diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8b3e4e56f63..b2dfed54595 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,6 +15,12 @@ repos: - id: isort name: isort (python) + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.0.282 + hooks: + - id: ruff + - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.33.0 hooks: diff --git a/.pylintrc b/.pylintrc index 03c773c92dd..d148a69df31 100644 --- a/.pylintrc +++ b/.pylintrc @@ -22,7 +22,8 @@ disable = arguments-differ, duplicate-code, consider-using-f-string, logging-fstring-interpolation, - cyclic-import + cyclic-import, + useless-import-alias max-line-length = 120 ignore-docstrings = yes diff --git a/README.md b/README.md index 5962454a9a7..482ade33933 100644 --- a/README.md +++ b/README.md @@ -406,3 +406,12 @@ Refer to the [CONTRIBUTING.md](./CONTRIBUTING.md) file for guidelines on contrib - [Notebooks](https://github.com/openvinotoolkit/openvino_notebooks#-model-training) - [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum/intel/optimization_ov) - [OpenVINO Model Optimization Guide](https://docs.openvino.ai/latest/openvino_docs_model_optimization_guide.html) + +## Telemetry + +NNCF as part of the OpenVINO™ toolkit collects anonymous usage data for the purpose of improving OpenVINO™ tools. +You can opt-out at any time by running the following command in the Python environment where you have NNCF installed: + +`opt_in_out --opt_out` + +More information is available at https://docs.openvino.ai/latest/openvino_docs_telemetry_information.html. diff --git a/docs/api/source/conf.py b/docs/api/source/conf.py index 3c88fa2cafa..f7a565f1581 100644 --- a/docs/api/source/conf.py +++ b/docs/api/source/conf.py @@ -67,7 +67,7 @@ def collect_api_entities() -> APIInfo: """ retval = APIInfo() modules = {} - skipped_modules = {} # type: Dict[str, str] + skipped_modules: Dict[str, str] = {} import nncf for _, modname, _ in pkgutil.walk_packages(path=nncf.__path__, prefix=nncf.__name__ + ".", onerror=lambda x: None): @@ -135,6 +135,7 @@ def collect_api_entities() -> APIInfo: "onnxruntime", "openvino", "tensorflow", + "keras", "tensorflow_addons", # Need add backend implementation functions to avoid endless loops on registered functions by mock module, "nncf.experimental.tensor.torch_functions", diff --git a/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py b/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py index e3748ce2295..20f3563681a 100644 --- a/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py +++ b/examples/tensorflow/object_detection/preprocessing/yolo_v4_preprocessing.py @@ -152,13 +152,19 @@ def _preprocess_true_boxes( true_boxes[..., 2:4] = boxes_wh / input_shape[::-1] batch_size = true_boxes.shape[0] - grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] + grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[layer_idx] for layer_idx in range(num_layers)] y_true = [ np.zeros( - (batch_size, grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + num_classes), + ( + batch_size, + grid_shapes[layer_idx][0], + grid_shapes[layer_idx][1], + len(anchor_mask[layer_idx]), + 5 + num_classes, + ), dtype="float32", ) - for l in range(num_layers) + for layer_idx in range(num_layers) ] # Expand dim to apply broadcasting. @@ -196,22 +202,22 @@ def _preprocess_true_boxes( best_anchors = np.expand_dims(best_anchors, -1) for t, row in enumerate(best_anchors): - for l in range(num_layers): + for layer_idx in range(num_layers): for n in row: # use different matching policy for single & multi anchor assign if multi_anchor_assign: - matching_rule = iou[t, n] > iou_thresh and n in anchor_mask[l] + matching_rule = iou[t, n] > iou_thresh and n in anchor_mask[layer_idx] else: - matching_rule = n in anchor_mask[l] + matching_rule = n in anchor_mask[layer_idx] if matching_rule: - i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype("int32") - j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype("int32") - k = anchor_mask[l].index(n) + i = np.floor(true_boxes[b, t, 0] * grid_shapes[layer_idx][1]).astype("int32") + j = np.floor(true_boxes[b, t, 1] * grid_shapes[layer_idx][0]).astype("int32") + k = anchor_mask[layer_idx].index(n) c = true_boxes[b, t, 4].astype("int32") - y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] - y_true[l][b, j, i, k, 4] = 1 - y_true[l][b, j, i, k, 5 + c] = 1 + y_true[layer_idx][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] + y_true[layer_idx][b, j, i, k, 4] = 1 + y_true[layer_idx][b, j, i, k, 5 + c] = 1 return y_true def _preprocess2(self, image_data, box_data): diff --git a/examples/torch/__init__.py b/examples/torch/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/examples/torch/__init__.py +++ b/examples/torch/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/examples/torch/common/model_loader.py b/examples/torch/common/model_loader.py index 08f15b0698e..e688489f113 100644 --- a/examples/torch/common/model_loader.py +++ b/examples/torch/common/model_loader.py @@ -92,7 +92,7 @@ def is_url(uri): try: parsed_url = urllib.parse.urlparse(uri) return parsed_url.scheme and parsed_url.netloc - except: # pylint: disable=bare-except + except: # pylint: disable=bare-except # noqa: E722 return False diff --git a/examples/torch/object_detection/__init__.py b/examples/torch/object_detection/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/examples/torch/object_detection/__init__.py +++ b/examples/torch/object_detection/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/examples/torch/object_detection/datasets/__init__.py b/examples/torch/object_detection/datasets/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/examples/torch/object_detection/datasets/__init__.py +++ b/examples/torch/object_detection/datasets/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/examples/torch/object_detection/models/__init__.py b/examples/torch/object_detection/models/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/examples/torch/object_detection/models/__init__.py +++ b/examples/torch/object_detection/models/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/examples/torch/object_detection/utils/__init__.py b/examples/torch/object_detection/utils/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/examples/torch/object_detection/utils/__init__.py +++ b/examples/torch/object_detection/utils/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/licensing/third-party-programs.txt b/licensing/third-party-programs.txt index 5b981582cec..3d6ce1f410e 100644 --- a/licensing/third-party-programs.txt +++ b/licensing/third-party-programs.txt @@ -1544,3 +1544,29 @@ rwightman/pytorch-image-models limitations under the License. ------------------------------------------------------------- + +microsoft/Swin-Transformer + + MIT License + + Copyright (c) Microsoft Corporation. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE + +------------------------------------------------------------- diff --git a/nncf/__init__.py b/nncf/__init__.py index f61fe7ec3bc..d047a37e0b4 100644 --- a/nncf/__init__.py +++ b/nncf/__init__.py @@ -12,29 +12,29 @@ Neural Network Compression Framework (NNCF) for enhanced OpenVINO™ inference. """ -from nncf.common.logging import nncf_logger -from nncf.common.logging.logger import disable_logging -from nncf.common.logging.logger import set_log_level -from nncf.common.strip import strip -from nncf.config import NNCFConfig -from nncf.data import Dataset -from nncf.parameters import CompressWeightsMode -from nncf.parameters import DropType -from nncf.parameters import ModelType -from nncf.parameters import TargetDevice -from nncf.quantization import QuantizationPreset -from nncf.quantization import compress_weights -from nncf.quantization import quantize -from nncf.quantization import quantize_with_accuracy_control -from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters -from nncf.scopes import IgnoredScope -from nncf.version import __version__ +from nncf.common.logging import nncf_logger as nncf_logger +from nncf.common.logging.logger import disable_logging as disable_logging +from nncf.common.logging.logger import set_log_level as set_log_level +from nncf.common.strip import strip as strip +from nncf.config import NNCFConfig as NNCFConfig +from nncf.data import Dataset as Dataset +from nncf.parameters import CompressWeightsMode as CompressWeightsMode +from nncf.parameters import DropType as DropType +from nncf.parameters import ModelType as ModelType +from nncf.parameters import TargetDevice as TargetDevice +from nncf.quantization import QuantizationPreset as QuantizationPreset +from nncf.quantization import compress_weights as compress_weights +from nncf.quantization import quantize as quantize +from nncf.quantization import quantize_with_accuracy_control as quantize_with_accuracy_control +from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters as AdvancedQuantizationParameters +from nncf.scopes import IgnoredScope as IgnoredScope +from nncf.version import __version__ as __version__ _SUPPORTED_FRAMEWORKS = ["torch", "tensorflow", "onnx", "openvino"] -from importlib.util import find_spec as _find_spec # pylint:disable=wrong-import-position -from pathlib import Path as _Path # pylint:disable=wrong-import-position +from importlib.util import find_spec as _find_spec # noqa: E402 # pylint:disable=wrong-import-position +from pathlib import Path as _Path # noqa: E402 # pylint:disable=wrong-import-position _AVAILABLE_FRAMEWORKS = {} diff --git a/nncf/common/accuracy_aware_training/__init__.py b/nncf/common/accuracy_aware_training/__init__.py index 62a7a2c15dc..04411f01716 100644 --- a/nncf/common/accuracy_aware_training/__init__.py +++ b/nncf/common/accuracy_aware_training/__init__.py @@ -12,5 +12,7 @@ Accuracy Aware Training functionality. """ -from nncf.common.accuracy_aware_training.training_loop import AccuracyAwareTrainingMode -from nncf.common.accuracy_aware_training.training_loop import create_accuracy_aware_training_loop +from nncf.common.accuracy_aware_training.training_loop import AccuracyAwareTrainingMode as AccuracyAwareTrainingMode +from nncf.common.accuracy_aware_training.training_loop import ( + create_accuracy_aware_training_loop as create_accuracy_aware_training_loop, +) diff --git a/nncf/common/accuracy_aware_training/training_loop.py b/nncf/common/accuracy_aware_training/training_loop.py index c9c06f7cb48..11e63db07e1 100644 --- a/nncf/common/accuracy_aware_training/training_loop.py +++ b/nncf/common/accuracy_aware_training/training_loop.py @@ -21,6 +21,7 @@ from scipy.interpolate import interp1d from nncf.api.compression import CompressionAlgorithmController +from nncf.common.accuracy_aware_training.runner import BaseAccuracyAwareTrainingRunner from nncf.common.accuracy_aware_training.runner_factory import AdaptiveCompressionLevelTrainingRunnerCreator from nncf.common.accuracy_aware_training.runner_factory import EarlyExitTrainingRunnerCreator from nncf.common.accuracy_aware_training.statistics import TrainingLoopStatistics @@ -89,7 +90,7 @@ class BaseEarlyExitCompressionTrainingLoop(TrainingLoop, ABC): """ def __init__(self, compression_controller: CompressionAlgorithmController): - self.runner = None # type: BaseAccuracyAwareTrainingRunner + self.runner: BaseAccuracyAwareTrainingRunner = None self.compression_controller = compression_controller self._current_compression_rate = None diff --git a/nncf/common/factory.py b/nncf/common/factory.py index 3089a781d17..8c3a4ba2e19 100644 --- a/nncf/common/factory.py +++ b/nncf/common/factory.py @@ -45,7 +45,9 @@ def create(model: TModel) -> NNCFGraph: return GraphConverter.create_nncf_graph(model) if model_backend == BackendType.TORCH: return model.nncf.get_graph() - raise RuntimeError("Cannot create backend-specific graph because {} is not supported!".format(model_backend)) + raise RuntimeError( + "Cannot create backend-specific graph because {} is not supported!".format(model_backend.value) + ) class ModelTransformerFactory: @@ -71,7 +73,7 @@ def create(model: TModel) -> ModelTransformer: return PTModelTransformer(model) raise RuntimeError( - "Cannot create backend-specific model transformer because {} is not supported!".format(model_backend) + "Cannot create backend-specific model transformer because {} is not supported!".format(model_backend.value) ) @@ -103,7 +105,9 @@ def create(model: TModel) -> Engine: from nncf.torch.engine import PTEngine return PTEngine(model) - raise RuntimeError("Cannot create backend-specific engine because {} is not supported!".format(model_backend)) + raise RuntimeError( + "Cannot create backend-specific engine because {} is not supported!".format(model_backend.value) + ) class CommandCreatorFactory: @@ -121,7 +125,7 @@ def create(model: TModel) -> CommandCreator: return OVCommandCreator() raise RuntimeError( - "Cannot create backend-specific command creator because {} is not supported!".format(model_backend) + "Cannot create backend-specific command creator because {} is not supported!".format(model_backend.value) ) @@ -148,5 +152,7 @@ def create(model: TModel, dataset: Dataset) -> aggregator.StatisticsAggregator: return PTStatisticsAggregator(dataset) raise RuntimeError( - "Cannot create backend-specific statistics aggregator because {} is not supported!".format(model_backend) + "Cannot create backend-specific statistics aggregator because {} is not supported!".format( + model_backend.value + ) ) diff --git a/nncf/common/graph/__init__.py b/nncf/common/graph/__init__.py index 9c27a5a1437..e5320fa9b55 100644 --- a/nncf/common/graph/__init__.py +++ b/nncf/common/graph/__init__.py @@ -9,4 +9,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.common.graph.graph import * +# Make these names available from nncf.common.graph directly +from nncf.common.graph.graph import LayerName as LayerName +from nncf.common.graph.graph import NNCFGraph as NNCFGraph +from nncf.common.graph.graph import NNCFGraphEdge as NNCFGraphEdge +from nncf.common.graph.graph import NNCFGraphPatternIO as NNCFGraphPatternIO +from nncf.common.graph.graph import NNCFNode as NNCFNode +from nncf.common.graph.graph import NNCFNodeName as NNCFNodeName diff --git a/nncf/common/graph/graph.py b/nncf/common/graph/graph.py index 0b5b3cf3db4..16b073d0e47 100644 --- a/nncf/common/graph/graph.py +++ b/nncf/common/graph/graph.py @@ -194,11 +194,11 @@ def __init__(self): self._nx_graph = nx.DiGraph() self._node_id_to_key_dict = {} self._nodes: Dict[str, NNCFNode] = {} - self._input_nncf_nodes = {} # type: Dict[int, NNCFNode] - self._output_nncf_nodes = {} # type: Dict[int, NNCFNode] + self._input_nncf_nodes: Dict[int, NNCFNode] = {} + self._output_nncf_nodes: Dict[int, NNCFNode] = {} - self._node_ids_vs_layer_names = {} # type: Dict[int, LayerName] - self._layer_name_vs_shared_nodes = defaultdict(list) # type: Dict[LayerName, List[NNCFNode]] + self._node_ids_vs_layer_names: Dict[int, LayerName] = {} + self._layer_name_vs_shared_nodes: Dict[LayerName, List[NNCFNode]] = defaultdict(list) @property def nodes(self) -> Dict[str, NNCFNode]: diff --git a/nncf/common/graph/patterns/__init__.py b/nncf/common/graph/patterns/__init__.py index 70523c16efe..8092ba8d1ff 100644 --- a/nncf/common/graph/patterns/__init__.py +++ b/nncf/common/graph/patterns/__init__.py @@ -9,8 +9,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.common.graph.patterns.patterns import GraphPattern -from nncf.common.graph.patterns.patterns import HWFusedPatternNames -from nncf.common.graph.patterns.patterns import IgnoredPatternNames -from nncf.common.graph.patterns.patterns import Patterns -from nncf.common.graph.patterns.patterns import merge_two_types_of_operations +from nncf.common.graph.patterns.patterns import GraphPattern as GraphPattern +from nncf.common.graph.patterns.patterns import HWFusedPatternNames as HWFusedPatternNames +from nncf.common.graph.patterns.patterns import IgnoredPatternNames as IgnoredPatternNames +from nncf.common.graph.patterns.patterns import Patterns as Patterns +from nncf.common.graph.patterns.patterns import merge_two_types_of_operations as merge_two_types_of_operations diff --git a/nncf/common/insertion_point_graph.py b/nncf/common/insertion_point_graph.py index 4ae0027fff7..7e71a38cdc7 100644 --- a/nncf/common/insertion_point_graph.py +++ b/nncf/common/insertion_point_graph.py @@ -16,11 +16,11 @@ import networkx as nx -from nncf.common.graph import Dtype from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNodeName from nncf.common.graph.graph import NNCFNode from nncf.common.graph.graph_matching import find_subgraphs_matching_pattern +from nncf.common.graph.layer_attributes import Dtype from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.common.graph.patterns import GraphPattern from nncf.common.logging import nncf_logger @@ -107,11 +107,11 @@ def __init__( # Post-hook all nodes if an exact list is not specified allowed_post_hook_insertion_points = self._get_default_post_hook_ip_list(nncf_graph) - target_node_name_vs_pre_hook_ips = defaultdict(set) # type: Dict[NNCFNodeName, Set[PreHookInsertionPoint]] + target_node_name_vs_pre_hook_ips: Dict[NNCFNodeName, Set[PreHookInsertionPoint]] = defaultdict(set) for pre_hook_ip in allowed_pre_hook_insertion_points: target_node_name_vs_pre_hook_ips[pre_hook_ip.target_node_name].add(pre_hook_ip) - target_node_name_vs_post_hook_ips = defaultdict(set) # type: Dict[NNCFNodeName, Set[PostHookInsertionPoint]] + target_node_name_vs_post_hook_ips: Dict[NNCFNodeName, Set[PostHookInsertionPoint]] = defaultdict(set) for post_hook_ip in allowed_post_hook_insertion_points: target_node_name_vs_post_hook_ips[post_hook_ip.target_node_name].add(post_hook_ip) diff --git a/nncf/common/logging/__init__.py b/nncf/common/logging/__init__.py index 351c1e72b6b..be651872924 100644 --- a/nncf/common/logging/__init__.py +++ b/nncf/common/logging/__init__.py @@ -9,4 +9,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.common.logging.logger import nncf_logger +from nncf.common.logging.logger import nncf_logger as nncf_logger diff --git a/nncf/common/pruning/clusterization.py b/nncf/common/pruning/clusterization.py index e89d2c888c4..8e8459e1e62 100644 --- a/nncf/common/pruning/clusterization.py +++ b/nncf/common/pruning/clusterization.py @@ -40,8 +40,8 @@ class Clusterization(Generic[T]): """ def __init__(self, id_fn: Callable[[T], Hashable] = None): - self.clusters = {} # type: Dict[int, Cluster[T]] - self._element_to_cluster = {} # type: Dict[Hashable, int] + self.clusters: Dict[int, Cluster[T]] = {} + self._element_to_cluster: Dict[Hashable, int] = {} if id_fn is None: self._id_fn = lambda x: x.id else: diff --git a/nncf/common/pruning/mask_propagation.py b/nncf/common/pruning/mask_propagation.py index 9fa3b7a3d32..63b7da58027 100644 --- a/nncf/common/pruning/mask_propagation.py +++ b/nncf/common/pruning/mask_propagation.py @@ -109,7 +109,7 @@ def symbolic_mask_propagation( input_masks = get_input_masks(node, self._graph) if any(input_masks): assert len(input_masks) == 1 - input_mask = input_masks[0] # type: SymbolicMask + input_mask: SymbolicMask = input_masks[0] for producer in input_mask.mask_producers: previously_dims_equal = ( diff --git a/nncf/common/pruning/operations.py b/nncf/common/pruning/operations.py index 4caadd69a81..905285b6396 100644 --- a/nncf/common/pruning/operations.py +++ b/nncf/common/pruning/operations.py @@ -227,7 +227,7 @@ def generate_output_mask( input_mask[node.node_name] if isinstance(input_mask, dict) else input_mask for input_mask in input_masks ] - not_empty_masks = [mask for mask in input_masks if mask is not None] # type: List[NNCFTensor] + not_empty_masks: List[NNCFTensor] = [mask for mask in input_masks if mask is not None] if not not_empty_masks: return None diff --git a/nncf/common/pruning/utils.py b/nncf/common/pruning/utils.py index fcded91c95d..e119d017b92 100644 --- a/nncf/common/pruning/utils.py +++ b/nncf/common/pruning/utils.py @@ -295,9 +295,9 @@ def __init__( self.decision = decision if not isinstance(possible_reasons, list): possible_reasons = [possible_reasons] - self._reasons = ( + self._reasons: Optional[List[PruningAnalysisReason]] = ( possible_reasons if not decision and possible_reasons else None - ) # type: Optional[List[PruningAnalysisReason]] + ) def __repr__(self) -> str: representation = f"Prunable: {self.decision}" @@ -380,7 +380,7 @@ def get_input_channels(node: NNCFNode) -> int: :param node: Given prunable node. :return: Count of input channels of the given node. """ - layer_attrs = node.layer_attributes # type: Union[ConvolutionLayerAttributes, LinearLayerAttributes] + layer_attrs: Union[ConvolutionLayerAttributes, LinearLayerAttributes] = node.layer_attributes if isinstance(layer_attrs, ConvolutionLayerAttributes): return layer_attrs.in_channels if isinstance(layer_attrs, LinearLayerAttributes): @@ -395,7 +395,7 @@ def get_output_channels(node: NNCFNode) -> int: :param node: Given prunable node. :return: Count of output channels of the given node. """ - layer_attrs = node.layer_attributes # type: Union[ConvolutionLayerAttributes, LinearLayerAttributes] + layer_attrs: Union[ConvolutionLayerAttributes, LinearLayerAttributes] = node.layer_attributes if isinstance(layer_attrs, ConvolutionLayerAttributes): return layer_attrs.out_channels if isinstance(layer_attrs, LinearLayerAttributes): diff --git a/nncf/common/quantization/config_assignment.py b/nncf/common/quantization/config_assignment.py index abaddf95427..15a4283c1b7 100644 --- a/nncf/common/quantization/config_assignment.py +++ b/nncf/common/quantization/config_assignment.py @@ -72,7 +72,7 @@ def assign_qconfig_lists_to_modules( :return: A dict of each weighted node vs. the list of quantizer configs allowed for quantizing the associated weights """ - retval = {} # type: Dict[NNCFNode, List[QuantizerConfig]] + retval: Dict[NNCFNode, List[QuantizerConfig]] = {} default_qconfig = deepcopy(default_weight_qconfig) if global_weight_constraints is not None: default_qconfig = global_weight_constraints.apply_constraints_to(default_qconfig) diff --git a/nncf/common/quantization/quantizer_propagation/graph.py b/nncf/common/quantization/quantizer_propagation/graph.py index 3baba6c1e44..47963285937 100644 --- a/nncf/common/quantization/quantizer_propagation/graph.py +++ b/nncf/common/quantization/quantizer_propagation/graph.py @@ -13,17 +13,17 @@ from copy import copy from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union +from typing import Any, Callable, Deque, Dict, List, Optional, Set, Tuple, Type, Union import networkx as nx from nncf import nncf_logger -from nncf.common.graph import INPUT_NOOP_METATYPES -from nncf.common.graph import OUTPUT_NOOP_METATYPES from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import NoopMetatype +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.insertion_point_graph import InsertionPointGraphNodeType @@ -85,13 +85,13 @@ def __init__( self._ignored_scopes = list(ignored_scopes.keys()) if ignored_scopes is not None else None self._target_scopes = deepcopy(target_scopes) - self.ignored_node_keys = {} # type: Dict[str, IgnoreReason] + self.ignored_node_keys: Dict[str, IgnoreReason] = {} self._unified_scale_group_manager = UnifiedScalePropagatingQuantizerGroupManager() - self._input_node_keys_vs_nncf_nodes = {} # type: Dict[str, NNCFNode] - self._output_node_keys_vs_nncf_nodes = {} # type: Dict[str, NNCFNode] - self._pqs_after_weight_dependent_output_quantized_nodes = {} # type: Dict[PropagatingQuantizer, str] - self.op_node_keys_to_underlying_nodes_mapping = {} # type: Dict[str, List[NNCFNode]] + self._input_node_keys_vs_nncf_nodes: Dict[str, NNCFNode] = {} + self._output_node_keys_vs_nncf_nodes: Dict[str, NNCFNode] = {} + self._pqs_after_weight_dependent_output_quantized_nodes: Dict[PropagatingQuantizer, str] = {} + self.op_node_keys_to_underlying_nodes_mapping: Dict[str, List[NNCFNode]] = {} iteration_scope_node_keys = [] for node_key, node in ip_graph.nodes.items(): @@ -117,7 +117,7 @@ def __init__( qpg_node[self.AFFECTING_PROPAGATING_QUANTIZERS_ATTR] = [] qpg_node[self.IS_IN_IGNORED_SCOPES] = False - nncf_node_ref = node[InsertionPointGraph.REGULAR_NODE_REF_NODE_ATTR] # type: NNCFNode + nncf_node_ref: NNCFNode = node[InsertionPointGraph.REGULAR_NODE_REF_NODE_ATTR] qpg_node[self.IS_MERGED_NODE_ATTR] = node[InsertionPointGraph.IS_MERGED_NODE_ATTR] if node[InsertionPointGraph.IS_MERGED_NODE_ATTR]: @@ -269,7 +269,7 @@ def is_insertion_point(qpsg_node_type: QuantizerPropagationStateGraphNodeType) - def merge_quantizer_into_path(self, prop_quantizer: PropagatingQuantizer, path: PropagationPath): curr_node = self.nodes[prop_quantizer.current_location_node_key] curr_node[QuantizerPropagationStateGraph.PROPAGATING_QUANTIZER_NODE_ATTR] = None - surviving_quantizers = [] # type: List[PropagatingQuantizer] + surviving_quantizers: List[PropagatingQuantizer] = [] for from_node_key, to_node_key in path: edge = self.edges[from_node_key, to_node_key] edge_affecting_quantizers = edge[QuantizerPropagationStateGraph.AFFECTING_PROPAGATING_QUANTIZERS_ATTR] @@ -618,7 +618,7 @@ def _verify_nodes_and_edges_for_pq(self, prop_quantizer: PropagatingQuantizer): def _verify_qconfig_matching( prop_quantizer: PropagatingQuantizer, existing_prop_quantizers: List[PropagatingQuantizer] ): - for existing_pq in existing_prop_quantizers: # type: PropagatingQuantizer + for existing_pq in existing_prop_quantizers: if existing_pq.potential_quant_configs != prop_quantizer.potential_quant_configs: raise RuntimeError( "Configurations of the quantizer to be registered are conflicting with " @@ -850,7 +850,7 @@ def recursive_helper(curr_edge, curr_path, all_paths, curr_group): return paths def get_propagating_quantizers_immediately_dominated_by_node(self, node_key: str) -> Set[PropagatingQuantizer]: - retval = set() # type: Set[PropagatingQuantizer] + retval: Set[PropagatingQuantizer] = set() def traverse_fn( curr_node_key: str, all_pqs: Set[PropagatingQuantizer] @@ -918,19 +918,19 @@ def is_branching_node_dominating_outputs(self, from_node_key: str) -> bool: def get_visualized_graph(self): out_graph = nx.DiGraph() - unified_scale_group_vs_pq_node_id_dict = {} # type: Dict[int, List[str]] + unified_scale_group_vs_pq_node_id_dict: Dict[int, List[str]] = {} for node_key, node in self.nodes.items(): node_type = node[QuantizerPropagationStateGraph.NODE_TYPE_NODE_ATTR] if self.is_insertion_point(node_type): - insertion_point_data = node[ + insertion_point_data: TargetPoint = node[ QuantizerPropagationStateGraph.QUANT_INSERTION_POINT_DATA_NODE_ATTR - ] # type: TargetPoint + ] label = "TP: {}".format(str(insertion_point_data)) out_graph.add_node(node_key, label=label, color="red") if node[QuantizerPropagationStateGraph.PROPAGATING_QUANTIZER_NODE_ATTR] is not None: - prop_quantizer = node[ + prop_quantizer: PropagatingQuantizer = node[ QuantizerPropagationStateGraph.PROPAGATING_QUANTIZER_NODE_ATTR - ] # type: PropagatingQuantizer + ] quant_node_key = "Quantizer #{}".format(prop_quantizer.id) if prop_quantizer.potential_quant_configs: quant_configs_str_list = [str(conf) for conf in prop_quantizer.potential_quant_configs] @@ -1007,8 +1007,8 @@ def traverse_graph( traverse_forward: bool = True, dfs: bool = True, ) -> Any: - visited_node_keys = set() # type: Set[str] - node_keys_to_visit = deque() # type: Deque[Tuple[str, Any]] + visited_node_keys: Set[str] = set() + node_keys_to_visit: Deque[Tuple[str, Any]] = deque() next_node_keys_indexer = self.succ if traverse_forward else self.pred # Storing the node-specific operation output is required so that this function # interface could generalize to situations where 'output' is not a global storage @@ -1064,7 +1064,7 @@ def get_unified_scale_group_id_by_propagating_quantizer_id(self, pqid: int) -> i return self._unified_scale_group_manager.get_group_id_by_propagating_quantizer_id(pqid) def get_quantizers_at_input_nncf_nodes(self) -> Dict[NNCFNode, List[int]]: - retval = {} # type: Dict[NNCFNode, List[int]] + retval: Dict[NNCFNode, List[int]] = {} def recursive_helper(curr_node_key: str, curr_input_quantizer_ids_list: List[int]): curr_node = self.nodes[curr_node_key] @@ -1162,7 +1162,7 @@ def merge_traverse_fn( self.traverse_graph(graph_root_key, merge_traverse_fn, (None, graph_root_key)) def collect_all_propagating_quantizers(self) -> Set[PropagatingQuantizer]: - retval = set() # type: Set[PropagatingQuantizer] + retval: Set[PropagatingQuantizer] = set() def traverse_fn( curr_node_key: str, all_pqs: Set[PropagatingQuantizer] @@ -1206,7 +1206,7 @@ class Grouper: """ def __init__(self): - self._group_vs_node_keys_and_pqs = {} # type: Dict[int, SharedAffectedOpsPropagatingQuantizerGroup] + self._group_vs_node_keys_and_pqs: Dict[int, SharedAffectedOpsPropagatingQuantizerGroup] = {} self._next_gid = 0 def _get_next_gid(self): @@ -1224,7 +1224,7 @@ def add_pq(self, pq: PropagatingQuantizer): {pq}, set(pq.quantized_input_sink_operator_nodes) ) new_group_data = self._group_vs_node_keys_and_pqs[new_gid] - gids_to_merge = set() # type: Set[int] + gids_to_merge: Set[int] = set() for gid, group_data in self._group_vs_node_keys_and_pqs.items(): if gid == new_gid: continue @@ -1258,8 +1258,8 @@ def create_quantizer_setup( same_op_groups = self._get_all_quantizers_grouped_by_affecting_op_set() setup = MultiConfigQuantizerSetup() - pqid_vs_qpid = {} # type: Dict[int, QuantizationPointId] - qm_node_vs_same_op_gid = {} # type: Dict[NNCFNodeName, int] + pqid_vs_qpid: Dict[int, QuantizationPointId] = {} + qm_node_vs_same_op_gid: Dict[NNCFNodeName, int] = {} for group in same_op_groups: grouped_ids = set() for pq in group.affecting_prop_quants: @@ -1300,7 +1300,7 @@ def create_quantizer_setup( max_aq_id = 0 next_wq_id = max_aq_id + 1 - wao_op_node_key_vs_wq_id = {} # type: Dict[str, QuantizationPointId] + wao_op_node_key_vs_wq_id: Dict[str, QuantizationPointId] = {} for weighted_node_name, qconfig_list in weight_quantizable_node_names_vs_configs.items(): quant_point = MultiConfigQuantizationPoint( WeightQuantizationInsertionPoint(weighted_node_name), qconfig_list, [weighted_node_name] @@ -1459,7 +1459,7 @@ def traverse_fn(curr_node_key: str, unused) -> Tuple[bool, Any]: node = self.nodes[curr_node_key] node_type = node[QuantizerPropagationStateGraph.NODE_TYPE_NODE_ATTR] if self.is_insertion_point(node_type): - pq = node[QuantizerPropagationStateGraph.PROPAGATING_QUANTIZER_NODE_ATTR] # type: PropagatingQuantizer + pq: PropagatingQuantizer = node[QuantizerPropagationStateGraph.PROPAGATING_QUANTIZER_NODE_ATTR] affecting_pqs = node[QuantizerPropagationStateGraph.AFFECTING_PROPAGATING_QUANTIZERS_ATTR] if pq is not None: assert pq in affecting_pqs diff --git a/nncf/common/quantization/quantizer_propagation/grouping.py b/nncf/common/quantization/quantizer_propagation/grouping.py index 8fa73018677..2d58238c6d1 100644 --- a/nncf/common/quantization/quantizer_propagation/grouping.py +++ b/nncf/common/quantization/quantizer_propagation/grouping.py @@ -23,7 +23,7 @@ class UnifiedScalePropagatingQuantizerGroupManager: def __init__(self): self._next_gid = 0 - self._group_vs_prop_quants_dict = {} # type: Dict[int, Set[PropagatingQuantizer]] + self._group_vs_prop_quants_dict: Dict[int, Set[PropagatingQuantizer]] = {} def _get_next_gid(self) -> int: retval = self._next_gid @@ -111,8 +111,8 @@ class QuantizersWaitingForMergeManager: """ def __init__(self): - self._branching_node_keys_vs_quantizers_waiting_for_merge = {} # type: Dict[str, Set[PropagatingQuantizer]] - self._quantizers_vs_branching_node_keys = {} # type: Dict[PropagatingQuantizer, str] + self._branching_node_keys_vs_quantizers_waiting_for_merge: Dict[str, Set[PropagatingQuantizer]] = {} + self._quantizers_vs_branching_node_keys: Dict[PropagatingQuantizer, str] = {} def add_propagating_quantizer_to_wait_on_node_key(self, pq: PropagatingQuantizer, branching_node_key: str): """ diff --git a/nncf/common/quantization/quantizer_propagation/solver.py b/nncf/common/quantization/quantizer_propagation/solver.py index 2746af06a55..2a9f8e31efa 100644 --- a/nncf/common/quantization/quantizer_propagation/solver.py +++ b/nncf/common/quantization/quantizer_propagation/solver.py @@ -19,10 +19,10 @@ import networkx as nx -from nncf.common.graph import INPUT_NOOP_METATYPES -from nncf.common.graph import OUTPUT_NOOP_METATYPES from nncf.common.graph import NNCFNodeName -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.hardware.config import HWConfig from nncf.common.insertion_point_graph import InsertionPointGraph @@ -125,7 +125,7 @@ def __init__( self.quantizer_setup = quantizer_setup self._quant_prop_graph = quant_prop_graph self._quantization_point_id_vs_prop_quantizer = quantization_point_id_vs_prop_quantizer - self._prop_quantizer_vs_quantization_point_id = {} # type: Dict[PropagatingQuantizer, QuantizationPointId] + self._prop_quantizer_vs_quantization_point_id: Dict[PropagatingQuantizer, QuantizationPointId] = {} for qp_id, pq in self._quantization_point_id_vs_prop_quantizer.items(): self._prop_quantizer_vs_quantization_point_id[pq] = qp_id @@ -394,7 +394,7 @@ def __init__( else: self._default_trait_to_metatype_map = default_trait_to_metatype_map self.default_global_qconfig_list = default_qconfig_list - self._hw_config = hw_config # type: HWConfig + self._hw_config: HWConfig = hw_config self._visualizer = None if is_debug(): from nncf.common.quantization.quantizer_propagation.visualizer import ( @@ -417,8 +417,8 @@ def __init__( if scope_overrides is None: self._scope_overrides = {} else: - self._scope_overrides = scope_overrides # type: Dict - self._global_constraints = global_constraints # type: Dict['QuantizerGroup', 'QuantizationConstraints'] + self._scope_overrides: Dict = scope_overrides + self._global_constraints: Dict["QuantizerGroup", "QuantizationConstraints"] = global_constraints self._run_consistency_checks = run_consistency_checks self._unified_scales_operation_set = set() @@ -435,7 +435,7 @@ def __init__( if HWConfig.is_qconf_list_corresponding_to_unspecified_op(qconf_list): self._operator_allowed_qconfigs_map[op_meta] = default_qconfig_list self._active_propagating_quantizers_queue = deque() - self._finished_propagating_quantizers = [] # type: List[PropagatingQuantizer] + self._finished_propagating_quantizers: List[PropagatingQuantizer] = [] self._quantizers_waiting_for_branch_merge = QuantizersWaitingForMergeManager() self._potential_quantizers = {} @@ -553,7 +553,7 @@ def _map_quantization_points_to_prop_quantizers( quant_prop_graph: QuantizerPropagationStateGraph, quantizer_setup: MultiConfigQuantizerSetup, ) -> Dict[QuantizationPointId, PropagatingQuantizer]: - qps_vs_associated_prop_quants_dict = {} # type: Dict[QuantizationPointId, PropagatingQuantizer] + qps_vs_associated_prop_quants_dict: Dict[QuantizationPointId, PropagatingQuantizer] = {} for finished_prop_quantizer in prop_quant_list: qip = quant_prop_graph.get_quant_insertion_point_for_propagating_quantizer(finished_prop_quantizer) @@ -846,7 +846,7 @@ def get_operator_quantization_traits_map(self) -> Dict[OperatorMetatype, Quantiz retval = {} if self._hw_config is None: for trait, meta_list in self._default_trait_to_metatype_map.items(): - for op_meta in meta_list: # type: OperatorMetatype + for op_meta in meta_list: retval[op_meta] = trait else: op_meta_vs_qconfs_map = self._hw_config.get_metatype_vs_quantizer_configs_map() @@ -894,13 +894,13 @@ def _get_operator_qconfigs_map(self) -> Dict[OperatorMetatype, List[QuantizerCon if self._hw_config is None: for trait, meta_list in self._default_trait_to_metatype_map.items(): if trait == QuantizationTrait.INPUTS_QUANTIZABLE: - for op_meta in meta_list: # type: OperatorMetatype + for op_meta in meta_list: if self.default_global_qconfig_list is not None: retval[op_meta] = deepcopy(self.default_global_qconfig_list) else: retval[op_meta] = deepcopy(self.DEFAULT_QUANTIZATION_TYPES) elif trait == QuantizationTrait.NON_QUANTIZABLE: - for op_meta in meta_list: # type: OperatorMetatype + for op_meta in meta_list: retval[op_meta] = None else: retval = self._hw_config.get_metatype_vs_quantizer_configs_map() @@ -960,7 +960,7 @@ def setup_initial_quantizers( if self._additional_unified_scale_op_scopes is not None: # Link the prop quantizers according to specification in NNCF config - occupied_insertion_points_vs_pqs = {} # type: Dict[TargetPoint, PropagatingQuantizer] + occupied_insertion_points_vs_pqs: Dict[TargetPoint, PropagatingQuantizer] = {} for pq in self._active_propagating_quantizers_queue: ip_node_key = pq.current_location_node_key ip_node = quant_prop_graph.nodes[ip_node_key] @@ -1039,7 +1039,7 @@ def coalesce_insertion_points( if i not in insertion_point_indices_vs_group_id: insertion_point_indices_vs_group_id[i] = None - group_indices_list = [[] for _ in linked_scopes_groups_list] # type: List[List[int]] + group_indices_list: List[List[int]] = [[] for _ in linked_scopes_groups_list] for insertion_point_idx, group_idx in insertion_point_indices_vs_group_id.items(): if group_idx is not None: group_indices_list[group_idx].append(insertion_point_idx) @@ -1482,7 +1482,7 @@ def __assign_priorities_to_configs_in_merged_list( # takes 0-th config in the list as the final config file. Without an external, unambiguous # priority mechanism or manual config selection there is no way to do a consistent, branch order-independent # merge. - qconfig_and_priority_list = [] # type: List[Tuple[QuantizerConfig, int]] + qconfig_and_priority_list: List[Tuple[QuantizerConfig, int]] = [] for merged_qconfig in merged_qconfig_list: priority = 0 max_original_list_len = max(len(x) for x in potential_qconfigs_for_each_branch) diff --git a/nncf/common/quantization/quantizer_propagation/structs.py b/nncf/common/quantization/quantizer_propagation/structs.py index 581bf5b2556..0831da49f48 100644 --- a/nncf/common/quantization/quantizer_propagation/structs.py +++ b/nncf/common/quantization/quantizer_propagation/structs.py @@ -64,10 +64,10 @@ def __init__( :param unified_scale_type: The type of unified scales for this quantizer - if unspecified, this quantizer won't require unified scales. """ - self.potential_quant_configs = quant_configs # type: List[QuantizerConfig] + self.potential_quant_configs: List[QuantizerConfig] = quant_configs self.affected_edges = set() - self.affected_ip_nodes = set() # type: Set[str] - self.propagation_path = [] # type: PropagationPath + self.affected_ip_nodes: Set[str] = set() + self.propagation_path: PropagationPath = [] self.current_location_node_key = init_location_node_key self.last_accepting_location_node_key = None self.id = id_ @@ -94,8 +94,8 @@ class SharedAffectedOpsPropagatingQuantizerGroup: """Combines propagating quantizers that share affected operations""" def __init__(self, affecting_prop_quants: Set[PropagatingQuantizer], affected_op_node_keys: Set[str]): - self.affecting_prop_quants = affecting_prop_quants # type: Set[PropagatingQuantizer] - self.affected_op_node_keys = affected_op_node_keys # type: Set[str] + self.affecting_prop_quants: Set[PropagatingQuantizer] = affecting_prop_quants + self.affected_op_node_keys: Set[str] = affected_op_node_keys def update(self, other: "SharedAffectedOpsPropagatingQuantizerGroup"): self.affected_op_node_keys.update(other.affected_op_node_keys) diff --git a/nncf/common/quantization/quantizer_setup.py b/nncf/common/quantization/quantizer_setup.py index e03347096d5..caf2359a876 100644 --- a/nncf/common/quantization/quantizer_setup.py +++ b/nncf/common/quantization/quantizer_setup.py @@ -235,9 +235,9 @@ def get_all_configs_list(self) -> List[QuantizerConfig]: class QuantizerSetupBase: def __init__(self): - self.quantization_points = {} # type: Dict[QuantizationPointId, QuantizationPointBase] - self.unified_scale_groups = {} # type: Dict[int, Set[QuantizationPointId]] - self.shared_input_operation_set_groups = {} # type: Dict[int, Set[QuantizationPointId]] + self.quantization_points: Dict[QuantizationPointId, QuantizationPointBase] = {} + self.unified_scale_groups: Dict[int, Set[QuantizationPointId]] = {} + self.shared_input_operation_set_groups: Dict[int, Set[QuantizationPointId]] = {} self._next_unified_scale_gid = 0 self._next_shared_inputs_gid = 0 @@ -331,11 +331,11 @@ def remove_unified_scale_from_point(self, qp_id: QuantizationPointId): self.unified_scale_groups.pop(gid) def equivalent_to(self, other: "QuantizerSetupBase") -> bool: - this_qp_id_to_other_qp_id_dict = {} # type: Dict[QuantizationPointId, QuantizationPointId] + this_qp_id_to_other_qp_id_dict: Dict[QuantizationPointId, QuantizationPointId] = {} def _compare_qps(first: "QuantizerSetupBase", second: "QuantizerSetupBase") -> bool: for this_qp_id, this_qp in first.quantization_points.items(): - matches = [] # type: List[QuantizationPointId] + matches: List[QuantizationPointId] = [] for other_qp_id, other_qp in second.quantization_points.items(): if this_qp == other_qp: matches.append(other_qp_id) @@ -395,7 +395,7 @@ class SingleConfigQuantizerSetup(QuantizerSetupBase): def __init__(self): super().__init__() - self.quantization_points = {} # type: Dict[QuantizationPointId, SingleConfigQuantizationPoint] + self.quantization_points: Dict[QuantizationPointId, SingleConfigQuantizationPoint] = {} def get_state(self) -> Dict: """ @@ -445,8 +445,8 @@ def list2set(pair): class MultiConfigQuantizerSetup(QuantizerSetupBase): def __init__(self): super().__init__() - self.quantization_points = {} # type: Dict[QuantizationPointId, MultiConfigQuantizationPoint] - self._unified_scale_qpid_vs_type = {} # type: Dict[QuantizationPointId, UnifiedScaleType] + self.quantization_points: Dict[QuantizationPointId, MultiConfigQuantizationPoint] = {} + self._unified_scale_qpid_vs_type: Dict[QuantizationPointId, UnifiedScaleType] = {} def register_unified_scale_group_with_types( self, qp_group: List[QuantizationPointId], us_types: List[UnifiedScaleType] @@ -509,7 +509,7 @@ def select_qconfigs( return retval def select_first_qconfig_for_each_point(self) -> SingleConfigQuantizerSetup: - qp_id_vs_qconfig_dict = {} # type: Dict[QuantizationPointId, QuantizerConfig] + qp_id_vs_qconfig_dict: Dict[QuantizationPointId, QuantizerConfig] = {} for qp_id, qp in self.quantization_points.items(): qp_id_vs_qconfig_dict[qp_id] = qp.possible_qconfigs[0] return self.select_qconfigs(qp_id_vs_qconfig_dict) diff --git a/nncf/common/stateful_classes_registry.py b/nncf/common/stateful_classes_registry.py index b8fbcfa885f..524b9078779 100644 --- a/nncf/common/stateful_classes_registry.py +++ b/nncf/common/stateful_classes_registry.py @@ -21,8 +21,8 @@ class StatefulClassesRegistry: REQUIRED_METHOD_NAME = "from_state" def __init__(self): - self._name_vs_class_map = {} # type: Dict[str, object] - self._class_vs_name_map = {} # type: Dict[object, str] + self._name_vs_class_map: Dict[str, object] = {} + self._class_vs_name_map: Dict[object, str] = {} def register(self, name: str = None) -> Callable: """ diff --git a/nncf/common/utils/logger/__init__.py b/nncf/common/utils/logger/__init__.py index 40fc5969d12..d074af82b7d 100644 --- a/nncf/common/utils/logger/__init__.py +++ b/nncf/common/utils/logger/__init__.py @@ -20,6 +20,6 @@ ) # pylint:disable=unused-import -from nncf.common.logging.logger import disable_logging -from nncf.common.logging.logger import nncf_logger as logger -from nncf.common.logging.logger import set_log_level +from nncf.common.logging.logger import disable_logging as disable_logging # noqa: E402 +from nncf.common.logging.logger import nncf_logger as logger # noqa: E402, F401 +from nncf.common.logging.logger import set_log_level as set_log_level # noqa: E402 diff --git a/nncf/common/utils/patcher.py b/nncf/common/utils/patcher.py index da00e541e5a..097f466762a 100644 --- a/nncf/common/utils/patcher.py +++ b/nncf/common/utils/patcher.py @@ -54,7 +54,7 @@ def patch( # noqa: C901 else: if inspect.isclass(obj_cls): try: - fn = obj_cls.__getattribute__(obj_cls, fn_name) # type: ignore + fn = obj_cls.__getattribute__(obj_cls, fn_name) except AttributeError: return self._patch_class_fn(obj_cls, fn_name, fn, wrapper, force) diff --git a/nncf/config/__init__.py b/nncf/config/__init__.py index 7df0d43a6bd..d98056274d3 100644 --- a/nncf/config/__init__.py +++ b/nncf/config/__init__.py @@ -11,4 +11,4 @@ """ NNCF configuration file schemata and associated structures. """ -from nncf.config.config import NNCFConfig +from nncf.config.config import NNCFConfig as NNCFConfig diff --git a/nncf/config/config.py b/nncf/config/config.py index 72cfdb28997..cf1cff31358 100644 --- a/nncf/config/config.py +++ b/nncf/config/config.py @@ -37,7 +37,7 @@ class NNCFConfig(dict): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.__nncf_extra_structs = {} # type: dict[str, NNCFExtraConfigStruct] + self.__nncf_extra_structs: Dict[str, NNCFExtraConfigStruct] = {} @classmethod def from_dict(cls, nncf_dict: Dict) -> "NNCFConfig": diff --git a/nncf/config/extractors.py b/nncf/config/extractors.py index da7ac722ef8..2bee6557083 100644 --- a/nncf/config/extractors.py +++ b/nncf/config/extractors.py @@ -105,7 +105,7 @@ def extract_range_init_params(config: NNCFConfig, algorithm_name: str = "quantiz max_num_init_samples = 0 global_range_init_config = None - scope_overrides = [] # type: List[PerLayerRangeInitConfig] + scope_overrides: List[PerLayerRangeInitConfig] = [] if isinstance(init_range_config_dict_or_list, dict): global_range_init_config = RangeInitConfig.from_dict(init_range_config_dict_or_list) max_num_init_samples = global_range_init_config.num_init_samples diff --git a/nncf/data/__init__.py b/nncf/data/__init__.py index 97fb8e39dfd..d80c0ca5d52 100644 --- a/nncf/data/__init__.py +++ b/nncf/data/__init__.py @@ -9,4 +9,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.data.dataset import Dataset +from nncf.data.dataset import Dataset as Dataset diff --git a/nncf/experimental/common/pruning/nodes_grouping.py b/nncf/experimental/common/pruning/nodes_grouping.py index e0fe392b4d7..3212ecbb103 100644 --- a/nncf/experimental/common/pruning/nodes_grouping.py +++ b/nncf/experimental/common/pruning/nodes_grouping.py @@ -72,7 +72,7 @@ def get_pruning_groups( :return: list of groups with parameters of pruning. """ # 1. Initialize masks for producing nodes - all_nodes_to_prune = graph.get_nodes_by_types(prune_operations_types) # type: List[NNCFNode] + all_nodes_to_prune: List[NNCFNode] = graph.get_nodes_by_types(prune_operations_types) roots = {} for node in all_nodes_to_prune: assert isinstance(node.layer_attributes, (LinearLayerAttributes, ConvolutionLayerAttributes)) diff --git a/nncf/experimental/common/tensor_statistics/statistical_functions.py b/nncf/experimental/common/tensor_statistics/statistical_functions.py new file mode 100644 index 00000000000..24fc115a058 --- /dev/null +++ b/nncf/experimental/common/tensor_statistics/statistical_functions.py @@ -0,0 +1,30 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nncf.experimental.tensor import Tensor +from nncf.experimental.tensor import functions as fns + + +def mean_per_channel(x: Tensor, axis: int) -> Tensor: + """ + Computes the mean of elements across given channel dimension of Tensor. + + :param x: Tensor to reduce. + :param axis: The channel dimensions to reduce. + :return: Reduced Tensor. + """ + if len(x.shape) < 3: + return fns.mean(x, axis=0) + pos_axis = axis + x.ndim if axis < 0 else axis + if pos_axis < 0 or pos_axis >= x.ndim: + raise ValueError(f"axis {axis} is out of bounds for array of dimension {x.ndim}") + axis = tuple(i for i in range(x.ndim) if i != pos_axis) + return fns.mean(x, axis=axis) diff --git a/nncf/experimental/tensor/README.md b/nncf/experimental/tensor/README.md index 09e3dc6a1e0..ea8ae2168c9 100644 --- a/nncf/experimental/tensor/README.md +++ b/nncf/experimental/tensor/README.md @@ -6,7 +6,7 @@ making them more portable and reusable. ## Usage -The main idea is common algorithms should use wrapped tensors and provide to backend-specific function unwrapped tensor. +Common algorithms should use wrapped tensors and provide the unwrapped tensor to the backend-specific function. ### Initialization Tensor @@ -32,6 +32,8 @@ tenor_b = Tensor(np.array([1,2])) tensor_a + tenor_b # Tensor(array([2, 4])) ``` +**NOTE** Division operations for the numpy backend are performed with warnings disabled for the same for all backends. + ### Comparison operators All math operations are overrided to operated with wrapped object and return `Tensor` @@ -55,16 +57,16 @@ nncf_tensor.max() # Tensor(2) All available functions you can found in [functions.py](functions.py). ```python -from nncf.experimental.tensor import functions -functions.max(nncf_tensor) # Tensor(2) +from nncf.experimental.tensor import functions as fns +fns.max(nncf_tensor) # Tensor(2) ``` **NOTE** A function requires at least one positional argument, which is used to dispatch the function to the appropriate implementation depending on the type of argument. ```python -functions.max(nncf_tensor) # Correct -functions.max(a=nncf_tensor) # TypeError: wrapper requires at least 1 positional argument +fns.max(nncf_tensor) # Correct +fns.max(a=nncf_tensor) # TypeError: wrapper requires at least 1 positional argument ``` ### Loop over Tensor @@ -100,7 +102,7 @@ tensor_a[0:2] # Tensor(array([[1],[2]])) class Tensor: ... def foo(self, arg1: Type) -> "Tensor": - return functions.foo(self, arg1) + return fns.foo(self, arg1) ``` 2. Add function to [function.py](function.py) @@ -120,28 +122,36 @@ tensor_a[0:2] # Tensor(array([[1],[2]])) return NotImplemented(f"Function `foo` is not implemented for {type(a)}") ``` -3. Add function name to `__all__` in [function.py](function.py) + **NOTE** For the case when the first argument has type `List[Tensor]`, use the `_dispatch_list` function. This function dispatches function by first element in the first argument. + + ```python + @functools.singledispatch + def foo(x: List[Tensor], axis: int = 0) -> Tensor: + if isinstance(x, List): + unwrapped_x = [i.data for i in x] + return Tensor(_dispatch_list(foo, unwrapped_x, axis=axis)) + raise NotImplementedError(f"Function `foo` is not implemented for {type(x)}") + ``` -4. Add backend specific implementation of method to: +3. Add backend specific implementation of method to: - - [numpy_function.py](numpy_function.py) + - [numpy_function.py](numpy_functions.py) ```python - @functions.foo.register(np.ndarray) - @functions.foo.register(np.number) + @_register_numpy_types(fns.foo) def _(a: TType, arg1: Type) -> np.ndarray: return np.foo(a, arg1) ``` - - [torch_function.py](torch_function.py) + - [torch_function.py](torch_functions.py) ```python - @functions.foo.register(torch.Tensor) + @fns.foo.register(torch.Tensor) def _(a: torch.Tensor, arg1: Type) -> torch.Tensor: return torch.foo(a, arg1) ``` -5. Add test of method to [test template](tests/shared/test_templates/template_test_nncf_tensor.py) for Tensor class +4. Add test of method to [test template](../../../tests/shared/test_templates/template_test_nncf_tensor.py) for Tensor class ### Add new backend diff --git a/nncf/experimental/tensor/__init__.py b/nncf/experimental/tensor/__init__.py index 96c8cb31f82..dbf4bc868e5 100644 --- a/nncf/experimental/tensor/__init__.py +++ b/nncf/experimental/tensor/__init__.py @@ -9,8 +9,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.experimental.tensor.enums import TensorBackendType -from nncf.experimental.tensor.enums import TensorDataType -from nncf.experimental.tensor.enums import TensorDeviceType -from nncf.experimental.tensor.tensor import Tensor -from nncf.experimental.tensor.tensor import unwrap_tensor_data +from nncf.experimental.tensor.enums import TensorBackendType as TensorBackendType +from nncf.experimental.tensor.enums import TensorDataType as TensorDataType +from nncf.experimental.tensor.enums import TensorDeviceType as TensorDeviceType +from nncf.experimental.tensor.tensor import Tensor as Tensor +from nncf.experimental.tensor.tensor import unwrap_tensor_data as unwrap_tensor_data diff --git a/nncf/experimental/tensor/functions.py b/nncf/experimental/tensor/functions.py index 30f27a65cce..a71ac209012 100644 --- a/nncf/experimental/tensor/functions.py +++ b/nncf/experimental/tensor/functions.py @@ -10,14 +10,12 @@ # limitations under the License. import functools -from typing import List, Optional, Tuple, TypeVar, Union +from typing import Callable, List, Optional, Tuple, Union -from nncf.experimental.tensor import Tensor -from nncf.experimental.tensor import unwrap_tensor_data from nncf.experimental.tensor.enums import TensorDataType from nncf.experimental.tensor.enums import TensorDeviceType - -TTensor = TypeVar("TTensor") +from nncf.experimental.tensor.tensor import Tensor +from nncf.experimental.tensor.tensor import unwrap_tensor_data def _tensor_guard(func: callable): @@ -36,7 +34,7 @@ def wrapper(*args, **kwargs): @functools.singledispatch @_tensor_guard -def device(a: TTensor) -> TensorDeviceType: +def device(a: Tensor) -> TensorDeviceType: """ Return the device of the tensor. @@ -48,7 +46,7 @@ def device(a: TTensor) -> TensorDeviceType: @functools.singledispatch @_tensor_guard -def squeeze(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: +def squeeze(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: """ Remove axes of length one from a. @@ -63,7 +61,7 @@ def squeeze(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTenso @functools.singledispatch @_tensor_guard -def flatten(a: TTensor) -> TTensor: +def flatten(a: Tensor) -> Tensor: """ Return a copy of the tensor collapsed into one dimension. @@ -75,7 +73,7 @@ def flatten(a: TTensor) -> TTensor: @functools.singledispatch @_tensor_guard -def max(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: # pylint: disable=redefined-builtin +def max(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: # pylint: disable=redefined-builtin """ Return the maximum of an array or maximum along an axis. @@ -88,7 +86,7 @@ def max(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: @functools.singledispatch @_tensor_guard -def min(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: # pylint: disable=redefined-builtin +def min(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: # pylint: disable=redefined-builtin """ Return the minimum of an array or minimum along an axis. @@ -101,7 +99,7 @@ def min(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: @functools.singledispatch @_tensor_guard -def abs(a: TTensor) -> TTensor: # pylint: disable=redefined-builtin +def abs(a: Tensor) -> Tensor: # pylint: disable=redefined-builtin """ Calculate the absolute value element-wise. @@ -113,7 +111,7 @@ def abs(a: TTensor) -> TTensor: # pylint: disable=redefined-builtin @functools.singledispatch @_tensor_guard -def astype(a: TTensor, data_type: TensorDataType) -> TTensor: +def astype(a: Tensor, data_type: TensorDataType) -> Tensor: """ Copy of the tensor, cast to a specified type. @@ -127,7 +125,7 @@ def astype(a: TTensor, data_type: TensorDataType) -> TTensor: @functools.singledispatch @_tensor_guard -def dtype(a: TTensor) -> TensorDataType: +def dtype(a: Tensor) -> TensorDataType: """ Return data type of the tensor. @@ -139,7 +137,7 @@ def dtype(a: TTensor) -> TensorDataType: @functools.singledispatch @_tensor_guard -def reshape(a: TTensor, shape: List[int]) -> TTensor: +def reshape(a: Tensor, shape: Tuple[int, ...]) -> Tensor: """ Gives a new shape to a tensor without changing its data. @@ -152,7 +150,7 @@ def reshape(a: TTensor, shape: List[int]) -> TTensor: @functools.singledispatch @_tensor_guard -def all(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: # pylint: disable=redefined-builtin +def all(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: # pylint: disable=redefined-builtin """ Test whether all tensor elements along a given axis evaluate to True. @@ -165,7 +163,9 @@ def all(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: @functools.singledispatch @_tensor_guard -def allclose(a: TTensor, b: TTensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> TTensor: +def allclose( + a: Tensor, b: Union[Tensor, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False +) -> Tensor: """ Returns True if two arrays are element-wise equal within a tolerance. @@ -191,7 +191,7 @@ def allclose(a: TTensor, b: TTensor, rtol: float = 1e-05, atol: float = 1e-08, e @functools.singledispatch @_tensor_guard -def any(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: # pylint: disable=redefined-builtin +def any(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: # pylint: disable=redefined-builtin """ Test whether any tensor elements along a given axis evaluate to True. @@ -204,7 +204,7 @@ def any(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: @functools.singledispatch @_tensor_guard -def count_nonzero(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> TTensor: +def count_nonzero(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: """ Counts the number of non-zero values in the tensor input. @@ -218,19 +218,21 @@ def count_nonzero(a: TTensor, axis: Optional[Union[int, Tuple[int]]] = None) -> @functools.singledispatch @_tensor_guard -def isempty(a: TTensor) -> TTensor: +def isempty(a: Tensor) -> bool: """ Return True if input tensor is empty. :param a: The input tensor. :return: True if tensor is empty, otherwise False. """ - return Tensor(isempty(a.data)) + return isempty(a.data) @functools.singledispatch @_tensor_guard -def isclose(a: TTensor, b: TTensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> TTensor: +def isclose( + a: Tensor, b: Union[Tensor, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False +) -> Tensor: """ Returns a boolean array where two arrays are element-wise equal within a tolerance. @@ -256,7 +258,7 @@ def isclose(a: TTensor, b: TTensor, rtol: float = 1e-05, atol: float = 1e-08, eq @functools.singledispatch @_tensor_guard -def maximum(x1: TTensor, x2: TTensor) -> TTensor: +def maximum(x1: Tensor, x2: Union[Tensor, float]) -> Tensor: """ Element-wise maximum of tensor elements. @@ -269,7 +271,7 @@ def maximum(x1: TTensor, x2: TTensor) -> TTensor: @functools.singledispatch @_tensor_guard -def minimum(x1: TTensor, x2: TTensor) -> TTensor: +def minimum(x1: Tensor, x2: Union[Tensor, float]) -> Tensor: """ Element-wise minimum of tensor elements. @@ -282,7 +284,7 @@ def minimum(x1: TTensor, x2: TTensor) -> TTensor: @functools.singledispatch @_tensor_guard -def ones_like(a: TTensor) -> TTensor: +def ones_like(a: Tensor) -> Tensor: """ Return a tensor of ones with the same shape and type as a given tensor. @@ -294,7 +296,7 @@ def ones_like(a: TTensor) -> TTensor: @functools.singledispatch @_tensor_guard -def where(condition: TTensor, x: TTensor, y: TTensor) -> TTensor: +def where(condition: Tensor, x: Union[Tensor, float], y: Union[Tensor, float]) -> Tensor: """ Return elements chosen from x or y depending on condition. @@ -314,7 +316,7 @@ def where(condition: TTensor, x: TTensor, y: TTensor) -> TTensor: @functools.singledispatch @_tensor_guard -def zeros_like(a: TTensor) -> TTensor: +def zeros_like(a: Tensor) -> Tensor: """ Return an tensor of zeros with the same shape and type as a given tensor. @@ -324,28 +326,114 @@ def zeros_like(a: TTensor) -> TTensor: return Tensor(zeros_like(a.data)) -__all__ = [ - "device", - "squeeze", - "flatten", - "max", - "min", - "abs", - "astype", - "reshape", - "all", - "allclose", - "any", - "count_nonzero", - "isempty", - "isclose", - "maximum", - "minimum", - "ones_like", - "minimum", - "where", - "zeros_like", -] +@functools.singledispatch +def stack(x: List[Tensor], axis: int = 0) -> Tensor: + """ + Stacks a list of Tensors rank-R tensors into one Tensor rank-(R+1) tensor. + + :param x: List of Tensors. + :param axis: The axis to stack along. + :return: Stacked Tensor. + """ + if isinstance(x, List): + return Tensor(_dispatch_list(stack, x, axis=axis)) + raise NotImplementedError(f"Function `stack` is not implemented for {type(x)}") + + +@functools.singledispatch +@_tensor_guard +def unstack(a: Tensor, axis: int = 0) -> List[Tensor]: + """ + Unstack a Tensor into list. + + :param a: Tensor to unstack. + :param axis: The axis to unstack along. + :return: List of Tensor. + """ + res = unstack(a.data, axis=axis) + return [Tensor(i) for i in res] + + +@functools.singledispatch +@_tensor_guard +def moveaxis(a: Tensor, source: Union[int, Tuple[int, ...]], destination: Union[int, Tuple[int, ...]]) -> Tensor: + """ + Move axes of an array to new positions. + + :param a: The array whose axes should be reordered. + :param source: Original positions of the axes to move. These must be unique. + :param destination: Destination positions for each of the original axes. These must also be unique. + :return: Array with moved axes. + """ + return Tensor(moveaxis(a.data, source, destination)) + + +@functools.singledispatch +@_tensor_guard +def mean(a: Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Tensor: + """ + Compute the arithmetic mean along the specified axis. + + :param a: Array containing numbers whose mean is desired. + :param axis: Axis or axes along which the means are computed. + :param keepdims: Destination positions for each of the original axes. These must also be unique. + :return: Array with moved axes. + """ + return Tensor(mean(a.data, axis, keepdims)) + + +@functools.singledispatch +@_tensor_guard +def round(a: Tensor, decimals=0) -> Tensor: # pylint: disable=redefined-builtin + """ + Evenly round to the given number of decimals. + + :param a: Input data. + :param decimals: Number of decimal places to round to (default: 0). If decimals is negative, + it specifies the number of positions to the left of the decimal point. + :return: An array of the same type as a, containing the rounded values. + """ + return Tensor(round(a.data, decimals)) + + +@functools.singledispatch +@_tensor_guard +def _binary_op_nowarn(a: Tensor, b: Union[Tensor, float], operator_fn: Callable) -> Tensor: + """ + Applies a binary operation with disable warnings. + + :param a: The first tensor. + :param b: The second tensor. + :param operator_fn: The binary operation function. + :return: The result of the binary operation. + """ + return Tensor(_binary_op_nowarn(a.data, unwrap_tensor_data(b), operator_fn)) + + +@functools.singledispatch +@_tensor_guard +def _binary_reverse_op_nowarn(a: Tensor, b: Union[Tensor, float], operator_fn: Callable) -> Tensor: + """ + Applies a binary reverse operation with disable warnings. + + :param a: The first tensor. + :param b: The second tensor. + :param operator_fn: The binary operation function. + :return: The result of the binary operation. + """ + return Tensor(_binary_reverse_op_nowarn(a.data, unwrap_tensor_data(b), operator_fn)) + + +def _dispatch_list(fn: "functools._SingleDispatchCallable", tensor_list: List[Tensor], *args, **kwargs): + """ + Dispatches the function to the type of the wrapped data of the first element in tensor_list. + + :param fn: A function wrapped by `functools.singledispatch`. + :param tensor_list: List of Tensors. + :return: The result value of the function call. + """ + unwrapped_list = [i.data for i in tensor_list] + return fn.dispatch(type(unwrapped_list[0]))(unwrapped_list, *args, **kwargs) def _initialize_backends(): @@ -353,7 +441,7 @@ def _initialize_backends(): import nncf.experimental.tensor.numpy_functions try: - import nncf.experimental.tensor.torch_functions + import nncf.experimental.tensor.torch_functions # noqa: F401 except ImportError: pass diff --git a/nncf/experimental/tensor/numpy_functions.py b/nncf/experimental/tensor/numpy_functions.py index be070db4bdb..b4c515b1da1 100644 --- a/nncf/experimental/tensor/numpy_functions.py +++ b/nncf/experimental/tensor/numpy_functions.py @@ -9,11 +9,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional, Tuple, Union +from typing import Callable, List, Optional, Tuple, Union import numpy as np -from nncf.experimental.tensor import functions +from nncf.experimental.tensor import functions as fns from nncf.experimental.tensor.enums import TensorDataType from nncf.experimental.tensor.enums import TensorDeviceType @@ -28,137 +28,179 @@ DTYPE_MAP_REV = {v: k for k, v in DTYPE_MAP.items()} -@functions.device.register(np.ndarray) -@functions.device.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> TensorDeviceType: +def _register_numpy_types(singledispatch_fn): + """ + Decorator to register function to singledispatch for numpy classes. + + :param singledispatch_fn: singledispatch function. + """ + + def inner(func): + singledispatch_fn.register(np.ndarray)(func) + singledispatch_fn.register(np.generic)(func) + return func + + return inner + + +@_register_numpy_types(fns.device) +def _(a: Union[np.ndarray, np.generic]) -> TensorDeviceType: return TensorDeviceType.CPU -@functions.squeeze.register(np.ndarray) -@functions.squeeze.register(np.number) -def _(a: Union[np.ndarray, np.number], axis: Optional[Union[int, Tuple[int]]] = None) -> np.ndarray: +@_register_numpy_types(fns.squeeze) +def _( + a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None +) -> Union[np.ndarray, np.generic]: return np.squeeze(a, axis=axis) -@functions.flatten.register(np.ndarray) -@functions.flatten.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> np.ndarray: +@_register_numpy_types(fns.flatten) +def _(a: Union[np.ndarray, np.generic]) -> np.ndarray: return a.flatten() -@functions.max.register(np.ndarray) -@functions.max.register(np.number) -def _(a: Union[np.ndarray, np.number], axis: Optional[Union[int, Tuple[int]]] = None) -> np.ndarray: +@_register_numpy_types(fns.max) +def _(a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None) -> np.ndarray: return np.max(a, axis=axis) -@functions.min.register(np.ndarray) -@functions.min.register(np.number) -def _(a: Union[np.ndarray, np.number], axis: Optional[Union[int, Tuple[int]]] = None) -> np.ndarray: +@_register_numpy_types(fns.min) +def _( + a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None +) -> Union[np.ndarray, np.generic]: return np.min(a, axis=axis) -@functions.abs.register(np.ndarray) -@functions.abs.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> np.ndarray: +@_register_numpy_types(fns.abs) +def _(a: Union[np.ndarray, np.generic]) -> Union[np.ndarray, np.generic]: return np.absolute(a) -@functions.astype.register(np.ndarray) -@functions.astype.register(np.number) -def _(a: Union[np.ndarray, np.number], dtype: TensorDataType) -> np.ndarray: +@_register_numpy_types(fns.astype) +def _(a: Union[np.ndarray, np.generic], dtype: TensorDataType) -> Union[np.ndarray, np.generic]: return a.astype(DTYPE_MAP[dtype]) -@functions.dtype.register(np.ndarray) -@functions.dtype.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> TensorDataType: +@_register_numpy_types(fns.dtype) +def _(a: Union[np.ndarray, np.generic]) -> TensorDataType: return DTYPE_MAP_REV[np.dtype(a.dtype)] -@functions.reshape.register(np.ndarray) -@functions.reshape.register(np.number) -def _(a: Union[np.ndarray, np.number], shape: Union[int, Tuple[int]]) -> np.ndarray: +@_register_numpy_types(fns.reshape) +def _(a: Union[np.ndarray, np.generic], shape: Union[int, Tuple[int, ...]]) -> np.ndarray: return a.reshape(shape) -@functions.all.register(np.ndarray) -@functions.all.register(np.number) -def _(a: Union[np.ndarray, np.number], axis: Optional[Union[int, Tuple[int]]] = None) -> Union[np.ndarray, bool]: +@_register_numpy_types(fns.all) +def _(a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Union[np.ndarray, bool]: return np.all(a, axis=axis) -@functions.allclose.register(np.ndarray) -@functions.allclose.register(np.number) +@_register_numpy_types(fns.allclose) def _( - a: Union[np.ndarray, np.number], - b: Union[np.ndarray, np.number], + a: Union[np.ndarray, np.generic], + b: Union[np.ndarray, np.generic, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False, -) -> bool: +) -> np.ndarray: return np.allclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) -@functions.any.register(np.ndarray) -@functions.any.register(np.number) -def _(a: Union[np.ndarray, np.number], axis: Optional[Union[int, Tuple[int]]] = None) -> Union[np.ndarray, bool]: +@_register_numpy_types(fns.any) +def _(a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Union[np.ndarray, bool]: return np.any(a, axis=axis) -@functions.count_nonzero.register(np.ndarray) -@functions.count_nonzero.register(np.number) -def _(a: Union[np.ndarray, np.number], axis: Optional[Union[int, Tuple[int]]] = None) -> np.ndarray: - return np.count_nonzero(a, axis=axis) +@_register_numpy_types(fns.count_nonzero) +def _(a: Union[np.ndarray, np.generic], axis: Optional[Union[int, Tuple[int, ...]]] = None) -> np.ndarray: + return np.array(np.count_nonzero(a, axis=axis)) -@functions.isempty.register(np.ndarray) -@functions.isempty.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> bool: +@_register_numpy_types(fns.isempty) +def _(a: Union[np.ndarray, np.generic]) -> bool: return a.size == 0 -@functions.isclose.register(np.ndarray) -@functions.isclose.register(np.number) +@_register_numpy_types(fns.isclose) def _( - a: Union[np.ndarray, np.number], - b: np.ndarray, + a: Union[np.ndarray, np.generic], + b: Union[np.ndarray, np.generic, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False, -): +) -> Union[np.ndarray, bool]: return np.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) -@functions.maximum.register(np.ndarray) -@functions.maximum.register(np.number) -def _(x1: Union[np.ndarray, np.number], x2: np.ndarray) -> np.ndarray: +@_register_numpy_types(fns.maximum) +def _(x1: Union[np.ndarray, np.generic], x2: Union[np.ndarray, np.generic, float]) -> np.ndarray: return np.maximum(x1, x2) -@functions.minimum.register(np.ndarray) -@functions.minimum.register(np.number) -def _(x1: Union[np.ndarray, np.number], x2: np.ndarray) -> np.ndarray: +@_register_numpy_types(fns.minimum) +def _(x1: Union[np.ndarray, np.generic], x2: Union[np.ndarray, np.generic, float]) -> np.ndarray: return np.minimum(x1, x2) -@functions.ones_like.register(np.ndarray) -@functions.ones_like.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> np.ndarray: +@_register_numpy_types(fns.ones_like) +def _(a: Union[np.ndarray, np.generic]) -> np.ndarray: return np.ones_like(a) -@functions.where.register(np.ndarray) -@functions.where.register(np.number) +@_register_numpy_types(fns.where) def _( - condition: Union[np.ndarray, np.number], - x: Union[np.ndarray, np.number, float, bool], - y: Union[np.ndarray, float, bool], + condition: Union[np.ndarray, np.generic], + x: Union[np.ndarray, np.generic, float], + y: Union[np.ndarray, np.generic, float], ) -> np.ndarray: return np.where(condition, x, y) -@functions.zeros_like.register(np.ndarray) -@functions.zeros_like.register(np.number) -def _(a: Union[np.ndarray, np.number]) -> np.ndarray: +@_register_numpy_types(fns.zeros_like) +def _(a: Union[np.ndarray, np.generic]) -> np.ndarray: return np.zeros_like(a) + + +@_register_numpy_types(fns.stack) +def _(x: Union[np.ndarray, np.generic], axis: int = 0) -> List[np.ndarray]: + return np.stack(x, axis=axis) + + +@_register_numpy_types(fns.unstack) +def _(x: Union[np.ndarray, np.generic], axis: int = 0) -> List[np.ndarray]: + return [np.squeeze(e, axis) for e in np.split(x, x.shape[axis], axis=axis)] + + +@_register_numpy_types(fns.moveaxis) +def _(a: np.ndarray, source: Union[int, Tuple[int, ...]], destination: Union[int, Tuple[int, ...]]) -> np.ndarray: + return np.moveaxis(a, source, destination) + + +@_register_numpy_types(fns.mean) +def _(a: Union[np.ndarray, np.generic], axis: Union[int, Tuple[int, ...]] = None, keepdims: bool = False) -> np.ndarray: + return np.mean(a, axis=axis, keepdims=keepdims) + + +@_register_numpy_types(fns.round) +def _(a: Union[np.ndarray, np.generic], decimals: int = 0) -> np.ndarray: + return np.round(a, decimals=decimals) + + +@_register_numpy_types(fns._binary_op_nowarn) # pylint: disable=protected-access +def _( + a: Union[np.ndarray, np.generic], b: Union[np.ndarray, np.generic, float], operator_fn: Callable +) -> Union[np.ndarray, np.generic]: + # Run operator with disabled warning + with np.errstate(invalid="ignore", divide="ignore"): + return operator_fn(a, b) + + +@_register_numpy_types(fns._binary_reverse_op_nowarn) # pylint: disable=protected-access +def _( + a: Union[np.ndarray, np.generic], b: Union[np.ndarray, np.generic, float], operator_fn: Callable +) -> Union[np.ndarray, np.generic]: + # Run operator with disabled warning + with np.errstate(invalid="ignore", divide="ignore"): + return operator_fn(b, a) diff --git a/nncf/experimental/tensor/tensor.py b/nncf/experimental/tensor/tensor.py index daa8e37aff4..76fd05c4ff1 100644 --- a/nncf/experimental/tensor/tensor.py +++ b/nncf/experimental/tensor/tensor.py @@ -8,9 +8,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations - -from typing import Any, List, Optional, Tuple, TypeVar, Union +import operator +from typing import Any, Optional, Tuple, TypeVar, Union from nncf.experimental.tensor.enums import TensorDataType from nncf.experimental.tensor.enums import TensorDeviceType @@ -31,8 +32,12 @@ def data(self) -> TTensor: return self._data @property - def shape(self) -> List[int]: - return list(self.data.shape) + def shape(self) -> Tuple[int, ...]: + return tuple(self.data.shape) + + @property + def ndim(self) -> int: + return self.data.ndim @property def device(self) -> TensorDeviceType: @@ -48,7 +53,7 @@ def __bool__(self) -> bool: def __iter__(self): return TensorIterator(self.data) - def __getitem__(self, index: int) -> "Tensor": + def __getitem__(self, index: int) -> Tensor: return Tensor(self.data[index]) def __str__(self) -> str: @@ -59,86 +64,86 @@ def __repr__(self) -> str: # built-in operations - def __add__(self, other: TTensor) -> "Tensor": + def __add__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data + unwrap_tensor_data(other)) - def __radd__(self, other: TTensor) -> "Tensor": + def __radd__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(unwrap_tensor_data(other) + self.data) - def __sub__(self, other: TTensor) -> "Tensor": + def __sub__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data - unwrap_tensor_data(other)) - def __rsub__(self, other: TTensor) -> "Tensor": + def __rsub__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(unwrap_tensor_data(other) - self.data) - def __mul__(self, other: TTensor) -> "Tensor": + def __mul__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data * unwrap_tensor_data(other)) - def __rmul__(self, other: TTensor) -> "Tensor": + def __rmul__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(unwrap_tensor_data(other) * self.data) - def __pow__(self, other: TTensor) -> "Tensor": + def __pow__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data ** unwrap_tensor_data(other)) - def __truediv__(self, other: TTensor) -> "Tensor": - return Tensor(self.data / unwrap_tensor_data(other)) + def __truediv__(self, other: Union[Tensor, float]) -> Tensor: + return _call_function("_binary_op_nowarn", self, other, operator.truediv) - def __rtruediv__(self, other: TTensor) -> "Tensor": - return Tensor(unwrap_tensor_data(other) / self.data) + def __rtruediv__(self, other: Union[Tensor, float]) -> Tensor: + return _call_function("_binary_reverse_op_nowarn", self, other, operator.truediv) - def __floordiv__(self, other: TTensor) -> "Tensor": - return Tensor(self.data // unwrap_tensor_data(other)) + def __floordiv__(self, other: Union[Tensor, float]) -> Tensor: + return _call_function("_binary_op_nowarn", self, other, operator.floordiv) - def __rfloordiv__(self, other: TTensor) -> "Tensor": - return Tensor(unwrap_tensor_data(other) // self.data) + def __rfloordiv__(self, other: Union[Tensor, float]) -> Tensor: + return _call_function("_binary_reverse_op_nowarn", self, other, operator.floordiv) - def __neg__(self) -> "Tensor": + def __neg__(self) -> Tensor: return Tensor(-self.data) # Comparison operators - def __lt__(self, other: TTensor) -> "Tensor": + def __lt__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data < unwrap_tensor_data(other)) - def __le__(self, other: TTensor) -> "Tensor": + def __le__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data <= unwrap_tensor_data(other)) - def __eq__(self, other: TTensor) -> "Tensor": + def __eq__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data == unwrap_tensor_data(other)) - def __ne__(self, other: TTensor) -> "Tensor": + def __ne__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data != unwrap_tensor_data(other)) - def __gt__(self, other: TTensor) -> "Tensor": + def __gt__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data > unwrap_tensor_data(other)) - def __ge__(self, other: TTensor) -> "Tensor": + def __ge__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data >= unwrap_tensor_data(other)) # Tensor functions - def squeeze(self, axis: Optional[Union[int, Tuple[int]]] = None) -> "Tensor": + def squeeze(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: return _call_function("squeeze", self, axis) - def flatten(self) -> "Tensor": + def flatten(self) -> Tensor: return _call_function("flatten", self) - def max(self, axis: Optional[TTensor] = None) -> "Tensor": + def max(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: return _call_function("max", self, axis) - def min(self, axis: Optional[TTensor] = None) -> "Tensor": + def min(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: return _call_function("min", self, axis) - def abs(self) -> "Tensor": + def abs(self) -> Tensor: return _call_function("abs", self) - def isempty(self) -> "Tensor": + def isempty(self) -> bool: return _call_function("isempty", self) - def astype(self, dtype: TensorDataType): + def astype(self, dtype: TensorDataType) -> Tensor: return _call_function("astype", self, dtype) - def reshape(self, shape: TTensor) -> "Tensor": + def reshape(self, shape: Tuple[int, ...]) -> Tensor: return _call_function("reshape", self, shape) diff --git a/nncf/experimental/tensor/torch_functions.py b/nncf/experimental/tensor/torch_functions.py index 09ef0f1b886..273d5419781 100644 --- a/nncf/experimental/tensor/torch_functions.py +++ b/nncf/experimental/tensor/torch_functions.py @@ -9,13 +9,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Optional, Tuple, Union +from typing import Callable, List, Optional, Tuple, Union import torch from nncf.experimental.tensor import TensorDataType from nncf.experimental.tensor import TensorDeviceType -from nncf.experimental.tensor import functions +from nncf.experimental.tensor import functions as fns DTYPE_MAP = { TensorDataType.float16: torch.float16, @@ -28,7 +28,7 @@ DTYPE_MAP_REV = {v: k for k, v in DTYPE_MAP.items()} -@functions.device.register(torch.Tensor) +@fns.device.register(torch.Tensor) def _(a: torch.Tensor) -> TensorDeviceType: DEVICE_MAP = { "cpu": TensorDeviceType.CPU, @@ -37,112 +37,162 @@ def _(a: torch.Tensor) -> TensorDeviceType: return DEVICE_MAP[a.device.type] -@functions.squeeze.register(torch.Tensor) -def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int]]] = None) -> torch.Tensor: +@fns.squeeze.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> torch.Tensor: if axis is None: return a.squeeze() + if isinstance(axis, Tuple) and any(1 != a.shape[i] for i in axis): + # Make Numpy behavior, torch.squeeze skips axes that are not equal to one.. + raise ValueError("Cannot select an axis to squeeze out which has size not equal to one") return a.squeeze(axis) -@functions.flatten.register(torch.Tensor) +@fns.flatten.register(torch.Tensor) def _(a: torch.Tensor) -> torch.Tensor: return a.flatten() -@functions.max.register(torch.Tensor) -def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int]]] = None) -> torch.Tensor: +@fns.max.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> torch.Tensor: + # Analog of numpy.max is torch.amax if axis is None: - return torch.max(a) - return torch.max(a, dim=axis).values + return torch.amax(a) + return torch.amax(a, dim=axis) -@functions.min.register(torch.Tensor) -def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int]]] = None) -> torch.Tensor: +@fns.min.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> torch.Tensor: + # Analog of numpy.min is torch.amin if axis is None: - return torch.min(a) - return torch.min(a, dim=axis).values + return torch.amin(a) + return torch.amin(a, dim=axis) -@functions.abs.register(torch.Tensor) +@fns.abs.register(torch.Tensor) def _(a: torch.Tensor) -> torch.Tensor: return torch.absolute(a) -@functions.astype.register(torch.Tensor) +@fns.astype.register(torch.Tensor) def _(a: torch.Tensor, dtype: TensorDataType) -> torch.Tensor: return a.type(DTYPE_MAP[dtype]) -@functions.dtype.register(torch.Tensor) +@fns.dtype.register(torch.Tensor) def _(a: torch.Tensor) -> TensorDataType: return DTYPE_MAP_REV[a.dtype] -@functions.reshape.register(torch.Tensor) -def _(a: torch.Tensor, shape: List[int]) -> torch.Tensor: +@fns.reshape.register(torch.Tensor) +def _(a: torch.Tensor, shape: Tuple[int, ...]) -> torch.Tensor: return a.reshape(shape) -@functions.all.register(torch.Tensor) -def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int]]] = None) -> Union[torch.Tensor, bool]: +@fns.all.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Union[torch.Tensor, bool]: if axis is None: return torch.all(a) return torch.all(a, dim=axis) -@functions.allclose.register(torch.Tensor) -def _(a: torch.Tensor, b: torch.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> bool: +@fns.allclose.register(torch.Tensor) +def _( + a: torch.Tensor, b: Union[torch.Tensor, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False +) -> bool: + if not isinstance(b, torch.Tensor): + b = torch.tensor(b, device=a.device) return torch.allclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) -@functions.any.register(torch.Tensor) -def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int]]] = None) -> Union[torch.Tensor, bool]: +@fns.any.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Union[torch.Tensor, bool]: if axis is None: return torch.any(a) return torch.any(a, dim=axis) -@functions.count_nonzero.register(torch.Tensor) -def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int]]] = None) -> torch.Tensor: +@fns.count_nonzero.register(torch.Tensor) +def _(a: torch.Tensor, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> torch.Tensor: return torch.count_nonzero(a, dim=axis) -@functions.isempty.register(torch.Tensor) +@fns.isempty.register(torch.Tensor) def _(a: torch.Tensor) -> bool: return a.numel() == 0 -@functions.isclose.register(torch.Tensor) -def _(a: torch.Tensor, b: torch.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False): +@fns.isclose.register(torch.Tensor) +def _( + a: torch.Tensor, b: Union[torch.Tensor, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False +): + if not isinstance(b, torch.Tensor): + b = torch.tensor(b, device=a.device) return torch.isclose(a, b, atol=atol, rtol=rtol, equal_nan=equal_nan) -@functions.maximum.register(torch.Tensor) -def _(x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor: +@fns.maximum.register(torch.Tensor) +def _(x1: torch.Tensor, x2: Union[torch.Tensor, float]) -> torch.Tensor: if not isinstance(x2, torch.Tensor): x2 = torch.tensor(x2, device=x1.data.device) return torch.maximum(x1, x2) -@functions.minimum.register(torch.Tensor) -def _(x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor: +@fns.minimum.register(torch.Tensor) +def _(x1: torch.Tensor, x2: Union[torch.Tensor, float]) -> torch.Tensor: if not isinstance(x2, torch.Tensor): x2 = torch.tensor(x2, device=x1.data.device) return torch.minimum(x1, x2) -@functions.ones_like.register(torch.Tensor) +@fns.ones_like.register(torch.Tensor) def _(a: torch.Tensor) -> torch.Tensor: return torch.ones_like(a) -@functions.where.register(torch.Tensor) +@fns.where.register(torch.Tensor) def _( condition: torch.Tensor, x: Union[torch.Tensor, float, bool], y: Union[torch.Tensor, float, bool] ) -> torch.Tensor: return torch.where(condition, x, y) -@functions.zeros_like.register(torch.Tensor) +@fns.zeros_like.register(torch.Tensor) def _(a: torch.Tensor) -> torch.Tensor: return torch.zeros_like(a) + + +@fns.stack.register(torch.Tensor) +def _(x: List[torch.Tensor], axis: int = 0) -> List[torch.Tensor]: + return torch.stack(x, dim=axis) + + +@fns.unstack.register(torch.Tensor) +def _(x: torch.Tensor, axis: int = 0) -> List[torch.Tensor]: + if not list(x.shape): + x = x.unsqueeze(0) + return torch.unbind(x, dim=axis) + + +@fns.moveaxis.register(torch.Tensor) +def _(a: torch.Tensor, source: Union[int, Tuple[int, ...]], destination: Union[int, Tuple[int, ...]]) -> torch.Tensor: + return torch.moveaxis(a, source, destination) + + +@fns.mean.register(torch.Tensor) +def _(a: torch.Tensor, axis: Union[int, Tuple[int, ...]] = None, keepdims: bool = False) -> torch.Tensor: + return torch.mean(a, axis=axis, keepdims=keepdims) + + +@fns.round.register(torch.Tensor) +def _(a: torch.Tensor, decimals=0) -> torch.Tensor: + return torch.round(a, decimals=decimals) + + +@fns._binary_op_nowarn.register(torch.Tensor) # pylint: disable=protected-access +def _(a: torch.Tensor, b: Union[torch.Tensor, float], operator_fn: Callable) -> torch.Tensor: + return operator_fn(a, b) + + +@fns._binary_reverse_op_nowarn.register(torch.Tensor) # pylint: disable=protected-access +def _(a: torch.Tensor, b: Union[torch.Tensor, float], operator_fn: Callable) -> torch.Tensor: + return operator_fn(b, a) diff --git a/nncf/experimental/tensorflow/__init__.py b/nncf/experimental/tensorflow/__init__.py index 17d0ed33bb6..4ca939c3d73 100644 --- a/nncf/experimental/tensorflow/__init__.py +++ b/nncf/experimental/tensorflow/__init__.py @@ -9,4 +9,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.experimental.tensorflow.quantization import algorithm as experimental_quantization_algorithm +from nncf.experimental.tensorflow.quantization import algorithm as experimental_quantization_algorithm # noqa: F401 diff --git a/nncf/experimental/tensorflow/graph/converter.py b/nncf/experimental/tensorflow/graph/converter.py index 2a911aa04b9..d288a0b5f40 100644 --- a/nncf/experimental/tensorflow/graph/converter.py +++ b/nncf/experimental/tensorflow/graph/converter.py @@ -294,9 +294,9 @@ def _collect_tfgraph_descs(graph: tf.Graph, op_names: List[str]) -> Tuple[List[N # If `op_name` in `visited_ops` then operation `visited_ops[op_name]` # (i.e. operation with `op_name` name) is reachable from the `input_ops`. - input_ops = [graph.get_operation_by_name(name) for name in op_names] # type: List[tf.Operation] + input_ops: List[tf.Operation] = [graph.get_operation_by_name(name) for name in op_names] queue = deque(input_ops) - visited_ops = {op.name: op for op in input_ops} # type: Dict[str, tf.Operation] + visited_ops: Dict[str, tf.Operation] = {op.name: op for op in input_ops} while len(queue) != 0: v = queue.popleft() # A successor of node `v` is a node `u` such that exists @@ -398,7 +398,7 @@ def _get_op_name_to_const_op_names_map( """ const_ops = [op for op in graph.get_operations() if op.type == "Const"] - const_op_name_to_op_names_map = {} # type: Dict[str, List[str]] + const_op_name_to_op_names_map: Dict[str, List[str]] = {} for const_op in const_ops: # Traverse the `graph` from the `const_op` and find the reachable ops # which are marked as visited (i.e. it's name in `marked_ops` dict) @@ -418,7 +418,7 @@ def _get_op_name_to_const_op_names_map( queue.append(u) visited_ops[u.name] = u - op_name_to_const_op_names_map = {} # type: Dict[str, List[Tuple[str, bool]]] + op_name_to_const_op_names_map: Dict[str, List[Tuple[str, bool]]] = {} for const_op_name, op_names in const_op_name_to_op_names_map.items(): is_shared = len(op_names) > 1 for op_name in op_names: diff --git a/nncf/experimental/tensorflow/nncf_network.py b/nncf/experimental/tensorflow/nncf_network.py index 173d2f18d87..2ced9be22d8 100644 --- a/nncf/experimental/tensorflow/nncf_network.py +++ b/nncf/experimental/tensorflow/nncf_network.py @@ -51,8 +51,8 @@ def __init__(self, model: tf.keras.Model, input_signature: InputSignature, **kwa # This workaround allows not add dependencies from hooks to the model. # See `tensorflow.python.training.tracking.autotrackable.AutoTrackable` # class for more details. - self.__dict__["_pre_hooks"] = {} # type: Dict[str, List[Hook]] - self.__dict__["_post_hooks"] = {} # type: Dict[str, List[Hook]] + self.__dict__["_pre_hooks"]: Dict[str, List[Hook]] = {} + self.__dict__["_post_hooks"]: Dict[str, List[Hook]] = {} @property def nncf_operations(self) -> List[NNCFOperation]: diff --git a/nncf/experimental/tensorflow/quantization/algorithm.py b/nncf/experimental/tensorflow/quantization/algorithm.py index 7ab32da19ee..6d7857ce7b0 100644 --- a/nncf/experimental/tensorflow/quantization/algorithm.py +++ b/nncf/experimental/tensorflow/quantization/algorithm.py @@ -258,8 +258,8 @@ def _get_quantizer_setup(self, model: NNCFNetwork) -> TFQuantizationSetupV2: # Logic of the TFQuantizationSetupV2 creation quantization_setup = TFQuantizationSetupV2() - node_name_to_qconfig_map = {} # type: Dict[str, QuantizerConfig] - qp_id_to_setup_index_map = {} # type: Dict[QuantizationPointId, int] + node_name_to_qconfig_map: Dict[str, QuantizerConfig] = {} + qp_id_to_setup_index_map: Dict[QuantizationPointId, int] = {} first_conv_nodes = get_first_nodes_of_type(nncf_graph, ["Conv2D", "Conv3D"]) for idx, (qp_id, qp) in enumerate(qp_solution.quantization_points.items()): diff --git a/nncf/experimental/torch/nas/bootstrapNAS/__init__.py b/nncf/experimental/torch/nas/bootstrapNAS/__init__.py index a8bf9502d27..917610120ec 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/__init__.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/__init__.py @@ -8,6 +8,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +# noqa from nncf.experimental.torch.nas.bootstrapNAS.elasticity import elasticity_builder as elasticity_algo from nncf.experimental.torch.nas.bootstrapNAS.search.search import SearchAlgorithm from nncf.experimental.torch.nas.bootstrapNAS.training import progressive_shrinking_builder as ps_algo diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py index 739ba4ce9c6..8599d00f845 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_depth.py @@ -66,7 +66,7 @@ def __init__( """ super().__init__() self._target_model = target_model - self._tracing_context = self._target_model.nncf.get_tracing_context() # type: TracingContext + self._tracing_context: TracingContext = self._target_model.nncf.get_tracing_context() self._skipped_blocks = skipped_blocks self._skip_dependencies = skip_dependencies self._node_names_per_block = node_names_per_block @@ -384,8 +384,8 @@ def __init__( """ super().__init__(ignored_scopes, target_scopes) self._params = params - self._skipped_blocks = None # type: Optional[ExtendedBuildingBlocks] - self._skip_dependencies = None # type: Optional[GroupedBlockIDs] + self._skipped_blocks: Optional[ExtendedBuildingBlocks] = None + self._skip_dependencies: Optional[GroupedBlockIDs] = None if self._params.skipped_blocks is not None: self._skipped_blocks = [BuildingBlock(*b) for b in self._params.skipped_blocks] self._skip_dependencies = get_group_of_dependent_blocks(self._skipped_blocks) diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py index 56a3f3dd211..383653b0a9f 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_kernel.py @@ -453,7 +453,7 @@ def __init__( target_scopes: Optional[List[str]] = None, ): super().__init__(ignored_scopes, target_scopes) - self._node_names_to_make_elastic = [] # type: List[NNCFNodeName] + self._node_names_to_make_elastic: List[NNCFNodeName] = [] self._params = params def build(self, target_model: NNCFNetwork) -> ElasticKernelHandler: @@ -464,7 +464,7 @@ def build(self, target_model: NNCFNetwork) -> ElasticKernelHandler: :param target_model: a target NNCFNetwork for adding modifications :return: a handler object that can manipulate the elastic kernel. """ - elastic_kernel_ops = [] # type: List[ElasticKernelOp] + elastic_kernel_ops: List[ElasticKernelOp] = [] transformation_commands = [] graph = target_model.nncf.get_original_graph() @@ -473,7 +473,7 @@ def build(self, target_model: NNCFNetwork) -> ElasticKernelHandler: if not self._node_names_to_make_elastic: elastic_kernel_types = [NNCFConv2d.op_func_name] - all_elastic_kernel_nodes = graph.get_nodes_by_types(elastic_kernel_types) # type: List[NNCFNode] + all_elastic_kernel_nodes: List[NNCFNode] = graph.get_nodes_by_types(elastic_kernel_types) self._node_names_to_make_elastic = [node.node_name for node in all_elastic_kernel_nodes] for node_name in self._node_names_to_make_elastic: diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py index dfb9f49e430..fd918a54cc7 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py @@ -19,8 +19,8 @@ import torch from torch import nn -from nncf.common.graph import BaseLayerAttributes from nncf.common.graph import NNCFNodeName +from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes from nncf.common.graph.layer_attributes import GenericWeightedLayerAttributes from nncf.common.graph.layer_attributes import LinearLayerAttributes @@ -718,8 +718,8 @@ def get_active_in_out_width_values(self) -> Tuple[Dict[NNCFNodeName, int], Dict[ assert all( out_channels[group.elements[0].node_name] == out_channels[node.node_name] for node in group.elements ) - first_elastic_width_info = group.elements[0] # type: ElasticWidthInfo - first_elastic_op = first_elastic_width_info.elastic_op # type: ElasticOutputWidthOp + first_elastic_width_info: ElasticWidthInfo = group.elements[0] + first_elastic_op: ElasticOutputWidthOp = first_elastic_width_info.elastic_op new_out_channels_num = first_elastic_op.get_active_width() num_of_pruned_elems = first_elastic_op.max_width - new_out_channels_num self._shape_pruning_processor.prune_cluster_shapes( @@ -925,9 +925,9 @@ def __init__( super().__init__(params, ignored_scopes, target_scopes) self._weights_normalizer = None self._overwriting_pruning_groups = params.overwrite_groups is not None - self._grouped_node_names_to_prune = ( + self._grouped_node_names_to_prune: List[List[NNCFNodeName]] = ( params.overwrite_groups if params.overwrite_groups is not None else [] - ) # type: List[List[NNCFNodeName]] + ) self._overwrite_groups_widths = params.overwrite_groups_widths self._add_dynamic_inputs = params.add_dynamic_inputs self._params = params diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py index ac0921a0c85..a94b67914c0 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/elasticity_builder.py @@ -64,7 +64,7 @@ def __init__(self, nncf_config: NNCFConfig, should_init: bool = True): all_elasticity_dims = {e.value for e in ElasticityDim} available_elasticity_dims_str = self._algo_config.get("available_elasticity_dims", all_elasticity_dims) self._available_elasticity_dims = list(map(ElasticityDim, available_elasticity_dims_str)) - self._elasticity_builders = OrderedDict() # type: Dict[ElasticityDim, SingleElasticityBuilder] + self._elasticity_builders: Dict[ElasticityDim, SingleElasticityBuilder] = OrderedDict() self._builder_states = None def initialize(self, model: NNCFNetwork) -> None: diff --git a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py index 97e6e11bc36..9901fb9060e 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/elasticity/visualization.py @@ -88,7 +88,7 @@ def _get_original_node_from_compressed_node_name( node_name: NNCFNodeName, width_handler: ElasticWidthHandler ) -> Optional[NNCFNode]: try: - propagation_graph = width_handler.propagation_graph # type: PTNNCFGraph + propagation_graph: PTNNCFGraph = width_handler.propagation_graph result = propagation_graph.get_node_by_name(node_name) except RuntimeError: result = None diff --git a/nncf/experimental/torch/nas/bootstrapNAS/search/search.py b/nncf/experimental/torch/nas/bootstrapNAS/search/search.py index a20faf6eb43..60c539d46bc 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/search/search.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/search/search.py @@ -24,7 +24,6 @@ from pymoo.operators.sampling.rnd import IntegerRandomSampling from pymoo.optimize import minimize from torch.utils.data.dataloader import DataLoader -from torch.utils.tensorboard import SummaryWriter from nncf import NNCFConfig from nncf.common.initialization.batchnorm_adaptation import BatchnormAdaptationAlgorithm @@ -174,7 +173,7 @@ def run( checkpoint_save_dir: str, efficiency_evaluator: Optional[BaseEvaluator] = None, ref_acc: Optional[float] = 100, - tensorboard_writer: Optional[SummaryWriter] = None, + tensorboard_writer: Optional["SummaryWriter"] = None, # noqa: F821 ) -> Tuple[ElasticityController, SubnetConfig, Tuple[float, ...]]: """This method should implement how to run the search algorithm.""" @@ -329,7 +328,7 @@ def run( checkpoint_save_dir: str, efficiency_evaluator: Optional[BaseEvaluator] = None, ref_acc: Optional[float] = 100, - tensorboard_writer: Optional[SummaryWriter] = None, + tensorboard_writer: Optional["SummaryWriter"] = None, # noqa: F821 evaluator_checkpoint=None, ) -> Tuple[ElasticityController, SubnetConfig, Tuple[float, ...]]: """ diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py b/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py index 9191bce742a..49bb88dc912 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/progressive_shrinking_builder.py @@ -22,6 +22,7 @@ from nncf.torch.algo_selector import PT_COMPRESSION_ALGORITHMS from nncf.torch.algo_selector import ZeroCompressionLoss from nncf.torch.compression_method_api import PTCompressionAlgorithmBuilder +from nncf.torch.compression_method_api import PTCompressionLoss from nncf.torch.graph.transformations.layout import PTTransformationLayout from nncf.torch.knowledge_distillation.knowledge_distillation_loss import KnowledgeDistillationLoss from nncf.torch.model_creation import create_compression_algorithm_builder @@ -102,7 +103,7 @@ def _build_controller(self, model: NNCFNetwork) -> "ProgressiveShrinkingControll compression_loss_func, ) - def _build_compression_loss_function(self, model: NNCFNetwork) -> "PTCompressionLoss": + def _build_compression_loss_function(self, model: NNCFNetwork) -> PTCompressionLoss: compression_builder = create_compression_algorithm_builder(self._algo_config) compressed_model = compression_builder.apply_to(model) compression_ctrl = compression_builder.build_controller(compressed_model) diff --git a/nncf/experimental/torch/sparsity/movement/scheduler.py b/nncf/experimental/torch/sparsity/movement/scheduler.py index 5137cef524f..fc06c2617c3 100644 --- a/nncf/experimental/torch/sparsity/movement/scheduler.py +++ b/nncf/experimental/torch/sparsity/movement/scheduler.py @@ -139,7 +139,7 @@ class MovementPolynomialThresholdScheduler(BaseCompressionScheduler): scheduler will start calculation only after `steps_per_epoch` is calculated. """ - def __init__(self, controller: "MovementSparsityController", params: MovementSchedulerParams): + def __init__(self, controller: "MovementSparsityController", params: MovementSchedulerParams): # noqa: F821 """ Initializes a movement sparsity scheduler with a polynomial decay schedule. diff --git a/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py b/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py index c7626dac4b4..f7928708849 100644 --- a/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py +++ b/nncf/experimental/torch/sparsity/movement/structured_mask_handler.py @@ -95,7 +95,7 @@ def __init__( """ self.sparsifier_operand = sparsifier_operand self.module_node_name = module_node_name - operand_mask: torch.Tensor = sparsifier_operand.weight_ctx.binary_mask # type: ignore + operand_mask: torch.Tensor = sparsifier_operand.weight_ctx.binary_mask self.operand_mask_shape = operand_mask.shape self.grid_size = self._resolve_grid_size(grid_size) self.structured_mask_shape = torch.Size( diff --git a/nncf/onnx/graph/metatypes/onnx_metatypes.py b/nncf/onnx/graph/metatypes/onnx_metatypes.py index 35d532caeac..3ff663a897f 100644 --- a/nncf/onnx/graph/metatypes/onnx_metatypes.py +++ b/nncf/onnx/graph/metatypes/onnx_metatypes.py @@ -9,21 +9,24 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Optional, Type +from typing import Dict, List, Optional, Type import onnx from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.operator_metatypes import OperatorMetatypeRegistry from nncf.common.hardware.opset import HWConfigOpName -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_parent +from nncf.onnx.graph.onnx_helper import get_parents_node_mapping +from nncf.onnx.graph.onnx_helper import get_tensor +from nncf.onnx.graph.onnx_helper import has_tensor ONNX_OPERATION_METATYPES = OperatorMetatypeRegistry("onnx_operator_metatypes") class ONNXOpMetatype(OperatorMetatype): - op_names = [] # type: List[str] - subtypes = [] # type: List[Type[OperatorMetatype]] + op_names: List[str] = [] + subtypes: List[Type[OperatorMetatype]] = [] @classmethod def get_all_aliases(cls) -> List[str]: @@ -648,7 +651,12 @@ def get_metatype(model: onnx.ModelProto, node: onnx.NodeProto) -> ONNXOpMetatype return metatype -def get_tensor_edge_name(onnx_graph: ONNXGraph, node: onnx.NodeProto, port_id: int) -> Optional[str]: +def get_tensor_edge_name( + model: onnx.ModelProto, + node: onnx.NodeProto, + port_id: int, + parents_node_mapping: Dict[str, onnx.NodeProto], +) -> Optional[str]: """ Returns an edge name associated with a weight of a node laying on an input port_id. @@ -665,9 +673,10 @@ def get_tensor_edge_name(onnx_graph: ONNXGraph, node: onnx.NodeProto, port_id: i ONNXTransposeMetatype ONNXQuantizeLinearMetatype - :param onnx_graph: ONNXGraph. + :param model: ONNX model. :param node: Node. :param port_id: Port id on which a weight edge is seeking. + :param parents_node_mapping: Mapping from edge name to node which outputs this edge. :return: Edge name associated with a weight. """ PROPAGATING_NODES = ( @@ -678,14 +687,14 @@ def get_tensor_edge_name(onnx_graph: ONNXGraph, node: onnx.NodeProto, port_id: i + ONNXDequantizeLinearMetatype.get_all_aliases() ) END_NODES = ONNXConstantMetatype.get_all_aliases() - parent = onnx_graph.get_parent(node, port_id) + parent = get_parent(node, port_id, parents_node_mapping) if not parent: - if onnx_graph.has_tensor(node.input[port_id]): + if has_tensor(model, node.input[port_id]): return node.input[port_id] elif parent.op_type in END_NODES: return node.input[port_id] elif parent.op_type in PROPAGATING_NODES: - return get_tensor_edge_name(onnx_graph, parent, 0) + return get_tensor_edge_name(model, parent, 0, parents_node_mapping) return None @@ -734,12 +743,12 @@ def _is_embedding(model: onnx.ModelProto, node: onnx.NodeProto) -> bool: :return: True if the layer is embedding, False - otherwise. """ tensor_port_id = ONNXEmbeddingMetatype.weight_port_ids[0] - onnx_graph = ONNXGraph(model) allowed_types_list = ["TensorProto.FLOAT"] - weight_edge_name = get_tensor_edge_name(onnx_graph, node, tensor_port_id) + parents_node_mapping = get_parents_node_mapping(model) + weight_edge_name = get_tensor_edge_name(model, node, tensor_port_id, parents_node_mapping) if weight_edge_name is not None: - tensor_data_type = onnx_graph.get_tensor(weight_edge_name).data_type + tensor_data_type = get_tensor(model, weight_edge_name).data_type if onnx.helper.tensor_dtype_to_string(tensor_data_type) in allowed_types_list: return True return False diff --git a/nncf/onnx/graph/model_transformer.py b/nncf/onnx/graph/model_transformer.py index a8f5355babf..877edb6838b 100644 --- a/nncf/onnx/graph/model_transformer.py +++ b/nncf/onnx/graph/model_transformer.py @@ -19,7 +19,13 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout from nncf.onnx.graph.node_utils import get_input_edge -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_children +from nncf.onnx.graph.onnx_helper import get_children_node_mapping +from nncf.onnx.graph.onnx_helper import get_edge_dtype +from nncf.onnx.graph.onnx_helper import get_edge_info_mapping +from nncf.onnx.graph.onnx_helper import get_name_to_node_map +from nncf.onnx.graph.onnx_helper import get_node_index +from nncf.onnx.graph.onnx_helper import get_tensor from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand @@ -40,7 +46,8 @@ class ONNXModelTransformer(ModelTransformer): ZERO_POINT_NAME_PREFIX = "zero_point_" def __init__(self, model: onnx.ModelProto): - super().__init__(model) + infered_model = onnx.shape_inference.infer_shapes(model) + super().__init__(infered_model) self.onnx_model_extractor = onnx.utils.Extractor(self._model) def _get_target_edge( @@ -48,7 +55,7 @@ def _get_target_edge( port_id: int, node_name: str, transform_type: TargetType, - onnx_graph: ONNXGraph, + node_mapping: Dict[str, onnx.NodeProto], input_edges_mapping: Dict[str, str], ) -> str: """ @@ -57,16 +64,16 @@ def _get_target_edge( :param port_id: Edge number of port. :param node_name: Node name. :param transform_type: Type of transformation. - :param onnx_graph: ONNXGraph. + :param node_mapping: Mapping from a node name to the node. :param input_edges_mapping: Mapping between NNCF Input nodes and - the following ONNX nodes and corresponding input port id. + the following ONNX nodes and corresponding input port id. :return: Target edge name. """ if transform_type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: - return onnx_graph.get_node_edge_names(node_name)["input"][port_id] + return node_mapping[node_name].input[port_id] if node_name in input_edges_mapping: # ADD INPUT NODE CASE - return get_input_edge(node_name, input_edges_mapping, onnx_graph) - return onnx_graph.get_node_edge_names(node_name)["output"][port_id] + return get_input_edge(node_name, input_edges_mapping, node_mapping) + return node_mapping[node_name].output[port_id] def transform(self, transformation_layout: TransformationLayout) -> onnx.ModelProto: """ @@ -123,15 +130,15 @@ def _apply_output_insertion_transformations( :param transformations: ONNXOutputInsertionCommand transformations. :return: New model with inserted outputs. """ - onnx_graph = ONNXGraph(self._model) - model_outputs = set(output.name for output in onnx_graph.get_model_outputs()) + model_outputs = set(output.name for output in self._model.graph.output) + node_mapping = get_name_to_node_map(self._model) for transformation in transformations: port_id = transformation.target_point.port_id node_name = transformation.target_point.target_node_name transform_type = transformation.target_point.type input_edges_mapping = transformation.input_edges_mapping target_edge_name = self._get_target_edge( - port_id, node_name, transform_type, onnx_graph, input_edges_mapping + port_id, node_name, transform_type, node_mapping, input_edges_mapping ) model_outputs.add(target_edge_name) @@ -146,11 +153,11 @@ def _insert_outputs(model: onnx.ModelProto, outputs: Union[List[str], Set[str]]) :param outputs: Edge names to use as outputs. :return: New model with inserted outputs. """ - onnx_graph = ONNXGraph(model) model_outputs = [] + edge_info_mapping = get_edge_info_mapping(model) for output in outputs: - edge = onnx_graph.get_edge(output) - onnx_dtype = ONNXGraph.get_edge_dtype(edge) + edge = edge_info_mapping[output] + onnx_dtype = get_edge_dtype(edge) type_proto = onnx.helper.make_tensor_type_proto(onnx_dtype, shape=None) model_outputs.append(onnx.helper.make_value_info(name=output, type_proto=type_proto)) @@ -192,8 +199,10 @@ def _apply_quantizer_insertion_transformations( :return: New model with inserted QuantizeLinear-DequantizeLinear nodes pairs. """ self._added_target_edges = Counter() + node_mapping = get_name_to_node_map(model) + children_node_mapping = get_children_node_mapping(model) for transformation in transformations: - model = self._insert_quantizer_dequantizer(model, transformation) + model = self._insert_quantizer_dequantizer(model, transformation, node_mapping, children_node_mapping) return model def _get_quantize_dequantize_nodes( @@ -274,35 +283,40 @@ def _get_scale_zero_point_tensors( return onnx_scale_tensor, onnx_zero_point_tensor def _get_quantizer_dequantizer_edge_name( - self, transformation: ONNXQuantizerInsertionCommand, onnx_graph: ONNXGraph + self, transformation: ONNXQuantizerInsertionCommand, node_mapping: Dict[str, onnx.NodeProto] ) -> str: """ Returns an edge name on which QuantizeLinear-DequantizeLinear nodes pair has to be inserted. :param transformation: QuantizeLinear-DequantizeLinear insertion transformation. - :param onnx_graph: ONNXGraph. + :param node_mapping: Mapping from a node name to the node. :return: Edge name to insert QuantizeLinear-DequantizeLinear nodes pair. """ port_id = transformation.target_point.port_id node_name = transformation.target_point.target_node_name transform_type = transformation.target_point.type input_edges_mapping = transformation.input_edges_mapping - target_edge_name = self._get_target_edge(port_id, node_name, transform_type, onnx_graph, input_edges_mapping) + target_edge_name = self._get_target_edge(port_id, node_name, transform_type, node_mapping, input_edges_mapping) self._added_target_edges[target_edge_name] += 1 return target_edge_name def _insert_quantizer_dequantizer( - self, model: onnx.ModelProto, transformation: ONNXQuantizerInsertionCommand + self, + model: onnx.ModelProto, + transformation: ONNXQuantizerInsertionCommand, + node_mapping: Dict[str, onnx.NodeProto], + children_node_mapping: Dict[str, List[onnx.ValueInfoProto]], ) -> onnx.ModelProto: """ Inserts QuantizeLinear-DequantizeLinear nodes pair. :param model: Model to insert new nodes. :param transformation: QuantizeLinear-DequantizeLinear insertion transformation. + :param node_mapping: Mapping from node name to the node. + :param children_node_mapping: Mapping from edge name to nodes which consume this edge as an input. :return: Updated model with inserted QuantizeLinear-DequantizeLinear pair. """ - onnx_graph = ONNXGraph(model) - target_edge_name = self._get_quantizer_dequantizer_edge_name(transformation, onnx_graph) + target_edge_name = self._get_quantizer_dequantizer_edge_name(transformation, node_mapping) quantizer, dequantizer = self._get_quantize_dequantize_nodes(transformation, target_edge_name) onnx_scale_tensor, onnx_zero_point_tensor = ONNXModelTransformer._get_scale_zero_point_tensors( transformation, quantizer, dequantizer @@ -310,7 +324,7 @@ def _insert_quantizer_dequantizer( # If several nodes on one edge input_nodes = [] - input_nodes.extend(onnx_graph.get_nodes_by_input(target_edge_name)) + input_nodes.extend(children_node_mapping[target_edge_name]) if not input_nodes: raise RuntimeError( f"Can not add the quantizer to the {target_edge_name} edge. This edge does not have end node." @@ -318,7 +332,7 @@ def _insert_quantizer_dequantizer( if transformation.target_point.type == TargetType.PRE_LAYER_OPERATION: # If we need to change only target nodes input - target_node = onnx_graph.get_node_by_name(transformation.target_point.target_node_name) + target_node = node_mapping[transformation.target_point.target_node_name] for i, inp in enumerate(target_node.input): if inp == target_edge_name: target_node.input[i] = dequantizer.output[0] @@ -336,7 +350,7 @@ def _insert_quantizer_dequantizer( ) model.graph.initializer.extend([onnx_scale_tensor, onnx_zero_point_tensor]) model.graph.value_info.extend([onnx_scale_value_info, onnx_zero_point_info]) - insert_index = onnx_graph.get_node_index(input_nodes[0].name) + insert_index = get_node_index(model, input_nodes[0].name) model.graph.node.insert(insert_index, quantizer) model.graph.node.insert(insert_index + 1, dequantizer) return model @@ -351,13 +365,13 @@ def _apply_bias_correction_transformations( :param transformations: Bias correction transformations. :return: Copy of original model with updated biases. """ - onnx_graph = ONNXGraph(model) + node_mapping = get_name_to_node_map(model) for transformation in transformations: bias_tensor_position = transformation.target_point.port_id node_name = transformation.target_point.target_node_name - onnx_node = onnx_graph.get_node_by_name(node_name) + onnx_node = node_mapping[node_name] bias_initializer_name = onnx_node.input[bias_tensor_position] - bias_initializer = onnx_graph.get_tensor(bias_initializer_name) + bias_initializer = get_tensor(model, bias_initializer_name) new_bias_tensor = onnx.numpy_helper.from_array(transformation.bias_value, bias_initializer_name) bias_initializer.CopyFrom(new_bias_tensor) @@ -370,20 +384,19 @@ def _apply_model_extraction_transformation(self, transformation: ONNXModelExtrac :param transformation: Model extraction transformation. :return: Extracted sub-model. """ - onnx_graph = ONNXGraph(self._model) - input_tensor_names = [] + node_mapping = get_name_to_node_map(self._model) for input_node_name in transformation.inputs: - input_onnx_node = onnx_graph.get_node_by_name(input_node_name) + input_onnx_node = node_mapping[input_node_name] input_tensor_names.append(input_onnx_node.input[0]) output_tensor_names = [] for output_node_name in transformation.outputs: - output_onnx_node = onnx_graph.get_node_by_name(output_node_name) + output_onnx_node = node_mapping[output_node_name] output_tensor_names.append(output_onnx_node.output[0]) if not output_tensor_names: - output_tensor_names = [n.name for n in onnx_graph.get_model_outputs()] + output_tensor_names = [n.name for n in self._model.graph.output] return self.onnx_model_extractor.extract_model(input_tensor_names, output_tensor_names) @@ -397,11 +410,12 @@ def _apply_qdq_node_removing_transformations( :param transformations: Nodes removing transformations. :return: Model with removed nodes. """ - onnx_graph = ONNXGraph(model) for transformation in transformations: - node = onnx_graph.get_node_by_name(transformation.target_point.target_node_name) + node_mapping = get_name_to_node_map(model) + children_node_mapping = get_children_node_mapping(model) + node = node_mapping[transformation.target_point.target_node_name] - node_children = onnx_graph.get_children(node) + node_children = get_children(node, children_node_mapping) for node_child in node_children: for input_id, input_obj in enumerate(node_child.input): if input_obj == node.output[0]: diff --git a/nncf/onnx/graph/nncf_graph_builder.py b/nncf/onnx/graph/nncf_graph_builder.py index b728b4020a8..148de756c30 100644 --- a/nncf/onnx/graph/nncf_graph_builder.py +++ b/nncf/onnx/graph/nncf_graph_builder.py @@ -29,7 +29,16 @@ from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXOpWithWeightsMetatype from nncf.onnx.graph.metatypes.onnx_metatypes import get_metatype from nncf.onnx.graph.metatypes.onnx_metatypes import get_tensor_edge_name -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_children_node_mapping +from nncf.onnx.graph.onnx_helper import get_edge_dtype +from nncf.onnx.graph.onnx_helper import get_edge_info_mapping +from nncf.onnx.graph.onnx_helper import get_edge_shape +from nncf.onnx.graph.onnx_helper import get_input_port_id_for_node_after_input +from nncf.onnx.graph.onnx_helper import get_model_inputs +from nncf.onnx.graph.onnx_helper import get_output_port_id_for_node_before_output +from nncf.onnx.graph.onnx_helper import get_parents_node_mapping +from nncf.onnx.graph.onnx_helper import get_port_ids_between_nodes +from nncf.onnx.graph.onnx_helper import is_node_has_shared_weight class ONNXLayerAttributes(BaseLayerAttributes): @@ -103,23 +112,28 @@ def get_bias_tensor_port_id(metatype: ONNXOpWithWeightsMetatype) -> Optional[int return None -def _get_weight_port_ids(node: onnx.NodeProto, onnx_graph: ONNXGraph) -> Set[int]: +def _get_weight_port_ids( + node: onnx.NodeProto, + model: onnx.ModelProto, + parents_node_mapping: Dict[str, onnx.NodeProto], +) -> Set[int]: """ Returns all weight input ports. First, add constant weight port ids from metatype. Second, add weight port ids determined dynamically if metatype could have them. :param node: ONNX node. - :param onnx_graph: ONNXGraph. + :param model: ONNX model. + :param parents_node_mapping: Mapping from edge name to node which outputs this edge. :return: Port ids with weights. """ port_ids = set() - metatype = get_metatype(onnx_graph.onnx_model, node) + metatype = get_metatype(model, node) constant_port_ids = get_constant_weight_port_ids(metatype) port_ids.update(constant_port_ids) possible_port_ids = get_possible_weight_port_ids(metatype) for port_id in possible_port_ids: - if get_tensor_edge_name(onnx_graph, node, port_id): + if get_tensor_edge_name(model, node, port_id, parents_node_mapping): port_ids.add(port_id) return port_ids @@ -129,7 +143,7 @@ def _is_node_with_bias(node: onnx.NodeProto, model: onnx.ModelProto) -> bool: Returns True if node has bias tensor, otherwise - False. :param node: ONNX node. - :param onnx_graph: ONNXGraph. + :param model: ONNX model. :return: True if node has bias tensor, otherwise - False. """ metatype = get_metatype(model, node) @@ -139,23 +153,6 @@ def _is_node_with_bias(node: onnx.NodeProto, model: onnx.ModelProto) -> bool: return False -def _get_weight_attr(node: onnx.NodeProto, onnx_graph: ONNXGraph, weight_port_id: int) -> Dict[int, Dict]: - """ - Returns weight attributes. - - :param node: ONNX node. - :param onnx_graph: ONNXGraph. - :param weight_port_ids: Port ids with weights location. - :return: Weight attributes. - """ - weight_attrs = {} - weight_edge_name = node.input[weight_port_id] - edge = onnx_graph.get_edge(weight_edge_name) - weight_shape = ONNXGraph.get_edge_shape(edge) - weight_attrs[weight_port_id] = {"name": weight_edge_name, "shape": weight_shape} - return weight_attrs - - def _get_gemm_attrs(node: onnx.NodeProto) -> Dict[str, int]: """ Returns transpose attrbiutes of GEMM node. @@ -176,7 +173,7 @@ def _get_node_attrs(node: onnx.NodeProto, model: onnx.ModelProto) -> Dict[str, A Returns node attributes. :param node: Node. - :param onnx_graph: ONNXGraph. + :param model: ONNX model. :return : Node attributes. """ metatype = get_metatype(model, node) @@ -185,19 +182,24 @@ def _get_node_attrs(node: onnx.NodeProto, model: onnx.ModelProto) -> Dict[str, A return {} -def _get_bias_attr(node: onnx.NodeProto, onnx_graph: ONNXGraph) -> Dict[str, str]: +def _get_bias_attr( + node: onnx.NodeProto, + model: onnx.ModelProto, + parents_node_mapping: Dict[str, onnx.NodeProto], +) -> Dict[str, str]: """ Returns bias tensor attributes. :param node: ONNX node. - :param onnx_graph: ONNXGraph. + :param model: ONNX model. + :param parents_node_mapping: Mapping from edge name to node which outputs this edge. :return: Bias tensor attributes. """ bias_attrs = {} - metatype = get_metatype(onnx_graph.onnx_model, node) - if _is_node_with_bias(node, onnx_graph.onnx_model): + metatype = get_metatype(model, node) + if _is_node_with_bias(node, model): bias_tensor_port_id = get_bias_tensor_port_id(metatype) - bias_edge_name = get_tensor_edge_name(onnx_graph, node, bias_tensor_port_id) + bias_edge_name = get_tensor_edge_name(model, node, bias_tensor_port_id, parents_node_mapping) bias_attrs["name"] = bias_edge_name return bias_attrs @@ -232,15 +234,22 @@ def _replace_empty_node_name(model: onnx.ModelProto) -> onnx.ModelProto: return model @staticmethod - def _add_nncf_input_nodes(onnx_graph: ONNXGraph, nncf_graph: NNCFGraph) -> None: + def _add_nncf_input_nodes( + model: onnx.ModelProto, + nncf_graph: NNCFGraph, + edge_info_mapping: Dict[str, onnx.ValueInfoProto], + children_node_mapping: Dict[str, List[onnx.NodeProto]], + ) -> None: """ Adds special NNCF Input nodes to NNCFGraph. For all the ONNX model inputs, the special NNCF Input node is placed and then corresponding edges are added. - :param onnx_graph: ONNXGraph, which helps to get information about the ONNX model. + :param model: ONNX model. :param nncf_graph: NNCFGraph, in which the new nodes will be added. + :param edge_info_mapping: Mapping from edge name to the edge info. + :param children_node_mapping: Mapping from edge name to nodes which consume this edge as an input. :return: None. """ - for i, _input in enumerate(onnx_graph.get_model_inputs()): + for i, _input in enumerate(get_model_inputs(model)): input_name = _input.name layer_attributes = ONNXLayerAttributes() input_node = nncf_graph.add_nncf_node( @@ -249,18 +258,18 @@ def _add_nncf_input_nodes(onnx_graph: ONNXGraph, nncf_graph: NNCFGraph) -> None: node_metatype=InputNoopMetatype, layer_attributes=layer_attributes, ) - to_nodes = onnx_graph.get_nodes_by_input(input_name) + to_nodes = children_node_mapping[input_name] input_node_node_id = input_node.node_id - edge = onnx_graph.get_edge(input_name) - input_shape = ONNXGraph.get_edge_shape(edge) - onnx_dtype = ONNXGraph.get_edge_dtype(edge) + edge = edge_info_mapping[input_name] + input_shape = get_edge_shape(edge) + onnx_dtype = get_edge_dtype(edge) nncf_dtype = GraphConverter.convert_onnx_dtype_to_nncf_dtype(onnx_dtype) output_port_id = 0 for node in to_nodes: to_node_id = nncf_graph.get_node_by_name(node.name).node_id - input_port_id = ONNXGraph.get_input_port_id_for_node_after_input(input_name, node) + input_port_id = get_input_port_id_for_node_after_input(input_name, node) nncf_graph.add_edge_between_nncf_nodes( from_node_id=input_node_node_id, to_node_id=to_node_id, @@ -272,15 +281,22 @@ def _add_nncf_input_nodes(onnx_graph: ONNXGraph, nncf_graph: NNCFGraph) -> None: output_port_id += 1 @staticmethod - def _add_nncf_output_nodes(onnx_graph: ONNXGraph, nncf_graph: NNCFGraph) -> None: + def _add_nncf_output_nodes( + model: onnx.ModelProto, + nncf_graph: NNCFGraph, + edge_info_mapping: Dict[str, onnx.ValueInfoProto], + parents_node_mapping: Dict[str, onnx.NodeProto], + ) -> None: """ Adds special NNCF Output nodes to NNCFGraph. For all the ONNX model outputs, the special NNCF Output node is placed and then corresponding edges are added. - :param onnx_graph: ONNXGraph, which helps to get information about the ONNX model. + :param model: ONNX model. :param nncf_graph: NNCFGraph, in which the new nodes will be added. + :param edge_info_mapping: Mapping from edge name to the edge info. + :param parents_node_mapping: Mapping from edge name to node which outputs this edge. :return: None. """ - for i, _output in enumerate(onnx_graph.get_model_outputs()): + for i, _output in enumerate(model.graph.output): output_name = _output.name layer_attributes = ONNXLayerAttributes() output_node = nncf_graph.add_nncf_node( @@ -289,16 +305,16 @@ def _add_nncf_output_nodes(onnx_graph: ONNXGraph, nncf_graph: NNCFGraph) -> None node_metatype=OutputNoopMetatype, layer_attributes=layer_attributes, ) - from_node = onnx_graph.get_node_by_output(output_name) + from_node = parents_node_mapping[output_name] output_node_node_id = output_node.node_id - edge = onnx_graph.get_edge(output_name) - output_shape = ONNXGraph.get_edge_shape(edge) - onnx_dtype = ONNXGraph.get_edge_dtype(edge) + edge = edge_info_mapping[output_name] + output_shape = get_edge_shape(edge) + onnx_dtype = get_edge_dtype(edge) nncf_dtype = GraphConverter.convert_onnx_dtype_to_nncf_dtype(onnx_dtype) input_port_id = 0 from_node_id = nncf_graph.get_node_by_name(from_node.name).node_id - output_port_id = ONNXGraph.get_output_port_id_for_node_before_output(output_name, from_node) + output_port_id = get_output_port_id_for_node_before_output(output_name, from_node) nncf_graph.add_edge_between_nncf_nodes( from_node_id=from_node_id, to_node_id=output_node_node_id, @@ -330,21 +346,27 @@ def create_nncf_graph(onnx_model: onnx.ModelProto) -> NNCFGraph: :return: NNCFGraph. """ onnx_model = GraphConverter._replace_empty_node_name(onnx_model) + onnx_model = onnx.shape_inference.infer_shapes(onnx_model) + edge_info_mapping = get_edge_info_mapping(onnx_model) + children_node_mapping = get_children_node_mapping(onnx_model) + parents_node_mapping = get_parents_node_mapping(onnx_model) nncf_graph = NNCFGraph() - onnx_graph = ONNXGraph(onnx_model) - for node in onnx_graph.get_all_nodes(): + for node in onnx_model.graph.node: metatype = get_metatype(onnx_model, node) - weight_port_ids = _get_weight_port_ids(node, onnx_graph) + weight_port_ids = _get_weight_port_ids(node, onnx_model, parents_node_mapping) is_shared = None weight_attrs = {} node_attrs = _get_node_attrs(node, onnx_model) - bias_attrs = _get_bias_attr(node, onnx_graph) + bias_attrs = _get_bias_attr(node, onnx_model, parents_node_mapping) if weight_port_ids: # If node has weight weight_edge_names = [] for weight_port_id in weight_port_ids: - weight_edge_names.append(node.input[weight_port_id]) - weight_attrs.update(_get_weight_attr(node, onnx_graph, weight_port_id)) - if not is_shared and onnx_graph.is_node_has_shared_weight(node, weight_port_id): + weight_edge_name = node.input[weight_port_id] + weight_edge_names.append(weight_edge_name) + edge = edge_info_mapping[weight_edge_name] + weight_shape = get_edge_shape(edge) + weight_attrs[weight_port_id] = {"name": weight_edge_name, "shape": weight_shape} + if not is_shared and is_node_has_shared_weight(node, weight_port_id, children_node_mapping): is_shared = True layer_attributes = ONNXLayerAttributes( @@ -357,22 +379,23 @@ def create_nncf_graph(onnx_model: onnx.ModelProto) -> NNCFGraph: layer_attributes=layer_attributes, is_shared=is_shared, ) - for output_node in onnx_graph.get_all_nodes(): - output_edges = onnx_graph.get_node_edge_names(output_node.name)["output"] + + for output_node in onnx_model.graph.node: + output_edges = output_node.output for output_edge in output_edges: - edge = onnx_graph.get_edge(output_edge) + edge = edge_info_mapping.get(output_edge) if edge is None: # If the edge is None it means that the edge was not added during shape inference of ONNX model. # BatchNorm exported in Training mode has unused outputs edges: mean, var, saved_mean, saved_var. # NNCFGraph should not contain such edges. continue - tensor_shape = ONNXGraph.get_edge_shape(edge) - onnx_dtype = ONNXGraph.get_edge_dtype(edge) + tensor_shape = get_edge_shape(edge) + onnx_dtype = get_edge_dtype(edge) nncf_dtype = GraphConverter.convert_onnx_dtype_to_nncf_dtype(onnx_dtype) output_node_id = nncf_graph.get_node_by_name(output_node.name).node_id - input_nodes = onnx_graph.get_nodes_by_input(output_edge) + input_nodes = children_node_mapping[output_edge] for input_node in input_nodes: - port_ids = ONNXGraph.get_port_ids_between_nodes(output_node, input_node) + port_ids = get_port_ids_between_nodes(output_node, input_node) input_port_id = port_ids["input_port_id"] output_port_id = port_ids["output_port_id"] in_node_id = nncf_graph.get_node_by_name(input_node.name).node_id @@ -384,6 +407,7 @@ def create_nncf_graph(onnx_model: onnx.ModelProto) -> NNCFGraph: output_port_id=output_port_id, dtype=Dtype(nncf_dtype), ) - GraphConverter._add_nncf_input_nodes(onnx_graph, nncf_graph) - GraphConverter._add_nncf_output_nodes(onnx_graph, nncf_graph) + + GraphConverter._add_nncf_input_nodes(onnx_model, nncf_graph, edge_info_mapping, children_node_mapping) + GraphConverter._add_nncf_output_nodes(onnx_model, nncf_graph, edge_info_mapping, parents_node_mapping) return nncf_graph diff --git a/nncf/onnx/graph/node_utils.py b/nncf/onnx/graph/node_utils.py index 6575dff6f1c..1e9a162211d 100644 --- a/nncf/onnx/graph/node_utils.py +++ b/nncf/onnx/graph/node_utils.py @@ -21,7 +21,7 @@ from nncf.common.tensor_statistics.collectors import ReductionAxes from nncf.onnx.graph.metatypes import onnx_metatypes as om from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXDequantizeLinearMetatype -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_tensor_value from nncf.onnx.graph.transformations.commands import ONNXTargetPoint @@ -45,10 +45,9 @@ def get_bias_value(node_with_bias: NNCFNode, model: onnx.ModelProto) -> np.ndarr :param model: The model that contains this operation. :return: The bias value that is applied to the output tensor of the node's operation. """ - onnx_graph = ONNXGraph(model) assert node_with_bias.layer_attributes.has_bias() bias_name = node_with_bias.layer_attributes.bias_attrs["name"] - return onnx_graph.get_tensor_value(bias_name) + return get_tensor_value(model, bias_name) def get_input_edges_mapping(nncf_graph: NNCFGraph) -> Dict[str, Tuple[str, int]]: @@ -68,20 +67,25 @@ def get_input_edges_mapping(nncf_graph: NNCFGraph) -> Dict[str, Tuple[str, int]] return input_edges_mapping -def get_input_edge(input_node_name: str, input_edges_mapping: Dict[str, Tuple[str, int]], onnx_graph: ONNXGraph) -> str: +def get_input_edge( + input_node_name: str, + input_edges_mapping: Dict[str, Tuple[str, int]], + node_mapping: Dict[str, onnx.NodeProto], +) -> str: """ Returns input edge corresponding to the NNCF input node with the name input_node_name. :param input_node_name: Name of NNCF input node. :param input_edges_mapping: A mapping of NNCF input node names and - a tuple with the consumed node names and their input port ids. - :param onnx_graph: Instance of ONNXGraph of the model. + a tuple with the consumed node names and their input port ids. + :param node_mapping: Mapping of node names to the nodes. :return: Input edge name. """ input_edges = set() for node_info in input_edges_mapping[input_node_name]: name, port_id = node_info - input_edges.add(onnx_graph.get_node_edge_names(name)["input"][port_id]) + node = node_mapping[name] + input_edges.add(node.input[port_id]) assert len(input_edges) == 1 return input_edges.pop() diff --git a/nncf/onnx/graph/onnx_graph.py b/nncf/onnx/graph/onnx_graph.py deleted file mode 100644 index df754263c99..00000000000 --- a/nncf/onnx/graph/onnx_graph.py +++ /dev/null @@ -1,321 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Dict, Iterator, List, Optional, Union - -import numpy as np -import onnx -from onnx import numpy_helper - - -class ONNXGraph: - """ - The class provides the interface to get the necessary information from ONNX model. - """ - - def __init__(self, onnx_model: onnx.ModelProto): - self.onnx_model = onnx_model - self._node_name_to_node = None # type: Dict[str, onnx.NodeProto] - self._edge_name_to_value_info = None # type: Dict[str, onnx.ValueInfoProto] - - def _update_edges(self) -> None: - self.onnx_model = onnx.shape_inference.infer_shapes(self.onnx_model) - value_infos = [ - *self.onnx_model.graph.value_info, - *self.onnx_model.graph.input, - *self.onnx_model.graph.output, - *self.onnx_model.graph.initializer, - ] - self._edge_name_to_value_info = {tensor.name: tensor for tensor in value_infos} - - def _update_node_names(self) -> None: - self._node_name_to_node = {n.name: n for n in self.onnx_model.graph.node} - - def _get_all_tensors(self) -> Iterator[onnx.TensorProto]: - """ - Iterate over all tensors of ONNX model. - - :yield: tensors of ONNX model. - """ - for initializer in self.onnx_model.graph.initializer: - yield initializer - for node in self.onnx_model.graph.node: - for attribute in node.attribute: - if attribute.HasField("t"): - yield attribute.t - yield from attribute.tensors - - def get_all_nodes(self) -> List[onnx.NodeProto]: - """ - Returns model nodes in the original order. - - :return: model nodes. - """ - return self.onnx_model.graph.node - - def get_node_by_name(self, node_name: str) -> Optional[onnx.NodeProto]: - """ - Returns a model node with the name equals to 'node_name' from self._node_name_to_node. - If the self._node_name_to_node is None, fills it with the nodes from the self.onnx_model. - If there is no node with such name returns None. - - :param node_name: Name of the node. - :return: None if the node with the specified name exists - otherwise returns the node. - """ - if self._node_name_to_node is None: - self._update_node_names() - return self._node_name_to_node[node_name] if node_name in self._node_name_to_node else None - - def get_edge(self, edge_name: str) -> Optional[onnx.ValueInfoProto]: - """ - Returns edge by its name or None if the model has no such edge. - If self._edge_name_to_value_info is not initialized runs an initialization. - - :param edge_name: Name of edge. - :return: Edge. - """ - if self._edge_name_to_value_info is None: - self._update_edges() - return self._edge_name_to_value_info.get(edge_name, None) - - def get_model_inputs(self) -> List[onnx.ValueInfoProto]: - """ - Returns all model inputs. - - :return: Model Inputs. - """ - inputs = [] - input_all = [node.name for node in self.onnx_model.graph.input] - input_initializer = [node.name for node in self.onnx_model.graph.initializer] - net_feed_input = list(set(input_all) - set(input_initializer)) - for node in self.onnx_model.graph.input: - if node.name in net_feed_input: - inputs.append(node) - return inputs - - def get_model_outputs(self) -> List[onnx.ValueInfoProto]: - """ - Returns all model outputs. - - :return: Model Outputs. - """ - return list(self.onnx_model.graph.output) - - def get_node_by_output(self, output_name: str) -> Optional[onnx.NodeProto]: - """ - Returns node that have output edge with the name 'output_name'. - - :param output_name: The name of output edge. - :return: Node with corresponding output. - """ - for node in self.get_all_nodes(): - if output_name in node.output: - return node - return None - - def get_nodes_by_input(self, input_name: str) -> List[onnx.NodeProto]: - """ - Returns all nodes that have input with the name 'input_name'. - - :param input_name: The name of input edge. - :return: Nodes with corresponding input. - """ - output = [] - for node in self.get_all_nodes(): - if input_name in node.input: - output.append(node) - return output - - def get_node_edge_names(self, node_name: str) -> Dict[str, List[str]]: - """ - Returns node edge names. - - :param node_name: The name of the node. - :return: Dict with two keys: 'input' and 'output', - which are corresponding to input and output edges accordingly. - """ - if self._node_name_to_node is None: - self._update_node_names() - if node_name in self._node_name_to_node: - return { - "input": list(self._node_name_to_node[node_name].input), - "output": list(self._node_name_to_node[node_name].output), - } - raise RuntimeError("There is no node with the name {}".format(node_name)) - - @staticmethod - def get_input_port_id_for_node_after_input(input_name: str, to_node: onnx.NodeProto) -> int: - """ - Returns input_port_id for 'to_node' connected with the model input with the name 'input_name'. - - :param input_name: Name of the ONNX model Input. - :param to_node: Node, which has input edge with 'input_name' name. - :return: input port number for 'to_node', which is connected to 'input_name'. - """ - for input_port_id, port in enumerate(to_node.input): - if port == input_name: - return input_port_id - raise RuntimeError(f"The node {to_node} does not have input edge with the name {input_name}") - - @staticmethod - def get_output_port_id_for_node_before_output(output_name: str, from_node: onnx.NodeProto) -> int: - """ - Returns output_port_id for 'from_node' connected with the model output with the name 'output_name'. - - :param output_name: Name of the ONNX model Output. - :param from_node: Node, which has output edge with 'output_name' name. - :return: output port number for 'from_node', which is connected to 'output_name'. - """ - for output_port_id, port in enumerate(from_node.output): - if port == output_name: - return output_port_id - raise RuntimeError(f"The node {from_node} does not have output edge with the name {output_name}") - - @staticmethod - def get_port_ids_between_nodes(from_node: onnx.NodeProto, to_node: onnx.NodeProto) -> Dict[str, int]: - """ - Returns input_port_id and output_port_id between 'from_node' and 'to_node'. - - :param from_node: Node, whose output is connected to 'to_node' node. - :param to_node: Node, whose input is connected to 'from_node' node. - :return: Dict{'input_port_id': input port id, 'output_port_id': output port id} - """ - output = {"input_port_id": None, "output_port_id": None} - for port_id, port in enumerate(to_node.input): - if port in from_node.output: - output["input_port_id"] = port_id - for port_id, port in enumerate(from_node.output): - if port in to_node.input: - output["output_port_id"] = port_id - if output["output_port_id"] is None or output["input_port_id"] is None: - raise RuntimeError(f"The nodes {from_node.name} and {to_node.name} do not have edges between.") - return output - - def get_node_index(self, node_name: str) -> int: - """ - Returns the node index in the model. - - :param node_name: Name of the node. - :return: Node index, -1 if there is no such node. - """ - for i, node in enumerate(self.get_all_nodes()): - if node.name == node_name: - return i - return -1 - - def has_tensor(self, tensor_name: str) -> bool: - """ - Returns True whether the model has the tensor with the name equals to tensor_name. - - :param tensor_name: Name of the tensor. - :return: True if the model has such tensor, False - otherwise. - """ - for tensor in self._get_all_tensors(): - if tensor.name == tensor_name: - return True - return False - - def get_tensor_value(self, tensor_name: str) -> np.ndarray: - """ - Returns tensor value of a tensor with the name 'tensor_name'. - - :param tensor_name: Name of the tensor. - :return: The value of the tensor. - """ - tensor = self.get_tensor(tensor_name) - return numpy_helper.to_array(tensor) - - def get_tensor(self, tensor_name: str) -> onnx.TensorProto: - """ - Returns a tensor with the name 'tensor_name'. - - :param initializer_name: Name of the Initializer. - :return: The Initializer. - """ - for tensor in self._get_all_tensors(): - if tensor.name == tensor_name: - return tensor - raise RuntimeError("There is no tensor with the name {}".format(tensor_name)) - - @staticmethod - def get_edge_shape(edge: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> List[int]: - """ - Returns edge shape. - - :param edge: The edge. - :return: Shape of the Tensor. - """ - if isinstance(edge, onnx.TensorProto): - return list(edge.dims) - tensor_type = edge.type.tensor_type - shape = [] - if tensor_type.HasField("shape"): - for d in tensor_type.shape.dim: - if d.HasField("dim_value"): - dim_value = d.dim_value - if isinstance(dim_value, int): - shape.append(dim_value) - else: - return shape - elif d.HasField("dim_param"): - # flexible shape make manually -1 - shape.append(-1) - else: - return shape - return shape - - @staticmethod - def get_edge_dtype(edge: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> int: - """ - Returns the data type of the edge. - - :param edge: The edge. - :return: Data type of the edge. - """ - if isinstance(edge, onnx.ValueInfoProto): - return edge.type.tensor_type.elem_type - return edge.data_type - - def get_parent(self, node: onnx.NodeProto, port_id: int) -> Optional[onnx.NodeProto]: - """ - Returns parents of the node. If there is no parent node, returns None. - - :param node: The child node. - :param port_id: Input port id on which the parent is seeked. - :return: Parent node. - """ - if port_id < len(node.input): - return self.get_node_by_output(node.input[port_id]) - return None - - def get_children(self, node: onnx.NodeProto) -> List[onnx.NodeProto]: - """ - Returns children of the node. - - :param node: The parent node. - :return: All children nodes. - """ - output = [] - node_edges = self.get_node_edge_names(node.name)["output"] - for node_edge in node_edges: - output.extend(self.get_nodes_by_input(node_edge)) - return output - - def is_node_has_shared_weight(self, node: onnx.NodeProto, weight_port_id: int) -> bool: - """ - Returns whether the node share a weight. - - :param node: Node. - :return: True whether node shares a weight - otherwise False. - """ - weight_tensor_edge = self.get_node_edge_names(node.name)["input"][weight_port_id] - nodes = self.get_nodes_by_input(weight_tensor_edge) - return len(nodes) > 1 diff --git a/nncf/onnx/graph/onnx_helper.py b/nncf/onnx/graph/onnx_helper.py new file mode 100644 index 00000000000..f6b082050a0 --- /dev/null +++ b/nncf/onnx/graph/onnx_helper.py @@ -0,0 +1,290 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import defaultdict +from typing import Dict, Iterator, List, Optional, Union + +import numpy as np +import onnx +from onnx import numpy_helper + + +def get_name_to_node_map(model: onnx.ModelProto) -> Dict[str, onnx.NodeProto]: + """ + Returns mapping from node name to the node. + + :param model: Model from mapping is built. + :return: Mapping. + """ + return {node.name: node for node in model.graph.node} + + +def get_edge_info_mapping(model: onnx.ModelProto) -> Dict[str, onnx.ValueInfoProto]: + """ + Retuns mapping from edge name to the edge info. + + :param model: Model from mapping is built. + :return: Mapping. + """ + return { + tensor.name: tensor + for tensor in (*model.graph.value_info, *model.graph.input, *model.graph.output, *model.graph.initializer) + } + + +def get_children_node_mapping(model: onnx.ModelProto) -> Dict[str, List[onnx.NodeProto]]: + """ + Returns a mapping from edge name to nodes which consume this edge as an input. + + :param model: ONNX model. + :return: Mapping from edge name to nodes which consume this edge as an input. + """ + output = defaultdict(list) + for node in model.graph.node: + for edge in node.input: + output[edge].append(node) + return output + + +def get_parents_node_mapping(model: onnx.ModelProto) -> Dict[str, onnx.NodeProto]: + """ + Returns a mapping from edge name to node which outputs this edge. + + :param model: ONNX model. + :return: Mapping from edge name to node which outputs this edge. + """ + output = {} + for node in model.graph.node: + for edge in node.output: + output[edge] = node + return output + + +def get_model_inputs(model: onnx.ModelProto) -> List[onnx.ValueInfoProto]: + """ + Returns all model inputs. + + :param model: ONNX model. + :return: Model Inputs. + """ + inputs = [] + input_all = [node.name for node in model.graph.input] + input_initializer = [node.name for node in model.graph.initializer] + net_feed_input = list(set(input_all) - set(input_initializer)) + for node in model.graph.input: + if node.name in net_feed_input: + inputs.append(node) + return inputs + + +def get_input_port_id_for_node_after_input(input_name: str, to_node: onnx.NodeProto) -> int: + """ + Returns input_port_id for 'to_node' connected with the model input with the name 'input_name'. + + :param input_name: Name of the ONNX model Input. + :param to_node: Node, which has input edge with 'input_name' name. + :return: input port number for 'to_node', which is connected to 'input_name'. + """ + for input_port_id, port in enumerate(to_node.input): + if port == input_name: + return input_port_id + raise RuntimeError(f"The node {to_node} does not have input edge with the name {input_name}") + + +def get_output_port_id_for_node_before_output(output_name: str, from_node: onnx.NodeProto) -> int: + """ + Returns output_port_id for 'from_node' connected with the model output with the name 'output_name'. + + :param output_name: Name of the ONNX model Output. + :param from_node: Node, which has output edge with 'output_name' name. + :return: output port number for 'from_node', which is connected to 'output_name'. + """ + for output_port_id, port in enumerate(from_node.output): + if port == output_name: + return output_port_id + raise RuntimeError(f"The node {from_node} does not have output edge with the name {output_name}") + + +def get_port_ids_between_nodes(from_node: onnx.NodeProto, to_node: onnx.NodeProto) -> Dict[str, int]: + """ + Returns input_port_id and output_port_id between 'from_node' and 'to_node'. + + :param from_node: Node, whose output is connected to 'to_node' node. + :param to_node: Node, whose input is connected to 'from_node' node. + :return: Dict{'input_port_id': input port id, 'output_port_id': output port id} + """ + output = {"input_port_id": None, "output_port_id": None} + for port_id, port in enumerate(to_node.input): + if port in from_node.output: + output["input_port_id"] = port_id + for port_id, port in enumerate(from_node.output): + if port in to_node.input: + output["output_port_id"] = port_id + if output["output_port_id"] is None or output["input_port_id"] is None: + raise RuntimeError(f"The nodes {from_node.name} and {to_node.name} do not have edges between.") + return output + + +def get_node_index(model: onnx.ModelProto, node_name: str) -> Optional[int]: + """ + Returns the node index in the model. + + :param model: ONNX model. + :param node_name: Name of the node. + :return: Node index, -1 if there is no such node. + """ + for i, node in enumerate(model.graph.node): + if node.name == node_name: + return i + return None + + +def _get_all_tensors(model: onnx.ModelProto) -> Iterator[onnx.TensorProto]: + """ + Iterate over all tensors of ONNX model. + + :param model: ONNX model. + :yield: tensors of ONNX model. + """ + for initializer in model.graph.initializer: + yield initializer + for node in model.graph.node: + for attribute in node.attribute: + if attribute.HasField("t"): + yield attribute.t + yield from attribute.tensors + + +def has_tensor(model: onnx.ModelProto, tensor_name: str) -> bool: + """ + Returns True whether the model has the tensor with the name equals to tensor_name. + + :param model: ONNX model. + :param tensor_name: Name of the tensor. + :return: True if the model has such tensor, False - otherwise. + """ + for tensor in _get_all_tensors(model): + if tensor.name == tensor_name: + return True + return False + + +def get_tensor(model: onnx.ModelProto, tensor_name: str) -> onnx.TensorProto: + """ + Returns a tensor with the name 'tensor_name'. + + :param model: ONNX model. + :param tensor_name: Name of the tensor. + :return: The Initializer. + """ + for tensor in _get_all_tensors(model): + if tensor.name == tensor_name: + return tensor + raise RuntimeError("There is no tensor with the name {}".format(tensor_name)) + + +def get_tensor_value(model: onnx.ModelProto, tensor_name: str) -> np.ndarray: + """ + Returns tensor value of a tensor with the name 'tensor_name'. + + :param model: ONNX model. + :param tensor_name: Name of the tensor. + :return: The value of the tensor. + """ + return numpy_helper.to_array(get_tensor(model, tensor_name)) + + +def get_edge_shape(edge: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> List[int]: + """ + Returns edge shape. + + :param edge: The edge. + :return: Shape of the Tensor. + """ + if isinstance(edge, onnx.TensorProto): + return list(edge.dims) + tensor_type = edge.type.tensor_type + shape = [] + if tensor_type.HasField("shape"): + for d in tensor_type.shape.dim: + if d.HasField("dim_value"): + dim_value = d.dim_value + if isinstance(dim_value, int): + shape.append(dim_value) + else: + return shape + elif d.HasField("dim_param"): + # flexible shape make manually -1 + shape.append(-1) + else: + return shape + return shape + + +def get_edge_dtype(edge: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> int: + """ + Returns the data type of the edge. + + :param edge: The edge. + :return: Data type of the edge. + """ + if isinstance(edge, onnx.ValueInfoProto): + return edge.type.tensor_type.elem_type + return edge.data_type + + +def get_parent( + node: onnx.NodeProto, + port_id: int, + parents_node_mapping: Dict[str, onnx.NodeProto], +) -> Optional[onnx.NodeProto]: + """ + Returns parents of the node. If there is no parent node, returns None. + + :param node: The child node. + :param port_id: Input port id on which the parent is seeked. + :param edge_node_mapping: Mapping describing start and consumed nodes of the edges. + :return: Parent node. + """ + if port_id < len(node.input): + return parents_node_mapping.get(node.input[port_id]) + return None + + +def get_children(node: onnx.NodeProto, children_node_mapping: Dict[str, List[onnx.NodeProto]]) -> List[onnx.NodeProto]: + """ + Returns children of the node. + + :param node: The parent node. + :param edge_node_mapping: Mapping describing start and consumed nodes of the edges. + :return: All children nodes. + """ + output = [] + for node_edge in node.output: + output.extend(children_node_mapping[node_edge]) + return output + + +def is_node_has_shared_weight( + node: onnx.NodeProto, + weight_port_id: int, + children_node_mapping: Dict[str, List[onnx.NodeProto]], +) -> bool: + """ + Returns whether the node share a weight. + + :param node: Node. + :param weight_port_id: Port id on which there is a weight. + :param edge_node_mapping: Mapping describing start and consumed nodes of the edges. + :return: True whether node shares a weight - otherwise False. + """ + weight_tensor_edge = node.input[weight_port_id] + nodes = children_node_mapping[weight_tensor_edge] + return len(nodes) > 1 diff --git a/nncf/onnx/hardware/config.py b/nncf/onnx/hardware/config.py index 548043f707a..85840435f2e 100644 --- a/nncf/onnx/hardware/config.py +++ b/nncf/onnx/hardware/config.py @@ -11,7 +11,7 @@ from typing import List, Type -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.hardware.config import HWConfig from nncf.onnx.graph.metatypes.onnx_metatypes import get_operator_metatypes diff --git a/nncf/onnx/quantization/quantizer_parameters.py b/nncf/onnx/quantization/quantizer_parameters.py index 71b3d976b50..e4470fdce1b 100644 --- a/nncf/onnx/quantization/quantizer_parameters.py +++ b/nncf/onnx/quantization/quantizer_parameters.py @@ -54,8 +54,8 @@ def convert_fq_params_to_onnx_params( if levels not in [255, 256]: raise ValueError("Can only export to INT8/UIN8 256-level ONNX Quantize/Dequantize pairs.") - input_low, input_high = parameters.input_low, parameters.input_high - output_low, output_high = parameters.output_low, parameters.output_high + input_low, input_high = parameters.input_low.data, parameters.input_high.data + output_low, output_high = parameters.output_low.data, parameters.output_high.data if not np.allclose(input_high, output_high) or not np.allclose(input_low, output_low): raise ValueError( "ONNX Quantize/Dequantize pairs only support input_high == output_high and input_low == output_low." diff --git a/nncf/onnx/statistics/aggregator.py b/nncf/onnx/statistics/aggregator.py index e3435382b5d..a768a855258 100644 --- a/nncf/onnx/statistics/aggregator.py +++ b/nncf/onnx/statistics/aggregator.py @@ -22,7 +22,7 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.onnx.graph.node_utils import get_input_edge from nncf.onnx.graph.node_utils import get_input_edges_mapping -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_name_to_node_map from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand from nncf.onnx.tensor import ONNXNNCFTensor @@ -30,28 +30,30 @@ class ONNXStatisticsAggregator(StatisticsAggregator): def collect_statistics(self, model: onnx.ModelProto, graph: NNCFGraph) -> None: self.input_edges_mapping = get_input_edges_mapping(graph) - self._onnx_graph = ONNXGraph(model) + self.node_mapping = get_name_to_node_map(model) self._registered_weights = set() super().collect_statistics(model, graph) def _register_statistics( self, outputs: Dict[str, ONNXNNCFTensor], statistic_points: StatisticPointsContainer ) -> None: - for node_name, _statistic_points in statistic_points.items(): + for _statistic_points in statistic_points.values(): for statistic_point in _statistic_points: target_point = statistic_point.target_point port_id = target_point.port_id if target_point.target_node_name in self.input_edges_mapping: # Input case edge_name = get_input_edge( - target_point.target_node_name, self.input_edges_mapping, self._onnx_graph + target_point.target_node_name, + self.input_edges_mapping, + self.node_mapping, ) - statistic_point.register_tensor(outputs[edge_name]) elif target_point.type == TargetType.POST_LAYER_OPERATION: - edge_name = self._onnx_graph.get_node_edge_names(node_name)["output"][port_id] - statistic_point.register_tensor(outputs[edge_name]) + node = self.node_mapping[target_point.target_node_name] + edge_name = node.output[port_id] elif target_point.type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]: - edge_name = self._onnx_graph.get_node_edge_names(node_name)["input"][port_id] - statistic_point.register_tensor(outputs[edge_name]) + node = self.node_mapping[target_point.target_node_name] + edge_name = node.input[port_id] + statistic_point.register_tensor(outputs[edge_name]) def _get_transformation_layout_extra_outputs( self, statistic_points: StatisticPointsContainer diff --git a/nncf/openvino/graph/metatypes/openvino_metatypes.py b/nncf/openvino/graph/metatypes/openvino_metatypes.py index 73b18efd3aa..07cd0aed29e 100644 --- a/nncf/openvino/graph/metatypes/openvino_metatypes.py +++ b/nncf/openvino/graph/metatypes/openvino_metatypes.py @@ -25,8 +25,8 @@ class OVOpMetatype(OperatorMetatype): - op_names = [] # type: List[str] - subtypes = [] # type: List[Type[OperatorMetatype]] + op_names: List[str] = [] + subtypes: List[Type[OperatorMetatype]] = [] @classmethod def get_all_aliases(cls) -> List[str]: diff --git a/nncf/openvino/graph/model_transformer.py b/nncf/openvino/graph/model_transformer.py index 19e43f4b131..16cad27bb65 100644 --- a/nncf/openvino/graph/model_transformer.py +++ b/nncf/openvino/graph/model_transformer.py @@ -249,10 +249,10 @@ def _convert_to_fp16(data): clip_data = np.clip(data, np.finfo(np.float16).min, np.finfo(np.float16).max) return clip_data.astype(np.float16) - input_low = _convert_to_fp16(fq_params.input_low) - input_high = _convert_to_fp16(fq_params.input_high) - output_low = _convert_to_fp16(fq_params.output_low) - output_high = _convert_to_fp16(fq_params.output_high) + input_low = _convert_to_fp16(fq_params.input_low.data) + input_high = _convert_to_fp16(fq_params.input_high.data) + output_low = _convert_to_fp16(fq_params.output_low.data) + output_high = _convert_to_fp16(fq_params.output_high.data) return input_low, input_high, output_low, output_high @staticmethod @@ -266,10 +266,10 @@ def _insert_fake_quantize_op( :param name_to_node_mapping: Mapping from node name to node instance. """ fq_params = transformation.quantizer_parameters - input_low = fq_params.input_low - input_high = fq_params.input_high - output_low = fq_params.output_low - output_high = fq_params.output_high + input_low = fq_params.input_low.data + input_high = fq_params.input_high.data + output_low = fq_params.output_low.data + output_high = fq_params.output_high.data levels = fq_params.levels node_name = transformation.target_point.target_node_name diff --git a/nncf/openvino/hardware/config.py b/nncf/openvino/hardware/config.py index afb0e446e64..d87ed4d7176 100644 --- a/nncf/openvino/hardware/config.py +++ b/nncf/openvino/hardware/config.py @@ -11,7 +11,7 @@ from typing import List, Type -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.hardware.config import HWConfig from nncf.openvino.graph.metatypes.openvino_metatypes import get_operator_metatypes diff --git a/nncf/openvino/quantization/quantize_ifmodel.py b/nncf/openvino/quantization/quantize_ifmodel.py index bf30e3bed91..3280ab4fb54 100644 --- a/nncf/openvino/quantization/quantize_ifmodel.py +++ b/nncf/openvino/quantization/quantize_ifmodel.py @@ -125,15 +125,9 @@ def _add_outputs_before_if_node(model_transformer: ModelTransformer, model: ov.M :param if_node: If node. :return: Model with extra outputs before If node. """ - assert if_node.metatype == OVIfMetatype transformation_layout = TransformationLayout() - name_to_node_mapping = {op.get_friendly_name(): op for op in model.get_ops()} - ov_node = name_to_node_mapping[if_node.node_name] - port_ids = range(len(ov_node.inputs())) - for port_id in port_ids: - transformation_layout.register( - OVOutputInsertionCommand(OVTargetPoint(TargetType.PRE_LAYER_OPERATION, if_node.node_name, port_id)) - ) + for command in OVBackend.create_output_insertion_commands_if_node(model, if_node): + transformation_layout.register(command) return model_transformer.transform(transformation_layout) @@ -165,12 +159,12 @@ def apply_algorithm_if_bodies( return quantized_model, current_model_num model_transformer_fp32 = factory.ModelTransformerFactory.create(parent_model) for if_node in parent_graph.get_nodes_by_metatypes(OVBackend.if_node_metatypes()): - then_model_input_names = OVBackend.get_if_body_input_names(parent_model, if_node, True) - else_model_input_names = OVBackend.get_if_body_input_names(parent_model, if_node, False) - if_cond_input_name = OVBackend.get_if_cond_input_name(parent_model, if_node) parent_model_with_additional_outputs = _add_outputs_before_if_node( model_transformer_fp32, parent_model, if_node ) + then_model_input_names = OVBackend.get_if_body_input_names(parent_model, if_node, True) + else_model_input_names = OVBackend.get_if_body_input_names(parent_model, if_node, False) + if_cond_input_name = OVBackend.get_if_cond_input_name(parent_model_with_additional_outputs, if_node) then_dataset, else_dataset = _make_dataset_for_if_bodies( factory.EngineFactory.create(parent_model_with_additional_outputs), parent_dataset, @@ -244,7 +238,8 @@ def get_if_body_input_names(model: ov.Model, if_node: NNCFNode, if_body_conditio desc.input_index for desc in ov_node.get_input_descriptions(OVBackend._get_if_body_port_id(if_body_condition)) ] - input_names.extend([ov_node.input_values()[index].any_name for index in input_indices]) + for index in input_indices: + input_names.append(ov_node.inputs()[index].get_tensor().get_any_name()) return input_names @staticmethod @@ -258,7 +253,7 @@ def get_if_cond_input_name(model: ov.Model, if_node: NNCFNode) -> str: """ name_to_node_mapping = {op.get_friendly_name(): op for op in model.get_ops()} ov_node = name_to_node_mapping[if_node.node_name] - return ov_node.input_values()[0].any_name + return ov_node.inputs()[0].get_tensor().get_any_name() @staticmethod def create_update_body_command(if_node: NNCFNode, if_body_condition: bool, body: ov.Model) -> OVUpdateIfBodyCommand: @@ -288,13 +283,13 @@ def create_extract_if_body_command(if_node: NNCFNode, if_body_condition: bool) - return OVExtractIfBodyCommand(if_node.node_name, if_body_condition) @staticmethod - def create_output_insertion_commands(model: ov.Model, if_node: NNCFNode) -> List[OVOutputInsertionCommand]: + def create_output_insertion_commands_if_node(model: ov.Model, if_node: NNCFNode) -> List[OVOutputInsertionCommand]: """ - Returns output insertion commands on + Returns output insertion commands on If node inputs. - :param ov.Model model: - :param NNCFNode if_node: - :return List[OVOutputInsertionCommand]: + :param ov.Model model: Model. + :param NNCFNode if_node: If node. + :return: Transformation commands to insert outputs before If node. """ assert if_node.metatype == OVIfMetatype commands = [] diff --git a/nncf/openvino/quantization/quantize_model.py b/nncf/openvino/quantization/quantize_model.py index ad07f6fdcf4..cbc4e03a842 100644 --- a/nncf/openvino/quantization/quantize_model.py +++ b/nncf/openvino/quantization/quantize_model.py @@ -24,8 +24,6 @@ from nncf.openvino.quantization.backend_parameters import BackendParameters from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed from nncf.openvino.quantization.quantize_ifmodel import apply_algorithm_if_bodies -from nncf.openvino.quantization.weights_compression import insert_pre_compression_operations -from nncf.parameters import CompressWeightsMode from nncf.parameters import DropType from nncf.parameters import ModelType from nncf.parameters import TargetDevice @@ -437,28 +435,3 @@ def quantize_with_accuracy_control_impl( advanced_quantization_parameters, advanced_accuracy_restorer_parameters, ) - - -def compress_weights_impl( - model: ov.Model, - mode: CompressWeightsMode = CompressWeightsMode.INT8, - ratio: Optional[float] = None, - group_size: Optional[int] = None, -) -> ov.Model: - """ - Implementation of the `compress_weights()` method for the OpenVINO backend. - - :param model: an OpenVINO model for compression. - :param mode: Defines a mode for weight compression. - INT8 stands for 8-bit integer quantization of all weights. - NF4 stands for a mixed-precision weights quantization to NF4 data type. The first and last layers - are always compressed to a backup precision which is 8-bit integer by default. All others are quantized whether - to NF4 or to a backup precision depending on criteria and the given ratio. - :param ratio: the ratio between baseline and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4 and - the rest to INT8). - :param group_size: number of weights (e.g. 128) in the channel dimension that share quantization parameters (scale). - The value -1 means no grouping. - :return: The non-trainable model with compressed weights and dequantization operations. - """ - insert_pre_compression_operations(model, mode, ratio, group_size) - return model diff --git a/nncf/quantization/__init__.py b/nncf/quantization/__init__.py index 92a36bcf31c..259d925f3e2 100644 --- a/nncf/quantization/__init__.py +++ b/nncf/quantization/__init__.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """Post-training quantization APIs.""" -from nncf.common.quantization.structs import QuantizationPreset -from nncf.quantization.quantize_model import compress_weights -from nncf.quantization.quantize_model import quantize -from nncf.quantization.quantize_model import quantize_with_accuracy_control +from nncf.common.quantization.structs import QuantizationPreset as QuantizationPreset +from nncf.quantization.quantize_model import compress_weights as compress_weights +from nncf.quantization.quantize_model import quantize as quantize +from nncf.quantization.quantize_model import quantize_with_accuracy_control as quantize_with_accuracy_control diff --git a/nncf/quantization/algorithms/bias_correction/algorithm.py b/nncf/quantization/algorithms/bias_correction/algorithm.py index 67a5d5855fd..614dc241349 100644 --- a/nncf/quantization/algorithms/bias_correction/algorithm.py +++ b/nncf/quantization/algorithms/bias_correction/algorithm.py @@ -124,7 +124,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = OVBiasCorrectionAlgoBackend() else: raise RuntimeError( - "Cannot return backend-specific entity because {} is not supported!".format(model_backend) + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) def apply( diff --git a/nncf/quantization/algorithms/bias_correction/backend.py b/nncf/quantization/algorithms/bias_correction/backend.py index a3f626ffe25..ea490f5fae1 100644 --- a/nncf/quantization/algorithms/bias_correction/backend.py +++ b/nncf/quantization/algorithms/bias_correction/backend.py @@ -180,7 +180,7 @@ def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: :param node: NNCFNode to check. :param nncf_graph: NNCFGraph instance with the node. - :return: boolean indicating whether the node has a quantized weights or not. + :return: Boolean indicating whether the node has a quantized weights or not. """ @staticmethod diff --git a/nncf/quantization/algorithms/bias_correction/onnx_backend.py b/nncf/quantization/algorithms/bias_correction/onnx_backend.py index 364c93acc5a..d7f34936bfd 100644 --- a/nncf/quantization/algorithms/bias_correction/onnx_backend.py +++ b/nncf/quantization/algorithms/bias_correction/onnx_backend.py @@ -22,7 +22,7 @@ from nncf.onnx.graph.node_utils import get_bias_value from nncf.onnx.graph.node_utils import is_any_weight_quantized from nncf.onnx.graph.node_utils import is_node_with_bias -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_name_to_node_map from nncf.onnx.graph.transformations.command_creation import create_bias_correction_command from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand @@ -101,15 +101,13 @@ def get_bias_value(node: NNCFNode, model: onnx.ModelProto, nncf_graph: NNCFGraph @staticmethod def get_input_name(model: onnx.ModelProto, node_name: str) -> str: - onnx_graph = ONNXGraph(model) - node = onnx_graph.get_node_by_name(node_name) - return node.input[0] + node_mapping = get_name_to_node_map(model) + return node_mapping[node_name].input[0] @staticmethod def get_output_name(model: onnx.ModelProto, node_name: str, output_id: int) -> List[str]: - onnx_graph = ONNXGraph(model) - node = onnx_graph.get_node_by_name(node_name) - return node.output[output_id] + node_mapping = get_name_to_node_map(model) + return node_mapping[node_name].output[output_id] @staticmethod def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: diff --git a/nncf/quantization/algorithms/channel_alignment/algorithm.py b/nncf/quantization/algorithms/channel_alignment/algorithm.py index 0170606c64e..f163ec3468c 100644 --- a/nncf/quantization/algorithms/channel_alignment/algorithm.py +++ b/nncf/quantization/algorithms/channel_alignment/algorithm.py @@ -265,7 +265,7 @@ def _check_consumer_conv_node(self, conv_node: NNCFNode) -> bool: return True def _check_producer_conv_node(self, conv_node: NNCFNode): - return not conv_node.layer_attributes is None + return conv_node.layer_attributes is not None def _get_target_patterns(self) -> GraphPattern: input_attrs = { diff --git a/nncf/quantization/algorithms/fast_bias_correction/algorithm.py b/nncf/quantization/algorithms/fast_bias_correction/algorithm.py index 7ed9182d004..919f56848c8 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/algorithm.py +++ b/nncf/quantization/algorithms/fast_bias_correction/algorithm.py @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from math import inf from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union from nncf import Dataset @@ -25,6 +26,9 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend +from nncf.experimental.common.tensor_statistics.statistical_functions import mean_per_channel +from nncf.experimental.tensor import Tensor +from nncf.experimental.tensor import functions as fns from nncf.quantization.algorithms.algorithm import Algorithm from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS @@ -114,7 +118,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = PTFastBiasCorrectionAlgoBackend() else: raise RuntimeError( - "Cannot return backend-specific entity because {} is not supported!".format(model_backend) + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) def apply( @@ -167,9 +171,9 @@ def apply( output_name=sub_output_name, ) - bias_shift = self.reshape_bias_shift(bias_shift, bias_value, channel_axis) + bias_shift = self._reshape_bias_shift(bias_shift, bias_value, channel_axis) updated_bias = bias_value + bias_shift - magnitude = self._backend_entity.get_bias_shift_magnitude(bias_value, updated_bias) + magnitude = self._get_bias_shift_magnitude(bias_value, updated_bias) if magnitude < self.threshold: nncf_logger.debug(f"{node_name} bias would be changed") @@ -185,7 +189,22 @@ def apply( return transformed_model - def reshape_bias_shift(self, bias_shift: TTensor, bias_value: TTensor, channel_axis: int) -> TTensor: + @staticmethod + def _get_bias_shift_magnitude(current_bias_value: Tensor, updated_bias_value: Tensor) -> float: + """ + Calculates bias shift magnitude based on the current and updated values. + + :param current_bias_value: The original bias value. + :param updated_bias_value: The updated bias value. + :return: Magnitude between original and updated bias values. + """ + bias_shift_magnitude = inf + if fns.count_nonzero(current_bias_value == 0) == 0: + bias_shift_magnitude = fns.max(fns.abs((updated_bias_value - current_bias_value) / current_bias_value)) + return bias_shift_magnitude + + @staticmethod + def _reshape_bias_shift(bias_shift: Tensor, bias_value: Tensor, channel_axis: int) -> Tensor: """ Reshape bias_shift tensor in case of dimensions of bias_value is more then 1. @@ -198,7 +217,7 @@ def reshape_bias_shift(self, bias_shift: TTensor, bias_value: TTensor, channel_a if bias_value.ndim > 1: new_shape = [1] * bias_value.ndim new_shape[channel_axis] = bias_shift.shape[0] - bias_shift = self._backend_entity.reshape_tensor(bias_shift, new_shape) + bias_shift = bias_shift.reshape(new_shape) return bias_shift def _get_fp_inputs(self, statistic_points: StatisticPointsContainer, node_name: str) -> Tuple[List, List]: @@ -222,7 +241,7 @@ def input_filter_func(point): node_name, input_filter_func, self._algorithm_key ): statistics = tensor_collector.get_statistics() - input_fp.extend(statistics.mean_values) + input_fp.extend(Tensor(statistics.mean_values)) input_shape.extend(statistics.shape) return input_fp, input_shape @@ -245,7 +264,7 @@ def output_filter_func(point): for tensor_collector in statistic_points.get_algo_statistics_for_node( node_name, output_filter_func, self._algorithm_key ): - output_fp.extend(tensor_collector.get_statistics().mean_values) + output_fp.extend(Tensor(tensor_collector.get_statistics().mean_values)) return output_fp def _extract_submodel(self, model_transformer: ModelTransformer, node_name: str) -> TModel: @@ -299,8 +318,8 @@ def _get_bias_shift( engine = EngineFactory.create(model) raw_output = engine.infer(input_blob) q_outputs = self._backend_entity.process_model_output(raw_output, output_name) - q_outputs = self._backend_entity.tensor_processor.mean_per_channel(q_outputs, channel_axis).tensor - bias_shift = self._backend_entity.post_process_output_data(output_fp) - q_outputs + q_outputs = mean_per_channel(q_outputs, channel_axis) + bias_shift = fns.stack(output_fp) - q_outputs return bias_shift def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: diff --git a/nncf/quantization/algorithms/fast_bias_correction/backend.py b/nncf/quantization/algorithms/fast_bias_correction/backend.py index 38618fe2efa..a9d2ef0ab74 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/backend.py @@ -20,9 +20,9 @@ from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.commands import TransformationCommand -from nncf.common.tensor import NNCFTensor from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase from nncf.common.utils.registry import Registry +from nncf.experimental.tensor import Tensor TModel = TypeVar("TModel") TTensor = TypeVar("TTensor") @@ -31,13 +31,6 @@ class FastBiasCorrectionAlgoBackend(ABC): - @property - @abstractmethod - def tensor_processor(self): - """ - Returns backend-specific instance of the NNCFCollectorTensorProcessor. - """ - @staticmethod @abstractmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> TargetPoint: @@ -120,7 +113,7 @@ def create_input_data( @staticmethod @abstractmethod - def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: TModel) -> np.ndarray: + def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: TModel) -> Tensor: """ Returns bias value in the NumPy format of provided node. @@ -151,12 +144,12 @@ def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: :param node: NNCFNode to check. :param nncf_graph: NNCFGraph instance. - :return: boolean indicating whether the node has a quantized weights or not + :return: Boolean indicating whether the node has a quantized weights or not """ @staticmethod @abstractmethod - def process_model_output(raw_data: OutputType, output_name: str) -> NNCFTensor: + def process_model_output(raw_data: OutputType, output_name: str) -> Tensor: """ Returns backend-specific processed output from the model. @@ -176,37 +169,6 @@ def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: :return: Boolean indicating whether the node has a bias or not. """ - @staticmethod - @abstractmethod - def get_bias_shift_magnitude(current_bias_value: TTensor, updated_bias_value: TTensor) -> float: - """ - Calculates bias shift magnitude based on the current and updated values. - - :param current_bias_value: The original bias value. - :param updated_bias_value: The updated bias value. - :return: Magnitude between original and updated bias values. - """ - - @staticmethod - @abstractmethod - def post_process_output_data(data: List[TTensor]) -> TTensor: - """ - Convert data to backend specific type. - - :param data: List of data. - :return: Converted data. - """ - - @staticmethod - @abstractmethod - def reshape_tensor(data: TTensor, new_shape: List[int]) -> TTensor: - """ - Reshape tensor. - - :param data: Tensor. - :param new_shape: New shape. - """ - @staticmethod @abstractmethod def get_node_names_for_input_output_statistics(node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[str, str]: diff --git a/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py b/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py index 02bb54106c6..d0646f6aeb2 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py @@ -18,6 +18,7 @@ from nncf.common.graph import NNCFNode from nncf.common.graph.transformations.commands import TargetType from nncf.common.utils.backend import BackendType +from nncf.experimental.tensor import Tensor from nncf.onnx.graph.node_utils import get_bias_value from nncf.onnx.graph.node_utils import is_any_weight_quantized from nncf.onnx.graph.node_utils import is_node_with_bias @@ -27,8 +28,6 @@ from nncf.onnx.graph.transformations.commands import ONNXNullBiasInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXTargetPoint from nncf.onnx.statistics.collectors import ONNXMeanStatisticCollector -from nncf.onnx.statistics.collectors import ONNXNNCFCollectorTensorProcessor -from nncf.onnx.tensor import ONNXNNCFTensor from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend @@ -39,10 +38,6 @@ class ONNXFastBiasCorrectionAlgoBackend(FastBiasCorrectionAlgoBackend): def types_to_insert_bias(self): return [] - @property - def tensor_processor(self) -> ONNXNNCFCollectorTensorProcessor: - return ONNXNNCFCollectorTensorProcessor - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> ONNXTargetPoint: return ONNXTargetPoint(target_type, target_node_name, port_id) @@ -53,9 +48,9 @@ def create_bias_insertion_command(node: NNCFNode) -> ONNXNullBiasInsertionComman @staticmethod def create_bias_correction_command( - node: NNCFNode, bias_value: np.ndarray, nncf_graph: NNCFGraph + node: NNCFNode, bias_value: Tensor, nncf_graph: NNCFGraph ) -> ONNXBiasCorrectionCommand: - return create_bias_correction_command(node, bias_value) + return create_bias_correction_command(node, bias_value.data) @staticmethod def model_extraction_command(inputs: List[str], outputs: List[str]) -> ONNXModelExtractionCommand: @@ -76,27 +71,26 @@ def get_sub_input_output_names(subgraph: onnx.ModelProto) -> Tuple[str, str]: @staticmethod def create_input_data( - shape: Tuple[int], data: List[np.ndarray], input_name: str, channel_axis: int + shape: Tuple[int], data: List[Tensor], input_name: str, channel_axis: int ) -> Dict[str, np.array]: - blob = np.zeros(shape) + blob = np.zeros(shape, dtype=data[0].data.dtype) for j, idx in enumerate(np.ndindex(blob.shape[channel_axis])): index = tuple(slice(None) if i != channel_axis else idx for i in range(blob.ndim)) - blob[index] = data[j] - blob = blob.astype(data[0].dtype) + blob[index] = data[j].data input_data = {input_name: blob} return input_data @staticmethod - def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: onnx.ModelProto) -> np.ndarray: - return get_bias_value(node, model) + def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: onnx.ModelProto) -> Tensor: + return Tensor(get_bias_value(node, model)) @staticmethod def get_activation_port_ids_for_bias_node(node: NNCFNode) -> Tuple[int, int]: return 0, 0 @staticmethod - def process_model_output(raw_data: Dict, output_name: str) -> ONNXNNCFTensor: - return ONNXNNCFTensor(raw_data[output_name]) + def process_model_output(raw_data: Dict, output_name: str) -> Tensor: + return Tensor(raw_data[output_name]) @staticmethod def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: @@ -106,21 +100,6 @@ def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: return is_node_with_bias(node) - @staticmethod - def get_bias_shift_magnitude(current_bias_value: np.ndarray, updated_bias_value: np.ndarray) -> float: - bias_shift_magnitude = np.inf - if np.count_nonzero(current_bias_value == 0) == 0: - bias_shift_magnitude = np.max(np.abs((updated_bias_value - current_bias_value) / current_bias_value)) - return bias_shift_magnitude - - @staticmethod - def post_process_output_data(data: List[np.ndarray]) -> np.ndarray: - return np.array(data) - - @staticmethod - def reshape_tensor(data: np.ndarray, new_shape: List[int]) -> np.ndarray: - return data.reshape(new_shape) - @staticmethod def get_node_names_for_input_output_statistics(node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[str, str]: return node.node_name, node.node_name diff --git a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py index 61ebb5a695b..d2744da5864 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py @@ -19,6 +19,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.utils.backend import BackendType from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.tensor import Tensor from nncf.openvino.graph.metatypes.groups import FAKE_QUANTIZE_OPERATIONS from nncf.openvino.graph.node_utils import get_bias_value from nncf.openvino.graph.node_utils import is_node_with_bias @@ -26,28 +27,22 @@ from nncf.openvino.graph.transformations.commands import OVBiasCorrectionCommand from nncf.openvino.graph.transformations.commands import OVModelExtractionCommand from nncf.openvino.graph.transformations.commands import OVTargetPoint -from nncf.openvino.statistics.collectors import OVNNCFCollectorTensorProcessor from nncf.openvino.statistics.collectors import get_mean_statistic_collector -from nncf.openvino.tensor import OVNNCFTensor from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend @ALGO_BACKENDS.register(BackendType.OPENVINO) class OVFastBiasCorrectionAlgoBackend(FastBiasCorrectionAlgoBackend): - @property - def tensor_processor(self) -> OVNNCFCollectorTensorProcessor: - return OVNNCFCollectorTensorProcessor - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> OVTargetPoint: return OVTargetPoint(target_type, target_node_name, port_id) @staticmethod def create_bias_correction_command( - node: NNCFNode, bias_value: np.ndarray, nncf_graph: NNCFGraph + node: NNCFNode, bias_value: Tensor, nncf_graph: NNCFGraph ) -> OVBiasCorrectionCommand: - return OVCommandCreator.create_command_to_update_bias(node, bias_value, nncf_graph) + return OVCommandCreator.create_command_to_update_bias(node, bias_value.data, nncf_graph) @staticmethod def model_extraction_command(inputs: List[str], outputs: List[str]) -> OVModelExtractionCommand: @@ -68,19 +63,18 @@ def get_sub_input_output_names(subgraph: ov.Model) -> Tuple[str, str]: @staticmethod def create_input_data( - shape: Tuple[int], data: List[np.ndarray], input_name: str, channel_axis: int + shape: Tuple[int], data: List[Tensor], input_name: str, channel_axis: int ) -> Dict[str, np.ndarray]: - blob = np.zeros(shape) + blob = np.zeros(shape, dtype=data[0].data.dtype) for j, idx in enumerate(np.ndindex(blob.shape[channel_axis])): index = tuple(slice(None) if i != channel_axis else idx for i in range(blob.ndim)) - blob[index] = data[j] - blob = blob.astype(data[0].dtype) + blob[index] = data[j].data input_data = {input_name: blob} return input_data @staticmethod - def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: ov.Model) -> np.ndarray: - return get_bias_value(node, nncf_graph, model) + def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: ov.Model) -> Tensor: + return Tensor(get_bias_value(node, nncf_graph, model)) @staticmethod def get_activation_port_ids_for_bias_node(node: NNCFNode) -> Tuple[int, int]: @@ -97,28 +91,13 @@ def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: return weight_node.metatype in FAKE_QUANTIZE_OPERATIONS @staticmethod - def process_model_output(raw_data: Dict, output_name: str) -> OVNNCFTensor: - return OVNNCFTensor(raw_data[output_name]) + def process_model_output(raw_data: Dict, output_name: str) -> Tensor: + return Tensor(raw_data[output_name]) @staticmethod def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: return is_node_with_bias(node, nncf_graph) - @staticmethod - def get_bias_shift_magnitude(current_bias_value: np.ndarray, updated_bias_value: np.ndarray) -> float: - bias_shift_magnitude = np.inf - if np.count_nonzero(current_bias_value == 0) == 0: - bias_shift_magnitude = np.max(np.abs((updated_bias_value - current_bias_value) / current_bias_value)) - return bias_shift_magnitude - - @staticmethod - def post_process_output_data(data: List[np.ndarray]) -> np.ndarray: - return np.array(data) - - @staticmethod - def reshape_tensor(data: np.ndarray, new_shape: List[int]) -> np.ndarray: - return data.reshape(new_shape) - @staticmethod def get_node_names_for_input_output_statistics(node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[str, str]: return node.node_name, node.node_name diff --git a/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py b/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py index cb9b0026e3f..193be8994d9 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py @@ -20,6 +20,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.utils.backend import BackendType from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.tensor import Tensor from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend from nncf.torch.graph.transformations.command_creation import create_bias_correction_command @@ -31,8 +32,6 @@ from nncf.torch.model_analyzer import is_node_with_fused_bias from nncf.torch.model_analyzer import is_quantized_weights from nncf.torch.nncf_network import NNCFNetwork -from nncf.torch.tensor import PTNNCFTensor -from nncf.torch.tensor_statistics.collectors import PTNNCFCollectorTensorProcessor from nncf.torch.tensor_statistics.collectors import get_mean_statistic_collector @@ -43,10 +42,6 @@ class PTFastBiasCorrectionAlgoBackend(FastBiasCorrectionAlgoBackend): TargetType.POST_LAYER_OPERATION: TargetType.OPERATOR_POST_HOOK, } - @property - def tensor_processor(self) -> PTNNCFCollectorTensorProcessor: - return PTNNCFCollectorTensorProcessor - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: if NNCFGraphNodeType.INPUT_NODE in target_node_name or target_type == TargetType.POST_LAYER_OPERATION: @@ -57,9 +52,9 @@ def target_point(target_type: TargetType, target_node_name: str, port_id: int) - @staticmethod def create_bias_correction_command( - node: NNCFNode, bias_value: np.ndarray, nncf_graph: NNCFGraph + node: NNCFNode, bias_value: Tensor, nncf_graph: NNCFGraph ) -> PTBiasCorrectionCommand: - return create_bias_correction_command(node, bias_value) + return create_bias_correction_command(node, bias_value.data) @staticmethod def model_extraction_command(inputs: List[str], outputs: List[str]) -> PTModelExtractionWithFusedBiasCommand: @@ -80,26 +75,24 @@ def get_sub_input_output_names(subgraph: NNCFNetwork) -> Tuple[str, str]: return None, None @staticmethod - def create_input_data( - shape: Tuple[int], data: List[torch.Tensor], input_name: str, channel_axis: int - ) -> torch.Tensor: - blob = torch.zeros(shape, dtype=data[0].dtype) + def create_input_data(shape: Tuple[int], data: List[Tensor], input_name: str, channel_axis: int) -> torch.Tensor: + blob = torch.zeros(shape, dtype=data[0].data.dtype, device=data[0].data.device) for j, idx in enumerate(np.ndindex(blob.shape[channel_axis])): index = tuple(slice(None) if i != channel_axis else idx for i in range(blob.ndim)) - blob[index] = data[j] + blob[index] = data[j].data return blob @staticmethod - def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: NNCFNetwork) -> np.ndarray: - return get_fused_bias_value(node, model) + def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: NNCFNetwork) -> Tensor: + return Tensor(get_fused_bias_value(node, model)) @staticmethod def get_activation_port_ids_for_bias_node(node: NNCFNode) -> Tuple[int, int]: return 0, 0 @staticmethod - def process_model_output(raw_data: Dict, output_name: str) -> PTNNCFTensor: - return PTNNCFTensor(raw_data) + def process_model_output(raw_data: Dict, output_name: str) -> Tensor: + return Tensor(raw_data) @staticmethod def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: @@ -109,21 +102,6 @@ def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: return is_node_with_fused_bias(node, nncf_graph) - @staticmethod - def get_bias_shift_magnitude(current_bias_value: torch.Tensor, updated_bias_value: torch.Tensor) -> float: - bias_shift_magnitude = torch.inf - if torch.count_nonzero(current_bias_value == 0) == 0: - bias_shift_magnitude = torch.max(torch.abs((updated_bias_value - current_bias_value) / current_bias_value)) - return bias_shift_magnitude - - @staticmethod - def post_process_output_data(data: List[torch.Tensor]) -> torch.Tensor: - return torch.Tensor(data) - - @staticmethod - def reshape_tensor(data: torch.Tensor, new_shape: List[int]) -> torch.Tensor: - return data.reshape(new_shape) - @staticmethod def get_node_names_for_input_output_statistics(node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[str, str]: input_node_name = node.node_name diff --git a/nncf/quantization/algorithms/min_max/algorithm.py b/nncf/quantization/algorithms/min_max/algorithm.py index 861e0226873..1b199409683 100644 --- a/nncf/quantization/algorithms/min_max/algorithm.py +++ b/nncf/quantization/algorithms/min_max/algorithm.py @@ -171,9 +171,9 @@ def __init__( def _reset_cache(self): # It prevents the duplicate weight quantizers from being added. # It can happen when you have layers that share the identical weight tensor. - self._quantization_target_points_to_qconfig = ( - collections.OrderedDict() - ) # type: OrderedDict[TargetPoint, QuantizerConfig] + self._quantization_target_points_to_qconfig: OrderedDict[ + TargetPoint, QuantizerConfig + ] = collections.OrderedDict() self._unified_scale_groups = [] @property @@ -227,7 +227,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = PTMinMaxAlgoBackend() else: raise RuntimeError( - "Cannot return backend-specific entity because {} is not supported!".format(model_backend) + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) def _get_range_estimator_parameters( diff --git a/nncf/quantization/algorithms/min_max/onnx_backend.py b/nncf/quantization/algorithms/min_max/onnx_backend.py index 47cf5695832..d3c1e25d0ae 100644 --- a/nncf/quantization/algorithms/min_max/onnx_backend.py +++ b/nncf/quantization/algorithms/min_max/onnx_backend.py @@ -103,7 +103,7 @@ def create_quantizer_insertion_command( quantizer_config: QuantizerConfig, parameters: FakeQuantizeParameters, ): - tensor_type = np.int8 if np.any(parameters.input_low < 0) else np.uint8 + tensor_type = np.int8 if np.any(parameters.input_low.data < 0) else np.uint8 if target_point.is_weight_target_point(): tensor_type = np.int8 # The weight is restricted to have only signed range nncf_input_node_next_nodes = ONNXMinMaxAlgoBackend._get_input_edges_mapping(nncf_graph) diff --git a/nncf/quantization/algorithms/min_max/openvino_backend.py b/nncf/quantization/algorithms/min_max/openvino_backend.py index 4ad4e309dc8..4edba0c0a37 100644 --- a/nncf/quantization/algorithms/min_max/openvino_backend.py +++ b/nncf/quantization/algorithms/min_max/openvino_backend.py @@ -138,7 +138,7 @@ def _get_reduction_axes_and_use_abs_max( else: raise NotImplementedError(f"Unsupported target point type {target_point.type}.") - # TODO (l-bat): Disable quantizer propogation through layout changing operations + # TODO (l-bat): Disable quantizer propagation through layout changing operations channel_axis = 1 # OpenVINO activations have channel first layout: [N, C, Z, Y, X] axes = get_channel_agnostic_reduction_axes([channel_axis], shape) return axes, use_abs_max @@ -171,12 +171,12 @@ def get_statistic_collector( [range_estimator_params.min, range_estimator_params.max], [OVMinMaxTensorStatistic.MIN_STAT, OVMinMaxTensorStatistic.MAX_STAT], ): - if not params.statistics_type in OV_REDUCERS_MAP: + if params.statistics_type not in OV_REDUCERS_MAP: raise RuntimeError( f"Statistic type: {params.statistics_type} is not supported for OpenVino PTQ backend yet." ) - if not params.aggregator_type in AGGREGATORS_MAP: + if params.aggregator_type not in AGGREGATORS_MAP: raise RuntimeError( f"Aggregator type: {params.aggregator_type} is not supported for OpenVino PTQ backend yet." ) diff --git a/nncf/quantization/algorithms/min_max/torch_backend.py b/nncf/quantization/algorithms/min_max/torch_backend.py index 0a8fe5778c5..e4a765a2d59 100644 --- a/nncf/quantization/algorithms/min_max/torch_backend.py +++ b/nncf/quantization/algorithms/min_max/torch_backend.py @@ -11,7 +11,6 @@ from typing import Dict, List, Optional, Set, Tuple -import numpy as np import torch import nncf.torch.graph.operator_metatypes as om @@ -19,7 +18,6 @@ from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode from nncf.common.graph.layer_attributes import WeightedLayerAttributes -from nncf.common.graph.model_transformer import ModelTransformer from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetType from nncf.common.hardware.config import HWConfig @@ -38,7 +36,6 @@ from nncf.torch.graph.graph import PTTargetPoint from nncf.torch.graph.transformations.commands import PTQuantizerInsertionCommand from nncf.torch.hardware.config import PTHWConfig -from nncf.torch.model_transformer import PTModelTransformer from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.quantization.default_quantization import DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT from nncf.torch.quantization.init_range import PTRangeInitCollectorParams @@ -112,10 +109,6 @@ def hw_config(self) -> HWConfig: def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]: return DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT - @staticmethod - def model_transformer(model: NNCFNetwork) -> ModelTransformer: - return PTModelTransformer(model) - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: if NNCFGraphNodeType.INPUT_NODE in target_node_name or target_type == TargetType.POST_LAYER_OPERATION: @@ -139,10 +132,10 @@ def create_quantizer_insertion_command( def unify_statistics(statistics: List[PTMinMaxTensorStatistic]) -> PTMinMaxTensorStatistic: max_values, min_values = [], [] for statistic in statistics: - max_values.append(torch.tensor(statistic.max_values).flatten()) - min_values.append(torch.tensor(statistic.min_values).flatten()) - max_values = torch.max(torch.tensor(max_values)) - min_values = torch.min(torch.tensor(min_values)) + max_values.append(statistic.max_values.flatten()) + min_values.append(statistic.min_values.flatten()) + max_values = torch.amax(torch.stack(max_values), dim=0) + min_values = torch.amin(torch.stack(min_values), dim=0) return PTMinMaxTensorStatistic(min_values=min_values, max_values=max_values) @staticmethod @@ -163,12 +156,12 @@ def get_statistic_collector( [range_estimator_params.min, range_estimator_params.max], [PTMinMaxTensorStatistic.MIN_STAT, PTMinMaxTensorStatistic.MAX_STAT], ): - if not params.statistics_type in PT_REDUCERS_MAP: + if params.statistics_type not in PT_REDUCERS_MAP: raise RuntimeError( f"Statistic type: {params.statistics_type} is not supported for Torch PTQ backend yet." ) - if not params.aggregator_type in AGGREGATORS_MAP: + if params.aggregator_type not in AGGREGATORS_MAP: raise RuntimeError( f"Aggregator type: {params.aggregator_type} is not supported for Torch PTQ backend yet." ) @@ -279,13 +272,12 @@ def _create_quantizer( def _fill_quantizer_parameters(quantizer: BaseQuantizer, parameters: FakeQuantizeParameters) -> None: quantizer.eps = 0 if isinstance(quantizer, AsymmetricQuantizer): - quantizer.input_low = torch.nn.Parameter(torch.from_numpy(parameters.input_low)) - quantizer.input_range = torch.nn.Parameter( - torch.from_numpy(np.array(parameters.input_high - parameters.input_low)) - ) + quantizer.input_low = torch.nn.Parameter(parameters.input_low.data) + input_range = parameters.input_high - parameters.input_low + quantizer.input_range = torch.nn.Parameter(input_range.data) else: - quantizer.signed = np.any(parameters.input_low < 0) - quantizer.scale = torch.nn.Parameter(torch.from_numpy(parameters.input_high)) + quantizer.signed = bool(torch.any(parameters.input_low.data < 0)) + quantizer.scale = torch.nn.Parameter(parameters.input_high.data) @staticmethod def _create_quantizer_insertion_command( diff --git a/nncf/quantization/algorithms/smooth_quant/algorithm.py b/nncf/quantization/algorithms/smooth_quant/algorithm.py index 90c3baa7a13..780a462d280 100644 --- a/nncf/quantization/algorithms/smooth_quant/algorithm.py +++ b/nncf/quantization/algorithms/smooth_quant/algorithm.py @@ -91,7 +91,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = OVSmoothQuantAlgoBackend() else: raise RuntimeError( - "Cannot return backend-specific entity because {} is not supported!".format(model_backend) + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) def apply( diff --git a/nncf/quantization/algorithms/smooth_quant/backend.py b/nncf/quantization/algorithms/smooth_quant/backend.py index c6385d8b4f1..e0ccfc955df 100644 --- a/nncf/quantization/algorithms/smooth_quant/backend.py +++ b/nncf/quantization/algorithms/smooth_quant/backend.py @@ -54,7 +54,7 @@ def is_node_with_weights(node: NNCFNode) -> bool: Checks whether the node with weights or not. :param node: NNCFNode to check. - :return: boolean indicating whether the node has weights or not. + :return: Boolean indicating whether the node has weights or not. """ @staticmethod diff --git a/nncf/quantization/algorithms/weight_compression/__init__.py b/nncf/quantization/algorithms/weight_compression/__init__.py new file mode 100644 index 00000000000..9b29b47534a --- /dev/null +++ b/nncf/quantization/algorithms/weight_compression/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/nncf/quantization/algorithms/weight_compression/algorithm.py b/nncf/quantization/algorithms/weight_compression/algorithm.py new file mode 100644 index 00000000000..20346cfcc6d --- /dev/null +++ b/nncf/quantization/algorithms/weight_compression/algorithm.py @@ -0,0 +1,127 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Optional, TypeVar + +from nncf import Dataset +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.graph import NNCFNode +from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType +from nncf.common.utils.backend import get_backend +from nncf.parameters import CompressWeightsMode +from nncf.quantization.algorithms.algorithm import Algorithm +from nncf.quantization.algorithms.weight_compression.backend import ALGO_BACKENDS + +TModel = TypeVar("TModel") +TTensor = TypeVar("TTensor") + + +class WeightCompression(Algorithm): + """ + Post-training Weight Compression algorithm implementation. + + Compresses weights of Linear and Embedding layers to 8-bit integer or + to nf4 depending on mode, ratio and group size. + """ + + def __init__( + self, + mode: CompressWeightsMode, + ratio: float = None, + group_size: int = None, + ): + """ + :param mode: Defines a mode for weight compression. + INT8 stands for 8-bit integer quantization of all weights. + NF4 stands for a mixed-precision weights quantization to NF4 data type. The first and last layers + are always compressed to a backup precision which is 8-bit integer by default. All others are quantized + whether to NF4 or to a backup precision depending on criteria and the given ratio. + :param ratio: the ratio between baseline and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4 + and the rest to INT8). + :param group_size: number of weights (e.g. 128) in the channel dimension + that share quantization parameters (scale). The value -1 means no grouping. + """ + super().__init__() + self._mode = mode + self._group_size = group_size + self._ratio = ratio + self._backend_entity = None + self._algorithm_key = f"CW_{hash(self)}" + + @property + def available_backends(self) -> Dict[str, BackendType]: + return ALGO_BACKENDS.registry_dict + + def _set_backend_entity(self, model: TModel) -> None: + """ + Creates a helper class with a backed-specific logic of the algorithm. + + :param model: Backend-specific input model. + """ + model_backend = get_backend(model) + if model_backend == BackendType.OPENVINO: + from nncf.quantization.algorithms.weight_compression.openvino_backend import OVWeightCompressionAlgoBackend + + self._backend_entity = OVWeightCompressionAlgoBackend() + else: + raise RuntimeError( + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) + ) + + def apply( + self, + model: TModel, + graph: NNCFGraph, + statistic_points: Optional[StatisticPointsContainer] = None, + dataset: Optional[Dataset] = None, + ) -> TModel: + self._set_backend_entity(model) + self._backend_entity.validate_params(self._mode) + nodes_to_compress = self._get_nodes_to_compress(graph) + transformed_model = self._backend_entity.do_compression( + model, nodes_to_compress, self._mode, self._ratio, self._group_size + ) + return transformed_model + + def _get_nodes_to_compress(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: + """ + Collects nodes in the model's graph corresponding to the layers for weight compression. + + :param nncf_graph: NNCFGraph instance. + :return: List with the data for each layer. + """ + weighted_metatypes = self._backend_entity.weighted_metatypes + ordered_nodes_to_compress = [] + for node in nncf_graph.topological_sort(): + is_node_with_weights = self._backend_entity.is_node_with_weights(node) + if node.metatype in weighted_metatypes and is_node_with_weights: + ordered_nodes_to_compress.append(node) + return ordered_nodes_to_compress + + def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: + """ + Returns statistic points, for which StatisticsCollector should collect statistics. + + :param model: Model for statistics collection. + :param graph: Model graph. + :return: Statistic points, for which StatisticsCollector should collect statistics. + """ diff --git a/nncf/quantization/algorithms/weight_compression/backend.py b/nncf/quantization/algorithms/weight_compression/backend.py new file mode 100644 index 00000000000..ffa0253b0c2 --- /dev/null +++ b/nncf/quantization/algorithms/weight_compression/backend.py @@ -0,0 +1,83 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC +from abc import abstractmethod +from typing import List, TypeVar + +from nncf.common.graph import NNCFNode +from nncf.common.graph.operator_metatypes import OperatorMetatype +from nncf.common.utils.registry import Registry +from nncf.parameters import CompressWeightsMode + +TModel = TypeVar("TModel") +ALGO_BACKENDS = Registry("algo_backends") + + +class WeightCompressionAlgoBackend(ABC): + @property + @abstractmethod + def weighted_metatypes(self) -> List[OperatorMetatype]: + """ + Property for the backend-specific metatypes. + """ + + @staticmethod + @abstractmethod + def is_node_with_weights(node: NNCFNode) -> bool: + """ + Checks whether the node with weights or not. + + :param node: NNCFNode to check. + :return: Boolean indicating whether the node has weights or not. + """ + + @staticmethod + @abstractmethod + def validate_params(mode: CompressWeightsMode) -> None: + """ + Performs validation of the algorithm's parameters and raises an error for unsupported configuration of + parameters. Should be called on early algorithm steps to prevent execution of time-consuming operations. + + :param mode: Defines a mode for weight compression. + INT8 stands for 8-bit integer quantization of all weights. + NF4 stands for a mixed-precision weights quantization to NF4 data type. The first and last layers + are always compressed to a backup precision which is 8-bit integer by default. All others are quantized + whether to NF4 or to a backup precision depending on criteria and the given ratio. + """ + + @staticmethod + @abstractmethod + def do_compression( + model: TModel, + nodes_to_compress: List[NNCFNode], + mode: CompressWeightsMode, + ratio: float = None, + group_size: int = None, + ) -> TModel: + """ + Compress weights of Linear and Embedding layers to 8-bit integer or to nf4 + depending on mode, ratio and group size. + + :param model: Model for applying weight compression. + :param nodes_to_compress: List of nodes in the model's graph, + corresponding to the layers for weight compression. + :param mode: Defines a mode for weight compression. + INT8 stands for 8-bit integer quantization of all weights. + NF4 stands for a mixed-precision weights quantization to NF4 data type. The first and last layers + are always compressed to a backup precision which is 8-bit integer by default. All others are quantized + whether to NF4 or to a backup precision depending on criteria and the given ratio. + :param ratio: The ratio between baseline and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4 + and the rest to INT8). + :param group_size: Number of weights (e.g. 128) in the channel dimension + that share quantization parameters (scale). The value -1 means no grouping. + :return: A resulting model with compressed weights. + """ diff --git a/nncf/openvino/quantization/weights_compression.py b/nncf/quantization/algorithms/weight_compression/openvino_backend.py similarity index 69% rename from nncf/openvino/quantization/weights_compression.py rename to nncf/quantization/algorithms/weight_compression/openvino_backend.py index fa588cd6c63..8142fb35556 100644 --- a/nncf/openvino/quantization/weights_compression.py +++ b/nncf/quantization/algorithms/weight_compression/openvino_backend.py @@ -10,25 +10,114 @@ # limitations under the License. from dataclasses import dataclass -from typing import List, Optional, Tuple, Type, TypeVar, Union +from typing import List, Optional, Tuple, TypeVar, Union import numpy as np import openvino.runtime as ov from openvino.runtime import opset9 as opset +from nncf.common.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.logging import nncf_logger -from nncf.common.quantization.statistics import _proportion_str +from nncf.common.utils.backend import BackendType from nncf.common.utils.helpers import create_table from nncf.openvino.graph.metatypes.openvino_metatypes import OVEmbeddingMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype -from nncf.openvino.graph.metatypes.openvino_metatypes import get_node_metatype -from nncf.openvino.graph.metatypes.openvino_metatypes import get_operation_const_op +from nncf.openvino.graph.node_utils import get_channel_agnostic_reduction_axes from nncf.openvino.graph.node_utils import get_const_value -from nncf.openvino.graph.node_utils import get_matmul_channel_axes +from nncf.openvino.graph.node_utils import get_weight_channel_axes from nncf.parameters import CompressWeightsMode +from nncf.quantization.algorithms.weight_compression.backend import ALGO_BACKENDS +from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend from nncf.quantization.fake_quantize import calculate_scale_zero_point + +@ALGO_BACKENDS.register(BackendType.OPENVINO) +class OVWeightCompressionAlgoBackend(WeightCompressionAlgoBackend): + @property + def weighted_metatypes(self) -> List[OperatorMetatype]: + return [OVMatMulMetatype, OVEmbeddingMetatype] + + @staticmethod + def is_node_with_weights(node: NNCFNode) -> bool: + return node.layer_attributes and node.layer_attributes.constant_attributes + + @staticmethod + def validate_params(mode: CompressWeightsMode) -> None: + pass + + @staticmethod + def do_compression( + model: ov.Model, + nodes_to_compress: List[NNCFNode], + mode: CompressWeightsMode, + ratio: float = None, + group_size: int = None, + ) -> ov.Model: + all_weight_params: List[WeightNodeParams] = [] + quantized_nodes_ids = set() + + friendly_name_to_op_map = {op.get_friendly_name(): op for op in model.get_ops()} + + for nncf_node in nodes_to_compress: + weight_port_ids = nncf_node.layer_attributes.get_const_port_ids() + for weight_port_id in weight_port_ids: + weight_op_friendly_name = nncf_node.layer_attributes.constant_attributes[weight_port_id]["name"] + weight_node = friendly_name_to_op_map[weight_op_friendly_name] + if weight_node is None: + continue + if id(weight_node) in quantized_nodes_ids: + continue + weight_output = weight_node.output(0) + + original_weight_dtype = weight_output.get_element_type().to_dtype() + if original_weight_dtype not in [np.float32, np.float16, np.float64]: + continue + const_shape = nncf_node.layer_attributes.constant_attributes[weight_port_id]["shape"] + channel_axes = get_weight_channel_axes(nncf_node, weight_port_id) + axes = get_channel_agnostic_reduction_axes(channel_axes, const_shape) + fq_name = f"{weight_op_friendly_name}/fq_weights_{weight_port_id}" + num_weights = np.prod(const_shape) + weight_params = WeightNodeParams(axes, num_weights, fq_name, weight_node, original_weight_dtype) + all_weight_params.append(weight_params) + quantized_nodes_ids.add(id(weight_node)) + + if mode == CompressWeightsMode.NF4: + _assign_mixed_precision(all_weight_params, ratio, group_size) + + for wp in all_weight_params: + weight_node = wp.weight_node + original_weight_dtype = wp.original_weight_dtype + + weight_output = weight_node.output(0) + weight_name = weight_node.get_friendly_name() + target_inputs = weight_output.get_target_inputs() + + weight = get_const_value(weight_node) + config = wp.compression_config + + if config.is_nf4: + original_shape = weight.shape + norm_weight, scale = _get_norm_weight_and_nf4_scale(weight, wp.reduction_axes, group_size) + compressed_const = opset.constant(norm_weight, dtype=ov.Type.nf4, name=weight_name) + convert = opset.convert(compressed_const, original_weight_dtype) + mul = opset.multiply(convert, scale.astype(original_weight_dtype), name=wp.fq_name) + if config.group_size != -1: + mul = opset.reshape(mul, output_shape=original_shape, special_zero=False) + last_output = mul.output(0) + else: + compressed_weights, scale, zero_point = _int8_compress(weight, wp.reduction_axes) + compressed_const = opset.constant(compressed_weights, dtype=np.uint8, name=weight_name) + convert = opset.convert(compressed_const, original_weight_dtype) + sub = opset.subtract(convert, zero_point.astype(original_weight_dtype)) + mul = opset.multiply(sub, scale.astype(original_weight_dtype), name=wp.fq_name) + last_output = mul.output(0) + + for target_input in target_inputs: + target_input.replace_source_output(last_output) + return model + + TWeightType = TypeVar("TWeightType") NF4_QUANTILES = np.array( @@ -52,26 +141,7 @@ ] ) - -CENTER_OF_NF4_QUANTILES = np.array( - [ - -0.8480964004993439, - -0.6106329262256622, - -0.4599952697753906, - -0.33967943489551544, - -0.23460740596055984, - -0.13791173323988914, - -0.045525018125772476, - 0.03979014977812767, - 0.1202552504837513, - 0.2035212516784668, - 0.2920137718319893, - 0.3893125355243683, - 0.5016634166240692, - 0.6427869200706482, - 0.8614784181118011, - ] -) +CENTER_OF_NF4_QUANTILES = (NF4_QUANTILES[1:] + NF4_QUANTILES[:-1]) / 2 @dataclass @@ -322,110 +392,3 @@ def _assign_mixed_precision(all_weight_params: List[WeightNodeParams], ratio: fl for weight_param in all_weight_params[1:-1]: weight_param.compression_config = nf4_config nncf_logger.info(_get_bitwidth_distribution_str(all_weight_params)) - - -def insert_pre_compression_operations( - model: ov.Model, - mode: CompressWeightsMode, - ratio: float, - group_size: int, -) -> None: - """ - Compress weights of Linear and Embedding layers to 8-bit integer or to nf4 depending on mode, ratio and group size. - - :param model: The model to be transformed. - :param mode: Defines a mode for weight compression. - INT8 stands for 8-bit integer quantization of all weights. - NF4 stands for a mixed-precision weights quantization to NF4 data type. The first and last layers - are always compressed to a backup precision which is 8-bit integer by default. All others are quantized whether - to NF4 or to a backup precision depending on criteria and the given ratio. - :param ratio: the ratio between baseline and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4 - and the rest to INT8). - :param group_size: number of weights (e.g. 128) in the channel dimension that share quantization parameters (scale). - The value -1 means no grouping. - """ - allowed_metatypes_to_const_port = {OVEmbeddingMetatype: [0], OVMatMulMetatype: [0, 1]} - - all_weight_params: List[WeightNodeParams] = [] - quantized_nodes_ids = set() - for node in model.get_ordered_ops(): - metatype = get_node_metatype(node) - if metatype not in allowed_metatypes_to_const_port: - continue - - for const_port_id in allowed_metatypes_to_const_port[metatype]: - weight_node = get_operation_const_op(node, const_port_id) - if weight_node is None: - continue - if id(weight_node) in quantized_nodes_ids: - continue - weight_output = weight_node.output(0) - weight_name = weight_node.get_friendly_name() - target_inputs = weight_output.get_target_inputs() - - original_weight_dtype = weight_output.get_element_type().to_dtype() - if original_weight_dtype not in [np.float32, np.float16, np.float64]: - continue - axes = _get_reduction_axes(metatype, node, const_port_id) - fq_name = f"{node.get_friendly_name()}/fq_weights_{const_port_id}" - weight = get_const_value(weight_node) - num_weights = weight.size - weight_params = WeightNodeParams(axes, num_weights, fq_name, weight_node, original_weight_dtype) - all_weight_params.append(weight_params) - quantized_nodes_ids.add(id(weight_node)) - - if mode == CompressWeightsMode.NF4: - _assign_mixed_precision(all_weight_params, ratio, group_size) - - for wp in all_weight_params: - weight_node = wp.weight_node - original_weight_dtype = wp.original_weight_dtype - - weight_output = weight_node.output(0) - weight_name = weight_node.get_friendly_name() - target_inputs = weight_output.get_target_inputs() - - weight = get_const_value(weight_node) - config = wp.compression_config - - if config.is_nf4: - original_shape = weight.shape - norm_weight, scale = _get_norm_weight_and_nf4_scale(weight, wp.reduction_axes, group_size) - compressed_const = opset.constant(norm_weight, dtype=ov.Type.nf4, name=weight_name) - convert = opset.convert(compressed_const, original_weight_dtype) - mul = opset.multiply(convert, scale.astype(original_weight_dtype), name=wp.fq_name) - if config.group_size != -1: - mul = opset.reshape(mul, output_shape=original_shape, special_zero=False) - last_output = mul.output(0) - else: - compressed_weights, scale, zero_point = _int8_compress(weight, wp.reduction_axes) - compressed_const = opset.constant(compressed_weights, dtype=np.uint8, name=weight_name) - convert = opset.convert(compressed_const, original_weight_dtype) - sub = opset.subtract(convert, zero_point.astype(original_weight_dtype)) - mul = opset.multiply(sub, scale.astype(original_weight_dtype), name=wp.fq_name) - last_output = mul.output(0) - - for target_input in target_inputs: - target_input.replace_source_output(last_output) - - -def _get_reduction_axes(metatype: Type[OperatorMetatype], node: ov.Node, weight_port_id: int) -> Union[int, Tuple[int]]: - """ - Determines reduction axes by given metatype and node information. - - :param metatype: The metatype of the operator. - :param node: The OpenVINO node. - :param weight_port_id: The weight port ID. - - :return: The reduction axes as an integer or a tuple of integers. - """ - if metatype is OVMatMulMetatype: - transpose = node.get_attributes()[f"transpose_{'a' if weight_port_id == 0 else 'b'}"] - ndims = node.input(weight_port_id).get_partial_shape().rank.get_max_length() - channel_axes = get_matmul_channel_axes(weight_port_id, ndims, transpose) - axes = tuple(i for i in range(ndims) if i not in channel_axes) - elif metatype is OVEmbeddingMetatype: - axes = (metatype.const_channel_axis[0] + 1) % 2 - else: - RuntimeError("Unsupported metatype to find reduction axes.") - return axes diff --git a/nncf/quantization/fake_quantize.py b/nncf/quantization/fake_quantize.py index f1744813c54..38b56c97019 100644 --- a/nncf/quantization/fake_quantize.py +++ b/nncf/quantization/fake_quantize.py @@ -21,6 +21,9 @@ from nncf.common.quantization.structs import QuantizerConfig from nncf.common.quantization.structs import QuantizerGroup from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic +from nncf.experimental.tensor import Tensor +from nncf.experimental.tensor import TensorDataType +from nncf.experimental.tensor import functions as fns @dataclass @@ -35,14 +38,14 @@ class FakeQuantizeParameters: :param levels: Number of quantization levels. """ - input_low: np.ndarray - input_high: np.ndarray - output_low: np.ndarray - output_high: np.ndarray + input_low: Tensor + input_high: Tensor + output_low: Tensor + output_high: Tensor levels: int -def fix_zero_filters_symmetric(max_values: np.ndarray, eps: float = 0.01) -> np.ndarray: +def fix_zero_filters_symmetric(max_values: Tensor, eps: float = 0.01) -> Tensor: """ Fixes zero filters for symmetric quantizer. @@ -50,14 +53,12 @@ def fix_zero_filters_symmetric(max_values: np.ndarray, eps: float = 0.01) -> np. :param eps: Correction coefficient. :return: Fixed the high quant number. """ - max_range = np.max(max_values) - lower_threshold = np.maximum(8e-5, eps * max_range) - return np.maximum(lower_threshold, max_values) + max_range = fns.max(max_values) + lower_threshold = fns.maximum(max_range * eps, 8e-5) + return fns.maximum(lower_threshold, max_values) -def fix_zero_filters_asymmetric( - min_values: np.ndarray, max_values: np.ndarray, eps: float = 1e-8 -) -> Tuple[np.ndarray, np.ndarray]: +def fix_zero_filters_asymmetric(min_values: Tensor, max_values: Tensor, eps: float = 1e-8) -> Tuple[Tensor, Tensor]: """ Fixes zero filters for asymmetric quantizer. @@ -69,20 +70,17 @@ def fix_zero_filters_asymmetric( level_high - fixed the high quant number """ ranges = max_values - min_values - ranges = ranges.flatten() if isinstance(ranges, np.ndarray) else np.array([ranges]) min_correction = 8e-4 - corrections = [ - (np.maximum(eps * rng, rng) - rng) * 0.5 if rng > min_correction else min_correction for rng in ranges - ] - corrections = np.array(corrections).reshape(max_values.shape) + corrections = fns.where(ranges > min_correction, (fns.maximum(eps * ranges, ranges) - ranges) * 0.5, min_correction) + level_low = min_values - corrections level_high = max_values + corrections return level_low, level_high def tune_range( - left_border: np.ndarray, right_border: np.ndarray, num_bits: int, unify_zp: bool = False -) -> Tuple[np.ndarray, np.ndarray]: + left_border: Tensor, right_border: Tensor, num_bits: int, unify_zp: bool = False +) -> Tuple[Tensor, Tensor]: """ Tunes asymmetric quantization range to unify the zero point of all channels if `unify_zp` is True, or sets zero quant precisely to zero value otherwise. @@ -101,22 +99,21 @@ def tune_range( if unify_zp: scale = (right_border - left_border) / level_high zero_point = -left_border / scale - avg_zpts = np.round(np.mean(zero_point)) - qval = np.ones_like(left_border) * avg_zpts + avg_zpts = fns.round(fns.mean(zero_point)) + qval = fns.ones_like(left_border) * avg_zpts else: s = level_high / (right_border - left_border) fval = -left_border * s - qval = np.round(fval) + qval = fns.round(fval) - with np.errstate(invalid="ignore", divide="ignore"): - ra = np.where(qval < level_high, qval / (qval - level_high) * right_border, left_border) - rb = np.where(qval > 0.0, (qval - level_high) / qval * left_border, right_border) + ra = fns.where(qval < level_high, qval / (qval - level_high) * right_border, left_border) + rb = fns.where(qval > 0.0, (qval - level_high) / qval * left_border, right_border) range_a = right_border - ra range_b = rb - left_border - mask = np.where(range_a > range_b, 1.0, 0.0) - inv_mask = np.abs(1.0 - mask) + mask = fns.where(range_a > range_b, 1.0, 0.0) + inv_mask = fns.abs(1.0 - mask) ra = mask * ra + inv_mask * left_border rb = inv_mask * rb + mask * right_border @@ -125,12 +122,12 @@ def tune_range( def symmetric_range( - min_values: np.ndarray, - max_values: np.ndarray, + min_values: Tensor, + max_values: Tensor, levels: int, quantizer_config: QuantizerConfig, q_group: QuantizerGroup, -) -> Tuple[np.ndarray, np.ndarray]: +) -> Tuple[Tensor, Tensor]: """ Calculates the numbers of the low and high quant for the symmetric quantization scheme. @@ -148,21 +145,23 @@ def symmetric_range( else: signed = quantizer_config.signedness_to_force is True level_low = ( - np.zeros_like(level_high) if np.all(min_values >= 0) and not signed else -level_high * levels / (levels - 2) + fns.zeros_like(level_high) + if fns.all(min_values >= 0) and not signed + else -level_high * levels / (levels - 2) ) - level_low = level_low.astype(np.float32) - level_high = level_high.astype(np.float32) + level_low = level_low.astype(TensorDataType.float32) + level_high = level_high.astype(TensorDataType.float32) return level_low, level_high def asymmetric_range( - min_values: np.ndarray, - max_values: np.ndarray, + min_values: Tensor, + max_values: Tensor, quantizer_config: QuantizerConfig, q_group: QuantizerGroup, unify_zp: bool = False, -) -> Tuple[np.ndarray, np.ndarray]: +) -> Tuple[Tensor, Tensor]: """ Calculates the numbers of the low and high quant for the asymmetric quantization scheme. @@ -176,15 +175,15 @@ def asymmetric_range( level_high - the high quant number """ level_low, level_high = fix_zero_filters_asymmetric(min_values, max_values) - level_low = np.where(level_low < 0.0, level_low, 0.0) - level_high = np.where(level_high > 0.0, level_high, 0.0) + level_low = fns.where(level_low < 0.0, level_low, 0.0) + level_high = fns.where(level_high > 0.0, level_high, 0.0) if unify_zp and q_group == QuantizerGroup.ACTIVATIONS: raise NotImplementedError("Unified zero point is not supported for activations.") level_low, level_high = tune_range(level_low, level_high, quantizer_config.num_bits, unify_zp=unify_zp) - level_low = level_low.astype(np.float32) - level_high = level_high.astype(np.float32) + level_low = level_low.astype(TensorDataType.float32) + level_high = level_high.astype(TensorDataType.float32) return level_low, level_high @@ -221,8 +220,8 @@ def calculate_quantizer_parameters( False - the full range is used. :return: Parameters of the FakeQuantize layer. """ - min_values = np.array(statistics.min_values).astype(np.float32) - max_values = np.array(statistics.max_values).astype(np.float32) + min_values = Tensor(statistics.min_values).astype(TensorDataType.float32) + max_values = Tensor(statistics.max_values).astype(TensorDataType.float32) if half_range: input_low, input_high, levels = _calculate_scaled_parameters( @@ -240,21 +239,20 @@ def calculate_quantizer_parameters( input_low, input_high = asymmetric_range(min_values, max_values, quantizer_config, quant_group) if not quantizer_config.per_channel: - input_low = np.squeeze(input_low) - input_high = np.squeeze(input_high) + input_low = fns.squeeze(input_low) + input_high = fns.squeeze(input_high) - input_low, input_high = np.array(input_low), np.array(input_high) output_low, output_high = input_low, input_high return FakeQuantizeParameters(input_low, input_high, output_low, output_high, levels) def _calculate_scaled_parameters( - min_values: np.ndarray, - max_values: np.ndarray, + min_values: Tensor, + max_values: Tensor, quantizer_config: QuantizerConfig, quant_group: QuantizerGroup, narrow_range: bool, -) -> Tuple[np.ndarray, np.ndarray, int]: +) -> Tuple[Tensor, Tensor, int]: """ Calculates FakeQuantize layer attributes scaled to effectively use a half range of the quantization range. diff --git a/nncf/quantization/quantize_model.py b/nncf/quantization/quantize_model.py index c87479fa401..76e5242c4a2 100644 --- a/nncf/quantization/quantize_model.py +++ b/nncf/quantization/quantize_model.py @@ -12,6 +12,7 @@ from typing import Any, Callable, Iterable, List, Optional, Tuple, TypeVar, Union from nncf.api.compression import TModel +from nncf.common.factory import NNCFGraphFactory from nncf.common.quantization.structs import QuantizationPreset from nncf.common.utils.api_marker import api from nncf.common.utils.backend import BackendType @@ -27,6 +28,7 @@ from nncf.quantization.algorithms.hyperparameter_tuner.algorithm import HyperparameterTuner from nncf.quantization.algorithms.hyperparameter_tuner.param_grid import get_quantization_param_grid from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization +from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression from nncf.scopes import IgnoredScope TTensor = TypeVar("TTensor") @@ -228,7 +230,10 @@ def quantize_with_accuracy_control( @api(canonical_alias="nncf.compress_weights") def compress_weights( - model: TModel, mode=CompressWeightsMode.INT8, ratio: Optional[float] = None, group_size: Optional[int] = None + model: TModel, + mode=CompressWeightsMode.INT8, + ratio: Optional[float] = None, + group_size: Optional[int] = None, ) -> TModel: """ Compress model weights. @@ -245,7 +250,6 @@ def compress_weights( The value -1 means no grouping. :return: The non-trainable model with compressed weights. """ - backend = get_backend(model) if mode == CompressWeightsMode.INT8: if ratio is None: ratio = 1 @@ -262,16 +266,15 @@ def compress_weights( if group_size is None: group_size = 128 + backend = get_backend(model) if backend == BackendType.TORCH: from nncf.torch.quantization.quantize_model import compress_weights_impl return compress_weights_impl(model, mode, ratio, group_size) - if backend == BackendType.OPENVINO: - from nncf.openvino.quantization.quantize_model import compress_weights_impl - return compress_weights_impl(model, mode, ratio, group_size) - - raise RuntimeError(f"Unsupported type of backend: {backend}") + compression_algorithm = WeightCompression(mode, ratio, group_size) + graph = NNCFGraphFactory.create(model) + return compression_algorithm.apply(model, graph) def quantize_with_tune_hyperparams( diff --git a/nncf/telemetry/__init__.py b/nncf/telemetry/__init__.py index 8219f26d6bd..3c0c3936eb1 100644 --- a/nncf/telemetry/__init__.py +++ b/nncf/telemetry/__init__.py @@ -9,6 +9,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.telemetry.decorator import tracked_function -from nncf.telemetry.extractors import TelemetryExtractor -from nncf.telemetry.wrapper import telemetry +from nncf.telemetry.decorator import tracked_function as tracked_function +from nncf.telemetry.extractors import TelemetryExtractor as TelemetryExtractor +from nncf.telemetry.wrapper import telemetry as telemetry diff --git a/nncf/telemetry/decorator.py b/nncf/telemetry/decorator.py index 67ee5a88c24..42ae45f58e3 100644 --- a/nncf/telemetry/decorator.py +++ b/nncf/telemetry/decorator.py @@ -14,6 +14,7 @@ from nncf.telemetry.events import get_current_category from nncf.telemetry.events import telemetry_category +from nncf.telemetry.extractors import CollectedEvent from nncf.telemetry.extractors import TelemetryExtractor from nncf.telemetry.extractors import VerbatimTelemetryExtractor from nncf.telemetry.wrapper import telemetry @@ -48,7 +49,7 @@ def __call__(self, fn: Callable) -> Callable: def wrapped(*args, **kwargs): bound_args = fn_signature.bind(*args, **kwargs) bound_args.apply_defaults() - events = [] # type: CollectedEvent + events: List[CollectedEvent] = [] for collector in self._collectors: argname = collector.argname argvalue = bound_args.arguments[argname] if argname is not None else None diff --git a/nncf/telemetry/wrapper.py b/nncf/telemetry/wrapper.py index 341f3c8c0c3..e6a4040a677 100644 --- a/nncf/telemetry/wrapper.py +++ b/nncf/telemetry/wrapper.py @@ -93,7 +93,13 @@ class NNCFTelemetry(ITelemetry): def __init__(self): try: - self._impl = Telemetry(app_name="nncf", app_version=__version__, tid=self.MEASUREMENT_ID, backend="ga4") + self._impl = Telemetry( + app_name="nncf", + app_version=__version__, + tid=self.MEASUREMENT_ID, + backend="ga4", + enable_opt_in_dialog=False, + ) # pylint:disable=broad-except except Exception as e: nncf_logger.debug(f"Failed to instantiate telemetry object: exception {e}") diff --git a/nncf/tensorflow/__init__.py b/nncf/tensorflow/__init__.py index 09e605b5b16..e98802e35a9 100644 --- a/nncf/tensorflow/__init__.py +++ b/nncf/tensorflow/__init__.py @@ -11,13 +11,12 @@ """ Base subpackage for NNCF TensorFlow functionality. """ +# pylint:skip-file import tensorflow from packaging import version from nncf import nncf_logger from nncf.common.logging.logger import warn_bkc_version_mismatch - -# pylint: skip-file from nncf.version import BKC_TF_VERSION try: @@ -36,11 +35,15 @@ ) -from nncf.common.accuracy_aware_training.training_loop import AdaptiveCompressionTrainingLoop -from nncf.common.accuracy_aware_training.training_loop import EarlyExitCompressionTrainingLoop -from nncf.tensorflow.helpers import create_compressed_model -from nncf.tensorflow.helpers.callback_creation import create_compression_callbacks -from nncf.tensorflow.initialization import register_default_init_args +from nncf.common.accuracy_aware_training.training_loop import ( + AdaptiveCompressionTrainingLoop as AdaptiveCompressionTrainingLoop, +) +from nncf.common.accuracy_aware_training.training_loop import ( + EarlyExitCompressionTrainingLoop as EarlyExitCompressionTrainingLoop, +) +from nncf.tensorflow.helpers import create_compressed_model as create_compressed_model +from nncf.tensorflow.helpers.callback_creation import create_compression_callbacks as create_compression_callbacks +from nncf.tensorflow.initialization import register_default_init_args as register_default_init_args from nncf.tensorflow.pruning.filter_pruning import algorithm as filter_pruning_algorithm # Required for correct COMPRESSION_ALGORITHMS registry functioning diff --git a/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py b/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py index feb37eb200c..25763c77e7a 100644 --- a/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py +++ b/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py @@ -39,7 +39,7 @@ def accuracy_aware_fit( result_dict_to_val_metric_fn = lambda metric: metric # pylint:disable=line-too-long - with cls_instance.distribute_strategy.scope(), tf_internals.keras_engine.training_utils.RespectCompiledTrainableState( + with cls_instance.distribute_strategy.scope(), tf_internals.keras_engine.training_utils.RespectCompiledTrainableState( # noqa: E501 cls_instance ): # pylint: disable=protected-access diff --git a/nncf/tensorflow/graph/converter.py b/nncf/tensorflow/graph/converter.py index 9c62dcd342a..9b9a6a3607a 100644 --- a/nncf/tensorflow/graph/converter.py +++ b/nncf/tensorflow/graph/converter.py @@ -20,7 +20,6 @@ from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNodeName -from nncf.common.graph import OperatorMetatype from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.graph import NNCFNode from nncf.common.graph.layer_attributes import BaseLayerAttributes @@ -32,6 +31,7 @@ from nncf.common.graph.layer_attributes import MultipleOutputLayerAttributes from nncf.common.graph.layer_attributes import PermuteLayerAttributes from nncf.common.graph.layer_attributes import ReshapeLayerAttributes +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.operator_metatypes import OutputNoopMetatype from nncf.common.graph.utils import get_concat_axis from nncf.common.graph.utils import get_split_axis @@ -139,12 +139,12 @@ class CustomLayerInfo: """ def __init__(self): - self.requires_inputs_from_nodes = set() # type: Set[str] - self.gives_outputs_from_nodes = set() # type: Set[str] - self.shared_weight_node_names_vs_weighted_op_node_names = defaultdict(set) # type: Dict[str, Set[str]] - self.node_infos = {} # type: Dict[str, CustomLayerNodeInfo] - self.edge_infos = {} # type: Dict[Tuple[str, str], CustomLayerEdgeInfo] - self.graphdef_node_name_to_pretty_node_name = {} # type: Dict[str, str] + self.requires_inputs_from_nodes: Set[str] = set() + self.gives_outputs_from_nodes: Set[str] = set() + self.shared_weight_node_names_vs_weighted_op_node_names: Dict[str, Set[str]] = defaultdict(set) + self.node_infos: Dict[str, CustomLayerNodeInfo] = {} + self.edge_infos: Dict[Tuple[str, str], CustomLayerEdgeInfo] = {} + self.graphdef_node_name_to_pretty_node_name: Dict[str, str] = {} class TFModelConverter(ABC): @@ -171,9 +171,9 @@ class BaseFunctionalSequentialConverter(TFModelConverter): def __init__(self, model: tf.keras.Model): self._model = model - self._node_info = {} # type: Dict[str, Dict] + self._node_info: Dict[str, Dict] = {} self._custom_layer_infos = self._collect_custom_layer_infos(self._model) - self._nncf_node_names_vs_custom_layer_name = {} # type: Dict[NNCFNodeName, str] + self._nncf_node_names_vs_custom_layer_name: Dict[NNCFNodeName, str] = {} @staticmethod def _get_type_spec(tensor): @@ -411,7 +411,7 @@ def _get_layer(self, layer_name: str) -> tf.keras.layers.Layer: def _add_custom_layer_subgraph(self, nncf_graph: NNCFGraph, custom_layer_name: str) -> NNCFGraph: # TODO (vshampor): filter meaningless ops such as Identity, resource read etc. custom_layer_info = self._custom_layer_infos[custom_layer_name] - node_name_vs_nncf_node_ids = {} # type: Dict[NNCFNodeName, int] + node_name_vs_nncf_node_ids: Dict[NNCFNodeName, int] = {} for node_info in custom_layer_info.node_infos.values(): weight_node_name = node_info.weight_node_name is_shared = False @@ -469,11 +469,11 @@ class FunctionalConverter(BaseFunctionalSequentialConverter): def __init__(self, model: tf.keras.Model): super().__init__(model) self._model_config = self._model.get_config() - self._layer_info = {} # type: Dict[str, Dict] + self._layer_info: Dict[str, Dict] = {} self._collect_layer_information() self._layer_name_to_node_names = defaultdict(set) self._collect_node_information() - self._edge_info = {} # type: Dict[Tuple[str, str], Dict] + self._edge_info: Dict[Tuple[str, str], Dict] = {} self._collect_edge_information() def _collect_layer_information(self): @@ -576,8 +576,8 @@ def _collect_edge_information(self): def convert(self) -> NNCFGraph: nncf_graph = NNCFGraph() - node_name_vs_nncf_node_ids = {} # type: Dict[str, int] - output_node_id_vs_model_output_idx = {} # type: Dict[int, int] + node_name_vs_nncf_node_ids: Dict[str, int] = {} + output_node_id_vs_model_output_idx: Dict[int, int] = {} # Regular nodes for node_name, node_info in self._node_info.items(): diff --git a/nncf/tensorflow/graph/metatypes/common.py b/nncf/tensorflow/graph/metatypes/common.py index af35b07bb29..e745578adec 100644 --- a/nncf/tensorflow/graph/metatypes/common.py +++ b/nncf/tensorflow/graph/metatypes/common.py @@ -11,9 +11,9 @@ from typing import List, Type -from nncf.common.graph import INPUT_NOOP_METATYPES -from nncf.common.graph import OUTPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.tensorflow.graph.metatypes import keras_layers as layer_metatypes from nncf.tensorflow.graph.metatypes import tf_ops as op_metatypes diff --git a/nncf/tensorflow/graph/metatypes/keras_layers.py b/nncf/tensorflow/graph/metatypes/keras_layers.py index 23eed1d3d5f..210e78ceb93 100644 --- a/nncf/tensorflow/graph/metatypes/keras_layers.py +++ b/nncf/tensorflow/graph/metatypes/keras_layers.py @@ -27,8 +27,8 @@ class TFLayerMetatype(OperatorMetatype): - keras_layer_names = [] # type: List[str] - subtypes = [] # type: List[Type[OperatorMetatype]] + keras_layer_names: List[str] = [] + subtypes: List[Type[OperatorMetatype]] = [] @classmethod def get_subtypes(cls) -> List[Type[OperatorMetatype]]: @@ -72,8 +72,8 @@ def _determine_subtype( class TFLayerWithWeightsMetatype(TFLayerMetatype): - weight_definitions = [] # type: List[WeightDef] - bias_attr_name = None # type: Optional[str] + weight_definitions: List[WeightDef] = [] + bias_attr_name: Optional[str] = None @KERAS_LAYER_METATYPES.register() diff --git a/nncf/tensorflow/graph/metatypes/tf_ops.py b/nncf/tensorflow/graph/metatypes/tf_ops.py index 2f41c0e8ca8..ec38997b516 100644 --- a/nncf/tensorflow/graph/metatypes/tf_ops.py +++ b/nncf/tensorflow/graph/metatypes/tf_ops.py @@ -19,7 +19,7 @@ class TFOpMetatype(OperatorMetatype): - op_names = [] # type: List[str] + op_names: List[str] = [] @classmethod def get_all_aliases(cls) -> List[str]: @@ -46,7 +46,7 @@ def __init__(self, port_id: int, channel_axes): class TFOpWithWeightsMetatype(TFOpMetatype): - weight_definitions = [] # type: List[OpWeightDef] + weight_definitions: List[OpWeightDef] = [] @TF_OPERATION_METATYPES.register() diff --git a/nncf/tensorflow/hardware/config.py b/nncf/tensorflow/hardware/config.py index 907d6b75fd8..d74412c709c 100644 --- a/nncf/tensorflow/hardware/config.py +++ b/nncf/tensorflow/hardware/config.py @@ -11,7 +11,7 @@ from typing import List, Type -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.hardware.config import HWConfig from nncf.tensorflow.graph.metatypes.common import get_operator_metatypes diff --git a/nncf/tensorflow/helpers/__init__.py b/nncf/tensorflow/helpers/__init__.py index b68e342e333..c04c78955a9 100644 --- a/nncf/tensorflow/helpers/__init__.py +++ b/nncf/tensorflow/helpers/__init__.py @@ -9,4 +9,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from nncf.tensorflow.helpers.model_creation import create_compressed_model +from nncf.tensorflow.helpers.model_creation import create_compressed_model as create_compressed_model diff --git a/nncf/tensorflow/layers/wrapper.py b/nncf/tensorflow/layers/wrapper.py index 56ab120df1b..d6e2ab9743b 100644 --- a/nncf/tensorflow/layers/wrapper.py +++ b/nncf/tensorflow/layers/wrapper.py @@ -50,7 +50,7 @@ def __init__(self, layer, **kwargs): super().__init__(layer, **kwargs) self._track_trackable(layer, name="layer") - self.weights_attr_ops = {} # type: Dict[str, Dict[str, NNCFOperation]] + self.weights_attr_ops: Dict[str, Dict[str, NNCFOperation]] = {} self._init_layer_call_fn_args() self._trainable_weights = [] diff --git a/nncf/tensorflow/pruning/filter_pruning/algorithm.py b/nncf/tensorflow/pruning/filter_pruning/algorithm.py index 2e0d33cefed..d095498bee0 100644 --- a/nncf/tensorflow/pruning/filter_pruning/algorithm.py +++ b/nncf/tensorflow/pruning/filter_pruning/algorithm.py @@ -500,7 +500,7 @@ def _calculate_filters_importance_in_group(self, group: Cluster[PrunedLayerInfo] cumulative_filters_importance = tf.zeros(filters_num) # Calculate cumulative importance for all filters in this group - shared_nodes = set() # type: Set[str] + shared_nodes: Set[str] = set() for minfo in group.elements: layer_name = minfo.layer_name if layer_name in shared_nodes: diff --git a/nncf/tensorflow/pruning/utils.py b/nncf/tensorflow/pruning/utils.py index 2248d834022..839fef680bd 100644 --- a/nncf/tensorflow/pruning/utils.py +++ b/nncf/tensorflow/pruning/utils.py @@ -70,7 +70,7 @@ def broadcast_filter_mask(filter_mask, shape, dim): return broadcasted_filter_mask -def collect_output_shapes(model: "NNCFNetwork", graph: NNCFGraph) -> Dict[NNCFNodeName, List[int]]: +def collect_output_shapes(model: "NNCFNetwork", graph: NNCFGraph) -> Dict[NNCFNodeName, List[int]]: # noqa: F821 """ Collects output dimension shapes for convolutions and fully connected layers from the connected edges in the NNCFGraph. diff --git a/nncf/tensorflow/quantization/__init__.py b/nncf/tensorflow/quantization/__init__.py index d659c28de8d..6cf84541e81 100644 --- a/nncf/tensorflow/quantization/__init__.py +++ b/nncf/tensorflow/quantization/__init__.py @@ -13,6 +13,6 @@ """ # Required for correct registry functioning -from nncf.tensorflow.quantization.layers import FakeQuantize -from nncf.tensorflow.quantization.quantizers import AsymmetricQuantizer -from nncf.tensorflow.quantization.quantizers import SymmetricQuantizer +from nncf.tensorflow.quantization.layers import FakeQuantize as FakeQuantize +from nncf.tensorflow.quantization.quantizers import AsymmetricQuantizer as AsymmetricQuantizer +from nncf.tensorflow.quantization.quantizers import SymmetricQuantizer as SymmetricQuantizer diff --git a/nncf/tensorflow/quantization/algorithm.py b/nncf/tensorflow/quantization/algorithm.py index c1a11f129e2..e977e1debc0 100644 --- a/nncf/tensorflow/quantization/algorithm.py +++ b/nncf/tensorflow/quantization/algorithm.py @@ -18,12 +18,12 @@ from nncf.api.compression import CompressionScheduler from nncf.api.compression import CompressionStage from nncf.common.compression import BaseCompressionAlgorithmController -from nncf.common.graph import INPUT_NOOP_METATYPES -from nncf.common.graph import OUTPUT_NOOP_METATYPES from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName from nncf.common.graph.layer_attributes import ConvertDtypeLayerAttributes +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.commands import TransformationPriority @@ -164,8 +164,8 @@ class TFQuantizationSetup: def __init__(self): super().__init__() - self._quantization_points = [] # type: List[TFQuantizationPoint] - self._unified_scale_groups = [] # type: List[List[QuantizationPointId]] + self._quantization_points: List[TFQuantizationPoint] = [] + self._unified_scale_groups: List[List[QuantizationPointId]] = [] def add_quantization_point(self, point: TFQuantizationPoint): """ @@ -458,8 +458,8 @@ def _get_quantizer_setup(self, model: tf.keras.Model) -> TFQuantizationSetup: ) setup = TFQuantizationSetup() - quantized_layer_names_vs_qconfigs = {} # type: Dict[str, QuantizerConfig] - qp_id_to_index = {} # type: Dict[QuantizationPointId, int] + quantized_layer_names_vs_qconfigs: Dict[str, QuantizerConfig] = {} + qp_id_to_index: Dict[QuantizationPointId, int] = {} tf_setup_qp_index = 0 applied_overflow_fix = False first_conv_nodes = get_first_nodes_of_type(nncf_graph, ["Conv2D", "Conv3D"]) @@ -578,7 +578,7 @@ def _get_quantizable_weighted_layer_nodes(self, nncf_graph: NNCFGraph) -> List[Q if metatype in OUTPUT_NOOP_METATYPES: continue - if not metatype in QUANTIZATION_LAYER_METATYPES: + if metatype not in QUANTIZATION_LAYER_METATYPES: continue assert issubclass(metatype, TFLayerWithWeightsMetatype) or issubclass(metatype, TFOpWithWeightsMetatype) @@ -610,7 +610,7 @@ def _get_quantizer_propagation_solution( input_preprocessing_nodes = self._get_input_preprocessing_nodes(nncf_graph, model) input_preprocessing_node_names = [n.node_name for n in input_preprocessing_nodes] if custom_layer_node_names: - custom_layer_node_names_str = ", ".join([str(l) for l in custom_layer_node_names]) + custom_layer_node_names_str = ", ".join([str(n) for n in custom_layer_node_names]) nncf_logger.warning( f"Following custom layers will be ignored during quantization (custom layer quantization not supported " f"by NNCF yet):\n[{custom_layer_node_names_str}]" diff --git a/nncf/tensorflow/quantization/init_range.py b/nncf/tensorflow/quantization/init_range.py index 900f3248134..6ff881c026c 100644 --- a/nncf/tensorflow/quantization/init_range.py +++ b/nncf/tensorflow/quantization/init_range.py @@ -63,7 +63,7 @@ def get_init_config_for_quantization_point(self, layer: tf.keras.layers.Layer, i return self.get_init_config_for_scope_and_group(node_name, group) def get_init_config_for_scope_and_group(self, node_name: str, group: QuantizerGroup) -> RangeInitConfig: - matches = [] # type: List[RangeInitConfig] + matches: List[RangeInitConfig] = [] for pl_config in self.per_layer_range_init_configs: if should_consider_scope( node_name, ignored_scopes=pl_config.ignored_scopes, target_scopes=pl_config.target_scopes diff --git a/nncf/tensorflow/quantization/quantize_model.py b/nncf/tensorflow/quantization/quantize_model.py index f6f15d79d8e..4d726d88651 100644 --- a/nncf/tensorflow/quantization/quantize_model.py +++ b/nncf/tensorflow/quantization/quantize_model.py @@ -53,7 +53,7 @@ def batch_size(self) -> int: if isinstance(batch_size, tf.Tensor): batch_size = batch_size.numpy() batch_size = int(batch_size) - except: # pylint: disable=W0702 + except: # pylint: disable=W0702 # noqa: E722 batch_size = 1 return batch_size diff --git a/nncf/tensorflow/sparsity/magnitude/algorithm.py b/nncf/tensorflow/sparsity/magnitude/algorithm.py index 0f6e7f3d270..0ef45f5ec9d 100644 --- a/nncf/tensorflow/sparsity/magnitude/algorithm.py +++ b/nncf/tensorflow/sparsity/magnitude/algorithm.py @@ -18,7 +18,7 @@ from nncf.api.compression import CompressionScheduler from nncf.api.compression import CompressionStage from nncf.common.accuracy_aware_training.training_loop import ADAPTIVE_COMPRESSION_CONTROLLERS -from nncf.common.graph import OUTPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES from nncf.common.graph.transformations.commands import TransformationPriority from nncf.common.initialization.batchnorm_adaptation import BatchnormAdaptationAlgorithm from nncf.common.schedulers import StubCompressionScheduler @@ -67,7 +67,7 @@ def get_transformation_layout(self, model: tf.keras.Model) -> TFTransformationLa transformations = TFTransformationLayout() - processed_shared_layer_names = set() # type: Set[str] + processed_shared_layer_names: Set[str] = set() for node in nncf_graph.get_all_nodes(): if node.is_shared(): diff --git a/nncf/tensorflow/sparsity/rb/algorithm.py b/nncf/tensorflow/sparsity/rb/algorithm.py index 44ec4074804..e014b99b8bc 100644 --- a/nncf/tensorflow/sparsity/rb/algorithm.py +++ b/nncf/tensorflow/sparsity/rb/algorithm.py @@ -58,7 +58,7 @@ def get_transformation_layout(self, model: tf.keras.Model) -> TFTransformationLa transformations = TFTransformationLayout() - processed_shared_layer_names = set() # type: Set[str] + processed_shared_layer_names: Set[str] = set() for node in nncf_graph.get_all_nodes(): if node.is_shared(): diff --git a/nncf/tensorflow/tf_internals.py b/nncf/tensorflow/tf_internals.py index 566365ce2c4..94b6ca13534 100644 --- a/nncf/tensorflow/tf_internals.py +++ b/nncf/tensorflow/tf_internals.py @@ -8,26 +8,26 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + # pylint: disable=unused-import +# pylint: disable=import-error +# pylint: disable=no-name-in-module -from packaging import version -from tensorflow.python.eager import context as eager_context +from keras import backend as backend +from keras import layers as layers +from keras.layers import Rescaling as Rescaling +from packaging import version as version +from tensorflow.python.eager import context as eager_context # noqa: F401 from nncf.tensorflow import tensorflow_version if version.parse(tensorflow_version) < version.parse("2.13"): - from keras import backend - from keras import engine as keras_engine - from keras import layers - from keras.applications import imagenet_utils - from keras.engine.keras_tensor import KerasTensor - from keras.layers import Rescaling - from keras.utils.control_flow_util import smart_cond + from keras import engine as keras_engine # noqa: F401 + from keras.applications import imagenet_utils as imagenet_utils + from keras.engine.keras_tensor import KerasTensor as KerasTensor + from keras.utils.control_flow_util import smart_cond as smart_cond else: - from keras import backend - from keras import layers - from keras.layers import Rescaling - from keras.src import engine as keras_engine # pylint: disable=no-name-in-module,import-error - from keras.src.applications import imagenet_utils # pylint: disable=no-name-in-module,import-error - from keras.src.engine.keras_tensor import KerasTensor # pylint: disable=no-name-in-module,import-error - from keras.src.utils.control_flow_util import smart_cond # pylint: disable=no-name-in-module,import-error + from keras.src import engine as keras_engine # noqa: F401 + from keras.src.applications import imagenet_utils as imagenet_utils # noqa: E501 + from keras.src.engine.keras_tensor import KerasTensor as KerasTensor # noqa: E501 + from keras.src.utils.control_flow_util import smart_cond as smart_cond # noqa: E501 diff --git a/nncf/torch/__init__.py b/nncf/torch/__init__.py index c1275f4ea7f..f62e8e33fe8 100644 --- a/nncf/torch/__init__.py +++ b/nncf/torch/__init__.py @@ -25,7 +25,7 @@ try: _torch_version = torch.__version__ torch_version = version.parse(_torch_version).base_version -except: +except: # noqa: E722 nncf_logger.debug("Could not parse torch version") _torch_version = "0.0.0" torch_version = version.parse(_torch_version).base_version diff --git a/nncf/torch/automl/environment/quantization_env.py b/nncf/torch/automl/environment/quantization_env.py index 8e162d95903..59de15e524a 100644 --- a/nncf/torch/automl/environment/quantization_env.py +++ b/nncf/torch/automl/environment/quantization_env.py @@ -176,9 +176,9 @@ def __init__( self.model_bitwidth_space = sorted(list(self.model_bitwidth_space)) # Create mapping of QuantizerId to the space of the corresponding quantizer's allowed qconfigs - self.qconfig_space_map = OrderedDict.fromkeys( + self.qconfig_space_map: Dict[QuantizerId, List[QuantizerConfig]] = OrderedDict.fromkeys( self.qctrl.all_quantizations.keys() - ) # type: Dict[QuantizerId, List[QuantizerConfig]] + ) if self.hw_cfg_type is None: for qid in self.qconfig_space_map.keys(): conf = self.qctrl.all_quantizations[qid].get_quantizer_config() @@ -554,7 +554,7 @@ def is_final_step(): return self.evaluate_strategy(self.collected_strategy, skip_constraint=self.skip_constraint) def select_config_for_actions(self, actions) -> Dict[QuantizationPointId, QuantizerConfig]: - retval = OrderedDict() # type: Dict[QuantizationPointId, QuantizerConfig] + retval: Dict[QuantizationPointId, QuantizerConfig] = OrderedDict() for action, qp_id_set, qconf_space in zip(actions, self.master_df["qp_id_set"], self.master_df["qconf_space"]): matches = [] for qconf in qconf_space: @@ -681,7 +681,7 @@ def _align_bw_action(self): intersecting_bw_space = self.adjq_groupwise_intersecting_bw_space[i] # Determine the lowest realizable hardware precision given current action of the groups - if not group_min_predicted_bw in intersecting_bw_space: + if group_min_predicted_bw not in intersecting_bw_space: group_final_min_bw = min(intersecting_bw_space) else: group_final_min_bw = group_min_predicted_bw diff --git a/nncf/torch/binarization/algo.py b/nncf/torch/binarization/algo.py index 6af5d4ae15e..998c4d6ecf1 100644 --- a/nncf/torch/binarization/algo.py +++ b/nncf/torch/binarization/algo.py @@ -39,7 +39,7 @@ from nncf.torch.layers import NNCFConv2d from nncf.torch.module_operations import UpdateInputs from nncf.torch.nncf_network import NNCFNetwork -from nncf.torch.quantization.algo import QuantizationControllerBase +from nncf.torch.quantization.base_ctrl import QuantizationControllerBase from nncf.torch.quantization.schedulers import QUANTIZATION_SCHEDULERS from nncf.torch.utils import get_model_device diff --git a/nncf/torch/checkpoint_loading.py b/nncf/torch/checkpoint_loading.py index 942509bded5..7931b200094 100644 --- a/nncf/torch/checkpoint_loading.py +++ b/nncf/torch/checkpoint_loading.py @@ -92,7 +92,7 @@ class ProcessedKeys: """Contains checkpoint keys with their status of matching with model keys""" def __init__(self): - self._keys = {} # type: Dict[ProcessedKeyStatus, Set[str]] + self._keys: Dict[ProcessedKeyStatus, Set[str]] = {} for key_status in ProcessedKeyStatus: self._keys[key_status] = set() diff --git a/nncf/torch/debug.py b/nncf/torch/debug.py index 1ffb4ba4d75..8ced0814eaf 100644 --- a/nncf/torch/debug.py +++ b/nncf/torch/debug.py @@ -62,8 +62,10 @@ def init_actual(self, owner_model): def debuggable_forward(forward_func): + from nncf.torch.nncf_network import NNCFNetwork + @functools.wraps(forward_func) - def decorated(self: "NNCFNetwork", *args, **kwargs): + def decorated(self: NNCFNetwork, *args, **kwargs): if hasattr(self, "nncf") and self.nncf.debug_interface is not None: self.nncf.debug_interface.pre_forward_actions(module=self) retval = forward_func(self, *args, **kwargs) @@ -76,12 +78,12 @@ def decorated(self: "NNCFNetwork", *args, **kwargs): class CombinedDebugInterface(DebugInterface): def __init__(self): - self._interfaces = [] # type: List[DebugInterface] + self._interfaces: List[DebugInterface] = [] def add_interface(self, interface: "DebugInterface"): self._interfaces.append(interface) - def init_actual(self, owner_model: "NNCFNetwork"): + def init_actual(self, owner_model: "NNCFNetwork"): # noqa: F821 for interface in self._interfaces: interface.init_actual(owner_model) diff --git a/nncf/torch/dynamic_graph/context.py b/nncf/torch/dynamic_graph/context.py index a56cd0ec2f5..9d9e1a645c3 100644 --- a/nncf/torch/dynamic_graph/context.py +++ b/nncf/torch/dynamic_graph/context.py @@ -24,11 +24,13 @@ from nncf.torch.dynamic_graph.graph import DynamicGraph from nncf.torch.dynamic_graph.graph import DynamicGraphNode from nncf.torch.dynamic_graph.graph import DynamicGraphNodeParameters +from nncf.torch.dynamic_graph.graph import TensorMetaComparator from nncf.torch.dynamic_graph.op_input_processing import OperatorInput from nncf.torch.dynamic_graph.operation_address import OperationAddress from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.dynamic_graph.scope import ScopeElement from nncf.torch.dynamic_graph.trace_tensor import TensorMeta +from nncf.torch.dynamic_graph.trace_tensor import TracedTensor class ThreadLocalGlobalContext(threading.local): @@ -91,7 +93,7 @@ def __init__(self): self._save_context = None self._post_hooks = {} - self._pre_hooks = {} # type: Dict[PreHookId, List[Callable]] + self._pre_hooks: Dict[PreHookId, List[Callable]] = {} self._num_nested_hooks = 0 self._threading = CopySafeThreadingVars() @@ -157,7 +159,7 @@ def find_operator_node( def register_global_buffer(self, name: str, buffer): self.global_buffer_store[name] = buffer - def register_traced_tensor(self, tt: "TracedTensor"): + def register_traced_tensor(self, tt: TracedTensor): """ Registers a weak reference to a traced tensor in the context so that in case the block under context retains a reference to an intermediate tensor somewhere, @@ -318,7 +320,7 @@ def enable_node_additions(self): def disable_node_additions(self): self._may_add_nodes = False - def add_node_comparators(self, scopes_to_apply: List[str], node_input_comparator: "TensorMetaComparator" = None): + def add_node_comparators(self, scopes_to_apply: List[str], node_input_comparator: TensorMetaComparator = None): self._input_comparators_per_scope.append((node_input_comparator, scopes_to_apply)) @property @@ -421,7 +423,7 @@ def set_active_skipped_block(self, block_indexes: List[int]): self.start_node_name_of_skipped_block.append(self.skipped_blocks[block_index].start_node_name) self.end_node_name_of_skipped_block.append(self.skipped_blocks[block_index].end_node_name) - def set_elastic_blocks(self, blocks: List["BuildingBlock"] = None): + def set_elastic_blocks(self, blocks: List["BuildingBlock"] = None): # noqa: F821 if blocks is not None: if isinstance(blocks, list): if len(blocks) == 0: diff --git a/nncf/torch/dynamic_graph/graph.py b/nncf/torch/dynamic_graph/graph.py index 80db3559dc9..bdfdf647820 100644 --- a/nncf/torch/dynamic_graph/graph.py +++ b/nncf/torch/dynamic_graph/graph.py @@ -16,8 +16,9 @@ from torch import Tensor from nncf import nncf_logger -from nncf.common.graph import Dtype from nncf.common.graph.layer_attributes import BaseLayerAttributes +from nncf.common.graph.layer_attributes import Dtype +from nncf.torch.dynamic_graph.op_input_processing import OperatorInput from nncf.torch.dynamic_graph.operation_address import OperationAddress from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.dynamic_graph.trace_tensor import TensorMeta @@ -272,7 +273,7 @@ class DefaultScopeNodeMatcher: def __init__(self, node_id_to_key_dict, nx_graph): self._node_id_to_key_dict = node_id_to_key_dict self._nx_graph = nx_graph - self._inputless_nodes = {} # type: Dict[str, DynamicGraphNode] + self._inputless_nodes: Dict[str, DynamicGraphNode] = {} def get_node_by_id(self, node_id): return self._nx_graph.nodes[self._node_id_to_key_dict[node_id]] @@ -299,7 +300,7 @@ def _find_nodes_with_matching_context_and_inputs( if op_exec_context.matches_saved_inputs_from(successor_node[DynamicGraph.OP_EXEC_CONTEXT_NODE_ATTR]): nx_node_candidates[successor_node_key] = successor_node - node_candidates = {} # type: Dict[str, DynamicGraphNode] + node_candidates: Dict[str, DynamicGraphNode] = {} for nx_node_key, nx_node_dict in nx_node_candidates.items(): node_candidates[nx_node_key] = DynamicGraphNode.build_from_nx_node(nx_node_dict) @@ -392,9 +393,9 @@ def find_node( class IterationScopeNodeMatcher(DefaultScopeNodeMatcher): def __init__(self, node_id_to_key_dict, nx_graph): super().__init__(node_id_to_key_dict, nx_graph) - self._first_iteration_nodes = {} # type: {str: {str: DynamicGraphNode}} + self._first_iteration_nodes: {str: {str: DynamicGraphNode}} = {} - def save_first_iteration_node(self, inputs: "OperatorInput", node: DynamicGraphNode): + def save_first_iteration_node(self, inputs: OperatorInput, node: DynamicGraphNode): """ It finds and saves "starting" points of iteration for further matching with them on next iteration, instead of adding new nodes for each iteration. "Starting" points of iteration are nodes diff --git a/nncf/torch/dynamic_graph/graph_tracer.py b/nncf/torch/dynamic_graph/graph_tracer.py index 4516a60adee..3da1bf41f3d 100644 --- a/nncf/torch/dynamic_graph/graph_tracer.py +++ b/nncf/torch/dynamic_graph/graph_tracer.py @@ -14,6 +14,7 @@ import torch +from nncf.torch.dynamic_graph.context import TracingContext from nncf.torch.dynamic_graph.graph import DynamicGraph from nncf.torch.utils import get_model_device @@ -93,12 +94,10 @@ def __init__(self, custom_forward_fn: Callable[[torch.nn.Module], Any]): self.custom_forward_fn = custom_forward_fn def trace_graph( - self, model: torch.nn.Module, context_to_use: Optional["TracingContext"] = None, as_eval: bool = False + self, model: torch.nn.Module, context_to_use: Optional[TracingContext] = None, as_eval: bool = False ) -> DynamicGraph: sd = deepcopy(model.state_dict()) - from nncf.torch.dynamic_graph.context import TracingContext # pylint: disable=cyclic-import - if context_to_use is None: context_to_use = TracingContext() diff --git a/nncf/torch/dynamic_graph/io_handling.py b/nncf/torch/dynamic_graph/io_handling.py index 04448d04e10..65812d6cbec 100644 --- a/nncf/torch/dynamic_graph/io_handling.py +++ b/nncf/torch/dynamic_graph/io_handling.py @@ -12,7 +12,7 @@ from collections import OrderedDict from inspect import Parameter from inspect import Signature -from typing import Any, List +from typing import Any, List, Set import torch @@ -60,7 +60,7 @@ def replicate_same_tensors(obj: Any) -> Any: at runtime one and the same tensor could be wrapped by input/output wrappers twice, which will disrupt the traced graph structure and possibly hook calls. """ - observed_tensor_object_ids = set() # type: Set[int] + observed_tensor_object_ids: Set[int] = set() def replicate_fn(tensor: torch.Tensor) -> torch.Tensor: tensor_object_id = id(tensor) @@ -104,7 +104,7 @@ def __init__( bound_params = fwd_signature.bind(*arg_iis, **kwarg_iis) self._fwd_params_to_input_infos_odict = bound_params.arguments - self._fwd_signature = fwd_signature # type: Signature + self._fwd_signature: Signature = fwd_signature def set_device(self, device: str): self._device = device diff --git a/nncf/torch/dynamic_graph/op_input_processing.py b/nncf/torch/dynamic_graph/op_input_processing.py index 25308a2ec16..135002368e1 100644 --- a/nncf/torch/dynamic_graph/op_input_processing.py +++ b/nncf/torch/dynamic_graph/op_input_processing.py @@ -11,6 +11,7 @@ from collections import OrderedDict from functools import partial from itertools import islice +from typing import Dict from nncf.torch.nested_objects_traversal import InputIndexEntry from nncf.torch.nested_objects_traversal import NestedObjectIndex @@ -24,7 +25,7 @@ class OperatorInput: def __init__(self, op_args, op_kwargs): self.op_args = op_args self.op_kwargs = op_kwargs - self._index = OrderedDict() # type: Dict[int, InputIndexEntry] + self._index: Dict[int, InputIndexEntry] = OrderedDict() op_args_index_entries = NestedObjectIndex(self.op_args, previous_level_setter=partial(setattr, self, "op_args")) op_kwargs_index_entries = NestedObjectIndex(self.op_kwargs) diff --git a/nncf/torch/dynamic_graph/patch_pytorch.py b/nncf/torch/dynamic_graph/patch_pytorch.py index 899a7e75ee3..f17901a6989 100644 --- a/nncf/torch/dynamic_graph/patch_pytorch.py +++ b/nncf/torch/dynamic_graph/patch_pytorch.py @@ -24,6 +24,7 @@ from nncf import nncf_logger from nncf.common.utils.api_marker import api from nncf.torch.dynamic_graph.structs import NamespaceTarget +from nncf.torch.dynamic_graph.structs import PatchedOperatorInfo from nncf.torch.dynamic_graph.trace_tensor import TracedTensor from nncf.torch.dynamic_graph.wrappers import ignore_scope from nncf.torch.dynamic_graph.wrappers import wrap_module_call @@ -52,20 +53,6 @@ def get_namespace_to_extract_functions_from(namespace_target: NamespaceTarget) - # pylint: enable=protected-access -class PatchedOperatorInfo: - def __init__(self, name: str, operator_namespace: NamespaceTarget, skip_trace: bool = False): - """ - Information about patched operator. - :param name: Operator name - :param operator_namespace: Python module, from which operator was gotten. - :param skip_trace: If it is set to True, the both operator and its internal calls - to otherwise traced functions do not appear into the model graph. - """ - self.name = name - self.operator_namespace = operator_namespace - self.skip_trace = skip_trace - - class FunctionsToPatchWithoutTracing: TENSOR_CREATING_FUNCTIONS = [ "arange", @@ -246,7 +233,7 @@ def __init__(self, name: str, namespace, op): self.op = op -ORIGINAL_OPERATORS = [] # type: List[OriginalOpInfo] +ORIGINAL_OPERATORS: List[OriginalOpInfo] = [] ORIGINAL_CALL = torch.nn.Module.__call__ _JIT_ALREADY_WRAPPED = False _OPERATORS_ALREADY_WRAPPED = False diff --git a/nncf/torch/dynamic_graph/structs.py b/nncf/torch/dynamic_graph/structs.py index d06b02c7f53..20fcbefada2 100644 --- a/nncf/torch/dynamic_graph/structs.py +++ b/nncf/torch/dynamic_graph/structs.py @@ -22,3 +22,17 @@ class NamespaceTarget(Enum): TORCH_TENSOR = "torch.tensor" TORCH = "torch" EXTERNAL = "external_function" + + +class PatchedOperatorInfo: + def __init__(self, name: str, operator_namespace: NamespaceTarget, skip_trace: bool = False): + """ + Information about patched operator. + :param name: Operator name + :param operator_namespace: Python module, from which operator was gotten. + :param skip_trace: If it is set to True, the both operator and its internal calls + to otherwise traced functions do not appear into the model graph. + """ + self.name = name + self.operator_namespace = operator_namespace + self.skip_trace = skip_trace diff --git a/nncf/torch/dynamic_graph/trace_tensor.py b/nncf/torch/dynamic_graph/trace_tensor.py index b1cdbbb50a1..a776185aa44 100644 --- a/nncf/torch/dynamic_graph/trace_tensor.py +++ b/nncf/torch/dynamic_graph/trace_tensor.py @@ -14,7 +14,8 @@ import torch from nncf import nncf_logger -from nncf.common.graph import Dtype +from nncf.common.graph.layer_attributes import Dtype +from nncf.torch.dynamic_graph.op_input_processing import OperatorInput class TensorMeta: @@ -119,7 +120,7 @@ def get_dtype(x: torch.Tensor) -> Dtype: def trace_tensors( - operator_output: TensorOrTupleOrList, node: "DynamicGraphNode", ctx: "TracingContext" = None + operator_output: TensorOrTupleOrList, node: "DynamicGraphNode", ctx: "TracingContext" = None # noqa: F821 ) -> TensorOrTupleOrList: """ Dynamically turn torch.Tensor instances in `operator_output` into TracedTensor instances. `operator_output` is @@ -154,7 +155,7 @@ def trace_tensors( return operator_output -def make_tensor_metas(inputs: "OperatorInput") -> List[Optional[TensorMeta]]: +def make_tensor_metas(inputs: OperatorInput) -> List[Optional[TensorMeta]]: """ Produces TensorMeta data for each torch.Tensor or TracedTensor in `inputs`. :param inputs: An OperatorInput representation of input arguments to an operation in the traced model. diff --git a/nncf/torch/dynamic_graph/wrappers.py b/nncf/torch/dynamic_graph/wrappers.py index a8eade450dd..33a1122020c 100644 --- a/nncf/torch/dynamic_graph/wrappers.py +++ b/nncf/torch/dynamic_graph/wrappers.py @@ -23,6 +23,7 @@ from nncf.torch.dynamic_graph.layer_attributes_handlers import get_layer_attributes_from_args_and_kwargs from nncf.torch.dynamic_graph.layer_attributes_handlers import get_layer_attributes_from_module from nncf.torch.dynamic_graph.op_input_processing import OperatorInput +from nncf.torch.dynamic_graph.structs import PatchedOperatorInfo from nncf.torch.dynamic_graph.trace_tensor import make_tensor_metas from nncf.torch.dynamic_graph.trace_tensor import trace_tensors from nncf.torch.layer_utils import _NNCFModuleMixin @@ -47,7 +48,7 @@ def ignore_scope(cls): return cls -def wrap_operator(operator, operator_info: "PatchedOperatorInfo"): +def wrap_operator(operator, operator_info: PatchedOperatorInfo): """ Wraps the input callable object (`operator`) with the functionality that allows the calls to this object to be tracked by the currently set global TracingContext. The wrapped functions can be then intercepted, @@ -159,7 +160,7 @@ def wrapped(self, *args, **kwargs): def _execute_op( - op_address: "OperationAddress", + op_address: "OperationAddress", # noqa: F821 operator_info: "PatchedOperatorInfo", operator: Callable, ctx: "TracingContext", diff --git a/nncf/torch/extensions/__init__.py b/nncf/torch/extensions/__init__.py index 58dd0fe29d3..e4eab098dfc 100644 --- a/nncf/torch/extensions/__init__.py +++ b/nncf/torch/extensions/__init__.py @@ -113,7 +113,7 @@ def get(self, fn_name: str) -> Callable: Or disable timeout by set: {EXTENSION_LOAD_TIMEOUT_ENV_VAR}=0 For more information, see FAQ entry at: https://github.com/openvinotoolkit/nncf/blob/develop/docs/FAQ.md#importing-anything-from-nncftorch-hangs - """ + """ # noqa: E501 ) raise ExtensionLoaderTimeoutException(msg) from error diff --git a/nncf/torch/graph/graph_builder.py b/nncf/torch/graph/graph_builder.py index f98c2f7b43e..993bc6a21f4 100644 --- a/nncf/torch/graph/graph_builder.py +++ b/nncf/torch/graph/graph_builder.py @@ -9,15 +9,17 @@ # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, Set import torch -from nncf.common.graph import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.torch.dynamic_graph.context import TracingContext from nncf.torch.dynamic_graph.graph import DynamicGraph from nncf.torch.dynamic_graph.graph_tracer import GraphTracer from nncf.torch.dynamic_graph.graph_tracer import ModelInputInfo from nncf.torch.dynamic_graph.layer_attributes_handlers import set_nodes_attributes_in_nncf_graph +from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.graph.graph import PTNNCFGraph from nncf.torch.graph.operator_metatypes import PT_OPERATOR_METATYPES @@ -29,7 +31,7 @@ def __init__(self, custom_forward_fn: Callable[[torch.nn.Module], Any]): def build_graph( self, model: torch.nn.Module, - context_to_use: Optional["TracingContext"] = None, + context_to_use: Optional[TracingContext] = None, as_eval: bool = False, input_infos: List[ModelInputInfo] = None, ) -> PTNNCFGraph: @@ -42,7 +44,7 @@ class GraphConverter: @staticmethod def convert(dynamic_graph: DynamicGraph, input_infos: List[ModelInputInfo] = None) -> PTNNCFGraph: # pylint:disable=too-many-branches - module_id_vs_known_op_addrs_map = defaultdict(set) # type: Dict[int, Set[Scope]] + module_id_vs_known_op_addrs_map: Dict[int, Set[Scope]] = defaultdict(set) for dynamic_graph_node in dynamic_graph.get_all_nodes(): module_id_vs_known_op_addrs_map[dynamic_graph_node.calling_module_id].add( dynamic_graph_node.op_exec_context.op_address diff --git a/nncf/torch/graph/transformations/commands.py b/nncf/torch/graph/transformations/commands.py index dbd75c6d10f..5f873539c29 100644 --- a/nncf/torch/graph/transformations/commands.py +++ b/nncf/torch/graph/transformations/commands.py @@ -141,8 +141,8 @@ def __init__( priority: TransformationPriority = TransformationPriority.DEFAULT_PRIORITY, ): super().__init__(TransformationType.INSERT, point) - self.fn = fn # type: Callable - self.priority = priority # type: TransformationPriority + self.fn: Callable = fn + self.priority: TransformationPriority = priority def union(self, other: "PTTransformationCommand") -> "PTTransformationCommand": # TODO: keep all TransformationCommands atomic, refactor TransformationLayout instead @@ -166,7 +166,7 @@ class PTQuantizerInsertionCommand(PTTransformationCommand): def __init__( self, point: PTTargetPoint, - quantizer: "BaseQuantizer", + quantizer: "BaseQuantizer", # noqa: F821 ): super().__init__(TransformationType.INSERT, point) self.quantizer = quantizer diff --git a/nncf/torch/hardware/config.py b/nncf/torch/hardware/config.py index 48e5bb2cc2b..3fe97e8b960 100644 --- a/nncf/torch/hardware/config.py +++ b/nncf/torch/hardware/config.py @@ -11,7 +11,7 @@ from typing import List, Type -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.hardware.config import HWConfig from nncf.torch.graph.operator_metatypes import get_operator_metatypes diff --git a/nncf/torch/initialization.py b/nncf/torch/initialization.py index 5e06f395b02..7d5982c3c60 100644 --- a/nncf/torch/initialization.py +++ b/nncf/torch/initialization.py @@ -18,6 +18,8 @@ from torch.nn.modules.loss import _Loss from torch.utils.data import DataLoader +from nncf import NNCFConfig +from nncf.api.compression import CompressionAlgorithmController from nncf.common.initialization.dataloader import NNCFDataLoader from nncf.common.logging import nncf_logger from nncf.common.logging.progress_bar import ProgressBar @@ -28,6 +30,7 @@ from nncf.torch.nested_objects_traversal import objwalk from nncf.torch.structures import AutoQPrecisionInitArgs from nncf.torch.structures import DistributedCallbacksArgs +from nncf.torch.structures import ExecutionParameters from nncf.torch.structures import LeGRInitArgs from nncf.torch.structures import QuantizationPrecisionInitArgs from nncf.torch.utils import default_distributed_unwrapper @@ -241,7 +244,7 @@ def default_criterion_fn(outputs: Any, target: Any, criterion: Any) -> torch.Ten @api(canonical_alias="nncf.torch.register_default_init_args") def register_default_init_args( - nncf_config: "NNCFConfig", + nncf_config: NNCFConfig, train_loader: torch.utils.data.DataLoader, criterion: _Loss = None, criterion_fn: Callable[[Any, Any, _Loss], torch.Tensor] = None, @@ -250,7 +253,7 @@ def register_default_init_args( torch.utils.data.DataLoader, torch.nn.Module, torch.optim.Optimizer, - "CompressionAlgorithmController", + CompressionAlgorithmController, Optional[int], ], type(None), @@ -260,10 +263,10 @@ def register_default_init_args( autoq_eval_fn: Callable[[torch.nn.Module, torch.utils.data.DataLoader], float] = None, model_eval_fn: Callable[[torch.nn.Module, torch.utils.data.DataLoader], float] = None, distributed_callbacks: Tuple[Callable, Callable] = None, - execution_parameters: "ExecutionParameters" = None, + execution_parameters: ExecutionParameters = None, legr_train_optimizer: torch.optim.Optimizer = None, device: str = None, -) -> "NNCFConfig": +) -> NNCFConfig: nncf_config.register_extra_structs( [ QuantizationRangeInitArgs(data_loader=wrap_dataloader_for_init(train_loader), device=device), diff --git a/nncf/torch/layers.py b/nncf/torch/layers.py index f22dba2dac1..134845ad128 100644 --- a/nncf/torch/layers.py +++ b/nncf/torch/layers.py @@ -10,7 +10,7 @@ # limitations under the License. import math import numbers -from typing import Dict, Optional, Tuple +from typing import Dict, Tuple, Union import torch import torch.nn.functional as F @@ -513,8 +513,7 @@ def check_forward_input(self, input_): "input_ has inconsistent input_size: got {}, expected {}".format(input_.size(1), self.input_size) ) - def check_forward_hidden(self, input_, hx, hidden_label=""): - # type: (Tensor, Tensor, str) -> None + def check_forward_hidden(self, input_: torch.Tensor, hx: torch.Tensor, hidden_label: str = ""): if input_.size(0) != hx.size(0): raise RuntimeError( "Input batch size {} doesn't match hidden{} batch size {}".format( @@ -548,8 +547,7 @@ def __init__(self, input_linear, hidden_linear): self.input_linear = input_linear self.hidden_linear = hidden_linear - def forward(self, input_, hidden): - # type: (Tensor, Tuple[Tensor, Tensor]) -> (Tensor, Tensor) + def forward(self, input_: torch.Tensor, hidden: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: hx, cx = hidden gates = self.input_linear(input_) + self.hidden_linear(hx) @@ -611,8 +609,8 @@ def forward(self, input_, hidden, batch_sizes): for i in range(self.num_layers): all_output = [] for j in range(self.num_directions): - l = i * self.num_directions + j - hy, output = self.inners[l](input_, hidden[l], batch_sizes) + k = i * self.num_directions + j + hy, output = self.inners[k](input_, hidden[k], batch_sizes) next_hidden.append(hy) all_output.append(output) @@ -898,18 +896,19 @@ def all_weights(self): return [[getattr(self, weight) for weight in weights] for weights in self._all_weights] @staticmethod - def apply_permutation(tensor, permutation, dim=1): - # type: (Tensor, Tensor, int) -> Tensor + def apply_permutation(tensor: torch.Tensor, permutation: torch.Tensor, dim: int = 1) -> torch.Tensor: return tensor.index_select(dim, permutation) - def permute_hidden(self, hx, permutation): - # type: (Tuple[Tensor, Tensor], Optional[Tensor]) -> Tuple[Tensor, Tensor] + def permute_hidden( + self, hx: torch.Tensor, permutation: torch.Tensor + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: if permutation is None: return hx return self.apply_permutation(hx[0], permutation), self.apply_permutation(hx[1], permutation) - def prepare_hidden(self, hx, permutation): - # type: (Tuple[Tuple[Tensor], Tuple[Tensor]], Optional[Tensor]) -> Tuple[Tuple[Tensor], Tuple[Tensor]] + def prepare_hidden( + self, hx: torch.Tensor, permutation: torch.Tensor + ) -> Union[torch.Tensor, Tuple[Tuple[torch.Tensor, ...], Tuple[torch.Tensor, ...]]]: if permutation is None: return hx split_size = len(hx[0]) diff --git a/nncf/torch/nested_objects_traversal.py b/nncf/torch/nested_objects_traversal.py index 1233d0855c6..5736fab0b04 100644 --- a/nncf/torch/nested_objects_traversal.py +++ b/nncf/torch/nested_objects_traversal.py @@ -65,7 +65,7 @@ def __call__(self, value): class NestedObjectIndex: def __init__(self, obj, path=(), memo=None, previous_level_setter=None): - self._flat_nested_obj_indexing = [] # type: List[InputIndexEntry] + self._flat_nested_obj_indexing: List[InputIndexEntry] = [] self._nested_object_paths_generator(obj, self._flat_nested_obj_indexing, path, memo, previous_level_setter) @staticmethod @@ -142,7 +142,7 @@ def objwalk(obj, unary_predicate: Callable[[Any], bool], apply_fn: Callable, mem if id(obj) not in memo: memo.add(id(obj)) indices_to_apply_fn_to = set() - indices_vs_named_tuple_data = {} # type: Dict[Any, Tuple[list, Type, List[str]]] + indices_vs_named_tuple_data: Dict[Any, Tuple[list, Type, List[str]]] = {} for idx, value in iterator(obj): next_level_it = maybe_get_iterator(value) if next_level_it is None: diff --git a/nncf/torch/nncf_module_replacement.py b/nncf/torch/nncf_module_replacement.py index b582eb9ca6b..281ea83255a 100644 --- a/nncf/torch/nncf_module_replacement.py +++ b/nncf/torch/nncf_module_replacement.py @@ -170,8 +170,8 @@ def replace_modules_by_nncf_modules( The dictionary will also include the extended modules that have already been present in the model. """ modules_vs_scopes_dict = collect_all_scopes_for_extendable_and_extended_modules(model, predicate=predicate_fn) - inter_dict = {} # type: Dict[nn.Module, Set[Scope]] - ret_dict = {} # type: Dict[nn.Module, List[Scope]] + inter_dict: Dict[nn.Module, Set[Scope]] = {} + ret_dict: Dict[nn.Module, List[Scope]] = {} for module, scope_set in modules_vs_scopes_dict.items(): if is_nncf_module(module): # The module has already been extended, track it in the return value diff --git a/nncf/torch/nncf_network.py b/nncf/torch/nncf_network.py index 3b70228f988..9d0d5bdcf8c 100644 --- a/nncf/torch/nncf_network.py +++ b/nncf/torch/nncf_network.py @@ -23,6 +23,7 @@ from torch import nn from nncf import nncf_logger +from nncf.api.compression import CompressionAlgorithmController from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName from nncf.common.graph.definitions import MODEL_INPUT_OP_NAME @@ -239,13 +240,11 @@ def __init__( else: self._wrap_outputs_fn = wrap_nncf_model_outputs_with_objwalk - self._nncf_replaced_modules = {} # type: Dict[torch.nn.Module, List[Scope]] + self._nncf_replaced_modules: Dict[torch.nn.Module, List[Scope]] = {} self._scopes_without_shape_matching = scopes_without_shape_matching self.debug_interface = CombinedDebugInterface() if is_debug() else None - self._extra_module_types = [] # type: List[ExtraCompressionModuleType] - self._insertions_into_original_graph = ( - {} - ) # type: Dict[PTTargetPoint, List[Tuple[Callable, TransformationPriority]]] + self._extra_module_types: List[ExtraCompressionModuleType] = [] + self._insertions_into_original_graph: Dict[PTTargetPoint, List[Tuple[Callable, TransformationPriority]]] = {} _orig_graph_build_forward_fn = self._get_dummy_forward_fn_for_graph_building( with_input_tracing=True, with_output_tracing=True @@ -271,7 +270,7 @@ def __init__( model, _orig_context, as_eval=True ) self._original_graph = GraphConverter.convert(self._original_dynamic_graph, input_infos=self._input_infos) - self._compressed_graph = None # type: PTNNCFGraph + self._compressed_graph: PTNNCFGraph = None self._compressed_context = TracingContext() @@ -288,7 +287,7 @@ def __init__( ) self._load_listener = None - self.compression_controller = None # type: PTCompressionAlgorithmController + self.compression_controller: CompressionAlgorithmController = None @property def _original_unbound_forward(self): @@ -602,8 +601,8 @@ def do_dummy_forward(self, force_eval: bool = False): def get_insertion_point_graph(self) -> InsertionPointGraph: # Set up a pre- and post-hooks on almost every op in PyTorch nncf_graph = self.get_original_graph() - pre_hooks = [] # type: List[PreHookInsertionPoint] - post_hooks = [] # type: List[PostHookInsertionPoint] + pre_hooks: List[PreHookInsertionPoint] = [] + post_hooks: List[PostHookInsertionPoint] = [] for node in nncf_graph.get_all_nodes(): # Pre-hook insertion point nodes # Will insert a pre-hook IP for each input edge. The input edge must be marked with @@ -744,7 +743,7 @@ def get_node_to_op_address_mapping(self) -> Dict[NNCFNodeName, OperationAddress] retval[nncf_node.node_name] = op_address return retval - def set_compression_controller(self, ctrl: "PTCompressionAlgorithmController"): + def set_compression_controller(self, ctrl: CompressionAlgorithmController): self.compression_controller = ctrl def strip(self, do_copy: bool = True) -> "NNCFNetwork": @@ -927,7 +926,7 @@ def forward(self, *args, **kwargs): graph tracing and calling compression-related hooks. """ # pylint:disable=protected-access - with self.nncf._compressed_context as ctx: # type: TracingContext + with self.nncf._compressed_context as ctx: ctx.base_module_thread_local_replica = self args, kwargs = replicate_same_tensors((args, kwargs)) if not self.nncf._in_user_dummy_forward: diff --git a/nncf/torch/pruning/filter_pruning/algo.py b/nncf/torch/pruning/filter_pruning/algo.py index 589583d723a..b59aad5817c 100644 --- a/nncf/torch/pruning/filter_pruning/algo.py +++ b/nncf/torch/pruning/filter_pruning/algo.py @@ -147,10 +147,10 @@ def __init__( pruning_operations_metatype=PT_PRUNING_OPERATOR_METATYPES, prunable_types=prunable_types ) self.pruning_quotas = {} - self.nodes_flops = {} # type: Dict[NNCFNodeName, int] - self.nodes_params_num = {} # type: Dict[NNCFNodeName, int] - self._next_nodes = {} # type: Dict[int, List[NNCFNodeName]] - self._output_shapes = {} # type: Dict[NNCFNodeName, int] + self.nodes_flops: Dict[NNCFNodeName, int] = {} + self.nodes_params_num: Dict[NNCFNodeName, int] = {} + self._next_nodes: Dict[int, List[NNCFNodeName]] = {} + self._output_shapes: Dict[NNCFNodeName, int] = {} _, modules_out_channels = get_prunable_layers_in_out_channels(self._graph) self._init_pruned_layers_params(modules_out_channels) self.flops_count_init() diff --git a/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py b/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py +++ b/nncf/torch/pruning/filter_pruning/global_ranking/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py b/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py index fec955d61db..042c3233634 100644 --- a/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py +++ b/nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py @@ -270,7 +270,7 @@ class LeGRPruner: and resetting all changes in the model made by the environment. """ - def __init__(self, filter_pruner_ctrl: "FilterPruningController", model: nn.Module): + def __init__(self, filter_pruner_ctrl: "FilterPruningController", model: nn.Module): # noqa: F821 self.filter_pruner = filter_pruner_ctrl self.scheduler = copy(self.filter_pruner.scheduler) self.model = model diff --git a/nncf/torch/pruning/filter_pruning/global_ranking/legr.py b/nncf/torch/pruning/filter_pruning/global_ranking/legr.py index f2fb172166a..6a8b641d131 100644 --- a/nncf/torch/pruning/filter_pruning/global_ranking/legr.py +++ b/nncf/torch/pruning/filter_pruning/global_ranking/legr.py @@ -31,7 +31,7 @@ class LeGR: def __init__( self, - pruning_ctrl: "FilterPruningController", + pruning_ctrl: "FilterPruningController", # noqa: F821 target_model: nn.Module, legr_init_args: LeGRInitArgs, train_steps: int = PRUNING_LEGR_TRAIN_STEPS, diff --git a/nncf/torch/quantization/__init__.py b/nncf/torch/quantization/__init__.py index f5db2a833ee..993ee97325a 100644 --- a/nncf/torch/quantization/__init__.py +++ b/nncf/torch/quantization/__init__.py @@ -13,4 +13,4 @@ """ # Required for correct QUANTIZATION_MODULES registry functioning -from . import layers +from . import layers as layers diff --git a/nncf/torch/quantization/algo.py b/nncf/torch/quantization/algo.py index 673d2918311..f0aad112574 100644 --- a/nncf/torch/quantization/algo.py +++ b/nncf/torch/quantization/algo.py @@ -94,6 +94,7 @@ from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.quantization.adjust_padding import AdjustPaddingArgs from nncf.torch.quantization.adjust_padding import CalculatePaddingAdjustment +from nncf.torch.quantization.base_ctrl import QuantizationControllerBase from nncf.torch.quantization.debug_interface import QuantizationDebugInterface from nncf.torch.quantization.default_quantization import DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT from nncf.torch.quantization.default_quantization import QUANTIZATION_LAYER_METATYPES @@ -176,9 +177,9 @@ def __init__( self.ignored_scopes = self._quantization_config.get("ignored_scopes") self.target_scopes = self._quantization_config.get("target_scopes") - self.global_quantizer_constraints = {} # type: Dict[QuantizerGroup, QuantizationConstraints] - self._ignored_scopes_per_group = {} # type: Dict[QuantizerGroup, List[str]] - self._target_scopes_per_group = {} # type: Dict[QuantizerGroup, List[str]] + self.global_quantizer_constraints: Dict[QuantizerGroup, QuantizationConstraints] = {} + self._ignored_scopes_per_group: Dict[QuantizerGroup, List[str]] = {} + self._target_scopes_per_group: Dict[QuantizerGroup, List[str]] = {} for quantizer_group in QuantizerGroup: self._parse_group_params(self._quantization_config, quantizer_group) @@ -442,15 +443,15 @@ def __init__(self, config, should_init: bool = True): self._weight_quantizers = OrderedDict() # Quantizers applied via UpdateWeights self._non_weight_quantizers = OrderedDict() # All the other quantizers self._quantizers_input_shapes = OrderedDict() - self._processed_insertion_points = set() # type: Set[PTTargetPoint] - self._groups_of_adjacent_quantizers = GroupsOfAdjacentQuantizers() # type: GroupsOfAdjacentQuantizers - self._setup_to_module_id_translation_dict = {} # type: Dict[QuantizationPointId, QuantizerId] + self._processed_insertion_points: Set[PTTargetPoint] = set() + self._groups_of_adjacent_quantizers: GroupsOfAdjacentQuantizers = GroupsOfAdjacentQuantizers() + self._setup_to_module_id_translation_dict: Dict[QuantizationPointId, QuantizerId] = {} self.eval_ops_exec_ctx = [] - self._build_time_metric_infos = None # type: Optional[NetworkQuantizationShareMetricBuildTimeInfo] + self._build_time_metric_infos: Optional[QuantizationShareBuildTimeInfo] = None self.hw_config = None - self._legacy_single_config_quantizer_setup_from_comp_state = None # type: Optional[SingleConfigQuantizerSetup] - self._pt_quantizer_setup = None # type: Optional[PTQuantizerSetup] - self._minmax_values_for_range_init = {} # type: Optional[Dict[QuantizationPointId, MinMaxTensorStatistic]] + self._legacy_single_config_quantizer_setup_from_comp_state: Optional[SingleConfigQuantizerSetup] = None + self._pt_quantizer_setup: Optional[PTQuantizerSetup] = None + self._minmax_values_for_range_init: Optional[Dict[QuantizationPointId, MinMaxTensorStatistic]] = {} # can be False to disable setting of adjust padding operations on precision init, because it may add unnecessary # noise on model evaluation (e.g. in AutoQ) @@ -474,7 +475,7 @@ def __init__(self, config, should_init: bool = True): if self.should_init: self._parse_init_params() - self._use_logarithm_scale_per_group = {} # type: Dict[QuantizerGroup, bool] + self._use_logarithm_scale_per_group: Dict[QuantizerGroup, bool] = {} for quantizer_group in QuantizerGroup: group_name = quantizer_group.value @@ -885,10 +886,10 @@ def _build_insertion_commands_list_for_quantizer_setup( minmax_values_for_range_init: Dict[QuantizationPointId, MinMaxTensorStatistic], ) -> Tuple[List[PTInsertionCommand], Dict[QuantizationPointId, QuantizerId]]: insertion_commands = [] - qp_id_vs_quant_module_id_dict = {} # type: Dict[QuantizationPointId, QuantizerId] + qp_id_vs_quant_module_id_dict: Dict[QuantizationPointId, QuantizerId] = {} target_model_graph = target_model.nncf.get_original_graph() non_unified_scales_quantization_point_ids = set(quantizer_setup.quantization_points.keys()) - already_weight_quantized_shared_layers = {} # type: Dict[str, QuantizerId] + already_weight_quantized_shared_layers: Dict[str, QuantizerId] = {} for unified_scales_group in quantizer_setup.unified_scale_groups.values(): for us_qp_id in unified_scales_group: @@ -1158,7 +1159,7 @@ def is_weights(ip: PTTargetPoint) -> bool: quantizer.apply_minmax_init(min_values=min_values, max_values=max_values, log_module_name=str(primary_ip)) - qids = [] # type: List[QuantizerId] + qids: List[QuantizerId] = [] for ip in insertion_points: if is_weights(ip): qids.append(WeightQuantizerId(ip.target_node_name)) @@ -1264,27 +1265,6 @@ def initialize(self, model: NNCFNetwork) -> None: bn_adaptation.run(model) -class QuantizationControllerBase(PTCompressionAlgorithmController): - """ - Base controller class for the quantization controllers in PT. - """ - - def enable_activation_quantization(self): - raise NotImplementedError - - def enable_weight_quantization(self): - raise NotImplementedError - - def disable_activation_quantization(self): - raise NotImplementedError - - def disable_weight_quantization(self): - raise NotImplementedError - - def init_range(self): - raise NotImplementedError - - @api() class QuantizationController(QuantizationControllerBase): """ @@ -1311,9 +1291,9 @@ def __init__( algo_config = self._get_algo_config() self._build_time_range_init_params = build_time_range_init_params - self.weight_quantizers = weight_quantizers # type: Dict[WeightQuantizerId, WeightQuantizerInfo] - self.non_weight_quantizers = non_weight_quantizers # type: Dict[NonWeightQuantizerId, NonWeightQuantizerInfo] - self.all_quantizations = OrderedDict() # type: Dict[QuantizerId, BaseQuantizer] + self.weight_quantizers: Dict[WeightQuantizerId, WeightQuantizerInfo] = weight_quantizers + self.non_weight_quantizers: Dict[NonWeightQuantizerId, NonWeightQuantizerInfo] = non_weight_quantizers + self.all_quantizations: Dict[QuantizerId, BaseQuantizer] = OrderedDict() self.all_quantizations.update({k: v.quantizer_module_ref for k, v in self.weight_quantizers.items()}) self.all_quantizations.update({k: v.quantizer_module_ref for k, v in self.non_weight_quantizers.items()}) self._quantizers_input_shapes = quantizers_input_shapes @@ -1336,7 +1316,7 @@ def __init__( else: export_mode = QuantizerExportMode.FAKE_QUANTIZE - for quantizer in self.all_quantizations.values(): # type: BaseQuantizer + for quantizer in self.all_quantizations.values(): quantizer.set_export_mode(export_mode) params = algo_config.get("params", None) @@ -1374,7 +1354,7 @@ def _get_algo_config(self) -> Dict: def _broadcast_initialized_params_for_each_quantizer(self): # NOTE: Order of quantization modules must be the same on GPUs to correctly broadcast num_bits sorted_quantizers = OrderedDict(sorted(self.all_quantizations.items(), key=lambda x: str(x[0]))) - for quantizer in sorted_quantizers.values(): # type: BaseQuantizer + for quantizer in sorted_quantizers.values(): quantizer.broadcast_initialized_params() def _do_runtime_range_init(self, range_init_params: PTRangeInitParams): @@ -1532,7 +1512,7 @@ def _get_statistics_for_final_range_init( def _build_controller(self, model: NNCFNetwork) -> "ExperimentalQuantizationController": groups_of_adjacent_quantizers = GroupsOfAdjacentQuantizers() - all_quantizations = {} # type: Dict[QuantizerId, BaseQuantizer] + all_quantizations: Dict[QuantizerId, BaseQuantizer] = {} all_quantizations.update({k: v.quantizer_module_ref for k, v in self._weight_quantizers.items()}) all_quantizations.update({k: v.quantizer_module_ref for k, v in self._non_weight_quantizers.items()}) @@ -1604,7 +1584,7 @@ def __init__( self._initial_quantizer_setup = initial_quantizer_setup self._tensor_stats = tensor_stats self.setup_to_module_id_translation_dict = setup_to_module_id_translation_dict - self.module_id_to_qp_id_translation_dict = {} # type: Dict[QuantizerId, Set[QuantizationPointId]] + self.module_id_to_qp_id_translation_dict: Dict[QuantizerId, Set[QuantizationPointId]] = {} for qp_id, qid in self.setup_to_module_id_translation_dict.items(): if qid in self.module_id_to_qp_id_translation_dict: self.module_id_to_qp_id_translation_dict[qid].add(qp_id) diff --git a/nncf/torch/quantization/base_ctrl.py b/nncf/torch/quantization/base_ctrl.py new file mode 100644 index 00000000000..c9f2e442ab9 --- /dev/null +++ b/nncf/torch/quantization/base_ctrl.py @@ -0,0 +1,32 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nncf.torch.compression_method_api import PTCompressionAlgorithmController + + +class QuantizationControllerBase(PTCompressionAlgorithmController): + """ + Base controller class for the quantization controllers in PT. + """ + + def enable_activation_quantization(self): + raise NotImplementedError + + def enable_weight_quantization(self): + raise NotImplementedError + + def disable_activation_quantization(self): + raise NotImplementedError + + def disable_weight_quantization(self): + raise NotImplementedError + + def init_range(self): + raise NotImplementedError diff --git a/nncf/torch/quantization/default_quantization.py b/nncf/torch/quantization/default_quantization.py index 79856820dc9..d108d3ba359 100644 --- a/nncf/torch/quantization/default_quantization.py +++ b/nncf/torch/quantization/default_quantization.py @@ -17,7 +17,7 @@ # If a metatype is not in this list, then it is considered to be QuantizationTrait.NON_QUANTIZABLE. -DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT = { +DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT: Dict[QuantizationTrait, List[PTOperatorMetatype]] = { QuantizationTrait.INPUTS_QUANTIZABLE: [ operator_metatypes.PTConv2dMetatype, operator_metatypes.PTModuleConv2dMetatype, @@ -88,7 +88,7 @@ operator_metatypes.PTEmbeddingBagMetatype, operator_metatypes.PTModuleEmbeddingBagMetatype, ], -} # type: Dict[QuantizationTrait, List[PTOperatorMetatype]] +} -QUANTIZATION_LAYER_METATYPES = OPERATORS_WITH_WEIGHTS_METATYPES # type: List[PTOperatorMetatype] +QUANTIZATION_LAYER_METATYPES: List[PTOperatorMetatype] = OPERATORS_WITH_WEIGHTS_METATYPES diff --git a/nncf/torch/quantization/hessian_trace.py b/nncf/torch/quantization/hessian_trace.py index 43c5c67982a..477eca9f21d 100644 --- a/nncf/torch/quantization/hessian_trace.py +++ b/nncf/torch/quantization/hessian_trace.py @@ -132,7 +132,7 @@ def get_average_traces(self, max_iter=500, tolerance=1e-5) -> Tensor: :return: Tensor with average hessian trace per parameter """ avg_total_trace = 0.0 - avg_traces_per_iter = [] # type: List[Tensor] + avg_traces_per_iter: List[Tensor] = [] mean_avg_traces_per_param = None for i in range(max_iter): diff --git a/nncf/torch/quantization/init_range.py b/nncf/torch/quantization/init_range.py index 28a5204ce22..8357ae649da 100644 --- a/nncf/torch/quantization/init_range.py +++ b/nncf/torch/quantization/init_range.py @@ -69,7 +69,7 @@ def get_init_config_for_quantization_point(self, qp: QuantizationPointBase) -> R return self.get_init_config_for_scope_and_group(qid, group) def get_init_config_for_scope_and_group(self, qid: QuantizerId, group: QuantizerGroup) -> RangeInitConfig: - matches = [] # type: List[RangeInitConfig] + matches: List[RangeInitConfig] = [] for pl_config in self.per_layer_range_init_configs: if should_consider_scope(qid, pl_config.ignored_scopes, pl_config.target_scopes): if group == pl_config.target_group or pl_config.target_group is None: @@ -113,7 +113,7 @@ def get_reduction_axes(self, per_sample_stats: bool) -> ReductionAxes: :return: Shape to reduce to. """ ndims = len(self._input_shape) - reduction_axes = list(range(ndims)) # type: List[int] + reduction_axes: List[int] = list(range(ndims)) if self._per_channel: val = (ndims + self._channel_idx) % ndims reduction_axes.remove(val) @@ -282,9 +282,9 @@ def __init__( self.modules_to_init = modules_to_init_vs_init_configs self.progressbar_description = "Range parameters initialization" - self.collectors_and_modules_to_init = ( - OrderedDict() - ) # type: Dict[str, Tuple[TensorStatisticCollectorBase, BaseQuantizer]] + self.collectors_and_modules_to_init: Dict[ + str, Tuple[TensorStatisticCollectorBase, BaseQuantizer] + ] = OrderedDict() self.hook_handles = [] self.batch_size = batch_size diff --git a/nncf/torch/quantization/layers.py b/nncf/torch/quantization/layers.py index 4a588ddaa8d..fafd1353918 100644 --- a/nncf/torch/quantization/layers.py +++ b/nncf/torch/quantization/layers.py @@ -558,12 +558,12 @@ class QuantizersSwitcher: """Enables/disables quantizers with saving and restoring original state""" def __init__(self, quantizers: List[BaseQuantizer]): - self.originally_disabled = [] # type: List[BaseQuantizer] - self.originally_enabled = [] # type: List[BaseQuantizer] + self.originally_disabled: List[BaseQuantizer] = [] + self.originally_enabled: List[BaseQuantizer] = [] self._quantizers = quantizers def disable_quantizers(self): - for module in self._quantizers: # type: BaseQuantizer + for module in self._quantizers: if not module.is_enabled_quantization(): self.originally_disabled.append(module) if module not in self.originally_enabled: @@ -571,7 +571,7 @@ def disable_quantizers(self): self.originally_enabled = [] def enable_quantizers(self): - for module in self._quantizers: # type: BaseQuantizer + for module in self._quantizers: if module.is_enabled_quantization(): self.originally_enabled.append(module) if module not in self.originally_disabled: diff --git a/nncf/torch/quantization/metrics.py b/nncf/torch/quantization/metrics.py index 3b4960a28aa..fc7d6adcc0c 100644 --- a/nncf/torch/quantization/metrics.py +++ b/nncf/torch/quantization/metrics.py @@ -232,9 +232,11 @@ class ShareEdgesQuantizedDataPathStatisticsCollector(StatisticsCollector): NODES_GRAPH_ATTR = "nodes" IS_MERGED_GRAPH_ATTR = "is_merged" - def __init__(self, compressed_model: NNCFNetwork, qctrl: "QuantizationController", target_device: TargetDevice): + def __init__( + self, compressed_model: NNCFNetwork, qctrl: "QuantizationController", target_device: TargetDevice # noqa: F821 + ): # noqa: E501, F821 self._compressed_model = compressed_model - self._qctrl = qctrl # type: QuantizationController + self._qctrl = qctrl self.stats = QuantizationConfigurationStatistics(0, 0) self._target_device = target_device diff --git a/nncf/torch/quantization/precision_constraints.py b/nncf/torch/quantization/precision_constraints.py index 872c2f6adf1..ce060622cab 100644 --- a/nncf/torch/quantization/precision_constraints.py +++ b/nncf/torch/quantization/precision_constraints.py @@ -17,7 +17,7 @@ class HardwareQuantizationConstraints: def __init__(self): - self._constraints = {} # type: Dict[QuantizerId, List[QuantizerConfig]] + self._constraints: Dict[QuantizerId, List[QuantizerConfig]] = {} def add(self, quantizer_id: QuantizerId, qconfigs: List[QuantizerConfig]): self._constraints[quantizer_id] = qconfigs @@ -28,7 +28,7 @@ def get(self, quantizer_id: QuantizerId) -> List[QuantizerConfig]: return [] def get_bitwidth_vs_qconfigs_dict(self, quantizer_id: QuantizerId) -> Dict[int, List[QuantizerConfig]]: - bitwidths_vs_qconfigs = {} # type: Dict[int, List[QuantizerConfig]] + bitwidths_vs_qconfigs: Dict[int, List[QuantizerConfig]] = {} for qc in self.get(quantizer_id): if qc.num_bits not in bitwidths_vs_qconfigs: bitwidths_vs_qconfigs[qc.num_bits] = [qc] diff --git a/nncf/torch/quantization/precision_init/adjacent_quantizers.py b/nncf/torch/quantization/precision_init/adjacent_quantizers.py index 6a08cf35fd8..21855176d39 100644 --- a/nncf/torch/quantization/precision_init/adjacent_quantizers.py +++ b/nncf/torch/quantization/precision_init/adjacent_quantizers.py @@ -68,11 +68,11 @@ def parse_from_quantizer_setup( quantization_point_id_vs_quantizer_id: Dict[QuantizationPointId, QuantizerId], ): for group_idx, group in quantizer_setup.shared_input_operation_set_groups.items(): - act_quant_tuples = [] # type: List[Tuple[QuantizerId, BaseQuantizer]] - wt_quant_tuples = [] # type: List[Tuple[QuantizerId, BaseQuantizer]] + act_quant_tuples: List[Tuple[QuantizerId, BaseQuantizer]] = [] + wt_quant_tuples: List[Tuple[QuantizerId, BaseQuantizer]] = [] - quantized_node_per_activation_qp_id = {} # type: Dict[NNCFNodeName, QuantizationPointId] - module_scope_per_weight_qp_id = {} # type: Dict[NNCFNodeName, QuantizationPointId] + quantized_node_per_activation_qp_id: Dict[NNCFNodeName, QuantizationPointId] = {} + module_scope_per_weight_qp_id: Dict[NNCFNodeName, QuantizationPointId] = {} for qp_id in group: qp = quantizer_setup.quantization_points[qp_id] diff --git a/nncf/torch/quantization/precision_init/autoq_init.py b/nncf/torch/quantization/precision_init/autoq_init.py index 3b55139f05a..4a4a176e677 100644 --- a/nncf/torch/quantization/precision_init/autoq_init.py +++ b/nncf/torch/quantization/precision_init/autoq_init.py @@ -32,6 +32,7 @@ from nncf.config.schemata.defaults import AUTOQ_ITER_NUMBER from nncf.config.schemata.defaults import AUTOQ_WARMUP_ITER_NUMBER from nncf.config.schemata.defaults import PRECISION_INIT_BITWIDTHS +from nncf.torch.automl.agent.ddpg.ddpg import DDPG from nncf.torch.quantization.precision_constraints import HardwareQuantizationConstraints from nncf.torch.quantization.precision_init.base_init import BasePrecisionInitializer from nncf.torch.quantization.precision_init.base_init import BasePrecisionInitParams @@ -103,7 +104,7 @@ def from_config( class AutoQPrecisionInitializer(BasePrecisionInitializer): def __init__( self, - algo: "ExperimentalQuantizationController", + algo: "ExperimentalQuantizationController", # noqa: F821 params: AutoQPrecisionInitParams, hw_precision_constraints: HardwareQuantizationConstraints, ): @@ -119,7 +120,6 @@ def __init__( def apply_init(self) -> SingleConfigQuantizerSetup: from nncf.common.utils.debug import DEBUG_LOG_DIR # pylint: disable=cyclic-import - from nncf.torch.automl.agent.ddpg.ddpg import DDPG # pylint: disable=cyclic-import from nncf.torch.automl.environment.quantization_env import QuantizationEnv # pylint: disable=cyclic-import if self._dump_autoq_data or is_debug(): @@ -216,7 +216,7 @@ def apply_init(self) -> SingleConfigQuantizerSetup: nncf_logger.info("[AutoQ] Elapsed time of AutoQ Precision Initialization (): {}".format(end_ts - start_ts)) return final_quantizer_setup - def _search(self, agent: "DDPG", env: "QuantizationEnv") -> Tuple[pd.Series, float]: + def _search(self, agent: DDPG, env: "QuantizationEnv") -> Tuple[pd.Series, float]: # noqa: F821 # pylint: disable=too-many-branches,too-many-statements best_reward = -math.inf episode = 0 @@ -360,7 +360,7 @@ def _search(self, agent: "DDPG", env: "QuantizationEnv") -> Tuple[pd.Series, flo return best_policy, best_reward - def _dump_best_episode(self, info_tuple: Tuple, bit_stats_df: pd.DataFrame, env: "QuantizationEnv"): + def _dump_best_episode(self, info_tuple: Tuple, bit_stats_df: pd.DataFrame, env: "QuantizationEnv"): # noqa: F821 if self._dump_autoq_data: episode = info_tuple[0] self.best_policy_dict[episode] = env.master_df["action"].astype("int") @@ -385,7 +385,7 @@ def _dump_best_episode(self, info_tuple: Tuple, bit_stats_df: pd.DataFrame, env: self.tb_writer.add_text("AutoQ/best_policy", best_policy_string, episode) def _dump_episode( - self, episodic_info_tuple: Tuple, bit_stats_df: pd.DataFrame, env: "QuantizationEnv", agent: "DDPG" + self, episodic_info_tuple: Tuple, bit_stats_df: pd.DataFrame, env: "QuantizationEnv", agent: DDPG # noqa: F821 ): if self._dump_autoq_data: episode, final_reward, _, accuracy, model_ratio, bop_ratio, _, _, _ = episodic_info_tuple @@ -437,7 +437,7 @@ def _dump_episode( if episode % int((self._iter_number + 10) / 10) == 0: agent.save_model(self.dump_dir) - def _add_to_tensorboard(self, tb_writer: "SummaryWriter", log_tuple: Tuple): + def _add_to_tensorboard(self, tb_writer: "SummaryWriter", log_tuple: Tuple): # noqa: F821 episode, final_reward, best_reward, accuracy, model_ratio, bop_ratio, value_loss, policy_loss, delta = log_tuple tb_writer.add_scalar("AutoQ/reward/last", final_reward, episode) diff --git a/nncf/torch/quantization/precision_init/base_init.py b/nncf/torch/quantization/precision_init/base_init.py index 4009ab9f4dc..652ac2d0389 100644 --- a/nncf/torch/quantization/precision_init/base_init.py +++ b/nncf/torch/quantization/precision_init/base_init.py @@ -37,12 +37,12 @@ def __init__(self, user_init_args: NNCFExtraConfigStruct = None): class BasePrecisionInitializer: def __init__( self, - algo: "ExperimentalQuantizationController", + algo: "ExperimentalQuantizationController", # noqa: F821 params: BasePrecisionInitParams, hw_precision_constraints: HardwareQuantizationConstraints = None, ): self._algo = algo - self._model = self._algo._model # type: NNCFNetwork + self._model: NNCFNetwork = self._algo._model all_quantizers = algo.all_quantizations self._hw_precision_constraints = hw_precision_constraints self.original_precisions = {q_id: quantizer.num_bits for q_id, quantizer in all_quantizers.items()} @@ -100,11 +100,11 @@ def __init__( constraints: HardwareQuantizationConstraints, ): self._wq_affected_module_node_name_vs_qid_dict = {k.target_node_name: k for k in weight_quantizers.keys()} - self._quantizer_module_scope_vs_qid_dict = {} # type: Dict[Scope, WeightQuantizerId] + self._quantizer_module_scope_vs_qid_dict: Dict[Scope, WeightQuantizerId] = {} self._skipped_quantized_weight_node_names = [] - self._skipped_weight_quantizers = {} # type: Dict[WeightQuantizerId, BaseQuantizer] - self._weight_quantizers_in_execution_order_per_scope = OrderedDict() # type: Dict[Scope, BaseQuantizer] - self._weight_quantizers_in_execution_order = OrderedDict() # type: Dict[WeightQuantizerId, BaseQuantizer] + self._skipped_weight_quantizers: Dict[WeightQuantizerId, BaseQuantizer] = {} + self._weight_quantizers_in_execution_order_per_scope: Dict[Scope, BaseQuantizer] = OrderedDict() + self._weight_quantizers_in_execution_order: Dict[WeightQuantizerId, BaseQuantizer] = OrderedDict() quantization_types = [class_type.__name__ for class_type in QUANTIZATION_MODULES.registry_dict.values()] weight_module_dict = model diff --git a/nncf/torch/quantization/precision_init/bitwidth_graph.py b/nncf/torch/quantization/precision_init/bitwidth_graph.py index 3561557872e..0416a6fc533 100644 --- a/nncf/torch/quantization/precision_init/bitwidth_graph.py +++ b/nncf/torch/quantization/precision_init/bitwidth_graph.py @@ -10,12 +10,12 @@ # limitations under the License. from collections import defaultdict -from typing import Dict +from typing import Dict, Set, Tuple import networkx as nx from nncf.common.graph import NNCFGraph -from nncf.common.graph.graph import NNCFNode +from nncf.common.graph import NNCFNode from nncf.common.logging import nncf_logger from nncf.common.quantization.structs import NonWeightQuantizerId from nncf.torch.layers import NNCFConv2d @@ -40,7 +40,7 @@ def __init__( if add_flops: flops_per_module = model.nncf.get_flops_per_module() - flops_vs_node_group = defaultdict(set) # type: Dict[int, Tuple[int, Set[NNCFNode]]] + flops_vs_node_group: Dict[int, Tuple[int, Set[NNCFNode]]] = defaultdict(set) for idx, module_node_name_and_flops in enumerate(flops_per_module.items()): module_node_name, flops = module_node_name_and_flops node_set = set(nncf_graph.get_op_nodes_in_scope(nncf_graph.get_scope_by_node_name(module_node_name))) diff --git a/nncf/torch/quantization/precision_init/compression_ratio.py b/nncf/torch/quantization/precision_init/compression_ratio.py index 2122cf80474..95a923f8a5d 100644 --- a/nncf/torch/quantization/precision_init/compression_ratio.py +++ b/nncf/torch/quantization/precision_init/compression_ratio.py @@ -32,7 +32,7 @@ def __init__( weight_qp_id_per_activation_qp_id: Dict[QuantizationPointId, QuantizationPointId], ): self._weight_qp_id_per_activation_qp_id = weight_qp_id_per_activation_qp_id - self._flops_per_weight_qp_id = {} # type: Dict[QuantizationPointId, float] + self._flops_per_weight_qp_id: Dict[QuantizationPointId, float] = {} for qp_id, qp in quantizer_setup.quantization_points.items(): if qp.is_weight_quantization_point(): target_node_name = qp.insertion_point.target_node_name diff --git a/tools/debug/compare_activations.py b/nncf/torch/quantization/precision_init/definitions.py similarity index 51% rename from tools/debug/compare_activations.py rename to nncf/torch/quantization/precision_init/definitions.py index 82868dce213..cdcbe26b5a5 100644 --- a/tools/debug/compare_activations.py +++ b/nncf/torch/quantization/precision_init/definitions.py @@ -8,24 +8,9 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import List -import argparse +from nncf.common.quantization.structs import QuantizerConfig -from tools.debug.common import compare_activations -from tools.debug.common import print_args - -argparser = argparse.ArgumentParser() -argparser.add_argument( - "-i", "--ir-dump-txt", help="IE dump file in text format (ieb from mkldnn_graph.cpp)", required=True -) -argparser.add_argument("-p", "--torch-dump-npy", help="PyTorch dump file in npy format", required=True) -args = argparser.parse_args() -print_args(args) - - -def main(): - compare_activations(args.ir_dump_txt, args.torch_dump_npy) - - -if __name__ == "__main__": - main() +QConfigSequenceForHAWQToEvaluate = List[QuantizerConfig] +CoveringQConfigSequenceForQuantNoiseCalculation = List[QuantizerConfig] diff --git a/nncf/torch/quantization/precision_init/hawq_debug.py b/nncf/torch/quantization/precision_init/hawq_debug.py index b2165e09e5d..694ea76c501 100644 --- a/nncf/torch/quantization/precision_init/hawq_debug.py +++ b/nncf/torch/quantization/precision_init/hawq_debug.py @@ -25,6 +25,9 @@ from nncf.torch.quantization.adjust_padding import add_adjust_padding_nodes from nncf.torch.quantization.layers import QUANTIZATION_MODULES from nncf.torch.quantization.precision_init.adjacent_quantizers import GroupsOfAdjacentQuantizers + +# pylint:disable=unused-import +from nncf.torch.quantization.precision_init.definitions import QConfigSequenceForHAWQToEvaluate from nncf.torch.quantization.precision_init.perturbations import PerturbationObserver from nncf.torch.quantization.precision_init.perturbations import Perturbations from nncf.torch.quantization.precision_init.traces_order import TracesPerLayer @@ -159,7 +162,7 @@ def dump_metric_flops( @skip_if_dependency_unavailable(dependencies=["matplotlib.pyplot"]) def dump_density_of_quantization_noise(self): - noise_per_config = [] # type: List[Tensor] + noise_per_config: List[Tensor] = [] for qconfig_sequence in self._weight_qconfig_sequences_in_trace_order: qnoise = 0 for i in range(self._num_weights): @@ -213,7 +216,7 @@ def dump_perturbations_ratio(self): def dump_bitwidth_graph( self, - algo_ctrl: "QuantizationController", + algo_ctrl: "QuantizationController", # noqa: F821 model: NNCFNetwork, groups_of_adjacent_quantizers: GroupsOfAdjacentQuantizers, ): diff --git a/nncf/torch/quantization/precision_init/hawq_init.py b/nncf/torch/quantization/precision_init/hawq_init.py index 7dcc3403ed6..c21cdd684a8 100644 --- a/nncf/torch/quantization/precision_init/hawq_init.py +++ b/nncf/torch/quantization/precision_init/hawq_init.py @@ -45,6 +45,8 @@ from nncf.torch.quantization.precision_init.base_init import BasePrecisionInitializer from nncf.torch.quantization.precision_init.base_init import BasePrecisionInitParams from nncf.torch.quantization.precision_init.compression_ratio import CompressionRatioCalculator +from nncf.torch.quantization.precision_init.definitions import CoveringQConfigSequenceForQuantNoiseCalculation +from nncf.torch.quantization.precision_init.definitions import QConfigSequenceForHAWQToEvaluate from nncf.torch.quantization.precision_init.hawq_debug import HAWQDebugger from nncf.torch.quantization.precision_init.perturbations import PerturbationObserver from nncf.torch.quantization.precision_init.perturbations import Perturbations @@ -104,10 +106,6 @@ def from_config( ) -QConfigSequenceForHAWQToEvaluate = List[QuantizerConfig] -CoveringQConfigSequenceForQuantNoiseCalculation = List[QuantizerConfig] - - class TraceOrderBitwidthMatcher: def __init__(self, available_bitwidths: List[int], traces_order: TracesOrder): self._available_bitwidths = available_bitwidths @@ -152,12 +150,12 @@ def _deduplicate( @staticmethod def _generate_covering_qconfig_sequences(observed_qconfs: List[Dict[QuantizerConfig, QuantizerConfig]]): - covering_qconfig_sequences = [] # type: List[CoveringQConfigSequenceForQuantNoiseCalculation] + covering_qconfig_sequences: List[CoveringQConfigSequenceForQuantNoiseCalculation] = [] # For each index, put the largest qconf subset that only varies in bitwidth on top # so that the associated covering configurations would not require model regeneration - optimized_observed_qconfs = [] # type: List[List[QuantizerConfig]] + optimized_observed_qconfs: List[List[QuantizerConfig]] = [] for qconf_oset in observed_qconfs: - variants = [] # type: List[List[QuantizerConfig]] + variants: List[List[QuantizerConfig]] = [] for qconf in qconf_oset.keys(): variants.append(list(filter(qconf.is_a_bitwidth_variant, qconf_oset.keys()))) max_bw_varying_variant = max(variants, key=len) @@ -166,7 +164,7 @@ def _generate_covering_qconfig_sequences(observed_qconfs: List[Dict[QuantizerCon max_depth = max([len(qconfs_for_trace_idx) for qconfs_for_trace_idx in optimized_observed_qconfs]) for i in range(max_depth): - covering_conf = [] # type: CoveringQConfigSequenceForQuantNoiseCalculation + covering_conf: CoveringQConfigSequenceForQuantNoiseCalculation = [] for qconfs_for_trace_idx in optimized_observed_qconfs: if i < len(qconfs_for_trace_idx): covering_conf.append(qconfs_for_trace_idx[i]) @@ -188,10 +186,10 @@ def get_qconfig_sequences_constrained_by_trace_order( """ if len(possible_qconfigs_sequence_in_trace_order) != len(self._traces_order): raise ValueError("The size of the qconfig space and the traces do not match!") - retval = [] # type: List[QConfigSequenceForHAWQToEvaluate] + retval: List[QConfigSequenceForHAWQToEvaluate] = [] observed_qconfs_in_retval = [OrderedDict() for _ in range(len(self._traces_order))] for bitwidth_sequence in self._bitwidth_sequences: - current_qconfig_sequence_in_trace_order = [] # type: QConfigSequenceForHAWQToEvaluate + current_qconfig_sequence_in_trace_order: QConfigSequenceForHAWQToEvaluate = [] for trace_idx, bitwidth in enumerate(bitwidth_sequence): if trace_idx in indices_for_bitwidth_adjustment_only: bitwidth_adjusted_default_qconfig = deepcopy( @@ -220,7 +218,7 @@ def get_qconfig_sequences_constrained_by_trace_order( class HAWQPrecisionInitializer(BasePrecisionInitializer): def __init__( self, - algo: "ExperimentalQuantizationController", + algo: "ExperimentalQuantizationController", # noqa: F821 params: HAWQPrecisionInitParams, hw_precision_constraints: HardwareQuantizationConstraints, ): @@ -464,7 +462,7 @@ def disable_all_gradients_except_weights_of_quantized_modules( for wq_id in weight_quantizers.values(): quantized_module = wq_id.quantized_module for param_name, param in quantized_module.named_parameters(): - if (quantized_module, param_name) in gradients_to_enable and not "bias" in param_name: + if (quantized_module, param_name) in gradients_to_enable and "bias" not in param_name: param.requires_grad = True return HAWQPrecisionInitializer.ParamsToRestore(originally_disabled_gradients, skipped_gradients_to_enable) @@ -536,8 +534,8 @@ def restore_disabled_gradients( def get_qconfig_sequences_constrained_by_traces_order( self, traces_order: TracesOrder ) -> Tuple[List[QConfigSequenceForHAWQToEvaluate], List[CoveringQConfigSequenceForQuantNoiseCalculation]]: - possible_qconfigs_sequence_in_trace_order = [] # type: List[List[QuantizerConfig]] - trace_order_indices_of_defaulted_qconfig_sequence = set() # type: Set[int] + possible_qconfigs_sequence_in_trace_order: List[List[QuantizerConfig]] = [] + trace_order_indices_of_defaulted_qconfig_sequence: Set[int] = set() quantizer_ids_in_exec_order = list(self._weight_quantizations_by_execution_order.keys()) assert len(quantizer_ids_in_exec_order) == len(traces_order) for trace_idx in range(len(traces_order)): @@ -586,14 +584,12 @@ def calc_quantization_noise( perturbations = Perturbations() qp_ids_in_trace_order = self._get_weight_qp_ids_in_trace_order(traces_order) ctrl = self._algo - observers_for_all_qconfig_sequences = [] # type: List[List[PerturbationObserver]] + observers_for_all_qconfig_sequences: List[List[PerturbationObserver]] = [] for qconfig_sequence in qconfig_sequences_to_run: quantizer_setup_to_run = self._apply_qconfig_sequence_to_quantizer_setup( qconfig_sequence, qp_ids_in_trace_order, ctrl.get_quantizer_setup_for_current_state() ) - ctrl, model = ctrl.apply_new_quantizer_setup( - quantizer_setup_to_run - ) # type: Tuple[ExperimentalQuantizationController, NNCFNetwork] + ctrl, model = ctrl.apply_new_quantizer_setup(quantizer_setup_to_run) hook_handles = [] observers = [] diff --git a/nncf/torch/quantization/precision_init/manual_init.py b/nncf/torch/quantization/precision_init/manual_init.py index ddced58fcd3..98ae0b23ae6 100644 --- a/nncf/torch/quantization/precision_init/manual_init.py +++ b/nncf/torch/quantization/precision_init/manual_init.py @@ -30,7 +30,7 @@ def from_config(cls, manual_init_params_dict: Dict): class ManualPrecisionInitializer(BasePrecisionInitializer): def __init__( self, - algo: "ExperimentalQuantizationController", + algo: "ExperimentalQuantizationController", # noqa: F821 params: ManualPrecisionInitParams, hw_precision_constraints: HardwareQuantizationConstraints = None, ): diff --git a/nncf/torch/quantization/precision_init/perturbations.py b/nncf/torch/quantization/precision_init/perturbations.py index 8d7b136d1af..9daacbfcedd 100644 --- a/nncf/torch/quantization/precision_init/perturbations.py +++ b/nncf/torch/quantization/precision_init/perturbations.py @@ -47,7 +47,7 @@ def get_input_norm(self): class Perturbations: def __init__(self): - self._perturbations = {} # type: Dict[int, Dict[QuantizerConfig, Tensor]] + self._perturbations: Dict[int, Dict[QuantizerConfig, Tensor]] = {} def add(self, layer_id: int, qconfig: QuantizerConfig, perturbation: Tensor): if layer_id in self._perturbations: diff --git a/nncf/torch/quantization/schedulers.py b/nncf/torch/quantization/schedulers.py index 80f515948b3..4f726a28f82 100644 --- a/nncf/torch/quantization/schedulers.py +++ b/nncf/torch/quantization/schedulers.py @@ -23,7 +23,7 @@ @QUANTIZATION_SCHEDULERS.register("staged") class StagedQuantizationScheduler(BaseCompressionScheduler): - def __init__(self, quantization_ctrl: "QuantizationController", params=None): + def __init__(self, quantization_ctrl: "QuantizationController", params=None): # noqa: F821 super().__init__() if params is None: params = {} diff --git a/nncf/torch/quantization/weights_compression.py b/nncf/torch/quantization/weights_compression.py index 2cf8947fd89..9fcdc0e61d8 100644 --- a/nncf/torch/quantization/weights_compression.py +++ b/nncf/torch/quantization/weights_compression.py @@ -54,7 +54,7 @@ def _insert_pre_compression_operations( if compression_hist is None: compression_hist = {} for _, layer in module.named_children(): - if not type(layer) in allowed_types: + if type(layer) not in allowed_types: _insert_pre_compression_operations(layer, allowed_types, level_high, compression_hist) continue diff --git a/nncf/torch/structures.py b/nncf/torch/structures.py index 2cb63c1b4ce..567a2c27f17 100644 --- a/nncf/torch/structures.py +++ b/nncf/torch/structures.py @@ -18,6 +18,8 @@ from torch.nn.modules.loss import _Loss from torch.utils.data import DataLoader +from nncf import NNCFConfig +from nncf.api.compression import CompressionAlgorithmController from nncf.common.utils.api_marker import api from nncf.config.structures import NNCFExtraConfigStruct @@ -82,7 +84,7 @@ def __init__( self, data_loader: DataLoader, eval_fn: Callable[[torch.nn.Module, torch.utils.data.DataLoader], float], - nncf_config: "NNCFConfig", + nncf_config: NNCFConfig, ): self.data_loader = data_loader self.eval_fn = eval_fn @@ -115,7 +117,7 @@ def __init__( torch.utils.data.DataLoader, torch.nn.Module, torch.optim.Optimizer, - "CompressionAlgorithmController", + CompressionAlgorithmController, Optional[int], ], type(None), @@ -123,7 +125,7 @@ def __init__( val_loader: torch.utils.data.DataLoader, val_fn: Callable[[torch.nn.Module, torch.utils.data.DataLoader], Tuple[float, float]], train_optimizer: Optional[torch.optim.Optimizer], - nncf_config: "NNCFConfig", + nncf_config: NNCFConfig, ): self.train_loader = train_loader self.train_steps_fn = train_fn diff --git a/nncf/torch/tensor_statistics/reduction.py b/nncf/torch/tensor_statistics/reduction.py deleted file mode 100644 index 4911bc2fdf2..00000000000 --- a/nncf/torch/tensor_statistics/reduction.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import List, Tuple - -import numpy as np -import torch - - -def max_reduce_like(input_: torch.Tensor, ref_tensor_shape: List[int]) -> torch.Tensor: - numel = np.prod(ref_tensor_shape) - if numel == 1: - retval = input_.max() - for _ in ref_tensor_shape: - retval.unsqueeze_(-1) - return retval - tmp_max = input_ - for dim_idx, dim in enumerate(ref_tensor_shape): - if dim == 1: - tmp_max, _ = torch.max(tmp_max, dim_idx, keepdim=True) - return tmp_max - - -def min_reduce_like(input_: torch.Tensor, ref_tensor_shape: List[int]): - numel = np.prod(ref_tensor_shape) - if numel == 1: - retval = input_.min() - for _ in ref_tensor_shape: - retval.unsqueeze_(-1) - return retval - tmp_min = input_ - for dim_idx, dim in enumerate(ref_tensor_shape): - if dim == 1: - tmp_min, _ = torch.min(tmp_min, dim_idx, keepdim=True) - return tmp_min - - -def get_channel_count_and_dim_idx(scale_shape: List[int]) -> Tuple[int, int]: - channel_dim_idx = 0 - channel_count = 1 - for dim_idx, dim in enumerate(scale_shape): - if dim != 1: - channel_dim_idx = dim_idx - channel_count = dim - return channel_count, channel_dim_idx - - -def expand_like(input_: torch.Tensor, scale_shape: List[int]) -> torch.Tensor: - retval = input_ - count, idx = get_channel_count_and_dim_idx(scale_shape) - assert input_.numel() == count - assert len(input_.size()) == 1 - for _ in range(0, idx): - retval = retval.unsqueeze(0) - for _ in range(idx + 1, len(scale_shape)): - retval = retval.unsqueeze(-1) - return retval diff --git a/nncf/torch/tensor_statistics/statistics.py b/nncf/torch/tensor_statistics/statistics.py index ba51df16ce9..187f125d813 100644 --- a/nncf/torch/tensor_statistics/statistics.py +++ b/nncf/torch/tensor_statistics/statistics.py @@ -9,8 +9,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional, Tuple - import torch from nncf.common.tensor_statistics.statistics import MeanTensorStatistic @@ -20,10 +18,6 @@ from nncf.common.tensor_statistics.statistics import TensorStatistic -def _reshape_all(targets: Tuple[torch.Tensor, ...], target_shape: Tuple[int, ...]): - return map(lambda stat: torch.reshape(stat, target_shape), targets) - - class PTMinMaxTensorStatistic(MinMaxTensorStatistic): @staticmethod def tensor_eq(tensor1: torch.Tensor, tensor2: torch.Tensor, rtol=1e-6) -> bool: diff --git a/nncf/torch/utils.py b/nncf/torch/utils.py index 5ac8f142594..646e939282e 100644 --- a/nncf/torch/utils.py +++ b/nncf/torch/utils.py @@ -24,8 +24,11 @@ from nncf.common.graph import NNCFNodeName from nncf.common.logging import nncf_logger from nncf.common.scopes import matches_any +from nncf.torch.dynamic_graph.scope import Scope +from nncf.torch.dynamic_graph.scope import ScopeElement from nncf.torch.dynamic_graph.trace_tensor import TracedTensor from nncf.torch.layer_utils import _NNCFModuleMixin +from nncf.torch.structures import ExecutionParameters def get_node_name(module, module_name, prefix): @@ -47,14 +50,12 @@ def get_all_modules(model, prefix=None): def get_all_modules_by_type( model, module_types=None, current_scope=None, ignored_scopes=None, target_scopes=None, memo=None -) -> Dict["Scope", Module]: +) -> Dict[Scope, Module]: if memo is None: memo = set() if isinstance(module_types, str): module_types = [module_types] found = OrderedDict() - from nncf.torch.dynamic_graph.scope import Scope # pylint: disable=cyclic-import - from nncf.torch.dynamic_graph.scope import ScopeElement # pylint: disable=cyclic-import if current_scope is None: current_scope = Scope() @@ -87,7 +88,7 @@ def get_all_modules_by_type( def get_state_dict_names_with_modules( - model: "NNCFNetwork", str_types: List[str] = None, prefix="" + model: torch.nn.Module, str_types: List[str] = None, prefix="" ) -> Dict[str, torch.nn.Module]: found = OrderedDict() for name, module in model.named_children(): @@ -214,8 +215,8 @@ def is_traced_tensor(obj): class _ModuleState: def __init__(self, base_module: Module = None): - self._training_state = {} # type: Dict[str, bool] - self._requires_grad_state = {} # type: Dict[str, bool] + self._training_state: Dict[str, bool] = {} + self._requires_grad_state: Dict[str, bool] = {} if base_module is not None: for module_name, module in base_module.named_modules(): self.training_state[module_name] = module.training @@ -286,7 +287,7 @@ def add_domain(name_operator: str) -> str: return DOMAIN_CUSTOM_OPS_NAME + "::" + name_operator -def default_distributed_wrapper(model: nn.Module, execution_parameters: "ExecutionParameters"): +def default_distributed_wrapper(model: nn.Module, execution_parameters: ExecutionParameters): """ Wrapping model for distributed training with DataParallel or DistributedDataParallel depending on execution mode chosen by user. @@ -334,7 +335,7 @@ def rename_legacy_names_in_state_dict( ): for name in legacy_names: tensor = state_dict_to_load.pop(name) - new_key = name.replace(legacy_name, new_name) if not new_name in name else name + new_key = name.replace(legacy_name, new_name) if new_name not in name else name state_dict_to_load[new_key] = tensor if legacy_names: @@ -397,7 +398,7 @@ def maybe_convert_legacy_names_in_compress_state(compression_state: Dict[str, An for point in qips.values(): name = point["qip"]["target_node_name"] for old_name, new_name in LEGACY_VS_NEW_BN_MAP.items(): - if old_name in name and not new_name in name: + if old_name in name and new_name not in name: detected_legacy_names[old_name] = True point["qip"]["target_node_name"] = name.replace(old_name, new_name) break diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 00000000000..fafd792e862 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,31 @@ +line-length = 120 +ignore-init-module-imports = true +ignore = ["E731"] +select = [ + "E", # pycodestyle rules + "F", # pyflakes rules + "CPY001" # copyright check +] +exclude = ["nncf/tensorflow/__init__.py"] + +[per-file-ignores] +"nncf/experimental/torch/nas/bootstrapNAS/__init__.py" = ["F401"] +"nncf/torch/__init__.py" = ["F401"] +"tests/**/*.py" = ["F403"] +"tests/**/__init__.py" = ["F401"] +"examples/**/*.py" = ["F403"] + + +[flake8-copyright] +notice-rgx = """\ +# Copyright \\(c\\) (\\d{4}|\\d{4}-\\d{4}) Intel Corporation +# Licensed under the Apache License, Version 2.0 \\(the "License"\\); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" diff --git a/setup.py b/setup.py index 4807a483c15..5790546f50c 100644 --- a/setup.py +++ b/setup.py @@ -107,7 +107,7 @@ def find_version(*file_paths): "networkx>=2.6, <=2.8.2", # see ticket 94048 or https://github.com/networkx/networkx/issues/5962 "ninja>=1.10.0.post2, <1.11", "numpy>=1.19.1, <1.25", - "openvino-telemetry>=2023.1.1", + "openvino-telemetry>=2023.2.0", "packaging>=20.0", "pandas>=1.1.5,<2.1", "psutil", diff --git a/tests/common/conftest.py b/tests/common/conftest.py index b9217f8077e..656c5a25b69 100644 --- a/tests/common/conftest.py +++ b/tests/common/conftest.py @@ -9,4 +9,5 @@ # See the License for the specific language governing permissions and # limitations under the License. -from tests.shared.logging import nncf_caplog # pylint:disable=unused-import +# pylint:disable=unused-import +from tests.shared.logging import nncf_caplog # noqa: F401 diff --git a/tests/common/graph/__init__.py b/tests/common/graph/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/common/graph/__init__.py +++ b/tests/common/graph/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/common/pruning/__init__.py b/tests/common/pruning/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/common/pruning/__init__.py +++ b/tests/common/pruning/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/common/pruning/test_symbolic_mask_processor.py b/tests/common/pruning/test_symbolic_mask_processor.py index 379c5b38799..352a74f5a86 100644 --- a/tests/common/pruning/test_symbolic_mask_processor.py +++ b/tests/common/pruning/test_symbolic_mask_processor.py @@ -37,7 +37,7 @@ def test_repeat(): mask = SymbolicMask(10, mask_producers) repeated_tensor = SymbolicMaskProcessor.repeat(mask, repeats) for idx, repeated_producer in enumerate(repeated_tensor.mask_producers): - assert not mask_producers[idx] is repeated_producer + assert mask_producers[idx] is not repeated_producer assert mask_producers[idx].id == repeated_producer.id assert mask_producers[idx].sparse_multiplier * repeats == repeated_producer.sparse_multiplier diff --git a/tests/common/quantization/metatypes.py b/tests/common/quantization/metatypes.py index 0e9a5bcfe43..8b8255c59a7 100644 --- a/tests/common/quantization/metatypes.py +++ b/tests/common/quantization/metatypes.py @@ -11,8 +11,8 @@ from typing import List -from nncf.common.graph import OperatorMetatype from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.operator_metatypes import OperatorMetatypeRegistry from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait diff --git a/tests/common/quantization/mock_graphs.py b/tests/common/quantization/mock_graphs.py index 7879066a35f..1234cd33287 100644 --- a/tests/common/quantization/mock_graphs.py +++ b/tests/common/quantization/mock_graphs.py @@ -15,12 +15,12 @@ import networkx as nx -from nncf.common.graph import BaseLayerAttributes -from nncf.common.graph import Dtype from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName -from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes +from nncf.common.graph.layer_attributes import Dtype from nncf.common.graph.operator_metatypes import UnknownMetatype from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.insertion_point_graph import PostHookInsertionPoint @@ -84,7 +84,7 @@ def get_nncf_graph_from_mock_nx_graph(nx_graph: nx.DiGraph, nncf_graph_cls=NNCFG # pylint:disable=too-many-branches mock_graph = nncf_graph_cls() key_vs_id = {} - edge_vs_output_idx_and_creator_id = {} # type: Dict[Tuple[str, str], Tuple[int, int]] + edge_vs_output_idx_and_creator_id: Dict[Tuple[str, str], Tuple[int, int]] = {} from networkx.algorithms.dag import lexicographical_topological_sort for idx, curr_node_key in enumerate(lexicographical_topological_sort(nx_graph)): diff --git a/tests/common/quantization/test_filter_constant_nodes.py b/tests/common/quantization/test_filter_constant_nodes.py index 1cccfcffd08..e92d271b859 100644 --- a/tests/common/quantization/test_filter_constant_nodes.py +++ b/tests/common/quantization/test_filter_constant_nodes.py @@ -14,7 +14,6 @@ import pytest -from nncf.common.graph.graph import NNCFNode from nncf.common.graph.operator_metatypes import InputNoopMetatype from nncf.common.graph.operator_metatypes import OutputNoopMetatype from nncf.common.insertion_point_graph import ConstantNodesFilter diff --git a/tests/common/quantization/test_quantizer_propagation_graph.py b/tests/common/quantization/test_quantizer_propagation_graph.py index 83ff4321572..45fe13a42a2 100644 --- a/tests/common/quantization/test_quantizer_propagation_graph.py +++ b/tests/common/quantization/test_quantizer_propagation_graph.py @@ -18,10 +18,10 @@ import networkx as nx import pytest -from nncf.common.graph import Dtype from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName -from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.layer_attributes import Dtype from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.insertion_point_graph import PostHookInsertionPoint from nncf.common.insertion_point_graph import PreHookInsertionPoint @@ -758,7 +758,7 @@ def test_merge_quantizer_into_path(self, model_graph_qpsg, merge_quantizer_into_ target_node = quantizers_test_struct.target_node_for_quantizer is_merged = quantizers_test_struct.is_merged prop_path = quantizers_test_struct.prop_path - node_key_vs_trait_dict = {} # type: Dict[str, QuantizationTrait] + node_key_vs_trait_dict: Dict[str, QuantizationTrait] = {} for node_key in quant_prop_graph.nodes: node_key_vs_trait_dict[node_key] = QuantizationTrait.QUANTIZATION_AGNOSTIC primary_prop_quant = None @@ -1310,7 +1310,7 @@ def create_graph_for_output_quant_as_weights() -> NNCFGraph: return get_nncf_graph_from_mock_nx_graph(mock_graph) -MODEL_GRAPH = create_graph_for_output_quant_as_weights() # type: NNCFGraph +MODEL_GRAPH: NNCFGraph = create_graph_for_output_quant_as_weights() class TestOutputQuantAsWeightsSetup: diff --git a/tests/common/quantization/test_quantizer_propagation_solver.py b/tests/common/quantization/test_quantizer_propagation_solver.py index dc065a2e7e2..19937966b17 100644 --- a/tests/common/quantization/test_quantizer_propagation_solver.py +++ b/tests/common/quantization/test_quantizer_propagation_solver.py @@ -18,11 +18,11 @@ import networkx as nx import pytest -from nncf.common.graph import Dtype from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode from nncf.common.graph import NNCFNodeName from nncf.common.graph.definitions import MODEL_INPUT_OP_NAME -from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.layer_attributes import Dtype from nncf.common.graph.operator_metatypes import OutputNoopMetatype from nncf.common.graph.operator_metatypes import UnknownMetatype from nncf.common.graph.transformations.commands import TargetType @@ -182,7 +182,7 @@ def __init__( self.expected_count_finished_quant = expected_count_finished_quant self.expected_count_active_quant = expected_count_active_quant self.ignored_scopes = ignored_scopes - self.retval_qps = {} # type: Dict[QuantizationPointId, MultiConfigQuantizationPoint] + self.retval_qps: Dict[QuantizationPointId, MultiConfigQuantizationPoint] = {} for id_, qp_data in retval_qp_data.items(): if qp_data.target_type is TargetType.OPERATION_WITH_WEIGHTS: qip = WeightQuantizationInsertionPoint(qp_data.node_name) @@ -1528,8 +1528,8 @@ def test_propagation_step(self, propagation_step_test_struct: PropagationStepTes assert quant_prop in active_propagating_quantizers_queue for pq in untouched_quantizers: - assert not pq in quant_prop_solver.get_active_propagating_quantizers_queue() - assert not pq in quant_prop_solver.get_finished_propagating_quantizers() + assert pq not in quant_prop_solver.get_active_propagating_quantizers_queue() + assert pq not in quant_prop_solver.get_finished_propagating_quantizers() # The quantizers that were added during preparation were not registered # as active for the solvers; but the ones that may have appeared due to an upward diff --git a/tests/common/test_progress_bar.py b/tests/common/test_progress_bar.py index 3b8f6605aa7..9e6c376fc60 100644 --- a/tests/common/test_progress_bar.py +++ b/tests/common/test_progress_bar.py @@ -13,9 +13,6 @@ import pytest from nncf.common.logging.progress_bar import ProgressBar -from tests.shared.logging import nncf_caplog # pylint:disable=unused-import - -# pylint:disable=redefined-outer-name TEST_RANGE = range(3) diff --git a/tests/common/test_statistics_aggregator.py b/tests/common/test_statistics_aggregator.py index 6c5fc043904..980ea2328d7 100644 --- a/tests/common/test_statistics_aggregator.py +++ b/tests/common/test_statistics_aggregator.py @@ -38,6 +38,16 @@ from nncf.quantization.range_estimator import StatisticsType +class BiasCorrectionAlgos(Enum): + BIAS_CORRECTION = "bias_correction" + FAST_BIAS_CORRECTION = "fast_bias_correction" + + +class BCStatsCollectors(Enum): + MEAN = "mean" + RAW = "raw" + + # pylint: disable=too-many-public-methods class TemplateTestStatisticsAggregator: @abstractmethod @@ -442,18 +452,10 @@ def filter_func(point): assert stat.min_values.shape == ref_shape assert stat.max_values.shape == ref_shape - class BiasCorrectionAlgos(Enum): - BIAS_CORRECTION = "bias_correction" - FAST_BIAS_CORRECTION = "fast_bias_correction" - - class BCStatsCollectors(Enum): - MEAN = "mean" - RAW = "raw" - @dataclass class BCTestParameters: - algo: "BiasCorrectionAlgos" - collector_type: "BCStatsCollectors" + algo: BiasCorrectionAlgos + collector_type: BCStatsCollectors target_type: TargetType ref_values: Any = None axis: int = 1 @@ -576,15 +578,15 @@ def test_statistics_aggregator_bias_correction( self, dataset_samples, test_params: BCTestParameters, inplace_statistics, is_stat_in_shape_of_scale ): name_to_algo_backend_map = { - self.BiasCorrectionAlgos.BIAS_CORRECTION: self.get_bias_correction_algo_backend_cls, - self.BiasCorrectionAlgos.FAST_BIAS_CORRECTION: self.get_fast_bias_correction_algo_backend_cls, + BiasCorrectionAlgos.BIAS_CORRECTION: self.get_bias_correction_algo_backend_cls, + BiasCorrectionAlgos.FAST_BIAS_CORRECTION: self.get_fast_bias_correction_algo_backend_cls, } algo_backend = name_to_algo_backend_map[test_params.algo]() - if test_params.collector_type == self.BCStatsCollectors.MEAN: + if test_params.collector_type == BCStatsCollectors.MEAN: tensor_collector = algo_backend.mean_statistic_collector( test_params.axis, inplace_statistics, len(dataset_samples) ) - elif test_params.collector_type == self.BCStatsCollectors.RAW: + elif test_params.collector_type == BCStatsCollectors.RAW: tensor_collector = algo_backend.raw_statistic_collector(inplace_statistics, len(dataset_samples)) else: raise RuntimeError() @@ -615,9 +617,9 @@ def filter_func(point): for tensor_collector in tensor_collectors: stat = tensor_collector.get_statistics() - if test_params.collector_type == self.BCStatsCollectors.MEAN: + if test_params.collector_type == BCStatsCollectors.MEAN: ret_val = [stat.mean_values, stat.shape] - elif test_params.collector_type == self.BCStatsCollectors.RAW: + elif test_params.collector_type == BCStatsCollectors.RAW: ret_val = stat.values test_params.ref_values = dataset_samples if not is_stat_in_shape_of_scale: diff --git a/tests/cross_fw/install/install_checks_tf.py b/tests/cross_fw/install/install_checks_tf.py index 239f612bdc3..11dea551a3c 100644 --- a/tests/cross_fw/install/install_checks_tf.py +++ b/tests/cross_fw/install/install_checks_tf.py @@ -13,7 +13,7 @@ # Do not remove - these imports are for testing purposes. # pylint:disable=unused-import -import nncf +import nncf # noqa: F401 from nncf.common.compression import BaseCompressionAlgorithmController from nncf.tensorflow.helpers.model_creation import create_compressed_model from tests.tensorflow.quantization.utils import get_basic_quantization_config diff --git a/tests/cross_fw/install/install_checks_torch.py b/tests/cross_fw/install/install_checks_torch.py index d22dd5e2302..c15983c08d8 100644 --- a/tests/cross_fw/install/install_checks_torch.py +++ b/tests/cross_fw/install/install_checks_torch.py @@ -21,8 +21,8 @@ # Do not remove - these imports are for testing purposes. # pylint:disable=unused-import # pylint:disable=wrong-import-position -import nncf -from nncf.torch import create_compressed_model +import nncf # noqa: F401, E402 +from nncf.torch import create_compressed_model # noqa: F401, E402 input_low_tensor = torch.zeros([1]) input_tensor = torch.ones([1, 1, 1, 1]) diff --git a/tests/onnx/__init__.py b/tests/onnx/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/onnx/__init__.py +++ b/tests/onnx/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/onnx/benchmarking/__init__.py b/tests/onnx/benchmarking/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/onnx/benchmarking/__init__.py +++ b/tests/onnx/benchmarking/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot b/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot index 0775c236c76..e2a0350705f 100644 --- a/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot +++ b/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot @@ -481,10 +481,10 @@ strict digraph { "479 QuantizeLinear_389_1" [id=479, type=QuantizeLinear]; "480 DequantizeLinear_389_1" [id=480, type=DequantizeLinear]; "481 390" [id=481, type=Conv]; -"482 QuantizeLinear_391_2" [id=482, type=QuantizeLinear]; -"483 DequantizeLinear_391_2" [id=483, type=DequantizeLinear]; -"484 QuantizeLinear_391_1" [id=484, type=QuantizeLinear]; -"485 DequantizeLinear_391_1" [id=485, type=DequantizeLinear]; +"482 QuantizeLinear_391_1" [id=482, type=QuantizeLinear]; +"483 DequantizeLinear_391_1" [id=483, type=DequantizeLinear]; +"484 QuantizeLinear_391_2" [id=484, type=QuantizeLinear]; +"485 DequantizeLinear_391_2" [id=485, type=DequantizeLinear]; "486 487" [id=486, type=MaxPool]; "487 QuantizeLinear_489_1" [id=487, type=QuantizeLinear]; "488 DequantizeLinear_489_1" [id=488, type=DequantizeLinear]; @@ -1749,14 +1749,14 @@ strict digraph { "1747 1172" [id=1747, type=Gather]; "1748 2479" [id=1748, type=Concat]; "1749 2490" [id=1749, type=Gather]; -"1750 QuantizeLinear_2527_4" [id=1750, type=QuantizeLinear]; -"1751 DequantizeLinear_2527_4" [id=1751, type=DequantizeLinear]; -"1752 QuantizeLinear_2527_3" [id=1752, type=QuantizeLinear]; -"1753 DequantizeLinear_2527_3" [id=1753, type=DequantizeLinear]; -"1754 QuantizeLinear_2527_2" [id=1754, type=QuantizeLinear]; -"1755 DequantizeLinear_2527_2" [id=1755, type=DequantizeLinear]; -"1756 QuantizeLinear_2527_1" [id=1756, type=QuantizeLinear]; -"1757 DequantizeLinear_2527_1" [id=1757, type=DequantizeLinear]; +"1750 QuantizeLinear_2527_1" [id=1750, type=QuantizeLinear]; +"1751 DequantizeLinear_2527_1" [id=1751, type=DequantizeLinear]; +"1752 QuantizeLinear_2527_2" [id=1752, type=QuantizeLinear]; +"1753 DequantizeLinear_2527_2" [id=1753, type=DequantizeLinear]; +"1754 QuantizeLinear_2527_3" [id=1754, type=QuantizeLinear]; +"1755 DequantizeLinear_2527_3" [id=1755, type=DequantizeLinear]; +"1756 QuantizeLinear_2527_4" [id=1756, type=QuantizeLinear]; +"1757 DequantizeLinear_2527_4" [id=1757, type=DequantizeLinear]; "1758 2532" [id=1758, type=Slice]; "1759 2534" [id=1759, type=Gather]; "1760 2525" [id=1760, type=Slice]; @@ -3692,14 +3692,14 @@ strict digraph { "3690 3030" [id=3690, type=Gather]; "3691 6518" [id=3691, type=Concat]; "3692 6530" [id=3692, type=Gather]; -"3693 QuantizeLinear_6568_4" [id=3693, type=QuantizeLinear]; -"3694 DequantizeLinear_6568_4" [id=3694, type=DequantizeLinear]; -"3695 QuantizeLinear_6568_3" [id=3695, type=QuantizeLinear]; -"3696 DequantizeLinear_6568_3" [id=3696, type=DequantizeLinear]; -"3697 QuantizeLinear_6568_2" [id=3697, type=QuantizeLinear]; -"3698 DequantizeLinear_6568_2" [id=3698, type=DequantizeLinear]; -"3699 QuantizeLinear_6568_1" [id=3699, type=QuantizeLinear]; -"3700 DequantizeLinear_6568_1" [id=3700, type=DequantizeLinear]; +"3693 QuantizeLinear_6568_1" [id=3693, type=QuantizeLinear]; +"3694 DequantizeLinear_6568_1" [id=3694, type=DequantizeLinear]; +"3695 QuantizeLinear_6568_2" [id=3695, type=QuantizeLinear]; +"3696 DequantizeLinear_6568_2" [id=3696, type=DequantizeLinear]; +"3697 QuantizeLinear_6568_3" [id=3697, type=QuantizeLinear]; +"3698 DequantizeLinear_6568_3" [id=3698, type=DequantizeLinear]; +"3699 QuantizeLinear_6568_4" [id=3699, type=QuantizeLinear]; +"3700 DequantizeLinear_6568_4" [id=3700, type=DequantizeLinear]; "3701 6576" [id=3701, type=Slice]; "3702 6578" [id=3702, type=Gather]; "3703 6569" [id=3703, type=Slice]; @@ -4788,16 +4788,16 @@ strict digraph { "478 DequantizeLinear_388_1" -> "481 390" [label="[1, 256, -1, -1]", style=solid]; "479 QuantizeLinear_389_1" -> "480 DequantizeLinear_389_1" [label="[256, 256, 3, 3]", style=dashed]; "480 DequantizeLinear_389_1" -> "481 390" [label="[256, 256, 3, 3]", style=solid]; -"481 390" -> "482 QuantizeLinear_391_2" [label="[1, 256, -1, -1]", style=solid]; -"481 390" -> "484 QuantizeLinear_391_1" [label="[1, 256, -1, -1]", style=solid]; +"481 390" -> "482 QuantizeLinear_391_1" [label="[1, 256, -1, -1]", style=solid]; +"481 390" -> "484 QuantizeLinear_391_2" [label="[1, 256, -1, -1]", style=solid]; "481 390" -> "784 536" [label="[1, 256, -1, -1]", style=solid]; "481 390" -> "787 533" [label="[1, 256, -1, -1]", style=solid]; "481 390" -> "1929 2620" [label="[1, 256, -1, -1]", style=solid]; "481 390" -> "3872 6664" [label="[1, 256, -1, -1]", style=solid]; -"482 QuantizeLinear_391_2" -> "483 DequantizeLinear_391_2" [label="[1, 256, -1, -1]", style=dashed]; -"483 DequantizeLinear_391_2" -> "722 506" [label="[1, 256, -1, -1]", style=solid]; -"484 QuantizeLinear_391_1" -> "485 DequantizeLinear_391_1" [label="[1, 256, -1, -1]", style=dashed]; -"485 DequantizeLinear_391_1" -> "486 487" [label="[1, 256, -1, -1]", style=solid]; +"482 QuantizeLinear_391_1" -> "483 DequantizeLinear_391_1" [label="[1, 256, -1, -1]", style=dashed]; +"483 DequantizeLinear_391_1" -> "486 487" [label="[1, 256, -1, -1]", style=solid]; +"484 QuantizeLinear_391_2" -> "485 DequantizeLinear_391_2" [label="[1, 256, -1, -1]", style=dashed]; +"485 DequantizeLinear_391_2" -> "722 506" [label="[1, 256, -1, -1]", style=solid]; "486 487" -> "489 510" [label="[1, 256, -1, -1]", style=solid]; "486 487" -> "555 542" [label="[1, 256, -1, -1]", style=solid]; "486 487" -> "558 539" [label="[1, 256, -1, -1]", style=solid]; @@ -6341,21 +6341,21 @@ strict digraph { "1746 1171" -> "1747 1172" [label="[-1]", style=dashed]; "1747 1172" -> "1748 2479" [label="[-1, 4]", style=solid]; "1748 2479" -> "1749 2490" [label="[-1, 4]", style=solid]; -"1749 2490" -> "1750 QuantizeLinear_2527_4" [label="[]", style=solid]; -"1749 2490" -> "1752 QuantizeLinear_2527_3" [label="[]", style=solid]; -"1749 2490" -> "1754 QuantizeLinear_2527_2" [label="[]", style=solid]; -"1749 2490" -> "1756 QuantizeLinear_2527_1" [label="[]", style=solid]; +"1749 2490" -> "1750 QuantizeLinear_2527_1" [label="[]", style=solid]; +"1749 2490" -> "1752 QuantizeLinear_2527_2" [label="[]", style=solid]; +"1749 2490" -> "1754 QuantizeLinear_2527_3" [label="[]", style=solid]; +"1749 2490" -> "1756 QuantizeLinear_2527_4" [label="[]", style=solid]; "1749 2490" -> "1803 2495" [label="[]", style=solid]; "1749 2490" -> "1807 2503" [label="[]", style=solid]; "1749 2490" -> "2009 2775" [label="[]", style=solid]; -"1750 QuantizeLinear_2527_4" -> "1751 DequantizeLinear_2527_4" [label="[]", style=dashed]; -"1751 DequantizeLinear_2527_4" -> "1768 2508" [label="[]", style=solid]; -"1752 QuantizeLinear_2527_3" -> "1753 DequantizeLinear_2527_3" [label="[]", style=dashed]; -"1753 DequantizeLinear_2527_3" -> "1766 2515" [label="[]", style=solid]; -"1754 QuantizeLinear_2527_2" -> "1755 DequantizeLinear_2527_2" [label="[]", style=dashed]; -"1755 DequantizeLinear_2527_2" -> "1760 2525" [label="[]", style=solid]; -"1756 QuantizeLinear_2527_1" -> "1757 DequantizeLinear_2527_1" [label="[]", style=dashed]; -"1757 DequantizeLinear_2527_1" -> "1758 2532" [label="[]", style=solid]; +"1750 QuantizeLinear_2527_1" -> "1751 DequantizeLinear_2527_1" [label="[]", style=dashed]; +"1751 DequantizeLinear_2527_1" -> "1758 2532" [label="[]", style=solid]; +"1752 QuantizeLinear_2527_2" -> "1753 DequantizeLinear_2527_2" [label="[]", style=dashed]; +"1753 DequantizeLinear_2527_2" -> "1760 2525" [label="[]", style=solid]; +"1754 QuantizeLinear_2527_3" -> "1755 DequantizeLinear_2527_3" [label="[]", style=dashed]; +"1755 DequantizeLinear_2527_3" -> "1766 2515" [label="[]", style=solid]; +"1756 QuantizeLinear_2527_4" -> "1757 DequantizeLinear_2527_4" [label="[]", style=dashed]; +"1757 DequantizeLinear_2527_4" -> "1768 2508" [label="[]", style=solid]; "1758 2532" -> "1759 2534" [label="[]", style=solid]; "1759 2534" -> "1762 2535" [label="[]", style=solid]; "1760 2525" -> "1761 2527" [label="[]", style=solid]; @@ -8988,21 +8988,21 @@ strict digraph { "3690 3030" -> "3691 6518" [label="[]", style=solid]; "3690 3030" -> "4259 3037" [label="[]", style=solid]; "3691 6518" -> "3692 6530" [label="[]", style=solid]; -"3692 6530" -> "3693 QuantizeLinear_6568_4" [label="[-1, 4]", style=solid]; -"3692 6530" -> "3695 QuantizeLinear_6568_3" [label="[-1, 4]", style=solid]; -"3692 6530" -> "3697 QuantizeLinear_6568_2" [label="[-1, 4]", style=solid]; -"3692 6530" -> "3699 QuantizeLinear_6568_1" [label="[-1, 4]", style=solid]; +"3692 6530" -> "3693 QuantizeLinear_6568_1" [label="[-1, 4]", style=solid]; +"3692 6530" -> "3695 QuantizeLinear_6568_2" [label="[-1, 4]", style=solid]; +"3692 6530" -> "3697 QuantizeLinear_6568_3" [label="[-1, 4]", style=solid]; +"3692 6530" -> "3699 QuantizeLinear_6568_4" [label="[-1, 4]", style=solid]; "3692 6530" -> "3746 6539" [label="[-1, 4]", style=solid]; "3692 6530" -> "3750 6547" [label="[-1, 4]", style=solid]; "3692 6530" -> "4281 nncf_model_output_0" [label="[-1, 4]", style=solid]; -"3693 QuantizeLinear_6568_4" -> "3694 DequantizeLinear_6568_4" [label="[-1, 4]", style=dashed]; -"3694 DequantizeLinear_6568_4" -> "3711 6552" [label="[-1, 4]", style=solid]; -"3695 QuantizeLinear_6568_3" -> "3696 DequantizeLinear_6568_3" [label="[-1, 4]", style=dashed]; -"3696 DequantizeLinear_6568_3" -> "3709 6559" [label="[-1, 4]", style=solid]; -"3697 QuantizeLinear_6568_2" -> "3698 DequantizeLinear_6568_2" [label="[-1, 4]", style=dashed]; -"3698 DequantizeLinear_6568_2" -> "3703 6569" [label="[-1, 4]", style=solid]; -"3699 QuantizeLinear_6568_1" -> "3700 DequantizeLinear_6568_1" [label="[-1, 4]", style=dashed]; -"3700 DequantizeLinear_6568_1" -> "3701 6576" [label="[-1, 4]", style=solid]; +"3693 QuantizeLinear_6568_1" -> "3694 DequantizeLinear_6568_1" [label="[-1, 4]", style=dashed]; +"3694 DequantizeLinear_6568_1" -> "3701 6576" [label="[-1, 4]", style=solid]; +"3695 QuantizeLinear_6568_2" -> "3696 DequantizeLinear_6568_2" [label="[-1, 4]", style=dashed]; +"3696 DequantizeLinear_6568_2" -> "3703 6569" [label="[-1, 4]", style=solid]; +"3697 QuantizeLinear_6568_3" -> "3698 DequantizeLinear_6568_3" [label="[-1, 4]", style=dashed]; +"3698 DequantizeLinear_6568_3" -> "3709 6559" [label="[-1, 4]", style=solid]; +"3699 QuantizeLinear_6568_4" -> "3700 DequantizeLinear_6568_4" [label="[-1, 4]", style=dashed]; +"3700 DequantizeLinear_6568_4" -> "3711 6552" [label="[-1, 4]", style=solid]; "3701 6576" -> "3702 6578" [label="[-1, 4]", style=solid]; "3702 6578" -> "3705 6579" [label="[-1]", style=solid]; "3703 6569" -> "3704 6571" [label="[-1, 4]", style=solid]; diff --git a/tests/onnx/data/reference_graphs/quantization/bertsquad-12.dot b/tests/onnx/data/reference_graphs/quantization/bertsquad-12.dot index 246765a6663..5e2502ba0c4 100644 --- a/tests/onnx/data/reference_graphs/quantization/bertsquad-12.dot +++ b/tests/onnx/data/reference_graphs/quantization/bertsquad-12.dot @@ -405,14 +405,14 @@ strict digraph { "403 bert/encoder/Reshape_13/shape_Concat__301" [id=403, type=Concat]; "404 bert/encoder/Reshape_13__471" [id=404, type=Cast]; "405 bert/encoder/Reshape_1" [id=405, type=Reshape]; -"406 QuantizeLinear_bert/encoder/Reshape_1^0_3" [id=406, label="406 QuantizeLinear_bert/encoder/Reshape_1:0_3", type=QuantizeLinear]; -"407 DequantizeLinear_bert/encoder/Reshape_1^0_3" [id=407, label="407 DequantizeLinear_bert/encoder/Reshape_1:0_3", type=DequantizeLinear]; -"408 QuantizeLinear_bert/encoder/Reshape_1^0_2" [id=408, label="408 QuantizeLinear_bert/encoder/Reshape_1:0_2", type=QuantizeLinear]; -"409 DequantizeLinear_bert/encoder/Reshape_1^0_2" [id=409, label="409 DequantizeLinear_bert/encoder/Reshape_1:0_2", type=DequantizeLinear]; -"410 QuantizeLinear_bert/encoder/Reshape_1^0_1" [id=410, label="410 QuantizeLinear_bert/encoder/Reshape_1:0_1", type=QuantizeLinear]; -"411 DequantizeLinear_bert/encoder/Reshape_1^0_1" [id=411, label="411 DequantizeLinear_bert/encoder/Reshape_1:0_1", type=DequantizeLinear]; -"412 QuantizeLinear_bert/encoder/layer_0/attention/self/value/kernel^0_1" [id=412, label="412 QuantizeLinear_bert/encoder/layer_0/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"413 DequantizeLinear_bert/encoder/layer_0/attention/self/value/kernel^0_1" [id=413, label="413 DequantizeLinear_bert/encoder/layer_0/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"406 QuantizeLinear_bert/encoder/Reshape_1^0_1" [id=406, label="406 QuantizeLinear_bert/encoder/Reshape_1:0_1", type=QuantizeLinear]; +"407 DequantizeLinear_bert/encoder/Reshape_1^0_1" [id=407, label="407 DequantizeLinear_bert/encoder/Reshape_1:0_1", type=DequantizeLinear]; +"408 QuantizeLinear_bert/encoder/layer_0/attention/self/value/kernel^0_1" [id=408, label="408 QuantizeLinear_bert/encoder/layer_0/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"409 DequantizeLinear_bert/encoder/layer_0/attention/self/value/kernel^0_1" [id=409, label="409 DequantizeLinear_bert/encoder/layer_0/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"410 QuantizeLinear_bert/encoder/Reshape_1^0_2" [id=410, label="410 QuantizeLinear_bert/encoder/Reshape_1:0_2", type=QuantizeLinear]; +"411 DequantizeLinear_bert/encoder/Reshape_1^0_2" [id=411, label="411 DequantizeLinear_bert/encoder/Reshape_1:0_2", type=DequantizeLinear]; +"412 QuantizeLinear_bert/encoder/Reshape_1^0_3" [id=412, label="412 QuantizeLinear_bert/encoder/Reshape_1:0_3", type=QuantizeLinear]; +"413 DequantizeLinear_bert/encoder/Reshape_1^0_3" [id=413, label="413 DequantizeLinear_bert/encoder/Reshape_1:0_3", type=DequantizeLinear]; "414 bert/encoder/layer_0/attention/self/value/MatMul" [id=414, type=MatMul]; "415 bert/encoder/layer_0/attention/self/value/BiasAdd" [id=415, type=Add]; "416 bert/encoder/layer_0/attention/self/Reshape_2" [id=416, type=Reshape]; @@ -495,14 +495,14 @@ strict digraph { "493 bert/encoder/layer_0/output/LayerNorm/batchnorm/sub" [id=493, type=Sub]; "494 bert/encoder/layer_0/output/LayerNorm/batchnorm/mul_1" [id=494, type=Mul]; "495 bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1" [id=495, type=Add]; -"496 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_3" [id=496, label="496 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"497 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_3" [id=497, label="497 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"498 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_2" [id=498, label="498 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"499 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_2" [id=499, label="499 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"500 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_1" [id=500, label="500 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"501 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_1" [id=501, label="501 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"502 QuantizeLinear_bert/encoder/layer_1/attention/self/value/kernel^0_1" [id=502, label="502 QuantizeLinear_bert/encoder/layer_1/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"503 DequantizeLinear_bert/encoder/layer_1/attention/self/value/kernel^0_1" [id=503, label="503 DequantizeLinear_bert/encoder/layer_1/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"496 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_1" [id=496, label="496 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"497 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_1" [id=497, label="497 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"498 QuantizeLinear_bert/encoder/layer_1/attention/self/value/kernel^0_1" [id=498, label="498 QuantizeLinear_bert/encoder/layer_1/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"499 DequantizeLinear_bert/encoder/layer_1/attention/self/value/kernel^0_1" [id=499, label="499 DequantizeLinear_bert/encoder/layer_1/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"500 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_2" [id=500, label="500 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"501 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_2" [id=501, label="501 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"502 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_3" [id=502, label="502 QuantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"503 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1^0_3" [id=503, label="503 DequantizeLinear_bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "504 bert/encoder/layer_1/attention/self/value/MatMul" [id=504, type=MatMul]; "505 bert/encoder/layer_1/attention/self/value/BiasAdd" [id=505, type=Add]; "506 bert/encoder/layer_1/attention/self/Reshape_2" [id=506, type=Reshape]; @@ -585,14 +585,14 @@ strict digraph { "583 bert/encoder/layer_1/output/LayerNorm/batchnorm/sub" [id=583, type=Sub]; "584 bert/encoder/layer_1/output/LayerNorm/batchnorm/mul_1" [id=584, type=Mul]; "585 bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1" [id=585, type=Add]; -"586 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" [id=586, label="586 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"587 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" [id=587, label="587 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"588 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" [id=588, label="588 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"589 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" [id=589, label="589 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"590 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" [id=590, label="590 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"591 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" [id=591, label="591 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"592 QuantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" [id=592, label="592 QuantizeLinear_bert/encoder/layer_2/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"593 DequantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" [id=593, label="593 DequantizeLinear_bert/encoder/layer_2/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"586 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" [id=586, label="586 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"587 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" [id=587, label="587 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"588 QuantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" [id=588, label="588 QuantizeLinear_bert/encoder/layer_2/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"589 DequantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" [id=589, label="589 DequantizeLinear_bert/encoder/layer_2/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"590 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" [id=590, label="590 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"591 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" [id=591, label="591 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"592 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" [id=592, label="592 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"593 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" [id=593, label="593 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "594 bert/encoder/layer_2/attention/self/value/MatMul" [id=594, type=MatMul]; "595 bert/encoder/layer_2/attention/self/value/BiasAdd" [id=595, type=Add]; "596 bert/encoder/layer_2/attention/self/Reshape_2" [id=596, type=Reshape]; @@ -675,14 +675,14 @@ strict digraph { "673 bert/encoder/layer_2/output/LayerNorm/batchnorm/sub" [id=673, type=Sub]; "674 bert/encoder/layer_2/output/LayerNorm/batchnorm/mul_1" [id=674, type=Mul]; "675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" [id=675, type=Add]; -"676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [id=676, label="676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [id=677, label="677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"678 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [id=678, label="678 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"679 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [id=679, label="679 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [id=680, label="680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [id=681, label="681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"682 QuantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" [id=682, label="682 QuantizeLinear_bert/encoder/layer_3/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"683 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" [id=683, label="683 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [id=676, label="676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [id=677, label="677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"678 QuantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" [id=678, label="678 QuantizeLinear_bert/encoder/layer_3/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"679 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" [id=679, label="679 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [id=680, label="680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [id=681, label="681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"682 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [id=682, label="682 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"683 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [id=683, label="683 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "684 bert/encoder/layer_3/attention/self/value/MatMul" [id=684, type=MatMul]; "685 bert/encoder/layer_3/attention/self/value/BiasAdd" [id=685, type=Add]; "686 bert/encoder/layer_3/attention/self/Reshape_2" [id=686, type=Reshape]; @@ -765,14 +765,14 @@ strict digraph { "763 bert/encoder/layer_3/output/LayerNorm/batchnorm/sub" [id=763, type=Sub]; "764 bert/encoder/layer_3/output/LayerNorm/batchnorm/mul_1" [id=764, type=Mul]; "765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" [id=765, type=Add]; -"766 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [id=766, label="766 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [id=767, label="767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"768 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [id=768, label="768 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"769 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [id=769, label="769 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"770 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DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" [id=767, label="767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"768 QuantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" [id=768, label="768 QuantizeLinear_bert/encoder/layer_4/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"769 DequantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" [id=769, label="769 DequantizeLinear_bert/encoder/layer_4/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"770 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [id=770, label="770 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"771 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [id=771, label="771 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"772 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [id=772, label="772 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"773 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [id=773, label="773 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "774 bert/encoder/layer_4/attention/self/value/MatMul" [id=774, type=MatMul]; "775 bert/encoder/layer_4/attention/self/value/BiasAdd" [id=775, type=Add]; "776 bert/encoder/layer_4/attention/self/Reshape_2" [id=776, type=Reshape]; @@ -855,14 +855,14 @@ strict digraph { "853 bert/encoder/layer_4/output/LayerNorm/batchnorm/sub" [id=853, type=Sub]; "854 bert/encoder/layer_4/output/LayerNorm/batchnorm/mul_1" [id=854, type=Mul]; "855 bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1" [id=855, type=Add]; -"856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" [id=856, label="856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" [id=857, label="857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"858 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [id=858, label="858 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"859 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [id=859, label="859 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [id=860, label="860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [id=861, label="861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"862 QuantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" [id=862, label="862 QuantizeLinear_bert/encoder/layer_5/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"863 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" [id=863, label="863 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [id=856, label="856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [id=857, label="857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"858 QuantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" [id=858, label="858 QuantizeLinear_bert/encoder/layer_5/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"859 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" [id=859, label="859 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [id=860, label="860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [id=861, label="861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"862 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" [id=862, label="862 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"863 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" [id=863, label="863 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "864 bert/encoder/layer_5/attention/self/value/MatMul" [id=864, type=MatMul]; "865 bert/encoder/layer_5/attention/self/value/BiasAdd" [id=865, type=Add]; "866 bert/encoder/layer_5/attention/self/Reshape_2" [id=866, type=Reshape]; @@ -945,14 +945,14 @@ strict digraph { "943 bert/encoder/layer_5/output/LayerNorm/batchnorm/sub" [id=943, type=Sub]; "944 bert/encoder/layer_5/output/LayerNorm/batchnorm/mul_1" [id=944, type=Mul]; "945 bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1" [id=945, type=Add]; -"946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [id=946, label="946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [id=947, label="947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"948 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [id=948, label="948 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"949 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [id=949, label="949 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [id=950, label="950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [id=951, label="951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"952 QuantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" [id=952, label="952 QuantizeLinear_bert/encoder/layer_6/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"953 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" [id=953, label="953 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [id=946, label="946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [id=947, label="947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"948 QuantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" [id=948, label="948 QuantizeLinear_bert/encoder/layer_6/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"949 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" [id=949, label="949 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [id=950, label="950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [id=951, label="951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"952 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [id=952, label="952 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"953 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [id=953, label="953 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "954 bert/encoder/layer_6/attention/self/value/MatMul" [id=954, type=MatMul]; "955 bert/encoder/layer_6/attention/self/value/BiasAdd" [id=955, type=Add]; "956 bert/encoder/layer_6/attention/self/Reshape_2" [id=956, type=Reshape]; @@ -1035,14 +1035,14 @@ strict digraph { "1033 bert/encoder/layer_6/output/LayerNorm/batchnorm/sub" [id=1033, type=Sub]; "1034 bert/encoder/layer_6/output/LayerNorm/batchnorm/mul_1" [id=1034, type=Mul]; "1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" [id=1035, type=Add]; -"1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [id=1036, label="1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [id=1037, label="1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"1038 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [id=1038, label="1038 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"1039 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [id=1039, label="1039 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [id=1040, label="1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [id=1041, label="1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"1042 QuantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" [id=1042, label="1042 QuantizeLinear_bert/encoder/layer_7/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"1043 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" [id=1043, label="1043 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [id=1036, label="1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [id=1037, label="1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"1038 QuantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" [id=1038, label="1038 QuantizeLinear_bert/encoder/layer_7/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"1039 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" [id=1039, label="1039 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [id=1040, label="1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [id=1041, label="1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"1042 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [id=1042, label="1042 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"1043 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [id=1043, label="1043 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "1044 bert/encoder/layer_7/attention/self/value/MatMul" [id=1044, type=MatMul]; "1045 bert/encoder/layer_7/attention/self/value/BiasAdd" [id=1045, type=Add]; "1046 bert/encoder/layer_7/attention/self/Reshape_2" [id=1046, type=Reshape]; @@ -1125,14 +1125,14 @@ strict digraph { "1123 bert/encoder/layer_7/output/LayerNorm/batchnorm/sub" [id=1123, type=Sub]; "1124 bert/encoder/layer_7/output/LayerNorm/batchnorm/mul_1" [id=1124, type=Mul]; "1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" [id=1125, type=Add]; -"1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [id=1126, label="1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [id=1127, label="1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"1128 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [id=1128, label="1128 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"1129 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [id=1129, label="1129 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [id=1130, label="1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [id=1131, label="1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"1132 QuantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" [id=1132, label="1132 QuantizeLinear_bert/encoder/layer_8/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"1133 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" [id=1133, label="1133 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [id=1126, label="1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [id=1127, label="1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"1128 QuantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" [id=1128, label="1128 QuantizeLinear_bert/encoder/layer_8/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"1129 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" [id=1129, label="1129 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [id=1130, label="1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [id=1131, label="1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"1132 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [id=1132, label="1132 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"1133 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [id=1133, label="1133 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "1134 bert/encoder/layer_8/attention/self/value/MatMul" [id=1134, type=MatMul]; "1135 bert/encoder/layer_8/attention/self/value/BiasAdd" [id=1135, type=Add]; "1136 bert/encoder/layer_8/attention/self/Reshape_2" [id=1136, type=Reshape]; @@ -1215,14 +1215,14 @@ strict digraph { "1213 bert/encoder/layer_8/output/LayerNorm/batchnorm/sub" [id=1213, type=Sub]; "1214 bert/encoder/layer_8/output/LayerNorm/batchnorm/mul_1" [id=1214, type=Mul]; "1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" [id=1215, type=Add]; -"1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [id=1216, label="1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [id=1217, label="1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"1218 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [id=1218, label="1218 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"1219 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [id=1219, label="1219 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [id=1220, label="1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [id=1221, label="1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"1222 QuantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" [id=1222, label="1222 QuantizeLinear_bert/encoder/layer_9/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"1223 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" [id=1223, label="1223 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [id=1216, label="1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [id=1217, label="1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"1218 QuantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" [id=1218, label="1218 QuantizeLinear_bert/encoder/layer_9/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"1219 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" [id=1219, label="1219 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [id=1220, label="1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [id=1221, label="1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"1222 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [id=1222, label="1222 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"1223 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [id=1223, label="1223 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "1224 bert/encoder/layer_9/attention/self/value/MatMul" [id=1224, type=MatMul]; "1225 bert/encoder/layer_9/attention/self/value/BiasAdd" [id=1225, type=Add]; "1226 bert/encoder/layer_9/attention/self/Reshape_2" [id=1226, type=Reshape]; @@ -1305,14 +1305,14 @@ strict digraph { "1303 bert/encoder/layer_9/output/LayerNorm/batchnorm/sub" [id=1303, type=Sub]; "1304 bert/encoder/layer_9/output/LayerNorm/batchnorm/mul_1" [id=1304, type=Mul]; "1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" [id=1305, type=Add]; -"1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [id=1306, label="1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [id=1307, label="1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"1308 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [id=1308, label="1308 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; -"1309 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [id=1309, label="1309 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; -"1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [id=1310, label="1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; -"1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [id=1311, label="1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; -"1312 QuantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" [id=1312, label="1312 QuantizeLinear_bert/encoder/layer_10/attention/self/value/kernel:0_1", type=QuantizeLinear]; -"1313 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" [id=1313, label="1313 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [id=1306, label="1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [id=1307, label="1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"1308 QuantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" [id=1308, label="1308 QuantizeLinear_bert/encoder/layer_10/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"1309 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" [id=1309, label="1309 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [id=1310, label="1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [id=1311, label="1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"1312 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [id=1312, label="1312 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"1313 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [id=1313, label="1313 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "1314 bert/encoder/layer_10/attention/self/value/MatMul" [id=1314, type=MatMul]; "1315 bert/encoder/layer_10/attention/self/value/BiasAdd" [id=1315, type=Add]; "1316 bert/encoder/layer_10/attention/self/Reshape_2" [id=1316, type=Reshape]; @@ -1395,14 +1395,14 @@ strict digraph { "1393 bert/encoder/layer_10/output/LayerNorm/batchnorm/sub" [id=1393, type=Sub]; "1394 bert/encoder/layer_10/output/LayerNorm/batchnorm/mul_1" [id=1394, type=Mul]; "1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" [id=1395, type=Add]; -"1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [id=1396, label="1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; -"1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [id=1397, label="1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; -"1398 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [id=1398, label="1398 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label="1403 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" [id=1396, label="1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_1", type=QuantizeLinear]; +"1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" [id=1397, label="1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_1", type=DequantizeLinear]; +"1398 QuantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" [id=1398, label="1398 QuantizeLinear_bert/encoder/layer_11/attention/self/value/kernel:0_1", type=QuantizeLinear]; +"1399 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" [id=1399, label="1399 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel:0_1", type=DequantizeLinear]; +"1400 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [id=1400, label="1400 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_2", type=QuantizeLinear]; +"1401 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [id=1401, label="1401 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_2", type=DequantizeLinear]; +"1402 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [id=1402, label="1402 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_3", type=QuantizeLinear]; +"1403 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [id=1403, label="1403 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1:0_3", type=DequantizeLinear]; "1404 bert/encoder/layer_11/attention/self/value/MatMul" [id=1404, type=MatMul]; "1405 bert/encoder/layer_11/attention/self/value/BiasAdd" [id=1405, type=Add]; "1406 bert/encoder/layer_11/attention/self/Reshape_2" [id=1406, type=Reshape]; @@ -1991,18 +1991,18 @@ strict 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bert/encoder/layer_2/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"586 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" -> "587 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"587 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_1" -> "594 bert/encoder/layer_2/attention/self/value/MatMul" [label="[]", style=solid]; +"588 QuantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" -> "589 DequantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"589 DequantizeLinear_bert/encoder/layer_2/attention/self/value/kernel^0_1" -> "594 bert/encoder/layer_2/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"590 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" -> "591 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"591 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_2" -> "600 bert/encoder/layer_2/attention/self/query/MatMul" [label="[]", style=solid]; +"592 QuantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" -> "593 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"593 DequantizeLinear_bert/encoder/layer_1/output/LayerNorm/batchnorm/add_1^0_3" -> "608 bert/encoder/layer_2/attention/self/key/MatMul" [label="[]", style=solid]; "594 bert/encoder/layer_2/attention/self/value/MatMul" -> "595 bert/encoder/layer_2/attention/self/value/BiasAdd" [label="[]", style=solid]; "595 bert/encoder/layer_2/attention/self/value/BiasAdd" -> "596 bert/encoder/layer_2/attention/self/Reshape_2" [label="[]", style=solid]; "596 bert/encoder/layer_2/attention/self/Reshape_2" -> "597 bert/encoder/layer_2/attention/self/transpose_2" [label="[]", style=solid]; @@ -2303,18 +2303,18 @@ strict digraph { "672 bert/encoder/layer_2/output/LayerNorm/batchnorm/mul_2" -> "673 bert/encoder/layer_2/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "673 bert/encoder/layer_2/output/LayerNorm/batchnorm/sub" -> "675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "674 bert/encoder/layer_2/output/LayerNorm/batchnorm/mul_1" -> "675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "678 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "682 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "675 bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1" -> "718 bert/encoder/layer_3/attention/output/add" [label="[]", style=solid]; -"676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" -> "677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" -> "698 bert/encoder/layer_3/attention/self/key/MatMul" [label="[]", style=solid]; -"678 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" -> "679 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"679 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" -> "690 bert/encoder/layer_3/attention/self/query/MatMul" [label="[]", style=solid]; -"680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" -> "681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" -> "684 bert/encoder/layer_3/attention/self/value/MatMul" [label="[]", style=solid]; -"682 QuantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" -> "683 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"683 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" -> "684 bert/encoder/layer_3/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"676 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" -> "677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"677 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_1" -> "684 bert/encoder/layer_3/attention/self/value/MatMul" [label="[]", style=solid]; +"678 QuantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" -> "679 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"679 DequantizeLinear_bert/encoder/layer_3/attention/self/value/kernel^0_1" -> "684 bert/encoder/layer_3/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"680 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" -> "681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"681 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_2" -> "690 bert/encoder/layer_3/attention/self/query/MatMul" [label="[]", style=solid]; +"682 QuantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" -> "683 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"683 DequantizeLinear_bert/encoder/layer_2/output/LayerNorm/batchnorm/add_1^0_3" -> "698 bert/encoder/layer_3/attention/self/key/MatMul" [label="[]", style=solid]; "684 bert/encoder/layer_3/attention/self/value/MatMul" -> "685 bert/encoder/layer_3/attention/self/value/BiasAdd" [label="[]", style=solid]; "685 bert/encoder/layer_3/attention/self/value/BiasAdd" -> "686 bert/encoder/layer_3/attention/self/Reshape_2" [label="[]", style=solid]; "686 bert/encoder/layer_3/attention/self/Reshape_2" -> "687 bert/encoder/layer_3/attention/self/transpose_2" [label="[]", style=solid]; @@ -2407,18 +2407,18 @@ strict digraph { "762 bert/encoder/layer_3/output/LayerNorm/batchnorm/mul_2" -> "763 bert/encoder/layer_3/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "763 bert/encoder/layer_3/output/LayerNorm/batchnorm/sub" -> "765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "764 bert/encoder/layer_3/output/LayerNorm/batchnorm/mul_1" -> "765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "766 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "768 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "770 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "766 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "770 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "772 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "765 bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1" -> "808 bert/encoder/layer_4/attention/output/add" [label="[]", style=solid]; -"766 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" -> "767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" -> "788 bert/encoder/layer_4/attention/self/key/MatMul" [label="[]", style=solid]; -"768 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" -> "769 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"769 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" -> "780 bert/encoder/layer_4/attention/self/query/MatMul" [label="[]", style=solid]; -"770 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" -> "771 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"771 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" -> "774 bert/encoder/layer_4/attention/self/value/MatMul" [label="[]", style=solid]; -"772 QuantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" -> "773 DequantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"773 DequantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" -> "774 bert/encoder/layer_4/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"766 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" -> "767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"767 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_1" -> "774 bert/encoder/layer_4/attention/self/value/MatMul" [label="[]", style=solid]; +"768 QuantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" -> "769 DequantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"769 DequantizeLinear_bert/encoder/layer_4/attention/self/value/kernel^0_1" -> "774 bert/encoder/layer_4/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"770 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" -> "771 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"771 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_2" -> "780 bert/encoder/layer_4/attention/self/query/MatMul" [label="[]", style=solid]; +"772 QuantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" -> "773 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"773 DequantizeLinear_bert/encoder/layer_3/output/LayerNorm/batchnorm/add_1^0_3" -> "788 bert/encoder/layer_4/attention/self/key/MatMul" [label="[]", style=solid]; "774 bert/encoder/layer_4/attention/self/value/MatMul" -> "775 bert/encoder/layer_4/attention/self/value/BiasAdd" [label="[]", style=solid]; "775 bert/encoder/layer_4/attention/self/value/BiasAdd" -> "776 bert/encoder/layer_4/attention/self/Reshape_2" [label="[]", style=solid]; "776 bert/encoder/layer_4/attention/self/Reshape_2" -> "777 bert/encoder/layer_4/attention/self/transpose_2" [label="[]", style=solid]; @@ -2511,18 +2511,18 @@ strict digraph { "852 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bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1" -> "856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"855 bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1" -> "860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"855 bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1" -> "862 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "855 bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1" -> "898 bert/encoder/layer_5/attention/output/add" [label="[]", style=solid]; -"856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" -> "857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_3" -> "878 bert/encoder/layer_5/attention/self/key/MatMul" [label="[]", style=solid]; -"858 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" -> "859 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"859 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" -> "870 bert/encoder/layer_5/attention/self/query/MatMul" [label="[]", style=solid]; -"860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" -> "861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" -> "864 bert/encoder/layer_5/attention/self/value/MatMul" [label="[]", style=solid]; -"862 QuantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" -> "863 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"863 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" -> "864 bert/encoder/layer_5/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"856 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" -> "857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"857 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_1" -> "864 bert/encoder/layer_5/attention/self/value/MatMul" [label="[]", style=solid]; +"858 QuantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" -> "859 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"859 DequantizeLinear_bert/encoder/layer_5/attention/self/value/kernel^0_1" -> "864 bert/encoder/layer_5/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"860 QuantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" -> "861 DequantizeLinear_bert/encoder/layer_4/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; 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bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1" -> "946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"945 bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1" -> "950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"945 bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1" -> "952 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "945 bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1" -> "988 bert/encoder/layer_6/attention/output/add" [label="[]", style=solid]; -"946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" -> "947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" -> "968 bert/encoder/layer_6/attention/self/key/MatMul" [label="[]", style=solid]; -"948 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" -> "949 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"949 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" -> "960 bert/encoder/layer_6/attention/self/query/MatMul" [label="[]", style=solid]; -"950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" -> "951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" -> "954 bert/encoder/layer_6/attention/self/value/MatMul" [label="[]", style=solid]; -"952 QuantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" -> "953 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"953 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" -> "954 bert/encoder/layer_6/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"946 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" -> "947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"947 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_1" -> "954 bert/encoder/layer_6/attention/self/value/MatMul" [label="[]", style=solid]; +"948 QuantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" -> "949 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"949 DequantizeLinear_bert/encoder/layer_6/attention/self/value/kernel^0_1" -> "954 bert/encoder/layer_6/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"950 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" -> "951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"951 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_2" -> "960 bert/encoder/layer_6/attention/self/query/MatMul" [label="[]", style=solid]; +"952 QuantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" -> "953 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"953 DequantizeLinear_bert/encoder/layer_5/output/LayerNorm/batchnorm/add_1^0_3" -> "968 bert/encoder/layer_6/attention/self/key/MatMul" [label="[]", style=solid]; "954 bert/encoder/layer_6/attention/self/value/MatMul" -> "955 bert/encoder/layer_6/attention/self/value/BiasAdd" [label="[]", style=solid]; "955 bert/encoder/layer_6/attention/self/value/BiasAdd" -> "956 bert/encoder/layer_6/attention/self/Reshape_2" [label="[]", style=solid]; "956 bert/encoder/layer_6/attention/self/Reshape_2" -> "957 bert/encoder/layer_6/attention/self/transpose_2" [label="[]", style=solid]; @@ -2719,18 +2719,18 @@ strict digraph { "1032 bert/encoder/layer_6/output/LayerNorm/batchnorm/mul_2" -> "1033 bert/encoder/layer_6/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "1033 bert/encoder/layer_6/output/LayerNorm/batchnorm/sub" -> "1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "1034 bert/encoder/layer_6/output/LayerNorm/batchnorm/mul_1" -> "1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1038 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1042 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "1035 bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1" -> "1078 bert/encoder/layer_7/attention/output/add" [label="[]", style=solid]; -"1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" -> "1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" -> "1058 bert/encoder/layer_7/attention/self/key/MatMul" [label="[]", style=solid]; -"1038 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" -> "1039 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"1039 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" -> "1050 bert/encoder/layer_7/attention/self/query/MatMul" [label="[]", style=solid]; -"1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" -> "1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" -> "1044 bert/encoder/layer_7/attention/self/value/MatMul" [label="[]", style=solid]; -"1042 QuantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" -> "1043 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"1043 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" -> "1044 bert/encoder/layer_7/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1036 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" -> "1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"1037 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_1" -> "1044 bert/encoder/layer_7/attention/self/value/MatMul" [label="[]", style=solid]; +"1038 QuantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" -> "1039 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"1039 DequantizeLinear_bert/encoder/layer_7/attention/self/value/kernel^0_1" -> "1044 bert/encoder/layer_7/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1040 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" -> "1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"1041 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_2" -> "1050 bert/encoder/layer_7/attention/self/query/MatMul" [label="[]", style=solid]; +"1042 QuantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" -> "1043 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"1043 DequantizeLinear_bert/encoder/layer_6/output/LayerNorm/batchnorm/add_1^0_3" -> "1058 bert/encoder/layer_7/attention/self/key/MatMul" [label="[]", style=solid]; "1044 bert/encoder/layer_7/attention/self/value/MatMul" -> "1045 bert/encoder/layer_7/attention/self/value/BiasAdd" [label="[]", style=solid]; "1045 bert/encoder/layer_7/attention/self/value/BiasAdd" -> "1046 bert/encoder/layer_7/attention/self/Reshape_2" [label="[]", style=solid]; "1046 bert/encoder/layer_7/attention/self/Reshape_2" -> "1047 bert/encoder/layer_7/attention/self/transpose_2" [label="[]", style=solid]; @@ -2823,18 +2823,18 @@ strict digraph { "1122 bert/encoder/layer_7/output/LayerNorm/batchnorm/mul_2" -> "1123 bert/encoder/layer_7/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "1123 bert/encoder/layer_7/output/LayerNorm/batchnorm/sub" -> "1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "1124 bert/encoder/layer_7/output/LayerNorm/batchnorm/mul_1" -> "1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1128 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1132 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "1125 bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1" -> "1168 bert/encoder/layer_8/attention/output/add" [label="[]", style=solid]; -"1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" -> "1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" -> "1148 bert/encoder/layer_8/attention/self/key/MatMul" [label="[]", style=solid]; -"1128 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" -> "1129 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"1129 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" -> "1140 bert/encoder/layer_8/attention/self/query/MatMul" [label="[]", style=solid]; -"1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" -> "1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" -> "1134 bert/encoder/layer_8/attention/self/value/MatMul" [label="[]", style=solid]; -"1132 QuantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" -> "1133 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"1133 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" -> "1134 bert/encoder/layer_8/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1126 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" -> "1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"1127 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_1" -> "1134 bert/encoder/layer_8/attention/self/value/MatMul" [label="[]", style=solid]; +"1128 QuantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" -> "1129 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"1129 DequantizeLinear_bert/encoder/layer_8/attention/self/value/kernel^0_1" -> "1134 bert/encoder/layer_8/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1130 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" -> "1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"1131 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_2" -> "1140 bert/encoder/layer_8/attention/self/query/MatMul" [label="[]", style=solid]; +"1132 QuantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" -> "1133 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"1133 DequantizeLinear_bert/encoder/layer_7/output/LayerNorm/batchnorm/add_1^0_3" -> "1148 bert/encoder/layer_8/attention/self/key/MatMul" [label="[]", style=solid]; "1134 bert/encoder/layer_8/attention/self/value/MatMul" -> "1135 bert/encoder/layer_8/attention/self/value/BiasAdd" [label="[]", style=solid]; "1135 bert/encoder/layer_8/attention/self/value/BiasAdd" -> "1136 bert/encoder/layer_8/attention/self/Reshape_2" [label="[]", style=solid]; "1136 bert/encoder/layer_8/attention/self/Reshape_2" -> "1137 bert/encoder/layer_8/attention/self/transpose_2" [label="[]", style=solid]; @@ -2927,18 +2927,18 @@ strict digraph { "1212 bert/encoder/layer_8/output/LayerNorm/batchnorm/mul_2" -> "1213 bert/encoder/layer_8/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "1213 bert/encoder/layer_8/output/LayerNorm/batchnorm/sub" -> "1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "1214 bert/encoder/layer_8/output/LayerNorm/batchnorm/mul_1" -> "1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1218 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1222 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "1215 bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1" -> "1258 bert/encoder/layer_9/attention/output/add" [label="[]", style=solid]; -"1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" -> "1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" -> "1238 bert/encoder/layer_9/attention/self/key/MatMul" [label="[]", style=solid]; -"1218 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" -> "1219 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"1219 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" -> "1230 bert/encoder/layer_9/attention/self/query/MatMul" [label="[]", style=solid]; -"1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" -> "1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" -> "1224 bert/encoder/layer_9/attention/self/value/MatMul" [label="[]", style=solid]; -"1222 QuantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" -> "1223 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"1223 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" -> "1224 bert/encoder/layer_9/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1216 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" -> "1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"1217 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_1" -> "1224 bert/encoder/layer_9/attention/self/value/MatMul" [label="[]", style=solid]; +"1218 QuantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" -> "1219 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"1219 DequantizeLinear_bert/encoder/layer_9/attention/self/value/kernel^0_1" -> "1224 bert/encoder/layer_9/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1220 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" -> "1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"1221 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_2" -> "1230 bert/encoder/layer_9/attention/self/query/MatMul" [label="[]", style=solid]; +"1222 QuantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" -> "1223 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"1223 DequantizeLinear_bert/encoder/layer_8/output/LayerNorm/batchnorm/add_1^0_3" -> "1238 bert/encoder/layer_9/attention/self/key/MatMul" [label="[]", style=solid]; "1224 bert/encoder/layer_9/attention/self/value/MatMul" -> "1225 bert/encoder/layer_9/attention/self/value/BiasAdd" [label="[]", style=solid]; "1225 bert/encoder/layer_9/attention/self/value/BiasAdd" -> "1226 bert/encoder/layer_9/attention/self/Reshape_2" [label="[]", style=solid]; "1226 bert/encoder/layer_9/attention/self/Reshape_2" -> "1227 bert/encoder/layer_9/attention/self/transpose_2" [label="[]", style=solid]; @@ -3031,18 +3031,18 @@ strict digraph { "1302 bert/encoder/layer_9/output/LayerNorm/batchnorm/mul_2" -> "1303 bert/encoder/layer_9/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "1303 bert/encoder/layer_9/output/LayerNorm/batchnorm/sub" -> "1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "1304 bert/encoder/layer_9/output/LayerNorm/batchnorm/mul_1" -> "1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1308 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1312 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "1305 bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1" -> "1348 bert/encoder/layer_10/attention/output/add" [label="[]", style=solid]; -"1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" -> "1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" -> "1328 bert/encoder/layer_10/attention/self/key/MatMul" [label="[]", style=solid]; -"1308 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" -> "1309 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"1309 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" -> "1320 bert/encoder/layer_10/attention/self/query/MatMul" [label="[]", style=solid]; -"1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" -> "1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" -> "1314 bert/encoder/layer_10/attention/self/value/MatMul" [label="[]", style=solid]; -"1312 QuantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" -> "1313 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"1313 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" -> "1314 bert/encoder/layer_10/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1306 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" -> "1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"1307 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_1" -> "1314 bert/encoder/layer_10/attention/self/value/MatMul" [label="[]", style=solid]; +"1308 QuantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" -> "1309 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"1309 DequantizeLinear_bert/encoder/layer_10/attention/self/value/kernel^0_1" -> "1314 bert/encoder/layer_10/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1310 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" -> "1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"1311 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_2" -> "1320 bert/encoder/layer_10/attention/self/query/MatMul" [label="[]", style=solid]; +"1312 QuantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" -> "1313 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"1313 DequantizeLinear_bert/encoder/layer_9/output/LayerNorm/batchnorm/add_1^0_3" -> "1328 bert/encoder/layer_10/attention/self/key/MatMul" [label="[]", style=solid]; "1314 bert/encoder/layer_10/attention/self/value/MatMul" -> "1315 bert/encoder/layer_10/attention/self/value/BiasAdd" [label="[]", style=solid]; "1315 bert/encoder/layer_10/attention/self/value/BiasAdd" -> "1316 bert/encoder/layer_10/attention/self/Reshape_2" [label="[]", style=solid]; "1316 bert/encoder/layer_10/attention/self/Reshape_2" -> "1317 bert/encoder/layer_10/attention/self/transpose_2" [label="[]", style=solid]; @@ -3135,18 +3135,18 @@ strict digraph { "1392 bert/encoder/layer_10/output/LayerNorm/batchnorm/mul_2" -> "1393 bert/encoder/layer_10/output/LayerNorm/batchnorm/sub" [label="[]", style=solid]; "1393 bert/encoder/layer_10/output/LayerNorm/batchnorm/sub" -> "1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; "1394 bert/encoder/layer_10/output/LayerNorm/batchnorm/mul_1" -> "1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" [label="[]", style=solid]; -"1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; -"1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1398 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; -"1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1400 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=solid]; +"1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1400 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=solid]; +"1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1402 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=solid]; "1395 bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1" -> "1438 bert/encoder/layer_11/attention/output/add" [label="[]", style=solid]; -"1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" -> "1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; -"1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" -> "1418 bert/encoder/layer_11/attention/self/key/MatMul" [label="[]", style=solid]; -"1398 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" -> "1399 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; -"1399 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" -> "1410 bert/encoder/layer_11/attention/self/query/MatMul" [label="[]", style=solid]; -"1400 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" -> "1401 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; -"1401 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" -> "1404 bert/encoder/layer_11/attention/self/value/MatMul" [label="[]", style=solid]; -"1402 QuantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" -> "1403 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; -"1403 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" -> "1404 bert/encoder/layer_11/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1396 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" -> "1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" [label="[]", style=dashed]; +"1397 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_1" -> "1404 bert/encoder/layer_11/attention/self/value/MatMul" [label="[]", style=solid]; +"1398 QuantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" -> "1399 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" [label="[768, 768]", style=dashed]; +"1399 DequantizeLinear_bert/encoder/layer_11/attention/self/value/kernel^0_1" -> "1404 bert/encoder/layer_11/attention/self/value/MatMul" [label="[768, 768]", style=solid]; +"1400 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" -> "1401 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" [label="[]", style=dashed]; +"1401 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_2" -> "1410 bert/encoder/layer_11/attention/self/query/MatMul" [label="[]", style=solid]; +"1402 QuantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" -> "1403 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" [label="[]", style=dashed]; +"1403 DequantizeLinear_bert/encoder/layer_10/output/LayerNorm/batchnorm/add_1^0_3" -> "1418 bert/encoder/layer_11/attention/self/key/MatMul" [label="[]", style=solid]; "1404 bert/encoder/layer_11/attention/self/value/MatMul" -> "1405 bert/encoder/layer_11/attention/self/value/BiasAdd" [label="[]", style=solid]; "1405 bert/encoder/layer_11/attention/self/value/BiasAdd" -> "1406 bert/encoder/layer_11/attention/self/Reshape_2" [label="[]", style=solid]; "1406 bert/encoder/layer_11/attention/self/Reshape_2" -> "1407 bert/encoder/layer_11/attention/self/transpose_2" [label="[]", style=solid]; diff --git a/tests/onnx/data/reference_graphs/quantization/retinanet-9.dot b/tests/onnx/data/reference_graphs/quantization/retinanet-9.dot index 7aa64281d32..9d2f66780d5 100644 --- a/tests/onnx/data/reference_graphs/quantization/retinanet-9.dot +++ b/tests/onnx/data/reference_graphs/quantization/retinanet-9.dot @@ -855,10 +855,10 @@ strict digraph { "853 QuantizeLinear_backbones.ResNet101FPN.pyramid6.weight_1" [id=853, type=QuantizeLinear]; "854 DequantizeLinear_backbones.ResNet101FPN.pyramid6.weight_1" [id=854, type=DequantizeLinear]; "855 Conv_349" [id=855, type=Conv]; -"856 QuantizeLinear_1028_2" [id=856, type=QuantizeLinear]; -"857 DequantizeLinear_1028_2" [id=857, type=DequantizeLinear]; -"858 QuantizeLinear_1028_1" [id=858, type=QuantizeLinear]; -"859 DequantizeLinear_1028_1" [id=859, type=DequantizeLinear]; +"856 QuantizeLinear_1028_1" [id=856, type=QuantizeLinear]; +"857 DequantizeLinear_1028_1" [id=857, type=DequantizeLinear]; +"858 QuantizeLinear_1028_2" [id=858, type=QuantizeLinear]; +"859 DequantizeLinear_1028_2" [id=859, type=DequantizeLinear]; "860 Relu_350" [id=860, type=Relu]; "861 QuantizeLinear_1029_1" [id=861, type=QuantizeLinear]; "862 DequantizeLinear_1029_1" [id=862, type=DequantizeLinear]; @@ -1983,13 +1983,13 @@ strict digraph { "852 Add_348" -> "866 QuantizeLinear_1027_1" [label="[1, 256, 60, 80]", style=solid]; "853 QuantizeLinear_backbones.ResNet101FPN.pyramid6.weight_1" -> "854 DequantizeLinear_backbones.ResNet101FPN.pyramid6.weight_1" [label="[256, 2048, 3, 3]", style=dashed]; "854 DequantizeLinear_backbones.ResNet101FPN.pyramid6.weight_1" -> "855 Conv_349" [label="[256, 2048, 3, 3]", style=solid]; -"855 Conv_349" -> "856 QuantizeLinear_1028_2" [label="[1, 256, 8, 10]", style=solid]; -"855 Conv_349" -> "858 QuantizeLinear_1028_1" [label="[1, 256, 8, 10]", style=solid]; +"855 Conv_349" -> "856 QuantizeLinear_1028_1" [label="[1, 256, 8, 10]", style=solid]; +"855 Conv_349" -> "858 QuantizeLinear_1028_2" [label="[1, 256, 8, 10]", style=solid]; "855 Conv_349" -> "860 Relu_350" [label="[1, 256, 8, 10]", style=solid]; -"856 QuantizeLinear_1028_2" -> "857 DequantizeLinear_1028_2" [label="[1, 256, 8, 10]", style=dashed]; -"857 DequantizeLinear_1028_2" -> "1041 Conv_427" [label="[1, 256, 8, 10]", style=solid]; -"858 QuantizeLinear_1028_1" -> "859 DequantizeLinear_1028_1" [label="[1, 256, 8, 10]", style=dashed]; -"859 DequantizeLinear_1028_1" -> "944 Conv_382" [label="[1, 256, 8, 10]", style=solid]; +"856 QuantizeLinear_1028_1" -> "857 DequantizeLinear_1028_1" [label="[1, 256, 8, 10]", style=dashed]; +"857 DequantizeLinear_1028_1" -> "944 Conv_382" [label="[1, 256, 8, 10]", style=solid]; +"858 QuantizeLinear_1028_2" -> "859 DequantizeLinear_1028_2" [label="[1, 256, 8, 10]", style=dashed]; +"859 DequantizeLinear_1028_2" -> "1041 Conv_427" [label="[1, 256, 8, 10]", style=solid]; "860 Relu_350" -> "861 QuantizeLinear_1029_1" [label="[1, 256, 8, 10]", style=solid]; "861 QuantizeLinear_1029_1" -> "862 DequantizeLinear_1029_1" [label="[1, 256, 8, 10]", style=dashed]; "862 DequantizeLinear_1029_1" -> "865 Conv_351" [label="[1, 256, 8, 10]", style=solid]; diff --git a/tests/onnx/quantization/common.py b/tests/onnx/quantization/common.py index 1d3464882fe..a5b9c8f47e3 100644 --- a/tests/onnx/quantization/common.py +++ b/tests/onnx/quantization/common.py @@ -16,8 +16,11 @@ import onnx from nncf import Dataset +from nncf.experimental.tensor import Tensor from nncf.onnx.graph.nncf_graph_builder import GraphConverter -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_edge_dtype +from nncf.onnx.graph.onnx_helper import get_edge_info_mapping +from nncf.onnx.graph.onnx_helper import get_edge_shape from nncf.onnx.statistics.statistics import ONNXMinMaxTensorStatistic from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization @@ -32,10 +35,18 @@ def mock_collect_statistics(mocker): - get_statistics_value = ONNXMinMaxTensorStatistic(min_values=-1, max_values=1) + get_statistics_value = ONNXMinMaxTensorStatistic( + min_values=np.array(-1, dtype=np.float32), max_values=np.array(1, dtype=np.float32) + ) _ = mocker.patch( "nncf.quantization.fake_quantize.calculate_quantizer_parameters", - return_value=FakeQuantizeParameters(np.array(0), np.array(0), np.array(0), np.array(0), 256), + return_value=FakeQuantizeParameters( + Tensor(np.array(0, dtype=np.float32)), + Tensor(np.array(0, dtype=np.float32)), + Tensor(np.array(0, dtype=np.float32)), + Tensor(np.array(0, dtype=np.float32)), + 256, + ), ) _ = mocker.patch( "nncf.common.tensor_statistics.aggregator.StatisticsAggregator.collect_statistics", return_value=None @@ -53,15 +64,15 @@ def _get_input_keys(original_model: onnx.ModelProto) -> str: def get_random_dataset_for_test(model: onnx.ModelProto, has_batch_dim: bool, length: Optional[int] = 10): keys = _get_input_keys(model) - onnx_graph = ONNXGraph(model) + edge_info_mapping = get_edge_info_mapping(model) def transform_fn(i): output = {} for key in keys: - edge = onnx_graph.get_edge(key) - input_dtype = ONNXGraph.get_edge_dtype(edge) + edge = edge_info_mapping[key] + input_dtype = get_edge_dtype(edge) input_np_dtype = onnx.helper.tensor_dtype_to_np_dtype(input_dtype) - shape = ONNXGraph.get_edge_shape(edge) + shape = get_edge_shape(edge) rng = get_random_generator() tensor = rng.uniform(-1, 1, shape).astype(input_np_dtype) if has_batch_dim: diff --git a/tests/onnx/quantization/test_qdq_params_calculation.py b/tests/onnx/quantization/test_qdq_params_calculation.py index bf16eb152b2..1b3367ab6fa 100644 --- a/tests/onnx/quantization/test_qdq_params_calculation.py +++ b/tests/onnx/quantization/test_qdq_params_calculation.py @@ -15,7 +15,7 @@ import pytest from nncf.common.quantization.structs import QuantizationPreset -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_tensor_value from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.advanced_parameters import OverflowFix from tests.onnx.conftest import ONNX_TEST_ROOT @@ -36,11 +36,10 @@ def get_q_nodes_params(model: onnx.ModelProto) -> Dict[str, np.ndarray]: output = {} - onnx_graph = ONNXGraph(model) - for node in onnx_graph.get_all_nodes(): + for node in model.graph.node: if node.op_type == "QuantizeLinear": - scale = onnx_graph.get_tensor_value(node.input[1]) - zero_point = onnx_graph.get_tensor_value(node.input[2]) + scale = get_tensor_value(model, node.input[1]) + zero_point = get_tensor_value(model, node.input[2]) output[node.name] = {"scale": scale, "zero_point": zero_point} return output diff --git a/tests/onnx/test_model_transformer.py b/tests/onnx/test_model_transformer.py index 4cf5cb4e332..da039ee2d1a 100644 --- a/tests/onnx/test_model_transformer.py +++ b/tests/onnx/test_model_transformer.py @@ -20,7 +20,8 @@ from nncf.common.graph.transformations.layout import TransformationLayout from nncf.onnx.graph.model_transformer import ONNXModelTransformer from nncf.onnx.graph.nncf_graph_builder import GraphConverter -from nncf.onnx.graph.onnx_graph import ONNXGraph +from nncf.onnx.graph.onnx_helper import get_tensor +from nncf.onnx.graph.onnx_helper import get_tensor_value from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand from nncf.onnx.graph.transformations.commands import ONNXQDQNodeRemovingCommand @@ -60,7 +61,7 @@ def test_quantizer_insertion(target_layers, should_raise, quantizer_number): if should_raise: try: _ = model_transformer.transform(transformation_layout) - except RuntimeError: + except KeyError: return transformed_model = model_transformer.transform(transformation_layout) onnx.checker.check_model(transformed_model) @@ -124,17 +125,15 @@ def test_inserted_quantizer_parameters(test_parameters): transformed_model = model_transformer.transform(transformation_layout) onnx.checker.check_model(transformed_model) - onnx_graph = ONNXGraph(transformed_model) - # pylint:disable=no-member for node in transformed_model.graph.node: op_type = node.op_type if op_type == "QuantizeLinear": for attr in node.attribute: assert test_parameters.onnx_attributes[attr.name] == onnx.helper.get_attribute_value(attr) - assert np.allclose(onnx_graph.get_tensor_value(node.input[1]), np.array(test_parameters.scale)) - assert np.allclose(onnx_graph.get_tensor_value(node.input[2]), np.array(test_parameters.zero_point)) - assert onnx_graph.get_tensor_value(node.input[2]).dtype == test_parameters.onnx_dtype + assert np.allclose(get_tensor_value(transformed_model, node.input[1]), np.array(test_parameters.scale)) + assert np.allclose(get_tensor_value(transformed_model, node.input[2]), np.array(test_parameters.zero_point)) + assert get_tensor_value(transformed_model, node.input[2]).dtype == test_parameters.onnx_dtype TARGET_LAYERS = [["ReLU1"], ["Conv1", "BN1"], ["Conv1", "BN1", "ReLU1"]] @@ -160,8 +159,7 @@ def test_output_insertion(target_layers, target_layer_outputs): transformed_model = model_transformer.transform(transformation_layout) - onnx_graph = ONNXGraph(transformed_model) - assert Counter([out.name for out in onnx_graph.get_model_outputs()]) == Counter(target_layer_outputs) + assert Counter([out.name for out in transformed_model.graph.output]) == Counter(target_layer_outputs) CONV_LAYERS = [["Conv1", "Conv2"]] @@ -182,11 +180,11 @@ def test_bias_correction(layers, values, refs): model_transformer = ONNXModelTransformer(model) transformed_model = model_transformer.transform(transformation_layout) - onnx_graph = ONNXGraph(transformed_model) + node_dict = {node.name: node for node in transformed_model.graph.node} for conv_layer, bias_reference in zip(layers, refs): - bias_tensor_name = onnx_graph.get_node_by_name(conv_layer).input[2] - bias_tensor = onnx_graph.get_tensor(bias_tensor_name) + bias_tensor_name = node_dict[conv_layer].input[2] + bias_tensor = get_tensor(transformed_model, bias_tensor_name) bias_value = onnx.numpy_helper.to_array(bias_tensor) assert np.all(bias_value == bias_reference) diff --git a/tests/onnx/weightless_model.py b/tests/onnx/weightless_model.py index 046568df8eb..6f34347ba38 100644 --- a/tests/onnx/weightless_model.py +++ b/tests/onnx/weightless_model.py @@ -19,8 +19,6 @@ from onnx import TensorProto # pylint:disable=no-name-in-module from onnx.external_data_helper import uses_external_data -from nncf.onnx.graph.onnx_graph import ONNXGraph - # pylint: disable=no-member @@ -32,8 +30,7 @@ def load_model_topology_with_zeros_weights(model_path: Union[str, Path]) -> onnx :return: Onnx model with filled the all external tensors by random values. """ model = onnx.load_model(model_path, load_external_data=False) - onnx_graph = ONNXGraph(model) - for tensor in onnx_graph.onnx_model.graph.initializer: + for tensor in model.graph.initializer: if uses_external_data(tensor): np_dtype = onnx.helper.tensor_dtype_to_np_dtype(tensor.data_type) np_tensor = np.zeros(list(tensor.dims)).astype(np_dtype) diff --git a/tests/openvino/conftest.py b/tests/openvino/conftest.py index 78a61be7a52..738ba7efc44 100644 --- a/tests/openvino/conftest.py +++ b/tests/openvino/conftest.py @@ -16,7 +16,7 @@ from tests.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS from tests.shared.case_collection import skip_marked_cases_if_options_not_specified -from tests.shared.install_fixtures import tmp_venv_with_nncf +from tests.shared.install_fixtures import tmp_venv_with_nncf # noqa: F401 from tests.shared.paths import TEST_ROOT diff --git a/tests/openvino/native/common.py b/tests/openvino/native/common.py index 46b238ebc57..709ebf6c324 100644 --- a/tests/openvino/native/common.py +++ b/tests/openvino/native/common.py @@ -13,6 +13,7 @@ import numpy as np import openvino.runtime as ov +from packaging import version from nncf import Dataset from nncf.openvino.graph.nncf_graph_builder import GraphConverter @@ -64,3 +65,15 @@ def default(self, o): def dump_to_json(local_path, data): with open(local_path, "w", encoding="utf8") as file: json.dump(deepcopy(data), file, indent=4, cls=NumpyEncoder) + + +def get_openvino_version() -> str: + ov_version = ov.__version__ + pos = ov_version.find("-") + if pos != -1: + ov_version = ov_version[:pos] + + ov_version = version.parse(ov_version).base_version + version_major, version_minor = ov_version.split(".")[:2] + + return f"{version_major}.{version_minor}" diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ComparisonBinaryModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/ConvModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ConvModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/ConvModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ConvModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ConvNotBiasModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/DynamicModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/DynamicModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/DynamicModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/DynamicModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/IntegerModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/IntegerModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/IntegerModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/IntegerModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/LSTMModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/LSTMModel.dot similarity index 100% rename from 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a/tests/openvino/native/data/reference_graphs/original_nncf_graph/MatMul2DModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/MatMul2DModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/MatMul2DModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/MatMul2DModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/SeBlockModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/SeBlockModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/SeBlockModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/SeBlockModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/ShapeOfModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ShapeOfModel.dot similarity index 100% rename from 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a/tests/openvino/native/data/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot 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rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot diff --git a/tests/openvino/native/data/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot b/tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot rename to tests/openvino/native/data/2023.1/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot diff --git a/tests/openvino/native/data/reference_graphs/quantized/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/quantized/ComparisonBinaryModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/quantized/ComparisonBinaryModel.dot rename to 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a/tests/openvino/native/data/reference_graphs/quantized/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/quantized/DepthwiseConvModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/quantized/DepthwiseConvModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/quantized/DepthwiseConvModel.dot diff --git a/tests/openvino/native/data/reference_graphs/quantized/DynamicModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/quantized/DynamicModel.dot similarity index 100% rename from tests/openvino/native/data/reference_graphs/quantized/DynamicModel.dot rename to tests/openvino/native/data/2023.1/reference_graphs/quantized/DynamicModel.dot diff --git a/tests/openvino/native/data/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot b/tests/openvino/native/data/2023.1/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot similarity index 100% rename from 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a/tests/openvino/native/data/reference_scales/ComparisonBinaryModel_mixed.json b/tests/openvino/native/data/2023.1/reference_scales/ComparisonBinaryModel_mixed.json similarity index 100% rename from tests/openvino/native/data/reference_scales/ComparisonBinaryModel_mixed.json rename to tests/openvino/native/data/2023.1/reference_scales/ComparisonBinaryModel_mixed.json diff --git a/tests/openvino/native/data/reference_scales/ComparisonBinaryModel_performance.json b/tests/openvino/native/data/2023.1/reference_scales/ComparisonBinaryModel_performance.json similarity index 100% rename from tests/openvino/native/data/reference_scales/ComparisonBinaryModel_performance.json rename to tests/openvino/native/data/2023.1/reference_scales/ComparisonBinaryModel_performance.json diff --git a/tests/openvino/native/data/reference_scales/ConvModel_mixed.json b/tests/openvino/native/data/2023.1/reference_scales/ConvModel_mixed.json similarity index 100% rename from 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a/tests/openvino/native/data/reference_scales/LSTMSequenceModel_mixed.json b/tests/openvino/native/data/2023.1/reference_scales/LSTMSequenceModel_mixed.json similarity index 100% rename from tests/openvino/native/data/reference_scales/LSTMSequenceModel_mixed.json rename to tests/openvino/native/data/2023.1/reference_scales/LSTMSequenceModel_mixed.json diff --git a/tests/openvino/native/data/reference_scales/LSTMSequenceModel_performance.json b/tests/openvino/native/data/2023.1/reference_scales/LSTMSequenceModel_performance.json similarity index 100% rename from tests/openvino/native/data/reference_scales/LSTMSequenceModel_performance.json rename to tests/openvino/native/data/2023.1/reference_scales/LSTMSequenceModel_performance.json diff --git a/tests/openvino/native/data/reference_scales/LinearModel_mixed.json b/tests/openvino/native/data/2023.1/reference_scales/LinearModel_mixed.json similarity index 100% rename from tests/openvino/native/data/reference_scales/LinearModel_mixed.json 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b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/DepthwiseConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot new file mode 120000 index 00000000000..3577725c492 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/DynamicModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/DynamicModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot new file mode 120000 index 00000000000..4790440afc8 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/GroupNormalizationModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot new file mode 120000 index 00000000000..e5a75e99702 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/IntegerModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/IntegerModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot new file mode 120000 index 00000000000..fa71d03d9ba --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/LSTMModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot new file mode 120000 index 00000000000..dd92e274992 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/LSTMSequenceModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LinearModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LinearModel.dot new file mode 120000 index 00000000000..9646848954c --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/LinearModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/LinearModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot new file mode 120000 index 00000000000..dfa3adb9b6e --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatMul2DModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/MatMul2DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot new file mode 120000 index 00000000000..a9efb23cec1 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/MatmulSoftmaxMatmulBlock.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot new file mode 120000 index 00000000000..b690d6c90bd --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/ScaleShiftReluModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot new file mode 120000 index 00000000000..d5bb68f136d --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SeBlockModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/SeBlockModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot new file mode 120000 index 00000000000..99aef792a39 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ShapeOfModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/ShapeOfModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot new file mode 120000 index 00000000000..f2389b194ea --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/SharedConvModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/SharedConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot new file mode 120000 index 00000000000..fcf92fa0732 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/UnifiedEmbeddingModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot new file mode 120000 index 00000000000..e290fd9bfe7 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/WeightsModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/WeightsModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot new file mode 120000 index 00000000000..e8418494fef --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/exctracted_ConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot new file mode 120000 index 00000000000..0ce2e696bfd --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/exctracted_QuantizedModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot new file mode 120000 index 00000000000..29073a517a4 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/googlenet-v3-pytorch.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot new file mode 120000 index 00000000000..2c928273ecb --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/mobilenet-v2-pytorch.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot new file mode 120000 index 00000000000..616c5905eb0 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/mobilenet-v3-small-1.0-224-tf.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot new file mode 120000 index 00000000000..8d5aea5575a --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/removed_nodes_in_QuantizedModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot new file mode 120000 index 00000000000..a10a9c052f0 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/resnet-18-pytorch.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot new file mode 120000 index 00000000000..0047688dd6f --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/original_nncf_graph/ssd_mobilenet_v1_coco.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot new file mode 100644 index 00000000000..64e4b4fe423 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/original_nncf_graph/yolo-v4-tiny-tf.dot @@ -0,0 +1,334 @@ +strict digraph { +"0 image_input" [id=0, type=Parameter]; +"1 Divide_2378" [id=1, type=Transpose]; +"2 Multiply_3747" [id=2, type=Convolution]; +"3 Transpose_1172" [id=3, type=Add]; +"4 Transpose_1178" [id=4, type=PRelu]; +"5 Multiply_3761" [id=5, type=Convolution]; +"6 Transpose_1229" [id=6, type=Add]; +"7 Transpose_1235" [id=7, type=PRelu]; +"8 Multiply_3775" [id=8, type=Convolution]; +"9 Transpose_1259" [id=9, type=Add]; +"10 Transpose_1265" [id=10, type=PRelu]; +"11 Transpose_1368" [id=11, type=Concat]; +"12 group_route_3/split" [id=12, type=Split]; +"13 MaxPool_307" [id=13, type=MaxPool]; +"14 Multiply_3789" [id=14, type=Convolution]; +"15 Multiply_3831" [id=15, type=Convolution]; +"16 Transpose_1294" [id=16, type=Add]; +"17 Transpose_1392" [id=17, type=Add]; +"18 Transpose_1300" [id=18, type=PRelu]; +"19 Transpose_1398" [id=19, type=PRelu]; +"20 Multiply_3803" [id=20, type=Convolution]; +"21 Transpose_1334" [id=21, type=Concat]; +"22 Transpose_1501" [id=22, type=Concat]; +"23 group_route_11/split" [id=23, type=Split]; +"24 Transpose_1324" [id=24, type=Add]; +"25 Multiply_3817" [id=25, type=Convolution]; +"26 MaxPool_433" [id=26, type=MaxPool]; +"27 Multiply_3845" [id=27, type=Convolution]; +"28 Transpose_1330" [id=28, type=PRelu]; +"29 Transpose_1358" [id=29, type=Add]; +"30 Multiply_3887" [id=30, type=Convolution]; +"31 Transpose_1427" [id=31, type=Add]; +"32 Transpose_1364" [id=32, type=PRelu]; +"33 Transpose_1525" [id=33, type=Add]; +"34 Transpose_1433" [id=34, type=PRelu]; +"35 Transpose_1531" [id=35, type=PRelu]; +"36 Multiply_3859" [id=36, type=Convolution]; +"37 Transpose_1467" [id=37, type=Concat]; +"38 Transpose_1634" [id=38, type=Concat]; +"39 group_route_19/split" [id=39, type=Split]; +"40 Transpose_1457" [id=40, type=Add]; +"41 Multiply_3873" [id=41, type=Convolution]; +"42 MaxPool_579" [id=42, type=MaxPool]; +"43 Multiply_3901" [id=43, type=Convolution]; +"44 Transpose_1463" [id=44, type=PRelu]; +"45 Transpose_1491" [id=45, type=Add]; +"46 Multiply_3943" [id=46, type=Convolution]; +"47 Transpose_1560" [id=47, type=Add]; +"48 Transpose_1497" [id=48, type=PRelu]; +"49 Transpose_1658" [id=49, type=Add]; +"50 Transpose_1566" [id=50, type=PRelu]; +"51 Transpose_1664" [id=51, type=PRelu]; +"52 Multiply_3915" [id=52, type=Convolution]; +"53 Transpose_1600" [id=53, type=Concat]; +"54 Multiply_3957" [id=54, type=Convolution]; +"55 Transpose_1590" [id=55, type=Add]; +"56 Multiply_3929" [id=56, type=Convolution]; +"57 Transpose_1688" [id=57, type=Add]; +"58 Transpose_1596" [id=58, type=PRelu]; +"59 Transpose_1624" [id=59, type=Add]; +"60 Transpose_1694" [id=60, type=PRelu]; +"61 Transpose_1630" [id=61, type=PRelu]; +"62 Multiply_3971" [id=62, type=Convolution]; +"63 Multiply_3999" [id=63, type=Convolution]; +"64 Transpose_1730" [id=64, type=Concat]; +"65 Transpose_1718" [id=65, type=Add]; +"66 Transpose_1790" [id=66, type=Add]; +"67 Multiply_3985" [id=67, type=Convolution]; +"68 Transpose_1724" [id=68, type=PRelu]; +"69 Transpose_1796" [id=69, type=PRelu]; +"70 Transpose_1754" [id=70, type=Add]; +"71 Gather_1726" [id=71, type=ShapeOf]; +"72 leaky_re_lu_17/LeakyRelu" [id=72, type=Transpose]; +"73 Convolution_749" [id=73, type=Convolution]; +"74 Transpose_1760" [id=74, type=PRelu]; +"75 up_sampling2d/Shape" [id=75, type=Gather]; +"76 up_sampling2d/resize/ResizeNearestNeighbor" [id=76, type=Interpolate]; +"77 Transpose_1802" [id=77, type=Add]; +"78 Convolution_706" [id=78, type=Convolution]; +"79 up_sampling2d/strided_slice" [id=79, type=StridedSlice]; +"80 Transpose_1728" [id=80, type=Transpose]; +"81 conv2d_17/BiasAdd" [id=81, type=Transpose]; +"82 Transpose_1766" [id=82, type=Add]; +"83 up_sampling2d/mul" [id=83, type=Multiply]; +"84 conv2d_17/BiasAdd^0" [id=84, label="84 conv2d_17/BiasAdd:0", type=Result]; +"85 conv2d_20/BiasAdd" [id=85, type=Transpose]; +"86 conv2d_20/BiasAdd^0" [id=86, label="86 conv2d_20/BiasAdd:0", type=Result]; +"87 Constant_1765" [id=87, type=Constant]; +"88 Transpose_1764" [id=88, type=Constant]; +"89 Transpose_705" [id=89, type=Constant]; +"90 Transpose_1758" [id=90, type=Constant]; +"91 Constant_3993" [id=91, type=Constant]; +"92 Multiply_4118" [id=92, type=Constant]; +"93 Transpose_1628" [id=93, type=Constant]; +"94 Constant_3937" [id=94, type=Constant]; +"95 Multiply_4094" [id=95, type=Constant]; +"96 Transpose_1564" [id=96, type=Constant]; +"97 Constant_3909" [id=97, type=Constant]; +"98 Multiply_4082" [id=98, type=Constant]; +"99 Constant_1532" [id=99, type=Constant]; +"100 Transpose_1529" [id=100, type=Constant]; +"101 Constant_3895" [id=101, type=Constant]; +"102 Multiply_4076" [id=102, type=Constant]; +"103 Transpose_1495" [id=103, type=Constant]; +"104 Constant_3881" [id=104, type=Constant]; +"105 Multiply_4070" [id=105, type=Constant]; +"106 Transpose_1431" [id=106, type=Constant]; +"107 Constant_3853" [id=107, type=Constant]; +"108 Multiply_4058" [id=108, type=Constant]; +"109 Constant_1399" [id=109, type=Constant]; +"110 Transpose_1396" [id=110, type=Constant]; +"111 Constant_3839" [id=111, type=Constant]; +"112 Multiply_4052" [id=112, type=Constant]; +"113 Transpose_1362" [id=113, type=Constant]; +"114 Constant_3825" [id=114, type=Constant]; +"115 Multiply_4046" [id=115, type=Constant]; +"116 Transpose_1298" [id=116, type=Constant]; +"117 Constant_3797" [id=117, type=Constant]; +"118 Multiply_4034" [id=118, type=Constant]; +"119 Constant_1266" [id=119, type=Constant]; +"120 Transpose_1263" [id=120, type=Constant]; +"121 Constant_3783" [id=121, type=Constant]; +"122 Multiply_4028" [id=122, type=Constant]; +"123 Transpose_1233" [id=123, type=Constant]; +"124 Constant_3769" [id=124, type=Constant]; +"125 Multiply_4022" [id=125, type=Constant]; +"126 Transpose_1176" [id=126, type=Constant]; +"127 Constant_3755" [id=127, type=Constant]; +"128 Gather_4293" [id=128, type=Constant]; +"129 Constant_2333" [id=129, type=Constant]; +"130 Transpose_1328" [id=130, type=Constant]; +"131 Constant_3811" [id=131, type=Constant]; +"132 Multiply_4040" [id=132, type=Constant]; +"133 Transpose_1461" [id=133, type=Constant]; +"134 Constant_3867" [id=134, type=Constant]; +"135 Multiply_4064" [id=135, type=Constant]; +"136 Transpose_1594" [id=136, type=Constant]; +"137 Constant_3923" [id=137, type=Constant]; +"138 Multiply_4088" [id=138, type=Constant]; +"139 Constant_1727" [id=139, type=Constant]; +"140 Constant_669" [id=140, type=Constant]; +"141 up_sampling2d/Const" [id=141, type=Constant]; +"142 up_sampling2d/strided_slice/stack_2" [id=142, type=Constant]; +"143 up_sampling2d/strided_slice/stack_1" [id=143, type=Constant]; +"144 up_sampling2d/strided_slice/stack" [id=144, type=Constant]; +"145 Constant_1725" [id=145, type=Constant]; +"146 Constant_1723" [id=146, type=Constant]; +"147 Transpose_1722" [id=147, type=Constant]; +"148 Constant_3979" [id=148, type=Constant]; +"149 Multiply_4112" [id=149, type=Constant]; +"150 Transpose_1692" [id=150, type=Constant]; +"151 Constant_3965" [id=151, type=Constant]; +"152 Multiply_4106" [id=152, type=Constant]; +"153 Transpose_1662" [id=153, type=Constant]; +"154 Constant_3951" [id=154, type=Constant]; +"155 Multiply_4100" [id=155, type=Constant]; +"156 Constant_1801" [id=156, type=Constant]; +"157 Transpose_1800" [id=157, type=Constant]; +"158 Transpose_748" [id=158, type=Constant]; +"159 Transpose_1794" [id=159, type=Constant]; +"160 Constant_4007" [id=160, type=Constant]; +"161 Multiply_4124" [id=161, type=Constant]; +"0 image_input" -> "1 Divide_2378" [label="[1, 416, 416, 3]", style=solid]; +"1 Divide_2378" -> "2 Multiply_3747" [label="[1, 3, 416, 416]", style=solid]; +"2 Multiply_3747" -> "3 Transpose_1172" [label="[1, 32, 208, 208]", style=solid]; +"3 Transpose_1172" -> "4 Transpose_1178" [label="[1, 32, 208, 208]", style=solid]; +"4 Transpose_1178" -> "5 Multiply_3761" [label="[1, 32, 208, 208]", style=solid]; +"5 Multiply_3761" -> "6 Transpose_1229" [label="[1, 64, 104, 104]", style=solid]; +"6 Transpose_1229" -> "7 Transpose_1235" [label="[1, 64, 104, 104]", style=solid]; +"7 Transpose_1235" -> "8 Multiply_3775" [label="[1, 64, 104, 104]", style=solid]; +"8 Multiply_3775" -> "9 Transpose_1259" [label="[1, 64, 104, 104]", style=solid]; +"9 Transpose_1259" -> "10 Transpose_1265" [label="[1, 64, 104, 104]", style=solid]; +"10 Transpose_1265" -> "11 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"10 Transpose_1265" -> "12 group_route_3/split" [label="[1, 64, 104, 104]", style=solid]; +"11 Transpose_1368" -> "13 MaxPool_307" [label="[1, 128, 104, 104]", style=solid]; +"12 group_route_3/split" -> "14 Multiply_3789" [label="[1, 32, 104, 104]", style=solid]; +"13 MaxPool_307" -> "15 Multiply_3831" [label="[1, 128, 52, 52]", style=solid]; +"14 Multiply_3789" -> "16 Transpose_1294" [label="[1, 32, 104, 104]", style=solid]; +"15 Multiply_3831" -> "17 Transpose_1392" [label="[1, 128, 52, 52]", style=solid]; +"16 Transpose_1294" -> "18 Transpose_1300" [label="[1, 32, 104, 104]", style=solid]; +"17 Transpose_1392" -> "19 Transpose_1398" [label="[1, 128, 52, 52]", style=solid]; +"18 Transpose_1300" -> "20 Multiply_3803" [label="[1, 32, 104, 104]", style=solid]; +"18 Transpose_1300" -> "21 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"19 Transpose_1398" -> "22 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"19 Transpose_1398" -> "23 group_route_11/split" [label="[1, 128, 52, 52]", style=solid]; +"20 Multiply_3803" -> "24 Transpose_1324" [label="[1, 32, 104, 104]", style=solid]; +"21 Transpose_1334" -> "25 Multiply_3817" [label="[1, 64, 104, 104]", style=solid]; +"22 Transpose_1501" -> "26 MaxPool_433" [label="[1, 256, 52, 52]", style=solid]; +"23 group_route_11/split" -> "27 Multiply_3845" [label="[1, 64, 52, 52]", style=solid]; +"24 Transpose_1324" -> "28 Transpose_1330" [label="[1, 32, 104, 104]", style=solid]; +"25 Multiply_3817" -> "29 Transpose_1358" [label="[1, 64, 104, 104]", style=solid]; +"26 MaxPool_433" -> "30 Multiply_3887" [label="[1, 256, 26, 26]", style=solid]; +"27 Multiply_3845" -> "31 Transpose_1427" [label="[1, 64, 52, 52]", style=solid]; +"28 Transpose_1330" -> "21 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"29 Transpose_1358" -> "32 Transpose_1364" [label="[1, 64, 104, 104]", style=solid]; +"30 Multiply_3887" -> "33 Transpose_1525" [label="[1, 256, 26, 26]", style=solid]; +"31 Transpose_1427" -> "34 Transpose_1433" [label="[1, 64, 52, 52]", style=solid]; +"32 Transpose_1364" -> "11 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"33 Transpose_1525" -> "35 Transpose_1531" [label="[1, 256, 26, 26]", style=solid]; +"34 Transpose_1433" -> "36 Multiply_3859" [label="[1, 64, 52, 52]", style=solid]; +"34 Transpose_1433" -> "37 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"35 Transpose_1531" -> "38 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"35 Transpose_1531" -> "39 group_route_19/split" [label="[1, 256, 26, 26]", style=solid]; +"36 Multiply_3859" -> "40 Transpose_1457" [label="[1, 64, 52, 52]", style=solid]; +"37 Transpose_1467" -> "41 Multiply_3873" [label="[1, 128, 52, 52]", style=solid]; +"38 Transpose_1634" -> "42 MaxPool_579" [label="[1, 512, 26, 26]", style=solid]; +"39 group_route_19/split" -> "43 Multiply_3901" [label="[1, 128, 26, 26]", style=solid]; +"40 Transpose_1457" -> "44 Transpose_1463" [label="[1, 64, 52, 52]", style=solid]; +"41 Multiply_3873" -> "45 Transpose_1491" [label="[1, 128, 52, 52]", style=solid]; +"42 MaxPool_579" -> "46 Multiply_3943" [label="[1, 512, 13, 13]", style=solid]; +"43 Multiply_3901" -> "47 Transpose_1560" [label="[1, 128, 26, 26]", style=solid]; +"44 Transpose_1463" -> "37 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"45 Transpose_1491" -> "48 Transpose_1497" [label="[1, 128, 52, 52]", style=solid]; +"46 Multiply_3943" -> "49 Transpose_1658" [label="[1, 512, 13, 13]", style=solid]; +"47 Transpose_1560" -> "50 Transpose_1566" [label="[1, 128, 26, 26]", style=solid]; +"48 Transpose_1497" -> "22 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"49 Transpose_1658" -> "51 Transpose_1664" [label="[1, 512, 13, 13]", style=solid]; +"50 Transpose_1566" -> "52 Multiply_3915" [label="[1, 128, 26, 26]", style=solid]; +"50 Transpose_1566" -> "53 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"51 Transpose_1664" -> "54 Multiply_3957" [label="[1, 512, 13, 13]", style=solid]; +"52 Multiply_3915" -> "55 Transpose_1590" [label="[1, 128, 26, 26]", style=solid]; +"53 Transpose_1600" -> "56 Multiply_3929" [label="[1, 256, 26, 26]", style=solid]; +"54 Multiply_3957" -> "57 Transpose_1688" [label="[1, 256, 13, 13]", style=solid]; +"55 Transpose_1590" -> "58 Transpose_1596" [label="[1, 128, 26, 26]", style=solid]; +"56 Multiply_3929" -> "59 Transpose_1624" [label="[1, 256, 26, 26]", style=solid]; +"57 Transpose_1688" -> "60 Transpose_1694" [label="[1, 256, 13, 13]", style=solid]; +"58 Transpose_1596" -> "53 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"59 Transpose_1624" -> "61 Transpose_1630" [label="[1, 256, 26, 26]", style=solid]; +"60 Transpose_1694" -> "62 Multiply_3971" [label="[1, 256, 13, 13]", style=solid]; +"60 Transpose_1694" -> "63 Multiply_3999" [label="[1, 256, 13, 13]", style=solid]; +"61 Transpose_1630" -> "38 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"61 Transpose_1630" -> "64 Transpose_1730" [label="[1, 256, 26, 26]", style=solid]; +"62 Multiply_3971" -> "65 Transpose_1718" [label="[1, 128, 13, 13]", style=solid]; +"63 Multiply_3999" -> "66 Transpose_1790" [label="[1, 512, 13, 13]", style=solid]; +"64 Transpose_1730" -> "67 Multiply_3985" [label="[1, 384, 26, 26]", style=solid]; +"65 Transpose_1718" -> "68 Transpose_1724" [label="[1, 128, 13, 13]", style=solid]; +"66 Transpose_1790" -> "69 Transpose_1796" [label="[1, 512, 13, 13]", style=solid]; +"67 Multiply_3985" -> "70 Transpose_1754" [label="[1, 256, 26, 26]", style=solid]; +"68 Transpose_1724" -> "71 Gather_1726" [label="[1, 128, 13, 13]", style=solid]; +"68 Transpose_1724" -> "72 leaky_re_lu_17/LeakyRelu" [label="[1, 128, 13, 13]", style=solid]; +"69 Transpose_1796" -> "73 Convolution_749" [label="[1, 512, 13, 13]", style=solid]; +"70 Transpose_1754" -> "74 Transpose_1760" [label="[1, 256, 26, 26]", style=solid]; +"71 Gather_1726" -> "75 up_sampling2d/Shape" [label="[4]", style=dashed]; +"72 leaky_re_lu_17/LeakyRelu" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[1, 13, 13, 128]", style=solid]; +"73 Convolution_749" -> "77 Transpose_1802" [label="[1, 255, 13, 13]", style=solid]; +"74 Transpose_1760" -> "78 Convolution_706" [label="[1, 256, 26, 26]", style=solid]; +"75 up_sampling2d/Shape" -> "79 up_sampling2d/strided_slice" [label="[4]", style=dashed]; +"76 up_sampling2d/resize/ResizeNearestNeighbor" -> "80 Transpose_1728" [label="[1, 26, 26, 128]", style=solid]; +"77 Transpose_1802" -> "81 conv2d_17/BiasAdd" [label="[1, 255, 13, 13]", style=solid]; +"78 Convolution_706" -> "82 Transpose_1766" [label="[1, 255, 26, 26]", style=solid]; +"79 up_sampling2d/strided_slice" -> "83 up_sampling2d/mul" [label="[2]", style=dashed]; +"80 Transpose_1728" -> "64 Transpose_1730" [label="[1, 128, 26, 26]", style=solid]; +"81 conv2d_17/BiasAdd" -> "84 conv2d_17/BiasAdd^0" [label="[1, 13, 13, 255]", style=solid]; +"82 Transpose_1766" -> "85 conv2d_20/BiasAdd" [label="[1, 255, 26, 26]", style=solid]; +"83 up_sampling2d/mul" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"85 conv2d_20/BiasAdd" -> "86 conv2d_20/BiasAdd^0" [label="[1, 26, 26, 255]", style=solid]; +"87 Constant_1765" -> "85 conv2d_20/BiasAdd" [label="[4]", style=dashed]; +"88 Transpose_1764" -> "82 Transpose_1766" [label="[1, 255, 1, 1]", style=solid]; +"89 Transpose_705" -> "78 Convolution_706" [label="[255, 256, 1, 1]", style=solid]; +"90 Transpose_1758" -> "74 Transpose_1760" [label="[1, 1, 1, 1]", style=solid]; +"91 Constant_3993" -> "70 Transpose_1754" [label="[1, 256, 1, 1]", style=solid]; +"92 Multiply_4118" -> "67 Multiply_3985" [label="[256, 384, 3, 3]", style=solid]; +"93 Transpose_1628" -> "61 Transpose_1630" [label="[1, 1, 1, 1]", style=solid]; +"94 Constant_3937" -> "59 Transpose_1624" [label="[1, 256, 1, 1]", style=solid]; +"95 Multiply_4094" -> "56 Multiply_3929" [label="[256, 256, 1, 1]", style=solid]; +"96 Transpose_1564" -> "50 Transpose_1566" [label="[1, 1, 1, 1]", style=solid]; +"97 Constant_3909" -> "47 Transpose_1560" [label="[1, 128, 1, 1]", style=solid]; +"98 Multiply_4082" -> "43 Multiply_3901" [label="[128, 128, 3, 3]", style=solid]; +"99 Constant_1532" -> "39 group_route_19/split" [label="[]", style=dashed]; +"100 Transpose_1529" -> "35 Transpose_1531" [label="[1, 1, 1, 1]", style=solid]; +"101 Constant_3895" -> "33 Transpose_1525" [label="[1, 256, 1, 1]", style=solid]; +"102 Multiply_4076" -> "30 Multiply_3887" [label="[256, 256, 3, 3]", style=solid]; +"103 Transpose_1495" -> "48 Transpose_1497" [label="[1, 1, 1, 1]", style=solid]; +"104 Constant_3881" -> "45 Transpose_1491" [label="[1, 128, 1, 1]", style=solid]; +"105 Multiply_4070" -> "41 Multiply_3873" [label="[128, 128, 1, 1]", style=solid]; +"106 Transpose_1431" -> "34 Transpose_1433" [label="[1, 1, 1, 1]", style=solid]; +"107 Constant_3853" -> "31 Transpose_1427" [label="[1, 64, 1, 1]", style=solid]; +"108 Multiply_4058" -> "27 Multiply_3845" [label="[64, 64, 3, 3]", style=solid]; +"109 Constant_1399" -> "23 group_route_11/split" [label="[]", style=dashed]; +"110 Transpose_1396" -> "19 Transpose_1398" [label="[1, 1, 1, 1]", style=solid]; +"111 Constant_3839" -> "17 Transpose_1392" [label="[1, 128, 1, 1]", style=solid]; +"112 Multiply_4052" -> "15 Multiply_3831" [label="[128, 128, 3, 3]", style=solid]; +"113 Transpose_1362" -> "32 Transpose_1364" [label="[1, 1, 1, 1]", style=solid]; +"114 Constant_3825" -> "29 Transpose_1358" [label="[1, 64, 1, 1]", style=solid]; +"115 Multiply_4046" -> "25 Multiply_3817" [label="[64, 64, 1, 1]", style=solid]; +"116 Transpose_1298" -> "18 Transpose_1300" [label="[1, 1, 1, 1]", style=solid]; +"117 Constant_3797" -> "16 Transpose_1294" [label="[1, 32, 1, 1]", style=solid]; +"118 Multiply_4034" -> "14 Multiply_3789" [label="[32, 32, 3, 3]", style=solid]; +"119 Constant_1266" -> "12 group_route_3/split" [label="[]", style=dashed]; +"120 Transpose_1263" -> "10 Transpose_1265" [label="[1, 1, 1, 1]", style=solid]; +"121 Constant_3783" -> "9 Transpose_1259" [label="[1, 64, 1, 1]", style=solid]; +"122 Multiply_4028" -> "8 Multiply_3775" [label="[64, 64, 3, 3]", style=solid]; +"123 Transpose_1233" -> "7 Transpose_1235" [label="[1, 1, 1, 1]", style=solid]; +"124 Constant_3769" -> "6 Transpose_1229" [label="[1, 64, 1, 1]", style=solid]; +"125 Multiply_4022" -> "5 Multiply_3761" [label="[64, 32, 3, 3]", style=solid]; +"126 Transpose_1176" -> "4 Transpose_1178" [label="[1, 1, 1, 1]", style=solid]; +"127 Constant_3755" -> "3 Transpose_1172" [label="[1, 32, 1, 1]", style=solid]; +"128 Gather_4293" -> "2 Multiply_3747" [label="[32, 3, 3, 3]", style=solid]; +"129 Constant_2333" -> "1 Divide_2378" [label="[4]", style=dashed]; +"130 Transpose_1328" -> "28 Transpose_1330" [label="[1, 1, 1, 1]", style=solid]; +"131 Constant_3811" -> "24 Transpose_1324" [label="[1, 32, 1, 1]", style=solid]; +"132 Multiply_4040" -> "20 Multiply_3803" [label="[32, 32, 3, 3]", style=solid]; +"133 Transpose_1461" -> "44 Transpose_1463" [label="[1, 1, 1, 1]", style=solid]; +"134 Constant_3867" -> "40 Transpose_1457" [label="[1, 64, 1, 1]", style=solid]; +"135 Multiply_4064" -> "36 Multiply_3859" [label="[64, 64, 3, 3]", style=solid]; +"136 Transpose_1594" -> "58 Transpose_1596" [label="[1, 1, 1, 1]", style=solid]; +"137 Constant_3923" -> "55 Transpose_1590" [label="[1, 128, 1, 1]", style=solid]; +"138 Multiply_4088" -> "52 Multiply_3915" [label="[128, 128, 3, 3]", style=solid]; +"139 Constant_1727" -> "80 Transpose_1728" [label="[4]", style=dashed]; +"140 Constant_669" -> "76 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"141 up_sampling2d/Const" -> "83 up_sampling2d/mul" [label="[2]", style=dashed]; +"142 up_sampling2d/strided_slice/stack_2" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"143 up_sampling2d/strided_slice/stack_1" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"144 up_sampling2d/strided_slice/stack" -> "79 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"145 Constant_1725" -> "75 up_sampling2d/Shape" [label="[]", style=dashed]; +"146 Constant_1723" -> "72 leaky_re_lu_17/LeakyRelu" [label="[4]", style=dashed]; +"146 Constant_1723" -> "75 up_sampling2d/Shape" [label="[4]", style=dashed]; +"147 Transpose_1722" -> "68 Transpose_1724" [label="[1, 1, 1, 1]", style=solid]; +"148 Constant_3979" -> "65 Transpose_1718" [label="[1, 128, 1, 1]", style=solid]; +"149 Multiply_4112" -> "62 Multiply_3971" [label="[128, 256, 1, 1]", style=solid]; +"150 Transpose_1692" -> "60 Transpose_1694" [label="[1, 1, 1, 1]", style=solid]; +"151 Constant_3965" -> "57 Transpose_1688" [label="[1, 256, 1, 1]", style=solid]; +"152 Multiply_4106" -> "54 Multiply_3957" [label="[256, 512, 1, 1]", style=solid]; +"153 Transpose_1662" -> "51 Transpose_1664" [label="[1, 1, 1, 1]", style=solid]; +"154 Constant_3951" -> "49 Transpose_1658" [label="[1, 512, 1, 1]", style=solid]; +"155 Multiply_4100" -> "46 Multiply_3943" [label="[512, 512, 3, 3]", style=solid]; +"156 Constant_1801" -> "81 conv2d_17/BiasAdd" [label="[4]", style=dashed]; +"157 Transpose_1800" -> "77 Transpose_1802" [label="[1, 255, 1, 1]", style=solid]; +"158 Transpose_748" -> "73 Convolution_749" [label="[255, 512, 1, 1]", style=solid]; +"159 Transpose_1794" -> "69 Transpose_1796" [label="[1, 1, 1, 1]", style=solid]; +"160 Constant_4007" -> "66 Transpose_1790" [label="[1, 512, 1, 1]", style=solid]; +"161 Multiply_4124" -> "63 Multiply_3999" [label="[512, 256, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot new file mode 120000 index 00000000000..46de4f3a01b --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ComparisonBinaryModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/ComparisonBinaryModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvModel.dot new file mode 120000 index 00000000000..61b0b019759 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/ConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvNotBiasModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvNotBiasModel.dot new file mode 120000 index 00000000000..be1ead77939 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ConvNotBiasModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/ConvNotBiasModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot new file mode 120000 index 00000000000..925cd74344e --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv3DModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/DepthwiseConv3DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot new file mode 120000 index 00000000000..a2f28a4ca4d --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv4DModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/DepthwiseConv4DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot new file mode 120000 index 00000000000..53ecd8222a4 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConv5DModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/DepthwiseConv5DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConvModel.dot new file mode 120000 index 00000000000..c7e309face6 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DepthwiseConvModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/DepthwiseConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/DynamicModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DynamicModel.dot new file mode 120000 index 00000000000..912944ed752 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/DynamicModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/DynamicModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot new file mode 120000 index 00000000000..3c96834ce5d --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_F.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot new file mode 120000 index 00000000000..01711f4a26c --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/GRUSequenceModel_linear_before_reset_T.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/GroupNormalizationModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GroupNormalizationModel.dot new file mode 120000 index 00000000000..bf9f263e91f --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/GroupNormalizationModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/GroupNormalizationModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_else.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_else.dot new file mode 120000 index 00000000000..9c7f8751cc3 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_else.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/IfModel_else.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_main.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_main.dot new file mode 120000 index 00000000000..039a21a9f27 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_main.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/IfModel_main.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_then.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_then.dot new file mode 120000 index 00000000000..d975f652815 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IfModel_then.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/IfModel_then.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/IntegerModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IntegerModel.dot new file mode 120000 index 00000000000..baca43a4f1e --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/IntegerModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/IntegerModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMModel.dot new file mode 120000 index 00000000000..10126fd058c --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/LSTMModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMSequenceModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMSequenceModel.dot new file mode 120000 index 00000000000..6cdc8a9ffaa --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LSTMSequenceModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/LSTMSequenceModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/LinearModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LinearModel.dot new file mode 120000 index 00000000000..bb4e769e70e --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/LinearModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/LinearModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatMul2DModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatMul2DModel.dot new file mode 120000 index 00000000000..8a4c93568cb --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatMul2DModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/MatMul2DModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot new file mode 120000 index 00000000000..5af75360301 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/MatmulSoftmaxMatmulBlock.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot new file mode 120000 index 00000000000..0843c2335c0 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ScaleShiftReluModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/ScaleShiftReluModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/SeBlockModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/SeBlockModel.dot new file mode 120000 index 00000000000..8dbbc00598d --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/SeBlockModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/SeBlockModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/ShapeOfModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ShapeOfModel.dot new file mode 120000 index 00000000000..2519aba5a06 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/ShapeOfModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/ShapeOfModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/SharedConvModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/SharedConvModel.dot new file mode 120000 index 00000000000..e9e2a4e9d92 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/SharedConvModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/SharedConvModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot new file mode 120000 index 00000000000..53096448b08 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/UnifiedEmbeddingModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/UnifiedEmbeddingModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/WeightsModel.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/WeightsModel.dot new file mode 120000 index 00000000000..d21f9c32761 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/WeightsModel.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/WeightsModel.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot new file mode 120000 index 00000000000..4d27a957555 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/mobilenet-v2-pytorch_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot new file mode 120000 index 00000000000..f0885dae083 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/mobilenet-v3-small-1.0-224-tf_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot new file mode 120000 index 00000000000..a347e66bdaf --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-18-pytorch_performance.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/resnet-18-pytorch_performance.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot new file mode 120000 index 00000000000..7d1a392f16e --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/resnet-50-pytorch_performance_CPU_SPR.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot new file mode 120000 index 00000000000..65e8723ba3b --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/swin-tiny-patch4-window7-224_performance_transformer.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot new file mode 120000 index 00000000000..40c2318499c --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot @@ -0,0 +1 @@ +../../../2023.1/reference_graphs/quantized/swin-tiny-patch4-window7-224_sq.dot \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot b/tests/openvino/native/data/2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot new file mode 100644 index 00000000000..073e63c11e0 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_graphs/quantized/yolo-v4-tiny-tf_performance.dot @@ -0,0 +1,754 @@ +strict digraph { +"0 image_input" [id=0, type=Parameter]; +"1 image_input/fq_output_0" [id=1, type=FakeQuantize]; +"2 Divide_2378" [id=2, type=Transpose]; +"3 Multiply_3747" [id=3, type=Convolution]; +"4 Transpose_1172" [id=4, type=Add]; +"5 Transpose_1178" [id=5, type=PRelu]; +"6 Transpose_1178/fq_output_0" [id=6, type=FakeQuantize]; +"7 Multiply_3761" [id=7, type=Convolution]; +"8 Transpose_1229" [id=8, type=Add]; +"9 Transpose_1235" [id=9, type=PRelu]; +"10 Transpose_1235/fq_output_0" [id=10, type=FakeQuantize]; +"11 Multiply_3775" [id=11, type=Convolution]; +"12 Transpose_1259" [id=12, type=Add]; +"13 Transpose_1265" [id=13, type=PRelu]; +"14 Transpose_1265/fq_output_0" [id=14, type=FakeQuantize]; +"15 Transpose_1368" [id=15, type=Concat]; +"16 group_route_3/split" [id=16, type=Split]; +"17 MaxPool_307" [id=17, type=MaxPool]; +"18 Multiply_3789" [id=18, type=Convolution]; +"19 Multiply_3831" [id=19, type=Convolution]; +"20 Transpose_1294" [id=20, type=Add]; +"21 Transpose_1392" [id=21, type=Add]; +"22 Transpose_1300" [id=22, type=PRelu]; +"23 Transpose_1398" [id=23, type=PRelu]; +"24 Transpose_1300/fq_output_0" [id=24, type=FakeQuantize]; +"25 Transpose_1398/fq_output_0" [id=25, type=FakeQuantize]; +"26 Multiply_3803" [id=26, type=Convolution]; +"27 Transpose_1334" [id=27, type=Concat]; +"28 Transpose_1501" [id=28, type=Concat]; +"29 group_route_11/split" [id=29, type=Split]; +"30 Transpose_1324" [id=30, type=Add]; +"31 Multiply_3817" [id=31, type=Convolution]; +"32 MaxPool_433" [id=32, type=MaxPool]; +"33 Multiply_3845" [id=33, type=Convolution]; +"34 Transpose_1330" [id=34, type=PRelu]; +"35 Transpose_1358" [id=35, type=Add]; +"36 Multiply_3887" [id=36, type=Convolution]; +"37 Transpose_1427" [id=37, type=Add]; +"38 Transpose_1330/fq_output_0" [id=38, type=FakeQuantize]; +"39 Transpose_1364" [id=39, type=PRelu]; +"40 Transpose_1525" [id=40, type=Add]; +"41 Transpose_1433" [id=41, type=PRelu]; +"42 Transpose_1364/fq_output_0" [id=42, type=FakeQuantize]; +"43 Transpose_1531" [id=43, type=PRelu]; +"44 Transpose_1433/fq_output_0" [id=44, type=FakeQuantize]; +"45 Transpose_1531/fq_output_0" [id=45, type=FakeQuantize]; +"46 Multiply_3859" [id=46, type=Convolution]; +"47 Transpose_1467" [id=47, type=Concat]; +"48 Transpose_1634" [id=48, type=Concat]; +"49 group_route_19/split" [id=49, type=Split]; +"50 Transpose_1457" [id=50, type=Add]; +"51 Multiply_3873" [id=51, type=Convolution]; +"52 MaxPool_579" [id=52, type=MaxPool]; +"53 Multiply_3901" [id=53, type=Convolution]; +"54 Transpose_1463" [id=54, type=PRelu]; +"55 Transpose_1491" [id=55, type=Add]; +"56 Multiply_3943" [id=56, type=Convolution]; +"57 Transpose_1560" [id=57, type=Add]; +"58 Transpose_1463/fq_output_0" [id=58, type=FakeQuantize]; +"59 Transpose_1497" [id=59, type=PRelu]; +"60 Transpose_1658" [id=60, type=Add]; +"61 Transpose_1566" [id=61, type=PRelu]; +"62 Transpose_1497/fq_output_0" [id=62, type=FakeQuantize]; +"63 Transpose_1664" [id=63, type=PRelu]; +"64 Transpose_1566/fq_output_0" [id=64, type=FakeQuantize]; +"65 Transpose_1664/fq_output_0" [id=65, type=FakeQuantize]; +"66 Multiply_3915" [id=66, type=Convolution]; +"67 Transpose_1600" [id=67, type=Concat]; +"68 Multiply_3957" [id=68, type=Convolution]; +"69 Transpose_1590" [id=69, type=Add]; +"70 Multiply_3929" [id=70, type=Convolution]; +"71 Transpose_1688" [id=71, type=Add]; +"72 Transpose_1596" [id=72, type=PRelu]; +"73 Transpose_1624" [id=73, type=Add]; +"74 Transpose_1694" [id=74, type=PRelu]; +"75 Transpose_1596/fq_output_0" [id=75, type=FakeQuantize]; +"76 Transpose_1630" [id=76, type=PRelu]; +"77 Transpose_1694/fq_output_0" [id=77, type=FakeQuantize]; +"78 Transpose_1630/fq_output_0" [id=78, type=FakeQuantize]; +"79 Multiply_3971" [id=79, type=Convolution]; +"80 Multiply_3999" [id=80, type=Convolution]; +"81 Transpose_1730" [id=81, type=Concat]; +"82 Transpose_1718" [id=82, type=Add]; +"83 Transpose_1790" [id=83, type=Add]; +"84 Multiply_3985" [id=84, type=Convolution]; +"85 Transpose_1724" [id=85, type=PRelu]; +"86 Transpose_1796" [id=86, type=PRelu]; +"87 Transpose_1754" [id=87, type=Add]; +"88 Transpose_1724/fq_output_0" [id=88, type=FakeQuantize]; +"89 Transpose_1796/fq_output_0" [id=89, type=FakeQuantize]; +"90 Transpose_1760" [id=90, type=PRelu]; +"91 Gather_1726" [id=91, type=ShapeOf]; +"92 leaky_re_lu_17/LeakyRelu" [id=92, type=Transpose]; +"93 Convolution_749" [id=93, type=Convolution]; +"94 Transpose_1760/fq_output_0" [id=94, type=FakeQuantize]; +"95 up_sampling2d/Shape" [id=95, type=Gather]; +"96 up_sampling2d/resize/ResizeNearestNeighbor" [id=96, type=Interpolate]; +"97 Transpose_1802" [id=97, type=Add]; +"98 Convolution_706" [id=98, type=Convolution]; +"99 up_sampling2d/strided_slice" [id=99, type=StridedSlice]; +"100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [id=100, type=FakeQuantize]; +"101 conv2d_17/BiasAdd" [id=101, type=Transpose]; +"102 Transpose_1766" [id=102, type=Add]; +"103 up_sampling2d/mul" [id=103, type=Multiply]; +"104 Transpose_1728" [id=104, type=Transpose]; +"105 conv2d_17/BiasAdd^0" [id=105, label="105 conv2d_17/BiasAdd:0", type=Result]; +"106 conv2d_20/BiasAdd" [id=106, type=Transpose]; +"107 conv2d_20/BiasAdd^0" [id=107, label="107 conv2d_20/BiasAdd:0", type=Result]; +"108 Constant_1765" [id=108, type=Constant]; +"109 Transpose_1764" [id=109, type=Constant]; +"110 Convolution_706/fq_weights_1" [id=110, type=FakeQuantize]; +"111 Constant_5545" [id=111, type=Constant]; +"112 Constant_5544" [id=112, type=Constant]; +"113 Constant_5543" [id=113, type=Constant]; +"114 Constant_5542" [id=114, type=Constant]; +"115 Transpose_705" [id=115, type=Constant]; +"116 Constant_5540" [id=116, type=Constant]; +"117 Constant_5539" [id=117, type=Constant]; +"118 Constant_5538" [id=118, type=Constant]; +"119 Constant_5537" [id=119, type=Constant]; +"120 Transpose_1758" [id=120, type=Constant]; +"121 Constant_3993" [id=121, type=Constant]; +"122 Multiply_3985/fq_weights_1" [id=122, type=FakeQuantize]; +"123 Constant_5535" [id=123, type=Constant]; +"124 Constant_5534" [id=124, type=Constant]; +"125 Constant_5533" [id=125, type=Constant]; +"126 Constant_5532" [id=126, type=Constant]; +"127 Multiply_4118" [id=127, type=Constant]; +"128 Constant_5405" [id=128, type=Constant]; +"129 Constant_5404" [id=129, type=Constant]; +"130 Constant_5403" [id=130, type=Constant]; +"131 Constant_5402" [id=131, type=Constant]; +"132 Transpose_1628" [id=132, type=Constant]; +"133 Constant_3937" [id=133, type=Constant]; +"134 Multiply_3929/fq_weights_1" [id=134, type=FakeQuantize]; +"135 Constant_5500" [id=135, type=Constant]; +"136 Constant_5499" [id=136, type=Constant]; +"137 Constant_5498" [id=137, type=Constant]; +"138 Constant_5497" [id=138, type=Constant]; +"139 Multiply_4094" [id=139, type=Constant]; +"140 Constant_5375" [id=140, type=Constant]; +"141 Constant_5374" [id=141, type=Constant]; +"142 Constant_5373" [id=142, type=Constant]; +"143 Constant_5372" [id=143, type=Constant]; +"144 Transpose_1564" [id=144, type=Constant]; +"145 Constant_3909" [id=145, type=Constant]; +"146 Multiply_3901/fq_weights_1" [id=146, type=FakeQuantize]; +"147 Constant_5490" [id=147, type=Constant]; +"148 Constant_5489" [id=148, type=Constant]; +"149 Constant_5488" [id=149, type=Constant]; +"150 Constant_5487" [id=150, type=Constant]; +"151 Multiply_4082" [id=151, type=Constant]; +"152 Constant_1532" [id=152, type=Constant]; +"153 Constant_5410" [id=153, type=Constant]; +"154 Constant_5409" [id=154, type=Constant]; +"155 Constant_5408" [id=155, type=Constant]; +"156 Constant_5407" [id=156, type=Constant]; +"157 Transpose_1529" [id=157, type=Constant]; +"158 Constant_3895" [id=158, type=Constant]; +"159 Multiply_3887/fq_weights_1" [id=159, type=FakeQuantize]; +"160 Constant_5485" [id=160, type=Constant]; +"161 Constant_5484" [id=161, type=Constant]; +"162 Constant_5483" [id=162, type=Constant]; +"163 Constant_5482" [id=163, type=Constant]; +"164 Multiply_4076" [id=164, type=Constant]; +"165 Constant_5400" [id=165, type=Constant]; +"166 Constant_5399" [id=166, type=Constant]; +"167 Constant_5398" [id=167, type=Constant]; +"168 Constant_5397" [id=168, type=Constant]; +"169 Transpose_1495" [id=169, type=Constant]; +"170 Constant_3881" [id=170, type=Constant]; +"171 Multiply_3873/fq_weights_1" [id=171, type=FakeQuantize]; +"172 Constant_5480" [id=172, type=Constant]; +"173 Constant_5479" [id=173, type=Constant]; +"174 Constant_5478" [id=174, type=Constant]; +"175 Constant_5477" [id=175, type=Constant]; +"176 Multiply_4070" [id=176, type=Constant]; +"177 Constant_5370" [id=177, type=Constant]; +"178 Constant_5369" [id=178, type=Constant]; +"179 Constant_5368" [id=179, type=Constant]; +"180 Constant_5367" [id=180, type=Constant]; +"181 Transpose_1431" [id=181, type=Constant]; +"182 Constant_3853" [id=182, type=Constant]; +"183 Multiply_3845/fq_weights_1" [id=183, type=FakeQuantize]; +"184 Constant_5470" [id=184, type=Constant]; +"185 Constant_5469" [id=185, type=Constant]; +"186 Constant_5468" [id=186, type=Constant]; +"187 Constant_5467" [id=187, type=Constant]; +"188 Multiply_4058" [id=188, type=Constant]; +"189 Constant_1399" [id=189, type=Constant]; +"190 Constant_5395" [id=190, type=Constant]; +"191 Constant_5394" [id=191, type=Constant]; +"192 Constant_5393" [id=192, type=Constant]; +"193 Constant_5392" [id=193, type=Constant]; +"194 Transpose_1396" [id=194, type=Constant]; +"195 Constant_3839" [id=195, type=Constant]; +"196 Multiply_3831/fq_weights_1" [id=196, type=FakeQuantize]; +"197 Constant_5465" [id=197, type=Constant]; +"198 Constant_5464" [id=198, type=Constant]; +"199 Constant_5463" [id=199, type=Constant]; +"200 Constant_5462" [id=200, type=Constant]; +"201 Multiply_4052" [id=201, type=Constant]; +"202 Constant_5390" [id=202, type=Constant]; +"203 Constant_5389" [id=203, type=Constant]; +"204 Constant_5388" [id=204, type=Constant]; +"205 Constant_5387" [id=205, type=Constant]; +"206 Transpose_1362" [id=206, type=Constant]; +"207 Constant_3825" [id=207, type=Constant]; +"208 Multiply_3817/fq_weights_1" [id=208, type=FakeQuantize]; +"209 Constant_5460" [id=209, type=Constant]; +"210 Constant_5459" [id=210, type=Constant]; +"211 Constant_5458" [id=211, type=Constant]; +"212 Constant_5457" [id=212, type=Constant]; +"213 Multiply_4046" [id=213, type=Constant]; +"214 Constant_5355" [id=214, type=Constant]; +"215 Constant_5354" [id=215, type=Constant]; +"216 Constant_5353" [id=216, type=Constant]; +"217 Constant_5352" [id=217, type=Constant]; +"218 Transpose_1298" [id=218, type=Constant]; +"219 Constant_3797" [id=219, type=Constant]; +"220 Multiply_3789/fq_weights_1" [id=220, type=FakeQuantize]; +"221 Constant_5450" [id=221, type=Constant]; +"222 Constant_5449" [id=222, type=Constant]; +"223 Constant_5448" [id=223, type=Constant]; +"224 Constant_5447" [id=224, type=Constant]; +"225 Multiply_4034" [id=225, type=Constant]; +"226 Constant_1266" [id=226, type=Constant]; +"227 Constant_5385" [id=227, type=Constant]; +"228 Constant_5384" [id=228, type=Constant]; +"229 Constant_5383" [id=229, type=Constant]; +"230 Constant_5382" [id=230, type=Constant]; +"231 Transpose_1263" [id=231, type=Constant]; +"232 Constant_3783" [id=232, type=Constant]; +"233 Multiply_3775/fq_weights_1" [id=233, type=FakeQuantize]; +"234 Constant_5445" [id=234, type=Constant]; +"235 Constant_5444" [id=235, type=Constant]; +"236 Constant_5443" [id=236, type=Constant]; +"237 Constant_5442" [id=237, type=Constant]; +"238 Multiply_4028" [id=238, type=Constant]; +"239 Constant_5440" [id=239, type=Constant]; +"240 Constant_5439" [id=240, type=Constant]; +"241 Constant_5438" [id=241, type=Constant]; +"242 Constant_5437" [id=242, type=Constant]; +"243 Transpose_1233" [id=243, type=Constant]; +"244 Constant_3769" [id=244, type=Constant]; +"245 Multiply_3761/fq_weights_1" [id=245, type=FakeQuantize]; +"246 Constant_5435" [id=246, type=Constant]; +"247 Constant_5434" [id=247, type=Constant]; +"248 Constant_5433" [id=248, type=Constant]; +"249 Constant_5432" [id=249, type=Constant]; +"250 Multiply_4022" [id=250, type=Constant]; +"251 Constant_5430" [id=251, type=Constant]; +"252 Constant_5429" [id=252, type=Constant]; +"253 Constant_5428" [id=253, type=Constant]; +"254 Constant_5427" [id=254, type=Constant]; +"255 Transpose_1176" [id=255, type=Constant]; +"256 Constant_3755" [id=256, type=Constant]; +"257 Multiply_3747/fq_weights_1" [id=257, type=FakeQuantize]; +"258 Constant_5425" [id=258, type=Constant]; +"259 Constant_5424" [id=259, type=Constant]; +"260 Constant_5423" [id=260, type=Constant]; +"261 Constant_5422" [id=261, type=Constant]; +"262 Gather_4293" [id=262, type=Constant]; +"263 Constant_2333" [id=263, type=Constant]; +"264 Constant_5420" [id=264, type=Constant]; +"265 Constant_5419" [id=265, type=Constant]; +"266 Constant_5418" [id=266, type=Constant]; +"267 Constant_5417" [id=267, type=Constant]; +"268 Constant_5360" [id=268, type=Constant]; +"269 Constant_5359" [id=269, type=Constant]; +"270 Constant_5358" [id=270, type=Constant]; +"271 Constant_5357" [id=271, type=Constant]; +"272 Transpose_1328" [id=272, type=Constant]; +"273 Constant_3811" [id=273, type=Constant]; +"274 Multiply_3803/fq_weights_1" [id=274, type=FakeQuantize]; +"275 Constant_5455" [id=275, type=Constant]; +"276 Constant_5454" [id=276, type=Constant]; +"277 Constant_5453" [id=277, type=Constant]; +"278 Constant_5452" [id=278, type=Constant]; +"279 Multiply_4040" [id=279, type=Constant]; +"280 Constant_5365" [id=280, type=Constant]; +"281 Constant_5364" [id=281, type=Constant]; +"282 Constant_5363" [id=282, type=Constant]; +"283 Constant_5362" [id=283, type=Constant]; +"284 Transpose_1461" [id=284, type=Constant]; +"285 Constant_3867" [id=285, type=Constant]; +"286 Multiply_3859/fq_weights_1" [id=286, type=FakeQuantize]; +"287 Constant_5475" [id=287, type=Constant]; +"288 Constant_5474" [id=288, type=Constant]; +"289 Constant_5473" [id=289, type=Constant]; +"290 Constant_5472" [id=290, type=Constant]; +"291 Multiply_4064" [id=291, type=Constant]; +"292 Constant_5380" [id=292, type=Constant]; +"293 Constant_5379" [id=293, type=Constant]; +"294 Constant_5378" [id=294, type=Constant]; +"295 Constant_5377" [id=295, type=Constant]; +"296 Transpose_1594" [id=296, type=Constant]; +"297 Constant_3923" [id=297, type=Constant]; +"298 Multiply_3915/fq_weights_1" [id=298, type=FakeQuantize]; +"299 Constant_5495" [id=299, type=Constant]; +"300 Constant_5494" [id=300, type=Constant]; +"301 Constant_5493" [id=301, type=Constant]; +"302 Constant_5492" [id=302, type=Constant]; +"303 Multiply_4088" [id=303, type=Constant]; +"304 Constant_1727" [id=304, type=Constant]; +"305 Constant_5415" [id=305, type=Constant]; +"306 Constant_5414" [id=306, type=Constant]; +"307 Constant_5413" [id=307, type=Constant]; +"308 Constant_5412" [id=308, type=Constant]; +"309 Constant_669" [id=309, type=Constant]; +"310 up_sampling2d/Const" [id=310, type=Constant]; +"311 up_sampling2d/strided_slice/stack_2" [id=311, type=Constant]; +"312 up_sampling2d/strided_slice/stack_1" [id=312, type=Constant]; +"313 up_sampling2d/strided_slice/stack" [id=313, type=Constant]; +"314 Constant_1725" [id=314, type=Constant]; +"315 Constant_1723" [id=315, type=Constant]; +"316 Constant_5530" [id=316, type=Constant]; +"317 Constant_5529" [id=317, type=Constant]; +"318 Constant_5528" [id=318, type=Constant]; +"319 Constant_5527" [id=319, type=Constant]; +"320 Transpose_1722" [id=320, type=Constant]; +"321 Constant_3979" [id=321, type=Constant]; +"322 Multiply_3971/fq_weights_1" [id=322, type=FakeQuantize]; +"323 Constant_5525" [id=323, type=Constant]; +"324 Constant_5524" [id=324, type=Constant]; +"325 Constant_5523" [id=325, type=Constant]; +"326 Constant_5522" [id=326, type=Constant]; +"327 Multiply_4112" [id=327, type=Constant]; +"328 Constant_5520" [id=328, type=Constant]; +"329 Constant_5519" [id=329, type=Constant]; +"330 Constant_5518" [id=330, type=Constant]; +"331 Constant_5517" [id=331, type=Constant]; +"332 Transpose_1692" [id=332, type=Constant]; +"333 Constant_3965" [id=333, type=Constant]; +"334 Multiply_3957/fq_weights_1" [id=334, type=FakeQuantize]; +"335 Constant_5515" [id=335, type=Constant]; +"336 Constant_5514" [id=336, type=Constant]; +"337 Constant_5513" [id=337, type=Constant]; +"338 Constant_5512" [id=338, type=Constant]; +"339 Multiply_4106" [id=339, type=Constant]; +"340 Constant_5510" [id=340, type=Constant]; +"341 Constant_5509" [id=341, type=Constant]; +"342 Constant_5508" [id=342, type=Constant]; +"343 Constant_5507" [id=343, type=Constant]; +"344 Transpose_1662" [id=344, type=Constant]; +"345 Constant_3951" [id=345, type=Constant]; +"346 Multiply_3943/fq_weights_1" [id=346, type=FakeQuantize]; +"347 Constant_5505" [id=347, type=Constant]; +"348 Constant_5504" [id=348, type=Constant]; +"349 Constant_5503" [id=349, type=Constant]; +"350 Constant_5502" [id=350, type=Constant]; +"351 Multiply_4100" [id=351, type=Constant]; +"352 Constant_1801" [id=352, type=Constant]; +"353 Transpose_1800" [id=353, type=Constant]; +"354 Convolution_749/fq_weights_1" [id=354, type=FakeQuantize]; +"355 Constant_5560" [id=355, type=Constant]; +"356 Constant_5559" [id=356, type=Constant]; +"357 Constant_5558" [id=357, type=Constant]; +"358 Constant_5557" [id=358, type=Constant]; +"359 Transpose_748" [id=359, type=Constant]; +"360 Constant_5555" [id=360, type=Constant]; +"361 Constant_5554" [id=361, type=Constant]; +"362 Constant_5553" [id=362, type=Constant]; +"363 Constant_5552" [id=363, type=Constant]; +"364 Transpose_1794" [id=364, type=Constant]; +"365 Constant_4007" [id=365, type=Constant]; +"366 Multiply_3999/fq_weights_1" [id=366, type=FakeQuantize]; +"367 Constant_5550" [id=367, type=Constant]; +"368 Constant_5549" [id=368, type=Constant]; +"369 Constant_5548" [id=369, type=Constant]; +"370 Constant_5547" [id=370, type=Constant]; +"371 Multiply_4124" [id=371, type=Constant]; +"0 image_input" -> "1 image_input/fq_output_0" [label="[1, 416, 416, 3]", style=solid]; +"1 image_input/fq_output_0" -> "2 Divide_2378" [label="[1, 416, 416, 3]", style=solid]; +"2 Divide_2378" -> "3 Multiply_3747" [label="[1, 3, 416, 416]", style=solid]; +"3 Multiply_3747" -> "4 Transpose_1172" [label="[1, 32, 208, 208]", style=solid]; +"4 Transpose_1172" -> "5 Transpose_1178" [label="[1, 32, 208, 208]", style=solid]; +"5 Transpose_1178" -> "6 Transpose_1178/fq_output_0" [label="[1, 32, 208, 208]", style=solid]; +"6 Transpose_1178/fq_output_0" -> "7 Multiply_3761" [label="[1, 32, 208, 208]", style=solid]; +"7 Multiply_3761" -> "8 Transpose_1229" [label="[1, 64, 104, 104]", style=solid]; +"8 Transpose_1229" -> "9 Transpose_1235" [label="[1, 64, 104, 104]", style=solid]; +"9 Transpose_1235" -> "10 Transpose_1235/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; +"10 Transpose_1235/fq_output_0" -> "11 Multiply_3775" [label="[1, 64, 104, 104]", style=solid]; +"11 Multiply_3775" -> "12 Transpose_1259" [label="[1, 64, 104, 104]", style=solid]; +"12 Transpose_1259" -> "13 Transpose_1265" [label="[1, 64, 104, 104]", style=solid]; +"13 Transpose_1265" -> "14 Transpose_1265/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; +"14 Transpose_1265/fq_output_0" -> "15 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"14 Transpose_1265/fq_output_0" -> "16 group_route_3/split" [label="[1, 64, 104, 104]", style=solid]; +"15 Transpose_1368" -> "17 MaxPool_307" [label="[1, 128, 104, 104]", style=solid]; +"16 group_route_3/split" -> "18 Multiply_3789" [label="[1, 32, 104, 104]", style=solid]; +"17 MaxPool_307" -> "19 Multiply_3831" [label="[1, 128, 52, 52]", style=solid]; +"18 Multiply_3789" -> "20 Transpose_1294" [label="[1, 32, 104, 104]", style=solid]; +"19 Multiply_3831" -> "21 Transpose_1392" [label="[1, 128, 52, 52]", style=solid]; +"20 Transpose_1294" -> "22 Transpose_1300" [label="[1, 32, 104, 104]", style=solid]; +"21 Transpose_1392" -> "23 Transpose_1398" [label="[1, 128, 52, 52]", style=solid]; +"22 Transpose_1300" -> "24 Transpose_1300/fq_output_0" [label="[1, 32, 104, 104]", style=solid]; +"23 Transpose_1398" -> "25 Transpose_1398/fq_output_0" [label="[1, 128, 52, 52]", style=solid]; +"24 Transpose_1300/fq_output_0" -> "26 Multiply_3803" [label="[1, 32, 104, 104]", style=solid]; +"24 Transpose_1300/fq_output_0" -> "27 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"25 Transpose_1398/fq_output_0" -> "28 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"25 Transpose_1398/fq_output_0" -> "29 group_route_11/split" [label="[1, 128, 52, 52]", style=solid]; +"26 Multiply_3803" -> "30 Transpose_1324" [label="[1, 32, 104, 104]", style=solid]; +"27 Transpose_1334" -> "31 Multiply_3817" [label="[1, 64, 104, 104]", style=solid]; +"28 Transpose_1501" -> "32 MaxPool_433" [label="[1, 256, 52, 52]", style=solid]; +"29 group_route_11/split" -> "33 Multiply_3845" [label="[1, 64, 52, 52]", style=solid]; +"30 Transpose_1324" -> "34 Transpose_1330" [label="[1, 32, 104, 104]", style=solid]; +"31 Multiply_3817" -> "35 Transpose_1358" [label="[1, 64, 104, 104]", style=solid]; +"32 MaxPool_433" -> "36 Multiply_3887" [label="[1, 256, 26, 26]", style=solid]; +"33 Multiply_3845" -> "37 Transpose_1427" [label="[1, 64, 52, 52]", style=solid]; +"34 Transpose_1330" -> "38 Transpose_1330/fq_output_0" [label="[1, 32, 104, 104]", style=solid]; +"35 Transpose_1358" -> "39 Transpose_1364" [label="[1, 64, 104, 104]", style=solid]; +"36 Multiply_3887" -> "40 Transpose_1525" [label="[1, 256, 26, 26]", style=solid]; +"37 Transpose_1427" -> "41 Transpose_1433" [label="[1, 64, 52, 52]", style=solid]; +"38 Transpose_1330/fq_output_0" -> "27 Transpose_1334" [label="[1, 32, 104, 104]", style=solid]; +"39 Transpose_1364" -> "42 Transpose_1364/fq_output_0" [label="[1, 64, 104, 104]", style=solid]; +"40 Transpose_1525" -> "43 Transpose_1531" [label="[1, 256, 26, 26]", style=solid]; +"41 Transpose_1433" -> "44 Transpose_1433/fq_output_0" [label="[1, 64, 52, 52]", style=solid]; +"42 Transpose_1364/fq_output_0" -> "15 Transpose_1368" [label="[1, 64, 104, 104]", style=solid]; +"43 Transpose_1531" -> "45 Transpose_1531/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; +"44 Transpose_1433/fq_output_0" -> "46 Multiply_3859" [label="[1, 64, 52, 52]", style=solid]; +"44 Transpose_1433/fq_output_0" -> "47 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"45 Transpose_1531/fq_output_0" -> "48 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"45 Transpose_1531/fq_output_0" -> "49 group_route_19/split" [label="[1, 256, 26, 26]", style=solid]; +"46 Multiply_3859" -> "50 Transpose_1457" [label="[1, 64, 52, 52]", style=solid]; +"47 Transpose_1467" -> "51 Multiply_3873" [label="[1, 128, 52, 52]", style=solid]; +"48 Transpose_1634" -> "52 MaxPool_579" [label="[1, 512, 26, 26]", style=solid]; +"49 group_route_19/split" -> "53 Multiply_3901" [label="[1, 128, 26, 26]", style=solid]; +"50 Transpose_1457" -> "54 Transpose_1463" [label="[1, 64, 52, 52]", style=solid]; +"51 Multiply_3873" -> "55 Transpose_1491" [label="[1, 128, 52, 52]", style=solid]; +"52 MaxPool_579" -> "56 Multiply_3943" [label="[1, 512, 13, 13]", style=solid]; +"53 Multiply_3901" -> "57 Transpose_1560" [label="[1, 128, 26, 26]", style=solid]; +"54 Transpose_1463" -> "58 Transpose_1463/fq_output_0" [label="[1, 64, 52, 52]", style=solid]; +"55 Transpose_1491" -> "59 Transpose_1497" [label="[1, 128, 52, 52]", style=solid]; +"56 Multiply_3943" -> "60 Transpose_1658" [label="[1, 512, 13, 13]", style=solid]; +"57 Transpose_1560" -> "61 Transpose_1566" [label="[1, 128, 26, 26]", style=solid]; +"58 Transpose_1463/fq_output_0" -> "47 Transpose_1467" [label="[1, 64, 52, 52]", style=solid]; +"59 Transpose_1497" -> "62 Transpose_1497/fq_output_0" [label="[1, 128, 52, 52]", style=solid]; +"60 Transpose_1658" -> "63 Transpose_1664" [label="[1, 512, 13, 13]", style=solid]; +"61 Transpose_1566" -> "64 Transpose_1566/fq_output_0" [label="[1, 128, 26, 26]", style=solid]; +"62 Transpose_1497/fq_output_0" -> "28 Transpose_1501" [label="[1, 128, 52, 52]", style=solid]; +"63 Transpose_1664" -> "65 Transpose_1664/fq_output_0" [label="[1, 512, 13, 13]", style=solid]; +"64 Transpose_1566/fq_output_0" -> "66 Multiply_3915" [label="[1, 128, 26, 26]", style=solid]; +"64 Transpose_1566/fq_output_0" -> "67 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"65 Transpose_1664/fq_output_0" -> "68 Multiply_3957" [label="[1, 512, 13, 13]", style=solid]; +"66 Multiply_3915" -> "69 Transpose_1590" [label="[1, 128, 26, 26]", style=solid]; +"67 Transpose_1600" -> "70 Multiply_3929" [label="[1, 256, 26, 26]", style=solid]; +"68 Multiply_3957" -> "71 Transpose_1688" [label="[1, 256, 13, 13]", style=solid]; +"69 Transpose_1590" -> "72 Transpose_1596" [label="[1, 128, 26, 26]", style=solid]; +"70 Multiply_3929" -> "73 Transpose_1624" [label="[1, 256, 26, 26]", style=solid]; +"71 Transpose_1688" -> "74 Transpose_1694" [label="[1, 256, 13, 13]", style=solid]; +"72 Transpose_1596" -> "75 Transpose_1596/fq_output_0" [label="[1, 128, 26, 26]", style=solid]; +"73 Transpose_1624" -> "76 Transpose_1630" [label="[1, 256, 26, 26]", style=solid]; +"74 Transpose_1694" -> "77 Transpose_1694/fq_output_0" [label="[1, 256, 13, 13]", style=solid]; +"75 Transpose_1596/fq_output_0" -> "67 Transpose_1600" [label="[1, 128, 26, 26]", style=solid]; +"76 Transpose_1630" -> "78 Transpose_1630/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; +"77 Transpose_1694/fq_output_0" -> "79 Multiply_3971" [label="[1, 256, 13, 13]", style=solid]; +"77 Transpose_1694/fq_output_0" -> "80 Multiply_3999" [label="[1, 256, 13, 13]", style=solid]; +"78 Transpose_1630/fq_output_0" -> "48 Transpose_1634" [label="[1, 256, 26, 26]", style=solid]; +"78 Transpose_1630/fq_output_0" -> "81 Transpose_1730" [label="[1, 256, 26, 26]", style=solid]; +"79 Multiply_3971" -> "82 Transpose_1718" [label="[1, 128, 13, 13]", style=solid]; +"80 Multiply_3999" -> "83 Transpose_1790" [label="[1, 512, 13, 13]", style=solid]; +"81 Transpose_1730" -> "84 Multiply_3985" [label="[1, 384, 26, 26]", style=solid]; +"82 Transpose_1718" -> "85 Transpose_1724" [label="[1, 128, 13, 13]", style=solid]; +"83 Transpose_1790" -> "86 Transpose_1796" [label="[1, 512, 13, 13]", style=solid]; +"84 Multiply_3985" -> "87 Transpose_1754" [label="[1, 256, 26, 26]", style=solid]; +"85 Transpose_1724" -> "88 Transpose_1724/fq_output_0" [label="[1, 128, 13, 13]", style=solid]; +"86 Transpose_1796" -> "89 Transpose_1796/fq_output_0" [label="[1, 512, 13, 13]", style=solid]; +"87 Transpose_1754" -> "90 Transpose_1760" [label="[1, 256, 26, 26]", style=solid]; +"88 Transpose_1724/fq_output_0" -> "91 Gather_1726" [label="[1, 128, 13, 13]", style=solid]; +"88 Transpose_1724/fq_output_0" -> "92 leaky_re_lu_17/LeakyRelu" [label="[1, 128, 13, 13]", style=solid]; +"89 Transpose_1796/fq_output_0" -> "93 Convolution_749" [label="[1, 512, 13, 13]", style=solid]; +"90 Transpose_1760" -> "94 Transpose_1760/fq_output_0" [label="[1, 256, 26, 26]", style=solid]; +"91 Gather_1726" -> "95 up_sampling2d/Shape" [label="[4]", style=dashed]; +"92 leaky_re_lu_17/LeakyRelu" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[1, 13, 13, 128]", style=solid]; +"93 Convolution_749" -> "97 Transpose_1802" [label="[1, 255, 13, 13]", style=solid]; +"94 Transpose_1760/fq_output_0" -> "98 Convolution_706" [label="[1, 256, 26, 26]", style=solid]; +"95 up_sampling2d/Shape" -> "99 up_sampling2d/strided_slice" [label="[4]", style=dashed]; +"96 up_sampling2d/resize/ResizeNearestNeighbor" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[1, 26, 26, 128]", style=solid]; +"97 Transpose_1802" -> "101 conv2d_17/BiasAdd" [label="[1, 255, 13, 13]", style=solid]; +"98 Convolution_706" -> "102 Transpose_1766" [label="[1, 255, 26, 26]", style=solid]; +"99 up_sampling2d/strided_slice" -> "103 up_sampling2d/mul" [label="[2]", style=dashed]; +"100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" -> "104 Transpose_1728" [label="[1, 26, 26, 128]", style=solid]; +"101 conv2d_17/BiasAdd" -> "105 conv2d_17/BiasAdd^0" [label="[1, 13, 13, 255]", style=solid]; +"102 Transpose_1766" -> "106 conv2d_20/BiasAdd" [label="[1, 255, 26, 26]", style=solid]; +"103 up_sampling2d/mul" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"104 Transpose_1728" -> "81 Transpose_1730" [label="[1, 128, 26, 26]", style=solid]; +"106 conv2d_20/BiasAdd" -> "107 conv2d_20/BiasAdd^0" [label="[1, 26, 26, 255]", style=solid]; +"108 Constant_1765" -> "106 conv2d_20/BiasAdd" [label="[4]", style=dashed]; +"109 Transpose_1764" -> "102 Transpose_1766" [label="[1, 255, 1, 1]", style=solid]; +"110 Convolution_706/fq_weights_1" -> "98 Convolution_706" [label="[255, 256, 1, 1]", style=solid]; +"111 Constant_5545" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"112 Constant_5544" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"113 Constant_5543" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"114 Constant_5542" -> "110 Convolution_706/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"115 Transpose_705" -> "110 Convolution_706/fq_weights_1" [label="[255, 256, 1, 1]", style=solid]; +"116 Constant_5540" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"117 Constant_5539" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"118 Constant_5538" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"119 Constant_5537" -> "94 Transpose_1760/fq_output_0" [label="[]", style=solid]; +"120 Transpose_1758" -> "90 Transpose_1760" [label="[1, 1, 1, 1]", style=solid]; +"121 Constant_3993" -> "87 Transpose_1754" [label="[1, 256, 1, 1]", style=solid]; +"122 Multiply_3985/fq_weights_1" -> "84 Multiply_3985" [label="[256, 384, 3, 3]", style=solid]; +"123 Constant_5535" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"124 Constant_5534" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"125 Constant_5533" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"126 Constant_5532" -> "122 Multiply_3985/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"127 Multiply_4118" -> "122 Multiply_3985/fq_weights_1" [label="[256, 384, 3, 3]", style=solid]; +"128 Constant_5405" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"129 Constant_5404" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"130 Constant_5403" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"131 Constant_5402" -> "78 Transpose_1630/fq_output_0" [label="[]", style=solid]; +"132 Transpose_1628" -> "76 Transpose_1630" [label="[1, 1, 1, 1]", style=solid]; +"133 Constant_3937" -> "73 Transpose_1624" [label="[1, 256, 1, 1]", style=solid]; +"134 Multiply_3929/fq_weights_1" -> "70 Multiply_3929" [label="[256, 256, 1, 1]", style=solid]; +"135 Constant_5500" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"136 Constant_5499" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"137 Constant_5498" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"138 Constant_5497" -> "134 Multiply_3929/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"139 Multiply_4094" -> "134 Multiply_3929/fq_weights_1" [label="[256, 256, 1, 1]", style=solid]; +"140 Constant_5375" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"141 Constant_5374" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"142 Constant_5373" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"143 Constant_5372" -> "64 Transpose_1566/fq_output_0" [label="[]", style=solid]; +"144 Transpose_1564" -> "61 Transpose_1566" [label="[1, 1, 1, 1]", style=solid]; +"145 Constant_3909" -> "57 Transpose_1560" [label="[1, 128, 1, 1]", style=solid]; +"146 Multiply_3901/fq_weights_1" -> "53 Multiply_3901" [label="[128, 128, 3, 3]", style=solid]; +"147 Constant_5490" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"148 Constant_5489" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"149 Constant_5488" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"150 Constant_5487" -> "146 Multiply_3901/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"151 Multiply_4082" -> "146 Multiply_3901/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"152 Constant_1532" -> "49 group_route_19/split" [label="[]", style=dashed]; +"153 Constant_5410" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"154 Constant_5409" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"155 Constant_5408" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"156 Constant_5407" -> "45 Transpose_1531/fq_output_0" [label="[]", style=solid]; +"157 Transpose_1529" -> "43 Transpose_1531" [label="[1, 1, 1, 1]", style=solid]; +"158 Constant_3895" -> "40 Transpose_1525" [label="[1, 256, 1, 1]", style=solid]; +"159 Multiply_3887/fq_weights_1" -> "36 Multiply_3887" [label="[256, 256, 3, 3]", style=solid]; +"160 Constant_5485" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"161 Constant_5484" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"162 Constant_5483" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"163 Constant_5482" -> "159 Multiply_3887/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"164 Multiply_4076" -> "159 Multiply_3887/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"165 Constant_5400" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"166 Constant_5399" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"167 Constant_5398" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"168 Constant_5397" -> "62 Transpose_1497/fq_output_0" [label="[]", style=solid]; +"169 Transpose_1495" -> "59 Transpose_1497" [label="[1, 1, 1, 1]", style=solid]; +"170 Constant_3881" -> "55 Transpose_1491" [label="[1, 128, 1, 1]", style=solid]; +"171 Multiply_3873/fq_weights_1" -> "51 Multiply_3873" [label="[128, 128, 1, 1]", style=solid]; +"172 Constant_5480" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"173 Constant_5479" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"174 Constant_5478" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"175 Constant_5477" -> "171 Multiply_3873/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"176 Multiply_4070" -> "171 Multiply_3873/fq_weights_1" [label="[128, 128, 1, 1]", style=solid]; +"177 Constant_5370" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"178 Constant_5369" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"179 Constant_5368" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"180 Constant_5367" -> "44 Transpose_1433/fq_output_0" [label="[]", style=solid]; +"181 Transpose_1431" -> "41 Transpose_1433" [label="[1, 1, 1, 1]", style=solid]; +"182 Constant_3853" -> "37 Transpose_1427" [label="[1, 64, 1, 1]", style=solid]; +"183 Multiply_3845/fq_weights_1" -> "33 Multiply_3845" [label="[64, 64, 3, 3]", style=solid]; +"184 Constant_5470" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"185 Constant_5469" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"186 Constant_5468" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"187 Constant_5467" -> "183 Multiply_3845/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"188 Multiply_4058" -> "183 Multiply_3845/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"189 Constant_1399" -> "29 group_route_11/split" [label="[]", style=dashed]; +"190 Constant_5395" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"191 Constant_5394" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"192 Constant_5393" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"193 Constant_5392" -> "25 Transpose_1398/fq_output_0" [label="[]", style=solid]; +"194 Transpose_1396" -> "23 Transpose_1398" [label="[1, 1, 1, 1]", style=solid]; +"195 Constant_3839" -> "21 Transpose_1392" [label="[1, 128, 1, 1]", style=solid]; +"196 Multiply_3831/fq_weights_1" -> "19 Multiply_3831" [label="[128, 128, 3, 3]", style=solid]; +"197 Constant_5465" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"198 Constant_5464" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"199 Constant_5463" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"200 Constant_5462" -> "196 Multiply_3831/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"201 Multiply_4052" -> "196 Multiply_3831/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"202 Constant_5390" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"203 Constant_5389" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"204 Constant_5388" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"205 Constant_5387" -> "42 Transpose_1364/fq_output_0" [label="[]", style=solid]; +"206 Transpose_1362" -> "39 Transpose_1364" [label="[1, 1, 1, 1]", style=solid]; +"207 Constant_3825" -> "35 Transpose_1358" [label="[1, 64, 1, 1]", style=solid]; +"208 Multiply_3817/fq_weights_1" -> "31 Multiply_3817" [label="[64, 64, 1, 1]", style=solid]; +"209 Constant_5460" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"210 Constant_5459" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"211 Constant_5458" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"212 Constant_5457" -> "208 Multiply_3817/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"213 Multiply_4046" -> "208 Multiply_3817/fq_weights_1" [label="[64, 64, 1, 1]", style=solid]; +"214 Constant_5355" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"215 Constant_5354" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"216 Constant_5353" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"217 Constant_5352" -> "24 Transpose_1300/fq_output_0" [label="[]", style=solid]; +"218 Transpose_1298" -> "22 Transpose_1300" [label="[1, 1, 1, 1]", style=solid]; +"219 Constant_3797" -> "20 Transpose_1294" [label="[1, 32, 1, 1]", style=solid]; +"220 Multiply_3789/fq_weights_1" -> "18 Multiply_3789" [label="[32, 32, 3, 3]", style=solid]; +"221 Constant_5450" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"222 Constant_5449" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"223 Constant_5448" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"224 Constant_5447" -> "220 Multiply_3789/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"225 Multiply_4034" -> "220 Multiply_3789/fq_weights_1" [label="[32, 32, 3, 3]", style=solid]; +"226 Constant_1266" -> "16 group_route_3/split" [label="[]", style=dashed]; +"227 Constant_5385" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"228 Constant_5384" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"229 Constant_5383" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"230 Constant_5382" -> "14 Transpose_1265/fq_output_0" [label="[]", style=solid]; +"231 Transpose_1263" -> "13 Transpose_1265" [label="[1, 1, 1, 1]", style=solid]; +"232 Constant_3783" -> "12 Transpose_1259" [label="[1, 64, 1, 1]", style=solid]; +"233 Multiply_3775/fq_weights_1" -> "11 Multiply_3775" [label="[64, 64, 3, 3]", style=solid]; +"234 Constant_5445" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"235 Constant_5444" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"236 Constant_5443" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"237 Constant_5442" -> "233 Multiply_3775/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"238 Multiply_4028" -> "233 Multiply_3775/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"239 Constant_5440" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"240 Constant_5439" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"241 Constant_5438" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"242 Constant_5437" -> "10 Transpose_1235/fq_output_0" [label="[]", style=solid]; +"243 Transpose_1233" -> "9 Transpose_1235" [label="[1, 1, 1, 1]", style=solid]; +"244 Constant_3769" -> "8 Transpose_1229" [label="[1, 64, 1, 1]", style=solid]; +"245 Multiply_3761/fq_weights_1" -> "7 Multiply_3761" [label="[64, 32, 3, 3]", style=solid]; +"246 Constant_5435" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"247 Constant_5434" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"248 Constant_5433" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"249 Constant_5432" -> "245 Multiply_3761/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"250 Multiply_4022" -> "245 Multiply_3761/fq_weights_1" [label="[64, 32, 3, 3]", style=solid]; +"251 Constant_5430" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"252 Constant_5429" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"253 Constant_5428" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"254 Constant_5427" -> "6 Transpose_1178/fq_output_0" [label="[]", style=solid]; +"255 Transpose_1176" -> "5 Transpose_1178" [label="[1, 1, 1, 1]", style=solid]; +"256 Constant_3755" -> "4 Transpose_1172" [label="[1, 32, 1, 1]", style=solid]; +"257 Multiply_3747/fq_weights_1" -> "3 Multiply_3747" [label="[32, 3, 3, 3]", style=solid]; +"258 Constant_5425" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"259 Constant_5424" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"260 Constant_5423" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"261 Constant_5422" -> "257 Multiply_3747/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"262 Gather_4293" -> "257 Multiply_3747/fq_weights_1" [label="[32, 3, 3, 3]", style=solid]; +"263 Constant_2333" -> "2 Divide_2378" [label="[4]", style=dashed]; +"264 Constant_5420" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"265 Constant_5419" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"266 Constant_5418" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"267 Constant_5417" -> "1 image_input/fq_output_0" [label="[]", style=solid]; +"268 Constant_5360" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"269 Constant_5359" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"270 Constant_5358" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"271 Constant_5357" -> "38 Transpose_1330/fq_output_0" [label="[]", style=solid]; +"272 Transpose_1328" -> "34 Transpose_1330" [label="[1, 1, 1, 1]", style=solid]; +"273 Constant_3811" -> "30 Transpose_1324" [label="[1, 32, 1, 1]", style=solid]; +"274 Multiply_3803/fq_weights_1" -> "26 Multiply_3803" [label="[32, 32, 3, 3]", style=solid]; +"275 Constant_5455" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"276 Constant_5454" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"277 Constant_5453" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"278 Constant_5452" -> "274 Multiply_3803/fq_weights_1" [label="[32, 1, 1, 1]", style=solid]; +"279 Multiply_4040" -> "274 Multiply_3803/fq_weights_1" [label="[32, 32, 3, 3]", style=solid]; +"280 Constant_5365" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"281 Constant_5364" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"282 Constant_5363" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"283 Constant_5362" -> "58 Transpose_1463/fq_output_0" [label="[]", style=solid]; +"284 Transpose_1461" -> "54 Transpose_1463" [label="[1, 1, 1, 1]", style=solid]; +"285 Constant_3867" -> "50 Transpose_1457" [label="[1, 64, 1, 1]", style=solid]; +"286 Multiply_3859/fq_weights_1" -> "46 Multiply_3859" [label="[64, 64, 3, 3]", style=solid]; +"287 Constant_5475" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"288 Constant_5474" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"289 Constant_5473" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"290 Constant_5472" -> "286 Multiply_3859/fq_weights_1" [label="[64, 1, 1, 1]", style=solid]; +"291 Multiply_4064" -> "286 Multiply_3859/fq_weights_1" [label="[64, 64, 3, 3]", style=solid]; +"292 Constant_5380" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"293 Constant_5379" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"294 Constant_5378" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"295 Constant_5377" -> "75 Transpose_1596/fq_output_0" [label="[]", style=solid]; +"296 Transpose_1594" -> "72 Transpose_1596" [label="[1, 1, 1, 1]", style=solid]; +"297 Constant_3923" -> "69 Transpose_1590" [label="[1, 128, 1, 1]", style=solid]; +"298 Multiply_3915/fq_weights_1" -> "66 Multiply_3915" [label="[128, 128, 3, 3]", style=solid]; +"299 Constant_5495" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"300 Constant_5494" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"301 Constant_5493" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"302 Constant_5492" -> "298 Multiply_3915/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"303 Multiply_4088" -> "298 Multiply_3915/fq_weights_1" [label="[128, 128, 3, 3]", style=solid]; +"304 Constant_1727" -> "104 Transpose_1728" [label="[4]", style=dashed]; +"305 Constant_5415" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"306 Constant_5414" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"307 Constant_5413" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"308 Constant_5412" -> "100 up_sampling2d/resize/ResizeNearestNeighbor/fq_output_0" [label="[]", style=solid]; +"309 Constant_669" -> "96 up_sampling2d/resize/ResizeNearestNeighbor" [label="[2]", style=dashed]; +"310 up_sampling2d/Const" -> "103 up_sampling2d/mul" [label="[2]", style=dashed]; +"311 up_sampling2d/strided_slice/stack_2" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"312 up_sampling2d/strided_slice/stack_1" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"313 up_sampling2d/strided_slice/stack" -> "99 up_sampling2d/strided_slice" [label="[1]", style=dashed]; +"314 Constant_1725" -> "95 up_sampling2d/Shape" [label="[]", style=dashed]; +"315 Constant_1723" -> "92 leaky_re_lu_17/LeakyRelu" [label="[4]", style=dashed]; +"315 Constant_1723" -> "95 up_sampling2d/Shape" [label="[4]", style=dashed]; +"316 Constant_5530" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"317 Constant_5529" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"318 Constant_5528" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"319 Constant_5527" -> "88 Transpose_1724/fq_output_0" [label="[]", style=solid]; +"320 Transpose_1722" -> "85 Transpose_1724" [label="[1, 1, 1, 1]", style=solid]; +"321 Constant_3979" -> "82 Transpose_1718" [label="[1, 128, 1, 1]", style=solid]; +"322 Multiply_3971/fq_weights_1" -> "79 Multiply_3971" [label="[128, 256, 1, 1]", style=solid]; +"323 Constant_5525" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"324 Constant_5524" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"325 Constant_5523" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"326 Constant_5522" -> "322 Multiply_3971/fq_weights_1" [label="[128, 1, 1, 1]", style=solid]; +"327 Multiply_4112" -> "322 Multiply_3971/fq_weights_1" [label="[128, 256, 1, 1]", style=solid]; +"328 Constant_5520" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"329 Constant_5519" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"330 Constant_5518" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"331 Constant_5517" -> "77 Transpose_1694/fq_output_0" [label="[]", style=solid]; +"332 Transpose_1692" -> "74 Transpose_1694" [label="[1, 1, 1, 1]", style=solid]; +"333 Constant_3965" -> "71 Transpose_1688" [label="[1, 256, 1, 1]", style=solid]; +"334 Multiply_3957/fq_weights_1" -> "68 Multiply_3957" [label="[256, 512, 1, 1]", style=solid]; +"335 Constant_5515" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"336 Constant_5514" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"337 Constant_5513" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"338 Constant_5512" -> "334 Multiply_3957/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"339 Multiply_4106" -> "334 Multiply_3957/fq_weights_1" [label="[256, 512, 1, 1]", style=solid]; +"340 Constant_5510" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"341 Constant_5509" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"342 Constant_5508" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"343 Constant_5507" -> "65 Transpose_1664/fq_output_0" [label="[]", style=solid]; +"344 Transpose_1662" -> "63 Transpose_1664" [label="[1, 1, 1, 1]", style=solid]; +"345 Constant_3951" -> "60 Transpose_1658" [label="[1, 512, 1, 1]", style=solid]; +"346 Multiply_3943/fq_weights_1" -> "56 Multiply_3943" [label="[512, 512, 3, 3]", style=solid]; +"347 Constant_5505" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"348 Constant_5504" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"349 Constant_5503" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"350 Constant_5502" -> "346 Multiply_3943/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"351 Multiply_4100" -> "346 Multiply_3943/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"352 Constant_1801" -> "101 conv2d_17/BiasAdd" [label="[4]", style=dashed]; +"353 Transpose_1800" -> "97 Transpose_1802" [label="[1, 255, 1, 1]", style=solid]; +"354 Convolution_749/fq_weights_1" -> "93 Convolution_749" [label="[255, 512, 1, 1]", style=solid]; +"355 Constant_5560" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"356 Constant_5559" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"357 Constant_5558" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"358 Constant_5557" -> "354 Convolution_749/fq_weights_1" [label="[255, 1, 1, 1]", style=solid]; +"359 Transpose_748" -> "354 Convolution_749/fq_weights_1" [label="[255, 512, 1, 1]", style=solid]; +"360 Constant_5555" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"361 Constant_5554" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"362 Constant_5553" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"363 Constant_5552" -> "89 Transpose_1796/fq_output_0" [label="[]", style=solid]; +"364 Transpose_1794" -> "86 Transpose_1796" [label="[1, 1, 1, 1]", style=solid]; +"365 Constant_4007" -> "83 Transpose_1790" [label="[1, 512, 1, 1]", style=solid]; +"366 Multiply_3999/fq_weights_1" -> "80 Multiply_3999" [label="[512, 256, 3, 3]", style=solid]; +"367 Constant_5550" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"368 Constant_5549" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"369 Constant_5548" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"370 Constant_5547" -> "366 Multiply_3999/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"371 Multiply_4124" -> "366 Multiply_3999/fq_weights_1" [label="[512, 256, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_mixed.json new file mode 120000 index 00000000000..a955b340aa9 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ComparisonBinaryModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_performance.json new file mode 120000 index 00000000000..97b5be03de3 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ComparisonBinaryModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ComparisonBinaryModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ConvModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/ConvModel_mixed.json new file mode 120000 index 00000000000..74d5d9d7984 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ConvModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ConvModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ConvModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/ConvModel_performance.json new file mode 120000 index 00000000000..ea4f322c0cf --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ConvModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ConvModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ConvNotBiasModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/ConvNotBiasModel_mixed.json new file mode 120000 index 00000000000..b39c9911bf5 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ConvNotBiasModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ConvNotBiasModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ConvNotBiasModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/ConvNotBiasModel_performance.json new file mode 120000 index 00000000000..1d4464e7950 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ConvNotBiasModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ConvNotBiasModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/DepthwiseConvModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/DepthwiseConvModel_mixed.json new file mode 120000 index 00000000000..9e3286c856e --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/DepthwiseConvModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/DepthwiseConvModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/DepthwiseConvModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/DepthwiseConvModel_performance.json new file mode 120000 index 00000000000..c0c4b37214b --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/DepthwiseConvModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/DepthwiseConvModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/DynamicModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/DynamicModel_mixed.json new file mode 120000 index 00000000000..8fac115c5eb --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/DynamicModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/DynamicModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/DynamicModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/DynamicModel_performance.json new file mode 120000 index 00000000000..d63cee3b0f9 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/DynamicModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/DynamicModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/GroupNormalizationModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/GroupNormalizationModel_mixed.json new file mode 120000 index 00000000000..2b03e597488 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/GroupNormalizationModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/GroupNormalizationModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/GroupNormalizationModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/GroupNormalizationModel_performance.json new file mode 120000 index 00000000000..13071df1272 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/GroupNormalizationModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/GroupNormalizationModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_compressed_weights.json b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_compressed_weights.json new file mode 120000 index 00000000000..a01109496e9 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_compressed_weights.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/IntegerModel_compressed_weights.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_compressed_weights_nf4.json b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_compressed_weights_nf4.json new file mode 120000 index 00000000000..07cc378b8aa --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_compressed_weights_nf4.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/IntegerModel_compressed_weights_nf4.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_mixed.json new file mode 120000 index 00000000000..44cf05c3f1c --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/IntegerModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_performance.json new file mode 120000 index 00000000000..4e488119c1d --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/IntegerModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/IntegerModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/LSTMModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/LSTMModel_mixed.json new file mode 120000 index 00000000000..857a7419cf5 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/LSTMModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/LSTMModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/LSTMModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/LSTMModel_performance.json new file mode 120000 index 00000000000..31fdb65f457 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/LSTMModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/LSTMModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/LSTMSequenceModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/LSTMSequenceModel_mixed.json new file mode 120000 index 00000000000..77b7882bf2d --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/LSTMSequenceModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/LSTMSequenceModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/LSTMSequenceModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/LSTMSequenceModel_performance.json new file mode 120000 index 00000000000..d1f4096ea99 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/LSTMSequenceModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/LSTMSequenceModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/LinearModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/LinearModel_mixed.json new file mode 120000 index 00000000000..5b853b78982 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/LinearModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/LinearModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/LinearModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/LinearModel_performance.json new file mode 120000 index 00000000000..ecdd693648a --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/LinearModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/LinearModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/MatMul2DModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/MatMul2DModel_mixed.json new file mode 120000 index 00000000000..ec9708bb9df --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/MatMul2DModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/MatMul2DModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/MatMul2DModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/MatMul2DModel_performance.json new file mode 120000 index 00000000000..e0b20eaf1df --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/MatMul2DModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/MatMul2DModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ScaleShiftReluModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/ScaleShiftReluModel_mixed.json new file mode 120000 index 00000000000..2de91d576ac --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ScaleShiftReluModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ScaleShiftReluModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/ScaleShiftReluModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/ScaleShiftReluModel_performance.json new file mode 120000 index 00000000000..cf1ef81f04a --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/ScaleShiftReluModel_performance.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/ScaleShiftReluModel_performance.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/SeBlockModel_mixed.json b/tests/openvino/native/data/2023.2/reference_scales/SeBlockModel_mixed.json new file mode 120000 index 00000000000..371cc670eab --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/SeBlockModel_mixed.json @@ -0,0 +1 @@ +../../2023.1/reference_scales/SeBlockModel_mixed.json \ No newline at end of file diff --git a/tests/openvino/native/data/2023.2/reference_scales/SeBlockModel_performance.json b/tests/openvino/native/data/2023.2/reference_scales/SeBlockModel_performance.json new file mode 120000 index 00000000000..0acc26e9075 --- /dev/null +++ b/tests/openvino/native/data/2023.2/reference_scales/SeBlockModel_performance.json @@ -0,0 +1 @@ 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tests.openvino.native.common import get_dataset_for_test +from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import SYNTHETIC_MODELS from tests.openvino.native.models import ConvModel from tests.openvino.native.models import FPModel @@ -33,7 +34,8 @@ from tests.shared.helpers import compare_stats from tests.shared.helpers import load_json -REFERENCE_SCALES_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / "reference_scales" +OV_VERSION = get_openvino_version() +REFERENCE_SCALES_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_scales" def get_fq_nodes_stats_algo(model): diff --git a/tests/openvino/native/quantization/test_graphs.py b/tests/openvino/native/quantization/test_graphs.py index e7913870285..9073a4bb581 100644 --- a/tests/openvino/native/quantization/test_graphs.py +++ b/tests/openvino/native/quantization/test_graphs.py @@ -28,6 +28,7 @@ from tests.openvino.native.common import compare_nncf_graphs from tests.openvino.native.common import dump_model from tests.openvino.native.common import get_dataset_for_test +from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import SYNTHETIC_MODELS from tests.openvino.native.models import DepthwiseConv3DModel from tests.openvino.native.models import DepthwiseConv4DModel @@ -39,7 +40,8 @@ from tests.openvino.omz_helpers import convert_model from tests.openvino.omz_helpers import download_model -QUANTIZED_REF_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / "reference_graphs" / "quantized" +OV_VERSION = get_openvino_version() +QUANTIZED_REF_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_graphs" / "quantized" @pytest.mark.parametrize("model_creator_func", SYNTHETIC_MODELS.values()) diff --git a/tests/openvino/native/quantization/test_weights_compression.py b/tests/openvino/native/quantization/test_weights_compression.py index 52d1d32229a..766561efe11 100644 --- a/tests/openvino/native/quantization/test_weights_compression.py +++ b/tests/openvino/native/quantization/test_weights_compression.py @@ -19,10 +19,10 @@ from nncf import CompressWeightsMode from nncf.openvino.graph.node_utils import get_const_value -from nncf.openvino.quantization.weights_compression import _calculate_scale_per_group -from nncf.openvino.quantization.weights_compression import _get_int8_err -from nncf.openvino.quantization.weights_compression import _get_nf4_error from nncf.quantization import compress_weights +from nncf.quantization.algorithms.weight_compression.openvino_backend import _calculate_scale_per_group +from nncf.quantization.algorithms.weight_compression.openvino_backend import _get_int8_err +from nncf.quantization.algorithms.weight_compression.openvino_backend import _get_nf4_error from tests.openvino.native.models import IntegerModel from tests.openvino.native.models import SequentialMatmulModel from tests.openvino.native.models import WeightsModel @@ -345,10 +345,10 @@ def test_raise_error_with_incorrect_group_size(): def test_raise_error_with_int8_and_non_default_ratio(mocker): - with pytest.raises(RuntimeError): + with pytest.raises(AttributeError): compress_weights(mocker.Mock(), mode=CompressWeightsMode.INT8, ratio=0.5) def test_raise_error_with_int8_and_non_default_group_size(mocker): - with pytest.raises(RuntimeError): + with pytest.raises(AttributeError): compress_weights(mocker.Mock(), mode=CompressWeightsMode.INT8, group_size=64) diff --git a/tests/openvino/native/test_bias_correction.py b/tests/openvino/native/test_bias_correction.py index 6b42f11d4e2..e539b26faf7 100644 --- a/tests/openvino/native/test_bias_correction.py +++ b/tests/openvino/native/test_bias_correction.py @@ -24,8 +24,11 @@ from nncf.quantization.algorithms.bias_correction.openvino_backend import OVBiasCorrectionAlgoBackend from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs +from tests.openvino.native.common import get_openvino_version from tests.post_training.test_templates.test_bias_correction import TemplateTestBCAlgorithm +OV_VERSION = get_openvino_version() + class TestOVBCAlgorithm(TemplateTestBCAlgorithm): @staticmethod @@ -67,7 +70,15 @@ def remove_fq_from_inputs(model: ov.Model) -> ov.Model: @staticmethod def get_ref_path(suffix: str) -> str: - return OPENVINO_NATIVE_TEST_ROOT / "data" / "reference_graphs" / "quantized" / "subgraphs" / f"{suffix}.dot" + return ( + OPENVINO_NATIVE_TEST_ROOT + / "data" + / OV_VERSION + / "reference_graphs" + / "quantized" + / "subgraphs" + / f"{suffix}.dot" + ) @staticmethod def compare_nncf_graphs(model: ov.Model, ref_path: str) -> None: diff --git a/tests/openvino/native/test_model_transformer.py b/tests/openvino/native/test_model_transformer.py index 608e98a5e26..febcb0a3036 100644 --- a/tests/openvino/native/test_model_transformer.py +++ b/tests/openvino/native/test_model_transformer.py @@ -39,6 +39,7 @@ from nncf.quantization.fake_quantize import FakeQuantizeParameters from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs +from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import ConvModel from tests.openvino.native.models import ConvNotBiasModel from tests.openvino.native.models import FPModel @@ -48,7 +49,8 @@ from tests.openvino.native.models import WeightsModel from tests.openvino.native.models import ZeroRankEltwiseModel -REFERENCE_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / "reference_graphs" / "original_nncf_graph" +OV_VERSION = get_openvino_version() +REFERENCE_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_graphs" / "original_nncf_graph" TARGET_INSERT_LAYERS = [["Add"], ["MatMul"], ["Add", "MatMul"]] TARGET_PRE_LAYER_FQS = [["Add/fq_input_0"], ["MatMul/fq_input_0"], ["Add/fq_input_0", "MatMul/fq_input_0"]] diff --git a/tests/openvino/native/test_nncf_graph_builder.py b/tests/openvino/native/test_nncf_graph_builder.py index 03ea07e9cc6..8b21431aa67 100644 --- a/tests/openvino/native/test_nncf_graph_builder.py +++ b/tests/openvino/native/test_nncf_graph_builder.py @@ -17,12 +17,14 @@ from nncf.openvino.graph.nncf_graph_builder import GraphConverter from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT from tests.openvino.native.common import compare_nncf_graphs +from tests.openvino.native.common import get_openvino_version from tests.openvino.native.models import SYNTHETIC_MODELS from tests.openvino.native.models import ParallelEdgesModel from tests.openvino.omz_helpers import convert_model from tests.openvino.omz_helpers import download_model -REFERENCE_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / "reference_graphs" / "original_nncf_graph" +OV_VERSION = get_openvino_version() +REFERENCE_GRAPHS_DIR = OPENVINO_NATIVE_TEST_ROOT / "data" / OV_VERSION / "reference_graphs" / "original_nncf_graph" @pytest.mark.parametrize("model_cls_to_test", SYNTHETIC_MODELS.values()) diff --git a/tests/openvino/tools/calibrate.py b/tests/openvino/tools/calibrate.py index 6ca576b0052..59e793ac1e3 100644 --- a/tests/openvino/tools/calibrate.py +++ b/tests/openvino/tools/calibrate.py @@ -125,15 +125,34 @@ class ACValidationFunction: "ndcg": "sigmoid_recom_loss", } - def __init__(self, model_evaluator: ModelEvaluator, metric_name: str, requests_number: Optional[int] = None): + SPECIAL_METRICS = [ + "cmc", + "reid_map", + "pairwise_accuracy_subsets", + "pairwise_accuracy", + "normalized_embedding_accuracy", + "face_recognition_tafa_pair_metric", + "localization_recall", + "coco_orig_keypoints_precision", + "coco_orig_segm_precision", + "coco_orig_keypoints_precision", + "spearman_correlation_coef", + "pearson_correlation_coef", + ] + + def __init__( + self, model_evaluator: ModelEvaluator, metric_name: str, metric_type: str, requests_number: Optional[int] = None + ): """ :param model_evaluator: Model Evaluator. :param metric_name: Name of a metric. + :param metric_type: Type of a metric. :param requests_number: A number of infer requests. If it is `None`, the count will be selected automatically. """ self._model_evaluator = model_evaluator self._metric_name = metric_name + self._metric_type = metric_type self._persample_metric_name = self.METRIC_TO_PERSAMPLE_METRIC.get(self._metric_name, self._metric_name) registered_metrics = model_evaluator.get_metrics_attributes() if self._persample_metric_name not in registered_metrics: @@ -141,6 +160,8 @@ def __init__(self, model_evaluator: ModelEvaluator, metric_name: str, requests_n self._requests_number = requests_number self._values_for_each_item = [] + self._collect_outputs = self._metric_type in self.SPECIAL_METRICS + def __call__(self, compiled_model: ov.CompiledModel, indices: Optional[Iterable[int]] = None) -> float: """ Calculates metrics for the provided model. @@ -203,6 +224,11 @@ def _output_callback(self, raw_predictions, **kwargs): return for sample_id, results in metrics_result.items(): + if self._collect_outputs: + output = list(raw_predictions.values())[0] + self._values_for_each_item.append({"sample_id": sample_id, "metric_value": output}) + continue + for metric_result in results: if metric_result.metric_name != self._persample_metric_name: continue @@ -940,10 +966,11 @@ def quantize_model_with_accuracy_control( ) model_evaluator.load_network([{"model": ov_model}]) + metric_type = accuracy_checker_config["models"][0]["datasets"][0]["metrics"][0]["type"] metric_name = accuracy_checker_config["models"][0]["datasets"][0]["metrics"][0].get("name", None) if metric_name is None: - metric_name = accuracy_checker_config["models"][0]["datasets"][0]["metrics"][0]["type"] - validation_fn = ACValidationFunction(model_evaluator, metric_name) + metric_name = metric_type + validation_fn = ACValidationFunction(model_evaluator, metric_name, metric_type) name_to_quantization_impl_map = { "pot": pot_quantize_with_native_accuracy_control, diff --git a/tests/post_training/pipelines/base.py b/tests/post_training/pipelines/base.py index 9984a4df18a..0306fc9594e 100644 --- a/tests/post_training/pipelines/base.py +++ b/tests/post_training/pipelines/base.py @@ -279,6 +279,19 @@ def run(self) -> None: self.save_quantized_model() self.get_num_fq() self.validate() + self.cleanup_torchscript_cache() + + @staticmethod + def cleanup_torchscript_cache(): + """ + Helper for removing cached model representation. + + After run torch.jit.trace in convert_model, PyTorch does not clear the trace cache automatically. + """ + # pylint: disable=protected-access + torch._C._jit_clear_class_registry() + torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore() + torch.jit._state._clear_class_state() def get_run_info(self) -> RunInfo: return self.run_info diff --git a/tests/post_training/test_templates/models.py b/tests/post_training/test_templates/models.py index 546a4104318..86d258066b4 100644 --- a/tests/post_training/test_templates/models.py +++ b/tests/post_training/test_templates/models.py @@ -144,7 +144,7 @@ def __init__( # Conv_2 # | # Output_1 - if use_one_layer_attrs and not conv_layer_attrs is None and conv_2_layer_attrs is None: + if use_one_layer_attrs and conv_layer_attrs is not None and conv_2_layer_attrs is None: conv_2_layer_attrs = conv_layer_attrs nodes = [ NodeWithType("Input_1", InputNoopMetatype), diff --git a/tests/post_training/test_templates/test_calculate_quantizer_parameters.py b/tests/post_training/test_templates/test_calculate_quantizer_parameters.py index bffe249d064..ebcc5df7e75 100644 --- a/tests/post_training/test_templates/test_calculate_quantizer_parameters.py +++ b/tests/post_training/test_templates/test_calculate_quantizer_parameters.py @@ -19,6 +19,7 @@ from nncf.common.quantization.structs import QuantizationMode from nncf.common.quantization.structs import QuantizerConfig from nncf.common.quantization.structs import QuantizerGroup +from nncf.experimental.tensor import functions as fns from nncf.quantization.fake_quantize import FakeQuantizeParameters from nncf.quantization.fake_quantize import calculate_quantizer_parameters from tests.post_training.conftest import FQ_CALCULATED_PARAMETERS_PATH @@ -32,10 +33,10 @@ def compare_fq_parameters(ref_params, params): assert ref_params.input_high.shape == params.input_high.shape assert ref_params.output_low.shape == params.output_low.shape assert ref_params.output_high.shape == params.output_high.shape - assert np.allclose(ref_params.input_low, params.input_low) - assert np.allclose(ref_params.input_high, params.input_high) - assert np.allclose(ref_params.output_low, params.output_low) - assert np.allclose(ref_params.output_high, params.output_high) + assert fns.allclose(ref_params.input_low, params.input_low) + assert fns.allclose(ref_params.input_high, params.input_high) + assert fns.allclose(ref_params.output_low, params.output_low) + assert fns.allclose(ref_params.output_high, params.output_high) def get_test_reference_key(q_group, q_config, narrow_range, hf_range): diff --git a/tests/post_training/test_templates/test_channel_alignment.py b/tests/post_training/test_templates/test_channel_alignment.py index 21f6cfdeaaf..0ad51ff76c7 100644 --- a/tests/post_training/test_templates/test_channel_alignment.py +++ b/tests/post_training/test_templates/test_channel_alignment.py @@ -240,9 +240,9 @@ def check_updated_values(updated_conv_in, updated_conv_out, updated_bias_in): def test_get_node_pairs(self, first_conv_attrs, second_conv_attrs, ref_match): algorithm = ChannelAlignment() algorithm._backend_entity = self.get_backend_cls() - if not first_conv_attrs is None: + if first_conv_attrs is not None: first_conv_attrs = self.convert_conv_layer_attrs(first_conv_attrs) - if not second_conv_attrs is None: + if second_conv_attrs is not None: second_conv_attrs = self.convert_conv_layer_attrs(second_conv_attrs) nncf_graph = NNCFGraphCA( self.get_conv_metatype(), diff --git a/tests/post_training/test_templates/test_fast_bias_correction.py b/tests/post_training/test_templates/test_fast_bias_correction.py index b972ce851cd..c4ea71d6551 100644 --- a/tests/post_training/test_templates/test_fast_bias_correction.py +++ b/tests/post_training/test_templates/test_fast_bias_correction.py @@ -67,7 +67,7 @@ def test_reshape_bias_shift(self, bias_value: list, bias_shift: list, channel_ax algo = FastBiasCorrection(subset_size=1, inplace_statistics=False) # pylint: disable=protected-access algo._backend_entity = self.get_backend() - new_bias_shift = algo.reshape_bias_shift(bias_shift, bias_value, channel_axis) + new_bias_shift = algo._reshape_bias_shift(bias_shift, bias_value, channel_axis) assert list(new_bias_shift.shape) == ref_shape @staticmethod diff --git a/tests/shared/__init__.py b/tests/shared/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/shared/__init__.py +++ b/tests/shared/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/shared/nx_graph.py b/tests/shared/nx_graph.py index 518627f7a10..1387a90207a 100644 --- a/tests/shared/nx_graph.py +++ b/tests/shared/nx_graph.py @@ -98,7 +98,7 @@ def graph_key(line: str) -> LineOrder: def _build_node_id_vs_attrs_dict( nx_graph: nx.DiGraph, id_from_attr: bool = False ) -> Dict[Union[int, str], Dict[str, str]]: - retval = {} # type: Dict[Union[int, str], Dict[str, str]] + retval: Dict[Union[int, str], Dict[str, str]] = {} for node_name, node_attrs in nx_graph.nodes.items(): # When read a dot graph dumped by pydot the extra '\n' symbol appears as a graph node. # https://github.com/networkx/networkx/issues/5686 diff --git a/tests/shared/test_templates/template_test_nncf_tensor.py b/tests/shared/test_templates/template_test_nncf_tensor.py index 9fff5e9de1c..461deb14fce 100644 --- a/tests/shared/test_templates/template_test_nncf_tensor.py +++ b/tests/shared/test_templates/template_test_nncf_tensor.py @@ -17,10 +17,11 @@ import pytest +from nncf.experimental.common.tensor_statistics import statistical_functions as s_fns from nncf.experimental.tensor import Tensor from nncf.experimental.tensor import TensorDataType from nncf.experimental.tensor import TensorDeviceType -from nncf.experimental.tensor import functions +from nncf.experimental.tensor import functions as fns TModel = TypeVar("TModel") TTensor = TypeVar("TTensor") @@ -68,6 +69,7 @@ def test_operators_tensor(self, op_name): assert res.dtype == res_nncf.data.dtype assert all(res == res_nncf.data) assert isinstance(res_nncf, Tensor) + assert res_nncf.device == nncf_tensor_a.device @pytest.mark.parametrize("op_name", OPERATOR_MAP.keys()) def test_operators_int(self, op_name): @@ -83,6 +85,7 @@ def test_operators_int(self, op_name): assert res.dtype == res_nncf.data.dtype assert all(res == res_nncf.data) assert isinstance(res_nncf, Tensor) + assert res_nncf.device == nncf_tensor_a.device @pytest.mark.parametrize("op_name", ("add", "sub", "mul", "truediv", "floordiv")) def test_operators_int_rev(self, op_name): @@ -98,6 +101,7 @@ def test_operators_int_rev(self, op_name): assert res.dtype == res_nncf.data.dtype assert all(res == res_nncf.data) assert isinstance(res_nncf, Tensor) + assert res_nncf.device == nncf_tensor_a.device @pytest.mark.parametrize("op_name", COMPARISON_OPERATOR_MAP.keys()) def test_comparison_tensor(self, op_name): @@ -150,6 +154,7 @@ def test_comparison_int_rev(self, op_name): ([[[[1], [2]], [[1], [2]]]], None, [[1, 2], [1, 2]]), ([[[[1], [2]], [[1], [2]]]], 0, [[[1], [2]], [[1], [2]]]), ([[[[1], [2]], [[1], [2]]]], -1, [[[1, 2], [1, 2]]]), + ([[[[1], [2]], [[1], [2]]]], (0, 3), [[1, 2], [1, 2]]), ), ) def test_squeeze(self, val, axis, ref): @@ -157,11 +162,23 @@ def test_squeeze(self, val, axis, ref): nncf_tensor = Tensor(tensor) ref_tensor = self.to_tensor(ref) res = nncf_tensor.squeeze(axis=axis) - if isinstance(ref, list): - assert functions.all(res == ref_tensor) - else: - assert res == ref_tensor assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor) + assert res.device == nncf_tensor.device + + @pytest.mark.parametrize( + "val, axis, exception_type, exception_match", + ( + ([[[[1], [2]], [[1], [2]]]], (0, 1), ValueError, "not equal to one"), + ([[[[1], [2]], [[1], [2]]]], 42, IndexError, "out of"), + ([[[[1], [2]], [[1], [2]]]], (0, 42), IndexError, "out of"), + ), + ) + def test_squeeze_axis_error(self, val, axis, exception_type, exception_match): + tensor = self.to_tensor(val) + nncf_tensor = Tensor(tensor) + with pytest.raises(exception_type, match=exception_match): + nncf_tensor.squeeze(axis=axis) @pytest.mark.parametrize( "val, axis, ref", @@ -177,12 +194,10 @@ def test_fn_squeeze(self, val, axis, ref): tensor = self.to_tensor(val) nncf_tensor = Tensor(tensor) ref_tensor = self.to_tensor(ref) - res = functions.squeeze(nncf_tensor, axis=axis) - if isinstance(ref, list): - assert functions.all(res == ref_tensor) - else: - assert res == ref_tensor + res = fns.squeeze(nncf_tensor, axis=axis) assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor) + assert res.device == nncf_tensor.device @pytest.mark.parametrize( "val,ref", @@ -197,31 +212,9 @@ def test_flatten(self, val, ref): nncf_tensor = Tensor(tensor) ref_tensor = self.to_tensor(ref) res = nncf_tensor.flatten() - if isinstance(ref, list): - assert all(res.data == ref_tensor) - else: - assert res.data == ref_tensor - assert isinstance(res, Tensor) - - @pytest.mark.parametrize( - "val, axis, ref", - ( - (1, None, 1), - ([1], None, 1), - ([[[[1], [2]], [[3], [4]]]], None, 4), - ([[1, 2], [3, 4]], 1, [2, 4]), - ), - ) - def test_max(self, val, axis, ref): - tensor = self.to_tensor(val) - nncf_tensor = Tensor(tensor) - ref_tensor = self.to_tensor(ref) - res = nncf_tensor.max(axis=axis) - if isinstance(ref, list): - assert all(res.data == ref_tensor) - else: - assert res.data == ref_tensor assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor) + assert res.device == nncf_tensor.device @pytest.mark.parametrize( "val, axis, ref", @@ -236,12 +229,10 @@ def test_fn_max(self, val, axis, ref): tensor = self.to_tensor(val) nncf_tensor = Tensor(tensor) ref_tensor = self.to_tensor(ref) - res = functions.max(nncf_tensor, axis=axis) - if isinstance(ref, list): - assert all(res.data == ref_tensor) - else: - assert res.data == ref_tensor + res = fns.max(nncf_tensor, axis=axis) assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor) + assert res.device == nncf_tensor.device @pytest.mark.parametrize( "val, axis, ref", @@ -256,30 +247,9 @@ def test_min(self, val, axis, ref): nncf_tensor = Tensor(self.to_tensor(val)) ref_tensor = self.to_tensor(ref) res = nncf_tensor.min(axis=axis) - if isinstance(ref, list): - assert all(res.data == ref_tensor) - else: - assert res.data == ref_tensor - assert isinstance(res, Tensor) - - @pytest.mark.parametrize( - "val, axis, ref", - ( - (1, None, 1), - ([1], None, 1), - ([[[[1], [2]], [[3], [4]]]], None, 1), - ([[1, 2], [3, 4]], 1, [1, 3]), - ), - ) - def test_fn_min(self, val, axis, ref): - nncf_tensor = Tensor(self.to_tensor(val)) - ref_tensor = self.to_tensor(ref) - res = functions.min(nncf_tensor, axis=axis) - if isinstance(ref, list): - assert all(res.data == ref_tensor) - else: - assert res.data == ref_tensor assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor) + assert res.device == nncf_tensor.device @pytest.mark.parametrize( "val, ref", @@ -292,11 +262,9 @@ def test_abs(self, val, ref): nncf_tensor = Tensor(self.to_tensor(val)) nncf_ref_tensor = Tensor(self.to_tensor(ref)) res = nncf_tensor.abs() - if isinstance(ref, list): - assert all(res == nncf_ref_tensor) - else: - assert res == nncf_ref_tensor assert isinstance(res, Tensor) + assert fns.allclose(res, nncf_ref_tensor) + assert res.device == nncf_tensor.device @pytest.mark.parametrize( "val, ref", @@ -308,12 +276,10 @@ def test_abs(self, val, ref): def test_fn_abs(self, val, ref): nncf_tensor = Tensor(self.to_tensor(val)) nncf_ref_tensor = Tensor(self.to_tensor(ref)) - res = functions.abs(nncf_tensor) - if isinstance(ref, list): - assert all(res == nncf_ref_tensor) - else: - assert res == nncf_ref_tensor + res = fns.abs(nncf_tensor) assert isinstance(res, Tensor) + assert fns.allclose(res, nncf_ref_tensor) + assert res.device == nncf_tensor.device def test_getitem(self): arr = [0, 1, 2] @@ -321,6 +287,7 @@ def test_getitem(self): res = nncf_tensor[1] assert res == 1 assert isinstance(res, Tensor) + assert res.device == nncf_tensor.device def test_iter(self): arr = [0, 1, 2] @@ -341,67 +308,72 @@ def test_iter(self): ), ) def test_fn_count_nonzero(self, axis, ref): - tensor = self.to_tensor([[1, 2], [1, 0]]) + tensor = self.to_tensor([[1.0, 2.0], [1.0, 0.0]]) nncf_tensor = Tensor(tensor) ref_tensor = self.to_tensor(ref) - res = functions.count_nonzero(nncf_tensor, axis=axis) - if axis is None: - assert res.data == ref_tensor - else: - assert all(res.data == self.to_tensor(ref)) + res = fns.count_nonzero(nncf_tensor, axis=axis) + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == nncf_tensor.device def test_fn_zeros_like(self): tensor = self.to_tensor([1, 2]) nncf_tensor = Tensor(tensor) - res = functions.zeros_like(nncf_tensor) + res = fns.zeros_like(nncf_tensor) assert all(res == Tensor(tensor * 0)) assert isinstance(res, Tensor) + assert res.device == nncf_tensor.device def test_fn_maximum(self): tensor_a = Tensor(self.to_tensor([1, 2])) tensor_b = Tensor(self.to_tensor([2, 1])) tensor_ref = self.to_tensor([2, 2]) - res = functions.maximum(tensor_a, tensor_b) + res = fns.maximum(tensor_a, tensor_b) assert all(res.data == tensor_ref) assert isinstance(res, Tensor) + assert res.device == tensor_a.device def test_fn_maximum_list(self): tensor_a = Tensor(self.to_tensor([1, 2])) tensor_b = [2, 1] tensor_ref = self.to_tensor([2, 2]) - res = functions.maximum(tensor_a, tensor_b) + res = fns.maximum(tensor_a, tensor_b) assert all(res.data == tensor_ref) assert isinstance(res, Tensor) + assert res.device == tensor_a.device def test_fn_minimum(self): tensor_a = Tensor(self.to_tensor([1, 2])) tensor_b = Tensor(self.to_tensor([2, 1])) tensor_ref = self.to_tensor([1, 1]) - res = functions.minimum(tensor_a, tensor_b) + res = fns.minimum(tensor_a, tensor_b) assert all(res.data == tensor_ref) assert isinstance(res, Tensor) + assert res.device == tensor_a.device def test_fn_minimum_list(self): tensor_a = Tensor(self.to_tensor([1, 2])) tensor_b = [2, 1] tensor_ref = self.to_tensor([1, 1]) - res = functions.minimum(tensor_a, tensor_b) + res = fns.minimum(tensor_a, tensor_b) assert all(res.data == tensor_ref) assert isinstance(res, Tensor) + assert res.device == tensor_a.device def test_fn_ones_like(self): tensor_a = Tensor(self.to_tensor([1, 2])) tensor_ref = self.to_tensor([1, 1]) - res = functions.ones_like(tensor_a) + res = fns.ones_like(tensor_a) assert all(res.data == tensor_ref) assert isinstance(res, Tensor) + assert res.device == tensor_a.device @pytest.mark.parametrize( "val, axis, ref", @@ -414,12 +386,10 @@ def test_fn_ones_like(self): ) def test_fn_all(self, val, axis, ref): tensor = Tensor(self.to_tensor(val)) - res = functions.all(tensor, axis=axis) - if isinstance(ref, list): - assert all(res.data == self.to_tensor(ref)) - else: - assert res.data == self.to_tensor(ref) + res = fns.all(tensor, axis=axis) assert isinstance(res, Tensor) + assert fns.allclose(res.data, self.to_tensor(ref)) + assert res.device == tensor.device @pytest.mark.parametrize( "val, axis, ref", @@ -432,19 +402,19 @@ def test_fn_all(self, val, axis, ref): ) def test_fn_any(self, val, axis, ref): tensor = Tensor(self.to_tensor(val)) - res = functions.any(tensor, axis=axis) - if isinstance(ref, list): - assert all(res.data == self.to_tensor(ref)) - else: - assert res == ref + res = fns.any(tensor, axis=axis) + assert isinstance(res, Tensor) + assert fns.allclose(res.data, self.to_tensor(ref)) + assert res.device == tensor.device def test_fn_where(self): tensor = Tensor(self.to_tensor([1, -1])) tensor_ref = self.to_tensor([1, 0]) - res = functions.where(tensor > 0, 1, 0) + res = fns.where(tensor > 0, 1, 0) assert all(res.data == tensor_ref) assert isinstance(res, Tensor) + assert res.device == tensor.device @pytest.mark.parametrize( "val, ref", @@ -456,9 +426,9 @@ def test_fn_where(self): ) def test_fn_isempty(self, val, ref): tensor = Tensor(self.to_tensor(val)) - res = functions.isempty(tensor) + res = fns.isempty(tensor) assert res == ref - assert isinstance(res, Tensor) + assert isinstance(res, bool) @pytest.mark.parametrize( "val, ref", @@ -472,7 +442,7 @@ def test_isempty(self, val, ref): tensor = Tensor(self.to_tensor(val)) res = tensor.isempty() assert res == ref - assert isinstance(res, Tensor) + assert isinstance(res, bool) @pytest.mark.parametrize( "x1, x2, rtol, atol, ref", @@ -482,19 +452,19 @@ def test_isempty(self, val, ref): ([0.1], [0.10001], 0.1, None, True), ([0.1], [0.10001], None, 0.1, True), ([0.1], [0.20001], None, 0.1, False), + ([0.1], 0.1, None, None, True), ), ) def test_fn_allclose(self, x1, x2, rtol, atol, ref): tensor1 = Tensor(self.to_tensor(x1)) tensor2 = Tensor(self.to_tensor(x2)) if rtol is not None: - res = functions.allclose(tensor1, tensor2, rtol=rtol) + res = fns.allclose(tensor1, tensor2, rtol=rtol) elif atol is not None: - res = functions.allclose(tensor1, tensor2, atol=atol) + res = fns.allclose(tensor1, tensor2, atol=atol) else: - res = functions.allclose(tensor1, tensor2) + res = fns.allclose(tensor1, tensor2) assert res == ref - assert isinstance(res, Tensor) @pytest.mark.parametrize( "x1, x2, rtol, atol, ref", @@ -503,17 +473,18 @@ def test_fn_allclose(self, x1, x2, rtol, atol, ref): ([0.1], [0.10001], None, None, [False]), ([0.1], [0.10001], 0.1, None, [True]), ([0.1], [0.10001], None, 0.1, [True]), + ([0.1], 0.1, None, None, [True]), ), ) def test_fn_isclose(self, x1, x2, rtol, atol, ref): tensor1 = Tensor(self.to_tensor(x1)) tensor2 = Tensor(self.to_tensor(x2)) if rtol is not None: - res = functions.isclose(tensor1, tensor2, rtol=rtol) + res = fns.isclose(tensor1, tensor2, rtol=rtol) elif atol is not None: - res = functions.isclose(tensor1, tensor2, atol=atol) + res = fns.isclose(tensor1, tensor2, atol=atol) else: - res = functions.isclose(tensor1, tensor2) + res = fns.isclose(tensor1, tensor2) assert all(res == self.to_tensor(ref)) assert isinstance(res, Tensor) @@ -526,23 +497,202 @@ def test_astype(self): res = tensor.astype(TensorDataType.int8) assert isinstance(res, Tensor) assert res.dtype == TensorDataType.int8 + assert res.device == tensor.device def test_fn_astype(self): tensor = Tensor(self.to_tensor([1])) - res = functions.astype(tensor, TensorDataType.int8) + res = fns.astype(tensor, TensorDataType.int8) assert isinstance(res, Tensor) assert res.dtype == TensorDataType.int8 def test_reshape(self): tensor = Tensor(self.to_tensor([1, 1])) - assert tensor.shape == [2] - assert tensor.reshape([1, 2]).shape == [1, 2] + res = tensor.reshape((1, 2)) + assert tensor.shape == (2,) + assert res.shape == (1, 2) + assert res.device == tensor.device def test_fn_reshape(self): tensor = Tensor(self.to_tensor([1, 1])) - assert tensor.shape == [2] - assert functions.reshape(tensor, [1, 2]).shape == [1, 2] + res = fns.reshape(tensor, (1, 2)) + assert tensor.shape == (2,) + assert res.shape == (1, 2) + assert res.device == tensor.device def test_not_implemented(self): with pytest.raises(NotImplementedError, match="is not implemented for"): - functions.device({}, [1, 2]) + fns.device({}, [1, 2]) + + @pytest.mark.parametrize( + "x, axis, ref", + ( + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + 0, + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + ), + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + 1, + [[0.8, 0.1], [0.2, 0.7], [0.2, 0.1]], + ), + ), + ) + def test_fn_unstack(self, x, axis, ref): + tensor = Tensor(self.to_tensor(x)) + ref = [self.to_tensor(r) for r in ref] + + res = fns.unstack(tensor, axis=axis) + + assert isinstance(res, list) + for i, _ in enumerate(ref): + assert all(res[i] == ref[i]) + assert res[i].device == tensor.device + + @pytest.mark.parametrize( + "x, axis, ref", + ( + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + 0, + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + ), + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + 1, + [[0.8, 0.1], [0.2, 0.7], [0.2, 0.1]], + ), + ), + ) + def test_fn_stack(self, x, axis, ref): + list_tensor = [Tensor(self.to_tensor(i)) for i in x] + ref = self.to_tensor(ref) + + res = fns.stack(list_tensor, axis=axis) + + assert isinstance(res, Tensor) + assert fns.all(res.data == ref) + assert res.device == list_tensor[0].device + + def test_fn_moveaxis(self): + tensor = [[0, 0, 0], [0, 0, 0]] + tensor = Tensor(self.to_tensor(tensor)) + + res = fns.moveaxis(tensor, 0, -1) + + assert res.shape == (3, 2) + + @pytest.mark.parametrize( + "x, axis, keepdims, ref", + ( + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + 0, + False, + [0.45, 0.45, 0.15], + ), + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + 0, + True, + [[0.45, 0.45, 0.15]], + ), + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + (0, 1), + True, + [[0.35]], + ), + ( + [[0.8, 0.2, 0.2], [0.1, 0.7, 0.1]], + None, + False, + 0.35, + ), + ), + ) + def test_fn_mean(self, x, axis, keepdims, ref): + tensor = Tensor(self.to_tensor(x)) + ref_tensor = self.to_tensor(ref) + + res = fns.mean(tensor, axis, keepdims) + + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == tensor.device + + @pytest.mark.parametrize( + "val, decimals, ref", + ( + (1.1, 0, 1.0), + ([1.1, 0.9], 0, [1.0, 1.0]), + ([1.11, 0.91], 1, [1.1, 0.9]), + ), + ) + def test_fn_round(self, val, decimals, ref): + tensor = Tensor(self.to_tensor(val)) + ref_tensor = self.to_tensor(ref) + + res = fns.round(tensor, decimals) + + assert isinstance(res, Tensor) + assert fns.allclose(res.data, ref_tensor) + assert res.device == tensor.device + + @pytest.mark.parametrize( + "val, axis, ref", + ( + ( + [[9.0, 9.0], [7.0, 1.0]], + 0, + [8.0, 5.0], + ), + ( + [[[9.0, 9.0], [0.0, 3.0]], [[5.0, 1.0], [7.0, 1.0]]], + 0, + [5.25, 3.5], + ), + ( + [[[9.0, 9.0], [0.0, 3.0]], [[5.0, 1.0], [7.0, 1.0]]], + 2, + [5.25, 3.5], + ), + ( + [ + [[[9.0, 6.0], [8.0, 5.0]], [[3.0, 9.0], [4.0, 6.0]]], + [[[3.0, 9.0], [9.0, 2.0]], [[2.0, 4.0], [2.0, 5.0]]], + ], + 0, + [6.25, 4.5], + ), + ( + [ + [[[9.0, 6.0], [8.0, 5.0]], [[3.0, 9.0], [4.0, 6.0]]], + [[[3.0, 9.0], [9.0, 2.0]], [[2.0, 4.0], [2.0, 5.0]]], + ], + 1, + [6.375, 4.375], + ), + ( + [ + [[[9.0, 6.0], [8.0, 5.0]], [[3.0, 9.0], [4.0, 6.0]]], + [[[3.0, 9.0], [9.0, 2.0]], [[2.0, 4.0], [2.0, 5.0]]], + ], + -1, + [5.0, 5.75], + ), + ), + ) + def test_fn_mean_per_channel(self, val, axis, ref): + tensor = Tensor(self.to_tensor(val)) + ref_tensor = self.to_tensor(ref) + res = s_fns.mean_per_channel(tensor, axis) + assert isinstance(res, Tensor) + assert fns.allclose(res, ref_tensor), f"{res.data}" + assert res.device == tensor.device + + @pytest.mark.parametrize("axis", (3, 4, -4, -5)) + def test_fn_mean_per_channel_incorrect_axis(self, axis): + tensor = Tensor(self.to_tensor([[[9.0, 9.0], [0.0, 3.0]], [[5.0, 1.0], [7.0, 1.0]]])) + with pytest.raises(ValueError, match="is out of bounds for array of dimension"): + s_fns.mean_per_channel(tensor, axis) diff --git a/tests/tensorflow/quantization/test_algorithm_quantization.py b/tests/tensorflow/quantization/test_algorithm_quantization.py index eb7757aacb0..ee50ca40b63 100644 --- a/tests/tensorflow/quantization/test_algorithm_quantization.py +++ b/tests/tensorflow/quantization/test_algorithm_quantization.py @@ -612,7 +612,7 @@ def __init__(self, input_type: InputType, data_format: DataFormat): } -def get_test_layers_desk(): +def get_test_layers_desc(): models = ["Conv1D", "Conv2D", "Conv3D", "DepthwiseConv2D", "Conv1DTranspose", "Conv2DTranspose", "Conv3DTranspose"] result = [] for model_name in models: @@ -626,7 +626,7 @@ def get_test_layers_desk(): @pytest.mark.parametrize( - "layer_name,input_type,data_type", get_test_layers_desk(), ids=[" ".join(l) for l in get_test_layers_desk()] + "layer_name,input_type,data_type", get_test_layers_desc(), ids=[" ".join(dsc) for dsc in get_test_layers_desc()] ) def test_quantize_pre_post_processing(layer_name, input_type, data_type): layer_desk = LAYERS_MAP[layer_name](input_type, data_type) diff --git a/tests/tensorflow/tensor_statistics/__init__.py b/tests/tensorflow/tensor_statistics/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/tensorflow/tensor_statistics/__init__.py +++ b/tests/tensorflow/tensor_statistics/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/tensorflow/test_model_converter.py b/tests/tensorflow/test_model_converter.py index 0874807bf93..04b67569492 100644 --- a/tests/tensorflow/test_model_converter.py +++ b/tests/tensorflow/test_model_converter.py @@ -17,10 +17,10 @@ from tensorflow.keras import layers from tensorflow.keras import models -from nncf.common.graph import INPUT_NOOP_METATYPES -from nncf.common.graph import OUTPUT_NOOP_METATYPES from nncf.common.graph.layer_attributes import MultipleInputLayerAttributes from nncf.common.graph.layer_attributes import PermuteLayerAttributes +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES from nncf.tensorflow.graph.converter import BaseFunctionalSequentialConverter from nncf.tensorflow.graph.converter import convert_keras_model_to_nncf_graph from nncf.tensorflow.graph.metatypes.common import DIMENSION_PERMUTATION_METATYPES diff --git a/tests/torch/conftest.py b/tests/torch/conftest.py index 0b5a1615a11..651dcd3eeb8 100644 --- a/tests/torch/conftest.py +++ b/tests/torch/conftest.py @@ -15,13 +15,13 @@ try: import torch -except: # pylint: disable=bare-except +except: # pylint: disable=bare-except # noqa: E722 torch = None from nncf.common.quantization.structs import QuantizationMode from tests.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS from tests.shared.case_collection import skip_marked_cases_if_options_not_specified -from tests.shared.install_fixtures import tmp_venv_with_nncf # pylint:disable=unused-import -from tests.shared.logging import nncf_caplog # pylint:disable=unused-import +from tests.shared.install_fixtures import tmp_venv_with_nncf # pylint:disable=unused-import # noqa: F401 +from tests.shared.logging import nncf_caplog # pylint:disable=unused-import # noqa: F401 pytest.register_assert_rewrite("tests.torch.helpers") diff --git a/tests/torch/modules/seq2seq/__init__.py b/tests/torch/modules/seq2seq/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/modules/seq2seq/__init__.py +++ b/tests/torch/modules/seq2seq/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/modules/test_rnn.py b/tests/torch/modules/test_rnn.py index a3e6e2016db..56adecdec8f 100644 --- a/tests/torch/modules/test_rnn.py +++ b/tests/torch/modules/test_rnn.py @@ -56,19 +56,18 @@ def replace_fn(module_): bias=module_.bias, ) - def get_param_names(bias): - # type: (bool) -> List[str] + def get_param_names(bias: bool) -> List[str]: suffixes = ["ih", "hh"] names = ["weight_" + suffix for suffix in suffixes] if bias: names += ["bias_" + suffix for suffix in suffixes] return names - for l in range(custom_lstm.num_layers): + for layer_idx in range(custom_lstm.num_layers): for d in range(custom_lstm.num_directions): for name in get_param_names(custom_lstm.bias): suffix = "_reverse" if d == 1 else "" - param_name = name + "_l{}{}".format(l, suffix) + param_name = name + "_l{}{}".format(layer_idx, suffix) param = getattr(module_, param_name) getattr(custom_lstm, param_name).data.copy_(param.data) custom_lstm.to(device) @@ -86,8 +85,7 @@ def get_param_names(bias): return model -def clone_test_data(data_list): - # type: (LSTMTestData) -> List[torch.Tensor] +def clone_test_data(data_list) -> List[torch.Tensor]: results = [] x = data_list[0] result = x if isinstance(x, PackedSequence) else x.clone() @@ -117,18 +115,17 @@ def clone_test_data(data_list): class TestLSTMCell: @staticmethod def generate_lstm_data( - p, - num_layers=1, - num_directions=1, - variable_length=False, - sorted_=True, - batch_first=True, - use_cuda=False, - bias=True, - empty_initial=False, - is_backward=False, - ): - # type: (LSTMTestSizes, int, int, bool, bool, bool, bool, bool, bool, bool) -> LSTMTestData + p: LSTMTestSizes, + num_layers: int = 1, + num_directions: int = 1, + variable_length: bool = False, + sorted_: bool = True, + batch_first: bool = True, + use_cuda: bool = False, + bias: bool = True, + empty_initial: bool = False, + is_backward: bool = False, + ) -> LSTMTestData: num_chunks = 4 seq_list = [] if variable_length: @@ -174,8 +171,7 @@ def wrap_tensor(tensor): return result @staticmethod - def set_weights(cell, data): - # type: (nn.LSTMCell, LSTMTestData) -> None + def set_weights(cell: nn.LSTMCell, data: LSTMTestData): for name in TestLSTM.get_param_names(bias=True): param = getattr(data, name) if param: @@ -389,13 +385,11 @@ def test_backward_lstm( torch.testing.assert_close(test, ref, rtol=1e-1, atol=1e-1) @classmethod - def flatten_nested_lists(cls, nested_list): - # type: (List) -> List[torch.Tensor] + def flatten_nested_lists(cls, nested_list: List) -> List[torch.Tensor]: return [tensor for tensor_tuple in nested_list for tensor in tensor_tuple] @classmethod - def get_test_lstm_hidden(cls, data): - # type: (LSTMTestData) -> List[Tuple[torch.Tensor, ...]] + def get_test_lstm_hidden(cls, data: LSTMTestData) -> List[Tuple[torch.Tensor, ...]]: result = [] hidden_names = ["h0", "c0"] for name in hidden_names: @@ -408,28 +402,27 @@ def get_test_lstm_hidden(cls, data): return result @classmethod - def get_ref_lstm_hidden(cls, data): - # type: (LSTMTestData) -> Tuple[torch.Tensor, torch.Tensor] + def get_ref_lstm_hidden(cls, data: LSTMTestData) -> Tuple[torch.Tensor, torch.Tensor]: hidden = cls.get_test_lstm_hidden(data) hidden_states = [torch.unsqueeze(tensor, dim=0) for tensor in hidden[0]] cell_states = [torch.unsqueeze(tensor, dim=0) for tensor in hidden[1]] return (torch.cat(hidden_states, dim=0), torch.cat(cell_states, dim=0)) @classmethod - def set_ref_lstm_weights(cls, data, nn_lstm, num_layers, num_directions, bias): - # type: (LSTMTestData, nn.LSTM, int, int, bool) -> None - for l in range(num_layers): + def set_ref_lstm_weights( + cls, data: LSTMTestData, nn_lstm: nn.LSTM, num_layers: int, num_directions: int, bias: bool + ): + for layer_idx in range(num_layers): for d in range(num_directions): - i = l * num_directions + d + i = layer_idx * num_directions + d for name in cls.get_param_names(bias): suffix = "_reverse" if d == 1 else "" param = getattr(data, name) - param_name = name + "_l{}{}".format(l, suffix) + param_name = name + "_l{}{}".format(layer_idx, suffix) getattr(nn_lstm, param_name).data.copy_(param[i].data) @classmethod - def get_param_names(cls, bias): - # type: (bool) -> List[str] + def get_param_names(cls, bias: bool) -> List[str]: suffixes = ["ih", "hh"] names = ["weight_" + suffix for suffix in suffixes] if bias: diff --git a/tests/torch/nas/test_ps_controller.py b/tests/torch/nas/test_ps_controller.py index c2f6cf0756f..a1b0ce0275d 100644 --- a/tests/torch/nas/test_ps_controller.py +++ b/tests/torch/nas/test_ps_controller.py @@ -26,7 +26,7 @@ from tests.torch.nas.helpers import move_model_to_cuda_if_available from tests.torch.nas.models.synthetic import ThreeConvModel from tests.torch.nas.models.synthetic import ThreeConvModelMode -from tests.torch.nas.test_scheduler import fixture_schedule_params # pylint: disable=unused-import +from tests.torch.nas.test_scheduler import fixture_schedule_params # pylint: disable=unused-import # noqa: F401 class PSControllerTestDesc(NamedTuple): diff --git a/tests/torch/nas/test_search.py b/tests/torch/nas/test_search.py index d35b6d71c52..0dc1ee1c374 100644 --- a/tests/torch/nas/test_search.py +++ b/tests/torch/nas/test_search.py @@ -24,7 +24,7 @@ from tests.torch.nas.creators import create_bnas_model_and_ctrl_by_test_desc from tests.torch.nas.creators import create_bootstrap_training_model_and_ctrl from tests.torch.nas.models.synthetic import ThreeConvModel -from tests.torch.nas.test_all_elasticity import fixture_nas_model_name # pylint: disable=unused-import +from tests.torch.nas.test_all_elasticity import fixture_nas_model_name # pylint: disable=unused-import # noqa: F401 class SearchTestDesc(NamedTuple): diff --git a/tests/torch/pruning/__init__.py b/tests/torch/pruning/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/pruning/__init__.py +++ b/tests/torch/pruning/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/pruning/filter_pruning/__init__.py b/tests/torch/pruning/filter_pruning/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/pruning/filter_pruning/__init__.py +++ b/tests/torch/pruning/filter_pruning/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/ptq/test_calculation_quantizer_params.py b/tests/torch/ptq/test_calculation_quantizer_params.py index 3c0cf83a64e..cea666d0065 100644 --- a/tests/torch/ptq/test_calculation_quantizer_params.py +++ b/tests/torch/ptq/test_calculation_quantizer_params.py @@ -25,6 +25,8 @@ from nncf.common.quantization.structs import QuantizationPreset from nncf.common.quantization.structs import QuantizerConfig from nncf.common.quantization.structs import QuantizerGroup +from nncf.experimental.tensor import Tensor +from nncf.experimental.tensor import functions as fn from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend from nncf.quantization.fake_quantize import FakeQuantizeParameters @@ -54,10 +56,10 @@ class CaseSymParams: SYM_CASES = ( CaseSymParams( fq_params=FakeQuantizeParameters( - np.array(-0.49920455, dtype=np.float32), - np.array(0.49530452, dtype=np.float32), - np.array(-0.49920455, dtype=np.float32), - np.array(0.49530452, dtype=np.float32), + Tensor(torch.tensor(-0.49920455, dtype=torch.float32)), + Tensor(torch.tensor(0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(-0.49920455, dtype=torch.float32)), + Tensor(torch.tensor(0.49530452, dtype=torch.float32)), 256, ), per_channel=False, @@ -66,10 +68,10 @@ class CaseSymParams: ), CaseSymParams( fq_params=FakeQuantizeParameters( - np.array(-0.49530452, dtype=np.float32), - np.array(0.49530452, dtype=np.float32), - np.array(-0.49530452, dtype=np.float32), - np.array(0.49530452, dtype=np.float32), + Tensor(torch.tensor(-0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(-0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(0.49530452, dtype=torch.float32)), 255, ), per_channel=False, @@ -78,33 +80,33 @@ class CaseSymParams: ), CaseSymParams( fq_params=FakeQuantizeParameters( - np.array([-0.4835594, -0.49530452, -0.49221927], dtype=np.float32).reshape(1, 3, 1, 1), - np.array([0.4797816, 0.49920455, 0.48837382], dtype=np.float32).reshape(1, 3, 1, 1), - np.array([-0.4835594, -0.49530452, -0.49221927], dtype=np.float32).reshape(1, 3, 1, 1), - np.array([0.4797816, 0.49920455, 0.48837382], dtype=np.float32).reshape(1, 3, 1, 1), + Tensor(torch.tensor([-0.4835594, -0.49530452, -0.49221927], dtype=torch.float32).reshape(1, 3, 1, 1)), + Tensor(torch.tensor([0.4797816, 0.49920455, 0.48837382], dtype=torch.float32).reshape(1, 3, 1, 1)), + Tensor(torch.tensor([-0.4835594, -0.49530452, -0.49221927], dtype=torch.float32).reshape(1, 3, 1, 1)), + Tensor(torch.tensor([0.4797816, 0.49920455, 0.48837382], dtype=torch.float32).reshape(1, 3, 1, 1)), 256, ), per_channel=True, quant_group=QuantizerGroup.ACTIVATIONS, - ref_scale=np.array([0.4797816, 0.49920455, 0.48837382]).reshape(1, 3, 1, 1), + ref_scale=torch.tensor([0.4797816, 0.49920455, 0.48837382]).reshape(1, 3, 1, 1), ), CaseSymParams( fq_params=FakeQuantizeParameters( - np.array([-0.48837382, -0.49530452], dtype=np.float32).reshape(2, 1, 1, 1), - np.array([0.48837382, 0.49530452], dtype=np.float32).reshape(2, 1, 1, 1), - np.array([-0.48837382, -0.49530452], dtype=np.float32).reshape(2, 1, 1, 1), - np.array([0.48837382, 0.49530452], dtype=np.float32).reshape(2, 1, 1, 1), + Tensor(torch.tensor([-0.48837382, -0.49530452], dtype=torch.float32).reshape(2, 1, 1, 1)), + Tensor(torch.tensor([0.48837382, 0.49530452], dtype=torch.float32).reshape(2, 1, 1, 1)), + Tensor(torch.tensor([-0.48837382, -0.49530452], dtype=torch.float32).reshape(2, 1, 1, 1)), + Tensor(torch.tensor([0.48837382, 0.49530452], dtype=torch.float32).reshape(2, 1, 1, 1)), 255, ), per_channel=True, quant_group=QuantizerGroup.WEIGHTS, - ref_scale=np.array([0.48837382, 0.49530452]).reshape(2, 1, 1, 1), + ref_scale=torch.tensor([0.48837382, 0.49530452]).reshape(2, 1, 1, 1), ), ) @pytest.mark.parametrize("case_to_test", SYM_CASES) -def test_quantizer_params_sym(case_to_test): +def test_quantizer_params_sym(case_to_test: CaseSymParams): per_ch = case_to_test.per_channel fq_params = case_to_test.fq_params quant_group = case_to_test.quant_group @@ -140,10 +142,10 @@ class CaseAsymParams: ASYM_CASES = ( CaseAsymParams( fq_params=FakeQuantizeParameters( - np.array(-0.49530452, dtype=np.float32), - np.array(0.49143496, dtype=np.float32), - np.array(-0.49530452, dtype=np.float32), - np.array(0.49143496, dtype=np.float32), + Tensor(torch.tensor(-0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(0.49143496, dtype=torch.float32)), + Tensor(torch.tensor(-0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(0.49143496, dtype=torch.float32)), 256, ), per_channel=False, @@ -153,10 +155,10 @@ class CaseAsymParams: ), CaseAsymParams( fq_params=FakeQuantizeParameters( - np.array(-0.49530452, dtype=np.float32), - np.array(0.49143496, dtype=np.float32), - np.array(-0.49530452, dtype=np.float32), - np.array(0.49143496, dtype=np.float32), + Tensor(torch.tensor(-0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(0.49143496, dtype=torch.float32)), + Tensor(torch.tensor(-0.49530452, dtype=torch.float32)), + Tensor(torch.tensor(0.49143496, dtype=torch.float32)), 256, ), per_channel=False, @@ -166,35 +168,35 @@ class CaseAsymParams: ), CaseAsymParams( fq_params=FakeQuantizeParameters( - np.array([-0.48051512, -0.49776307, -0.44099426], dtype=np.float32).reshape(1, 3, 1, 1), - np.array([0.4767611, 0.47861832, 0.48837382], dtype=np.float32).reshape(1, 3, 1, 1), - np.array([-0.48051512, -0.49776307, -0.44099426], dtype=np.float32).reshape(1, 3, 1, 1), - np.array([0.4767611, 0.47861832, 0.48837382], dtype=np.float32).reshape(1, 3, 1, 1), + Tensor(torch.tensor([-0.48051512, -0.49776307, -0.44099426], dtype=torch.float32).reshape(1, 3, 1, 1)), + Tensor(torch.tensor([0.4767611, 0.47861832, 0.48837382], dtype=torch.float32).reshape(1, 3, 1, 1)), + Tensor(torch.tensor([-0.48051512, -0.49776307, -0.44099426], dtype=torch.float32).reshape(1, 3, 1, 1)), + Tensor(torch.tensor([0.4767611, 0.47861832, 0.48837382], dtype=torch.float32).reshape(1, 3, 1, 1)), 256, ), per_channel=True, quant_group=QuantizerGroup.ACTIVATIONS, - ref_inp_low=np.array([-0.48051512, -0.49776307, -0.44099426]).reshape(1, 3, 1, 1), - ref_inp_range=np.array([0.9572762, 0.9763814, 0.9293681]).reshape(1, 3, 1, 1), + ref_inp_low=torch.tensor([-0.48051512, -0.49776307, -0.44099426]).reshape(1, 3, 1, 1), + ref_inp_range=torch.tensor([0.9572762, 0.9763814, 0.9293681]).reshape(1, 3, 1, 1), ), CaseAsymParams( fq_params=FakeQuantizeParameters( - np.array([-0.4845584, -0.49583155], dtype=np.float32).reshape(2, 1, 1, 1), - np.array([0.48837382, 0.4767611], dtype=np.float32).reshape(2, 1, 1, 1), - np.array([-0.4845584, -0.49583155], dtype=np.float32).reshape(2, 1, 1, 1), - np.array([0.48837382, 0.4767611], dtype=np.float32).reshape(2, 1, 1, 1), + Tensor(torch.tensor([-0.4845584, -0.49583155], dtype=torch.float32).reshape(2, 1, 1, 1)), + Tensor(torch.tensor([0.48837382, 0.4767611], dtype=torch.float32).reshape(2, 1, 1, 1)), + Tensor(torch.tensor([-0.4845584, -0.49583155], dtype=torch.float32).reshape(2, 1, 1, 1)), + Tensor(torch.tensor([0.48837382, 0.4767611], dtype=torch.float32).reshape(2, 1, 1, 1)), 256, ), per_channel=True, quant_group=QuantizerGroup.WEIGHTS, - ref_inp_low=np.array([-0.4845584, -0.49583155]).reshape(2, 1, 1, 1), - ref_inp_range=np.array([0.97293222, 0.97259265]).reshape(2, 1, 1, 1), + ref_inp_low=torch.tensor([-0.4845584, -0.49583155]).reshape(2, 1, 1, 1), + ref_inp_range=torch.tensor([0.97293222, 0.97259265]).reshape(2, 1, 1, 1), ), ) @pytest.mark.parametrize("case_to_test", ASYM_CASES) -def test_quantizer_params_asym(case_to_test): +def test_quantizer_params_asym(case_to_test: CaseSymParams): per_ch = case_to_test.per_channel fq_params = case_to_test.fq_params quant_group = case_to_test.quant_group @@ -212,8 +214,8 @@ def test_quantizer_params_asym(case_to_test): ) quantizer = PTMinMaxAlgoBackend._create_quantizer(qconfig, scale_shape, fq_params, target_type) assert quantizer.levels == fq_params.levels - assert np.allclose(quantizer.input_low.detach().numpy(), case_to_test.ref_inp_low) - assert np.allclose(quantizer.input_range.detach().numpy(), case_to_test.ref_inp_range) + assert fn.allclose(quantizer.input_low.data, case_to_test.ref_inp_low) + assert fn.allclose(quantizer.input_range.data, case_to_test.ref_inp_range) class LinearTestModel(nn.Module): @@ -270,10 +272,7 @@ def calculate_statistics(data, mode, qgroup, half_range=False): else: max_values = np.amax(data, axes) - statistics = PTMinMaxTensorStatistic( - min_values=torch.from_numpy(np.array(min_values)), - max_values=torch.from_numpy(np.array(max_values)), - ) + statistics = PTMinMaxTensorStatistic(min_values=torch.tensor(min_values), max_values=torch.tensor(max_values)) signedness_to_force = True if qgroup == QuantizerGroup.WEIGHTS else None qconfig = QuantizerConfig(num_bits=8, mode=mode, per_channel=per_ch, signedness_to_force=signedness_to_force) narrow_range = get_quantizer_narrow_range(qconfig, qgroup) @@ -346,8 +345,8 @@ def test_quantizer_parameters_export(tmp_path: Path): for name, param in fq_params.items(): assert name in torch_ptq_params - assert np.allclose(param["input_low"], torch_ptq_params[name]["input_low"]) - assert np.allclose(param["input_high"], torch_ptq_params[name]["input_high"]) + assert fn.allclose(param["input_low"], torch_ptq_params[name]["input_low"]) + assert fn.allclose(param["input_high"], torch_ptq_params[name]["input_high"]) class TestFQParams(TemplateTestFQParams): diff --git a/tests/torch/ptq/test_fast_bias_correction.py b/tests/torch/ptq/test_fast_bias_correction.py index b713aeb802c..7f5639aaeba 100644 --- a/tests/torch/ptq/test_fast_bias_correction.py +++ b/tests/torch/ptq/test_fast_bias_correction.py @@ -59,3 +59,30 @@ def check_bias(model: NNCFNetwork, ref_bias: list): assert torch.all(torch.isclose(bias_value, ref_bias, atol=0.02)), f"{bias_value} != {ref_bias}" return raise ValueError("Not found node with bias") + + +class TestTorchCudaFBCAlgorithm(TestTorchFBCAlgorithm): + @staticmethod + def list_to_backend_type(data: List) -> torch.Tensor: + return torch.Tensor(data).cuda() + + @staticmethod + def backend_specific_model(model: bool, tmp_dir: str): + return get_nncf_network(model.cuda(), model.INPUT_SIZE) + + @staticmethod + def fn_to_type(tensor): + return torch.Tensor(tensor).cuda() + + @staticmethod + def check_bias(model: NNCFNetwork, ref_bias: list): + ref_bias = torch.Tensor(ref_bias) + nncf_graph = NNCFGraphFactory.create(model) + for node in nncf_graph.get_all_nodes(): + if not is_node_with_fused_bias(node, nncf_graph): + continue + bias_value = get_fused_bias_value(node, model).cpu() + # TODO(AlexanderDokuchaev): return atol=0.0001 after fix 109189 + assert torch.all(torch.isclose(bias_value, ref_bias, atol=0.02)), f"{bias_value} != {ref_bias}" + return + raise ValueError("Not found node with bias") diff --git a/tests/torch/ptq/test_quantizer_config.py b/tests/torch/ptq/test_quantizer_config.py index 98e7de76ca2..951f79149d7 100644 --- a/tests/torch/ptq/test_quantizer_config.py +++ b/tests/torch/ptq/test_quantizer_config.py @@ -9,8 +9,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Tuple - import pytest from nncf.common.graph.transformations.commands import TargetType diff --git a/tests/torch/ptq/test_weights_compression.py b/tests/torch/ptq/test_weights_compression.py index d831aea7bb1..a42404ab43c 100644 --- a/tests/torch/ptq/test_weights_compression.py +++ b/tests/torch/ptq/test_weights_compression.py @@ -75,15 +75,16 @@ def test_compress_shared_weights(): def test_raise_error_with_int8_and_non_default_ratio(mocker): - with pytest.raises(RuntimeError): + with pytest.raises(AttributeError): compress_weights(mocker.Mock(), mode=CompressWeightsMode.INT8, ratio=0.5) def test_raise_error_with_int8_and_non_default_group_size(mocker): - with pytest.raises(RuntimeError): + with pytest.raises(AttributeError): compress_weights(mocker.Mock(), mode=CompressWeightsMode.INT8, group_size=64) def test_raise_error_with_nf4(mocker): - with pytest.raises(RuntimeError): - compress_weights(mocker.Mock(), mode=CompressWeightsMode.NF4) + with pytest.raises(AttributeError): + dummy_torch_model = torch.nn.Module() + compress_weights(dummy_torch_model, mode=CompressWeightsMode.NF4) diff --git a/tests/torch/pytorch_patch_isolated.py b/tests/torch/pytorch_patch_isolated.py index 049ddf653f1..c4648573883 100644 --- a/tests/torch/pytorch_patch_isolated.py +++ b/tests/torch/pytorch_patch_isolated.py @@ -38,7 +38,7 @@ def test_jit_if_tracing_script_source_equals(): # Get original torch.jit._script_if_tracing source torch_source = remove_comments_from_source(inspect.getsource(torch.jit._script_if_tracing)) - import nncf.torch # pylint: disable=unused-import + import nncf.torch # pylint: disable=unused-import # noqa: F401 # Get torch.jit._script_if_tracing source after patching was performed nncf_source = remove_comments_from_source(inspect.getsource(torch.jit._script_if_tracing)) @@ -69,7 +69,7 @@ def test_jit_script_exception_preserves_patching_isolated(): try: torch.jit.script(compressed_model) # supposed to fail since torch.jit.script does not support NNCF models - except: # pylint:disable=bare-except + except: # pylint:disable=bare-except # noqa: E722 pass # torch.nn.Module.__call__ is one of the fundamental patched functions, if the code object points to NNCF code, diff --git a/tests/torch/quantization/__init__.py b/tests/torch/quantization/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/quantization/__init__.py +++ b/tests/torch/quantization/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/quantization/test_algo_quantization.py b/tests/torch/quantization/test_algo_quantization.py index 7b7e395cad6..41abb2ef68e 100644 --- a/tests/torch/quantization/test_algo_quantization.py +++ b/tests/torch/quantization/test_algo_quantization.py @@ -193,7 +193,7 @@ def test_can_load_quant_algo__with_defaults(): assert len(model_conv) == len(quant_model_conv) for module_scope, _ in model_conv.items(): - quant_scope = deepcopy(module_scope) # type: Scope + quant_scope: Scope = deepcopy(module_scope) quant_scope.pop() quant_scope.push(ScopeElement("NNCFConv2d", "conv")) assert quant_scope in quant_model_conv.keys() diff --git a/tests/torch/quantization/test_range_init.py b/tests/torch/quantization/test_range_init.py index 6ef968a8bf9..5dec4eb8d06 100644 --- a/tests/torch/quantization/test_range_init.py +++ b/tests/torch/quantization/test_range_init.py @@ -966,7 +966,7 @@ def __init__(self, per_channel: bool, is_weights: bool, input_shape: List[int], def quantizer_range_init_scale_shape_idfn(fixture_value): - test_struct = fixture_value[0] # type: QRISSTS + test_struct: QRISSTS = fixture_value[0] postfix = "" if test_struct.is_weights: postfix += "-W" @@ -1002,7 +1002,7 @@ def test_quantize_range_init_sets_correct_scale_shapes(quantizer_range_init_test logarithm_scale=False, ) q_cls = QUANTIZATION_MODULES.get(quantization_mode) - quantizer = q_cls(qconfig) # type: BaseQuantizer + quantizer: BaseQuantizer = q_cls(qconfig) range_init_config = RangeInitConfig(init_type=initializer_type, num_init_samples=1) if test_struct.is_weights: diff --git a/tests/torch/quantization/test_scheduler.py b/tests/torch/quantization/test_scheduler.py index e514ae1c1e9..9ebcdc7d9a9 100644 --- a/tests/torch/quantization/test_scheduler.py +++ b/tests/torch/quantization/test_scheduler.py @@ -17,7 +17,7 @@ from nncf.torch import register_default_init_args from nncf.torch.dynamic_graph.graph_tracer import create_input_infos from nncf.torch.initialization import wrap_dataloader_for_init -from nncf.torch.quantization.algo import QuantizationControllerBase +from nncf.torch.quantization.base_ctrl import QuantizationControllerBase from nncf.torch.quantization.schedulers import StagedQuantizationScheduler from tests.torch.helpers import OnesDatasetMock from tests.torch.helpers import create_compressed_model_and_algo_for_test diff --git a/tests/torch/quantization/test_solver_quantization_traits.py b/tests/torch/quantization/test_solver_quantization_traits.py index 120a4107e4c..b9656d16b4a 100644 --- a/tests/torch/quantization/test_solver_quantization_traits.py +++ b/tests/torch/quantization/test_solver_quantization_traits.py @@ -8,9 +8,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import Dict, List, Type -from nncf.common.graph import INPUT_NOOP_METATYPES -from nncf.common.graph import OUTPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.quantization.quantizer_propagation.graph import QuantizerPropagationStateGraph as QPSG from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationSolver from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait @@ -25,7 +27,7 @@ def test_set_quantization_traits_for_quant_prop_graph_nodes(): # Test all patchable metatypes. If a patchable metatype is not registered # in quantization trait-to-metatype dict, the test will fail. - tested_op_metatypes = get_operator_metatypes() # type: List[Type[OperatorMetatype]] + tested_op_metatypes: List[Type[OperatorMetatype]] = get_operator_metatypes() tested_op_names = set() for op_meta in tested_op_metatypes: if op_meta not in INPUT_NOOP_METATYPES and op_meta not in OUTPUT_NOOP_METATYPES: @@ -59,7 +61,7 @@ def test_set_quantization_traits_for_quant_prop_graph_nodes(): def test_quantization_traits_are_unambiguous_for_op_names(): - op_name_to_trait_dict = {} # type: Dict[str, QuantizationTrait] + op_name_to_trait_dict: Dict[str, QuantizationTrait] = {} for trait, arches in DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT.items(): for op_meta in arches: aliases = op_meta.get_all_aliases() diff --git a/tests/torch/quantization/test_unified_scales.py b/tests/torch/quantization/test_unified_scales.py index 78f9ede3656..49544db5b34 100644 --- a/tests/torch/quantization/test_unified_scales.py +++ b/tests/torch/quantization/test_unified_scales.py @@ -561,7 +561,7 @@ def immediately_dominates_embedding(node: onnx.NodeProto, graph: onnx.GraphProto @staticmethod def group_nodes_by_output_target(nodes: List[onnx.NodeProto], graph: onnx.GraphProto) -> List[List[onnx.NodeProto]]: - output_nodes = {} # type: Dict[str, List[onnx.NodeProto]] + output_nodes: Dict[str, List[onnx.NodeProto]] = {} for node in nodes: succs = get_successors(node, graph) assert len(succs) == 1 diff --git a/tests/torch/sample_test_validator.py b/tests/torch/sample_test_validator.py index 78cb2a7442d..9969b9258ca 100644 --- a/tests/torch/sample_test_validator.py +++ b/tests/torch/sample_test_validator.py @@ -218,7 +218,7 @@ def batch_size(self, batch_size: int): def sample_type(self, sample_type: SampleType): self.sample_type_ = sample_type - sample_handler_cls = SAMPLE_HANDLERS.get(self.sample_type_) # type: Type[BaseSampleHandler] + sample_handler_cls: Type[BaseSampleHandler] = SAMPLE_HANDLERS.get(self.sample_type_) self.sample_handler = sample_handler_cls() return self diff --git a/tests/torch/sparsity/__init__.py b/tests/torch/sparsity/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/sparsity/__init__.py +++ b/tests/torch/sparsity/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/sparsity/const/__init__.py b/tests/torch/sparsity/const/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/sparsity/const/__init__.py +++ b/tests/torch/sparsity/const/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/sparsity/magnitude/test_algo.py b/tests/torch/sparsity/magnitude/test_algo.py index fd7ffc04844..63701aef36c 100644 --- a/tests/torch/sparsity/magnitude/test_algo.py +++ b/tests/torch/sparsity/magnitude/test_algo.py @@ -10,6 +10,7 @@ # limitations under the License. from copy import deepcopy +from typing import List import pytest import torch @@ -17,6 +18,7 @@ from nncf.api.compression import CompressionStage from nncf.torch.module_operations import UpdateWeight +from nncf.torch.sparsity.base_algo import SparseModuleInfo from nncf.torch.sparsity.layers import BinaryMask from nncf.torch.sparsity.magnitude.algo import MagnitudeSparsityController from nncf.torch.sparsity.magnitude.functions import normed_magnitude @@ -138,8 +140,8 @@ def test_magnitude_algo_binary_masks_are_applied(): config = get_empty_config() config["compression"] = {"algorithm": "magnitude_sparsity"} compressed_model, compression_ctrl = create_compressed_model_and_algo_for_test(model, config) - minfo_list = compression_ctrl.sparsified_module_info # type: List[SparseModuleInfo] - minfo = minfo_list[0] # type: SparseModuleInfo + minfo_list: List[SparseModuleInfo] = compression_ctrl.sparsified_module_info + minfo: SparseModuleInfo = minfo_list[0] minfo.operand.binary_mask = torch.ones_like(minfo.module.weight) # 1x1x2x2 input_ = torch.ones(size=(1, 1, 5, 5)) diff --git a/tests/torch/sparsity/movement/test_scheduler.py b/tests/torch/sparsity/movement/test_scheduler.py index 8a02462b5ee..81cfb334b79 100644 --- a/tests/torch/sparsity/movement/test_scheduler.py +++ b/tests/torch/sparsity/movement/test_scheduler.py @@ -25,7 +25,6 @@ from nncf.experimental.torch.sparsity.movement.scheduler import MovementSchedulerParams from nncf.experimental.torch.sparsity.movement.scheduler import MovementSchedulerStage from nncf.torch.model_creation import create_compressed_model -from tests.shared.logging import nncf_caplog # pylint:disable=unused-import from tests.torch.sparsity.movement.helpers import BaseMockRunRecipe from tests.torch.sparsity.movement.helpers import BertRunRecipe from tests.torch.sparsity.movement.helpers import LinearRunRecipe diff --git a/tests/torch/sparsity/movement/test_structured_mask.py b/tests/torch/sparsity/movement/test_structured_mask.py index 00e41e7354e..0349762472e 100644 --- a/tests/torch/sparsity/movement/test_structured_mask.py +++ b/tests/torch/sparsity/movement/test_structured_mask.py @@ -30,7 +30,6 @@ from nncf.experimental.torch.sparsity.movement.structured_mask_handler import StructuredMaskContextStatistics from nncf.experimental.torch.sparsity.movement.structured_mask_handler import StructuredMaskHandler from nncf.torch import create_compressed_model -from tests.shared.logging import nncf_caplog # pylint:disable=unused-import from tests.torch.sparsity.movement.helpers import BaseMockRunRecipe from tests.torch.sparsity.movement.helpers import BertRunRecipe from tests.torch.sparsity.movement.helpers import DictInTransformerBlockOrder diff --git a/tests/torch/sparsity/rb/__init__.py b/tests/torch/sparsity/rb/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/sparsity/rb/__init__.py +++ b/tests/torch/sparsity/rb/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/tensor_statistics/__init__.py b/tests/torch/tensor_statistics/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tests/torch/tensor_statistics/__init__.py +++ b/tests/torch/tensor_statistics/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch/test_compressed_graph.py b/tests/torch/test_compressed_graph.py index d2a1c725af5..07dfdbfa458 100644 --- a/tests/torch/test_compressed_graph.py +++ b/tests/torch/test_compressed_graph.py @@ -836,8 +836,8 @@ def _case_dir(type_hw_config): def prepare_potential_quantizer_graph(graph: PTNNCFGraph, quantizer_setup: SingleConfigQuantizerSetup) -> nx.DiGraph: quantizers_weights_attr = {} - pre_hooked_quantizers_activations_attr = {} # type: Dict[NNCFNodeName, Tuple[int, str]] - post_hooked_quantizers_activations_attr = {} # type: Dict[NNCFNodeName, str] + pre_hooked_quantizers_activations_attr: Dict[NNCFNodeName, Tuple[int, str]] = {} + post_hooked_quantizers_activations_attr: Dict[NNCFNodeName, str] = {} # pylint:disable=protected-access for qp in quantizer_setup.quantization_points.values(): diff --git a/tests/torch/test_get_modules_by_type.py b/tests/torch/test_get_modules_by_type.py index cb787ff2139..71ec01e75c4 100644 --- a/tests/torch/test_get_modules_by_type.py +++ b/tests/torch/test_get_modules_by_type.py @@ -90,14 +90,14 @@ def test_get_all_modules_by_type__for_multiple_type(): def test_get_all_modules_by_type__for_not_exact_type(): model = ModelForNameTest() - l = get_all_modules_by_type(model, "Avg") - assert not l + layer = get_all_modules_by_type(model, "Avg") + assert not layer def test_get_all_modules_by_type__for_subtype(): model = ModelForNameTest() - l = get_all_modules_by_type(model, "AvgPool2d_dummy") - assert not l + layer = get_all_modules_by_type(model, "AvgPool2d_dummy") + assert not layer IGNORED_SCOPES = [ diff --git a/tests/torch/test_graph_analysis.py b/tests/torch/test_graph_analysis.py index ddc322192c3..0bc381e3812 100644 --- a/tests/torch/test_graph_analysis.py +++ b/tests/torch/test_graph_analysis.py @@ -12,10 +12,10 @@ import networkx as nx -from nncf.common.graph import Dtype from nncf.common.graph import NNCFGraphEdge from nncf.common.graph import NNCFGraphPatternIO from nncf.common.graph import NNCFNodeName +from nncf.common.graph.layer_attributes import Dtype from tests.common.quantization.mock_graphs import get_mock_nncf_node_attrs from tests.common.quantization.mock_graphs import get_nncf_graph_from_mock_nx_graph from tests.common.quantization.mock_graphs import mark_input_ports_lexicographically_based_on_input_node_key diff --git a/tests/torch/test_graph_building.py b/tests/torch/test_graph_building.py index 52152772a7b..859fa6a7157 100644 --- a/tests/torch/test_graph_building.py +++ b/tests/torch/test_graph_building.py @@ -17,11 +17,11 @@ import torch.nn.functional as F from torch import nn -from nncf.common.graph import Dtype +from nncf.common.graph import NNCFGraphEdge from nncf.common.graph.definitions import MODEL_INPUT_OP_NAME from nncf.common.graph.definitions import MODEL_OUTPUT_OP_NAME from nncf.common.graph.definitions import NNCFGraphNodeType -from nncf.common.graph.graph import NNCFGraphEdge +from nncf.common.graph.layer_attributes import Dtype from nncf.common.graph.layer_attributes import GetItemLayerAttributes from nncf.common.graph.layer_attributes import MultipleInputLayerAttributes from nncf.common.graph.layer_attributes import MultipleOutputLayerAttributes @@ -571,7 +571,7 @@ def test_input_info_specification_from_config(mocker, input_info_test_struct): mock_model = MockModel(stub_fn) config = get_basic_quantization_config("symmetric") input_info_config_entry = input_info_test_struct[0] - target_argument_info = input_info_test_struct[1] # type: List[ModelInputInfo] + target_argument_info: List[ModelInputInfo] = input_info_test_struct[1] config["input_info"] = input_info_config_entry register_bn_adaptation_init_args(config) diff --git a/tests/torch/test_layer_attributes.py b/tests/torch/test_layer_attributes.py index 65c841cc890..e0c89127cb8 100644 --- a/tests/torch/test_layer_attributes.py +++ b/tests/torch/test_layer_attributes.py @@ -14,12 +14,12 @@ from torch import Size from torch import nn -from nncf.common.graph import BaseLayerAttributes -from nncf.common.graph import OperatorMetatype +from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes from nncf.common.graph.layer_attributes import GenericWeightedLayerAttributes from nncf.common.graph.layer_attributes import GroupNormLayerAttributes from nncf.common.graph.layer_attributes import LinearLayerAttributes +from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.torch.dynamic_graph.graph_tracer import ModelInputInfo from nncf.torch.graph.operator_metatypes import PTBatchNormMetatype from nncf.torch.graph.operator_metatypes import PTConv1dMetatype diff --git a/tests/torch/test_model_transformer.py b/tests/torch/test_model_transformer.py index 22fe633440a..4ba712ec18d 100644 --- a/tests/torch/test_model_transformer.py +++ b/tests/torch/test_model_transformer.py @@ -89,9 +89,7 @@ def forward(self, input_): class TestInsertionCommands: @pytest.fixture() def setup(self): - self.compressed_model = NNCFNetwork( - InsertionPointTestModel(), [ModelInputInfo([1, 1, 10, 10])] - ) # type: NNCFNetwork + self.compressed_model = NNCFNetwork(InsertionPointTestModel(), [ModelInputInfo([1, 1, 10, 10])]) conv1_node_name = "InsertionPointTestModel/NNCFConv2d[conv1]/conv2d_0" point_for_conv1_weights = PTTargetPoint( @@ -422,7 +420,7 @@ def forward(self, input_): nncf_network = NNCFNetwork(model, [ModelInputInfo([1, 3, 300, 300])]) nncf_graph = nncf_network.nncf.get_original_graph() - for nncf_node in nncf_graph.get_all_nodes(): # type: NNCFNode + for nncf_node in nncf_graph.get_all_nodes(): assert nncf_node.node_name in ref_scope_vs_metatype_dict ref_metatype = ref_scope_vs_metatype_dict[nncf_node.node_name] assert nncf_node.metatype == ref_metatype @@ -436,7 +434,7 @@ def test_get_ip_graph_with_merged_operations(self, mock_graph_factory, dot_file_ pattern = PatternsManager.get_full_hw_pattern_graph(backend=BackendType.TORCH, device=TargetDevice.ANY) merged_ip_graph = ip_graph.get_ip_graph_with_merged_hw_optimized_operations(pattern) - data_dir = TEST_ROOT / "torch/data/reference_graphs/pattern_merging" # type: Path + data_dir: Path = TEST_ROOT / "torch/data/reference_graphs/pattern_merging" path_to_dot_file = data_dir / "{}.dot".format(dot_file_name) diff --git a/tests/torch/test_nncf_network.py b/tests/torch/test_nncf_network.py index d75741aa635..f7a6bb7b064 100644 --- a/tests/torch/test_nncf_network.py +++ b/tests/torch/test_nncf_network.py @@ -77,7 +77,7 @@ def forward(self, inputs): ModelInputInfo(input_shape_1), ], scopes_without_shape_matching=["MatMulModel"], - ) # type: NNCFNetwork + ) context = qnet_no_shape.nncf.get_tracing_context() context.enable_trace_dynamic_graph() @@ -97,7 +97,7 @@ def forward(self, inputs): input_infos=[ ModelInputInfo(input_shape_1), ], - ) # type: NNCFNetwork + ) context = qnet.nncf.get_tracing_context() context.enable_trace_dynamic_graph() _ = qnet(torch.zeros(*input_shape_1)) @@ -110,7 +110,7 @@ def forward(self, inputs): def test_check_correct_modules_replacement(): model = TwoConvTestModel() - nncf_model = NNCFNetwork(TwoConvTestModel(), input_infos=[ModelInputInfo([1, 1, 4, 4])]) # type: NNCFNetwork + nncf_model: NNCFNetwork = NNCFNetwork(TwoConvTestModel(), input_infos=[ModelInputInfo([1, 1, 4, 4])]) _, detected_nncf_modules = check_correct_nncf_modules_replacement(model, nncf_model) replaced_modules_reported_by_nncf_network = { @@ -178,7 +178,7 @@ def forward(self, x): def test_custom_module_registering(): model = TwoConvTestModelWithUserModule() - nncf_model = NNCFNetwork(model, input_infos=[ModelInputInfo([1, 1, 4, 4])]) # type: NNCFNetwork + nncf_model: NNCFNetwork = NNCFNetwork(model, input_infos=[ModelInputInfo([1, 1, 4, 4])]) from nncf.torch.layers import UNWRAPPED_USER_MODULES @@ -222,7 +222,7 @@ def test_custom_module_registering(): def test_get_weighted_original_graph_nodes(): model = TwoConvTestModelWithUserModule() - nncf_model = NNCFNetwork(model, input_infos=[ModelInputInfo([1, 1, 4, 4])]) # type: NNCFNetwork + nncf_model: NNCFNetwork = NNCFNetwork(model, input_infos=[ModelInputInfo([1, 1, 4, 4])]) weighted_nodes = nncf_model.nncf.get_weighted_original_graph_nodes() ref_node_names = [ "TwoConvTestModelWithUserModule/Sequential[features]/Sequential[0]/NNCFConv2d[0]/conv2d_0", @@ -239,7 +239,7 @@ def test_get_weighted_original_graph_nodes(): # pylint: disable=protected-access def test_get_op_nodes_in_scope(): model = TwoConvTestModel() - nncf_model = NNCFNetwork(deepcopy(model), input_infos=[ModelInputInfo([1, 1, 4, 4])]) # type: NNCFNetwork + nncf_model: NNCFNetwork = NNCFNetwork(deepcopy(model), input_infos=[ModelInputInfo([1, 1, 4, 4])]) nncf_graph = nncf_model.nncf.get_original_graph() # Valid scopes should be successfully found diff --git a/tools/__init__.py b/tools/__init__.py index e69de29bb2d..9b29b47534a 100644 --- a/tools/__init__.py +++ b/tools/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tools/benchmark_quantize_layers.py b/tools/benchmark_quantize_layers.py index ec4f4e8a443..7d58785daa0 100644 --- a/tools/benchmark_quantize_layers.py +++ b/tools/benchmark_quantize_layers.py @@ -195,7 +195,7 @@ def get_module(params_struct: ParamStruct) -> BaseQuantizer: file_name = "benchmark_quantize_layers_result.csv" if len(sys.argv) == 1 else sys.argv[1] print(f"Benchmark results will be saved to file {file_name}") - benchmark_data = [] # type: List[Dict[str, Any]] + benchmark_data: List[Dict[str, Any]] = [] device_ids = range(torch.cuda.device_count()) ngpus_per_node = len(device_ids) world_size = ngpus_per_node @@ -208,7 +208,7 @@ def get_module(params_struct: ParamStruct) -> BaseQuantizer: input_size = param_struct.batch.input_size if param_struct.exec_type == ExecutionType.DISTRIBUTED_DATA_PARALLEL: - output = [] # type: List[Dict[str, float]] + output: List[Dict[str, float]] = [] try: mp.spawn( run_worker, @@ -216,7 +216,7 @@ def get_module(params_struct: ParamStruct) -> BaseQuantizer: args=(world_size, module, input_size, num_runs, param_struct.dtype, output), ) run_data = output[0] - except: # pylint:disable=bare-except + except: # pylint:disable=bare-except # noqa: E722 run_data = {"time": -1} else: run_data = call_fn(module, input_size, param_struct.device, num_runs, dtype=param_struct.dtype) diff --git a/tools/clip_dot.py b/tools/clip_dot.py index 23b5af45d2d..e14164a9a20 100644 --- a/tools/clip_dot.py +++ b/tools/clip_dot.py @@ -1,3 +1,14 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # pylint:skip-file import sys from argparse import ArgumentParser @@ -37,7 +48,6 @@ def main(argv): from_key, to_key, _ = edge id_portion = from_key.split()[0] has_id = id_portion.isdigit() - end_key = from_key if has_id: curr_id = int(id_portion) if curr_id >= int(args.finish_id): diff --git a/tools/collect_pylint_input_files_for_backend.py b/tools/collect_pylint_input_files_for_backend.py index 94d10c99d7b..ca54b3a16ed 100755 --- a/tools/collect_pylint_input_files_for_backend.py +++ b/tools/collect_pylint_input_files_for_backend.py @@ -1,3 +1,14 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import subprocess import sys diff --git a/tools/debug/__init__.py b/tools/debug/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tools/debug/common.py b/tools/debug/common.py deleted file mode 100644 index 070a9480b61..00000000000 --- a/tools/debug/common.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -from functools import partial - -import numpy as np -import torch -from torch import nn - -from examples.torch.common.model_loader import load_model -from nncf.torch.checkpoint_loading import load_state -from nncf.torch.layers import NNCFConv1d -from nncf.torch.layers import NNCFConv2d -from nncf.torch.layers import NNCFLinear -from nncf.torch.model_creation import create_compressed_model - - -def dump_in_out_hook(module, inputs, output): - dump_out_hook(module, inputs, output) - dump_path = get_dump_path(module) - if dump_path: - key = 0 - output_dir = os.path.abspath(os.path.join(dump_path, os.pardir)) - file_name = os.path.basename(dump_path) - for input_ in inputs: - key += 1 - input_data = input_.data.cpu().numpy().flatten() - dump_name = ".".join([file_name, "in", str(key)]) - npy_path, _ = save_dump(dump_name, output_dir, input_data) - add_full_dump_path(module, npy_path) - - -def dump_out_hook(module, inputs, output): - dump_path = get_dump_path(module) - if dump_path: - output_data = output.data.cpu().numpy().flatten() - output_dir = os.path.abspath(os.path.join(dump_path, os.pardir)) - file_name = os.path.basename(dump_path) - dump_name = ".".join([file_name, "out"]) - npy_path, _ = save_dump(dump_name, output_dir, output_data, force=False) - add_full_dump_path(module, npy_path) - - -def get_dump_path(module): - if hasattr(module, "dump_path"): - return module.dump_path - return None - - -def set_dump_path(layer, path): - layer.dump_path = path - - -def add_full_dump_path(layer, full_path): - if not hasattr(layer, "dump_full_paths"): - layer.dump_full_paths = [] - layer.dump_full_paths.append(full_path) - - -def get_full_dump_paths(layer): - if hasattr(layer, "dump_full_paths"): - return layer.dump_full_paths - return None - - -def is_weightable(layer): - return isinstance(layer, (nn.Conv1d, nn.Conv2d, nn.Linear)) and not isinstance( - layer, (NNCFConv1d, NNCFConv2d, NNCFLinear) - ) - - -def has_sparse_quant_weights(layer, name): - from nncf.torch.quantization.layers import SymmetricQuantizer - from nncf.torch.sparsity.rb.layers import RBSparsifyingWeight - - return (isinstance(layer, RBSparsifyingWeight) and ("sparsified_weight" in name)) or ( - isinstance(layer, SymmetricQuantizer) and ("quantized_weight" in name) - ) - - -def save_dump_(path, ext, saver, data, force=False): - full_path = ".".join([path, ext]) - if not os.path.exists(full_path) or force: - print("Saving dump to {}".format(full_path)) - saver(full_path, data) - else: - print("Dump already exists " + full_path) - return full_path - - -def save_dump(dump_name, output_dir, data, force=False): - path = os.path.join(output_dir, dump_name) - npy_path = save_dump_(path, "npy", np.save, data, force) - txt_path = save_dump_(path, "txt", partial(np.savetxt, fmt="%s"), data, force) - return npy_path, txt_path - - -def register_print_hooks(path, model, data_to_compare, num_layers=-1, dump_activations=False, prefix="", idx=0): - for name, children in model.named_children(): - name_full = "{}{}".format(prefix, name) - idx = register_print_hooks( - path, children, data_to_compare, num_layers, dump_activations, prefix=name_full + ".", idx=idx - ) - - within_range = (num_layers == -1) or idx < num_layers - has_weights = has_sparse_quant_weights(children, name_full) or is_weightable(children) - within_type = has_weights if not dump_activations else dump_activations - if within_range and within_type: - # always there for activations if dump_activation is enabled - # always there for weights if dump_activation is disabled - name_full = name_full.replace("/", "_") - dump_path = os.path.join(path, ".".join([str(idx), name_full])) - idx += 1 - if is_weightable(children): - output_dir = os.path.abspath(os.path.join(dump_path, os.pardir)) - file_name = os.path.basename(dump_path) - - def dump_attr(attr): - if hasattr(children, attr): - dump_name = ".".join([file_name, attr]) - data = children.weight.data.numpy() - save_dump(dump_name, output_dir, data, force=False) - data_to_compare[dump_name] = data - - dump_attr("weight") - dump_attr("bias") - else: - set_dump_path(children, dump_path) - hook = dump_in_out_hook if dump_activations else dump_out_hook - children.register_forward_hook(hook) - return idx - - -def load_torch_model(config, cuda=False): - weights = config.get("weights") - model = load_model( - config.get("model"), - pretrained=config.get("pretrained", True) if weights is None else False, - num_classes=config.get("num_classes", 1000), - model_params=config.get("model_params", {}), - ) - compression_ctrl, model = create_compressed_model(model, config) - if weights: - sd = torch.load(weights, map_location="cpu") - load_state(model, sd) - if cuda: - model = model.cuda() - model = torch.nn.DataParallel(model) - print(compression_ctrl.statistics().to_str()) - return model - - -def compare_activations(ir_dump_txt, torch_dump_npy): - with open(ir_dump_txt, "r", encoding="utf8") as fin: - first_line = fin.readline() - if "shape:" in first_line: - data = fin.read().splitlines(True) - with open(ir_dump_txt, "w", encoding="utf8") as fout: - fout.writelines(data) - ie = np.loadtxt(ir_dump_txt, dtype=np.float32) - pt = np.load(torch_dump_npy) - print("Size, [ MIN, MAX ]") - print_info = lambda np_array: print( - "{} [{:.3f}, {:.3f}]".format(np_array.size, np_array.min().item(), np_array.max().item()) - ) - print_info(ie) - print_info(pt) - print("Maximum of absolute difference: {:.7f}".format(abs(ie - pt).max())) - - -def print_args(args): - for arg in sorted(vars(args)): - print("{: <27s}: {}".format(arg, getattr(args, arg))) diff --git a/tools/debug/compare_accuracy.py b/tools/debug/compare_accuracy.py deleted file mode 100644 index 017748b1a9c..00000000000 --- a/tools/debug/compare_accuracy.py +++ /dev/null @@ -1,166 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import os -from functools import partial - -from openvino.inference_engine import IENetwork -from openvino.inference_engine import IEPlugin -from openvino.inference_engine import get_version - -from nncf.config import NNCFConfig -from nncf.torch.dynamic_graph.graph_tracer import create_input_infos -from tools.ir_utils import get_ir_paths - - -def getExecNet(plugin, net): - return plugin.load(network=net) - - -argparser = argparse.ArgumentParser() -argparser.add_argument("-m", "--model", help="input IR name", required=True) -argparser.add_argument("--bin", help="Input *.bin file name") -argparser.add_argument("-o", "--output-dir", help="Output directory to dump weights", required=True) -argparser.add_argument("-c", "--config", type=str, help="Model's config", required=True) -argparser.add_argument("--cuda", help="inference PyTorch model on CUDA", action="store_true") -argparser.add_argument("--data", metavar="DIR", help="path to dataset", required=True) -argparser.add_argument( - "--cpu-plugin-dir", - metavar="DIR", - help="path to the directory with CPU Plugin and CPU Extension libraries", - required=True, -) -argparser.add_argument("-n", "--num-layers", type=int, default=-1, help="Dump activations for given number of layers") -argparser.add_argument("--dump", action="store_true", help="Enables dump of activations") - -args = argparser.parse_args() - - -def validate_torch_model(output_dir, config, num_layers, dump, val_loader=None, cuda=False): - from tools.debug.common import load_torch_model - from tools.debug.common import register_print_hooks - - if not os.path.exists(output_dir): - os.makedirs(output_dir) - - model = load_torch_model(config, cuda) - - model_e = model.eval() - if dump: - register_print_hooks(output_dir, model_e, num_layers=num_layers, data_to_compare=None, dump_activations=True) - - validate_general(val_loader, model_e, infer_pytorch_model, cuda) - - -def main(): - model_bin, model_xml = get_ir_paths(args.model, args.bin) - - config = NNCFConfig.from_json(args.config) - - input_infos_list = create_input_infos(config) - image_size = input_infos_list[0].shape[-1] - - size = int(image_size / 0.875) - - print("IE version: {}".format(get_version())) - - # NOTE: importing torch after loading IE to plugin to avoid issue with built-in MKLDNN of PyTorch - plugin = IEPlugin(device="CPU", plugin_dirs=args.cpu_plugin_dir) - plugin.add_cpu_extension(os.path.join(args.cpu_plugin_dir, "libcpu_extension.so")) - net = IENetwork(model=model_xml, weights=model_bin) - exec_net = getExecNet(plugin, net) - from torch.utils.data import DataLoader - from torchvision import datasets - from torchvision import transforms - - val_loader = DataLoader( - datasets.ImageFolder( - args.data, - transforms.Compose( - [ - transforms.Resize(size), - transforms.CenterCrop(image_size), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ] - ), - ), - batch_size=1, - shuffle=False, - num_workers=4, - pin_memory=True, - ) - if not os.path.exists(args.output_dir): - os.makedirs(args.output_dir) - config["log_dir"] = args.output_dir - - infer_fn = partial(infer_ie_model, net=net) - validate_general(val_loader, exec_net, infer_fn) - - validate_torch_model( - os.path.join(args.output_dir, "PTH"), - config=config, - num_layers=args.num_layers, - dump=args.dump, - val_loader=val_loader, - cuda=args.cuda, - ) - - -def infer_ie_model(exec_net, inputs, net): - input_cpu = inputs.numpy() - input_name = next(iter(net.inputs)) - output_name = next(iter(net.outputs)) - res = exec_net.infer(inputs={input_name: input_cpu}) - output = res[output_name] - import torch - - torch_output = torch.from_numpy(output) - return torch_output - - -def infer_pytorch_model(model, inputs): - return model(inputs) - - -def validate_general(val_loader, model, infer_model_fn, cuda=False): - from examples.torch.classification.main import AverageMeter - from examples.torch.classification.main import accuracy - - top1 = AverageMeter() - top5 = AverageMeter() - - for i, (input_, target) in enumerate(val_loader): - # compute output - output = infer_model_fn(model, input_) - - if cuda: - target = target.cuda(None, non_blocking=True) - # measure accuracy and record loss - acc1, acc5 = accuracy(output, target, topk=(1, 5)) - - top1.update(acc1, input_.size(0)) - top5.update(acc5, input_.size(0)) - - if i % 10 == 0: - print( - "IE Test : [{0}/{1}]\t" - "Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t" - "Acc@5 {top5.val:.3f} ({top5.avg:.3f})".format(i, len(val_loader), top1=top1, top5=top5) - ) - - print(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}".format(top1=top1, top5=top5)) - return top1.avg, top5.avg - - -if __name__ == "__main__": - main() diff --git a/tools/debug/compare_dump.py b/tools/debug/compare_dump.py deleted file mode 100644 index 7476a7fd4c6..00000000000 --- a/tools/debug/compare_dump.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import os -from os.path import isdir -from os.path import isfile -from os.path import join - -import torch - -from tools.debug.common import print_args - -argparser = argparse.ArgumentParser() -argparser.add_argument( - "-r", - "--ref-dir", - help="Path to ref folder. Treated whether ref experiment, or dir to compare GPU dumps", - required=True, -) -argparser.add_argument( - "-c", "--cmp-dirs", help="List of path to dirs with experiments for comparison with ref folder 0,1,2...", nargs="+" -) -argparser.add_argument( - "--range", - type=int, - help="Right border of range, starting from ref dir number", -) -argparser.add_argument("--eps", help="Torelance for maximum of absolute difference", default=None, type=float) - -args = argparser.parse_args() -print_args(args) - - -def basename(x): - return os.path.basename(x) - - -def get_dirs(root_path): - return [join(root_path, f) for f in sorted(os.listdir(root_path), key=basename) if isdir(join(root_path, f))] - - -def get_files(root_path): - return [f for f in sorted(os.listdir(root_path), key=basename) if isfile(join(root_path, f))] - - -def get_dir_pairs(dump_dir): - dirs = get_dirs(dump_dir) - return [(dirs[0], cmp_dir) for cmp_dir in dirs[1:]] - - -def compare_dump_in_dir_pairs(pairs, eps=None): - global_max_diff = 0 - global_count_diff = 0 - for ref_dir, cmp_dir in pairs: - max_diff = compare_dump_in_file_pairs(ref_dir, cmp_dir, eps) - if max_diff != 0: - global_count_diff += 1 - if max_diff > global_max_diff: - global_max_diff = max_diff - print("\n\nGlobal MAX abs diff: {}\n{}/{} is different".format(global_max_diff, global_count_diff, len(pairs))) - - -def compare_dump_in_file_pairs(ref_dir, cmp_dir, eps): - ref_files = get_files(ref_dir) - cmp_files = get_files(cmp_dir) - max_diff = 0 - count_diff = 0 - print("\n\nCompare {} vs {}".format(ref_dir, cmp_dir)) - for rf in ref_files: - rt = torch.load(os.path.join(ref_dir, rf)) - if rf in cmp_files: - ct = torch.load(os.path.join(cmp_dir, rf)) - rn = os.path.basename(rf) - diff = abs(rt - ct).max() - if diff != 0: - count_diff += 1 - if diff > max_diff: - max_diff = diff - if eps is not None: - if diff >= eps: - if "scale" in rf: - print("____{} vs {}_____, diff={} for {}".format(rt.item(), ct.item(), diff, rn)) - else: - print("diff={} for {}".format(diff, rn)) - - else: - print("not matched file {}".format(rf)) - print("Max abs diff: {}\n{}/{} is different".format(max_diff, count_diff, len(ref_files))) - return max_diff - - -def main(): - ref_dir = args.ref_dir - cmp_dirs = args.cmp_dirs - range_ = args.range - dir_pairs = [] - if cmp_dirs: - for cmp_dir in cmp_dirs: - dir_pairs += list(zip(get_dirs(cmp_dir), get_dirs(ref_dir))) - elif range_: - lb = int(os.path.basename(ref_dir)) - parent_dir = os.path.abspath(os.path.join(ref_dir, os.pardir)) - for i in range(lb + 1, range_ + 1): - cmp_dir = os.path.join(parent_dir, str(i)) - dir_pairs += list(zip(get_dirs(cmp_dir), get_dirs(ref_dir))) - else: - dir_pairs = get_dir_pairs(ref_dir) - - compare_dump_in_dir_pairs(dir_pairs, args.eps) - - -if __name__ == "__main__": - main() diff --git a/tools/debug/compare_weights.py b/tools/debug/compare_weights.py deleted file mode 100644 index de2c89dc240..00000000000 --- a/tools/debug/compare_weights.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import json -import os -from collections import OrderedDict - -import defusedxml.cElementTree as ET -import numpy as np -from torch import randn - -from tools.debug.common import get_full_dump_paths -from tools.debug.common import load_torch_model -from tools.debug.common import print_args -from tools.debug.common import register_print_hooks -from tools.debug.common import save_dump -from tools.ir_utils import find_all_parameters -from tools.ir_utils import get_ir_paths - -argparser = argparse.ArgumentParser() -argparser.add_argument("-m", "--model", help="input IR name", required=True) -argparser.add_argument("--bin", help="Input *.bin file name") -argparser.add_argument("-o", "--output-dir", help="Output directory to dump weights", required=True) -argparser.add_argument("-c", "--config", type=str, default="config.json", help="Model's config", required=True) -argparser.add_argument("-n", "--num-layers", type=int, default=-1, help="Compare weights for given number of layers") -argparser.add_argument("--ignore", help="comma separated list of ignored layers", default="") -args = argparser.parse_args() -print_args(args) - - -def main(): - model_bin, model_xml = get_ir_paths(args.model, args.bin) - - if not os.path.exists(args.output_dir): - os.makedirs(args.output_dir) - - ir_weights = collect_IR_weights(os.path.join(args.output_dir, "IR"), model_xml, model_bin, args.num_layers) - - with open(args.config, encoding="utf8") as f: - config = json.load(f) - torch_weights = collect_torch_weights(os.path.join(args.output_dir, "PTH"), config, args.num_layers) - - assert len(ir_weights) == len(torch_weights), "{} vs {}".format(len(ir_weights), len(torch_weights)) - print("Maximum of absolute difference - IR vs Torch") - max_max = [] - for (k1, v1), (k2, v2) in zip(ir_weights.items(), torch_weights.items()): - max_diff = abs(v1 - v2).max() - max_max.append(max_diff) - print("{0:.5} - max diff [{1:}] vs [{2:}]".format(max_diff, k1, k2)) - print("Global maximum: {0:.5}".format(np.max(max_max))) - - -def collect_IR_weights(output_dir, model_xml, model_bin, num_layers): - data_to_compare = OrderedDict() - print("IR loaded from {}".format(model_bin)) - with open(model_bin, "rb") as f: - buffer = f.read() - - ignored = args.ignore.split(",") + get_ignored_layers(model_xml, args.num_layers) - - all_parameters = find_all_parameters(buffer, model_xml) - - if not os.path.exists(output_dir): - os.makedirs(output_dir) - idx = 0 - - for name, param in all_parameters.items(): - if name.split(".")[0] in ignored or "bias" in name: - continue - if (num_layers > 0 and idx < num_layers) or (num_layers == -1): - name = name.replace(os.path.sep, "_") - dump_name = ".".join([str(idx), name]) - output_data = param.data.flatten() - save_dump(dump_name, output_dir, output_data) - data_to_compare[dump_name] = output_data - idx += 1 - return data_to_compare - - -def collect_torch_weights(output_dir, config, num_layers): - if not os.path.exists(output_dir): - os.makedirs(output_dir) - - model = load_torch_model(config) - model_e = model.eval() - - data_to_compare = OrderedDict() - - register_print_hooks( - output_dir, model_e, num_layers=num_layers, data_to_compare=data_to_compare, dump_activations=False - ) - input_ = randn(config["input_sample_size"]) - model_e(input_) - - for _, module in enumerate(model_e.modules()): - paths = get_full_dump_paths(module) - if paths is not None: - for dump_path in paths: - if os.path.isfile(dump_path): - data_to_compare[os.path.splitext(os.path.basename(dump_path))[0]] = np.load(dump_path) - return data_to_compare - - -def get_ignored_layers(model_xml, num_layers=1): - ir_tree = ET.parse(model_xml) - ignored_layers = [] - all_supported = [l for l in ir_tree.iter("layer") if l.get("type") == ("Convolution", "FullyConnected")] - if num_layers > 0: - ignored_layers += [layer.get("name") for layer in all_supported[num_layers:]] - all_bns = [l for l in ir_tree.iter("layer") if l.get("type") == "ScaleShift"] - ignored_layers += [bn.get("name") for bn in all_bns] - return ignored_layers - - -if __name__ == "__main__": - main() diff --git a/tools/debug/debug_sample.py b/tools/debug/debug_sample.py deleted file mode 100644 index f2485cf0747..00000000000 --- a/tools/debug/debug_sample.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import re -from pathlib import Path - -import torch - - -def create_experiment_dir(dump_dir): - os.makedirs(dump_dir, exist_ok=True) - next_id = 0 - if not re.match(r"\d+", Path(dump_dir).parts[-1]): - ids = [int(f) for f in os.listdir(dump_dir) if f.isnumeric()] - if ids: - next_id = max(ids) + 1 - dump_path = os.path.join(dump_dir, str(next_id)) - os.makedirs(dump_path) - return dump_path - - -def register_dump_hooks(model, dump_dir, num_item_to_dump=10): - os.makedirs(dump_dir, exist_ok=True) - next_id = 0 - if not re.match(r"\d+/\d+", Path(dump_dir).parts[-1]): - ids = [int(f) for f in os.listdir(dump_dir) if f.isnumeric()] - if ids: - next_id = max(ids) + 1 - print(next_id) - dump_path = os.path.join(dump_dir, str(next_id)) - os.makedirs(dump_path) - handles = [] - name_idx = 0 - for name, module in model.named_modules(): - if name: - module.full_dump_path = os.path.join( - dump_path, str("{:04d}".format(name_idx)) + "_" + name.replace("/", "_") - ) - name_idx += 1 - - def hook(module, inputs, output): - idx = 0 - for input_ in inputs: - path = module.full_dump_path + "_input_{}".format(str(idx)) - # print('saving input to {}'.format(path)) - data = torch.flatten(input_)[:num_item_to_dump].cpu() - torch.save(data, path) - idx += 1 - path = module.full_dump_path + "_output" - # print('saving output to {}'.format(path)) - data = torch.flatten(output)[:num_item_to_dump].cpu() - torch.save(data, path) - if hasattr(module, "weight"): - data = torch.flatten(module.weight)[:num_item_to_dump].cpu() - path = module.full_dump_path + "_weight" - print("saving weight to {}".format(path)) - torch.save(data, path) - if hasattr(module, "scale"): - data = module.scale.cpu() - path = module.full_dump_path + "_scale" - print("saving scales to {}".format(path)) - torch.save(data, path) - - handles.append(module.register_forward_hook(hook)) - return handles diff --git a/tools/extract_ov_subgraph.py b/tools/extract_ov_subgraph.py new file mode 100644 index 00000000000..d169308156a --- /dev/null +++ b/tools/extract_ov_subgraph.py @@ -0,0 +1,306 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import shutil +import xml.etree.ElementTree as ET +from copy import copy +from copy import deepcopy +from pathlib import Path +from pprint import pprint +from typing import Any, Dict + +import defusedxml.ElementTree as dET +import networkx as nx + + +def xml_to_dict(element: ET.Element): + result = {} + if element.attrib: + result["attributes"] = element.attrib + for child in element: + child_dict = xml_to_dict(child) + if child.tag in result: + if isinstance(result[child.tag], list): + result[child.tag].append(child_dict) + else: + result[child.tag] = [result[child.tag], child_dict] + else: + result[child.tag] = child_dict + if element.text: + result["text"] = element.text + if element.tail: + result["tail"] = element.tail + return result + + +def dict_to_xml(data: Any, parent: ET.Element): + if isinstance(data, dict): + for tag_name, value in data.items(): + if tag_name == "attributes": + parent.attrib.update(value) + elif tag_name == "text": + parent.text = value + elif tag_name == "tail": + parent.tail = value + elif isinstance(value, list): + for item in value: + elem = ET.SubElement(parent, tag_name) + dict_to_xml(item, elem) + else: + elem = ET.SubElement(parent, tag_name) + dict_to_xml(value, elem) + else: + parent.text = str(data) + + +def get_edges(xml_dict: Dict): + def add_edge(edges: Dict, from_layer: int, from_port: int, to_layer: int, to_port: int): + if from_layer not in edges: + edges[from_layer] = {} + if from_port not in edges[from_layer]: + edges[from_layer][from_port] = {} + assert (to_layer, to_port) not in edges[from_layer][from_port] + edges[from_layer][from_port][(to_layer, to_port)] = {} + + edges = {} + for edge in xml_dict["edges"]["edge"]: + edge = edge["attributes"] + + from_layer = int(edge["from-layer"]) + from_port = int(edge["from-port"]) + to_layer = int(edge["to-layer"]) + to_port = int(edge["to-port"]) + + add_edge(edges, from_layer, from_port, to_layer, to_port) + add_edge(edges, to_layer, to_port, from_layer, from_port) + + return edges + + +def get_nodes(xml_dict: Dict, edges: Dict): + all_node_names = set() + nodes = {} + for node in xml_dict["layers"]["layer"]: + try: + attributes = node["attributes"] + data = node["data"]["attributes"] if "data" in node else None + inp = node["input"] if "input" in node else None + out = node["output"] if "output" in node else None + + node_id = int(attributes["id"]) + node_name = attributes["name"] + node_type = attributes["type"] + + assert node_name not in all_node_names + all_node_names.add(node_name) + + assert node_id not in nodes + nodes[node_id] = { + "name": node_name, + "type": node_type, + } + + node_dtype = data["element_type"] if data is not None and "element_type" in data else None + node_shape = data["shape"] if data is not None and "shape" in data else None + if node_dtype is not None: + nodes[node_id]["dtype"] = node_dtype + if node_shape is not None: + nodes[node_id]["shape"] = node_shape + + input_ports = [] if inp is None else inp["port"] + output_ports = [] if out is None else out["port"] + if isinstance(input_ports, dict): + input_ports = [input_ports] + if isinstance(output_ports, dict): + output_ports = [output_ports] + + for port, is_input in zip( + input_ports + output_ports, [True] * len(input_ports) + [False] * len(output_ports) + ): + from_port = int(port["attributes"]["id"]) + precision = port["attributes"]["precision"] + if "dim" in port["attributes"]: + dim = port["attributes"]["dim"] + elif "dim" in port: + dim = port["dim"] + else: + dim = [] + if isinstance(dim, dict): + dim = [dim] + shape = tuple(int(it["text"]) for it in dim) + + # Update properties of the edges leading from this port + if from_port not in edges[node_id]: + # Some edge descriptions may be missing in execution graph + continue + else: + edge = edges[node_id][from_port] + for (to_node_id, to_port), edge_properties_dict in edge.items(): + for name, value in zip(("precision", "shape", "is_input"), (precision, shape, is_input)): + assert name not in edge_properties_dict + edge_properties_dict[name] = value + except Exception as e: + pprint(node) + raise e + + return nodes + + +def create_nx_graph(xml_dict: Dict): + def get_node_label(nodes: Dict, node_id: int): + return nodes[node_id]["name"] + + def get_edge_label(edges: Dict, nodes: Dict, from_node: int, from_port: int, to_node: int, to_port: int): + edge_properties = edges[from_node][from_port][(to_node, to_port)] + return f'"{edge_properties["shape"]}\n{from_port}->{to_port}"' + + edges = get_edges(xml_dict) + nodes = get_nodes(xml_dict, edges) + + G = nx.Graph() + + # Add nodes + for node_id, node_properties in nodes.items(): + node_properties_copy = copy(node_properties) + node_properties_copy["id"] = node_id + G.add_node(get_node_label(nodes, node_id), **node_properties_copy) + + # Add edges + for node_id, from_port_dict in edges.items(): + for from_port, to_port_dict in from_port_dict.items(): + for (to_node_id, to_port), edge_properties in to_port_dict.items(): + G.add_edge( + u_of_edge=get_node_label(nodes, node_id), + v_of_edge=get_node_label(nodes, to_node_id), + label=get_edge_label(edges, nodes, node_id, from_port, to_node_id, to_port), + **edge_properties, + ) + + return G + + +def write_xml(xml_dict: Dict, filepath: Path): + write_root = ET.Element("net") + dict_to_xml(xml_dict, write_root) + xml_str = ET.tostring(write_root).decode() + xml_str = '<?xml version="1.0"?>\n' + xml_str + "\n" + with open(filepath, "w") as f: + f.write(xml_str) + + +def take_model_subgraph(xml_dict: Dict, source_node_name: str, distance: int): + # Create networkx graph from IR xml dictionary + G = create_nx_graph(xml_dict) + + # Traverse graph from target node + dfs_tree = nx.traversal.dfs_tree(G, source=source_node_name, depth_limit=distance) + node_names = set(dfs_tree.nodes) + node_ids = set([G.nodes[it]["id"] for it in node_names]) + + # Keep only the visited nodes + result_xml_dict = deepcopy(xml_dict) + result_xml_dict["layers"]["layer"] = [] + for layer in xml_dict["layers"]["layer"]: + node_name = layer["attributes"]["name"] + if node_name in node_names: + result_xml_dict["layers"]["layer"].append(layer) + + # Keep only the edges that connect the visited nodes + result_xml_dict["edges"]["edge"] = [] + for edge in xml_dict["edges"]["edge"]: + from_layer = int(edge["attributes"]["from-layer"]) + to_layer = int(edge["attributes"]["to-layer"]) + if from_layer in node_ids or to_layer in node_ids: + result_xml_dict["edges"]["edge"].append(edge) + + return result_xml_dict + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Extract a subgraph from a model in OpenVINO Intermediate Representation format.\n\nSubgraph is " + "taken around a given node. Use distance parameter to control how many nodes around the given one to include. " + "The resulting subgraph is saved next to the input .xml file or at --output_path if provided. Additionally, a " + "symbolic link targeting the original .bin file is created.", + epilog="Usage examples:\n" + ' python ir_subgraph.py openvino.xml "Constant_1116858"\n' + ' python ir_subgraph.py openvino.xml "Constant_1116858" --distance 5\n' + ' python ir_subgraph.py openvino.xml "Constant_1116858" --output-path ./subgraphs\n' + ' python ir_subgraph.py openvino.xml "Constant_1116858" --output-path ./subgraphs/Constant_1116858.xml\n', + formatter_class=argparse.RawTextHelpFormatter, + ) + + parser.add_argument("input-path", help="Input IR path.") + parser.add_argument("node", help="Target node name.") + parser.add_argument("--distance", type=int, default=10, help="Distance around the target node (default 10).") + parser.add_argument( + "--output-path", + dest="output_path", + help="Output IR path. Can either be a file path with .xml extension or a directory path.", + ) + + args = parser.parse_args() + + input_path = Path(args.__dict__["input-path"]) + node_name = args.node + distance = args.distance + output_path = Path(args.output_path) if args.output_path is not None else None + + if distance <= 0: + raise ValueError("Distance should be positive") + + if output_path is None or output_path.suffix == "": + output_filename = f"{input_path.stem}_{Path(node_name).stem}_{distance}.xml" + if output_path is None: + output_dir = input_path.parent + output_path = input_path.parent / output_filename + else: + output_dir = output_path + output_path = output_dir / output_filename + else: + output_dir = output_path.parent + + if output_path.exists(): + raise ValueError(f"There is already and IR at {output_path}. Exiting.") + + # Read IR xml as dict + tree = dET.parse(input_path) + root = tree.getroot() + xml_dict = xml_to_dict(root) + + # Take subgraph + subgraph_xml_dict = take_model_subgraph(xml_dict, source_node_name=node_name, distance=distance) + + # Save subgraph xml + if not output_dir.exists(): + output_dir.mkdir(parents=True) + write_xml(subgraph_xml_dict, output_path) + + # Create a symbolic link to original .bin file + bin_input_path = input_path.with_suffix(".bin") + bin_output_path = output_path.with_suffix(".bin") + if bin_output_path.exists(): + os.remove(bin_output_path) + try: + bin_output_path.symlink_to(os.path.relpath(bin_input_path, bin_output_path.parent)) + except OSError as e: + if "[WinError 1314]" in str(e): + if bin_input_path.exists(): + print("Copying original .bin file because can't create a symbolic link due to lack of admin privileges") + shutil.copy(bin_input_path, bin_output_path) + else: + print("Didn't create a copy of original .bin file because it is missing") + else: + raise e + + print("Saved at:", output_path) diff --git a/tools/optimize_tf_graph.py b/tools/optimize_tf_graph.py index d76c6d5d2b7..b83e1a12066 100644 --- a/tools/optimize_tf_graph.py +++ b/tools/optimize_tf_graph.py @@ -1,3 +1,14 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import tensorflow as tf from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import meta_graph_pb2 diff --git a/tools/pb_to_tb.py b/tools/pb_to_tb.py index 9240ac529d1..9b418f8dff5 100644 --- a/tools/pb_to_tb.py +++ b/tools/pb_to_tb.py @@ -1,3 +1,14 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # pylint:skip-file import shutil from pathlib import Path diff --git a/tools/render_dot_to_svg.py b/tools/render_dot_to_svg.py index 575a2307958..452a3b8e0dc 100644 --- a/tools/render_dot_to_svg.py +++ b/tools/render_dot_to_svg.py @@ -1,3 +1,14 @@ +# Copyright (c) 2023 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """ Useful when the .dot file is so large that no tool, online or offline, can effectively visualize it. """ diff --git a/tools/update_eval_results.py b/tools/update_eval_results.py index 58744599409..116821c9787 100644 --- a/tools/update_eval_results.py +++ b/tools/update_eval_results.py @@ -2,7 +2,7 @@ # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 +# http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. @@ -10,7 +10,6 @@ # limitations under the License. import argparse -import fileinput # pylint:skip-file import json @@ -63,7 +62,7 @@ class SampleDescriptor: TORCH_EXAMPLES_PATH = PROJECT_ROOT / "examples" / "torch" TF_EXAMPLES_PATH = PROJECT_ROOT / "examples" / "tensorflow" -TORCH_SAMPLE_TYPE_TO_DESCRIPTOR = { +TORCH_SAMPLE_TYPE_TO_DESCRIPTOR: Dict[str, SampleDescriptor] = { "classification": SampleDescriptor( path_to_readme=TORCH_EXAMPLES_PATH / "classification" / "README.md", result_table_anchor_in_main_readme='<a name="pytorch_classification"></a>', @@ -130,10 +129,9 @@ class SampleDescriptor: ) ], ), -} # type: Dict[str, SampleDescriptor] +} - -TF_SAMPLE_TYPE_TO_DESCRIPTOR = { +TF_SAMPLE_TYPE_TO_DESCRIPTOR: Dict[str, SampleDescriptor] = { "classification": SampleDescriptor( path_to_readme=TF_EXAMPLES_PATH / "classification" / "README.md", result_table_anchor_in_main_readme='<a name="tensorflow_classification"></a>', @@ -168,7 +166,7 @@ class SampleDescriptor: ) ], ), -} # type: Dict[str, SampleDescriptor] +} def get_fp32_and_compressed_metrics(