[TF Hub][Perf Tests]Implement performance tests in precommit to compa…

…re read_model and convert_model paths (openvinotoolkit#21023)

* begin

* fixes

* measure time and compare

* fix bug + update precommint models

* cleanup

* add wget to install requirements; add to linux.yml

* fixes + improvements

* output results to html

* remove unneeded code

* fix

* code review fixes

* store downloaded models in cache

* use 1000 runs

* use model paths from tf-hub tests

* use tf hub api to download instead of wget; measure some time stats

* small fixes

* remove unneeded files

* use own list of models

* remove uneeded if

* remove unstable models

* remove unstable models

* fix requirements

* code review fixes

* Update .github/workflows/linux.yml

Co-authored-by: Roman Kazantsev <[email protected]>

* fix round_num function

* remove unstable network

* Update tests/model_hub_tests/models_hub_common/test_performance_model.py

Co-authored-by: Roman Kazantsev <[email protected]>

* code review fixes

* build fix

* code review fixes

* code review fixes

* code review fixes

* Update tests/model_hub_tests/models_hub_common/test_performance_model.py

Co-authored-by: Roman Kazantsev <[email protected]>

* Update tests/model_hub_tests/performance_tests/test_tf_hub_perfomance_model.py

Co-authored-by: Roman Kazantsev <[email protected]>

* Update tests/model_hub_tests/performance_tests/test_tf_hub_perfomance_model.py

Co-authored-by: Roman Kazantsev <[email protected]>

* Update tests/model_hub_tests/models_hub_common/test_performance_model.py

Co-authored-by: Roman Kazantsev <[email protected]>

* code review fixes: use autoflake

* remove unneeded tensorflow_text

* Update .github/workflows/linux.yml

* Update linux.yml

Added TensorFlow_Hub_Performance_Models_Tests to final status check

---------

Co-authored-by: Roman Kazantsev <[email protected]>
Co-authored-by: Ilya Lavrenov <[email protected]>

.github/workflows/linux.yml

@@ -1357,6 +1357,91 @@ jobs:
             ${{ env.INSTALL_TEST_DIR }}/TEST*.html
           if-no-files-found: 'error'
 
+  TensorFlow_Hub_Performance_Models_Tests:
+    name: TensorFlow Hub Performance Models tests
+    needs: [Build, Smart_CI]
+    defaults:
+      run:
+        shell: bash
+    runs-on: ${{ github.event_name == 'schedule' && 'ubuntu-20.04-16-cores' || 'ubuntu-20.04-8-cores'}}
+    timeout-minutes: ${{ github.event_name == 'schedule' && 400 || 5 }}
+    env:
+      OPENVINO_REPO: ${{ github.workspace }}/openvino
+      INSTALL_DIR: ${{ github.workspace }}/install
+      INSTALL_TEST_DIR: ${{ github.workspace }}/install/tests
+      MODEL_HUB_TESTS_INSTALL_DIR: ${{ github.workspace }}/install/tests/model_hub_tests
+    if: fromJSON(needs.smart_ci.outputs.affected_components).TF_FE.test || 
+        fromJSON(needs.smart_ci.outputs.affected_components).TFL_FE.test
+
+    steps:
+      - name: Check sudo
+        run: if [ "$(id -u)" -eq 0 ]; then apt update && apt --assume-yes install sudo; fi
+
+      - name: Download OpenVINO package
+        uses: actions/download-artifact@v3
+        with:
+          name: openvino_package
+          path: ${{ env.INSTALL_DIR }}
+
+      - name: Download OpenVINO tests package
+        uses: actions/download-artifact@v3
+        with:
+          name: openvino_tests
+          path: ${{ env.INSTALL_TEST_DIR }}
+
+      - name: Extract OpenVINO packages
+        run: |
+          pushd ${INSTALL_DIR}
+            tar -xzf openvino_package.tar.gz -C ${INSTALL_DIR}
+          popd
+
+          pushd ${INSTALL_TEST_DIR}
+            tar -xzf openvino_tests.tar.gz -C ${INSTALL_DIR}
+          popd
+
+      - name: Fetch setup_python action
+        uses: actions/checkout@v4
+        with:
+          sparse-checkout: |
+            .github/actions/setup_python/action.yml
+          sparse-checkout-cone-mode: false
+          path: 'openvino'
+
+      - name: Setup Python ${{ env.PYTHON_VERSION }}
+        uses: ./openvino/.github/actions/setup_python
+        with:
+          version: ${{ env.PYTHON_VERSION }}
+          should-setup-pip-paths: 'false'
+          self-hosted-runner: 'false'
+
+      - name: Install OpenVINO Python wheels
+        run: python3 -m pip install ${INSTALL_DIR}/tools/openvino-*
+
+      - name: Install TF Hub tests requirements
+        run: |
+          python3 -m pip install -r ${MODEL_HUB_TESTS_INSTALL_DIR}/tf_hub_tests/requirements.txt
+
+      - name: Install Hub Performance tests requirements
+        run: |
+          python3 -m pip install -r ${MODEL_HUB_TESTS_INSTALL_DIR}/performance_tests/requirements.txt
+
+      - name: Performance Hub Tests
+        run: |
+          export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH
+          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/performance_tests/ -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-tf_hub_performance.html --self-contained-html -v
+        env:
+          TYPE: ${{ github.event_name == 'schedule' && 'nightly' || 'precommit'}}
+          TEST_DEVICE: CPU
+
+      - name: Upload Test Results
+        uses: actions/upload-artifact@v3
+        if: ${{ !cancelled() }}
+        with:
+          name: test-results-tensorflow-hub-performance-models
+          path: |
+            ${{ env.INSTALL_TEST_DIR }}/TEST*.html
+          if-no-files-found: 'error'
+
   PyTorch_Models_Tests:
     name: PyTorch Models tests
     needs: [Build, Smart_CI]
@@ -1590,7 +1675,7 @@ jobs:
   Overall_Status:
     name: ci/gha_overall_status
     needs: [Smart_CI, Build, Debian_Packages, Samples, Conformance, ONNX_Runtime, CXX_Unit_Tests, Python_Unit_Tests,
-            CPU_Functional_Tests, TensorFlow_Hub_Models_Tests, PyTorch_Models_Tests, NVIDIA_Plugin]
+            CPU_Functional_Tests, TensorFlow_Hub_Models_Tests, TensorFlow_Hub_Performance_Models_Tests, PyTorch_Models_Tests, NVIDIA_Plugin]
     if: ${{ always() }}
     runs-on: ubuntu-latest
     steps:

tests/model_hub_tests/models_hub_common/constants.py

@@ -9,8 +9,10 @@
                                   os.path.join(tempfile.gettempdir(), "tfhub_modules"))
 os.environ['TFHUB_CACHE_DIR'] = tf_hub_cache_dir
 
+no_clean_cache_dir = False
 hf_hub_cache_dir = tempfile.gettempdir()
 if os.environ.get('USE_SYSTEM_CACHE', 'True') == 'False':
+    no_clean_cache_dir = True
     os.environ['HUGGINGFACE_HUB_CACHE'] = hf_hub_cache_dir
 
 # supported_devices : CPU, GPU, GNA

tests/model_hub_tests/models_hub_common/test_performance_model.py

@@ -0,0 +1,218 @@
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+
+import sys
+import time
+from enum import Enum
+import traceback
+import pytest
+from openvino.runtime.utils.types import openvino_to_numpy_types_map
+
+import numpy as np
+from models_hub_common.multiprocessing_utils import multiprocessing_run
+import openvino as ov
+
+# noinspection PyUnresolvedReferences
+
+# set seed to have deterministic input data generation
+# to avoid sporadic issues in inference results
+rng = np.random.default_rng(seed=56190)
+
+
+def get_numpy_type(ov_type):
+    np_type = next(
+        (np_type_value for (ov_type_value, np_type_value) in openvino_to_numpy_types_map if ov_type_value == ov_type),
+        None,
+    )
+
+    if not np_type:
+        raise Exception('no numpy type for type {} found'.format(ov_type))
+
+    return np_type
+
+
+class Status(Enum):
+    OK = 0
+    LARGE_INFER_TIME_DIFF = 1
+    LOAD_MODEL = 2
+    GET_INPUTS_INFO = 3
+    PREPARE_INPUTS = 4
+    GET_CONVERTED_MODEL = 5
+    GET_READ_MODEL = 6
+    INFER_CONVERTED_MODEL = 7
+    INFER_READ_MODEL = 8
+    LARGE_INFER_TIME_DIFF_WITH_LARGE_VAR = 9
+
+
+class Results:
+    def __init__(self):
+        self.converted_infer_time = 0.0
+        self.converted_model_time_variance = 0.0
+        self.read_model_infer_time = 0.0
+        self.read_model_infer_time_variance = 0.0
+        self.infer_time_ratio = 0.0
+        self.error_message = ''
+        self.status = None
+
+
+def wrap_timer(func, args):
+    t0 = time.time()
+    retval = func(*args)
+    t1 = time.time()
+    return retval, t1 - t0
+
+
+class TestModelPerformance:
+    infer_timeout = 600
+    threshold_ratio = 0.1
+    num_heat_runs = 100
+    num_measure_runs = 500
+    threshold_var = 10.0
+
+    def load_model(self, model_name, model_link):
+        raise "load_model is not implemented"
+
+    def prepare_input(self, input_shape, input_type):
+        if input_type in [ov.Type.f32, ov.Type.f64]:
+            return 2.0 * rng.random(size=input_shape, dtype=get_numpy_type(input_type))
+        elif input_type in [ov.Type.u8, ov.Type.u16, ov.Type.i8, ov.Type.i16, ov.Type.i32, ov.Type.i64]:
+            return rng.integers(0, 5, size=input_shape).astype(get_numpy_type(input_type))
+        elif input_type in [str]:
+            return np.broadcast_to("Some string", input_shape)
+        elif input_type in [bool]:
+            return rng.integers(0, 2, size=input_shape).astype(get_numpy_type(input_type))
+        else:
+            assert False, "Unsupported type {}".format(input_type)
+
+    def prepare_inputs(self, inputs_info):
+        if len(inputs_info) > 0 and inputs_info[0] == 'list':
+            inputs = []
+            inputs_info = inputs_info[1:]
+            for input_name, input_shape, input_type in inputs_info:
+                inputs.append(self.prepare_input(input_shape, input_type))
+        else:
+            inputs = {}
+            for input_name, input_shape, input_type in inputs_info:
+                inputs[input_name] = self.prepare_input(input_shape, input_type)
+        return inputs
+
+    def get_inputs_info(self, model_path: str):
+        inputs_info = []
+        core = ov.Core()
+        model = core.read_model(model=model_path)
+        for param in model.inputs:
+            input_shape = []
+            param_shape = param.get_node().get_output_partial_shape(0)
+            shape_special_dims = [ov.Dimension(), ov.Dimension(), ov.Dimension(), ov.Dimension(3)]
+            if param_shape == ov.PartialShape(shape_special_dims) and param.get_element_type() == ov.Type.f32:
+                # image classification case, let us imitate an image
+                # that helps to avoid compute output size issue
+                input_shape = [1, 200, 200, 3]
+            else:
+                for dim in param_shape:
+                    if dim.is_dynamic:
+                        input_shape.append(1)
+                    else:
+                        input_shape.append(dim.get_length())
+            inputs_info.append((param.get_node().get_friendly_name(), input_shape, param.get_element_type()))
+        return inputs_info
+
+    def get_converted_model(self, model_path: str):
+        return ov.convert_model(model_path)
+
+    def get_read_model(self, model_path: str):
+        core = ov.Core()
+        return core.read_model(model=model_path)
+
+    def infer_model(self, ov_model, inputs):
+        infer_step_t0 = time.time()
+        # heat run
+        for _ in range(0, TestModelPerformance.num_heat_runs):
+            ov_model(inputs)
+        # measure
+        results = []
+        for _ in range(0, TestModelPerformance.num_measure_runs):
+            t0 = time.time()
+            ov_model(inputs)
+            t1 = time.time()
+            results.append(t1 - t0)
+        mean = np.mean(results)
+        var = np.std(results, ddof=1) * 100 / mean
+        infer_step_t1 = time.time()
+        print('inference measurement done in {} secs'.format(infer_step_t1 - infer_step_t0))
+        return mean, var
+
+    def compile_model(self, model, ie_device):
+        core = ov.Core()
+        return core.compile_model(model, ie_device)
+
+    def _run(self, model_name, model_link, ie_device):
+        results = Results()
+        results.status = None
+        try:
+            print("Load the model {} (url: {})".format(model_name, model_link))
+            results.status = Status.LOAD_MODEL
+            model_obj, timedelta = wrap_timer(self.load_model, (model_name, model_link))
+            print('Model {} loaded in {} secs'.format(model_name, timedelta))
+            print("Retrieve inputs info")
+            results.status = Status.GET_INPUTS_INFO
+            inputs_info, timedelta = wrap_timer(self.get_inputs_info, (model_obj,))
+            print('Got inputs info in {} secs'.format(timedelta))
+            print("Prepare input data")
+            results.status = Status.PREPARE_INPUTS
+            inputs = self.prepare_inputs(inputs_info)
+            print("Convert the model into ov::Model")
+            results.status = Status.GET_CONVERTED_MODEL
+            converted_model = self.compile_model(self.get_converted_model(model_obj), ie_device)
+            print("read the model into ov::Model")
+            results.status = Status.GET_READ_MODEL
+            read_model = self.compile_model(self.get_read_model(model_obj), ie_device)
+            print("Infer the converted model")
+            results.status = Status.INFER_CONVERTED_MODEL
+            converted_model_time, converted_model_time_variance = self.infer_model(converted_model, inputs)
+            print('converted model time infer {}'.format(converted_model_time))
+            print('converted model time infer var {}'.format(converted_model_time_variance))
+            print("Infer read model")
+            results.status = Status.INFER_READ_MODEL
+            read_model_time, read_model_time_variance = self.infer_model(read_model, inputs)
+            print('read model time infer {}'.format(read_model_time))
+            print('read model time infer var {}'.format(read_model_time_variance))
+
+            infer_time_ratio = converted_model_time/read_model_time
+
+            results.converted_infer_time = converted_model_time
+            results.converted_model_time_variance = converted_model_time_variance
+            results.read_model_infer_time = read_model_time
+            results.read_model_infer_time_variance = read_model_time_variance
+            results.infer_time_ratio = infer_time_ratio
+
+            if abs(infer_time_ratio - 1) > TestModelPerformance.threshold_ratio:
+                if (read_model_time_variance > TestModelPerformance.threshold_var
+                        or converted_model_time_variance > TestModelPerformance.threshold_var):
+                    results.status = Status.LARGE_INFER_TIME_DIFF_WITH_LARGE_VAR
+                    results.error_message = "too large ratio {} with large variance".format(infer_time_ratio)
+                else:
+                    results.status = Status.LARGE_INFER_TIME_DIFF
+                    results.error_message = "too large ratio {}".format(infer_time_ratio)
+            else:
+                results.status = Status.OK
+        except:
+            ex_type, ex_value, tb = sys.exc_info()
+            results.error_message = "{tb}\n{ex_type}: {ex_value}".format(tb=''.join(traceback.format_tb(tb)),
+                                                             ex_type=ex_type.__name__, ex_value=ex_value)
+        return results
+
+    def run(self, model_name, model_link, ie_device):
+        self.result = Results()
+        t0 = time.time()
+        self.result = multiprocessing_run(self._run, [model_name, model_link, ie_device], model_name, self.infer_timeout)
+        t1 = time.time()
+        print('test running time {}'.format(t1 - t0))
+        if self.result.status == Status.OK:
+            return
+        err_message = "\n{func} running failed: \n{msg}".format(func=model_name, msg=self.result.error_message)
+        if self.result.status == Status.LARGE_INFER_TIME_DIFF_WITH_LARGE_VAR:
+            pytest.xfail(err_message)
+        else:
+            pytest.fail(err_message)

tests/model_hub_tests/models_hub_common/utils.py

@@ -13,6 +13,7 @@ def get_models_list(file_name: str):
     models = []
     with open(file_name) as f:
         for model_info in f:
+            model_info = model_info.strip()
             # skip comment in model scope file
             if model_info.startswith('#'):
                 continue

tests/model_hub_tests/performance_tests/conftest.py

@@ -0,0 +1,52 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import inspect
+import pytest
+from py.xml import html
+
+from models_hub_common.utils import get_params
+
+
+def pytest_generate_tests(metafunc):
+    test_gen_attrs_names = list(inspect.signature(get_params).parameters)
+    params = get_params()
+    metafunc.parametrize(test_gen_attrs_names, params, scope="function")
+
+
+@pytest.mark.hookwrapper
+def pytest_runtest_makereport(item, call):
+    outcome = yield
+    report = outcome.get_result()
+    if call.when == 'teardown' and getattr(item.obj.__self__, 'result', None) is not None:
+        results = item.obj.__self__.result
+        report._results = results
+
+
+@pytest.mark.optionalhook
+def pytest_html_results_table_header(cells):
+    cells.insert(2, html.th('status', class_="sortable"))
+    cells.insert(3, html.th('converted model infer time'))
+    cells.insert(4, html.th('converted model infer time variance'))
+    cells.insert(5, html.th('read model infer time'))
+    cells.insert(6, html.th('read model infer time variance'))
+    cells.insert(7, html.th('model infer time ratio converted_model_time/read_model_time'))
+
+
+def round_num(n: float) -> str:
+    s = '{:.4E}'.format(n)
+    if s.endswith('E+00'):
+        return s[:-4]
+    return s
+
+
+@pytest.mark.optionalhook
+def pytest_html_results_table_row(report, cells):
+    if not getattr(report, '_results', None):
+        return
+    cells.insert(2, html.td(report._results.status))
+    cells.insert(3, html.td(round_num(report._results.converted_infer_time)))
+    cells.insert(4, html.td(round_num(report._results.converted_model_time_variance)))
+    cells.insert(5, html.td(round_num(report._results.read_model_infer_time)))
+    cells.insert(6, html.td(round_num(report._results.read_model_infer_time_variance)))
+    cells.insert(7, html.td(round_num(report._results.infer_time_ratio)))