* 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
* Changes to ensure the openvino-ep-2021.4 branch is created
* Fix failing cpp and python unit tests
* Fixed Myriad Tests for Ov_2021.4
* Disabled failing python tests for myriad
* Fixes models which were breaking w.r.t 2021.4
* Added fixes to Fix tinyyolov3 working on Myriad
and MaskRcnn, FasterRcnn using GPU_FP32
* Added FP16 output data type support for ngraph
* Implemented ReadNetwork() method
->Using Core::ReadNetwork() method for reading and creating a CNNNework
->Since OpenVINO™ 2020.4 version, Inference Engine enables reading ONNX models
via the Inference Engine Core API and there is no need to use directly the low-level
ONNX* Importer API anymore. To read ONNX* models, it's recommended to use the
Core::ReadNetwork() method that provide a uniform way to read models from ONNX format.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed ngraph f16 supported output type
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added comments in data_ops.cc
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed broken windows build
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disable failing CPP tests on CPU
Some of the convtranspose tests are failing on
OpenVINO-EP CPU due to accuracy mismatch w.r.t
default CPU. so currently we are disbaling
these tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated for ov version 2021.4
* Changes to include qdq ops in code
* Disabled failing python tests on GPU
Disabled two maxpool python tests on
GPU as they were passing but throwing
segfault
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix the backward compatibility issue
ReadNetwork() API has a bug and will only work
starting from OpenVINO 2021.4 version.
The previous versions will still have to use
onnx importer route
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix CMakeLists.txt for OpenVINO EP
If a directory with OpenVINO is sourced,
the latest OpenVINO settings have to
be imported.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: sfatimar <64512376+sfatimar@users.noreply.github.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
* Add ability to generate ios static framework
* Fix typos
* Add pod cache clean, update some comments of previous commit
* Fix CI failure with newly added cpuinfo library
* Update test model (CoreML requires node has a name)
* Addressed CR comments
Fix bug that shows up when running tests (in particular, GraphTransformationTests.ConcatSliceEliminationTest) with more verbose logging level.
There is a log statement that doesn't get evaluated at the default test logging level (warning). It was accessing the first element of an empty vector. This change moves that log statement before the point where that vector is cleared.
Updates to the iOS packaging pipeline:
- Make it harder to overwrite package archives accidentally when uploading (fails if the archive already exists)
- Only upload package archives for release builds
- Some clean up
* [ROCm] clear last status if hipErrorNotReady
* use hipEventDisableTiming in rocm_fence.cc
* fix syntax errors
* destroy event before handle becomes invalid
* 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
* Update submodule onnxruntime-extensions to latest.
* Add document for onnxruntime-extensions.
* Update cgmanifest.json for onnxruntime-extensions.
* Add example in JavaScript.
Co-authored-by: Zuwei Zhao <zuzhao@microsoft.com>
Pytorch cpuinfo library allows us to query current cpu features, micro-architecture and cache size, etc. These information is needed for targeted performance optimizations.
Unfortunately it does not work under Windows/ARM. We need to develop our own later
This PR purely extracts each kernel to a standalone file. No functionality change. It includes specifically:
leave the MlasGemm function and thread handling in the qgemm.cc
put dispatcher functions and the template functions (interfaces) that are required to implement a kernel into qgemm.h
put each kernel implementation in a separate file, which implements/specialize template functions: MlasGemmU8X8FixupZeroPointB, MlasGemmU8X8CopyPackA, MlasGemmU8X8CopyPackB, MlasGemmU8X8Kernel
determine the files to be compiled in cmake file
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
* Add metadata_props to ORT model
* Minor update
* Update python binding, and increase the minimal pipeline size threshold
* Fixed a small bug in serializing ir_version
* Remove temp ort.py.fbs and add it to .gitignore
* handle unsqueeze change in opset13
* fix the node arguments index check for square case (x * x)
* Revert "fix the node arguments index check for square case (x * x)"
This reverts commit c66344f0a82c35d8c24d31f2264cf7e9b235ce22.
* handle the square case (x * x) for node argument search
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* Add new transformer that can split node selection from node modification to allow just the modifications to be applied at runtime in a minimal build. This is the first step of a few to enable a QDQ model to be optimized for the NNAPI EP and/or the CPU EP at runtime in a mobile scenario.
Add generic and QDQ specific helpers for selection and modification.
Replace existing QDQ optimizers with optimizer based on new approach.
