### Description
Motivation for this PR is code cleanup.
1. Remove all deprecated python code related to orttrainer, old
checkpoint, related tests and utils
2. Cleanup orttraining_pybind_state.cc to remove all deprecated
bindings.
Bump ruff version and remove pylint from the linter list. Fix any new
error detected by ruff.
### Motivation and Context
Ruff covers many of the pylint rules. Since pylint is not enabled in
this repo and runs slow, we remove it from the linters
### Description
`lintrunner` is a linter runner successfully used by pytorch, onnx and
onnx-script. It provides a uniform experience running linters locally
and in CI. It supports all major dev systems: Windows, Linux and MacOs.
The checks are enforced by the `Python format` workflow.
This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors
in Python code. `lintrunner` now runs all required python lints
including `ruff`(replacing `flake8`), `black` and `isort`. Future lints
like `clang-format` can be added.
Most errors are auto-fixed by `ruff` and the fixes should be considered
robust.
Lints that are more complicated to fix are applied `# noqa` for now and
should be fixed in follow up PRs.
### Notable changes
1. This PR **removed some suboptimal patterns**:
- `not xxx in` -> `xxx not in` membership checks
- bare excepts (`except:` -> `except Exception`)
- unused imports
The follow up PR will remove:
- `import *`
- mutable values as default in function definitions (`def func(a=[])`)
- more unused imports
- unused local variables
2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than
flake8 and is more robust. We are using it successfully in onnx and
onnx-script. It also supports auto-fixing many flake8 errors.
3. Removed the legacy flake8 ci flow and updated docs.
4. The added workflow supports SARIF code scanning reports on github,
example snapshot:
5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Unified linting experience in CI and local.
Replacing https://github.com/microsoft/onnxruntime/pull/14306
---------
Signed-off-by: Justin Chu <justinchu@microsoft.com>
Description: Format all python files under onnxruntime with black and isort.
After checking in, we can use .git-blame-ignore-revs to ignore the formatting PR in git blame.
#11315, #11316
This change adds a new pipeline for checking Python code. Currently this pipeline only runs flake8.
flake8 is also run as part of the CMake project builds, but we can switch over completely to the new pipeline later.
The .flake8 config file was also updated to make it easier to run standalone (flake8 --config ./.flake8) and some Python formatting issues were addressed in files that were not previously scanned.
ORTModule requires two PyTorch CPP extensions that are currently JIT compiled. The runtime compilation can cause issues in some environments without all build requirements or in environments with multiple instances of ORTModule running in parallel
This PR creates a custom command to compile such extensions that must be manually executed before ORTModule is executed for the first time. When users try to use ORTModule before the extensions are compiled, an error with instructions are raised
PyTorch CPP Extensions for ORTModule can be compiled by running:
python -m onnxruntime.training.ortmodule.torch_cpp_extensions.install
Full build environment is needed for this
* Update torchtext usage for pytorch transformer sample
* Temporarily disable tests to unblock repo (failures are being worked on already)
* Update loss numbers for ORTTrainer UTs
* 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 b9f594341b.
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 472029ba25.
* 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>
* 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
* Add iOS test pipeline and a sample app.
* clean up the unused code.
* clean up.
* revert the unknown change
* disable the shared library for iOS.
* add open source notice text.
* ignore the skipped test.
* extract the common ortenv setup
* Add ORTTrainerOptions class for the new pytorch frontend (#4382)
Add ORTTrainerOptions class and some placeholders
* Add _ORTTrainerModelDesc to perform validation for model description (#4416)
* Add Loss Scaler classes to the new frontend (#4306)
* Add TrainStepInfo used on the new frontend API (#4256)
* Add Optimizer classes to the new frontend (#4280)
* Add LRScheduler implementation (#4357)
* Add basic ORTTrainer API (#4435)
This PR presents the public API for ORTTrainer for the short term
development.
It also validates and saves input parameters, which will be used in the
next stages, such as building ONNX model, post processing the model and
configuring the training session
* Add opset_version into ORTTrainerOptions and change type of ORTTrainer.loss_fn (#4592)
* Update ModelDescription and minor fix on ORTTrainer ctor (#4605)
* Update ModelDescription and minor fix on ORTTrainer/ORTTrainerOptions
This PR keeps the public API intact, but changes how model description is stored on the backend
Currently, users creates a dict with two lists of tuples.
One list called 'inputs' and each tuple has the following format tuple(name, shape).
The second list is called 'outputs' and each tuple can be either tuple(name, shape) or tuple(name, shape, is_loss).
With this PR, when this dict is passed in to ORTTrainer, it is fully validated as usual.
However, tuples are internally replaced by namedtuples and all output tuples will have
tuple(name, shape, is_loss) format instead of is_loss being optionally present.
Additionally to that normalization in the internal representation (which eases coding),
two internal methods were created to replace a namedtuple(name, shape) to namedtuple(name, shape, dtype)
or namedtuple(name, shape, is_loss, dtype) dependeing whether the tuple is an input or output.
This is necessary as ORTTRainer finds out data types of each input/output during model export to onnx.
Finally, a minor fix was done on ORTTrainer. It could initialize ORTTrainerOptions incorrectly when options=None
* Rename input name for test
* Add ONNX Model Export to New Frontend (#4612)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Create training session + minor improvements (#4668)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Save ONNX model in file (#4671)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add eval step (#4674)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add train_step (#4677)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add LR Scheduler (#4694)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add deterministic compute tests (#4716)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add legacy vs experimental ORTTrainer accuracy comparison (#4727)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add Mixed precision/LossScaler + several fixes (#4739)
Additionally to the mixed precision/loss scaler code, this PR includes:
* Fix CUDA training
* Add optimization_step into TrainStepInfo class
* Refactor LRSCheduler to use optimization_step instead of step
* Updated several default values at ORTTrainerOptions
* Add initial Gradient Accumulation supported. Untested
* Fix ONNX model post processing
* Refactor unit tests
* Add ONNX BERT example + minor fixes (#4757)
* Fix training issue when passing ONNX file into ORTTrainer
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add Dynamic Shape support (#4758)
* Update DeepSpeed Zero Stage option to a separate option group (#4772)
* Add support to fetches (#4777)
* Add Gradient Accumulation Steps support (#4793)
* Fix Dynamic Axes feature and add unit test (#4795)
* Add frozen weights test (#4807)
* Move new pytorch front-end to 'experimental' namespace (#4814)
* Fix build
Co-authored-by: Rayan-Krishnan <rayankrishnan@live.com>
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Ashwini Khade
Justin Chu
Edward Chen
Yulong Wang
Faith Xu
Changming Sun
Sherlock
Thiago Crepaldi
G. Ramalingam
Billy Franscois
Maajid khan
David Medine
M. Zeeshan Siddiqui
Nat Kershaw (MSFT)
Derek Murray
Vincent Wang
S. Manohar Karlapalem
Sergii Dymchenko
Keizo Fujiwara
Wenbing Li
Bowen Bao
Marcus Turewicz
Josh Bradley
gwang-msft
