Fix tests

nncf/experimental/torch/quantization/quantize_model.py

@@ -18,7 +18,7 @@
 from nncf.parameters import ModelType
 from nncf.parameters import TargetDevice
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
 from nncf.torch.dynamic_graph.context import no_nncf_trace
 from nncf.torch.dynamic_graph.io_handling import replicate_same_tensors
@@ -105,7 +105,7 @@ def quantize_impl(
 
     nncf_network = create_nncf_network(model.eval(), calibration_dataset)
 
-    quantization_algorithm = PostTrainingQuantization(
+    quantization_pipeline = PostTrainingQuantization(
         preset=preset,
         target_device=target_device,
         subset_size=subset_size,
@@ -115,9 +115,7 @@ def quantize_impl(
         advanced_parameters=advanced_parameters,
     )
 
-    quantized_model = quantization_algorithm.apply(
-        nncf_network, nncf_network.nncf.get_graph(), dataset=calibration_dataset
-    )
+    quantized_model = quantization_pipeline.run(nncf_network, calibration_dataset)
 
     quantized_model.nncf.disable_dynamic_graph_building()
 

nncf/onnx/quantization/quantize_model.py

@@ -20,7 +20,7 @@
 from nncf.parameters import ModelType
 from nncf.parameters import TargetDevice
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi
 from nncf.scopes import IgnoredScope
 from nncf.telemetry import tracked_function
@@ -56,7 +56,7 @@ def quantize_impl(
         advanced_parameters.weights_quantization_params.per_channel = False
         advanced_parameters.activations_quantization_params.per_channel = False
 
-    quantization_algorithm = PostTrainingQuantization(
+    quantization_pipeline = PostTrainingQuantization(
         preset=preset,
         target_device=target_device,
         subset_size=subset_size,
@@ -66,7 +66,6 @@ def quantize_impl(
         advanced_parameters=advanced_parameters,
     )
 
-    graph = GraphConverter.create_nncf_graph(model)
-    quantized_model = quantization_algorithm.apply(model, graph, dataset=calibration_dataset)
+    quantized_model = quantization_pipeline.run(model, calibration_dataset)
 
     return quantized_model

nncf/quantization/quantize_model.py

@@ -23,8 +23,8 @@
 from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.algorithms.accuracy_control.evaluator import MetricResults
-from nncf.quantization.pipelines.hyperparameter_tuner.pipeline import HyperparameterTuner
 from nncf.quantization.pipelines.hyperparameter_tuner.param_grid import get_quantization_param_grids
+from nncf.quantization.pipelines.hyperparameter_tuner.pipeline import HyperparameterTuner
 from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
 

tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py

@@ -16,9 +16,9 @@
 
 import pytest
 
-from nncf.quantization.algorithms.hyperparameter_tuner.algorithm import apply_combination
-from nncf.quantization.algorithms.hyperparameter_tuner.algorithm import create_combinations
-from nncf.quantization.algorithms.hyperparameter_tuner.algorithm import find_best_combination
+from nncf.quantization.pipelines.hyperparameter_tuner.pipeline import apply_combination
+from nncf.quantization.pipelines.hyperparameter_tuner.pipeline import create_combinations
+from nncf.quantization.pipelines.hyperparameter_tuner.pipeline import find_best_combination
 
 CombinationKey = Tuple[int, ...]
 Combination = Dict[str, Any]

tests/onnx/quantization/common.py

@@ -20,8 +20,8 @@
 from nncf.onnx.graph.onnx_graph import ONNXGraph
 from nncf.onnx.statistics.statistics import ONNXMinMaxTensorStatistic
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.fake_quantize import FakeQuantizeParameters
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from tests.onnx.common import get_random_generator
 from tests.onnx.opset_converter import convert_opset_version
 from tests.shared.nx_graph import check_nx_graph
@@ -95,7 +95,6 @@ def min_max_quantize_model(
 ) -> onnx.ModelProto:
     if convert_model_opset:
         original_model = convert_opset_version(original_model)
-    graph = GraphConverter.create_nncf_graph(original_model)
     dataset = get_random_dataset_for_test(original_model, dataset_has_batch_size)
     quantization_params = {} if quantization_params is None else quantization_params
 
@@ -105,7 +104,7 @@ def min_max_quantize_model(
 
     post_training_quantization = PostTrainingQuantization(subset_size=1, **quantization_params)
 
-    quantized_model = post_training_quantization.apply(original_model, graph, dataset=dataset)
+    quantized_model = post_training_quantization.run(original_model, dataset)
     return quantized_model
 
 
@@ -117,11 +116,10 @@ def ptq_quantize_model(
 ) -> onnx.ModelProto:
     if convert_model_opset:
         original_model = convert_opset_version(original_model)
-    graph = GraphConverter.create_nncf_graph(original_model)
     dataset = get_random_dataset_for_test(original_model, dataset_has_batch_size)
     quantization_params = {} if quantization_params is None else quantization_params
     post_training_quantization = PostTrainingQuantization(subset_size=1, **quantization_params)
-    quantized_model = post_training_quantization.apply(original_model, graph, dataset=dataset)
+    quantized_model = post_training_quantization.run(original_model, dataset)
     return quantized_model
 
 

tests/onnx/quantization/test_ptq_params.py

@@ -25,7 +25,7 @@
 from nncf.onnx.statistics.collectors import ONNXMinMaxStatisticCollector
 from nncf.parameters import TargetDevice
 from nncf.quantization.algorithms.min_max.onnx_backend import ONNXMinMaxAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
 from tests.common.quantization.metatypes import Conv2dTestMetatype
 from tests.common.quantization.metatypes import LinearTestMetatype
@@ -49,8 +49,8 @@ def get_ignored_patterns(device: TargetDevice = TargetDevice.ANY) -> GraphPatter
 
 @pytest.mark.parametrize("target_device", TargetDevice)
 def test_target_device(target_device):
-    algo = PostTrainingQuantization(target_device=target_device)
-    min_max_algo = algo.algorithms[0]
+    pipeline = PostTrainingQuantization(target_device=target_device)
+    min_max_algo = pipeline.pipeline_steps[-1][0]
     min_max_algo._backend_entity = ONNXMinMaxAlgoBackend()
     assert min_max_algo._target_device == target_device
 

tests/openvino/native/quantization/test_ptq_params.py

@@ -27,7 +27,7 @@
 from nncf.openvino.graph.transformations.commands import OVTargetPoint
 from nncf.parameters import TargetDevice
 from nncf.quantization.algorithms.min_max.openvino_backend import OVMinMaxAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
 from tests.common.quantization.metatypes import Conv2dTestMetatype
 from tests.common.quantization.metatypes import LinearTestMetatype
@@ -49,8 +49,8 @@ def get_ignored_patterns(device: TargetDevice = TargetDevice.ANY) -> GraphPatter
 # pylint: disable=protected-access
 @pytest.mark.parametrize("target_device", [TargetDevice.CPU, TargetDevice.GPU, TargetDevice.VPU])
 def test_target_device(target_device):
-    algo = PostTrainingQuantization(target_device=target_device)
-    min_max_algo = algo.algorithms[0]
+    pipelines = PostTrainingQuantization(target_device=target_device)
+    min_max_algo = pipelines.pipeline_steps[-1][0]
     min_max_algo._backend_entity = OVMinMaxAlgoBackend()
     assert min_max_algo._target_device.value == HW_CONFIG_TYPE_TARGET_DEVICE_MAP[target_device.value]
 

tests/post_training/test_templates/test_bias_correction.py

@@ -20,7 +20,7 @@
 from nncf.quantization.advanced_parameters import OverflowFix
 from nncf.quantization.algorithms.bias_correction.algorithm import BiasCorrection
 from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from tests.post_training.test_templates.helpers import ConvTestModel
 from tests.post_training.test_templates.helpers import MultipleConvTestModel
 from tests.post_training.test_templates.helpers import SplittedModel

tests/post_training/test_templates/test_fast_bias_correction.py

@@ -19,7 +19,7 @@
 from nncf.quantization.advanced_parameters import OverflowFix
 from nncf.quantization.algorithms.fast_bias_correction.algorithm import FastBiasCorrection
 from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from tests.post_training.test_templates.helpers import ConvBNTestModel
 from tests.post_training.test_templates.helpers import ConvTestModel
 from tests.post_training.test_templates.helpers import get_static_dataset

tests/post_training/test_templates/test_ptq_params.py

@@ -30,8 +30,8 @@
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.advanced_parameters import OverflowFix
 from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.passes import transform_to_inference_graph
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.quantization.range_estimator import RangeEstimatorParametersSet
 from nncf.scopes import IgnoredScope
 from tests.common.quantization.metatypes import Conv2dTestMetatype
@@ -131,12 +131,12 @@ def metatypes_mapping(self):
         "range_estimator_params", [RangeEstimatorParametersSet.MINMAX, RangeEstimatorParametersSet.MEAN_MINMAX, None]
     )
     def test_range_estimator_per_tensor(self, test_params, range_estimator_params):
-        algo = PostTrainingQuantization(
+        pipeline = PostTrainingQuantization(
             advanced_parameters=AdvancedQuantizationParameters(
                 activations_range_estimator_params=range_estimator_params
             )
         )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
         assert min_max_algo._range_estimator_params[QuantizerGroup.ACTIVATIONS] == range_estimator_params
 
@@ -161,10 +161,10 @@ def test_range_estimator_per_tensor(self, test_params, range_estimator_params):
 
     @pytest.mark.parametrize("quantize_outputs", [False, True])
     def test_quantize_outputs(self, test_params, quantize_outputs):
-        algo = PostTrainingQuantization(
+        pipeline = PostTrainingQuantization(
             advanced_parameters=AdvancedQuantizationParameters(quantize_outputs=quantize_outputs)
         )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
 
         nncf_graph = test_params["test_quantize_outputs"]["nncf_graph"]
@@ -189,8 +189,8 @@ def test_quantize_outputs(self, test_params, quantize_outputs):
 
     def test_ignored_scopes(self, test_params, ignored_scopes_data):
         ignored_scope, act_num_ref, weight_num_ref = ignored_scopes_data
-        algo = PostTrainingQuantization(ignored_scope=ignored_scope)
-        min_max_algo = algo.algorithms[0]
+        pipeline = PostTrainingQuantization(ignored_scope=ignored_scope)
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
         assert min_max_algo._ignored_scope == ignored_scope
 
@@ -215,8 +215,8 @@ def test_ignored_scopes(self, test_params, ignored_scopes_data):
 
     @pytest.mark.parametrize("model_type", [ModelType.TRANSFORMER])
     def test_model_type_pass(self, test_params, model_type):
-        algo = PostTrainingQuantization(preset=QuantizationPreset.MIXED, model_type=model_type)
-        min_max_algo = algo.algorithms[0]
+        pipeline = PostTrainingQuantization(preset=QuantizationPreset.MIXED, model_type=model_type)
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
 
         nncf_graph = test_params["test_model_type_pass"]["nncf_graph"]

tests/post_training/test_templates/test_quantizer_config.py

@@ -31,8 +31,8 @@
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.advanced_parameters import QuantizationParameters
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.passes import transform_to_inference_graph
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.quantization.range_estimator import RangeEstimatorParametersSet
 from tests.post_training.test_templates.models import NNCFGraphToTest
 from tests.post_training.test_templates.models import NNCFGraphToTestDepthwiseConv
@@ -81,8 +81,8 @@ def statistic_collector_parameters(self, request) -> TestGetStatisticsCollectorP
         pass
 
     def test_default_quantizer_config(self, single_conv_nncf_graph):
-        algo = PostTrainingQuantization()
-        min_max_algo = algo.algorithms[0]
+        pipeline = PostTrainingQuantization()
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
         nncf_graph = single_conv_nncf_graph.nncf_graph
         inference_nncf_graph = transform_to_inference_graph(
@@ -127,7 +127,7 @@ def test_quantizer_config_from_ptq_params_for_CPU(
         signed_activations,
         single_conv_nncf_graph,
     ):
-        algo = PostTrainingQuantization(
+        pipeline = PostTrainingQuantization(
             preset=preset,
             advanced_parameters=AdvancedQuantizationParameters(
                 activations_quantization_params=QuantizationParameters(
@@ -138,7 +138,7 @@ def test_quantizer_config_from_ptq_params_for_CPU(
                 ),
             ),
         )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
         nncf_graph = single_conv_nncf_graph.nncf_graph
         inference_nncf_graph = transform_to_inference_graph(
@@ -179,8 +179,8 @@ def test_quantizer_config_from_ptq_params_for_CPU(
                         assert quantization_point.qconfig.signedness_to_force == signed_activations
 
     def test_depthwise_conv_default_quantizer_config(self, depthwise_conv_nncf_graph):
-        algo = PostTrainingQuantization()
-        min_max_algo = algo.algorithms[0]
+        pipeline = PostTrainingQuantization()
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
         nncf_graph = depthwise_conv_nncf_graph.nncf_graph
         inference_nncf_graph = transform_to_inference_graph(
@@ -223,12 +223,12 @@ def test_get_stat_collector(
         statistic_collector_parameters: TestGetStatisticsCollectorParameters,
     ):
         params = statistic_collector_parameters
-        algo = PostTrainingQuantization(
+        pipeline = PostTrainingQuantization(
             advanced_parameters=AdvancedQuantizationParameters(
                 activations_range_estimator_params=range_estimator_params
             )
         )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = pipeline.pipeline_steps[-1][0]
         min_max_algo._backend_entity = self.get_algo_backend()
         q_config = QuantizerConfig(num_bits=8, mode=q_config_mode, per_channel=q_config_per_channel)
 

tests/post_training/test_templates/test_smooth_quant.py

@@ -22,9 +22,9 @@
 from nncf.parameters import ModelType
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.advanced_parameters import OverflowFix
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.algorithms.smooth_quant.algorithm import SmoothQuant
 from nncf.quantization.algorithms.smooth_quant.backend import SmoothQuantAlgoBackend
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from tests.post_training.test_templates.helpers import LinearMultiShapeModel
 from tests.post_training.test_templates.helpers import NonZeroLinearModel
 from tests.post_training.test_templates.helpers import get_static_dataset

tests/torch/ptq/test_fq_params_calculation.py

@@ -19,7 +19,7 @@
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.advanced_parameters import OverflowFix
 from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.torch.model_creation import create_nncf_network
 from nncf.torch.nncf_network import NNCFNetwork
 from nncf.torch.quantization.layers import QUANTIZATION_MODULES

tests/torch/ptq/test_ptq_params.py

@@ -25,7 +25,7 @@
 from nncf.quantization.advanced_parameters import QuantizationMode
 from nncf.quantization.advanced_parameters import QuantizationParameters
 from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
+from nncf.quantization.pipelines.post_training.pipeline import PostTrainingQuantization
 from nncf.quantization.range_estimator import RangeEstimatorParametersSet
 from nncf.scopes import IgnoredScope
 from nncf.torch.graph.graph import PTTargetPoint
@@ -94,8 +94,8 @@ def forward(self, x):
 
 @pytest.mark.parametrize("target_device", TargetDevice)
 def test_target_device(target_device):
-    algo = PostTrainingQuantization(target_device=target_device)
-    min_max_algo = algo.algorithms[0]
+    pipeline = PostTrainingQuantization(target_device=target_device)
+    min_max_algo = pipeline.pipeline_steps[-1][0]
     min_max_algo._backend_entity = PTMinMaxAlgoBackend()
     assert min_max_algo._target_device == target_device