* [Snippets][CI] Added Snippets Func Tests to Azure Windows
* [Snippets][CI] Added Snippets Func Tests to Azure Linux
* [Snippets][CI] Added Snippets Func Tests to GitHib workflow Linux
* [Snippets][CI] Added Snippets Func Tests to GitHib workflow Windows
* [Snippets][CI] Added Snippets Func Tests to Azure Linux Debian
* Remove inits, update main one
* Fix stacklevel
* Testing wrong solution
* Testing test test
* Fix test test test test
* mo modules mo problems
* Xfail test that check stdout/err?
* not so correct solution to circular imports
* Fix or not to fix
* CMake magic, co-authors: my best team
* Fix package imports
* Fix tools inits
* Fix ovc tf
* Fix Q000
* Fix F401
* Fix linters
* Add save_model
* Remove LayoutMap
* Move test_utils to 'internal modules'
* First testing
* Missing Type
* Expand main namespace
* Change some more tests
* Add OVAny to namespace
* Add Constant and Parameter to namespace
* More tests changes
* Fix inits
* Add layout_helpers to main namespace
* Revert CMake and linux.yml with ovc
* Update main inits
* Remove MO from tools inits
* changes to init files
* Fix tests
---------
Co-authored-by: Sergey Lyalin <sergey.lyalin@intel.com>
* [GNA] Fix for GeminiLake detection
* Added HWGeneration::GNA_1_0_E enumerator
Added DeviceVersion::GNAEmbedded1_0 enumerator, changed the meaning of DeviceVersion::GNA1_0.
Updated ConvLowPrecision test with all supported targets
* [GNA] Extended a few tests with GNA1.0
* Add debug messages
* Add more debug messages
* Add more messages
* Disable proxy plugin
* Enable proxy and suppress NVIDIA
* Remove disabling NVIDIA
* Update hidden device only if proxy settings were changed
* Use static unavailable device to load unavailable device only one time
for the process
* Removed debug messages and add global mutexes for unavailable plugins
* Modify the condition making batch interpretation true/false
- When the user is Convert for Constant node, and tensor is 1d,
- Set needBatchInterpretation to true
* Narrow down the range of the condition
* Merge the condition
* Add additional condition not to check self node
* Fix incomplete condition
* Check if all inputs to binary eltwise is 1d
* Change code style
* [MO] compress_to_fp16=True by default (2dn attempt)
* fix unit-tests
* second round of fixin unit-tests
* set compress_to_fp16 default to True in ovc/cli_parser.py
* use save_model in mo_python_api_tests
* enforce compress_to_fp16=False in test_zero_copy
* selectively compress depending on the path user has chosen to generate IR
* corrected doc
* allow compress_to_fp16=False/True for ovc
* doc and unit-tests failing fix
* user save_model in ovc cli tool
* revert back serialize and compress_model but into main instead of moc_emit_ir
* cover more argument combinations for cli tool and convert_model
* [GPU] Improvement for buffer dump
+ added OV_GPU_DumpLayersInput to support dump input layers
+ added OV_GPU_DumpLayersRawBinary to make binary dump
+ added OV_GPU_LoadDumpRawBinary to use binary dump as input
+ binary dump naming rule layername_datatype_tensor_format.bin
Signed-off-by: Min, Byungil <byungil.min@intel.com>
* Previously reorder / permute was not allocating its memory at build time thought the shape has upper bound
* Update src/plugins/intel_gpu/src/graph/permute.cpp
Co-authored-by: Sergey Shlyapnikov <Sergeishlyapnikov@gmail.com>
* Fix as review comment
---------
Co-authored-by: Sergey Shlyapnikov <Sergeishlyapnikov@gmail.com>
* Add static shape adapter
- Adapters holds CPU dimension which can be reference to it or vector
- Add ov::optional for holding optional result from shape inference
- Add new `infer` function in `IStaticShapeInfer`
* Temporary support of StaticShape
* Minor corrections in ShapeInferenceTA
* Migrate shape_infer to new interface version
* Replace StaticShape by adapter implementation
* Replace IShapeInferCommon by IStaticShapeInfer
* Correct code formatting
* Fix build issues
* NodeValidationFailure::create for StaticShapeRef
* Review RegionYolo for shape inference:
- Check dynamic shape and label propagation
- Check static shape inference
- Review shape_infer template implementation
- Update unit test
* Remove commented test code
* Correct flatten dim calculation
* Added Torchscript Backend
* First commit for backend with Torch FX Decoder
* Merging changes from Torch FX branch
* Torch FX initial fixes (Temporary)
* Fixed type/shape issues in Torch FX decoder
* Added translation for built-in getitem
* MaxPool update & Output shape fix (Torch FX)
* Torch FX graph outputs fix
* Torch FX support for sigmoid and slu_
* Torch FX graph module caching
* Torch Fx partitioner cache removed
* Torch FX initial getitem replacer added
* Index check for torch fx getitem replacer
* Debug print removed from partitioner
* Added environment variables for pytorch tracing mode and openvino device
* FX translation fix for getitem & getitem replacer removed
* Added checks for PyTorch tracing mode environment variable
* Adding compile mode for fallback
* Added more ops for resnet18
* Added a check for environment variable
* Generalized addmm to work with torchscript and torchfx
* Added the missing batch_norm.default translation
* fx_backend: include get_attr ops to the partitions
* AddeTODO note t to improvget_attr algorithm
* created function for adding get_attr nodes
* fx_backend: added aten.mul.Tensor, re-enabled aten.empty.memory_format
* fx_backend: Additional op support/improvement for Inception V3
* Added comment for fix 64-bit to 32-bit max int conversion
* fx_backend: Update for avg_poolnd to support 3 inputs
* Fixed erorr in decoder.py
* TorchFX caching fix
* Torch backend, op support for Stable Diff. & BERT
* Arranged ops in order and added torch tensor mapping
* Added support for more ops for super glue
* TorchFX: Initial permanent fallback
* TorchFX: New ops for improved TorchVision support
* TorchFX backend optimizations for partitioning and tmp fallback
* working operator updates for superglue
* Updates to operators for superglue
* Removed max.dim and stack
* Cleanup
* Cleanup
* Fixed a couple of syntax issues
* Fixed a couple of syntax issues
* Added missing method to TorchFX Decoder
* Added missing method to TorchFX Decoder
* Removed redundant code for transpose
* TorchFX: Initial StableDiffusion support
* PyTorch decoder ovtype to ctype fix for int64
* Added ops for distilbert
* Fixed few unnecessary include statements
* Seperated TorchFX and TorchScript decoders
* Modified import statements to reflect two decoders
* f64 fix for TorchFX
* Import fix for PyTorch backend modules
* TorchFX serialize graph for debugging (Temporary)
* Serialize and load back feature enabled for TorchFX
* Temporary optimization to remove Broadcast
* Temporary SoftmaxRehapeElimination pass is added
* TorchFX custom model cache directory
* PyTorch bitwise translation, conversion checks enabled
* Naming fix in make_list_construct
* TorchFX: Added comments to Softmax and Slice translations
* translate_chunk temporarily removed for TS backend
* Fixed linter issues
* Addressed clang formatting issues
* Fixed few more clang and linter issues
* Fixed tests to use ts_decoder
* Fixed naming convention issues
* Added missing import
* Added inlined_inputs to TorchScriptDecoder
* Added tests for torch fx backend
* Removed magic numbers in PyTorch decoder utils
* TorchFX decoder data type fix
* Added cast from size_t to int
* TorchFX output handling code cleanup
* TorchFX: Use detached input tensor
* Added missing cast from size_t to int
* Added static cast in group_norm
* Fixed casting issue in split
---------
Co-authored-by: ynimmaga <yamini.nimmagadda@intel.com>
Co-authored-by: Cavus Mustafa <mustafa.cavus@intel.com>
* Add subgraph body comparison
* Avoid confusing function name
* Skip failing snippet test
* Skip some ov_snippets_func_tests
* Derive comparison flags
* Skip snippet test
* Drop on bodies mismatch
---------
Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com>
Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com>
