[Good First Issue]: Enable parallel execution of TensorFlow Layer 2 python tests #20919

mryzhov · 2023-11-07T10:46:25Z

Context

Current CI (

Line 869 in c0381ab

Python_Unit_Tests:

) runs all the python tests sequentially one by one and it takes too much time.
The idea is to run all those tests in parallel by using pytest-xdist plugin, but tests should be modified for that.
Please find bellow the common tests issues:

Random inputs generation as parametrization arguments. Solution: all random test arguments should be generated inside of the tests
Tests create, use and remove the same artifacts (temporary data, models, etc). Hot fix solution: all test data should be generated in the system temporary directory (TMPDIR) which can be unique for each thread. Long term solution: all test data should be generated in the unique temporary directory or with unique names.

What needs to be done?

Modify TensorFlow Layer 2 tests to solve issues described in the Context. Tests are located here - https://github.com/openvinotoolkit/openvino/tree/master/tests/layer_tests/tensorflow2_keras_tests
Enable parallel execution of those test in the CI (

openvino/.github/workflows/linux.yml

Line 1057 in 4a0098b

- name: TensorFlow 2 Layer Tests - TF FE

)

Example Pull Requests

#20613

Resources

Contact points

@mryzhov @akashchi

Ticket

No response

LakshmiGayathri19 · 2023-11-12T05:12:53Z

Hello @mryzhov , I am new to contributing to open source projects, and would like to start with this issue. Could you please assign this to me ?
Thanks in advance.

LakshmiGayathri19 · 2023-11-16T02:04:32Z

Hey @ilya-lavrenov , Thanks for assigning this to me. I am starting working on this, and will post the updates in the comments.

LakshmiGayathri19 · 2023-11-16T23:53:44Z

Hey @ilya-lavrenov , the testcases which I saw in this link are using parametrization arguments with predefined test parameters. For example, if we take this example, test_data_float32 variable is pre-defined and is used as the parameter for the test case test_keras_conv_2d_float32 (Link).

All the test cases which I saw from starting until test_tf2_keras_conv_2d.py has the same format of parameter initialization and usage. And in the issue context it is mentioned that Random inputs generation as parametrization arguments. Solution: all random test arguments should be generated inside of the tests. However, i don't see any randomly generated parameter inputs for the test cases which I saw until now, all were pre-define "outside the test function" .

So I want to confirm if we should shift the parameter initialization from outside to inside the test function such that when parallelization happen, there wont be any issue. Or are there any random input generations and I should continue looking for them? Just wanted to confirm before I hunt further so that I wont waste my time going down the wrong way.

Thanks!

mryzhov · 2023-11-17T10:06:32Z

Hello @LakshmiGayathri19 , thank you for your interest and help.
You are right, there are no randomly generated parameters in the tests you mentioned.
Looks like this is another type of issue described in the xdist documentation - https://pytest-xdist.readthedocs.io/en/stable/known-limitations.html.

I would suggest fixing it by using lists instead of the dictionaries in the test parameters. Could you try it please?

And I would also suggest starting with the second part of the issue and try to run those tests in parallel to collect the particular list of problematic tests.

LakshmiGayathri19 · 2023-11-17T21:10:34Z

Hello @mryzhov,

Thank you for clarification. I will try to convert the dict to list in test parameters and also look into running test in parallel.

LakshmiGayathri19 · 2023-11-20T05:16:04Z

Hello @mryzhov ,

I made the changes to the linux.yml file to enable parallel execution. The line which i changed is:
python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/tensorflow2_keras_tests/ --use_new_frontend -m precommit_tf_fe -n auto --junitxml=${INSTALL_DIR}/TEST-tf2_fe.xml
by adding -n auto to enable parallel execution for TensorFlow 2 Layer Tests - TF FE. I pushed the changes to my forked repository and I am waiting for the Github workflow to get completed such that I can get the list of test problematic cases.

But the Github workflow is stuck in build stage showing the below status:
Requested labels: aks-linux-16-cores Job defined at: LakshmiGayathri19/openvino/.github/workflows/linux.yml@refs/heads/test-parallel-execution Waiting for a runner to pick up this job... .

I am waiting in this since yesterday. I am not understanding why is this taking so long. Did I miss any step or am I going in the right direction ?

Just wanted to confirm with you to be sure that I am doing the right steps.

Thanks

mryzhov · 2023-11-20T09:25:12Z

Hi @LakshmiGayathri19,
aks-linux-16-cores is self-hosted runner and it is not available on the forks, you would need to create PR in openvino-toolokit repository to use it.

LakshmiGayathri19 · 2023-11-22T05:24:59Z

Hey @mryzhov ,

I created the PR and the Github workflow ran. Few of the Python unit test cases which belong to TensorFlow layer 2 - TF FI failed due to Known-limitation. I am working on fixing these test cases.

LakshmiGayathri19 · 2023-11-24T13:46:32Z

Hello @mryzhov ,

I recently modified the test cases (only python test cases) which are failing due to enabling parallel execution. And i created a PR for that. But i see that my PR build is failing due to some pip issue. I am not sure if its due to my change. Could you please look into that and let me know if I need to do anything.

Thanks

mryzhov · 2023-11-24T15:31:26Z

Hi @LakshmiGayathri19,
There is an error in your changes: https://github.com/openvinotoolkit/openvino/actions/runs/6976462547/job/18988228421#step:26:176
pip error is just a warning.

LakshmiGayathri19 · 2023-11-25T05:01:44Z

Thanks for pointing that out @mryzhov . I will fix my test cases. But before raising a PR for these test case changes, is there any way that I can test locally if everything is working fine or not ?

mryzhov · 2023-11-28T12:39:48Z

@LakshmiGayathri19, you can build openvino manually and run those tests.

LakshmiGayathri19 · 2023-12-04T01:09:32Z

@mryzhov , Hey!

I set up my system required to build by following the docmentation. I should build in my local system to make sure my changes are not failing before raising another PR.

But I am little busy currently due to my end semester projects and exams. Will it be fine if I continue working on this after three weeks ?

mryzhov · 2023-12-04T11:17:04Z

Hi @LakshmiGayathri19 ,
Sure, it is not an urgent issue, please keep your time.

LakshmiGayathri19 · 2023-12-21T13:47:29Z

Hello @mryzhov,

I tried to build in my local system. But I am getting error in a header file onnx_openvino_onnx-ml.pb.h while I am executing the command cmake --build . --config Release --verbose -j8. I am following this documentation.

I am attaching the screenshot of the build error below:

Do you have any idea how to resolve this error ? Or is it that my system is missing any dependency ?

mryzhov · 2023-12-21T14:48:47Z

@LakshmiGayathri19, you can disable onnx frontend ("-DENABLE_OV_ONNX_FRONTEND:BOOL=OFF",) I think TensorFlow tests do not use it

LakshmiGayathri19 · 2023-12-27T06:09:40Z

Hello @mryzhov, I have raised the PR and i see that it is approved by a reviewer. But I see there are some merge conflicts with my branch. So, should I resolve the merge conflicts locally and raise the PR again ?

kamlesh-ops · 2024-01-21T07:44:25Z

Hi @mryzhov @akashchi I am new to OpenVINO and a novice in open source contribution. Would you like me to take up this issue.
Also, please let me know if some work has been already done( just for the sake of clarification).
Thanks :)

Ruman-12 · 2024-01-22T03:38:26Z

Hey @mryzhov i would like work on this issue.
could you please assign me this issue?

mlukasze · 2024-01-22T06:33:42Z

@Ruman-12 we've assigned to you another issue, please start there.

Ruman-12 · 2024-01-22T08:47:51Z

@mlukasze Thanks

kamlesh-ops · 2024-01-29T18:55:50Z

.take

github-actions · 2024-01-29T18:56:03Z

Thank you for looking into this issue! Please let us know if you have any questions or require any help.

p-wysocki · 2024-02-19T13:55:18Z

Hello @kamlesh-ops, are you still working on that issue?

kamlesh-ops · 2024-02-19T17:38:48Z

@p-wysocki no sir actually I was busy in some other issue, so I couldn't devote time to this. If you feel you can unassign me, as I may have to take more time in this.

Pranshu-S · 2024-03-01T10:16:05Z

.take

github-actions · 2024-03-01T10:16:17Z

Thank you for looking into this issue! Please let us know if you have any questions or require any help.

Pranshu-S · 2024-03-08T09:17:32Z

Hi @p-wysocki
Raised a PR addressing this issue. Do take a look

p-wysocki · 2024-03-11T12:50:48Z

Thank you for your contribution! I see that the review has started. :)

…23344) Addresses issue: #20919 ### Details: - Enables parallel execution of TensorFlow Layer 2 python tests - Fixes test_tf2_keras_conv_lstm_2d.py and test_tf2_map_fn.py to not fail during parallel execution - Appends args in github workflow to enable parallel execution Errors fixed: - Due to varying Kera activation function addresses causing the workers to get different parameter inputs and thus failing. See [known issue](https://pytest-xdist.readthedocs.io/en/stable/known-limitations.html#order-and-amount-of-test-must-be-consistent) ``` -tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 4, 'kernel_size': (3, 3), 'padding': 'same', 'return_sequences': False, 'activation': <function swish at 0x7f1fadf364d0>}, 'input_shapes': [[2, 5, 20, 30, 2]]} ] -tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 6, 'kernel_size': (2, 3), 'padding': 'valid', 'dilation_rate': 3, 'recurrent_activation': <function elu at 0x7f1fe6a1a830>, 'return_sequences': True, 'use_bias': True, 'data_format': 'channels_first'}, 'input_shapes': [[2, 5, 1, 40, 30]]} ] +tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 4, 'kernel_size': (3, 3), 'padding': 'same', 'return_sequences': False, 'activation': <function swish at 0x7f635e4d24d0>}, 'input_shapes': [[2, 5, 20, 30, 2]]} ] +tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 6, 'kernel_size': (2, 3), 'padding': 'valid', 'dilation_rate': 3, 'recurrent_activation': <function elu at 0x7f6396fa2830>, 'return_sequences': True, 'use_bias': True, 'data_format': 'channels_first'}, 'input_shapes': [[2, 5, 1, 40, 30]]} ] ``` - Due to lambda function definitions giving varying addresses as inputs ``` -tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP32 - params:{'fn': <function TestMapFN.<lambda> at 0x7f66c2c63c70>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] -tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP16 - params:{'fn': <function TestMapFN.<lambda> at 0x7f66c2c63c70>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] +tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP32 - params:{'fn': <function TestMapFN.<lambda> at 0x7f211b56fd00>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] +tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP16 - params:{'fn': <function TestMapFN.<lambda> at 0x7f211b56fd00>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] ``` --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>

[Specification] MaxPool-14 and AvgPool-14 - new ceiling mode `CEIL_TORCH` (openvinotoolkit#22930) - Add specification for `MaxPool-14` and `AvgPool-14` - They both introduce a new ceil mode: `ov::op::RoundingType::CEIL_TORCH` - The new ceiling mode does not allow the last pooling in a Dimension to start in the padding area - [Reference and Core](openvinotoolkit#22796) - [Python API](openvinotoolkit#22966) - [PT FE](openvinotoolkit#23027) - [Downgrade transformations](openvinotoolkit#23381) - 131961 openvinotoolkit#18731 --------- Co-authored-by: Tomasz Jankowski <tomasz1.jankowski@intel.com> Co-authored-by: Katarzyna Mitrus <katarzyna.mitrus@intel.com> [TF FE] Support ApproximateEqual operation for TensorFlow (openvinotoolkit#23351) - *Adding operation support for ApproximateEqual operation* - *Addresses issue openvinotoolkit#22082 * --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> [OV JS] Expose export_model()/import_model() (openvinotoolkit#23366) - Expose `compiledModel::export_model()`, a method to export a compiled model to the binary data stream. - Expose `core::import_model(model_file : Buffer, device_name : str)`, a method to import a compiled model from a previously exported one. - *134820* *134818* --------- Co-authored-by: Vishniakov Nikolai <nikolai.vishniakov@intel.com> [core] Low precision element iterator and `u2, u3, u6` types (openvinotoolkit#23279) - Introduce new low precision types `u2`, `u3`, `u6`. - Introduce `ov::element::Iterator` for low precision types like `u1, u2, u3, u4, i4, u6`: - Gives pointer like access to low precision values in Tensor, containers etc. - Can be used by STL algorithms to access data in unified algorithms for data manipulation. - Can be used in Constant, Convert operators to replace duplicate implementations for accessing low precision data (bin-size reduction). - Can be used for operator reference implementation or plugin if there is no hardware specific solution. - [CVS-126998](https://jira.devtools.intel.com/browse/CVS-126998) - Part of [CVS-128024](https://jira.devtools.intel.com/browse/CVS-128024) [DOCS] Updated file (openvinotoolkit#23509) - *item1* - *...* - *ticket-id* Add 'pad' operator support for ov::preprocess::PrePostProcessor (openvinotoolkit#23093) - Add 'pad' preprocessor operator - openvinotoolkit#23068 - [CVS-121548](https://jira.devtools.intel.com/browse/CVS-121548) [API][AUTO] Fail to get PERF_COUNT from compiled_model (openvinotoolkit#23123) - *Fail to get PERF_COUNT from compiled_model* - *CVS-130349* [GPU] Fix dynamic loop's not matched issue during multiple shapes are inferenced (openvinotoolkit#22806) - *Fix the issue which second infer with updated shape in dynamic loop doesn't update sliced layout.* - *Fix the issue that the optimized reshape doesn't reinterpret output memory in update_output_layout()* - *122739* - *131544* [DOCS] Add docs about ignored subgraphs (openvinotoolkit#23435) - Add documentation about `nncf.Subgraph` - 100999 [TRANSFORMATIONS] Fix Optional to match even with no inputs (openvinotoolkit#23471) [TRANSFORMATIONS] Fix Optional to match even with no inputs The Optional pattern type may create a wrong pattern to match if no inputs are provided to the Optional node. If no inputs present to the Optional type, it will not create an alternative branch(es) to check against resulting in the incorrect matching. Fix that by adding a check for the number of inputs being 0. Do a minor refactoring/renaming for the readability purposes. CSV-133523 Signed-off-by: Andrii Staikov <andrii.staikov@intel.com> --------- Signed-off-by: Andrii Staikov <andrii.staikov@intel.com> Enable Paddle FastSpeech2 model (openvinotoolkit#23311) - *Enable Paddle FastSpeech2 model* - *fix issue in 'set_value'* - *add 'round' op* - *CVS-134638* [Conformance Test] Fix cache test case failure for auto plugin (openvinotoolkit#23473) - check if the blob size remains the same as it was during the initial caching of the compiled model, rather than comparing it with a specified number, such as 1 in this case. - count the size of cached blobs after the model compilation is completed on all HW plugin within AUTO plugin. - CVS-130395 [GPU] Remove unused formats (openvinotoolkit#23431) + Most of them are in onednn weights format. - *119476* [CPU][ARM] Make f16 precision as default for CNN (openvinotoolkit#22839) Remove mentioning of compatibility folder in mac docs (openvinotoolkit#23542) - *item1* - *...* - *ticket-id* [TRANSFORMATIONS] Fix ReshapeAMatMul pattern to work with shared node as reshape input (openvinotoolkit#23535) - *`ReshapeAMatMul` worked incorrect in case of using shared nodes as reshape input* - *Fix: to reconnect reshape input to new `shape_of` pattern* - *[CVS-134625](https://jira.devtools.intel.com/browse/CVS-134625)* [TF FE] Support complex tensors for Reciprocal operations (openvinotoolkit#23355) - *Extended loader Reciprocal by propagating ComplexTypeMark from input to output and to represent output complex type tensor as a floating-point type tensor with an auxiliary dimension that concatenates real and imaginary parts of complex tensor.* - *Performed reciprocal for complex numbers.* - *Wrapped the complex result with ComplexTypeMark and returned the result* - openvinotoolkit#23234 --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> [GPU] Fix SIMD for non supporting platforms (openvinotoolkit#23540) - Check is simd 8 is supported - *[CVS-133769](https://jira.devtools.intel.com/browse/CVS-133769)* [PT FE] Fix typo and improve the error info. (openvinotoolkit#23507) - *Fix the typo of the code (then -> than)* - *Improve the error info here, to let developer know the size of output if the assertion fails.* - *No ticket id* [PT FE] Fix sporadic issue in quantized tests (openvinotoolkit#23520) - *Relax quantized tests condition to remove sporadicity.* - *CVS-129734* [GPU] Fixed not to set GATHER_AXIS_SHAPE_INFO_INDEX when input0 is static (openvinotoolkit#23548) - This PR fixes `Gather` not to set GATHER_AXIS_SHAPE_INFO_INDEX when input0 is static. - It enables some functional tests again. Add test for CoreImpl::get_versions() (openvinotoolkit#23336) Closes [23298](openvinotoolkit#23298) - [CVS-132140](https://jira.devtools.intel.com/browse/CVS-132140) --------- Co-authored-by: Oleg Pipikin <oleg.pipikin@intel.com> [PT FE] Add ModuleExtension (openvinotoolkit#23536) - *Continuation of openvinotoolkit#22867* - *CVS-133733* --------- Co-authored-by: Sergey Lyalin <sergey.lyalin@intel.com> [api conformance] Fix batch/hetero plugins config (openvinotoolkit#23547) - *item1* - *...* - *ticket-id* [Transformations] Added If operation to NMS path propagation for ignore negative indices in Gather (openvinotoolkit#23451) - *127874* [TF FE] Test TextVectorization on white-space string input and Equal on empty string tensor (openvinotoolkit#23572) **Details:** Test `tf.keras.TextVectorization` on white-space string input and Equal on empty string tensor. **Ticket:** 135749 --------- Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> [PT FE] Make ModuleExtension patching in independent function scope (openvinotoolkit#23584) - *Make ModuleExtension patching in independent function scope* - *ticket-id* [GPU] Increase FC tile_b size for INT4 shape agnostic kernel (openvinotoolkit#23532) - Increased FC tile_B size for INT4 shape agnostic kernel for improving context processing - 133444 [GPU] Enable 8bit compression support on dGPU via oneDNN (openvinotoolkit#22740) - Enable 8bit compression support on dGPU via oneDNN - Update oneDNN version - Enable oneDNN primitives cache Ticket: 124115 [CPU] Add PagedAttention support (openvinotoolkit#23524) - *Support PagedAttention support, depends on:* - openvino_contrib: openvinotoolkit/openvino_contrib#867 - vLLM: ilya-lavrenov/vllm#4 - *TODO* - Models with alibi feature - *[134329](https://jira.devtools.intel.com/browse/CVS-134329)* - *[134327](https://jira.devtools.intel.com/browse/CVS-134327)* [GPU] In gemm_tile_kernel, applied to use block read when N and K byte-size is aligned 4. (openvinotoolkit#23400) - *Element by element read is the bottle-neck in gemm_tiled kernel. Enable block-read when N and K size are aligned 4byte with N and K are leftover*. - *Increasing tile_n_size has performance improvement when m_size and n_size are not shallow and n_size is aligned at 32.* - *Add GEMM_TILE_M/N/K/SIMD environment variables for convenience.* - *134279* --------- Signed-off-by: hyunback <hyunback.kim@intel.com> [CPU] [ARM64] jit eltwise: int8 support (openvinotoolkit#22687) - *int8 support* - *CVS-128643* [ONNX] Extended ReduceMax by opsets 13,18,20 (openvinotoolkit#23475) - Extended ReduceMax by opsets 13,18,20 - Updated a using opset for ONNX to 20 - Added tests for additional supported types - Enabled backend tests - Closes openvinotoolkit#20555 [CPU] Enable concat nspc layout inplace for urlnet model cases (openvinotoolkit#23454) - *enable concat nspc layout inplace for channel only cases, with these concat node use inplace impl, urlnet model gain performance benefits, and this(intermediate concat node is nspc layout but actually is one dimension) could be common case especially for models with 1D input* - *130282* [CPU]Fix GPT-J RoPE fusion (openvinotoolkit#23519) - *Support new RoPE pattern of GPT-J* - *Local test shows 17 % improvement for 2nd token latency for BF16 in `Intel(R) Xeon(R) Platinum 8468`* - *CVS-134949* Torch Compile - New Op Support (openvinotoolkit#23310) New op support for: - torch.export updates - benchmarking model support - chatglm2 support --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: ynimmaga <yamini.nimmagadda@intel.com> Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com> [DOCS] Latency highlight for OV devices + update of Optimize Inference for master (openvinotoolkit#23575) Jira: 133389 * Added an indication on Latency being the default use for OV devices * Streamlined the Optimize Inference article for better clarity. [TF FE] Support complex tensors for OnesLike operation (openvinotoolkit#23445) - *Adding support for OnesLike operation on complex type tensor* - Closes openvinotoolkit#22953 --------- Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com> Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> [CI] [GHA] Remove usage of the `SimenB/github-actions-cpu-cores` action (openvinotoolkit#23583) - The action does not have a License. - `cmake` should figure out the # of cores for parallel. [CPU] [ARM64] jit select (openvinotoolkit#23450) - *[CPU] [AARCH64] jit select* - *CVS-135445* New DB schema for GitHub metrics script (openvinotoolkit#23606) Improvements and fixes for the script which sends GitHub Workflow metrics to a database. See also: [23484](openvinotoolkit#23484) [JS API] Extract code from CompiledModel getters (openvinotoolkit#23515) - Extract the same logic structure from `CompileModel::input` and `CompileModel::output` - Add a private `CompileModel::get_node` method that gets the specified input or output node. Note: No changes to argument validation or conversion. - *127617* constraints openvino-dev: Limit mpmath<1.4 (openvinotoolkit#23601) - Limit mpmath because of pytorch/pytorch#120995 and sympy/sympy#26273 [GPU] Re-enable memory reuse for gemm (openvinotoolkit#23600) - Since openvinotoolkit#22726 gemm is derived from multi-stage impl which had memory reuse flag enforced to false for all sub-classes. - This patch enables memory reuse back for gemm kernel to reduce memory consumption. - *135361* [TF FE] Support TensorFlow 2.16 (openvinotoolkit#23562) **Details:** Support TensorFlow 2.16 **Ticket:** TBD --------- Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> [IE TESTS][OP CONFORMANCE] Move `ConstRanges` range calculation to `InGenData` constructor (openvinotoolkit#23427) - *Move static const range initialization to `InData` structure* - *[125993](https://jira.devtools.intel.com/browse/CVS-125993)* Enable new property model_distribution_policy for CPU inference (openvinotoolkit#23077) - *Enable new property model_distribution_policy for CPU inference* -- *Add C++ interface and test cases* -- *Add Python interface and test cases* - *CVS-127844* [CPU] optimize PagedAttention's shape inference (openvinotoolkit#23603) - *Specific shape inference for PagedAttention* - *...* - *ticket-id* [CPU] [ARM64] jit equal (openvinotoolkit#23266) - *[CPU] [AARCH64] jit eltwise Equal - *CVS-134691* [GPU] Fix count non zero for empty input (openvinotoolkit#23597) - Adds buffer reset to 0 in `count_nonzero` impl in case of empty input tensor as currently we may try to allocate random amount of memory in subsequent `gather_nonzero` call [PyOV] Add Python API for MaxPool-14 and AvgPool-14 (openvinotoolkit#22966) - Extend Python API with`MaxPool-14` and `AvgPool-14` - They both introduce a new ceil mode: `ov::op::RoundingType::CEIL_TORCH` - The new ceiling mode does not allow the last pooling in a Dimension to start in the padding area - openvinotoolkit#22930 - openvinotoolkit#22796 - openvinotoolkit#23027 - openvinotoolkit#23381 - openvinotoolkit#23582 - 131961 openvinotoolkit#18731 --------- Co-authored-by: Katarzyna Mitrus <katarzyna.mitrus@intel.com> [Spec] Clarify specification for StridedSlice (openvinotoolkit#23039) - Add notes with descriptions of: Out of Bounds, Indexing in Reverse, Negative Indices - Clarified length of masks - Clarified the definition of `-1` value - Described in detail the behavior of masks, aligned with Reference Implementation - Added more latex-like style, add the examples for the missing masks. - 90128 [TRANSFORMATIONS] Remove use of legacy names from transformations (openvinotoolkit#23574) [TRANSFORMATIONS] Remove use of legacy names from transformations API function create_ie_output_name() and get_ie_output_name() are deprecated in a28a000 ("Deprecated functions to operate with legacy port names (openvinotoolkit#22717)") Remove usages of create_ie_output_name() in Transformations CVS-132087 Signed-off-by: Andrii Staikov andrii.staikov@intel.com --------- Signed-off-by: Andrii Staikov andrii.staikov@intel.com [Opset14][Spec] ConvertPromoteTypes-14 specification (openvinotoolkit#23264) - *This PR introduces specification for ConvertPromoteTypes-14 op - conversion op used to align two inputs to common type* - *Operator was introduced for PyTorch Frontend, rules also match Tensorflow https://www.tensorflow.org/guide/tf_numpy_type_promotion* - PR with core implementation: openvinotoolkit#22566 - Draft PR with improvements to core + replacement it PTFe: openvinotoolkit#22770 - *129197* --------- Co-authored-by: Katarzyna Mitrus <katarzyna.mitrus@intel.com> [CPU][ARM] Upgrade to ACL v24.02.1 (openvinotoolkit#22598) oneDNN PR: openvinotoolkit/oneDNN#227 [API CONFORMANCE] Modify API conformance suite for SW plugins (openvinotoolkit#23557) - *Move some properties from mandatory to optional for sw plugins* - *...* - *[133459](https://jira.devtools.intel.com/browse/CVS-133459)* Calculate model weights hash in parallel (openvinotoolkit#23605) - Calculate model weights hash in parallel in case of reading model from buffer - CVS-134771 [DOCS] improve legacy section formatting (openvinotoolkit#23512) [DOCS] ai legal disclaimer (openvinotoolkit#23587) [TRANSFORMATIONS] Create python binding for pattern::Optional (openvinotoolkit#23558) [TRANSFORMATIONS] Create python binding for pattern::Optional Expose the C++ op::pattern::Optional to Python in order to simplify patterns creation. Cover the functionality with the dedicated tests. CVS-133523 Signed-off-by: Andrii Staikov <andrii.staikov@intel.com> --------- Signed-off-by: Andrii Staikov <andrii.staikov@intel.com> [CPU] Fix SDPA pattern matching (openvinotoolkit#23581) Limit the Concat layer to have maximum 3 children. The third one is allowed to be a ShapeOf op only (to support Mixtral). - 135375 [chore] Use debug loglevel for github metrics script (openvinotoolkit#23633) We can switch log level for GitHub metrics script only when the workflow is restarted with debug logging [TF FE] Enable parallel execution of TensorFlow Layer 2 python tests (openvinotoolkit#23344) Addresses issue: openvinotoolkit#20919 - Enables parallel execution of TensorFlow Layer 2 python tests - Fixes test_tf2_keras_conv_lstm_2d.py and test_tf2_map_fn.py to not fail during parallel execution - Appends args in github workflow to enable parallel execution Errors fixed: - Due to varying Kera activation function addresses causing the workers to get different parameter inputs and thus failing. See [known issue](https://pytest-xdist.readthedocs.io/en/stable/known-limitations.html#order-and-amount-of-test-must-be-consistent) ``` -tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 4, 'kernel_size': (3, 3), 'padding': 'same', 'return_sequences': False, 'activation': <function swish at 0x7f1fadf364d0>}, 'input_shapes': [[2, 5, 20, 30, 2]]} ] -tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 6, 'kernel_size': (2, 3), 'padding': 'valid', 'dilation_rate': 3, 'recurrent_activation': <function elu at 0x7f1fe6a1a830>, 'return_sequences': True, 'use_bias': True, 'data_format': 'channels_first'}, 'input_shapes': [[2, 5, 1, 40, 30]]} ] +tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 4, 'kernel_size': (3, 3), 'padding': 'same', 'return_sequences': False, 'activation': <function swish at 0x7f635e4d24d0>}, 'input_shapes': [[2, 5, 20, 30, 2]]} ] +tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 6, 'kernel_size': (2, 3), 'padding': 'valid', 'dilation_rate': 3, 'recurrent_activation': <function elu at 0x7f6396fa2830>, 'return_sequences': True, 'use_bias': True, 'data_format': 'channels_first'}, 'input_shapes': [[2, 5, 1, 40, 30]]} ] ``` - Due to lambda function definitions giving varying addresses as inputs ``` -tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP32 - params:{'fn': <function TestMapFN.<lambda> at 0x7f66c2c63c70>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] -tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP16 - params:{'fn': <function TestMapFN.<lambda> at 0x7f66c2c63c70>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] +tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP32 - params:{'fn': <function TestMapFN.<lambda> at 0x7f211b56fd00>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] +tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP16 - params:{'fn': <function TestMapFN.<lambda> at 0x7f211b56fd00>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] ``` --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> [ IE TESTS ] Update tensor comparation function according plugin requirments (openvinotoolkit#23226) - *Comparation function was changed to compare tensors based on element comparation* - *`std::abs(ref_value - plugin_value) <= abs_threshold + rel_threshold * ref_value`* - *`abs_threshold ` = std::max(std::numeric_limits::eps<plugin_element_type>(), std::numeric_limits::eps<ref_element_type>())* - *`ref_threshold = eps_by_expected_type()`, which is based on half `bit length of mantissa`* - [CVS-133173](https://jira.devtools.intel.com/browse/CVS-133173) - [CVS-135540](https://jira.devtools.intel.com/browse/CVS-135540) --------- Co-authored-by: sbalandi <sofya.balandina@intel.com> [TF FE] Support Angle operation for TensorFlow models (openvinotoolkit#23028) - *Support Angle operation for TensorFlow models* - Closes openvinotoolkit#22083 --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> [GPU] Extend gemm to fuse broadcast and reshape layers (openvinotoolkit#23513) - Fuse `broadcast` and `reshape` layers into `gemm` layer for LLM's 2nd latency optimization - before : [`broadcast`] --> [`reshape`] --> `gemm` - after : `gemm` - `gemm` is extended to have `input0_target_shape`, `input1_target_shape`, `input0_output_pattern` and `input1_output_pattern` from `broadcast` and `reshape` layers - 128343 --------- Signed-off-by: Andrew Park <andrew.park@intel.com> [GPU] Extend pattern for ClampFP16Output (openvinotoolkit#23592) - By PR(openvinotoolkit#22245), `clamp_fp16_output` opt pass was moved to ngraph - Because nodes such as eltwise(`Add`, `Subtract`, `Multiply`, `Divide`) that were fused into target node `gemm` are not supported in pattern, corresponding pattern was extended for this purpose - 135060 Fix the aten::mv for pytorch models openvinotoolkit#22073 (openvinotoolkit#22677) - *item1* - *...* Add aten::mv operator close openvinotoolkit#22073 - *ticket-id* --------- Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com> Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com> Remove NGraphFunctions namespace (openvinotoolkit#23627) - Remove NGraphFunctions namespace - CVS-133379 [PY API] Fix the preoblem that Node.get_attributes() cannot return all attributes (openvinotoolkit#23530) - extend the `util::DictAttributeSerializer::on_adapter()` method, making it compatible with `ov::PartialShape` and `ov::op::util::Variable` types; - add extra tests to test the correctness of `Node.get_attributes()` - openvinotoolkit#23455 --------- Co-authored-by: Jan Iwaszkiewicz <jan.iwaszkiewicz@intel.com> [CPU] Correct type configuration for i8 inner_product with f16 output (openvinotoolkit#23610) - 136298 - 136163 Support aten::bucketize for pytorch models openvinotoolkit#23328 (openvinotoolkit#23527) ](openvinotoolkit#23328) - Support aten::bucketize for pytorch models Move ConvertConvertPromoteTypes transformation from Common to MOC (openvinotoolkit#23630) Move ConvertConvertPromoteTypes transformation from Common to MOC N/A [CPU][ARM] Enable both f16 and f32 kernels for aarch64 and introduce runtime f16 support check (openvinotoolkit#22992) Inherited from openvinotoolkit#22437 --------- Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com> [ONNX] Reduced memory consumption while running tests (openvinotoolkit#23628) - Significantly reduced amount of using RAM while testing - May introduce test regression in multi-worker scenario (-n auto), but it isn't detected while validation - 129958 [TF FE] Add testing StringLower and TextVectorization operations on non-ASCII sentences (openvinotoolkit#23641) **Details:** Add testing non-ASCII sentences for StringLower operation. Needs to be merged after openvinotoolkit/openvino_tokenizers#80. **Ticket:** 135752 --------- Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> Symbol Tracking API updated and made public (openvinotoolkit#23136) - dev_api `ov::DimensionTracker` and `ov::TableOfEquivalence` classes deleted, logic moved to `ov::Symbol` which is now stored by `ov::Dimension` - new implementation moves responsibility to store and report relations between Symbols directly to the Symbol object. Hence, there is no need for `ov::TableOfEquivalence` and no need for synchronization point anymore. - Equivalence is being tracked by using [Disjoint-set_data_structure](https://en.wikipedia.org/wiki/Disjoint-set_data_structure) which uses less memory than previous implementation. ![image](https://github.com/openvinotoolkit/openvino/assets/55839243/f1266f32-976d-44f9-a6ea-cd04dce07407) ![image](https://github.com/openvinotoolkit/openvino/assets/55839243/3108d1ad-0d30-4041-aa93-c4de1f1fb979) - *CVS-133123* Align friendly names uniqueization (openvinotoolkit#22729) Removed code that makes friendly names unique from Serialization and a name uniqueness check from Deserializator. Enabled the mode of ResolveNameCollisions transformation to uniqueize all friendly names, not only autogenerated in Frontends - *CVS-131567* --------- Co-authored-by: Evgenya Nugmanova <evgeniia.nugmanova@intel.com> Co-authored-by: Andrei Kochin <andrei.kochin@intel.com> [CPU][REFACTORING] Use memory access helper methods where possible (openvinotoolkit#23442) fix coverity issue 1540833 and 1540832 (openvinotoolkit#23635) - *fix coverity scan issue1540833 and issue1540832* - *ticket-id* [CPU] Prohibit fc avx2_vnni_2 decompression for bf16 input (openvinotoolkit#23638) - The FC changes made in scope of openvinotoolkit#20486 were missed when rebasing - The context is: Even the system and the node does support bf16 precision we have to fall back to f32 in/out precision due to lack of support for decompression with bf16 avx2_vnni_2 in oneDNN fork. - To cover this limitation an additional type mapping parameter in form of std::function was introduced for disabling particular type mapping entry using a runtime check (isa support in this case) - 122347 - 136163 Merged master changes Update src/frontends/tensorflow_common/src/op/gelu.cpp updated approximation access

…penvinotoolkit#23344) Addresses issue: openvinotoolkit#20919 ### Details: - Enables parallel execution of TensorFlow Layer 2 python tests - Fixes test_tf2_keras_conv_lstm_2d.py and test_tf2_map_fn.py to not fail during parallel execution - Appends args in github workflow to enable parallel execution Errors fixed: - Due to varying Kera activation function addresses causing the workers to get different parameter inputs and thus failing. See [known issue](https://pytest-xdist.readthedocs.io/en/stable/known-limitations.html#order-and-amount-of-test-must-be-consistent) ``` -tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 4, 'kernel_size': (3, 3), 'padding': 'same', 'return_sequences': False, 'activation': <function swish at 0x7f1fadf364d0>}, 'input_shapes': [[2, 5, 20, 30, 2]]} ] -tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 6, 'kernel_size': (2, 3), 'padding': 'valid', 'dilation_rate': 3, 'recurrent_activation': <function elu at 0x7f1fe6a1a830>, 'return_sequences': True, 'use_bias': True, 'data_format': 'channels_first'}, 'input_shapes': [[2, 5, 1, 40, 30]]} ] +tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 4, 'kernel_size': (3, 3), 'padding': 'same', 'return_sequences': False, 'activation': <function swish at 0x7f635e4d24d0>}, 'input_shapes': [[2, 5, 20, 30, 2]]} ] +tensorflow2_keras_tests/test_tf2_keras_conv_lstm_2d.py::TestKerasConvLSTM2D::test_keras_conv_lstm_2d_basic[ ie_device:CPU - precision:FP32 - params:{'params': {'filters': 6, 'kernel_size': (2, 3), 'padding': 'valid', 'dilation_rate': 3, 'recurrent_activation': <function elu at 0x7f6396fa2830>, 'return_sequences': True, 'use_bias': True, 'data_format': 'channels_first'}, 'input_shapes': [[2, 5, 1, 40, 30]]} ] ``` - Due to lambda function definitions giving varying addresses as inputs ``` -tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP32 - params:{'fn': <function TestMapFN.<lambda> at 0x7f66c2c63c70>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] -tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP16 - params:{'fn': <function TestMapFN.<lambda> at 0x7f66c2c63c70>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] +tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP32 - params:{'fn': <function TestMapFN.<lambda> at 0x7f211b56fd00>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] +tensorflow2_keras_tests/test_tf2_map_fn.py::TestMapFN::test_multiple_inputs_outputs_int32[ ie_device:CPU - precision:FP16 - params:{'fn': <function TestMapFN.<lambda> at 0x7f211b56fd00>, 'input_type': tf.int32, 'fn_output_signature': (tf.int32, tf.int32, tf.int32), 'back_prop': True, 'input_names': ['x1', 'x2', 'x3'], 'input_shapes': [[2, 1, 3, 4], [2, 1, 3, 4], [2, 1, 3, 4]]} ] ``` --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>

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LakshmiGayathri19 mentioned this issue Nov 19, 2023

Modify workflow to enable parallel execution #21166

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Modify Github workflow file - "linux.yml" to enable parallel execution. #21195

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Modify python test cases which are failing during parallel execution #21264

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Modify test cases which failed during parallel execution #21851

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[TF FE] Enable parallel execution of TensorFlow Layer 2 python tests #23344

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[Good First Issue]: Enable parallel execution of TensorFlow Layer 2 python tests #20919

[Good First Issue]: Enable parallel execution of TensorFlow Layer 2 python tests #20919

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[Good First Issue]: Enable parallel execution of TensorFlow Layer 2 python tests #20919

[Good First Issue]: Enable parallel execution of TensorFlow Layer 2 python tests #20919

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mryzhov commented Nov 7, 2023 •

edited by ilya-lavrenov

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LakshmiGayathri19 commented Nov 12, 2023 •

edited

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mryzhov commented Nov 17, 2023 •

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LakshmiGayathri19 commented Nov 20, 2023 •

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