* Q/DQ + mulichannel support
backup
fix interval
mfk_functiun.cpp
WIP moveDequantizationBefore
add moveDequantizationBefore function
add cpu and gpu tests
attribute cmp false
attribute cmp false
rm temp line
mkl-dnn update
concat with multichanels for mOve_fake_quantize_function, bad runtime info for q/dq
rm extra qualification
fix run time info for q/dq
add support of multichanel fakequantize, bad test for it
work tests for multi chanel FQ
rm workaround
cpplint fix
cpplint fix
don't worl Variadic split
ieFuncTest work
cpuFuncTest work
Fix benchmark_app build (#7577)
[GPU] Added onednn dependency. (#6564)
cpp lint
cpplint
fix get_shape
fix fq constants
cpp lint
some fix in mfk.cpp
resolve conversations, add spil_nodes function
add new tests for multi-chanels, rename NetworkHelper::split_consts_before_concat()
fix get fq constants
* add new multi-chanels test and use constant_fold to split constant
* remove extra spaces
fix namespase terminated
fix namespase terminated
* Updated requirements for MO and POT with telemetry.
* Added mock telemetry common class for unit tests.
* Used mock telemetry in preprocessing unit tests.
* Small correction.
* Fix in the transformation PreserveRuntimeInfo: now Transpose is inserted before input port 0 of Result only, not after data node of layer before Result layer.
* Deleted commented code.
* Added more tests for the MO transformation PreserveRuntimeInfo.
* Use fp16-int8 mixed precision, instead of fp32-int8 mixed precision for onednn
* Allow quantization fusion into bsv32_fsv16 conv
* For conv, do not select bsv16_fsv16. Select bsv32_fsv16 for mixed-layout
* depthwise conv is supported even though it is not fp16
* Allow resample kernel to work as cross-layout
* test case for cross-layout of resample_opt kernel
* Select onednn-friendly format from cldnn conv
* Optimization for fp16 mixed precision
* Choose mixed layout in case of mixed precision from reorder_inputs
* Support for mixed precision from depth_to_space
* Do not convert first conv format
* Use onednn for FC output of fp16
* Choose bsv8_fsv4 from quantization even when conv kernel size is not 7
* Select cldnn for first conv when input feature depth is 1
* For first conv, use onednn only when kernel size is 7x7
* Use short variable name and added is_i8_u8 helper function
Co-authored-by: Kim,SungEun <sungeun.kim@intel.com>
* [LPT] Documentation
* 1) ToC was removed 2) SVG => PNG temporary conversion
* [LPT] Refactoring + developer guide
* [LPT] attribute doxygen documentation was added
* [LPT] Developer Guide to Reference API links were added
* [LPT] comments fixes
* [LPT] Reference API to Developer Guide links were added
* [LPT] titles were changed
* [LPT] comments fixes#2
* [LPT] root document was moved to Plugin DG
* [LPT] Documentation: image link quick fix
* [LPT] Docummentation: PrecisionsAttribute description quick fix
* fix comments from Karol
* fixes
* movement
* directive was added
* movement #2
* LPT reference in Executable Network rollback
* snippets were updated ini accordance with new API
* Handle names collisions for old IR with new API
* Fixed load model
* Try to fix tests
* Try to fix tests
* Try to fix build
* Try to fix tests
* Fixed tests
* Revert "Fixed tests"
This reverts commit 35da307210.
* Refactoring
* Fixed functional test
* Try to fix CPU tests
Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
* [Python API] Remove offline transformations from old python api
* try to fix import error
* try to fix pylint
* try to fix pylint2
* Use new api in graph_utils
* Fix pylint
* Try to fix pylint
* Use serialize from pass manager
* try to skip tests
* try to use new ir
Co-authored-by: AlexeyLebedev1 <alexey.lebedev@intel.com>
* [GPU] Enable unet2d enable on DG2
Add to support is_os_yx_isa2_osa8_isv8_osv2 format, which is used in
weight reorder.
Signed-off-by: hyunback <hyunback.kim@intel.com>
* [GPU] Enable implicit concat batch1 in oneDNN.
* Use gpu_usm memory offset, enable implicit concat batch1 in oneDNN.
And optimized_out node doesn't always have to be mutable input,
so add to check whether mutable input is existed in optimized node.
* Update to check use_usm condition in implicit concat.
* Add the condition for implicit concat.
* implicit concat's dependency should not be fused_op with eltwise.
* Buffer reuse is required for onednn sum post operation, output padding
did the buffer reuse failure.
Signed-off-by: hyunback <hyunback.kim@intel.com>
+ cldnn supports hard sigmoid activation function but onednn doesn't.
+ split it into eltwise linear and eltwise clip in
add_onednn_optimization_attributes pass.
