diff --git a/nncf/common/graph/transformations/command_creation.py b/nncf/common/graph/transformations/command_creation.py index 6e166e6e0e8..257ea79ac47 100644 --- a/nncf/common/graph/transformations/command_creation.py +++ b/nncf/common/graph/transformations/command_creation.py @@ -47,6 +47,17 @@ def create_command_to_update_bias( :return: The command to update bias value. """ + @staticmethod + @abstractmethod + def create_command_to_insert_bias(node_without_bias: NNCFNode, bias_value: Any) -> TransformationCommand: + """ + Creates command to insert bias after given node. + + :param node_without_bias: The node that corresponds to the operation without bias. + :param bias_value: Bias value to insert. + :return: The command to insert bias value. + """ + @staticmethod @abstractmethod def create_command_to_update_weight( diff --git a/nncf/quantization/algorithms/channel_alignment/algorithm.py b/nncf/quantization/algorithms/channel_alignment/algorithm.py index db956997750..dcec35d910b 100644 --- a/nncf/quantization/algorithms/channel_alignment/algorithm.py +++ b/nncf/quantization/algorithms/channel_alignment/algorithm.py @@ -59,7 +59,6 @@ def __init__( self, subset_size: int = 100, inplace_statistics: bool = True, - backend_params: Optional[Dict[str, Any]] = None, ): """ :param subset_size: Size of a subset for the statistics collection, @@ -67,12 +66,10 @@ def __init__( :param inplace_statistics: Defines wheather to calculate quantizers statistics by backend graph operations or by default Python implementation, defaults to True. - :param backend_params: Backend specific parameters. """ super().__init__() self.subset_size = subset_size self.inplace_statistics = inplace_statistics - self.backend_params = backend_params self._backend_entity = None self._quantile = 1e-4 self._algorithm_key = f"CA_{hash(self)}" @@ -120,15 +117,14 @@ def filter_func(point: StatisticPoint) -> bool: conv_in_cont = ConvParamsContainer(conv_in, model, graph, self._backend_entity) conv_out_cont = ConvParamsContainer(conv_out, model, graph, self._backend_entity) - if conv_in_cont.has_bias() and conv_out_cont.has_bias(): - amean = (stat.max_values + stat.min_values) * 0.5 - conv_in_cont.bias, conv_out_cont.bias = self._align_means( - conv_in_cont.bias, - conv_out_cont.bias, - conv_out_cont.weight, - amean, - conv_out_cont.dims, - ) + amean = (stat.max_values + stat.min_values) * 0.5 + conv_in_cont.bias, conv_out_cont.bias = self._align_means( + conv_in_cont.bias, + conv_out_cont.bias, + conv_out_cont.weight, + amean, + conv_out_cont.dims, + ) ascale = (stat.max_values - stat.min_values).astype(np.float32) eps = np.finfo(ascale.dtype).eps @@ -153,9 +149,11 @@ def filter_func(point: StatisticPoint) -> bool: ) if container.stated_bias.is_modified(): - transformation_layout.register( - command_creator.create_command_to_update_bias(container.op, container.bias, graph), - ) + if container.bias_op_exist(): + command = command_creator.create_command_to_update_bias(container.op, container.bias, graph) + else: + command = command_creator.create_command_to_insert_bias(container.op, container.bias) + transformation_layout.register(command) transformed_model = model_transformer.transform(transformation_layout) return transformed_model @@ -239,10 +237,7 @@ def _align_scales( scale_in_shape[conv_in_descr.conv_weight_out_channels_dim] = scale_factor.shape[conv_in_descr.bias_channels_dim] updated_conv_in_value = conv_in_value / scale_factor.reshape(scale_in_shape) - if bias_in_value is not None: - updated_bias_in_value = bias_in_value / scale_factor.reshape(bias_in_value.shape) - else: - updated_bias_in_value = None + updated_bias_in_value = bias_in_value / scale_factor.reshape(bias_in_value.shape) scale_out_shape = np.ones(len(conv_out_value.shape), dtype=int) scale_out_shape[conv_out_descr.conv_weight_in_channels_dim] = scale_factor.shape[ @@ -433,18 +428,23 @@ class ConvParamsContainer: Convolution container class which is incapsulating common convolutional parameters collection. """ - def __init__(self, conv_op, model, nncf_graph, backend_entity: ChannelAlignmentAlgoBackend): + def __init__( + self, conv_op: NNCFNode, model: TModel, nncf_graph: NNCFGraph, backend_entity: ChannelAlignmentAlgoBackend + ): """ - :param conv_op: Backend-specific conv node. + :param conv_op: NNCF conv node. :param model: Backend-specific model instance. :param nncf_graph: NNCFGraph of given backend-specific model. :param backend_entity: Current backend entity to retrieve parameters from given conv node """ _, self._weights_port_id = backend_entity.get_weights_port_ids_for_node(conv_op) self.stated_weight = StatedTensor(backend_entity.get_weight_value(conv_op, model, self._weights_port_id)) - bias = None + self._bias_op_exist = False if backend_entity.is_node_with_bias(conv_op, nncf_graph): bias = backend_entity.get_bias_value(conv_op, model, nncf_graph) + self._bias_op_exist = True + else: + bias = backend_entity.create_bias_tensor(conv_op, nncf_graph, 0) self.stated_bias = StatedTensor(bias) self._op = conv_op self._dims = backend_entity.get_dims_descriptor(conv_op) @@ -477,5 +477,5 @@ def weight_port_id(self): def dims(self) -> LayoutDescriptor: return self._dims - def has_bias(self) -> bool: - return self.bias is not None + def bias_op_exist(self) -> bool: + return self._bias_op_exist diff --git a/nncf/quantization/algorithms/channel_alignment/backend.py b/nncf/quantization/algorithms/channel_alignment/backend.py index cf431604b7b..a02b788cf98 100644 --- a/nncf/quantization/algorithms/channel_alignment/backend.py +++ b/nncf/quantization/algorithms/channel_alignment/backend.py @@ -11,7 +11,7 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import Optional, Tuple, TypeVar +from typing import Any, Optional, Tuple, TypeVar import numpy as np @@ -142,3 +142,15 @@ def get_conv_layer_attributes(node: NNCFNode) -> Optional[ConvolutionLayerAttrib :param node: NNCFNode to take convolutional layer attributes from. :return: Convolutional layer attributes of given node if they are present and None otherwise """ + + @staticmethod + @abstractmethod + def create_bias_tensor(node: NNCFNode, nncf_graph: NNCFGraph, value: Any) -> np.ndarray: + """ + Creates bias value constant array filled by given value. + + :param node: NNCFNode to add bias to. + :param nncf_graph: Target NNCFgraph. + :param value: Value to fill bias constant array. + :return: Bias value constant array filled by given value. + """ diff --git a/nncf/quantization/algorithms/channel_alignment/openvino_backend.py b/nncf/quantization/algorithms/channel_alignment/openvino_backend.py index 9ab94bb8e70..0db705b676b 100644 --- a/nncf/quantization/algorithms/channel_alignment/openvino_backend.py +++ b/nncf/quantization/algorithms/channel_alignment/openvino_backend.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional, Tuple +from typing import Any, Optional, Tuple import numpy as np import openvino.runtime as ov @@ -29,6 +29,7 @@ from nncf.openvino.graph.metatypes.openvino_metatypes import OVGroupConvolutionMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVSubtractMetatype +from nncf.openvino.graph.node_utils import create_bias_tensor from nncf.openvino.graph.node_utils import get_bias_value from nncf.openvino.graph.node_utils import get_node_with_bias_value from nncf.openvino.graph.node_utils import get_weight_value @@ -146,3 +147,7 @@ def get_conv_layer_attributes(node: NNCFNode) -> Optional[ConvolutionLayerAttrib if node.layer_attributes is None: return None return node.layer_attributes.layer_attributes[1] + + @staticmethod + def create_bias_tensor(node: NNCFNode, nncf_graph: NNCFGraph, value: Any) -> np.ndarray: + return create_bias_tensor(node, nncf_graph, value) diff --git a/nncf/quantization/algorithms/post_training/algorithm.py b/nncf/quantization/algorithms/post_training/algorithm.py index 79630c3c652..d6e6b40de80 100644 --- a/nncf/quantization/algorithms/post_training/algorithm.py +++ b/nncf/quantization/algorithms/post_training/algorithm.py @@ -9,7 +9,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from dataclasses import dataclass from typing import Callable, Dict, List, Optional, TypeVar from nncf import Dataset @@ -34,7 +33,6 @@ from nncf.quantization.algorithms.fast_bias_correction.algorithm import FastBiasCorrection from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization from nncf.quantization.algorithms.smooth_quant.algorithm import SmoothQuant -from nncf.quantization.passes import insert_null_biases_pass from nncf.scopes import IgnoredScope TModel = TypeVar("TModel") @@ -49,11 +47,6 @@ class PostTrainingQuantization(Algorithm): 3) FastBiasCorrection or BiasCorrection """ - @dataclass - class FirstStageAlgorithm: - algorithm: "Algorithm" - pre_passes: List[TPass] - def __init__( self, preset: QuantizationPreset = QuantizationPreset.PERFORMANCE, @@ -87,7 +80,7 @@ def __init__( """ super().__init__() self.algorithms = [] - self.first_stage_algorithms: List[self.FirstStageAlgorithm] = [] + self.first_stage_algorithms: List[Algorithm] = [] if target_device is TargetDevice.VPU: warning_deprecated("VPU device is deprecated and will no longer be supported in the future.") @@ -101,15 +94,14 @@ def __init__( inplace_statistics=advanced_parameters.inplace_statistics, alpha=advanced_parameters.smooth_quant_alpha, ) - self.first_stage_algorithms.append(self.FirstStageAlgorithm(smooth_quant_algorithm, [])) + self.first_stage_algorithms.append(smooth_quant_algorithm) if not advanced_parameters.disable_channel_alignment: channel_alignment = ChannelAlignment( subset_size=subset_size, inplace_statistics=advanced_parameters.inplace_statistics, - backend_params=advanced_parameters.backend_params, ) - self.first_stage_algorithms.append(self.FirstStageAlgorithm(channel_alignment, [insert_null_biases_pass])) + self.first_stage_algorithms.append(channel_alignment) min_max_quantization = MinMaxQuantization( preset=preset, @@ -187,9 +179,7 @@ def apply( modified_model_graph = graph backend = get_backend(modified_model) - for first_stage_algorithm in self.first_stage_algorithms: - algorithm = first_stage_algorithm.algorithm - + for algorithm in self.first_stage_algorithms: if isinstance(algorithm, SmoothQuant) and backend != BackendType.OPENVINO: nncf_logger.debug(f"{backend.name} does not support SmoothQuant algorithm yet.") continue @@ -198,10 +188,6 @@ def apply( nncf_logger.debug(f"{backend.name} does not support ChannelAlignment algorithm yet.") continue - for pre_pass in first_stage_algorithm.pre_passes: - modified_model = pre_pass(modified_model, modified_model_graph) - modified_model_graph = NNCFGraphFactory.create(modified_model) - statistics_aggregator = StatisticsAggregatorFactory.create(modified_model, dataset) algo_statistic_points = algorithm.get_statistic_points(modified_model, modified_model_graph) statistics_aggregator.register_statistic_points(algo_statistic_points) diff --git a/tests/openvino/native/quantization/test_channel_alignment.py b/tests/openvino/native/quantization/test_channel_alignment.py index 080ec8a883d..432aa89a536 100644 --- a/tests/openvino/native/quantization/test_channel_alignment.py +++ b/tests/openvino/native/quantization/test_channel_alignment.py @@ -23,6 +23,7 @@ from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype from nncf.openvino.graph.transformations.command_creation import OVCommandCreator from nncf.openvino.graph.transformations.commands import OVBiasCorrectionCommand +from nncf.openvino.graph.transformations.commands import OVBiasInsertionCommand from nncf.openvino.graph.transformations.commands import OVTargetPoint from nncf.openvino.graph.transformations.commands import OVWeightUpdateCommand from nncf.quantization.algorithms.channel_alignment.backend import LayoutDescriptor @@ -64,7 +65,7 @@ def get_constant_metatype(self): return OVConstantMetatype def get_transformation_commands(self): - return OVBiasCorrectionCommand, OVWeightUpdateCommand + return OVBiasInsertionCommand, OVBiasCorrectionCommand, OVWeightUpdateCommand def mock_command_creation_factory(self, mocker) -> None: mocker.patch("nncf.common.factory.CommandCreatorFactory.create", return_value=OVCommandCreator) diff --git a/tests/openvino/native/test_model_utils.py b/tests/openvino/native/test_model_utils.py index cd6a36e6953..75992fc24e3 100644 --- a/tests/openvino/native/test_model_utils.py +++ b/tests/openvino/native/test_model_utils.py @@ -42,7 +42,7 @@ def get_nncf_graph_for_test(edge_shape, dtype): "edge_shape,dtype,ref_shape", [((2, 3, 4, 5), np.float32, (1, 3, 1, 1)), ((1, 1, 2, 3), np.float64, (1, 1, 1, 1))], ) -def test_create_bias_constant_value(edge_shape, dtype, ref_shape): +def test_create_bias_tensor(edge_shape, dtype, ref_shape): graph = get_nncf_graph_for_test(edge_shape, dtype) val = create_bias_tensor(graph.get_node_by_name("/Conv_1_0"), graph, 5) assert val.shape == ref_shape diff --git a/tests/post_training/test_templates/test_channel_alignment.py b/tests/post_training/test_templates/test_channel_alignment.py index 2b063e7d90f..d3b6dd045e5 100644 --- a/tests/post_training/test_templates/test_channel_alignment.py +++ b/tests/post_training/test_templates/test_channel_alignment.py @@ -183,18 +183,15 @@ def test_align_means(self, conv_out_value, refs, transposed): REF_UPDATED_CONV_OUT = np.array([[0.0, 2.0, 0.04, 600, 8], [10, 12, 0.14, 1600, 18]]) REF_UPDATED_BIAS_IN = np.array([2, 4, 600, 0.08, 10]) - @pytest.mark.parametrize("bias_in_value", [np.array([2, 4, 6, 8, 10]), None]) + @pytest.mark.parametrize("bias_in_value", [np.array([2, 4, 6, 8, 10])]) def test_align_scales(self, bias_in_value): def check_updated_values(updated_conv_in, updated_conv_out, updated_bias_in): assert updated_conv_in.shape == self.REF_UPDATED_CONV_IN.shape assert np.allclose(updated_conv_in, self.REF_UPDATED_CONV_IN) assert updated_conv_out.shape == self.REF_UPDATED_CONV_OUT.shape assert np.allclose(updated_conv_out, self.REF_UPDATED_CONV_OUT) - if bias_in_value is None: - assert updated_bias_in is None - else: - assert updated_bias_in.shape == self.REF_UPDATED_BIAS_IN.shape - assert np.allclose(updated_bias_in, self.REF_UPDATED_BIAS_IN) + assert updated_bias_in.shape == self.REF_UPDATED_BIAS_IN.shape + assert np.allclose(updated_bias_in, self.REF_UPDATED_BIAS_IN) conv_in_value = np.arange(5).reshape(5, 1) conv_out_value = np.arange(10).reshape(2, 5) * 2 @@ -225,7 +222,6 @@ def check_updated_values(updated_conv_in, updated_conv_out, updated_bias_in): updated_conv_in = updated_conv_in.reshape(updated_conv_in.shape[1:]) check_updated_values(updated_conv_in, updated_conv_out, updated_bias_in) - GET_NODES_TEST_CASES = [] GET_NODES_TEST_CASES = [(VALID_CONV_LAYER_ATTR, VALID_CONV_LAYER_ATTR, True)] GET_NODES_TEST_CASES.extend([(attr, VALID_CONV_LAYER_ATTR, True) for attr in INVALID_CONSUMER_CONV_LAYER_ATTRS]) GET_NODES_TEST_CASES.extend([(VALID_CONV_LAYER_ATTR, attr, False) for attr in INVALID_CONSUMER_CONV_LAYER_ATTRS]) @@ -370,27 +366,24 @@ class MockBackend(backend_cls): assert len(arg.transformations) == 0 return - align_means_called = 1 if num_biases == 2 else 0 - assert algorithm._align_means.call_count == align_means_called - if align_means_called: - algorithm._align_means.assert_called_once_with( - ref_bias_val + "1", - ref_bias_val + "2", - ref_weights_val + "2", - np.array(0.5, dtype=np.float32), - ref_dims_descr + "2", - ) + assert algorithm._align_means.call_count == 1 + args = [ + np.zeros((1, 1, 1, 1)), + np.zeros((1, 1, 1, 1)), + ref_weights_val + "2", + np.array(0.5, dtype=np.float32), + ref_dims_descr + "2", + ] + for i in range(num_biases): + args[i] = f"ref_bias_val{i + 1}" + + algorithm._align_means.assert_called_once_with(*args) assert algorithm._align_scales.call_count == 1 args = algorithm._align_scales.call_args.args assert args[0] == ref_weights_val + "1" assert args[1] == ref_weights_val + "2" - if num_biases == 2: - assert args[2] == ref_bias_in_after_align - elif num_biases == 1: - assert args[2] == ref_bias_val + "1" - else: - assert args[2] is None + assert args[2] == ref_bias_in_after_align assert ((args[3] - 3) < EPS).all() assert args[4] == ref_dims_descr + "1" assert args[5] == ref_dims_descr + "2" @@ -408,14 +401,20 @@ class MockBackend(backend_cls): }, "/Conv_2_0": {"weight_value": ref_weights_out_after_scale_align, "bias_value": ref_bias_out_after_align}, } - bias_update_cls, weights_update_cls = self.get_transformation_commands() + + bias_insert_cls, bias_update_cls, weights_update_cls = self.get_transformation_commands() for transformation in transformations: - assert transformation.type == TransformationType.CHANGE tp = transformation.target_point if isinstance(transformation, bias_update_cls): + assert transformation.type == TransformationType.CHANGE _class = bias_update_cls _attr = "bias_value" + elif isinstance(transformation, bias_insert_cls): + assert transformation.type == TransformationType.INSERT + _class = bias_insert_cls + _attr = "bias_value" elif isinstance(transformation, weights_update_cls): + assert transformation.type == TransformationType.CHANGE _class = weights_update_cls _attr = "weight_value" else: @@ -425,18 +424,16 @@ class MockBackend(backend_cls): assert ref_values[tp.target_node_name][_attr] == getattr(transformation, _attr) if num_biases == 2: - ref_len = {"/Conv_1_0": 2, "/Conv_2_0": 2} + ref_classes = {"/Conv_1_0": bias_update_cls, "/Conv_2_0": bias_update_cls} elif num_biases == 1: - ref_len = {"/Conv_1_0": 2, "/Conv_2_0": 1} + ref_classes = {"/Conv_1_0": bias_update_cls, "/Conv_2_0": bias_insert_cls} else: - ref_len = {"/Conv_1_0": 1, "/Conv_2_0": 1} + ref_classes = {"/Conv_1_0": bias_insert_cls, "/Conv_2_0": bias_insert_cls} for node_name, _transformations in target_names.items(): - _ref_len = ref_len[node_name] - assert len(_transformations) == _ref_len + assert len(_transformations) == 2 assert weights_update_cls in _transformations - if _ref_len == 2: - assert bias_update_cls in _transformations + assert ref_classes[node_name] in _transformations @pytest.mark.parametrize("num_biases", [0, 1, 2]) def test_get_statistic_points(self, num_biases, mocker):