* prepare for C# to configure provider options
* add c# code
* revert modification
* Add update provider info configuration in trt ep side
* fix bugs
* fix bug for compiler error C2259
* Add c# test
* fix bug
* fix bug
* Properly deal with string
* Add c# api for accepting trt provider options
* fix bug
* Modify C# test
* add shared lib test
* Add get provider options functionality
* clean up
* clean up
* fix bug
* fix bugs for CI
* Fix bugs for CI and documentation
* Move TRT EP provider options related functions out of C API
* revert
* fix bug
* refactor
* add check for provider options string
* code refactor
* fix CI bug
* Fix CI bugs
* clean up
* fix bug
* Fix bug for Post Analysis
* fix accidental bug
* Add API_IMPL_BEGIN/API_IMPL_END
* clean up
* code refactor
* code refactor
* fix CI fail
* fix bug
* use string append
* Change the code to better handle strncpy and string append
* 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
Numpy has binary compatibility, which means "binaries compiled against a given version of NumPy will still run correctly with newer NumPy versions, but not with older versions." So, if an onnx runtime package was built with numpy version A, then at run time it requires numpy version >=A. In this change, we read numpy version from the installed packages at build time, to avoid manually keeping the build time/runtime consistency.
This is an update to https://github.com/microsoft/onnxruntime/pull/8079
The sample application motivating the original update changed to use an updated version of the model. Now, fewer ops are required. This change removes the previously added ops which are no longer needed.
1. No padding branch performance is improved 8 times
2. Symmetric padding branch is generalized for asymmetric padding case (padding symmetry was not actually used) and further optimized by eliminating integer multiplications.
1. Remove some unused code and simplify tools/ci_build/github/linux/run_dockerbuild.sh.
2. Enable Nuget CUDA tests. The original design was we could leverage Directory.Build.props and let cmake generate the required properties(USE_CUDA/...) there. However, in nuget packaging pipeline we test the package on a different host that doesn't run cmake command and doesn't have the auto-generated Directory.Build.props file.
* Output error message to android log instead of stderr
* Address CR comments, move macro to a helper function
* Address CR comments
* Fix ort minimal build break
* Revert for testing TensorRT 7.1
* change to origianl googletest version
* change machine
* remove build arg
* change back machine
* revert back googletest version
* Make it ready to merge to master
* revert onnx-tensorrt to v7.1
* rename yml
* use [[ ]] in bash command
* add sudo
* add chmod
* add correct path
* change another way to revert onnx-tensorrt
* change docker image to manylinux build
* Added support for ReduceMean on DNNL EP for CPU and GPU
Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Added fix for a resnet model failure where it was failing to create dst shape for reducemean when it was part of a subgraph with other ops
Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Removing the DNNL EP from these unit tests. This is in anticipation of two changes:
- DNNL EP unit tests would be added in a different location later on, so addition of EP individually to these tests will not be necessary
- This was causing a memory leak fail in debug build. The bug is in the EP itself and not in the code added for reducemean. The fix for this is in the i/o handling overhaul which will be added later.
* Update reduction_ops_test.cc
Had accidentally deleted a new line. Making sure there are no unnecessary changes in this file
**Description**:
Enforce no repetition of n-grams. Scores are set to `-inf` for tokens that form a repeated n-gram if added to the back of the input_ids.
**Motivation and Context**
Needed by transformer models in sequence generation algorithms (greedy search and beam search). This module has heavy impact on performance, and can be highly parallelized.
* clean up builds for interop_torch
* add python dependency for executables
* disable onnxruntime_ENABLE_TRAINING_TORCH_INTEROP by default; enable it in ortmodule GPU training pipeline only
* disable training unrelated tests when torch interop is enabled
* simplify the python dependency.
* clean up and fix
- Allow anyone to kick off a perf test here. Customize: branch, eps, model selection, cuda version.
- Only run shape inference when required.
- Kill errored out memory processes.
- Remove warmup run.
- Clean up script.
- Standalone_TRT is it's own "EP" vs as an additional run with TRT EP
* Enable slice for NNAPI EP
* Add ANEURALNETWORKS_STRIDED_SLICE support
* Addressed CR comments
* Addressed CR comments, rename PrepareForCompute to PrepareForComputeHelper to avoid confusion
* Create a shared quantization util for all unit tests.
* Cleanup qlinear_binary_op_test.cc
* save
* save
* save
* cleanup
* save
* cleanup for linux build
* Fix PythonOp with input has no gradient
* Fix another bug which happens when inputs require gradient
* Remove comments
Co-authored-by: Peng Wang <pengwa@microsoft.com>
