* Add ORTTrainerOptions class for the new pytorch frontend (#4382)
Add ORTTrainerOptions class and some placeholders
* Add _ORTTrainerModelDesc to perform validation for model description (#4416)
* Add Loss Scaler classes to the new frontend (#4306)
* Add TrainStepInfo used on the new frontend API (#4256)
* Add Optimizer classes to the new frontend (#4280)
* Add LRScheduler implementation (#4357)
* Add basic ORTTrainer API (#4435)
This PR presents the public API for ORTTrainer for the short term
development.
It also validates and saves input parameters, which will be used in the
next stages, such as building ONNX model, post processing the model and
configuring the training session
* Add opset_version into ORTTrainerOptions and change type of ORTTrainer.loss_fn (#4592)
* Update ModelDescription and minor fix on ORTTrainer ctor (#4605)
* Update ModelDescription and minor fix on ORTTrainer/ORTTrainerOptions
This PR keeps the public API intact, but changes how model description is stored on the backend
Currently, users creates a dict with two lists of tuples.
One list called 'inputs' and each tuple has the following format tuple(name, shape).
The second list is called 'outputs' and each tuple can be either tuple(name, shape) or tuple(name, shape, is_loss).
With this PR, when this dict is passed in to ORTTrainer, it is fully validated as usual.
However, tuples are internally replaced by namedtuples and all output tuples will have
tuple(name, shape, is_loss) format instead of is_loss being optionally present.
Additionally to that normalization in the internal representation (which eases coding),
two internal methods were created to replace a namedtuple(name, shape) to namedtuple(name, shape, dtype)
or namedtuple(name, shape, is_loss, dtype) dependeing whether the tuple is an input or output.
This is necessary as ORTTRainer finds out data types of each input/output during model export to onnx.
Finally, a minor fix was done on ORTTrainer. It could initialize ORTTrainerOptions incorrectly when options=None
* Rename input name for test
* Add ONNX Model Export to New Frontend (#4612)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Create training session + minor improvements (#4668)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Save ONNX model in file (#4671)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add eval step (#4674)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add train_step (#4677)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add LR Scheduler (#4694)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add deterministic compute tests (#4716)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add legacy vs experimental ORTTrainer accuracy comparison (#4727)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add Mixed precision/LossScaler + several fixes (#4739)
Additionally to the mixed precision/loss scaler code, this PR includes:
* Fix CUDA training
* Add optimization_step into TrainStepInfo class
* Refactor LRSCheduler to use optimization_step instead of step
* Updated several default values at ORTTrainerOptions
* Add initial Gradient Accumulation supported. Untested
* Fix ONNX model post processing
* Refactor unit tests
* Add ONNX BERT example + minor fixes (#4757)
* Fix training issue when passing ONNX file into ORTTrainer
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add Dynamic Shape support (#4758)
* Update DeepSpeed Zero Stage option to a separate option group (#4772)
* Add support to fetches (#4777)
* Add Gradient Accumulation Steps support (#4793)
* Fix Dynamic Axes feature and add unit test (#4795)
* Add frozen weights test (#4807)
* Move new pytorch front-end to 'experimental' namespace (#4814)
* Fix build
Co-authored-by: Rayan-Krishnan <rayankrishnan@live.com>
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
1. Publish the image ACR, instead of building it every time for every PR
2. Make USE_MKLML and USE_OPENMP be able to co-exist. Currently both of them are enabled in our Linux CI build but indeed only one of them is taking effect.
3. Split nuphar and DNNL to separated pipelines.
4. Fix two warnings in onnxruntime/core/optimizer/matmul_scale_fusion.cc and onnxruntime/test/tvm/tvm_basic_test.cc.
5. Update the manylinux2010_x86_64 image to the latest.
* bump onnx to support bfloat16
* sign test code
* fix ut failures
* add bfloat type in gradient schema
* add bfloat16 to gathernd
* add bfloat16 into grad op defs
* temp disable gpu fusing transformers
* bfloat16 support fix
* more fix to bfloat
* bug ifx
* add bfloat16 to transpose matmul
* fix sce loss
* fix cast opset13 and other missing part of bfloat16
* Revert "temp disable gpu fusing transformers"
This reverts commit b627bc9019.
* add SCEloss back
* fix build break
* fix gpu failure due to missing kernel in opset13
* add tile opset 13 kernel
* Revert "fix gpu failure due to missing kernel in opset13"
This reverts commit 661d63d0599029757f240d29afd64b197b76b880.
* fix comments in pr
* fix cuda break due to opset13
* fix missing msdomain
* add nll loss tests into android build's broken list; disable bfloat16 cast tests due to the wrong type saved in onnx test data, will fix it in onnx first
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* test
* test
* add missing CUDA header include
* debug
* fix
* fix python package for dnnl and tensorrt.
* fix
* fix windows build.
* revert
* target_link_directories for tensorrt shared lib.
* Add experimental winrt api idl with dummy type to satisfy the build
* remove experimental from the api_lib target
* make experimental api available on windows builds also
* remove /y /d
* revert some pathing changes
* remove experimental api call from tests
* revert cppwinrt cmake changes
* switch to stdapi
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* update batch_norm test, enable dev_mode for nnapi, ignore onnx protobuf warning for nnapi ep
* fix some issues in concat and mark input without shape as not supported for now
* address review comments
* addressed comments
* Revert "Remove docstrigs if __ONNX_NO_DOC_STRINGS" (#4495)
This reverts commit bb4d331fa7bf1fe8d68b1527dda56e4739c80800.
* Bump version to 1.4.0 (#4496)
* Create N-1 threads in intra-op pool, given main thread now active (#4493)
Create N-1 threads in a thread pool when configured with intra-op parallelism of N. This ensures we have N active threads, given that the main thread also runs work. To avoid ambiguity on the value returned, rename ThreadPool::NumThreads method to ThreadPool::DegreeOfParallelism, and make corresponding updates in MLAS and operators.
* Conditionally compile without std::is_trivially_copyable to satisfy old GCC versions. (#4510)
* Adding CUDA arch flags for NVIDIA Jetson
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Added Dockerfile for Jetson and instructions to build wheel and image
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Removing guess about nvcc location
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Restoring pip3 setuptools install order
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Updated README with links and notes re NVIDIA Docker runtime
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Added mention of nvidia-docker
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Addressing code review comments
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
* Addressing code review comments
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
Co-authored-by: Tiago Koji Castro Shibata <ticastro@microsoft.com>
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
Co-authored-by: Tim Harris <tiharr@microsoft.com>
Co-authored-by: edgchen1 <18449977+edgchen1@users.noreply.github.com>
* Add BN to ArmNN EP
* Add Concat to ArmNN EP
* ACL logging improvements
* ArmNN logging improvements
* Fallback to CPU for 9x9 convolution in ACL EP
* Fallback to CPU for 9x9 convolution in ArmNN EP
* Enable python support for ACL and ArmNN EPs when compiled with BSP toolchain
* Removed the matmul operator
* Fix conv infer shape function
* Fix provider_names list for armnn
Co-authored-by: Andrei-Alexandru <andrei-alexandru.avram@nxp.com>
* Add python API for specifying CUDA device id
* Modification for providing session based python api for specifying
device id
* When include header file pybind11/stl.h, conversion between c++
containers and Python list, vector and dict data structure are
automatically enabled.
https://pybind11.readthedocs.io/en/stable/advanced/cast/stl.html#
Therefore, refactor the code for better leverage this advantage.
* Make struct CudaDeviceOptions as default cuda device options
* Implement sess.set_providers(list_of_providers, list_of_provider_option_dicts)
But still stay consistent with existing sess.set_providers(list_of_provider)
* Add cuda provider option default setting
* Add support for setting cuda cuda_mem_limit and arena_extend_strategy.
Also resolved the merge conflict on session.py
* Use python ctypes to call cuda library to help python unittest
* Refine the code with reviewer's suggestions
* Add the capability of getting execution provider's configuration
- Once we introduced the capability to set execution provider's
configuration, it makes sense to add capability of getting ep's configuration.
* Modify the code with reviewer's suggestions.
* Using stoull() and stoul() depends on 32/64-bits architecture.
* Rewrite the testcases for testing setting CUDA device id
Note: We need to make sure every ORT process be run on one CUDA device
at a time.
* Make sure old session object is destroyed by python gc before new
session object is being created
* Move testcases to original onnxruntime_test_python.py
* Fix bugs to pass CI build
* Make it pass CI build (cont.)
* Make it pass CI build (cont.)
* Revise pipeline schedule to consider communication ops
* Add test
* Fix warning
* inline some short functions
* Fix warnings
* Rename a class
* Add comment for test
* op renamed to task
* Fix NVTX wrapper's bug
* build engine in runtime for dynamic shape subgraphs
* Update TensorRT-ExecutionProvider.md
* Update TensorRT-ExecutionProvider.md
* fix build issue
* Add more instructions on how to use engine caching
* add precision to trt node name
* Update tensorrt_execution_provider.cc
* Update tensorrt_execution_provider.cc
* Add protobuf mutator library as a git submodule
* Added files and instructions to build the protobuf mutator library in CMake
* Added fuzzing flag to build system and added fuzzing dependency library. To run fuzzing test use the flags --fuzz_testing --build_shared_lib --use_full_protobuf --cmake_generator 'Visual Studio 16 2019'
* Added src files and build instructions for the main fuzzing engine
* Removed Random number generation test from inside the engine
* Added license header to files
* Removed all pep8 violations introduced by this change and other E501 violations
* Support quantization linear binary element wise math ops, implement QLinearAdd.
Support tests for quantization linear binary element wise math ops, implement test for QLinearAdd.
Add QlinearAdd with SSE2 intrisinc implemntation, Avx2 assembly implemntation, Neon intrisinc support.
QLinearAdd support VectorOnVector, VectorOnScalar, ScalarOnVector.
Generalized QlinearBinaryOp parallel related with broadcasting.
* Modify according to PR feedbacks. Mainly:
* template helper for generalize the qladd logic on v2v, s2v, v2s
* remove GetKernel related.
* change mixed lagecy MM/SSE code in the AVX code
* formater, typos, convensions, etc.
* Utilize MlasSubtractInt32x4 in MlasDequantizeLinearVector().
* Some format fix.
* More nature parallel parameter type.
* Fix build break for x86.
* Comment goes to 80 before wrap.
* Many change on assembly on Marco related.
Using vminps than vpminsd to handle NaN.
tested on windows.
* Using CLang Format to format the file.
* Fix arm32 build error.
* Remove some duplicate in different #if defined
* working add.u8.vector to vector
* Fix runtime bus error on real arm32 linux.
* fix typo in store last one lane.
* arm32 qlinearadd handle scalar.
* Move qladd to seperate c++ file
* Add neon64 qladd.
* refactor some, enhance two instructions on arm64 only instructions
* Fix typo for arm64
* use strict op in pure c++ (min/max on float value)
* sse2 new version.
* mrege arm/sse2/avx2
* pass arm/sse/avx2 linux test
* remove non-used assembly file.
* Remove unused data definition and tailing spaces.
* Fix broadcasting parallel issue.
* Enhance broadcasting scenarios. Allow testing result diff due to round
on half.
* Add Mlas or MLAS_ prefix for namespace safety.
* Handle alignment issue for arm32 for GCC/MSVC. remove some unused
signed/unsigned int ops.
* Specify /arch:AVX2 for qladd_avx2.cpp
* Fix type during copy/paste when unrolling. Better one GreatEqual
condition. Better formater by splitting two statements on single line.
* Arm neon alignment parameter is bits rather than bytes, change it.
* Move qladd_avx2.cpp to intrinsics/avx2/ folder
* Formatting using mlas style.
* Double check mlas style for these files.
* change indent 2 to 4 for qladd_avx2.cpp
* Fix windows x86 build error due to sse2 no _mm_cvtsi128_si64
* To re-trigger all as old failed pipeline updated.
Co-authored-by: Lei Zhang <phill.zhang@gmail.com>
* Move nnapi dnnlib to subfolder
* dnnlib compile settings
* add nnapi buildin build.py
* add onnxruntime_USE_NNAPI_BUILTIN
* compile using onnxruntime_USE_NNAPI_BUILTIN
* remove dnnlib from built in code
* Group onnxruntime_USE_NNAPI_BUILTIN sources
* add file stubs
* java 32bit compile error
* built in nnapi support 5-26
* init working version
* initializer support
* fix crash on free execution
* add dynamic input support
* bug fixes for dynamic input shape, add mul support, working on conv and batchnorm
* Add batchnormalization, add overflow check for int64 attributes
* add global average/max pool and reshape
* minor changes
* minor changes
* add skip relu and options to use different type of memory
* small bug fix for in operator relu
* bug fix for nnapi
* add transpose support, minor bug fix
* Add transpose support
* minor bug fixes, depthwise conv weight fix
* fixed the bug where the onnx model input has mismatch order than the nnapi model input
* add helper to add scalar operand
* add separated opbuilder to handle single operator
* add cast operator
* fixed reshape, moved some logs to verbose
* Add softmax and identity support, change shaper calling signature, and add support for int32 output
* changed the way to execute the NNAPI
* move NNMemory and InputOutputInfo into Model class
* add limited support for input dynamic shape
* add gemm support, fixed crash when allocating big array on stack
* add abs/exp/floor/log/sigmoid/neg/sin/sqrt/tanh support
* better dynamic input shape support;
* add more check for IsOpSupportedImpl, refactored some code
* some code style fix, switch to safeint
* Move opbuilders to a map with single instance, minor bug fixes
* add GetUniqueName for new temp tensors
* change from throw std to ort_throw
* build settings change and 3rd party notice update
* add readme for nnapi_lib, move to ort log, add comments to public functions, clean the code
* add android log sink and more logging changes, add new string for NnApiErrorDescription
* add nnapi execution options/fp16 relax
* fix a dnnlibrary build break
* addressed review comments
* address review comments, changed adding output for subgraph in NnapiExecutionProvider::GetCapability, minor issue fixes
* formatting in build.py
* more formatting fix in build.py, return fail status instead of throw in compute_func
* moved android_log_sink to platform folder, minor coding style changes
* addressed review comments
* Add build option to disable traditional ML ops from the binary.
* Fix python tests by splitting tests for ML ops to a separate file. Exclude ML tests from onnx_test_runner and C# tests. Exclude ML op sources.
* Update Edge pkg pipelines with new MLops env variable and fix C# packaging pipeline tests to skip ML ops.
* expose ACL/ARMNN providers to python
* add -acl / -armnn to package name when use_acl / use_armnn is specified
* build python wheel for ARMNN EP
* link ACL/ARMNN EPs into onnxruntime_pybind11_state
* wrong argument order in build_python_wheel for wheel_name_suffix
* Fix a bug and add code to profile memory
1. Compile Send/Recv again (currently broken because of
HOROVOD refactor).
2. Add code to print out initializer allocation size and
activation memory size.
* Address comments
* Split memory counts per locations
* Fix a metric
* ORT on CUDA 11
1. Seperate HOROVOD and MPI
2. Seperate NCCL from HOROVOD in CMakeLists.txt
2. Remove dependency on external cub
3. cudnnSetRNNDescriptor is changed in cuDNN 8.0
* polish the code about MPI/NCCL in CMakeLists.txt and build.py
* check CUDA version
* ${MPI_INCLUDE_DIRS} should be PUBLIC
* sm30, sm50 are deprecated in CUDA 11 Toolkit
* update change based on code review feedback.
* add sm_52
* improve MPI/NCCL build path
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Modify gradle build so artifactID has _gpu for GPU builds.
Pass USE_CUDA flag on CUDA build
Adjust publishing pipelines to extract POM from a correct path.
Co-Authored-By: @Craigacp
* Add ArmNN Execution Provider
Add a new execution provider targeting Arm architecture based on ArmNN.
Validated on NXP i.MX8QM CPU with ResNet50, MobileNetv2 and VGG models.
reviewed-by: mike.caraman@nxp.com
* Minor fixes
- renamed onnxruntime_ARMNN_RELU_USECPU to onnxruntime_ARMNN_RELU_USE_CPU
- fixed acl typo
* remove extra includes. added exception for ArmNN in test
* fix indentation
* Separated the activation implementation from the cpu and fixed the blockage from the endif
Co-authored-by: Andrei-Alexandru <andrei-alexandru.avram@nxp.com>
