Update tests

tests/onnx/quantization/common.py

@@ -100,7 +100,11 @@ def min_max_quantize_model(
     quantization_params = {} if quantization_params is None else quantization_params
 
     advanced_parameters = quantization_params.get("advanced_parameters", AdvancedQuantizationParameters())
+
+    # ONNX backend does not support these algorithms
     advanced_parameters.disable_bias_correction = True
+    advanced_parameters.disable_channel_alignment = True
+    advanced_parameters.smooth_quant_alpha = -1
     quantization_params["advanced_parameters"] = advanced_parameters
 
     post_training_quantization = PostTrainingQuantization(subset_size=1, **quantization_params)

tests/onnx/quantization/test_ptq_params.py

@@ -24,8 +24,8 @@
 from nncf.onnx.statistics.collectors import ONNXMeanMinMaxStatisticCollector
 from nncf.onnx.statistics.collectors import ONNXMinMaxStatisticCollector
 from nncf.parameters import TargetDevice
+from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization
 from nncf.quantization.algorithms.min_max.onnx_backend import ONNXMinMaxAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
 from tests.common.quantization.metatypes import Conv2dTestMetatype
 from tests.common.quantization.metatypes import LinearTestMetatype
@@ -49,8 +49,7 @@ 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]
+    min_max_algo = MinMaxQuantization(target_device=target_device)
     min_max_algo._backend_entity = ONNXMinMaxAlgoBackend()
     assert min_max_algo._target_device == target_device
 

tests/openvino/native/quantization/test_ptq_params.py

@@ -26,8 +26,8 @@
 from nncf.openvino.graph.nncf_graph_builder import GraphConverter
 from nncf.openvino.graph.transformations.commands import OVTargetPoint
 from nncf.parameters import TargetDevice
+from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization
 from nncf.quantization.algorithms.min_max.openvino_backend import OVMinMaxAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
 from tests.common.quantization.metatypes import Conv2dTestMetatype
 from tests.common.quantization.metatypes import LinearTestMetatype
@@ -49,8 +49,7 @@ 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]
+    min_max_algo = MinMaxQuantization(target_device=target_device)
     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_ptq_params.py

@@ -27,10 +27,8 @@
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic
 from nncf.parameters import ModelType
-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.range_estimator import RangeEstimatorParametersSet
 from nncf.scopes import IgnoredScope
@@ -131,12 +129,7 @@ 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(
-            advanced_parameters=AdvancedQuantizationParameters(
-                activations_range_estimator_params=range_estimator_params
-            )
-        )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = MinMaxQuantization(activations_range_estimator_params=range_estimator_params)
         min_max_algo._backend_entity = self.get_algo_backend()
         assert min_max_algo._range_estimator_params[QuantizerGroup.ACTIVATIONS] == range_estimator_params
 
@@ -161,10 +154,7 @@ 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(
-            advanced_parameters=AdvancedQuantizationParameters(quantize_outputs=quantize_outputs)
-        )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = MinMaxQuantization(quantize_outputs=quantize_outputs)
         min_max_algo._backend_entity = self.get_algo_backend()
 
         nncf_graph = test_params["test_quantize_outputs"]["nncf_graph"]
@@ -189,8 +179,7 @@ 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]
+        min_max_algo = MinMaxQuantization(ignored_scope=ignored_scope)
         min_max_algo._backend_entity = self.get_algo_backend()
         assert min_max_algo._ignored_scope == ignored_scope
 
@@ -215,8 +204,7 @@ 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]
+        min_max_algo = MinMaxQuantization(preset=QuantizationPreset.MIXED, model_type=model_type)
         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

@@ -29,9 +29,8 @@
 from nncf.experimental.common.tensor_statistics.collectors import MaxReducer
 from nncf.experimental.common.tensor_statistics.collectors import MinReducer
 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.algorithms.min_max.algorithm import MinMaxQuantization
 from nncf.quantization.passes import transform_to_inference_graph
 from nncf.quantization.range_estimator import RangeEstimatorParametersSet
 from tests.post_training.test_templates.models import NNCFGraphToTest
@@ -81,8 +80,7 @@ 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]
+        min_max_algo = MinMaxQuantization()
         min_max_algo._backend_entity = self.get_algo_backend()
         nncf_graph = single_conv_nncf_graph.nncf_graph
         inference_nncf_graph = transform_to_inference_graph(
@@ -127,18 +125,15 @@ def test_quantizer_config_from_ptq_params_for_CPU(
         signed_activations,
         single_conv_nncf_graph,
     ):
-        algo = PostTrainingQuantization(
+        min_max_algo = MinMaxQuantization(
             preset=preset,
-            advanced_parameters=AdvancedQuantizationParameters(
-                activations_quantization_params=QuantizationParameters(
-                    num_bits=activation_bits, per_channel=activation_per_channel, signedness_to_force=signed_activations
-                ),
-                weights_quantization_params=QuantizationParameters(
-                    num_bits=weight_bits, per_channel=weight_per_channel, signedness_to_force=signed_weights
-                ),
+            activations_quantization_params=QuantizationParameters(
+                num_bits=activation_bits, per_channel=activation_per_channel, signedness_to_force=signed_activations
+            ),
+            weights_quantization_params=QuantizationParameters(
+                num_bits=weight_bits, per_channel=weight_per_channel, signedness_to_force=signed_weights
             ),
         )
-        min_max_algo = algo.algorithms[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 +174,7 @@ 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]
+        min_max_algo = MinMaxQuantization()
         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 +217,7 @@ def test_get_stat_collector(
         statistic_collector_parameters: TestGetStatisticsCollectorParameters,
     ):
         params = statistic_collector_parameters
-        algo = PostTrainingQuantization(
-            advanced_parameters=AdvancedQuantizationParameters(
-                activations_range_estimator_params=range_estimator_params
-            )
-        )
-        min_max_algo = algo.algorithms[0]
+        min_max_algo = MinMaxQuantization(activations_range_estimator_params=range_estimator_params)
         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/torch/ptq/test_fq_params_calculation.py

@@ -18,7 +18,6 @@
 import nncf
 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.torch.model_creation import create_nncf_network
 from nncf.torch.nncf_network import NNCFNetwork
@@ -49,13 +48,14 @@ def transform_fn(sample):
 
     dataset = nncf.Dataset(dataloader, transform_func=transform_fn)
 
-    post_training_quantization = PostTrainingQuantization(subset_size=1, **quantization_params)
     # Using PTQ, but apply only MinMax
-    updated_algorithms = []
-    for algo in post_training_quantization.algorithms:
-        if isinstance(algo, MinMaxQuantization):
-            updated_algorithms.append(algo)
-    post_training_quantization.algorithms = updated_algorithms
+    advanced_parameters = quantization_params.get("advanced_parameters", AdvancedQuantizationParameters())
+    advanced_parameters.disable_bias_correction = True
+    advanced_parameters.disable_channel_alignment = True
+    advanced_parameters.smooth_quant_alpha = -1
+    quantization_params["advanced_parameters"] = advanced_parameters
+
+    post_training_quantization = PostTrainingQuantization(subset_size=1, **quantization_params)
 
     original_model.eval()
     nncf_network = create_nncf_network(original_model, config)

tests/torch/ptq/test_ptq_params.py

@@ -24,8 +24,8 @@
 from nncf.quantization.advanced_parameters import OverflowFix
 from nncf.quantization.advanced_parameters import QuantizationMode
 from nncf.quantization.advanced_parameters import QuantizationParameters
+from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization
 from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend
-from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.range_estimator import RangeEstimatorParametersSet
 from nncf.scopes import IgnoredScope
 from nncf.torch.graph.graph import PTTargetPoint
@@ -94,8 +94,7 @@ 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]
+    min_max_algo = MinMaxQuantization(target_device=target_device)
     min_max_algo._backend_entity = PTMinMaxAlgoBackend()
     assert min_max_algo._target_device == target_device