* create npm packaging pipeline
* fix indentations
* Update npm-packaging-pipeline.yml for Azure Pipelines
* Update npm-packaging-pipeline.yml for Azure Pipelines
* Update npm-packaging-pipeline.yml for Azure Pipelines
* react-native-ci as a template
* fix typos
* fix template paths
* add a depencendy
* change a stage name
* set different artifact name for each package
* fix typo
* Update npm-packaging-pipeline.yml for Azure Pipelines
Set a build Id for node npm package as a parameter
* Update npm-packaging-pipeline.yml for Azure Pipelines
Set a build Id for node npm package as a parameter
* Update npm-packaging-pipeline.yml for Azure Pipelines
* add c-api test for package
* fix bug for running c-api test for package
* refine run application script
* remove redundant code
* include CUDA test
* Remove testing CUDA EP temporarily
* fix bug
* Code refactor
* try to fix YAML bug
* try to fix YAML bug
* try to fix YAML bug
* fix bug for multiple directories in Pipelines
* fix bug
* add comments and fix bug
* Update c-api-noopenmp-packaging-pipelines.yml
* Remove failOnStandardError flag in Pipelines
* get inputs independently for trtexec
* track one process only
* remove engine and profile files
* change time to commit time
* add runtime option for io binding
* move to commit date
* fixes
* add option for graph optimization
* cleanup docker script
* include remaining changes
* choose graph optimization option
* add space in option
* Change storage container, simplify build definition parameters.
* Remove explicit version from Objective-C docs.
* Increase timeout.
* Use real storage account.
* Get static website URL with az cli.
* Add android package build settings for full build
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
* skip browserstack test at release pipeline
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* pool name as a parameter to run at lotus
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* Update web-ci-pipeline.yml for Azure Pipelines
* create a packaging pipeline for web
* Update web-packaging-pipeline.yml for Azure Pipelines
* make web-ci-pipeline as a template
* make web-ci-pipeline as a template
* make web-ci-pipeline as a template
* make web-ci-pipeline as a template
* change a paramter name checking a pipeline
* make a pool name changable for react native pipeline
* disable code sign validation for react native
* fix react native package.json publish
* fix indentation
* remove unnecessary comment
* test onnxruntime-common package publish
* ts and js files use lf as eol for windows
* use Linux style of ending line break
* change newLine at only tsconfig.json
* restore a commented code
* fix git restore directory for npm packaging
* fix a typo
* force eol to lf on windows for js directory in CI
* 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>
* apply the same policy for onnxruntime-common as web and node
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* remove old comment
* Pipeline for ONNX Runtime react native
* Fix a test failure
* test with custom built binaries
* add onnxruntime-common package back
* don't bob build when bootstrap
* revise Android test
* rename example to e2e
* remove onnxruntime packages from package.json
* remove release-it package
* upgrade gradle version to the same as CI
* add a pipeline for react native
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* android and ios mobile build for react native e2e
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* use android aar package template
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* use android aar package template
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* publish ios test results
* add e2e tests and publish a npm package
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* remove aar from npm package
* wait for view displayed
* change a waiting logic
* increase wait time for app launching
* give more time to launch an app
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* disable metro server on testing
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* test ios simulator launching
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* fix iOS e2e test
* use a publishing version of npm packages
* make pretty
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* make only one onnxruntime-common package after packaging
* make a powershell script of packaging universal
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Add a warning for file changes during a test
* clean up
* fix lint errors
* fix js npm packaging
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* Update mac-react-native-ci-pipeline.yml for Azure Pipelines
* resolve comments
* fix a typo
* move table names to one location
* remove session metadata
* reload trt inputs
* fix posting names
* Update linux-gpu-tensorrt-daily-perf-pipeline.yml for Azure Pipelines
* remove comments
* Split up anubis job and perf run
* add trt environ variables
* No embedded links
* add qdqgroup as input for NodeUnit
* minor update
* hookup nnapi_ep
* minor update
* update compiler setting
* Add a simple UT
* Pipeline change to add build minimal extended with NNAPI for Android
* move GetAllNodeUnits to node_unit.h, add UT for NodeUnits, minor updates
* minor updates
* address CR comments
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
Add abseil and inlined containers typedefs
Introduce TensorShapeVector for shape building.
Use gsl::span<const T> to make interfaces accept different types of vector like args.
Introduce InineShapeVectorT for shape capacity typed instantiations
Refactor cuda slice along with provider shared interfaces
Refactor Concat, Conv, Pad
Build with Conv Einsum and ConvTranspose refactored.
Remove TesnorShape::GetDimsAsVector()
Refactor SliceIterator and SliceIteratorBase
Refactor broadcast
Refactor Pads for twice as long
Remove memory planner intermediate shapes vector
Refactor orttraining
Fix passing TenshroShapeVector to tests
Remove abseil copy and submodule, use FetchContent_Declare/Fetch
Path with separate command
Make RocmAsyncBuffer accept anything convertible to span. Adjust Linux GPU pipeline.
* add back previous changes lost in merge
* post session to dashboard
* post session creation time to dashboard
* fix trt 8 functionality:
* add component governance
* Remove hardcoded values
* Update linux-gpu-tensorrt-daily-perf-pipeline.yml for Azure Pipelines
* cleanup errors
* post results only once
* checkout 8.0 GA
* try build 8.0 without building shared lib
* add back build_shared_lib, not the problem
* add upload_time to table
* use identifier to post
* Shorten to TRT x.x
* shorten commit hash using rev_parse
* use shortened commit hash
* use nvidia's default TRT_VERSION
Move binary size check(s) to a separate pipeline. In the future, other binary size-related builds can go here.
Add publishing of build artifacts for easier analysis.
Add optional build with debug info.
* migrate to 1ES Hosted Pool
* migrate to Kusto database
* refactor and organize ep names with ORT prefix
* standardize TRT benchmarking with save/load engine, input binding, and workspace
* Add TRT 8.2 to ep perf pipeline
* update model_list.json with full onnx zoo
* add anubis credentials
* add anubis credentials
* clarify trt variables
* get system info from docker image
* remove unwanted commenting
In a reduced ops build, some source files get updated. This change moves the updated files into the build directory. This way, it is easier to simultaneously manage different build directories (with possibly different reduced ops configurations) based on a single source directory.
* Include onnxruntime binary when not using pacakge referene or uap app.
* Remove the lib\uap10.0 build from the nuget package - causing conflicts
* Add UWP test
* remove build files
* remove local change
* reset mimalloc and onnx-tensorrt
* change username to Microsoft
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* update base image from 11.4.0 to 11.4.2
* update Linux TRT GPU pipeline to TRT 8.2
* update onnx-tensorrt to 8.2-GA
* disable failing TensorRT 8.2 tests.
* update pad test.
* fix
* update win trt ci pipeline to trt 8.2
* test run with cuda 11.4 and cudnn 8.2
* increase timeout
* revert
* revert
* update packaging pipelines to use trt 8.2
* fix typo
* update trt gpu perf pipeline to trt 8.2
* increase timeout
* delete deprecated ci-perf-pipeline.yml
* bump timeout
* adjust timeout packaging
* update to torch 1.10
* update torchvision version
* update torchtext version
* remove deprecated option enable_onnx_checker
* add unit test to test gradient of GatherElements
* add ORTMODULE_ONNX_OPSET_VERSION in a docker file
* add ortmodule and eager mode test
* add ortmodule dependency
* convert between aten ort tensor and ortvalue
* register the EP to ortmodule using ort device information
* remove duplicated test
* remove useless dependency
* handle half precision type for ortmodule outputs
* adjust the tensor conversion python code
Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* add ortmodule and eager mode test
* add ortmodule dependency
* fix eager pipeline
* skip tthe ortmodule test for windows due to win ci issue
* remove useless win ci change
* add torch
Co-authored-by: Abhishek Jindal <abjindal@microsoft.com>
* Add 2 builds to validate the cmake defines for excluding optional components work in both full and minimal builds.
* Create empty config for no-ops build
* Create empty config for no-ops build - attempt #2
* Create empty config for no-ops build - attempt #3
* Update python binding code to work when sparse tensors are disabled.
* Changes to ensure openvino build go through in Windows
* Modified Hetero plugin Logic
*Modified Hetero Feature logic. In Hetero,
if the operator to be marked true in getcapability(),
it should be supported by either of the devices
specified with HETERO in the device_type.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* OV updated to 2021.4.2 version
* OV updated to 2021.4.2 version
* Updated OV to 2021.4.2 version, mono download link and dotnet version
* Copying Managed nugets in openvino c# docker file
*Copying Managed nuget to nugets artifacts
directory
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: saharfraza <sfatima.3001@gmail.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
- Only set them as targets for the ORT nuget package
- Use OrtPackageId as the condition for inclusion, if installed
- need to do the nuget restore via msbuild so that this property is set correctly
- Add desktop-only version of the C# sln as there is no way to exclude the mobile specific csproj's from an sln
- use this when applicable if someone is running build.py with the `--build_nuget` flag
Other
- remove attempt to include symbols in the nuget package as nuget doesn't support symbols in native packages
- update build.py to use `nuget` and not a windows specific path and filename for a linux build with `--build_nuget`
* 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
* Update required operators for prebuilt package to add opsets 14 and 15.
Add helper script to check if the prebuilt package will support the model and if not why not.
* Add support for multiple opsets being specified on a single line in the required operators config. This makes it easier to update the pre-built package config.
It's also required for validation tools to work as they only have a single opset from the model and not per-operator opsets. If we only list the incremental ops we could merge in the ops from the previous opset, but that wouldn't give a way to drop an operator from being supported.
Left the info on which ops changed though so we have a better feel for the cost of supporting each opset.
Adding ARM64 depthwise convolution kernel for symmetric quantization
Motivation and Context
Two improvements against current kernel code :
1. Signed int8 based instructions, no need to extend from 8b to 16b before multiplication.
2. Unrolled loop with manual software pipelining
Co-authored-by: Chen Fu <fuchen@microsoft.com>
* Only serialize runtime optimization records container if non-empty.
* Remove runtime optimizations from onnxruntime/core/flatbuffers/schema/README.md as it's not completely implemented yet.
* Disable partial runtime optimization implementation by default.
ORT format model runtime optimization implementation is in progress.
This change adds a build.py option to disable the partial runtime optimization implementation, adds CI builds to test it, and disables runtime optimizations in mobile package builds.
Add Xamarin support to the ORT nuget packages.
- Update C# code to support Xamarin builds for iOS and Android
- refactor some things to split out common code
- include iOS and Android ORT native shared library in native nuget package
* Force Windows AI Nuget pipeline to use 19041 Windows SDK as 22000 casues a downlevel regression by importing LoadLibraryW
* move into quotes
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* implement cuda provider
* define profiler common
* call start after register
* add memcpy event
* add cuda correlation
* format code
* add cupti to test path
* switch to CUpti_ActivityKernel3
* reset cupti path
* fix test case
* fix trt pipeline
* add namespace
* format code
* exclude training from testing
* remove mutex
* make work for both rocm 4.2 and rocm 4.3.1
* fix rocm 4.3.1 docker image reference
* fix CUDA_VERSION to ROCM_VERSION
* fix ReduceConsts conflict def
* add ifdef to miopen_common.h as well
* trailing ws
* 2021.4.1 Docker and ci changes
* OV version change
* Removing Imagescaler op from the op's list
Reverting this change which was added in last
PR. Imagescaler is now deprecated. so removing
it from the supported list. Also this
op is causing regression in the performance
of the FP16 models.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Re-writing the help message for num_of_threads
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
* try to run inside 4.3.1 container
* no \ in container run command
* remove networking options
* try with adding video render groups
* add job to build docker image
* try without 1st stage
* change alpha, beta to float
* try adding service connection
* retain huggingface directory
* static video and render gid
* use runtime expression for variables
* install torch-ort
* pin sacrebleu==1.5.1
* update curves for rocm 4.3.1
* try again
* disable determinism and only check tail of loss curve and with a much larger threshold of 0.05
* disable RoBERTa due to high run variablity on ROCm 4.3.1
* put reduction unit tests back in
* install protobuf from source
* fix rm command in Dockerfile
* fix options on rm command
* fix cd into protobuf source directory
* try again
* remove strip step
* debug list the files
* ls on /usr
* more debug
* more debug
* adjust LD_LIBRARY_PATH
* try remove protobuf before ORT build
* Update to CUDA11.4 and TensorRT-8.0.3.4
* update trt pool, remove cudnn from setup_env_gpu.bat
* revert pool
* test gpu package pipeline on t4
* back out changes
* back out changes
Co-authored-by: George Wu <jywu@microsoft.com>
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* draft - enable model local function
* enable model local functions in ORT
* update to latest rel onnx commit
* plus tests
* plus more updates
* plus updates
* test updates
* Fix for nested functions + shape inference
* plus bug fix and updates per review
* plus fixes per review
* plus test updates
* plus updates per review
* plus fixes
* fix a test
* Add netstandard2.0 to nuget managed package.
Re-does PR that was backed out due to packaging pipeline changes.
Allows deprecation of netstandard1.1 in the following release as netstandard2 is the preferred lowest level framework.
* copy changes from trt_and_mem
* second edits
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* change to cuda 11.4
* build with cuda 11.4
* Update Dockerfile.ubuntu_cuda11_1_tensorrt7_2
* add cmake extra defines
* cmake architectures
* fix cmake arch
* Delete ubuntu-18.04.Dockerfile
* Rename Dockerfile.ubuntu_cuda11_1_tensorrt7_2 to Dockerfile.ubuntu_cuda11_4_tensorrt7_2
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* removing previous ort args
* rename to cuda 11.4
* remove cuda 10_2
* delete trt 7.1
* remove 7.1
* Passing in cuda architecture to reduce build time
* always add submodule sync due to recursive cloning
* fix run command
* add and
* take away unused arms and share python installation script
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml
* Update Dockerfile.tensorrt
* cleanup file
* install python directly on dockerfile - move to scripts in future
* Update Dockerfile.custom-trt-perf
* adding cuda 11.1 for missing Libnvrtc.so.11.1
* Delete install_python.sh
* test running hf bert-large
* try again
* try again
* include other models
* correct names
* disable deberta-v2-xxlarge
* avoid torch.distributed
* add compare json loss and perf for bert-large to test
* fix sed expression
* remove pytest
* add more models
* move unit tests u
* display samples/sec
The previous attempt to enable static analysis (#8842) didn't actually run the static analysis checks.
- Run clang-tidy directly.
- Address static analysis warnings.
* 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
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* prepare for PR
* Rename cuda directory to gpu directory in tarball
* Fix gpu java package
* fix bug
* fix small bug
* Add onnxruntime_providers_shared.dll into gpu nuget package
* Modify for test
* Temporarily remove for test
* Modify for test
* Modify for test
* Test packging Windows combined GPU
* Test packging Windows combined GPU
* Test packging Windows combined GPU
* Test packging Windows combined GPU
* modify for test
* modify for test
* fix bug
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Prepare for PR
* Prepare for PR
* Code refactor
* Rename proper Artifact name
* Rename intermediate Artifact names
* Revert Artifact Names
* Rename Artifact Names
* Modify Artifact name
* Modify Artifact name
* Modify Artifact name
* Update Java package
* Update Java package
* fix bug to change artifact name
* Fix bug for the wrong file path
* Fix no fetching correct artifact and test
* temporarily modify for test
* undo the change for test
* additional changes
* test package run
* minor fix
* minor fix
* minor fix
* Get around no arm64 simulator
* fix objc pod build failure
* downgrade_eigen
* update objc podspec template
* fix build - python.h not found
* disable --build_shared_lib for ortmodule tests
* fix
* fix the build flag
* disable --build_shared_lib for training path (not only for ortmodule)
* fix missing test model files
* disable test CApiTest.test_custom_op_library when ENABLE_TRAINING_TORCH_INTEROP is ON
* enable custom_op_library build
* fix build
* fix
* merge master and fix build failure
* build onnx_test_runner when onnxruntime_ENABLE_TRAINING_TORCH_INTEROP is ON
* resolve comments
* use --enable_training_torch_interop to replace "onnxruntime_ENABLE_TRAINING_TORCH_INTEROP=ON"
* Merge CPU/GPU nuget pipeline
* Include TensorRT EP libraries into existing GPU nuget package pipeline
* modify to use correct YAML
* Modify for test
* modify for test
* Add depedance
* Add depedance (cont.)
* modify for test
* Add create TensorRT nuget package
* modify for test
* modify for test
* Merge CPU/GPU nuget pipeline
* Include TensorRT EP libraries into existing GPU nuget package pipeline
* modify to use correct YAML
* Modify for test
* modify for test
* Add depedance
* Add depedance (cont.)
* modify for test
* Add create TensorRT nuget package
* modify for test
* fix merge bug
* code refactor
* code refactor
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* cleanup
* modify for test
* fix bug
* modify for test
* refactor
* fix bug and test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Prepare for PR
* Prepare for PR
* code refacotr from review
* Remove naming 'Microsoft.ML.OnnxRuntime.TensorRT' to avoid confusion
* Add linux TensorRT libraries
* Remove redundant variable in YMAL
* revert file
* undo revert file
* Modify regular expression so that it can capture the correct file
* Remove newline at end of file
* small fix
* Revert to CUDA11.1 on Windows
* Add unit tests for nuget package on Linux
Co-authored-by: Changming Sun <chasun@microsoft.com>
* initial update from 11.1 to 11.4
* change 11.4.1 to 11.4.0
* adjusting to match nvidia/cuda image tags
* adjusting to match nvidia/cuda image tags centos7
* correction to 11.4.0
* correction to 11.4.0
* update to cuda 11.4
* change training back to 11.1
* change training back to 11.1
* point to correct nvcr.io/nvidia/cuda 11.4.1 image
* change centos8 to centos7
* correct cudnn path
* Update linux-gpu-ci-pipeline.yml for Azure Pipelines
* Update c-api-noopenmp-packaging-pipelines.yml
* need to resolve centos images but remove space and change to 11.4
* Update linux-gpu-ci-pipeline.yml
* add cudnn to docker image
* bump devtoolset to 10
* revert cuda 11.4 change to setup_env_trt
* orttraining back to 11.1
* use nvcr.io
* Fix previous change back to cuda 11.1
* update cudnn path
* use cudnn image (revert if failure)
Add IsSparseTensor
Add CreateSparseTensor
Add utilities and test fully sparse instantiation
Fully sparse blocksparse
Add test and docs for fully sparse tensor instantiation
Rework creation API
Use API
Non string API
Retrofit of existing String API
Add tests
Add documentation
Address build issues (Winml pending)
Add inference test
Bump binary size
Add ifdef DISABLE CONTRIB
Merge CPU/GPU nuget pipeline. The old GPU nuget pipeline will be only for DML.
TODO: the result GPU package contains PDB files for some of the DLLs, but not all. It is due to the refactoring of CUDA EP to pluggable DLLs. At that time we forgot to copy the PDB files. However, I can't add them in now. Because currently the package is already 220MB large. If the missed PDB files were added, then it will be oversize. nuget.org doesn't accept >250MB packages.
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.
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* plus more fixes
* updates per review
* update to release commit
* add filters for optional type tests
* plus updates
* update onnx-tensorrt parser to master
* disable unsupported tests
* add cuda sm 75 for T4
* update tensorrt pipeline
* update trt pipelines
* update trt pipelines
* Update linux-gpu-tensorrt-ci-pipeline.yml
* update trt cid pipeline
* Update linux-gpu-tensorrt-ci-pipeline.yml
* Update Tensorrt Windows build pool and TensorRT/CUDA/CuDNN version
* update to cuda11.4 in trt ci pipeline
* update base image to cuda11.4
* update packaging pipeline to cuda11.4
* clean up
* remove cuda11.1 and cuda11.3 docker file
* disable unsupported tensorrt tests at runtime
* Update linux-multi-gpu-tensorrt-ci-pipeline.yml
1. Update SDLNativeRules from v2 to v3. The new one allows us setting excluded paths.
2. Update TSAUpload from v1 to v2. And add a config file ".gdn/.gdntsa" for it.
3. Fix some parentheses warnings
4. Update cmake to the latest.
5. Remove "--x86" build option from pipeline yaml files. Now we can auto-detect cpu architecture from python. So we don't need to ask user to specify it.
SparseTensor support
Implement Builder pattern
Fix support for 1-D and 2-D COO indices
Implement and test CSR support.
Handle shape inference for SparseTensors
Implement conversion for COO, CSR and tests.
Address the case where constant sparse initializer is the output.
Implement test infra for SparseTensors
Implement SparseDenseMatMul for Csr and COO and tested it.
Add hash for SparseToDenseMatMul
Finish shared provider refactor
Refactor GetOrCreate to Create
Working on py interface
Expose OrtDevice and use it in allocate_numpy
Adjust Sparse interfaces, add support for string SparseTensor. Add tests.
Add and test to_cuda()
Add accessors to format specific indices
Test values and indices views, read-only flag, after GC access
Add sparse related methods to OrtValue
Re-work SparseTensor wrapper, add OrtValue methods
Rework numpy_array_to_cuda/to_cpu
Add run_with_ort_values
Add models and test sparse_mat_mul with run_with_ort_values
Refactor sparse tensor to use a single buffer
Ifdef x86 Eigen CSR sparse matmul implementation
Exclude broken test, check for string type when copying cross device
Split pybind schema, regenerate docs, add exclusion
Conditionally exclude schema module
Update docs fix cuda build
Add test to a filter and renerate JS docs
Add conversion and test string support for sparse tensors
Exclude conversion utils from minimal build
Add CUDA Memcpy and adjust provider interfaces
* Changes to ensure the openvino-ep-2021.4 branch is created
* Fix failing cpp and python unit tests
* Fixed Myriad Tests for Ov_2021.4
* Disabled failing python tests for myriad
* Fixes models which were breaking w.r.t 2021.4
* Added fixes to Fix tinyyolov3 working on Myriad
and MaskRcnn, FasterRcnn using GPU_FP32
* Added FP16 output data type support for ngraph
* Implemented ReadNetwork() method
->Using Core::ReadNetwork() method for reading and creating a CNNNework
->Since OpenVINO™ 2020.4 version, Inference Engine enables reading ONNX models
via the Inference Engine Core API and there is no need to use directly the low-level
ONNX* Importer API anymore. To read ONNX* models, it's recommended to use the
Core::ReadNetwork() method that provide a uniform way to read models from ONNX format.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed ngraph f16 supported output type
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added comments in data_ops.cc
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed broken windows build
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disable failing CPP tests on CPU
Some of the convtranspose tests are failing on
OpenVINO-EP CPU due to accuracy mismatch w.r.t
default CPU. so currently we are disbaling
these tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated for ov version 2021.4
* Changes to include qdq ops in code
* Disabled failing python tests on GPU
Disabled two maxpool python tests on
GPU as they were passing but throwing
segfault
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix the backward compatibility issue
ReadNetwork() API has a bug and will only work
starting from OpenVINO 2021.4 version.
The previous versions will still have to use
onnx importer route
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix CMakeLists.txt for OpenVINO EP
If a directory with OpenVINO is sourced,
the latest OpenVINO settings have to
be imported.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: sfatimar <64512376+sfatimar@users.noreply.github.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
* Add ability to generate ios static framework
* Fix typos
* Add pod cache clean, update some comments of previous commit
* Fix CI failure with newly added cpuinfo library
* Update test model (CoreML requires node has a name)
* Addressed CR comments
Updates to the iOS packaging pipeline:
- Make it harder to overwrite package archives accidentally when uploading (fails if the archive already exists)
- Only upload package archives for release builds
- Some clean up
* Add memory check for TRT perf
* Revise test app
* Add memory check for TRT perf
* Revise test app
* add test cases
* Modify script and add pipeline YAML
* remove redundant code
* temporarily change
* Change YAML
* revise test app
* fix minor bug
* code refactor
* small fix
* temporarily change for test
* prepare result log
* rm container when it exits
* code refactor
Pytorch cpuinfo library allows us to query current cpu features, micro-architecture and cache size, etc. These information is needed for targeted performance optimizations.
Unfortunately it does not work under Windows/ARM. We need to develop our own later
* Add metadata_props to ORT model
* Minor update
* Update python binding, and increase the minimal pipeline size threshold
* Fixed a small bug in serializing ir_version
* Remove temp ort.py.fbs and add it to .gitignore
* first attempt share docker image across python and torch versons
* set dependency between jobs
* fix yaml grammer
* remove python version from first stage
* clean deepspeed directroy
* split into two images according torch version
* fix yaml syntax
* invalidate cache
* remove DS to prevent torch 1.9.0 upgrade
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
Switched the code to C++17. To build ONNX Runtime on old distros like CentOS 7, you need to install a newer GCC from additionary repos. If you build onnxruntime with the newer GCC, typically the result binary can't be distributed to other places because it depends on the new GCC's runtime libraries, something that the stock OS doesn't have. But on RHEL/CentOS, it can be better. We use Red Hat devtoolset 8/9/10 with CentOS7 building our code. The new library features(like std::filesystem) that not exists in the old C++ runtime will be statically linked into the applications with some restrictions:
1. GCC has dual ABI, but we can only use the old one. It means std::string is still copy-on-write and std::list::size() is still O(n). Also, if you build onnxruntime on CentOS 7 and link it with some binaries that were built on CentOS 8 or Ubuntu with the new ABI and export C++ symbols directly(instead of using a C API), the it won't work.
2. We still can't use std::optional. It is a limitation coming from macOS. We will solve it when we got macOS 11 build machines. It won't be too long.
3. Please avoid to use C++17 in CUDA files(*.cu). Also, the *.h files that they include(like core/framework/float16.h). This is Because CUDA 10.2 doesn't support C++17. You are welcome to use the new features in any *.cc files.
This is an update to https://github.com/microsoft/onnxruntime/pull/8079
The sample application motivating the original update changed to use an updated version of the model. Now, fewer ops are required. This change removes the previously added ops which are no longer needed.
1. Remove some unused code and simplify tools/ci_build/github/linux/run_dockerbuild.sh.
2. Enable Nuget CUDA tests. The original design was we could leverage Directory.Build.props and let cmake generate the required properties(USE_CUDA/...) there. However, in nuget packaging pipeline we test the package on a different host that doesn't run cmake command and doesn't have the auto-generated Directory.Build.props file.
* Revert for testing TensorRT 7.1
* change to origianl googletest version
* change machine
* remove build arg
* change back machine
* revert back googletest version
* Make it ready to merge to master
* revert onnx-tensorrt to v7.1
* rename yml
* use [[ ]] in bash command
* add sudo
* add chmod
* add correct path
* change another way to revert onnx-tensorrt
* change docker image to manylinux build
* clean up builds for interop_torch
* add python dependency for executables
* disable onnxruntime_ENABLE_TRAINING_TORCH_INTEROP by default; enable it in ortmodule GPU training pipeline only
* disable training unrelated tests when torch interop is enabled
* simplify the python dependency.
* clean up and fix
- Allow anyone to kick off a perf test here. Customize: branch, eps, model selection, cuda version.
- Only run shape inference when required.
- Kill errored out memory processes.
- Remove warmup run.
- Clean up script.
- Standalone_TRT is it's own "EP" vs as an additional run with TRT EP
