* rename BertOptimizationOptions to FusionOptions
* remove disable_onnxruntime, and use opt_level to control whether onnxruntime graph optimization is used.
* Change default opt_level for backward compatible. When opt_level is not specified, default value is based on model type.
Re-enable tests that disabled in PR 8530
Update import of test_optimizer.py so that the test could run in source directory.
Add a parameter to disable symbolic shape inference in fp16 conversion since it throws exception for some model.
* integrate eager mode source codde; build with cmake and integrate the python test
* Adding the python path for importing libraries in the Eager mode
* fix clang break;check if training and python enabled
* handling the linking of torch libraries across multiple platforms
* merge and fix the naming
* add build instruction
Co-authored-by: Abhishek Jindal <abjindal@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: ajindal1 <abjindal@microsoft.com>
* attention fusion kernel refactored
* consider the case of none in add_qk
* variabled added to check for pre-pack weights
* added a comment to PrePack()
* Optimized prepack and try to free the weights
* making comment sound better
* fixing a bug with optimizer.py
* commented out changes to be done
* removed comments
* make the private fn() private
* fix build
* making clean up fn static
* backed out optimizer tool change, needs more looking into
* atenop for inference
* assert if dtype mismatch
* atenop config in frontend
* fix orttrainer test
* gradient def not only for ATenOp
* bugfix
* fix gradient input shape and type issue
* fix after merge master
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 working to convert akv nodes
* changes to replace nodes
* changes to accomodate qkv hidden sizes as attributes
* kernel to accept qkv_hidden_size attributes
* Working till compute for varied dimension, todo applyattention()
* changes to make all regression tests work
* inference running successfully without prepack
* success inference with pre-pack weights
* add test for diff sizes
* bias shape need not be a mul of 3
* get the output_hidden_size from input
* infer output shape from input
* merge with master
* cleaning up files that got merged wrong
* accurancy at accepted level
* added unit test case for different dimensions
* all unit tests passing
* packed weights working for attention
* prepacked weights working
* added test case for newly added extra qk input
* updated unit test to test only extra add qk
* fixing build error
* removing few debugs
* reverting test changes
* all python test passing
* cleaning up
* new unit test added, major clean up of code
* removed extra code
* minor
* minor fix to tests
* prepack weights code cleaned up
* compacted compute() in attention.cc
* reformat compute()
* making a parameter T
* adding 3 q,k,v buffers in all cases
* fixing build
* running tests only on cpu
* Updating docs
* trigger ci builds
* Addressing comments in PR
* addressing some more comments
* get add_qk_str from add_qk node directly
* updating docs, added extra check to verify attn inputs
* Optimized the extra add by parallelizing
* added attention_shape to symbolic_shape_infer.py
* minor refactoring to address comments
* 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
* unregister registered python functions upon normal interpreter termination
* atexit.register(unregister_python_functions) should be called by __init__.py
* minor fix
1. Add support for sequence ops: ConcatFromSequence, SequenceAt, SequenceInsert. There are other sequence ops supported by onnx that worked well after adding these ops, so no need to add all of them in symbolic_shape_infer
2. For If node, the two branches output might have different shapes. In that case, for sequence output, use None in dimension; For tensor output, create a new symbolic dimension.
3. Fix a bug in Tile, where input for repeats might be of unknown value
4. Topological sort of nodes in graph need to consider implicit input in subgraphs for If/Loop/Scan ops
5. Generate unique prefix for new dimensions inside subgraph
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
* changes to fuse attention node and create varied dimensions
* added an option to optimizer to only do offline fusion
* fixing a typo
* merge with master
* removing extra changes
* added new unit test - test_attention_fusion_for_varied_qkv_dimensions()
* Unit test succesfull for q,k,v paths with varied dimensions
* adding test model for unit test case
* optimizing attention tests
* removing debugs
* minor change
* addressing comments
* addressing comments
* changed the new option to disable_onnxruntime
* replacing asserts with debugs
* make attn fusion backward compatible for head_size, hidden_size
* preserving behavior for shape_modified_tensor
* adding new option as the last parameter
* cleaning up
* line breaks and spaces
* formatting according to python
* making the changes to fuse attention node without user input
* changes to fusion_attention.py updated
* bringing the code up to python standard
- 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
* checkin transformers pipeline
* add docker requirements
* only trigger linux cpu
* temp remove tf instalation due to numpy version conflicts
* test numpy>=1.7
* revert numpy and disable transformers
* add coloredlogs
* enable shape_infer_helper and install transformers when needed
* pip3?
* testtest
* enable more tets
* line too long
* remove pytorch1.4 test and added back some onnx files
* add tests
* copy dir
* disable 2 teests
* trim lines
* add missing onnx
* fix type
* fix version conflicts
* install psutil
* change file path
* mfix path
* remove cached files
* add back attention fusion test
* labeled the shape infer test as slow
* fix
* enable tf2onnx test and enable pytest
* refactor path
* fix typo
* add cwd
* Register Torch Custom autograd.Function
* Add flag to supress pybind11 warning
* Avoid unnecessary include in cmake
* Add missing reference
* Add getter for registerred functions
* Format for making subsquent changes cleaner
* Fix interop feature build failure
* Forward pass, run PyOP on CPU EP
* clean up the code
* Fix build
* Define new ops
* refactor pyop - extract PyOpLibProxy class
* Hacks to run example
* implement the kernel compute func
* add back PyOP for comparision experiments
* debug info - thread id
* refine the kernels
* Polish code
(cherry picked from commit 4ed606f9a0)
* Fix a the Tensor address mismatch in C++ side
* PythonOpGrad compute
* add distributed test case
* refine test cases
* get dist.get_rank() in Autograd forward pass
* Add CUDA kernels
* Store float, int, and tuple of them as PythonOp's attributes
* Populate local changes
* Fix bugs
* PythonOp/PythonOpGrad CUDA kernels
* Support non-tensor inputs
* Single GPU FP16 Run Pass
(cherry picked from commit e539989e91e18ee997900292d3493b97d3eafa8a)
* Fix segement
* add basic test cases
* Save progress
* fix gradient builder for a Add op who have same inputs
* add test cases for auto grad fallback feature
* fix ref cnt issue. add thread id for debugging
* POC: remove interface class
* Remove interface classes
* Clean a bit
* Coarse-grained clean up after rebase master
* reset pyop and language_interop_ops to latest master
* Fix missing part during merge
* re-structure torch related language interop files
* Fix build
* Fix tests and build
* Fix build and basic unit tests
* Fix most of uts
* remove unnecessary import
* clean up and fix build when enabling language_interop_ops
* Fix single-GPU UTs
* Move runner register into ORT package
* Update dist UTs to new style
* Also fix distributed UTs and leaf gradient problem
* Static generation for constant args
* Move arg_positions_ to static field
* Rename some functions
* Move arg ceration into a function
* Clean output logic in PythonOp
* Move PythonOp's ctor
* Revise PythonOpGrad
* Fix "ORT only supports contiguous tensor for now" for inputs
* Fix evaulation mode error, add test & clean up
* clean up codes
* Fix issues introduced by recent master change (enabled symbolic shape infer)
* automatically register forward/backward function pointers && clean up
* Fix multi-output case
* Add a test back
* fix build and clean up
* RAII for function params PyObject
* Use new exporter
* Clean full name in new exporter
* Fix UTs
* Format a file
* Add "inplace" back
Remove a legacy comment
* Refine TorchProxy
1. Make TorchProxy a formal singleton class.
2. Remove unused Scope class.
3. Simplify the call to Forward and Backward. The two functions now
automatically acquire and release GIL state, so user doesn't need
any GIL-related calls.
* Format
* Add lock to avoid racing condition when registering Python objs
* Fix Python call param ref issues && Add RefcountTracker for debug build && Clean up
* clean up print
* Resolve part of comments && clean up
* Fix a potential bug
* track pyobject consistently
* move kernels to cpu provider as base class
* Refactor - 1. Extract PythonOpBase/PythonOpGradBase 2. Implement CPU kernels 3. Test coverage for CPU kernels
* Refine register code
* Add a missing macro
* Release python call result objects with PythonObjectPtr && Add UnRegisterContext && Track PyObject for Debugging && Clena up
* Fix random segfault issue - relasing a wrong ctx pointer for inplace cases
* put ref count in debug macro
* Move GIL out
* Refine tests
* Fix memory leak issue && forward output lifecycle issue:
1. Unregister the OrtValue PythonObject. Currently, the OrtValue shared same buffer with PythonOp/PythonOpGrad's output. So after those kernels outputs are released, the "leaked" OrtValue caused the shared buffer cannot be released.
2. According PyTorch forward+backward execution. The forward outputs (e.g. torch tensors) maintains the context/saved variables/dirty inputs, etc, which are used for backward execution, so its life should be after the backward runs. This change added such a depencencies between PythonOpGrad on PythonOp.
* Move dlpack->ortvalue into C++ to avoid temp object registration
* Fix the over released Py_False/Py_True && refine tests
* Clean up unused functions
* Always assume the first forward output is context so we don't need to test unused cases.
* Fix a memory leak
* move-copy unique_ptr & avoid C-style casting
* Use inplace attribute to determine if input tensors are copied
* Move DlpackCapsuleDestructor's to a common place
* Thread-safe TorchProxy
* Use OrtValue instead of OrtValue*
* Only keep checks for Debug build
* Wrap some long line per comment
* onnx_export_type --> kwargs
* Use requires_grads to create PythonOpGrad's inputs
* add missing files during master merge
* Fix build issue after merge
* Address two comments.
1. Internalize DlpackCapsuleDestructor
2. Change "(" to "]" for describing closed interval.
* Address some comments.
1. "override" -> "overwrite" to avoid using reserved keyword.
2. Call DLPack's helper to create OrtValue for avoiding repeated code.
* Address comments.
1. Pass std::mutex to registeration helpers so their callers don't
have to lock the mutex expclicitly.
2. Rename "func_context_pool_mutex_" to "mutex_". This mutex is the global mutex for OrtTorchFunctionPool.
* Add bridging code to make cuda kernels work with merged master
* put debue macro check within RefCountTracker && use default logger for debug info && remove useless ortvalue_ptr interface && typos && revert unncessary blank line changes
* fix some comments
* Resolve more comments
* Capitalize a word
* use unique_ptr instead of ObjectPointer for PyObject management && add converntion
* Support symbolic shape
* Remove unused variable
* fix build
* Enable function registration for training only && rectify ToDlpack/FromDlpack merge with master.
* Don't add context for non-PythonOp opeartors (for example AtenOp)
* Fix build error
* Polish frontend part.
1. Avoid adding kwargs to ORTModule's ctor
2. Use onnx_export_type rather than kwargs for type safty
3. Fix some build bugs.
* Resolve simpler comments
* Resolve export related comments
* sync master && fix tests && fix non-training build error
* Fix build errors
* add target link lib
* windows build error
* Fix orttraining-linux-ci build
* disable autograd test && clean up
* fix linux orttraining ci build
* try fixing win build error
* Revise append calls in runner
* Enable custom function using a function
* Rename to avoid using reservied keyword
* Use list comprehension
* Set ORT random seed in tests
* Remove print code and fix ctx shape
* [] -> list()
* Move autograd.Function and nn.Module into corresponding functions
* Move test helpers
* Polish dist test a bit. Tried move helpers to helper file but it causes a deadlock.
* trying fix undefined reference
* Context is not managed by global pool
* Polish dist test
* Polish dist test
* Add enable_custom_autograd_function
* Remove enable_custom_autograd_function from ctors
* Add doc strings
* Shorter code
* Address comments
* Add one empty line
* revert a minor and not needed change
* Address comments
* Back to reference
* Fix windows builds
* Fix windows debug build fail to find "'python39_d.lib'"
* fix mac build error
* revert _to_contiguous change
* add debugging tag for orttraining-cpu-ci
* Fix the wrong PYTHON_LIBRARIES which is affected by PYTHON_LIBRARY given in build command
* add debugging info
* Fix the build in this case: PYTHON_LIBDIR: /opt/_internal/cpython-3.7.10/lib, PYTHON_EXECUTABLE: /opt/python/cp37-cp37m/bin/python3, PYTHON_MULTIARCH: x86_64-linux-gnu
PYTHON_LIBRARY_PATH python3.7m
* fix build error due to python lib not found
* Fixes
1. Release PyObject's
2. Not useing deepcopy because we assume autograd.Function's
non-tensor inputs are static (constants) so there should
be no side effect after calling any autograd.Function
multiple times.
* Revert dtoc for decreasing refcnt
* add debugging log
* add debugging tag
* Fix a small leak
* Remove ONNX_FALLTHROUGH flag
* debug tag
* debug tag
* fix builds
* remove debug tag
* fix build
* fix builds
* fix build
* install python3 in centos, in case there is no libpython3.xm.so
* build python so for redhat
* add training cpu specific docker, build python so inside
* revert build-cpython change
* try fixing numpy include issue
* install_deps after re-installing cpython
* fix build && remove debug tag
* install openssl before cpython
* let's say: builds pass!
* add build flag for torch iterop, only enable it when training+Python is enabled
* skip ComputeBroadcastBackwardAxesDynamic for the shared inputs
* fix build
* add debug info for padgrad test
* Fix builds
* Split dlpack_converter into C++ and Python interfaces respecitively. Then different build use them as needed.
* clean up the changes
* fix addsubgradient builder
* Fix builds
* clean up
* clean up
* Address some comments.
1. Use pointer wraper to avoid calling Py_DECREF
2. Remove unregister_* functions
3. Allow repeated registration by skipping those with existing keys
4. Unregister context in PythonOpGrad
* Fix over-released Py_Boolean
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
* Update Vitis-AI EP support multiple DPU targets & specifically arm64 dpuczdx8g target
* Fix Vitis AI docker and default PyXIR versions
Co-authored-by: Jorn Tuyls <jornt@xilinx.com>
Co-authored-by: Jorn Tuyls <jornt.tuyls@gmail.com>
* Update the operator documentation generation
- Make layout a little nicer
- Update to latest supported operators including training
- Fix some links that are broken when the docs content is copied to github-pages
- Fix incorrect usage of 'onnx.ai.ml' as the default domain
- ML ops are now separated from the real default domain of 'onnx.ai'
- Include CPU, CUDA and training kernels
- exclude DNNL as it's not an EP we own
* There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library.
* Enable validation of both contrib ops and operator kernels in build
Filter generation so it's deterministic
Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs.
Remove workarounds for github-pages as that will now link to the github docs which display correctly
* There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library.
* Refine check
* Fix up constness in pybindings
Fix up return argument treatments.
Specifically, for all functions that return pointers or references
to the members of other pybind registered classes, we want not to copy
them, but internally bump up a reference to the hosting class so they do not
disappear before the reference to the returned members is re-claimed.
This policy is applied by default to def_property and def_readwrite but not to def_readonly
and other def methods.
See https://pybind11-jagerman.readthedocs.io/en/stable/advanced.html#return-value-policieshttps://pybind11.readthedocs.io/en/stable/advanced/functions.html#return-value-policies
Move OrtValue binding to a separate file
Move IOBinding into separate file.
* Use list comprehensions instead of list appends where possible
* Add OrtValueVector class as an opaque object in pybind
* Add dlpack methods to the OrtValueVector pybind class
* First iteration of making cuda a shared provider.
Separated out shared OpKernel change, so doing this to merge with that change.
* More cuda shared library refactoring
* More cuda shared library refactoring
* More build options tested, converted the training ops over.
* Fix merge breaks
* Fix submodules
* Fix submodules
* Fix submodules
* Fix python
* Fix compile errors
* Duplicate symbol fix
* Test fix for ROCM provider
* Another ROCM test workaround
* ROCM Build Test
* ROCM build fix
* ROCM
* ROCM
* ROCM
* ROCM
* ROCM
* ROCM test
* Reduce header dependencies
* Remove redundant namespace
* Test fix for linux
* Fix linux build
* Fix Eigen build error
* Fix unused parameter warning
* Test link error
* Another linker test
* Linker test
* Linker test
* Another test
* Another build test
* Fix linux link error
* Build test
* Fix control flow ops to use common base class with core code
* Remove extra qualifiers
* Fix template syntax for linux
* Fix cuda memory leak
* Fix pybind
* Test disabling cast
* Cleanup
* Restore cuda in test
* Remove more header dependencies
* Test not adding cuda provider to session
* Make GetProviderInfo_CUDA throw
* No-op cuda provider creation
* Fix some setup issues
* Fix memory cleanup on unload
* Diagnostics
* Don't unload library
* Add diagnostics
* Fix deleting registry at right time.
* Test disabling profiler
* Fix merge break
* Revert profiler change
* Move unloading of shared providers into Environment
* Free more global allocations before library unloads
* Add more diagnostics
* Move unloading back to the OrtEnv as there are multiple Environments created during a session.
Remove some library dependencies for tests.
* Fix more cmake files
* ERROR -> WARNING
* Fix python shutdown
* Test not using dml in pipeline
* Change python version and disable dml
* Update python version
* Test adding unload method for shared providers
* Disable DLL test
* Python test
* Revert "Python test"
This reverts commit c7ec2cfe98.
* Revert "Disable DLL test"
This reverts commit e901cb93aa.
* Revert "Test adding unload method for shared providers"
This reverts commit c427b78799.
* Point to RyanWinGPU
* Revert python version
* Fix id_to_allocator_map
* Another python exit test
* Remove extra debug messages
Try a more clean python shutdown through DllMain
* Revert DllMain idea, it didn't work
* Merge conflicts
* Fix merge with master issues.
* Comments
* Undo edit to file
* Cleanup + new training ops
* Revert yml changes
* Fix another merge error
* ROCM fix
* ROCM fix v2
* Put back Linux hack, it is necessary
* Stupid fixes
* Fix submodule out of sync
* ROCM fix 3
* ROCM 4
* Test java fix
* Fix typos
* Java test on my VM
* Fix build error
* Spotless fix
* Leave temp file around to load properly
* Fix cleanup on exit
* Fix break
* Java comments
* Remove LongformerAttentionBase workaround
* Spotless fix
* Switch yml back to regular build pool
* Revert "Switch yml back to regular build pool"
This reverts commit be35fc2a5a.
* Code review feedback
* Fix errors due to merge
* Spotless fix
* Fix minimal build
* Java fix for non cuda case
* Java fix for CPU build
* Fix Nuphar?
* Fix nuphar 2
* Fix formatting
* Revert "Remove LongformerAttentionBase workaround"
This reverts commit 648679b370.
* Training fix
* Another java fix
* Formatting
* Formatting
* For orttraining
* Last orttraining build fix...
* training fixes
* Fix test provider error
* Missing pass command
* Removed in wrong spot
* Python typo
* Python typos
* Python crash on exit, possibly due to unloading of libraries.
* Remove test_execution_provider from training build
Only enable python atexit on windows
Remove assert on provider library exit
* Still can't unload providers in python, alas.
* Disable Nvtx temporarily
* MPI Kernels for Training
* MPI Kernels part 2
* Patch through INcclService
* Oops, wrong CMakeLists
* Missing namespace
* Fix missing ()
* Move INcclService::GetInstance around to link nicer
* Missing }
* Missing MPI libraries for Cuda
* Add extra GetType functions used by MPI
* Missing Nccl library
* Remove LOGS statements as a test
* Add in a couple more missing GetType methods
* Update comments
* Missed a logging reference in mpi_context.h
* Convert aten_op to shared (due to marge with master)
* Test moving DistributedRunContext instance into shared provider layer
(with purpose error to verify it's being built properly)
* Test passed, now with fix
* Missing static
* Oops, scope DistributedRunContext to just NCCL
* Merge related issues and code review feedback.
* Merge error
* Bump to rel-1.9.1 (#7684)
* Formatting
* Code review feedback for Java build on non Windows
* Remove cupti library dependency from core library
* Test Java pipeline fix
* Linux build fix
* Revert "Linux build fix"
This reverts commit a73a811516.
* Revert "Remove cupti library dependency from core library"
This reverts commit 6a889ee8bf.
* Packaging pipeline fixes to copy cuda shared provider for tensorrt & standard packages
* Add cuda to Tensorrt nuget package
* onnxruntime_common still has a cuda header dependency
Co-authored-by: ashbhandare <ash.bhandare@gmail.com>
* GPT2 with one step search tutorial
* remove quantization section
Co-authored-by: Xiaoyu Liu <xiaoyu@xiaoyu-VM.z4vh1dzj5eoevgybsksdpz2izh.jx.internal.cloudapp.net>
* Add FBGEMM submodule
* Add fbgemm based per-channel quantization
* Add missing logic for pre-layernorm transformer model fusion
* add support for structured pruning architecture -fastformers
* Fix windows build
* Add a default behavior when head_size is not present for the backward compatibility
* Remove FBGEMM and default to tensor-wise quantization, column-wise quantization will be enabled later
* Fixed some unit test errors
* Fix windows compile error and unit test errors
* delete the option removed from the upstream
* Addresses review comments and fixes a merge error
* Remove commented out code
* add non-zero zp support
* support A and B scale with any dimensions
* fix build breaks
* fix warning in MSVC
* Fix bug for not checking original float value names when treat it as not existing.
* Clean up head size
* Clean up python tools
* Enable per column quantization
* fix quant weight cleanup bug
* A few code clean up
* Some code clean-up
* Some code clean-up
* Change option name
* update default value
* Rename option and parameter names
* Missing argument name change
* Add tests for quantization options for attention and matmul
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
catch symbolic shape inference exception.
no prune graph when there is inner graph (Loop/If/Scan)
add an wrapper for numpy_helper.to_array so that we can debug onnx graph without external data
remove fuse_mask that is not used any more in onnx_model_bert_tf.py
* Use positivity everywhere; handle negative index in Slice
* limit positivity to inputs
* make handle_negative_index private
* strengthen sympy comparison
* further strengthen compariso
n and a minor refactoring
* Add flip test
* Fall through if -int_max in handle_negative_index()
* minor fix for infer_Concat to include initializers
* Add more tests
* use simplify
* more tests
* check in early stop search as separate type
* rename to beam search configurations
* update do sample configuration flag help
* rename to configurable search step
* add option groups
* add more unit tests
Co-authored-by: Xiaoyu Liu <xiaoyu@xiaoyu-VM.z4vh1dzj5eoevgybsksdpz2izh.jx.internal.cloudapp.net>
* Update symbolic_shape_infer.py
don't rely on static code infer in _infer_Squeeze_
* checking if dorpped axes might be =! 1
* Checking opset. Logging assumption that symbolic dimensions are unequal to 1.
* more checks
* Implement qlinear concat and unit test.
Add quantization tools for QLinearConcat and it quantization tests.
* Add kernel def hash for QLinearConcat.
* Change according to PR. Add qdq transformer support for QLinearConcat.
* Add QDQ Transformer unittest. Fix typo on domain.
* remove dup logic of no use.
* fix x86 build error.
* Update operator docs.
* initial dynamic load example
* support load EP in the provider options
* support dynamic load EP in orttrainer
* split the provider interface; fix comments in pr
* remove experiment code
* add test
* remove useless file
* add test model file;fix linux brewak
* fix linux build and missing file
* fix python build
* fix python build
* fix python binding
* fix python test
* fix runtime path for posix env
* exclude the shared library from minimal build
* fix comments in pr;
* seperate the provider shared lib loading
* excluded from minimal / macos / ios build
* skip copy the provider shared lib for minimal build and mac os
* fix macos build
* exclude the test for macos build
* exclude from andorid build
* exclude from web assembly build
* enable the invalid ep test
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* beam search refactoring checkin
* add factory class and deduplicate code
* one step beam search works on gpu
Co-authored-by: Xiaoyu Liu <xiaoyu@xiaoyu-VM.z4vh1dzj5eoevgybsksdpz2izh.jx.internal.cloudapp.net>
* Enabling save/Load blob feature for OpenVINO-EP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes to enhance save/load feature
->This feature applies only for MYRIAD device target
->cleaned up the code and added error checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabled the feature only for MyriadX and only for Linux
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed compilation issues on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes to fix const subgraph issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed issues on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes for the feature
-> Removed default location dir dump using cmake
-> Enabled saving blob dumps at the executable path
by default
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Made save/load dump path configurable
-> The save/load blob dump path is now also made configurable
using a c/python Api's.
-> Introduced a flag named blob_dump_path
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor fixes added
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed python API issues
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Using GetEnvironmentVar to get the path
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed python runtime option issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes import network issue on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabled rocm support for graph transformations
* Support for external Hip allocator
* Added const_cast to reinterpret_cast to fix compiler issue
* Another crack at fixing the compile error
* More compilation fixes
* Added compilation flags to load_inline extension
* Added ROCM, ROCM_PINNED constants
* Changes to address PR comments
* Changed gpu identifier from ROCM to CUDA
* Added HIP compilation flag for torch inline functions
* Fixed a typo in header allocator string formatting
* Fix for runtime error with external_cuda_allocator
* Removed cuda/rocm specific code paths for allocators
* More name changes to generic gpu from rocm/cuda
* Removed duplicate allocator creation
* Rename cuda_external_ config options as gpu_external_
* Rename hip_mem_limit to gpu_mem_limit
* Rename cuda_mem_limit to gpu_mem_limit
With this change, differentiating CUDA EP and ROCm EP is not needed in training script when mem_limit option needs to be set.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* Updates to some operators to always support int32 and int64 based on testing of Android package build config with a minimal build.
If an operator can be used for shape manipulation (int64) it is frequently used for indices manipulation (int32), so we enable both types for that set of ops.
- e.g. BERT models take indices as input
- Scatter/Gather ops utilize indices
Misc. fix to python bindings to exclude call that fails in a minimal build.
* Allow specific optimizers to be disabled.
- replace unused ability to specify just the optimizers to run
- never used so not needed
Allow the disabled list to be specified via the python bindings
- expected usage is internal, so using kwargs for that so as not to pollute the documentation with stuff no user is likely to need
Update the ORT format model conversion script to disable NCHWc transformer when level is 'all'
- currently there aren't any known use cases where we'd want the NCHWc transformations to run as they create a device specific model and aren't used on ARM
- the ORT format model is not expected to be generated on the target device (e.g. generate on Windows/Linux/macOS to deploy to Android/iOS so there's a good chance we'd generate a useless/invalid model
- default to 'all' as ARM and MLAS prefer NHWC and the NHWC transformer runs at that level
* Add matching changes to optimizer generation in training code
* Code refactor
* Modify code to tackle OOM when calibrating on larget dataset
* Fix mismatch issue when setting keepdims on ReduceMin/ReduceMax
* Add COCO val 2017 annotation
* Fix mismatch issue when setting keepdims on ReduceMin/ReduceMax
* Fix bug of "No module named:onnxruntime.quantization.CalTableFlatBuffers"
* Check and install flatbuffers module
* Add script to donwload coco dataset image and refactor example
* Fix bug of "No module
named:onnxruntime.quantization.CalTableFlatBuffers"
* Add CalTableFaltBuffers as module
* Remove annotation, user can download by themselves.
* Uncommet code
* Add back instances_val2017.json
* Make sure flatbuffers installed when ORT is installed
* Refactor code to call coco api
* Enable FP16 for example
Changes include:
* Revert Event Pool changes
* Add copyright and revert unrelated changes
* Add DLPack as submodule and remove to_dlpack and from_dlpack from public API
* Update golden numbers for DHP Parallel tests
* Update ORTTrainer unit test numbers
* Rollback to DLPack v0.3
* Disable flaky test
* Update third party notices and CG manifest file
* Minor refactoring of ORTValue API
* fusion support runtime edge shape checking
* trim ctor
* add test
* fix
* Update test_shape_infer_helper.py
* use torch input size as dynamic axis hints
* check dir
* update
* support longformerattention
* update and add support for bert ops
* trim
* review comments
* review comments
Implemented following change to avoid the error when using both --use_external_data_form and --precision int8 with GPT2LMHeadModel, which results in
line 161, in save_external_data; open(external_data_file_path, 'ab').close()
FileNotFoundError: [Errno 2] No such file or directory:
This may also be related to the identified bug #6047.
* Handle case where bias_name is already quantized
If bias is shared between multiple nodes and we've already quantized it, just return the quantized name from the map
* Remove qType attribute from QuantizedValue and QuantizedInitializer
These are unused (and were incorrectly set in the case of int8 quantization)
* Add Reshape op to quantizer
* Add test for Reshape quant
* Introduce OrtTasks to replace EventPool
* return run_id to frontend
* pass run_id to backward
* OrtTasks support multiple bg_events
* make message_queue a member of orttask
* Replace MessageQueue with std::promise
* Move status_promise into Task
* Move terminate flag into Task
* Reenable previously disabled UTs
* Add unit tests
* Replace condition variables with std::promise
* Move to CreateBackgroundTask in the main thread
* return status and output in forward_future
* use throw for terminating background thread
* cleanup tasks at destructor
* reenable test_mixed_nnmodule_ortmodules_training
* add mutex for ORTTasks functions
* add mutex for bg_threads
* delay tests before start
* add ut for multi-task common backbone
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* update benchmark for transformers 4.* and ORT 1.7
* Fix gpt2 onnx conversion for transformers 4.3.*. Add a check of transformer version >= 3.1.
* remove code related to openmp
* update pretrain model list: keep representitive models only
* Add robust dependency check for Python package
* Add version_info.py to .gitignore
* Fix Linux build
* Fix Windows CPU build
* Fix Windows 32-bit build
* Minor tweak
* Generate version_info.py earlier in onnxruntime_python.cmake
* Print a user-friendly message if cuDNN is not found in
* Relax version requirements for CUDA 11 - only the major version has to match
* Fix PATH environment variable to include CUDA 11 in 'Python packaging pipeline' (Windows/GPU)
* Fix the build with cuDNN 7
* Add support for custom ops library to the ORT model conversion script
Simplify model conversion now that we read ops from the ORT format model.
Enable custom ops in the python bindings if custom ops are turned on in a minimal build.
* Add test of model conversion involving custom ops.
* Integrate memory improvements from NVidia
* compute max_global_num before buffer allocation
* update conversion script to support transformers 4.0
* update benchmark script for creating dummy inputs for different batch_size
* Use a wrapper of cuda event to avoid memory leak
* ortmodule v0.2
* use pt module for eval
* get user outputs in yield op
* pass output grads to yield output without copy
* Disable mem_pattern for ORTModule
* Avoid allocating output buffer for Yield op
* Change to WaitAndReset to avoid overriding signal
* remove unnecessory signal/wait at the end of bg thread
* Return Session.Run result as a std::future
* export model with torch.no_grad()
* Handle bg thread's early return in Forward call
* Removed duplicated Yield kernel
* Silence "CUDA kernel missing log"
* Add missing transforms, clear iobinding (#6532)
* revert ortmodule.py to a working state first
* Apply ortmodule.py change from dev branch
* Rename to YieldOp
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: ashbhandare <ash.bhandare@gmail.com>
Co-authored-by: Sherlock <baihan.huang@gmail.com>
* Support to allow user to specify compute stream per session
Create computation cuda stream explicitly rather than use default legacy stream or per-thread default stream.
remove some redudant cudaStreamSynchronize
fix gpt2 model test failures
don't use default stream in nccl either.
add stream schronization in OnRunEnd()
using cub::DeviceScan::InclusiveSum which can be called with stream specified.
fix topK failure due to latest rebase
fix tensorrt
support user specified stream
add user_stream support in tensorrt EP
use same stream for both tensort and CUDA EP.
fix ScatterND
specify stream for adasum and p2p kernels.
fix loop
fix CApiTest.custom_op_handler
fix CApiTest.varied_input_custom_op_handler
change for cudaMemcpyFromSymbol
improve provider options for user specified compute stream
* add changes for ROCM EP
* fix GatherGrad UT for ROCM EP
* clean code and fix NonMaxSuppression
* use default stream for ROCM now
* fix CApiTest.custom_op_handler:OrtFormatCustomOpTests.ConvertOnnxModelToOrt
* fix tensorrt ut: CApiTest.io_binding_cuda
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* Deprecate Python global configuration functions [Part 1] (#5923)
Enable options to be set via execution provider (EP)-specific options and log deprecation warning from current global configuration functions.
* remove dnnl_dll_path from post build copy (#6142)
* Model Fusion For Bart (#6105)
Fusion fix for Bart models
* Unify IExecutionProvider and IExecutionProviderFactory interfaces (#6108)
* Remove Provider_IExecutionProvider and make the internal IExecutionProvider usable by shared providers
* Change Provider_IExecutionProviderFactory to be the core version.
* Enable running the mnist_training sample without cuda (#6085)
Signed-off-by: George Nash <george.nash@intel.com>
* nnapi add min max support (#6117)
* Fix CUDA test hang: (#6138)
- Make condition check in `CUDAAllocatorTest` to ensure CUDA device is present.
* Fix TensorRT kernel conflict issue for subgraphs of control flow operators (#6115)
* add static subgraph kernel index
* change kernel naming to avoid conflicts
* Add gradient registration for Abs. (#6139)
* Partition initial optimizer state for Zero-1 (#6093)
* Initial changes
* Working changes
* Working changes
* Cleanup
* fix windows CI
* Review comments
* review comments
* Fix edge case in BFCArena where allocation failures could lead to an infinite loop. (#6145)
#4656
* Revert "work around of the build break in mac (#6069)" (#6150)
This reverts commit 3cae28699b.
* Fix clean_docker_image_cache.py detection of image pushes. (#6151)
Fix clean_docker_image_cache.py detection of image pushes. They were being ignored because the expected HTTP status code was wrong. For pushes, it's 201 instead of 200.
* MLAS: add NEON version of int8 depthwise convolution (#6152)
* Using a map of of ops to stages as input of partition function. (#5940)
* New partition algorithm running before AD
* Convert cut_group_info into device map. Work in progress -- works for bert-tiny with pp=2
* Removing code for partition of bwd graphs
* Remove old code
* Adding some verification code
* Handle Shared Initializer
* Renaming rank with stage
* Added first unit test
* new test
* redundant check
* undo change in bert
* Moved cut-based partition to testing utils file
Co-authored-by: xzhu1900
Co-authored-by: wschin
* New conversion function and tests
* minor
* remove test that is not needed2
* improve GetDeviceAssignment and PR comments
* minor changes
* PR comments
* improving documentation and variable naming
* add documentation
* Variable naming and docs
* more doc improvements
* more doc improvements
* missing static cast
* Fix test file for windows
* Fix test file for windows
* Fix test file for windows
* stage id is not the same as rank id
* PR comments
* PR comments
* More comments
* More comments
* Minor fix to satisfy c++14 (#6162)
* Deprecating Horovod and refactored Adasum computations (#5468)
deprecated horovod submodule
refactored adasum logic to be ort-native
added tests for native kernel and e2e tests
* Update TensorRT-ExecutionProvider.md (#6161)
* Bugfix for topk cuda kernel (#6164)
* fix the issue that std::numeric_limits cannot handle half type
* adding a test
Co-authored-by: Du Li <duli@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Revert "Fuse MatMulIntegerToFloat only when scales are scalar (#6008)" (#6169)
This reverts commit f2dcba7afe.
* Remove ignored build warnings for pybind on Mac (#6165)
* save_checkpoint, load_checkpoint and aggregate_checkpoints (#6136)
* save_checkpoint and load_checkpoint implementations
* checkpoint aggregation logic
* unit tests for save_checkpoint, load_checkpoint and aggregate_checkpoints
* Don't try to bind unused inputs in the Training frontend (#6166)
* Update documentation for contributing a PR and add deprecation notices for PyOp and ORT server. (#6172)
* aggregate model states only for the case when mixed precision was true (#6176)
* [NNAPI EP] Enable per-channel quantization for QlinearConv (#6155)
* Enable qlinearconv per-channel quantization
* Fix the android CI test failure
* Add Android Version Check for Per-Channel Quant
* Address PR comments
* Fix some minor issues
* Add verification of per-channel zero points
* Make the error tolerance configurable
* Fix typo in BERT pretraining script (#6175)
A misplaced `}` meant that the `'enable_adasum'` option was interpreted incorrectly, causing the test to fail.
* Update get_docker_image.py to enable use without image cache container registry. (#6177)
Update get_docker_image.py to enable use without image cache container registry.
* Helper for compiling EP to generate deterministic unique ids for use in MetaDef names (#6156)
* Create a helper for generating unique ids that can be used by an EP that creates compiled nodes and needs ids to be deterministic for a model when used in multiple sessions.
Added to IExecutionProvider as this can potentially be used by all compiling EPs and is more robust than a simplistic counter (although EP implementer is free to choose either approach).
* Restructure the helper so it can be called across the EP bridge.
Add ability to call id generation helper from EP bridge
- convert DNNL EP to use helper to validate
Address issue where a new Model may be loaded into the same address as a previous one.
- hash the bytes in the Graph instance (1728 bytes currently) to use as the key to the full hash for the model
Add lock around id generation to ensure no issues if multiple sessions partitions graphs at exactly the same time.
- Extremely unlikely but would be hard to debug and the locking cost is not an issue as it's only incurred during graph partitioning and not execution.
* Backend APIs for checkpointing (#5803)
* Add backend API GetOptimizerState and GetModelState
* add GetPartitionInfoMap
* Android coverage dashboard (#6163)
* Write the report to a file.
* Post code coverage to the Dashboard database.
* Add usage details of unified MCR container image (#6182)
Going forward, a single unifed docker image will be published in
MCR. The hardware accelerator target choice will have to be made
in the application using OpenVINO EP's runtime config options.
* improve perf for softmax (#6128)
* improve perf for both gathergrad and softmax
* revert the change in gathergrad and will be done in another PR.
* address comments from code review.
* Tune fast Gelu to use exp(x) instead of tanh(x) on Rocm platform (#6174)
* tune fast gelu to use exp(x) instead of tanh(x) on rocm
* update to use expression 2/(1+exp(-2x))-1 for stability
* Add Status.csv to EP Perf Tool (#6167)
* merge master, keep postprocess status commit
* download float16.py everytime
* removing hardcoded values
* Lochi/quantization tool for trt (#6103)
* Initial implementation of generating calibration dynamic range table
* Initialize validation support for Quantization
* Initialize validation support for Quantization (cont.)
* Improve validation support for Quantization
* Improve validation support for Quantization
* Rewrite/Refine for calibration and validation
* Rewrite/Refine for calibration and validation (cont.)
* Refine code
* Refine code
* Add data reader for BERT
* Add flatbuffers to serialize calibration table
* Refine code and add BERT evaluation
* Refine the code
* minor modification
* Add preprocess/postprocess of vision team yolov3 and refine the code
* Update annotation
* Make bbox cooridates more accurate
* Fix bug
* Add support of batch processing
* Batch processing for model zoo yolov3
* Add batch inference for evaluation
* Refine the code
* Add README
* Add comments
* Refine the code for PR
* Remove batch support checking in data_reader and refine the code
* Refine the code for PR
* Refine the code for PR review
Co-authored-by: Olivia Jain <oljain@microsoft.com>
* Implement ScatterND for CUDA EP (#6184)
* Condition fix in Resize operator (#6193)
* Clean up checkpoint tests to use the new checkpoint functions (#6188)
* add deprecation warning for old checkpoint functions
* update all the distributed checkpoint tests to use new checkpoint functions
* Implement comparing outputs that are sequence of maps of strings to floats (#6180)
* Implement conversion from ortvalue to Itensor for string tensors and comparing sequence of maps of strings to floats
* PR comments
* Dockerfile to build onnxruntime with ROCm 4.0
* Add ability to skip GPU tests based on GPU adapter name (#6198)
* Implement conversion from ortvalue to Itensor for string tensors and comparing sequence of maps of strings to floats
* PR comments
* Add ability to skip gpu tests according to adapter description
* spacing
* spacing
* spacing
* Openvino ep 2021.2 (#6196)
* Enabling fasterrcnn variant and vehicle detector
* changes for 2021_2 branch
* yolov3_pytorch commit
* fixed braces in basic_backend.cc
* ci information added
* faster rcnn variant and vehicle detector changes were made in 2021.1 and not in 2021.2
* some changes to support unit tests
* disable some tests which are failing
* fix myriad tests for vehicle detector
* Did some cleanup
*cleaned up comments
*Disabled Add_Broadcast_0x1 and Add_Broadcast_1x0
tests on MYRIAD_FP16 backend due to a bug
*cleaned up capability_2021_2.cc file
*Removed extra conditions which were added
for some validation in backend_utils
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* yolov3 pytorch workaround to ensure that the output names are matched
* gemmoptest fixed on myriad
* Fixed MYRIADX CPP Test Failures
*Expand,GatherND,Range,Round op's
are only supported in model
*where op with float input data
types are not supported and fixed
*Scatter and ScatterElements op's with
negative axis are fixed
*Reshape op with 0 dim value are not
supported and fixed
*Disabled InstanceNorm_2 test on MYRIADX
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* make changes to yolov3 pytorch
* Fixed python unit tests
*Fixed failing python tests on vpu,
GPU and CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes POW op failures on GPU_FP16
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Clean up capability_2021_2.cc
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated docx for MultiThreading option
*Added extra info on setting the num_of_threads
option using the API and it's actual usage
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* fixed slice and removed extra prints
* Disabled failing python tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor changes added in capabilty_2021_2
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* made changes to slice to avoid failures
* Disabling FP16 support for GPU_FP32
->Inferencing an FP16 model on GPU_FP32
leads to accuracy mismatches. so, we would
rather use GPU_FP16 to infer an FP16 model
on GPU Device
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated docx for Inferencing a FP16 Model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* fix for mask rcnn
* Script for installing openvino from source
* Updated with openvino 2021.2 online installation
* code comment fixes
fixed accuracy mismatch for div
* Update OpenvinoEP-ExecutionProvider.md
updated for 2021.2 branch
* Update README.md
updated dockerfile documentation
* Update BUILD.md
build.md update documentation
* permissiong change of install_openvino.sh
* made changes to align with microsoft onnxruntime changes
* Updated with ov 2021.2.200
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: mohdansx <mohdx.ansari@intel.com>
* Fix a memory leak in test_inference.cc (#6201)
* Fix a memory leak in test_inference.cc
* Use TArray in AMD element-wise kernels, rather than manually copying memory to device.
* Remove most ROCm-specific element-wise code and reuse CUDA element-wise code.
* Minor change to improve performance for operator Pad. (#5537)
* small improvment for pad
* Support double for operators Log, Reciprocal, Sum (CPU) (#6032)
* Support double for operators Log, Reciprocal, Sum
* remove tesdt erf_double
* Support double for operators Where, LpNormalisation (#6034)
* Support double for operators Relu, Tanh, Sigmoid (#6221)
* Fix ImportError in build.py (#6231)
There is a possible ImportError where build.py can import the wrong 'util' package if there are others present in `sys.path` already
* Removed executor todo that looks dead. (#6234)
* Remove MKLML/openblas/jemalloc build config (#6212)
* Remove python 3.5
* Update the readme file
* Upgrade build.py to assert for python 3.6+
Upgrade build.py to assert for python 3.6+
as python 3.5 cannot build anymore todays master.
* Support MLFloat16 type in Pow opset-12 CUDA kernel (#6233)
* MLAS: handle MlasGemm(M/N/K==0) cases (#6238)
* Support double for operator TopK + fix one bug in TopK implementation for GPU for double (#6220)
* Support double for operator TopK
* add static classes for topk/double
* fix cast issue in topk
* Support double for operator Gemm + fix bug in gemm implementation for cuda, rocm when sizeof(type) != sizeof(float) (#6223)
* Support double for operator Gemm
* fix type size while copying data in gemm operator for GPU
* fix type in gemm implementation for rocm
* Support double for operator ReduceMean, ReduceLogSumExp (#6217)
* Support double for operators ReduceMean, ReduceLogSumExp
* Support double for operator ArgMin (#6222)
* Support double for operator ArgMin
* add test specifically for double
* add new test on pai-excluded-tests.txt
* Update BUILD.md
* Update manylinux docker image to the latest (#6242)
* Fix allocator issue for TensorRT IOBinding (#6240)
* Fix issue: https://github.com/microsoft/onnxruntime/issues/6094
Root cause: we didn't expose the OrtMemoryInfo for TRT, so it will cause issue if user want use IObinding for Tensorrt.
Short term fix, add the OrtMemoryInfo for TRT. Long term should unify the allocator for CUDA and TRT
* Tune BiasGeluGradDx kernel in approximation mode to avoid tanh(...) on Rocm (#6239)
* bias gelu grad use exp(...) instead
* update cuda to rocm
* missing semicolon
* comment
* remove dockerfile
* missing factor of two
* Refactor EP Perf Tool (#6202)
* merge master, keep postprocess status commit
* download float16.py everytime
* using variables to reference eps
* adding ACL EP to ep perf tool
* accuracy with absolute tolerance configurable
* add acl to dict + remove commented line
* Documentation for distributed CI tests pipeline (#6140)
* Remove a debug log in provider_test_utils.cc (#6200)
* Add the Concat Slice Elimination transform, fix constant_folding transform (#5457)
* Add concat slice transform + test
* Cosmetic improvements in concat slice transform
* Remove unrelated file, fix comment, fix constant folding bug
* Add test onnx graph
* fix windows build
* Review comments
* review comment
Co-authored-by: Aishwarya <aibhanda@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add MakeStringLite which uses current locale, update some MakeString call sites to use it instead. (#6252)
* Add MakeStringLite which uses current locale, update macros to use that to generate messages.
* Convert calls to MakeStringLite().
* Liqun/speech model loop to scan (#6070)
Provide a tool to convert Loop to Scan for Nuphar performance
Fix Nuphar CI pipeline failures.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* model parallel refinement (#6244)
* Megatron Transformation as a seperate step
* remove useless header
* clang formating
* Re-Structure megatron transformer for subsquent changes
* fix comments
* Allow querying a GraphProto's doc_string as part of ModelMetadata (#6248)
* Fix Linux/Mac error message on input type mismatch (#6256)
* add bfloat16 to gathergrad type constrains (#6267)
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* Fix VS 2017 build break (#6276)
* Deprecate Python global configuration functions [Part 2] (#6171)
Update Python API to allow more flexibility for setting providers and provider options.
The providers argument (InferenceSession/TrainingSession constructors, InferenceSession.set_providers()) now also accepts a tuple of (name, options dict).
Fix get_available_providers() API (and the corresponding function in the C API) to return the providers in default priority order. Now it can be used as a starting point for the providers argument and maintain the default priority order.
Convert some usages of the deprecated global configuration functions to use EP-specific options instead.
Update some EP-specific option parsing to fail on unknown options.
Other clean up.
* Add script to preprocess python documentation before publishing (#6129)
* add script to preprocessing python documentation before publishing
* rename past to past_key_values for GPT-2 (#6269)
rename past to past_key_values for transformers 4.*
* Rename MakeString and ParseString functions. (#6272)
Rename MakeString to MakeStringWithClassicLocale, MakeStringLite to MakeString, *ParseString to *ParseStringWithClassicLocale.
Add missing pass-through versions of MakeStringWithClassicLocale for string types.
* Increase timeout for Linux GPU CUDA11 build. (#6280)
* Add helper to compare model with different precision (#6270)
* add parity_check_helper.py
* add real example
* remove lines
* Fix Min/Max CPU kernels for float16 type (#6205)
* fix data_ptr assertion error for past_sequence_length=0 in GPT-2 (#6284)
fix io binding crash for past_sequence_length=0
* A list of changes in transformers tool (#6224)
* longformer fp16 e2e
* add fp16/fp32 parity check helper file
* excludes nodes with subgraph in profiling
* use onnxconverter_common to do fp32->fp16
* add version check for onnxconverter_common
* remove helper file
* add pkg installation on notebooks and script
* Workaround for static_cast<double>(half)
* Add workaround to remove ROCm-specific binary-elementwise files.
* Update nuget build (#6297)
1. Update the ProtoSrc path. The old one is not used anymore.
2. Regenerate OnnxMl.cs
3. Delete some unused code in tools/ci_build/build.py
4. Avoid set intra_op_param.thread_pool_size in ModelTests in OpenMP build.
5. Fix a typo in the C API pipeline.
* Enable ONNX backend test of SequenceProto input/output (#6043)
* assert sequence tensor and remove skips
* update testdata json
* use ONNX 1.8 in cgmanifest.json
* use previous commit to workaround
* update ONNX commit ID in docker
* skip test_maxpool_2d_dilations test for now
* update function name
* add --sequence_lengths option (#6285)
* more dtype for Equal CUDA kernel (#6288)
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
* Force reinstall onnx python package on Windows (#6309)
* update transformers required package versions (#6315)
* Remove abs in LpPool (#6303)
* Support 1D input for Conv + Mul/Add fusion optimizer with test (#6295)
* Support 1D input (N C H) for Conv + Mul/Add fusion optimizer with test cases and test models.
* Add longformer to python package (#6314)
* add longformer to python package
* move test related script and data to a new folder
* Avoid false sharing on thread pool data structures (#6298)
Description: This change adds alignment and padding to avoid false sharing on fields in the thread pool. It also adds a new microbenchmark to profile thread-pool performance over short loops.
Motivation and Context
MobileNet on a 2*12-core system showed a performance gap between the ORT thread pool and OpenMP. One cause appeared to be false sharing on fields in the thread pool: ThreadPoolParallelSection::tasks_finished (which the main thread spins on waiting for workers to complete a loop), and the RunQueue::front_ and back_ fields (used respectively by the worker thread and the main thread).
The additional micro-benchmark BM_ThreadPoolSimpleParallelFor tests performance of loops of different sizes at different thread counts. The results below are on a machine with 2*14-core processors (E5-2690 v4) running with 1, 14, 15, and 28 threads. For each test, the microbenchmark has N threads run a loop with N iterations; hence a perfect result is for the time taken to be constant as additional threads are added (although we will also see power management effects helping at very low thread counts). The loop durations (100000, 10000, 1000) correspond roughly to 200us, 20us, and 2us on this machine.
Before change:
BM_ThreadPoolSimpleParallelFor/1/1/100000/real_time 17153 us 17154 us 32
BM_ThreadPoolSimpleParallelFor/14/14/100000/real_time 22553 us 22553 us 30
BM_ThreadPoolSimpleParallelFor/15/15/100000/real_time 21521 us 21521 us 29
BM_ThreadPoolSimpleParallelFor/28/28/100000/real_time 24111 us 24111 us 24
BM_ThreadPoolSimpleParallelFor/1/1/10000/real_time 1719 us 1719 us 407
BM_ThreadPoolSimpleParallelFor/14/14/10000/real_time 3409 us 3409 us 200
BM_ThreadPoolSimpleParallelFor/15/15/10000/real_time 3541 us 3541 us 201
BM_ThreadPoolSimpleParallelFor/28/28/10000/real_time 4576 us 4576 us 151
BM_ThreadPoolSimpleParallelFor/1/1/1000/real_time 174 us 174 us 4017
BM_ThreadPoolSimpleParallelFor/14/14/1000/real_time 1586 us 1586 us 402
BM_ThreadPoolSimpleParallelFor/15/15/1000/real_time 1586 us 1586 us 397
BM_ThreadPoolSimpleParallelFor/28/28/1000/real_time 2864 us 2864 us 232
After change:
BM_ThreadPoolSimpleParallelFor/1/1/100000/real_time 17160 us 17160 us 33
BM_ThreadPoolSimpleParallelFor/14/14/100000/real_time 20989 us 20989 us 31
BM_ThreadPoolSimpleParallelFor/15/15/100000/real_time 22286 us 22286 us 31
BM_ThreadPoolSimpleParallelFor/28/28/100000/real_time 24631 us 24631 us 25
BM_ThreadPoolSimpleParallelFor/1/1/10000/real_time 1718 us 1718 us 407
BM_ThreadPoolSimpleParallelFor/14/14/10000/real_time 2868 us 2868 us 242
BM_ThreadPoolSimpleParallelFor/15/15/10000/real_time 2907 us 2907 us 240
BM_ThreadPoolSimpleParallelFor/28/28/10000/real_time 3872 us 3872 us 186
BM_ThreadPoolSimpleParallelFor/1/1/1000/real_time 175 us 175 us 3938
BM_ThreadPoolSimpleParallelFor/14/14/1000/real_time 933 us 933 us 659
BM_ThreadPoolSimpleParallelFor/15/15/1000/real_time 912 us 912 us 591
BM_ThreadPoolSimpleParallelFor/28/28/1000/real_time 1976 us 1976 us 317
* fix opset imports for function body (#6287)
* fix function opsets
* add tests and update onnx
* changes per review comments
* add comments
* plus updates
* build fix
* Remove false positive prefast warning from threadpool (#6324)
* Java: add Semmle to Java publishing pipelines (#6326)
Add Semmle to Java API pipeline
Add security results publishing and add Java GPU.
* Quantization support for split operator with its NHWC support (#6107)
* Make split working for quantization.
* NHWC transformer support for split operator
* Refactor some according to Feedback. Will add test cases soon.
* Fix build error on windows.
* Add test case for split op on uint8_t support
* Add nhwc_transformer_test for split uint8_t support
* Some change according to PR feedbacks.
* Liqun/enable pipeline parallel test (#6331)
enable pipeline parallel test
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Use onnxruntime_USE_FULL_PROTOBUF=OFF for the cuda execution provider (#6340)
This removes a special case of the cuda EP.
* MLAS: add fallback implementation for quantized GEMM (#6335)
Add a non-vectorized version of the kernel used for the quantized version of MlasGemm.
* Delete float16.py (#6336)
No longer needed. Also doesn't pass policheck.
* Enable add + softmax fusion for Rocm platform (#6259)
* add bias softmax; tests appear to pass
* check fusion occurs for rocm as well
* check for rocm provider compatible as well
* build for cpu scenario as well
* try again; broader cope
* proper scope on kGpuExecutionProvider
* been editing wrong file
* remove commented #include lines
* try again due to mac os ci error
* try again
* test fusion both cuda and rocm to avoid mac ci error
* add external data support to tensor proto utils (#6257)
* update unpack tensor utilities to support loading external data
* more updates
* fix test
* fix nuphar build
* minor build fix
* add tests
* fix Android CI
* fix warning
* fix DML build failure and some warnings
* more updates
* more updates
* plus few updates
* plus some refactoring
* changes per review
* plus some change
* remove temp code
* plus updates to safeint usage
* build fix
* fix for safeint
* changed wording. (#6337)
* Remove OpSchema dummy definition. Only needed for Function now, and we can just exclude the method in Function (#6321)
* remove gemmlowp submodule (#6341)
* [NNAPI] Add pow support (#6310)
* Add support for running Android emulator from build.py on Windows. (#6317)
* fix the pipeline failure (#6346)
* Train BERT Using BFloat16 on A100 (#6090)
* traing bert using bf16
* Adam support bf16
* bugfix
* add fusedmatmul support
* fix after merge from master.
* bugfix
* bugfix after merge from master
* fast reduction for bf16.
* resolve comments
* fix win build
* bugfix
* change header file.
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
* Fix DerefNullPtr issues raised by SDLNativeRules. (#6348)
* update quantize to support basic optimization and e2e example for image classification (#6313)
update the resnet50-v1 to standard one from onnx zoo.
add an example for mobilenet
run basic optimization before quantization
fix a bug in Clip
* Enable graph save for orttrainer (#6333)
* Enable graph save for orttrainer
* Fix CI
* Update orttraining/orttraining/python/training/orttrainer_options.py
* Update orttraining/orttraining/python/training/orttrainer_options.py
* Update orttraining/orttraining/python/training/orttrainer_options.py
* Update orttraining/orttraining/python/training/orttrainer_options.py
* Update orttraining/orttraining/python/training/orttrainer_options.py
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add PREfast to python packaging pipeline (#6343)
* Add PREfast to python packaging pipeline
* fix longformer benchmark io_binding output_buffers (#6345)
* fix longformer benchmark io_binding output_buffers
* format
* import benchmark_helper from parent directory.
* Use readelf for minimal build binary size checks. (#6338)
* Use readelf for minimal build binary size checks.
The on-disk size grows in 4KB chunks which makes it hard to see how much growth an individual checkin causes.
Only downside is that the sum of the sections is larger than the on-disk size (assumably things get packed smaller on disk and some of the section alignment constraints can be ignored)
* Remove unused function
* Java: Set C language warnings to W4 and adjust JNI code (#6347)
Set /W3 for C language and fix up JNI warnings.
* Pipeline Parallel Experimental Python API (#5815)
* Add create session to WinML telemetry to track WinML Usage (#6356)
* Fix one more SDL warning (#6359)
* fix -Wdangling-gsl (#6357)
* Add python example of TensorRT INT8 inference on ResNet model (#6255)
* add trt int8 example on resnet model
* Update e2e_tensorrt_resnet_example.py
* remove keras dependency and update class names
* move ImageNetDataReader and ImageClassificationEvaluator to tensorrt resnet example
* simplify e2e_tensorrt_resnet_example.py
* Update preprocessing.py
* merge tensorrt_calibrate
* Update calibrate.py
* Update calibrate.py
* generalize calibrate
* Update calibrate.py
* fix issues
* fix formating
* remove augment_all
* This added telemetry isn't needed (#6363)
* Wezuo/memory analysis (#5658)
* merged alloc_plan
* pass compilation
* Start running, incorrect allocation memory info
* add in comments
* fix a bug of recording pattern too early.
* debugging lifetime
* fix lifetime
* passed mnist
* in process of visualization
* Add code to generate chrome trace for allocations.
* in process of collecting fragmentation
* before rebuild
* passed mnist
* passed bert tiny
* fix the inplace reuse
* fix the exception of weight in pinned memory
* add guards to ensure the tensor is in AllocPlan
* add customized profiling
* debugging
* debugging
* fix the reuse of differnt location type
* add rank
* add the rank
* add fragmentation
* add time_step_trace
* Add summary for each execution step (total bytes, used/free bytes).
* add top k
* change type of top k parameter
* remove prints
* change heap to set{
* add the name pattern
* add the useage for pattern
* add partition
* change to static class
* add custom group
* remove const
* update memory_info
* in process of adding it as runtime config
* change the memory profiling to be an argument
* add some comments
* add checks to recored meomry_info in traaining session
* set the "local rank setting" to correct argument.
* addressing comments
* format adjustment
* formatting
* remove alloc_interval
* update memory_info.cc to skip session when there is no tensor for a particular memory type
* fix memory_info multiple iteration seg-fault
* consolidate mainz changes
* fixed some minor errors
* guard by ORT_MINIMAL_BUILD
* add ORT_MEMORY_PROFILE flag
* added compiler flag to turn on/off memory profiling related code
* clean up the code regarding comments
* add comments
* revoke the onnx version
* clean up the code to match master
* clean up the code to match master
* clean up the code to match master
Co-authored-by: Jesse Benson <benson.jesse@gmail.com>
Co-authored-by: Wei Zuo <wezuo@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: wezuo <wezuo@az-eus-v100-32gb-5-worker-mgtbby.eastus.cloudapp.azure.com>
Co-authored-by: wezuo <wezuo@az-eus-v100-32gb-5-worker-yclzsf.eastus.cloudapp.azure.com>
* Support MLFloat16 in CumSum Cuda op for Opset 14 (#6355)
* Add CumSum-14 for Cuda
* fix convert_common version retrival (#6382)
* Refine auto_pad based pad computation in ConvTranspose (#6305)
* Fix SDL warning (#6390)
* Add max_norm for gradient clipping. (#6289)
* add max_norm as user option for gradient clipping
* add adam and lamb test cases for clip norm
* add frontend tests
* Add the custom op project information (#6334)
* Dont use default string marshalling in C# (#6219)
* Fix Windows x86 compiler warnings in the optimizers project (#6377)
* [Perf] Optimize Tile CPU and CUDA kernels for a corner case (#6376)
* Unblock Android CI code coverage failure (#6393)
* fix build on cuda11 (#6394)
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
* Load the model path correctly (#6369)
* Fix some compile warnings (#6316)
* OpenVino docker file changes to bypass privileged mode
Description: Builds and installs libusb without UDEV support, which is used for communicating with the VPU device.
Motivation and Context
This enables the resulting docker container to be run without '--privileged' and '--network host' options which may not be suitable in deployment environments.
* Megatron checkpointing (#6293)
* Add bart fairseq run script
* Add frontend change to enable megatron
* Initial changes for checkpointing
* Megatron optim state loading, checkpoint aggregation, frontend distributed tests for H, D+H
* Add load_checkpoint changes
* Fix CI
* Cleanup
* Fix CI
* review comments
* review comments
* review comments:
* Fix generate_submodule_cgmanifest.py Windows issues. (#6404)
* Continue memory planning when unknown shape tensor is encountered. (#6413)
* Reintroduce experimental api changes and fix remote build break (#6385)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* Add support for custom ops to minimal build. (#6228)
* Add support for custom ops to minimal build.
Cost is only ~8KB so including in base minimal build.
* enable pipeline to run quantization tests (#6416)
* enable pipeline to run quantization tests
setup test pipeline for quantization
* Minor cmake change (#6431)
* Liqun/liqun/enable pipeline parallel test2 (#6399)
* enable data and pipeline parallism test
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Farewell TrainableDropout (#5793)
* Deprecate TrainableDropout kernel.
* Update bert_toy_postprocessed.onnx to opset 12.
* Add more dropout tests.
* Fix BiasDropout kernel.
Co-authored-by: Ubuntu <OrtTrainingDev3@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Sergii Dymchenko <sedymche@microsoft.com>
* fix null dereference warning (#6437)
* Expose graph ModelPath to TensorRT shared library (#6353)
* Update graph_viewer.cc
* Update tensorrt_execution_provider.cc
* Update graph_viewer.h
* Update tensorrt_execution_provider.cc
* Update tensorrt_execution_provider.cc
* Update provider_api.h
* Update provider_bridge_ort.cc
* Update provider_interfaces.h
* Update provider_interfaces.h
* expose GraphViewer ModelPath API to TRT shared lib
* add modelpath to compile
* update
* add model_path to onnx tensorrt parser
* use GenerateMetaDefId to generate unique TRT kernel name
* use GenerateMetaDefId to generate unique TRT engine name
* fix issue
* Update tensorrt_execution_provider.cc
* remove GetVecHash
* Update tensorrt_execution_provider.h
* convert wchar_t to char for tensorrt parser
* update tensorrt parser to include latest changes
* fix issues
* Update tensorrt_execution_provider.cc
* merge trt parser latest change
* add PROVIDER_DISALLOW_ALL(Path)
* add tool for generating test data for longformer (#6415)
* only build experimental api in redist (#6465)
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Add an option to save the training graph after optimization (#6410)
* expose optimized_model_filepath in SessionOptions as `debug.graph_save_paths.model_with_training_graph_after_optimization_path` in `ORTTrainerOptions`
* Share allocator between CUDA EP & TRT EP. (#6332)
* Share allocator between CUDA EP & TRT EP.
limitation:
1. Does not cover the per-thread allocator created by CUDA EP, still need to figure out the way to remove it
2. Need to have more identifiers to make it able to share CPU allocator across all EPs
* fix max norm clipping test in python packaging pipeline test (#6468)
* fix python packaging pipeline
* make clip norm test compatabile with both V100 and M60 GPUs
* Initial version of CoreML EP (#6392)
* Bug 31463811: Servicing: Redist (Nuget) conflicts with Microsoft.AI.MachineLearning starting 21H1+ (#6460)
* update load library code to have the fullly qualified path
* make it work for syswow32
* git Revert "make it work for syswow32"
This reverts commit b9f594341b7cf07241b18d0c376af905edcabae3.
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* dequantize 1st input of lstm back if it is quantized (#6444)
* [java] Adds support for OrtEnvironment thread pools (#6406)
* Updates for Gradle 7.
* Adding support for OrtThreadingOptions into the Java API.
* Fixing a typo in the JNI code.
* Adding a test for the environment's thread pool.
* Fix cuda test, add comment to failure.
* Updating build.gradle
* fix SDL native rule warning #6246 (#6461)
* fix SDL rule (#6464)
* use tickcount64 (#6447)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* Update pypi package metadata (#6354)
* Update setup file data
* add missing comma
* remove python 3.5
* fix typo bracket
* Delete nuget extra configs (#6477)
* Op kernel type reduction infrastructure. (#6466)
Add infrastructure to support type reduction in Op kernel implementations.
Update Cast and IsInf CPU kernels to use it.
* Fixing a leak in OnnxSequences with String keys or values. (#6473)
* Increase the distributes tests pipeline timeout to 120 minutes (#6479)
* [CoreML EP] Add CI for CoreML EP (macOS) and add coreml_flags for EP options (#6481)
* Add macos coreml CI and coreml_flags
* Move save debuggubg model to use environment var
* Move pipeline off from macos CI template
* Fix an issue building using unix make, add parallel to build script
* Fixed build break for shared_lib and cmpile warning
* Fix a compile warning
* test
* Revert the accidental push from another branch
This reverts commit 472029ba25d50f9508474c9eeceb3454cead7877.
* Add ability to track per operator types in reduced build config. (#6428)
* Add ability to generate configuration that includes required types for individual operators, to allow build size reduction based on that.
- Add python bindings for ORT format models
- Add script to update bindings and help info
- Add parsing of ORT format models
- Add ability to enable type reduction to config generation
- Update build.py to only allow operator/type reduction via config
- simpler to require config to be generated first
- can't mix a type aware (ORT format model only) and non-type aware config as that may result in insufficient types being enabled
- Add script to create reduced build config
- Update CIs
* merge e2e with distributed pipeline (#6443)
merge e2e with distributed pipeline
* Fix test breaks in Windows ingestion pipeline (#6476)
* fix various build breaks with Windows build
* fix runtime errors loading libraries from system32
* add build_inbox check to winml_test_common
* use raw string
* cleanup
* fix dll load
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Speed up the Mac CI runs (#6483)
* expose learningmodelpixelrange property (#5877)
* Fix of support api version bug for [de]quantize (#6492)
* SDL fixes: add proper casts/format specifiers (#6446)
* SDL annotation fixes (#6448)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* [OpenVINO-EP] Remove support for OpenVINO 2020.2 (#6493)
* Removed OpenVINO 2020.2 support
* Updated documentation and build.py
* Removed unnecessary libraries from setup.py
* Support pad operator in quantization and quantized nhwc transformer. Fix Pad operator bug. (#6325)
Support pad operator in quantization tool.
Support pad operator in quantized nhwc transformer.
Fix pad() operator bug when pad input's inner(right) most axis value is zero for Edge and Reflect mode, it copied wrong value to the cells to be padded. Note the Constant mode will not trigger this bug, as Edge/Reflect need copy value from the already copied array while Constant mode only fill specified value.
Add more test cases to cover pad() operator bug fixed here.
Fix quantization tools uint8/int8 value overflow issue when quantize weights in python.
* Improve work distribution for Expand operator, and sharded LoopCounter configuration (#6454)
Description: This PR makes two changes identified while looking at a PGAN model.
First, it uses ThreadPool::TryParallelFor for the main parallel loops in the Expand operator. This lets the thread pool decide on the granularity at which to distribute work (unlike TrySimpleParallelFor). Profiling showed high costs when running "simple" loops with 4M iterations each of which copied only 4 bytes.
Second, it updates the sharded loop counter in the thread pool so that the number of shards is capped by the number of threads. This helps make the performance of any other high-contention "simple" loops more robust at low thread counts by letting each thread work on its own "home" shard for longer.
Motivation and Context
Profiling showed a PGAN model taking 2x+ longer with the non-OpenMP build. The root cause was that the OpenMP build uses simple static scheduling of loop iterations, while the non-OpenMP build uses dynamic scheduling. The combination of large numbers of tiny iterations is less significant with static scheduling --- although still desirable to avoid, given that each iteration incurs a std::function invocation.
* Update document of transformer optimization (#6487)
* nuphar test to avoid test data download to improve passing rate (#6467)
nuphar test to avoid test data download to improve passing rate
* Fuse cuda conv with activation (#6351)
* optimize cuda conv by fused activation
* remove needless print out
* exclude test from cpu
* handle status error from cudnn 8.x
* add reference to base class
* add hipify
* [CoreML EP] Add support for some activations/Transpose, move some shared helpers from NNAPI to shared space (#6498)
* Init change
* Move some helper from nnapi ep to shared
* Add transpose support
* Fix trt ci build break
* Refine transformers profiler output (#6502)
* output nodes in the original order; grouped by node name
* add document for profiler
* Update to match new test setup. (#6496)
* Update to match new test setup.
* Add Gemm(7) manually for now.
Will fix properly on Monday. It's used by mnist.ort as that is created by optimizing mnist.onnx to level 1 causing 2 nodes to be replaced by a Gemm and the op to be missing from the required list as that is created using the original onnx model.
* Enable dense sequence optimized version of Pytorch exported BERT-L on AMD GPU (#6504)
* Permit dense seq optimization on BERT-L pytorch export by enabling ReduceSumTraining, Equal, and NonZero on AMD
* enable Equal tests
* enable fast_matrix_reduction test case
* Optimize GatherGrad for AMD GPU (#6381)
* optimize gathergrad
* address comments
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* add explicit barriers for buffer overread and overrwrite (#6484)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* fix sdl bugs for uninitialized variables and returns (#6450)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* handle hr error conditions (#6449)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* Dnnl training (#6045)
* Add ReluGrad and ConvGrad ops for the dnnl provider
* the mnist sample is updated to add the --use_dnnl option that
will cause the sample to use the dnnl execution provider for
nodes that exist in dnnl provider.
* Added the ability to find forward ops. Dnnl backward gradient
ops require the forward primitive description and workspace
from the forward operation.
* Enable specifying the execution provider for Gradient Checker Tests
* Prevent memory leak when running dnnl_provider in training mode
Prevent creating a SubgraphPrimitivePool when the code is built with the
ENABLE_TRAINING build flag. Instead create a SubgraphPrimitive directly.
The SubgraphPrimitivePool was causing a pool of SubgraphPrimitives to be
stashed in a map for reuse. Due to the way the Training Loop uses threads
the pool of SubgraphPrimitives were not being reuse instead a new pool of
SubgraphPrimitives being created each run. The old pool was not instantly
freed. This behavior could be a language error when using thread_local
memory.
Signed-off-by: George Nash <george.nash@intel.com>
* Added fixes to maxpoolgrad and memory leak.
Maxpoolgrad will now pass all unit tests.
With the conv and convgrad disabled for dnnl, mnist is able to train till 95%
Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Fixed misc issues when testing training code with dnnl provider
* fix conv_grad dnnl tests with dilation to run dnnl execution provider
* update mnist training sample to accept convolution type models
convolution models require the input shape to be {1, 28, 28}
instead of the flat {728} image that is used for the gemm models
this will enable models that require the different shape by adding
`--model_type conv` to the command line when running the mnist sample.
(while testing a workaround was used see #4762)
* Disable weight caching in dnnl conv operator when using training
When training we can not use cached weights because the weight
will be updated each run. This re-enables dnnl Conv and ConvGrad Ops.
The weight caching was the source of the error from Conv when training.
* Fix issues found when building grad ops on Linux
* The dnnl_convgrad code was over using the scope operator
causing a compilation problem.
* The dnnl_maxpoolgrad code had a logic error that is was
comparing with the source description when it should have
been comparing with the destination despription.
* Update BUILD.md so it shows DNNL for training
* Updated the table of contents. Since the same providers
are listed twice. Once for Infrance and again for Training
an HTML anchor was added to distinguish the second header
from the first for the TOC.
* Fix build failure when not using --enable-training build option
* reorganize the gradient operators so they are grouped together
* Fix issues found when running onnx_backend_test_series.py
* Pooling code only supports 2 outputs when built with --enable-training
* Address code review feedback
* class member variables end in underscore_
* use dst instead of dist to match pattern use elsewhere in DNNL code.
* Remove workaround that was introduced to handle problems running
convolution based training models. See issue #4762
Signed-off-by: George Nash <george.nash@intel.com>
* Isolate training code and code cleanup
* Do not build if dnnl_gpu_runtime if enable_training is set training code
does not support dnnl_gpu_runtime yet.
* Isolated Training code inside ifdefs so that they wont affect
project if built without training enabled
* Inadvertant changes in whitespace were removed to make code review simpler
* Undid some code reordering that was not needed
* comments added to closing #endif statments to simplify reading complex ifdefs
* Modified the GetPrimitiveDesc functions to return shared_ptr instead of raw
pointer. This matches what was done in Pool code and is safer memory code.
Signed-off-by: George Nash <george.nash@intel.com>
* Address code review issues
- whitespace changes caused by running clang-format on the code
- Several spelling errors fixed
- Removed/changed some ifdefs to improve readability
- other misc. changes in responce to code review.
Signed-off-by: George Nash <george.nash@intel.com>
* Code changes to address code review
- Simplify iteration code using `auto` keyword
- remove C style cast that was not needed
- remove instance variable that was not needed [relugrad.h]
- added the execution providers to `ComputeGradientErrorInternal()`
and `ComputeTheoreticalJacobianTranspose()` instead of using
a pointer to an instance varaible [gradient_checker.h/.cc]
Signed-off-by: George Nash <george.nash@intel.com>
* Combined the default gradient ops test and dnnl gradient ops test for ConvGrad and MaxPoolGrad into one function with the help of a helper function.
This will reduce repeated code.
Signed-off-by: Palangotu Keshava, Chethan's avatarChethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Replaced the stack used by convgrad to vector so that the vector(used as stack) can be easily cleared everytime the graph is created.
This will prevent memory leak from convolution kernels being pushed constantly onto the stack.
Signed-off-by: chethan.palangotu.keshava@intel.com
* Code clean up and formating updates
- Removed empty else statment
- updated indentation of code that was causing double curly brackets to look unususal
- Changed check for NumDimensions to Size in Relu and ReluGrad error checking code.
- isolated training code
Signed-off-by: George Nash <george.nash@intel.com>
* Restore inadvertantly removed ConvGrad tests
When combining the DNNL and CPU version of the ConvGrad
tests two test were inadvertantly excluded. This adds
back the Conv3d and Conv3d with strides test cases.
Signed-off-by: George Nash <george.nash@intel.com>
* Add validation to ConvGrad
This validates the dimensions of the ConvGrad match the
passed in Convolution forward primitive description.
The current code for DNNL ConvGrad makes the assumption that the ConvGrad
nodes will be visited in the reverse order from the corresponding Conv nodes
The added validation will return an error if this assumption is not true.
Signed-off-by: George Nash <george.nash@intel.com>
* Do not create new execution providers in provider_test_utils
This removes the code that generated new execution providers in the
OpTester::Run function. This was added because the std::move was
leaving the `entry` value empty so subsequent calls would cause a
segfault.
Problem is this potentially changed the execution_provider because it
would create the default provider dropping any custom arguments.
When the now removed code was originally added the std::move was causing
crashes when the GradientChecker unit tests were run. However, it is no
longer causing problems even with the code removed.
Signed-off-by: George Nash <george.nash@intel.com>
* Change the forward conv stack to a forward conv map
This changes how the forward conv kernel is mapped to the bwd ConvGrad
kernel the problematic stack is no longer used.
The convolution stack made the assumption that the corresponding
ConvGrad operator would be visited in reverse order of the forward
Conv operators. This was always problematic and was unlikely to
work for inception models.
Important changes:
- The weight_name is added to the ConvGrad dnnl_node making it
possible to use the weight_name as a lookup key to find the
Conv forward Kernel
- the `std::vector fwd_conv_stack_` has been replaced with a
`std::map fwd_conv_kernel_map_`
- Although it is not needed lock_guards were added when writing
to and reading from the fwd_conv_kernel_map_ as well as the
fwd_kernel_map_. These should always be accessed by a single
thread when preparing the dnnl subgraphs so the guard should not
be needed but its added just in case.
- Updated the comments ConvGrad.h code to no longer mention the
stack. The error check is not removed. It will be good to verify
there are no errors as we continue to test against more models.
Signed-off-by: George Nash <george.nash@intel.com>
Co-authored-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
Co-authored-by: unknown <63478620+jeyblu@users.noreply.github.com>
* Lochi/refactor yolov3 quantization (#6290)
* Refactor the code and move data reader, preprocessing, evaluation to
E2E_example_mode
* Refactor the code.
Move data reader, preprocessing, evaluation to model specific example
under E2E_example_mode
* refactor code
* Move yolov3 example to specific folder and add additional pre/post
processing
* Print a warning message for using newer c_api header on old binary (#6507)
* Fix issues with ArmNN build setup (#6495)
* ArmNN build fixes
* Update BUILD.md to document that the ACL paths must be specified to build ArmNN
* Fix CUDA build error. We don't setup the link libraries correctly/consistently so improve that.
* Fix Windows CI builds by updating test scripts to work with numpy 1.20. (#6518)
* Update onnxruntime_test_python.py to work with numpy 1.20.
Some aliases are deprecated in favor of the built-in python types. See https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
np.array with bytes for entries and dtype of np.void no longer automatically pads. Change a test to adjust for that.
* Fix another test script
* Fix ORTModule branch for orttraining-* pipelines
* Update pytorch nightly version dependency
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: Cecilia Liu <ziyue.liu7@gmail.com>
Co-authored-by: Ryan Hill <38674843+RyanUnderhill@users.noreply.github.com>
Co-authored-by: George Nash <george.nash@intel.com>
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
Co-authored-by: Yateng Hong <toothache9010@gmail.com>
Co-authored-by: stevenlix <38092805+stevenlix@users.noreply.github.com>
Co-authored-by: Derek Murray <Derek.Murray@microsoft.com>
Co-authored-by: ashbhandare <ash.bhandare@gmail.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Tracy Sharpe <42477615+tracysh@users.noreply.github.com>
Co-authored-by: Juliana Franco <jufranc@microsoft.com>
Co-authored-by: Pranav Sharma <prs@microsoft.com>
Co-authored-by: Tixxx <tix@microsoft.com>
Co-authored-by: Jay Rodge <jayrodge@live.com>
Co-authored-by: Du Li <duli1@microsoft.com>
Co-authored-by: Du Li <duli@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
Co-authored-by: baijumeswani <bmeswani@microsoft.com>
Co-authored-by: Sergii Dymchenko <sedymche@microsoft.com>
Co-authored-by: jingyanwangms <47403504+jingyanwangms@users.noreply.github.com>
Co-authored-by: satyajandhyala <satya.k.jandhyala@gmail.com>
Co-authored-by: S. Manohar Karlapalem <manohar.karlapalem@intel.com>
Co-authored-by: Weixing Zhang <weixingzhang@users.noreply.github.com>
Co-authored-by: Suffian Khan <sukha@microsoft.com>
Co-authored-by: Olivia Jain <oljain@microsoft.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com>
Co-authored-by: Ryan Lai <rylai@microsoft.com>
Co-authored-by: Jesse Benson <jesseb@microsoft.com>
Co-authored-by: sfatimar <64512376+sfatimar@users.noreply.github.com>
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: mohdansx <mohdx.ansari@intel.com>
Co-authored-by: Xavier Dupré <xadupre@users.noreply.github.com>
Co-authored-by: Michael Goin <mgoin@vols.utk.edu>
Co-authored-by: Michael Giba <michaelgiba@gmail.com>
Co-authored-by: William Tambellini <wtambellini@sdl.com>
Co-authored-by: Hector Li <hecli@microsoft.com>
Co-authored-by: Aishwarya <aibhanda@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: liqunfu <liqfu@microsoft.com>
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: pengwa <pengwa@microsoft.com>
Co-authored-by: Tang, Cheng <souptc@gmail.com>
Co-authored-by: Cheng Tang <chenta@microsoft.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com>
Co-authored-by: Chun-Wei Chen <jacky82226@gmail.com>
Co-authored-by: Vincent Wang <wangwchpku@outlook.com>
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
Co-authored-by: Luyao Ren <375833274@qq.com>
Co-authored-by: Zhang Lei <zhang.huanning@hotmail.com>
Co-authored-by: Tim Harris <tiharr@microsoft.com>
Co-authored-by: Ashwini Khade <askhade@microsoft.com>
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
Co-authored-by: Alberto Magni <49027342+alberto-magni@users.noreply.github.com>
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: wezuo <49965641+wezuo@users.noreply.github.com>
Co-authored-by: Jesse Benson <benson.jesse@gmail.com>
Co-authored-by: Wei Zuo <wezuo@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: wezuo <wezuo@az-eus-v100-32gb-5-worker-mgtbby.eastus.cloudapp.azure.com>
Co-authored-by: wezuo <wezuo@az-eus-v100-32gb-5-worker-yclzsf.eastus.cloudapp.azure.com>
Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com>
Co-authored-by: Martin Man <supermt@gmail.com>
Co-authored-by: M. Zeeshan Siddiqui <mzs@microsoft.com>
Co-authored-by: Ori Levari <ori.levari@microsoft.com>
Co-authored-by: Ori Levari <orlevari@microsoft.com>
Co-authored-by: Ubuntu <OrtTrainingDev3@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Sheil Kumar <smk2007@gmail.com>
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Co-authored-by: Ryota Tomioka <ryoto@microsoft.com>
Co-authored-by: Adam Pocock <adam.pocock@oracle.com>
Co-authored-by: Yulong Wang <f.s@qq.com>
Co-authored-by: Faith Xu <faxu@microsoft.com>
Co-authored-by: Xiang Zhang <xianz@microsoft.com>
Co-authored-by: suryasidd <48925384+suryasidd@users.noreply.github.com>
Co-authored-by: RandySheriffH <48490400+RandySheriffH@users.noreply.github.com>
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Co-authored-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
Co-authored-by: unknown <63478620+jeyblu@users.noreply.github.com>
* Refactor the code and move data reader, preprocessing, evaluation to
E2E_example_mode
* Refactor the code.
Move data reader, preprocessing, evaluation to model specific example
under E2E_example_mode
* refactor code
* Move yolov3 example to specific folder and add additional pre/post
processing
Support pad operator in quantization tool.
Support pad operator in quantized nhwc transformer.
Fix pad() operator bug when pad input's inner(right) most axis value is zero for Edge and Reflect mode, it copied wrong value to the cells to be padded. Note the Constant mode will not trigger this bug, as Edge/Reflect need copy value from the already copied array while Constant mode only fill specified value.
Add more test cases to cover pad() operator bug fixed here.
Fix quantization tools uint8/int8 value overflow issue when quantize weights in python.
* Make split working for quantization.
* NHWC transformer support for split operator
* Refactor some according to Feedback. Will add test cases soon.
* Fix build error on windows.
* Add test case for split op on uint8_t support
* Add nhwc_transformer_test for split uint8_t support
* Some change according to PR feedbacks.
* longformer fp16 e2e
* add fp16/fp32 parity check helper file
* excludes nodes with subgraph in profiling
* use onnxconverter_common to do fp32->fp16
* add version check for onnxconverter_common
* remove helper file
* add pkg installation on notebooks and script
Update Python API to allow more flexibility for setting providers and provider options.
The providers argument (InferenceSession/TrainingSession constructors, InferenceSession.set_providers()) now also accepts a tuple of (name, options dict).
Fix get_available_providers() API (and the corresponding function in the C API) to return the providers in default priority order. Now it can be used as a starting point for the providers argument and maintain the default priority order.
Convert some usages of the deprecated global configuration functions to use EP-specific options instead.
Update some EP-specific option parsing to fail on unknown options.
Other clean up.
* Fix typo in ORTModule.to()
The `args` and `kwargs` should be expanded in the call to `super(...).to()`.
* Add fixes for multiple CUDA devices.
* Add simple DeepSpeed test script and configuration.
* Fixes for test script and config.
* Add trailing newline.
* Fix formatting for config.
* Set InferenceSession provider options at construction.
* Make the local_rank arg required.
* Convert ORTModule._device to a torch.device() before using its accessors.
* Refactor device handling and fix regressions on BERT fine tuning
Co-authored-by: Derek Murray <demurra@microsoft.com>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Provide a tool to convert Loop to Scan for Nuphar performance
Fix Nuphar CI pipeline failures.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* merge master, keep postprocess status commit
* download float16.py everytime
* using variables to reference eps
* adding ACL EP to ep perf tool
* accuracy with absolute tolerance configurable
* add acl to dict + remove commented line
* Initial implementation of generating calibration dynamic range table
* Initialize validation support for Quantization
* Initialize validation support for Quantization (cont.)
* Improve validation support for Quantization
* Improve validation support for Quantization
* Rewrite/Refine for calibration and validation
* Rewrite/Refine for calibration and validation (cont.)
* Refine code
* Refine code
* Add data reader for BERT
* Add flatbuffers to serialize calibration table
* Refine code and add BERT evaluation
* Refine the code
* minor modification
* Add preprocess/postprocess of vision team yolov3 and refine the code
* Update annotation
* Make bbox cooridates more accurate
* Fix bug
* Add support of batch processing
* Batch processing for model zoo yolov3
* Add batch inference for evaluation
* Refine the code
* Add README
* Add comments
* Refine the code for PR
* Remove batch support checking in data_reader and refine the code
* Refine the code for PR
* Refine the code for PR review
Co-authored-by: Olivia Jain <oljain@microsoft.com>
* Support to pass initial optimizer states to optimizer graph builder
* Changes for passing init optim state to training session config
* Pass optimizer state through cpp and python frontend
* Cleanup
* Review comments
* Fix windows and mac CI
* Review comments
* review comments
* Review comments
* Frontend review changes
* Fix CI
* support gpt2 and longformer in profiler tool
* rename bert_profiler to profiler
* Add --basic_optimization to allow user to use basic level of graph optimization
* Add --kernel_time_only to filter kernel time and exclude fence time
* Add --threshold to filter nodes that with low run time percentage.
* initial implementation of longformer tools for onnx conversion and benchmark
* Support ONNX conversion for transformers 4.0
Add an option to optimize onnx model, and export fp16 model
* optimize a bert model converted using tf2onnx
* add test data
* update
* remove comments
* format
* Revert "format"
This reverts commit f8ae88cb564bce5caf4780e56561403f3ba3d524.
* Revert "remove comments"
This reverts commit 59d8a693581a731fd0291b70fe2c9cec6c4950fe.
* add a squeeze node to convert a 3-d mask to 2-d
* update
* update
* verify and add comments
* build off a specific commit and archive wheel file
* rename to fp32, prefix results w/ commit, add CPU col
* rename 99th to 90 percentile
* get symbolic_shape from master each time
* add install archive wheel, parallel build
* shortening hash
Update ORT model conversion script
- add args for specifying optimization level and whether to use NNAPI
- add logic to create a list of required ops and ORT format model that can be used with NNAPI
* Remove nGraph Execution Provider
Pursuant to nGraph deprecation notice: https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md#deprecation-notice
**Deprecation Notice**
| | |
| --- | --- |
| Deprecation Begins | June 1, 2020 |
| Removal Date | December 1, 2020 |
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features
previously available through nGraph have been merged into the OpenVINO™
toolkit. As a result, all the features previously available through
ONNX RT Execution Provider for nGraph have been merged with ONNX RT
Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated
starting June 1, 2020 and will be completely removed on December 1,
2020. Users are recommended to migrate to the ONNX RT Execution Provider
for OpenVINO™ toolkit as the unified solution for all AI inferencing on
Intel® hardware.
* Remove nGraph Licence info from ThirdPartyNotices.txt
* Use simple Test.Run() for tests without EP exclusions
To be consistent with rest of test code.
* Remove nGraph EP functions from Java code
Quantize LSTM:
1. dynamically quantizes MatMul inside the LSTM. It doesn't quantize activation function.
2. support per-channel on the input weight and recurrent weight.
* Enabling Multi Device support for UEP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor fix added
*Added a simple fix to determine OpenVINO
version for Arm build as well
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Add YAML file for pipeline
* Modify typo
* Add working directory
* Modify and test
* Modfiy and test
* Modify and test
* Modify and test
* Modify
* Modify
* Modify
* Modify
* Make sure to copy all the result files
* Add clearn up
* Modify
* Modify agent pool name
* Upload only specific artifacts
* Modify
* Integrated CI Pipeline for running TRT perf as well as added the “large amount of models” into perf model target
* Fix bug
* Fix bug
* Add reading the information regarding previously known failing models
and then skip testing them during benchmark/validation
* Modify the script file for CI
* Replace print with logger.info
* Fix bug
* Fix bug
* Refine the code
* Modify the script so that it can capture script segmentation fault while
running ORT
* Fix bug
* fix bug
* fix bug
* Add debug info
* fix bug
* Refine perf code
* Refine the code
* fix bug
* Code refactoring
* change many-models path
* remove metadata after validation/benchmark are done
* Update README.md
* Fix bug so that metadata doesn't hold stale value
* Remove hardcode and update README
* Add arguments to the script to make it run correctly
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Fix bug so that metadata doesn't hold stale value
* Fix small bug of finding test dataset directory for FP16 test data, as
well as modification of some output information
* use -i random for perf test of TRT changes
Co-authored-by: Olivia Jain <oljain@microsoft.com>
* Implement Hetero in UEP
* Added security checks to take valid Hetero combinations
as device type
* Integrating Hetero features
* Get the statistics Report in Debug Mode
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Passing right device type for vadm_baackend
Added simple fix to pick the right device type
when using vadm_backend with Hetero as well.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed batching logic for 2020.4 and above
* Fixed flake8 PEP8 errors
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor Fixes Added
*Added security checks for device_type passed
in for Hetero build during run time
*code cleanup
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor changes Added
*Fixed batch_size bug in vadm_backend
*code cleanup
*Documentation updated for Hetero
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Some fixes to symbolic shape inference
1. Topological sort before iteration in graph
2. Fix a case in slice: start=100000, end=-100000, step=-1, dim=2
3. Fix Nuphar Gemm test's random seed
4. Slice opset 1 axes is optional
The ROCm EP is designed and implemented based on AMD GPU software stack named ROCm. Here is the link for the details about ROCm: https://rocmdocs.amd.com/en/latest/
ROCm EP was created based on the following things:
1. AMD GPU programming language: HIP
2. AMD GPU HIP language runtime: amdhip64
3. BLAS: rocBLAS, hipBLAS
4. DNN: miOpen
5. Collective Communication library: RCCL
6. cub: hipCub
7. …
Current status:
BERT-L and GPT2 training can be ran on AMD GPU with data parallel.
Next:
1. Make more GPU code be sharable between ROCm EP and CUDA EP since HIP language and HIP runtime API are very close to CUDA.
2. Continue improving the implementation.
3. Continue GPU kernel optimization.
4. Support model parallelism on ROCm EP.
……
The rocm kernels have been removed from this commit and will be in a separate PR. Since the original PR was too big(~180 files), it was suggested to split the PR into two parts, one is rocm-kernels, the other is non rocm kernels.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Co-authored-by: sabreshao <sabre.shao@amd.com>
Co-authored-by: anghostcici <11013544+anghostcici@users.noreply.github.com>
Co-authored-by: Suffian Khan <sukha@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
* Enabled multi-threading for OpenVino EP
->Enabled support for concurrent_session_runs
*Run UEP using concurrent_session_runs > 1
*Enabled support for ORT_PARALLEL ExecutionMode
->Documentation Added for Enabling MultiThreading
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor Fixes added
*Configure the value of nireq during Runtime
*Documentation typos rectified and details
added for Multi_Threaded Inference
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Some checks added for this fix
*Added checks to invalidate wrong nireq value
and assigned it to default value of 8
*Added new config options for enable_vpu_fast_compile
which were changed w.r.t OpenVINO_2021.1 Release
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* add Python API for getProfilingStartTime
* debug for using Python API
* add in C# api
* use uint intead of uint64_t to prevent warning
* typo for GetProfilingStartTimeNs
* remove const
* Update onnxruntime/python/session.py
Co-authored-by: Pranav Sharma <emailpranav@gmail.com>
* remove unnecessary return
* Add Python unit test
* Add C# unit test and refactor Python test
* use ulong in C# for uint64_t in C++
* remove time.monotonic_ns
* syntax: remove public for inner function
* correct the API's order
* getprofilingstarttime after run
* Correct the right order in NativeMethod.cs
* update order
* nit: remove spaces
* Update csharp/src/Microsoft.ML.OnnxRuntime/InferenceSession.cs
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
* use the updated function
* add comment about the precision
* add more comments
* add session.py back
* fix flake8
* remove session.py
* Add comments in C, C#, Python APIs about precision
Co-authored-by: Pranav Sharma <emailpranav@gmail.com>
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
* Add CUDA option to run copy in default stream
This change fixes#4829. Thanks @maherzog for providing the repro!
The bug is caused by memory reuse in BFC arena, where copy and
compute stream in CUDA has a racing condition.
BFC arena is an arena allocator on top of cudaMalloc/Free to
reduce the cost in syncing CPU and GPU when alloc/free. It means
when CPU alloc/free the memory, GPU might not finished previous
work on the memory, so that CPU and GPU could run asynchronously.
This is OK if there's only one stream, where the execution order
in CPU and GPU are consistent. For example, if we have two kernels
A and B, CPU runs allocA->computeA->freeA->allocB->computeB->freeB,
A and B could shares the same memory since computeA and computeB
will not have racing as long as they run in the same GPU compute
stream.
However, if CPU runs allocA->CopyA->freeA->allocB->computeB->freeB,
the order of execution in GPU could have copyA happen after computeB,
if copy and compute happens in different GPU streams.
This change makes copy to run in default compute stream, while adding
an option to fall back to previous behavior if there's perf hit. This
is a short term fix before BFC arena could support multiple streams.
User may use following options to revert to previous behavior:
C API:
struct OrtCUDAProviderOptions cudaProviderOpt;
cudaProviderOpt.do_copy_in_default_stream = false;
C++ API:
CUDAExecutionProviderInfo cudaEPInfo;
cudaEPInfo.do_copy_in_default_stream = false;
C# API:
pending...
Python:
import onnxruntime
onnxruntime.capi._pybind_state.set_do_copy_in_default_stream(False)
* Confirmed the test failes in CI when doing copy in separate stream
Revert the test to get CI pass now
* Fix Windows test
* Address CR
