* Enabled several ARM CPU tests
* Removed not-valid tests
* Fixed several template plugin tests
* Removed non-working suppressions
* Disabled 2 tests on ARM CPU
* [LPT][CPU] Added callback for AddTransformation
* [WIP] Convolution scales fusion
* Force to use weight sclae to test performance.
* Update on interface.
* Use weight scale to adapt to ONEDNN 3.x API changes.
* Update the code.
* Update ONEDNN fix for gemm_x8s8s32x_conv kernel
* Fix the bug in ONEDNN and deconvFusingScale.
* Fuse FC Bias when having DQscale.
* WR to perf regression on
* Update onednn version.
* Fix bug and clean code.
* FC fusing dq scale bug fix.
* Add more comments and debug information.
* Fix CI issues.
* Merge ONEDNN changes.
* Fix CI issues and bugs.
* Apply review comments.
* Update comments.
* Apply reveiw comments.
* Avoid using LPT BiasAttribute RTInfo.
* Applied review comments.
---------
Co-authored-by: Vladislav Golubev <vladislav.golubev@intel.com>
* Remove exclusive_asyc_requests property from AUTO plugin
* Update test case
* Add test case to test incorrect config
* Remove the test case related to exclusive_asyc_requests property of AUTO plugin
* [CPU] ARM architecture support
This patch extends existing CPU plugin capabilities with ARM CPUs optimized support
* Fixed undefined reference in unit tests
* refactoring
* Fixed Eltwise node behavior for ARM
* init commit
* tests passed
* fix skip failures
* Apply suggestions from code review
---------
Co-authored-by: dmitrygo <dmitry.gorokhov@intel.com>
Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
* Benchmark_app set ov::hint::allow_auto_batching through compile_model
* Remove the process about allow_auto_batching in set_property of core
* Remove allow_auto_batching and auto_batch_timeout property from AUTO plugin
* Reserve the info logs and add API to check auto_batching
* Update test case, rm AB property test from core config tests
* Update some API in AUTO plugin config
* [LPT][TESTS] GrConv: added test cases with per channel dq on weights and without reshape
* FoldFQ: don't transform FQ with quantization by several dimensions
* ConvolutionTransformation: supported GrConv with per channel dq on weights and without reshape
* fold_reshape: refactoring
* Remove cache_dir property from AUTO plugin
* Pass the secondary property to hardware plugin
* Update test case
* Update test case, meta plugin will pass the properties to device without checking
[MULTI] pass through to AUTO with CTPUT hint
After this change
-- MULTI doesn't support setting infer request via CPU(4),GPU(8).
-- MULTI doesn't support CompiledModel::set_property() and ExecutableNetwork::GetConfig().
* add reshape shapeinfer in cpu plugin
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* add squeeze and unsqueeze
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* add precision i8 i64 on test
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* fix code out of bounds risk
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* test performance of this PR
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* fix code issue
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* Revert "test performance of this PR"
This reverts commit f4f9f002de28d03bc1c55c24067f75b74824904c.
* fix reviewer comment
fix throw message
not create ov::shape instance
remove i8 test case
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* fix pytorch layer test failed issue
inputShape(1,0) outpattern(-1) is a valid input
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* fix windows compile issue
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* fix rebase mistaken
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
---------
Signed-off-by: Hu Yuan2 <yuan2.hu@intel.com>
* Review adaptive max pool shape inference
* Review AvgPool and MaxPool
* Review convolution operator
* Review GroupConvolution shape inference
* Review ConvolutionBackpropData operator
* Review GroupConvolutionBackpropData op
* Review BinaryConvolution operator
- add common bases for convolution ops
- refactor convolution ops
* Review DeformableConvolution operator
* Use new convolution shape_infer in GPU
* Fix build and test issues
* Correct set output spatial shape
in default constructed back prop convolutions
* The convolution shape_infer use pads as parameters
the external padding can be operators or other class padding properties shape_infer should not modify operators padding when
called from plugin
* Apply code formatting
* Fix padding validation and update
* Use shape inference with padding instead fallback
for DeformableConvolution from opset1
* Update convertPadding function to be template
Mateusz Mikolajczyk
Ilya Lavrenov
Vitaliy Urusovskij
Aleksandr Voron
Shen, Wanglei
Egor Duplenskii
Sofya Balandina
Edward Shogulin
Gorokhov Dmitriy
Luwei ZhouandVladislav Golubev
Wang Wangwang
Oleg Pipikin
Yuan Hu
Nadezhda Ageeva
Katarzyna Mitrus
Anton Voronov
guozhong wang
Chen Xu
Tingqian Li
Pawel Raasz
Alexandra Sidorova