* Add function body to SoftmaxGrad schema
* Add type context and cleanup
* Add test case with symbolic dimensions
* Add opset specification to function
* handle opset dependence
* Exclude from minimal build
* Promote BiasDropout from orttraining to onnxruntime
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Liqun/ort module perf1 (#6806)
add mysql script to log perf data
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Resolve HTTP Error 503: Service Unavailable for MNIST dataset (#6989)
* Reduce logging for ORTModule for the end user (#6982)
* Support none types in forward output (#7001)
* Missed test case for none type output (#7014)
* save iobinding to ctx
* save run_options to ctx
* remove debug tests
* PR comments and clean up
* add RunStateInfo
* remove whitespace edits
* PR comments
* remove test changes
* fix test failure
* Fit unit test test_nesting_forward_backward_calls
Co-authored-by: liqunfu <liqfu@microsoft.com>
Co-authored-by: baijumeswani <bmeswani@microsoft.com>
Co-authored-by: Jingyan Wang <jingywa@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add missing Python dependencies for training
cerberus - option parsing
h5py - checkpoint
onnx - model proto
packaging/sympy - symbolic shape inference
* Separate requirements.txt for inference and training Python packages.
Enable type reduction for Shrink, Sign, SplitToSequence CPU kernels.
Some other type reduction changes including refactoring to specify element types in a single place.
Add types support for DenseToSparse and SparseToDense conversions
Address the case of empty sparse values and indicies when the initializer does
not contain any NNZ.
Add sparsify script.
Update the kernel def hashing in ORT format models. The new hashing logic ignores the ordering of type constraint types.
This is a backward compatibility breaking change, but we don't guarantee backward compatibility yet.
* 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
* Added new Transpose+Cast+MatMul => Cast+FusedMatMul test scenarios.
* The Cast node may feed more than one node.
* Transpose node may feed multiple nodes and still may be fused with MatMul nodes.
* Liqun/ort module perf1 (#6806)
add mysql script to log perf data
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Resolve HTTP Error 503: Service Unavailable for MNIST dataset (#6989)
* Reduce logging for ORTModule for the end user (#6982)
* Support none types in forward output (#7001)
* Missed test case for none type output (#7014)
* Fix code style according to autopep8
Co-authored-by: liqunfu <liqfu@microsoft.com>
Co-authored-by: baijumeswani <bmeswani@microsoft.com>
Change int32_t->ptrdiff_t when interacting with the threadpool.
Migrate more code from MlasMaskMoveAvx->MlasMaskMoveTableAvx.
Update more code to use FUNCTION_ENTRY macro.
