diff --git a/nncf/common/graph/graph_matching.py b/nncf/common/graph/graph_matching.py index 1e8991fed99..50784176f54 100644 --- a/nncf/common/graph/graph_matching.py +++ b/nncf/common/graph/graph_matching.py @@ -15,10 +15,12 @@ from nncf.common.graph.patterns import GraphPattern +ATTRS_TO_SKIP = [GraphPattern.LABEL_ATTR, GraphPattern.PATTERN_NODE_TO_EXCLUDE] + def _are_nodes_matched(node_1, node_2) -> bool: for attr in node_2: - if attr == GraphPattern.LABEL_ATTR: + if attr in ATTRS_TO_SKIP: continue if attr == GraphPattern.METATYPE_ATTR: # GraphPattern.ANY_PATTERN_NODE_TYPE and GraphPattern.NON_PATTERN_NODE_TYPE @@ -103,7 +105,8 @@ def _is_subgraph_matching_strict(graph: nx.DiGraph, pattern: nx.DiGraph, subgrap def _copy_subgraph_excluding_non_pattern_node(subgraph: Dict[str, str], pattern_graph: GraphPattern) -> Dict[str, str]: """ - Copies a matching subgraph excluding the nodes having GraphPattern.NON_PATTERN_NODE_TYPE. + Copies a matching subgraph excluding the nodes having GraphPattern.NON_PATTERN_NODE_TYPE + or GraphPattern.PATTERN_NODE_TO_EXCLUDE. :param subgraph: Subgraph :param pattern_graph: A graph consists of patterns to match. @@ -113,8 +116,12 @@ def _copy_subgraph_excluding_non_pattern_node(subgraph: Dict[str, str], pattern_ for node_from_graph, node_from_pattern in subgraph.items(): pattern_node = pattern_graph.graph.nodes[node_from_pattern] pattern_node_types = pattern_node.get(GraphPattern.METATYPE_ATTR, []) - if GraphPattern.NON_PATTERN_NODE_TYPE not in pattern_node_types: - output[node_from_graph] = node_from_pattern + if GraphPattern.NON_PATTERN_NODE_TYPE in pattern_node_types: + continue + if pattern_node.get(GraphPattern.PATTERN_NODE_TO_EXCLUDE, False): + continue + output[node_from_graph] = node_from_pattern + return output diff --git a/nncf/common/graph/patterns/patterns.py b/nncf/common/graph/patterns/patterns.py index 0c97a9d1eec..08e4719feff 100644 --- a/nncf/common/graph/patterns/patterns.py +++ b/nncf/common/graph/patterns/patterns.py @@ -80,6 +80,7 @@ class GraphPattern: NODE_TYPE_ATTR = "metatype" ANY_PATTERN_NODE_TYPE = "ANY_PATTERN_NODE" NON_PATTERN_NODE_TYPE = "NON_PATTERN_NODE" + PATTERN_NODE_TO_EXCLUDE = "PATTERN_NODE_TO_EXCLUDE" def __init__(self): self._graph = nx.DiGraph() @@ -298,7 +299,6 @@ class HWFusedPatternNames(Enum): NORMALIZE_L2_MULTIPLY = PatternDesc("normalize_l2_multiply") SCALE_SHIFT = PatternDesc("scale_shift") SHIFT_SCALE = PatternDesc("shift_scale") - SE_BLOCK = PatternDesc("se_block") SOFTMAX_DIV = PatternDesc("softmax_div") # ACTIVATIONS @@ -396,5 +396,6 @@ class IgnoredPatternNames(Enum): model_types=[ModelType.TRANSFORMER], devices=[TargetDevice.ANY, TargetDevice.CPU, TargetDevice.GPU, TargetDevice.VPU], ) + SE_BLOCK = PatternDesc("se_block") FC_BN_HSWISH_ACTIVATION = PatternDesc("fc_bn_hswish_activation") EQUAL_LOGICALNOT = PatternDesc("equal_logicalnot") diff --git a/nncf/openvino/hardware/fused_patterns.py b/nncf/openvino/hardware/fused_patterns.py index 6e1596e47e0..df11b83aa4e 100644 --- a/nncf/openvino/hardware/fused_patterns.py +++ b/nncf/openvino/hardware/fused_patterns.py @@ -147,48 +147,6 @@ def create_shift_scale() -> GraphPattern: return pattern -@OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SE_BLOCK) -def create_se_block() -> GraphPattern: - pattern = GraphPattern() - any_node = pattern.add_node( - **{GraphPattern.LABEL_ATTR: "ANY", GraphPattern.METATYPE_ATTR: GraphPattern.NON_PATTERN_NODE_TYPE} - ) - reduce_mean_node = pattern.add_node( - **{GraphPattern.LABEL_ATTR: "REDUCE_MEAN", GraphPattern.METATYPE_ATTR: om.OVReduceMeanMetatype} - ) - linear_node_1 = pattern.add_node( - **{GraphPattern.METATYPE_ATTR: LINEAR_OPERATIONS, GraphPattern.LABEL_ATTR: "LINEAR"} - ) - add_node_1 = pattern.add_node(**{GraphPattern.LABEL_ATTR: "ADD_BIAS", GraphPattern.METATYPE_ATTR: om.OVAddMetatype}) - activation_node_1 = pattern.add_node( - **{ - GraphPattern.LABEL_ATTR: "RELU, PRELU, SWISH", - GraphPattern.METATYPE_ATTR: [om.OVReluMetatype, om.OVPReluMetatype, om.OVSwishMetatype], - } - ) - linear_node_2 = pattern.add_node( - **{GraphPattern.METATYPE_ATTR: LINEAR_OPERATIONS, GraphPattern.LABEL_ATTR: "LINEAR"} - ) - add_node_2 = pattern.add_node(**{GraphPattern.LABEL_ATTR: "ADD_BIAS", GraphPattern.METATYPE_ATTR: om.OVAddMetatype}) - activation_node_2 = pattern.add_node( - **{GraphPattern.LABEL_ATTR: "SIGMOID", GraphPattern.METATYPE_ATTR: om.OVSigmoidMetatype} - ) - multiply_node = pattern.add_node( - **{GraphPattern.LABEL_ATTR: "MULTIPLY", GraphPattern.METATYPE_ATTR: om.OVMultiplyMetatype} - ) - - pattern.add_edge(any_node, reduce_mean_node) - pattern.add_edge(reduce_mean_node, linear_node_1) - pattern.add_edge(linear_node_1, add_node_1) - pattern.add_edge(add_node_1, activation_node_1) - pattern.add_edge(activation_node_1, linear_node_2) - pattern.add_edge(linear_node_2, add_node_2) - pattern.add_edge(add_node_2, activation_node_2) - pattern.add_edge(activation_node_2, multiply_node) - pattern.add_edge(any_node, multiply_node) - return pattern - - @OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SOFTMAX_DIV) def create_softmax_div() -> GraphPattern: pattern = GraphPattern() diff --git a/nncf/openvino/quantization/ignored_patterns.py b/nncf/openvino/quantization/ignored_patterns.py index b61d11e933e..96a89a5fa86 100644 --- a/nncf/openvino/quantization/ignored_patterns.py +++ b/nncf/openvino/quantization/ignored_patterns.py @@ -12,6 +12,7 @@ from nncf.common.graph.patterns.patterns import IgnoredPatternNames from nncf.common.utils.registry import Registry from nncf.openvino.graph.metatypes import openvino_metatypes as om +from nncf.openvino.graph.metatypes.groups import LINEAR_OPERATIONS OPENVINO_IGNORED_PATTERNS = Registry("IGNORED_PATTERNS") @@ -108,3 +109,53 @@ def create_equal_logicalnot() -> GraphPattern: pattern.add_edge(equal_node, logical_not_node) return pattern + + +@OPENVINO_IGNORED_PATTERNS.register(IgnoredPatternNames.SE_BLOCK) +def create_se_block() -> GraphPattern: + pattern = GraphPattern() + any_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "ANY", GraphPattern.METATYPE_ATTR: GraphPattern.NON_PATTERN_NODE_TYPE} + ) + reduce_mean_node = pattern.add_node( + **{ + GraphPattern.LABEL_ATTR: "REDUCE_MEAN", + GraphPattern.METATYPE_ATTR: om.OVReduceMeanMetatype, + GraphPattern.PATTERN_NODE_TO_EXCLUDE: True, + } + ) + linear_node_1 = pattern.add_node( + **{GraphPattern.METATYPE_ATTR: LINEAR_OPERATIONS, GraphPattern.LABEL_ATTR: "LINEAR"} + ) + add_node_1 = pattern.add_node(**{GraphPattern.LABEL_ATTR: "ADD_BIAS", GraphPattern.METATYPE_ATTR: om.OVAddMetatype}) + activation_node_1 = pattern.add_node( + **{ + GraphPattern.LABEL_ATTR: "RELU, PRELU, SWISH", + GraphPattern.METATYPE_ATTR: [om.OVReluMetatype, om.OVPReluMetatype, om.OVSwishMetatype], + } + ) + linear_node_2 = pattern.add_node( + **{GraphPattern.METATYPE_ATTR: LINEAR_OPERATIONS, GraphPattern.LABEL_ATTR: "LINEAR"} + ) + add_node_2 = pattern.add_node(**{GraphPattern.LABEL_ATTR: "ADD_BIAS", GraphPattern.METATYPE_ATTR: om.OVAddMetatype}) + activation_node_2 = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "SIGMOID", GraphPattern.METATYPE_ATTR: om.OVSigmoidMetatype} + ) + multiply_node = pattern.add_node( + **{ + GraphPattern.LABEL_ATTR: "MULTIPLY", + GraphPattern.METATYPE_ATTR: om.OVMultiplyMetatype, + GraphPattern.PATTERN_NODE_TO_EXCLUDE: True, + } + ) + + pattern.add_edge(any_node, reduce_mean_node) + pattern.add_edge(reduce_mean_node, linear_node_1) + pattern.add_edge(linear_node_1, add_node_1) + pattern.add_edge(add_node_1, activation_node_1) + pattern.add_edge(activation_node_1, linear_node_2) + pattern.add_edge(linear_node_2, add_node_2) + pattern.add_edge(add_node_2, activation_node_2) + pattern.add_edge(activation_node_2, multiply_node) + pattern.add_edge(any_node, multiply_node) + return pattern diff --git a/nncf/torch/quantization/ignored_patterns.py b/nncf/torch/quantization/ignored_patterns.py index d4816cc482e..3f081d1341d 100644 --- a/nncf/torch/quantization/ignored_patterns.py +++ b/nncf/torch/quantization/ignored_patterns.py @@ -11,6 +11,8 @@ from nncf.common.graph.patterns.patterns import GraphPattern from nncf.common.graph.patterns.patterns import IgnoredPatternNames from nncf.common.utils.registry import Registry +from nncf.torch.graph.pattern_operations import ATOMIC_ACTIVATIONS_OPERATIONS +from nncf.torch.graph.pattern_operations import LINEAR_OPERATIONS PT_IGNORED_PATTERNS = Registry("IGNORED_PATTERNS") @@ -88,3 +90,126 @@ def create_multihead_attention_output() -> GraphPattern: transpose_aliases=transpose_aliases, ) return pattern + + +# pylint:disable=too-many-statements +@PT_IGNORED_PATTERNS.register(IgnoredPatternNames.SE_BLOCK) +def create_se_block() -> GraphPattern: + MEAN_OPERATIONS = { + GraphPattern.LABEL_ATTR: "REDUCE_MEAN", + GraphPattern.METATYPE_ATTR: ["avg_pool2d", "adaptive_avg_pool2d", "avg_pool3d", "adaptive_avg_pool3d", "mean"], + GraphPattern.PATTERN_NODE_TO_EXCLUDE: True, + } + SYGMOID_OPERATIONS = { + GraphPattern.LABEL_ATTR: "SIGMOID", + GraphPattern.METATYPE_ATTR: ["sigmoid", "hardsigmoid"], + } + MUL_OPERATION = { + GraphPattern.LABEL_ATTR: "MUL", + GraphPattern.METATYPE_ATTR: "__mul__", + GraphPattern.PATTERN_NODE_TO_EXCLUDE: True, + } + + def get_se_block_pattern() -> GraphPattern: + pattern = GraphPattern() + any_node = pattern.add_node(label="NON_PATTERN_NODE", type=GraphPattern.NON_PATTERN_NODE_TYPE) + reduce_mean_node = pattern.add_node(**MEAN_OPERATIONS) + linear_node_1 = pattern.add_node(**LINEAR_OPERATIONS) + activation_node_1 = pattern.add_node(**ATOMIC_ACTIVATIONS_OPERATIONS) + linear_node_2 = pattern.add_node(**LINEAR_OPERATIONS) + activation_node_2 = pattern.add_node(**SYGMOID_OPERATIONS) + multiply_node = pattern.add_node(**MUL_OPERATION) + + pattern.add_edge(any_node, reduce_mean_node) + pattern.add_edge(reduce_mean_node, linear_node_1) + pattern.add_edge(linear_node_1, activation_node_1) + pattern.add_edge(activation_node_1, linear_node_2) + pattern.add_edge(linear_node_2, activation_node_2) + pattern.add_edge(activation_node_2, multiply_node) + pattern.add_edge(any_node, multiply_node) + return pattern + + def get_se_block_with_bias_pattern() -> GraphPattern: + pattern = GraphPattern() + any_node = pattern.add_node(label="NON_PATTERN_NODE", type=GraphPattern.NON_PATTERN_NODE_TYPE) + reduce_mean_node = pattern.add_node(**MEAN_OPERATIONS) + linear_node_1 = pattern.add_node(**LINEAR_OPERATIONS) + add_node_1 = pattern.add_node(label="ADD_BIAS", type=["__add__", "__sub__"]) + activation_node_1 = pattern.add_node(**ATOMIC_ACTIVATIONS_OPERATIONS) + linear_node_2 = pattern.add_node(**LINEAR_OPERATIONS) + add_node_2 = pattern.add_node(label="ADD_BIAS", type=["__add__", "__sub__"]) + activation_node_2 = pattern.add_node(**SYGMOID_OPERATIONS) + multiply_node = pattern.add_node(**MUL_OPERATION) + + pattern.add_edge(any_node, reduce_mean_node) + pattern.add_edge(reduce_mean_node, linear_node_1) + pattern.add_edge(linear_node_1, add_node_1) + pattern.add_edge(add_node_1, activation_node_1) + pattern.add_edge(activation_node_1, linear_node_2) + pattern.add_edge(linear_node_2, add_node_2) + pattern.add_edge(add_node_2, activation_node_2) + pattern.add_edge(activation_node_2, multiply_node) + pattern.add_edge(any_node, multiply_node) + return pattern + + RESHAPE_NODES = { + GraphPattern.LABEL_ATTR: "RESHAPE", + GraphPattern.METATYPE_ATTR: ["reshape", "view", "flatten", "unsqueeze"], + } + + def get_se_block_with_reshape() -> GraphPattern: + pattern = GraphPattern() + any_node = pattern.add_node(label="NON_PATTERN_NODE", type=GraphPattern.NON_PATTERN_NODE_TYPE) + reduce_mean_node = pattern.add_node(**MEAN_OPERATIONS) + reshape_node_1 = pattern.add_node(**RESHAPE_NODES) + linear_node_1 = pattern.add_node(**LINEAR_OPERATIONS) + activation_node_1 = pattern.add_node(**ATOMIC_ACTIVATIONS_OPERATIONS) + linear_node_2 = pattern.add_node(**LINEAR_OPERATIONS) + activation_node_2 = pattern.add_node(**SYGMOID_OPERATIONS) + reshape_node_2 = pattern.add_node(**RESHAPE_NODES) + multiply_node = pattern.add_node(**MUL_OPERATION) + + pattern.add_edge(any_node, reduce_mean_node) + pattern.add_edge(reduce_mean_node, reshape_node_1) + pattern.add_edge(reshape_node_1, linear_node_1) + pattern.add_edge(linear_node_1, activation_node_1) + pattern.add_edge(activation_node_1, linear_node_2) + pattern.add_edge(linear_node_2, activation_node_2) + pattern.add_edge(activation_node_2, reshape_node_2) + pattern.add_edge(reshape_node_2, multiply_node) + pattern.add_edge(any_node, multiply_node) + return pattern + + def get_se_block_with_bias_and_reshape() -> GraphPattern: + pattern = GraphPattern() + any_node = pattern.add_node(label="NON_PATTERN_NODE", type=GraphPattern.NON_PATTERN_NODE_TYPE) + reduce_mean_node = pattern.add_node(**MEAN_OPERATIONS) + reshape_node_1 = pattern.add_node(**RESHAPE_NODES) + linear_node_1 = pattern.add_node(**LINEAR_OPERATIONS) + add_node_1 = pattern.add_node(label="ADD_BIAS", type=["__add__", "__sub__"]) + activation_node_1 = pattern.add_node(**ATOMIC_ACTIVATIONS_OPERATIONS) + linear_node_2 = pattern.add_node(**LINEAR_OPERATIONS) + add_node_2 = pattern.add_node(label="ADD_BIAS", type=["__add__", "__sub__"]) + activation_node_2 = pattern.add_node(**SYGMOID_OPERATIONS) + reshape_node_2 = pattern.add_node(**RESHAPE_NODES) + multiply_node = pattern.add_node(**MUL_OPERATION) + + pattern.add_edge(any_node, reduce_mean_node) + pattern.add_edge(reduce_mean_node, reshape_node_1) + pattern.add_edge(reshape_node_1, linear_node_1) + pattern.add_edge(linear_node_1, add_node_1) + pattern.add_edge(add_node_1, activation_node_1) + pattern.add_edge(activation_node_1, linear_node_2) + pattern.add_edge(linear_node_2, add_node_2) + pattern.add_edge(add_node_2, activation_node_2) + pattern.add_edge(activation_node_2, reshape_node_2) + pattern.add_edge(reshape_node_2, multiply_node) + pattern.add_edge(any_node, multiply_node) + return pattern + + main_pattern = GraphPattern() + main_pattern.add_pattern_alternative(get_se_block_pattern()) + main_pattern.add_pattern_alternative(get_se_block_with_bias_pattern()) + main_pattern.add_pattern_alternative(get_se_block_with_reshape()) + main_pattern.add_pattern_alternative(get_se_block_with_bias_and_reshape()) + return main_pattern diff --git a/tests/common/graph/test_graph_matching.py b/tests/common/graph/test_graph_matching.py index 089cd84f9dc..e6cdff2be24 100644 --- a/tests/common/graph/test_graph_matching.py +++ b/tests/common/graph/test_graph_matching.py @@ -227,3 +227,28 @@ def test_not_match_edges_inside_pattern(): pattern.add_edge(node_1, node_3) matches = find_subgraphs_matching_pattern(ref_graph, pattern) assert matches == [["1", "2", "3"]] + + +def test_non_pattern_graph_with_type(): + for match in [False, True]: + ref_graph = nx.DiGraph() + ref_graph.add_node("0", **{GraphPattern.METATYPE_ATTR: "0"}) + ref_graph.add_node("1", **{GraphPattern.METATYPE_ATTR: "a" if match else "0"}) + ref_graph.add_node("2", **{GraphPattern.METATYPE_ATTR: "b"}) + ref_graph.add_node("3", **{GraphPattern.METATYPE_ATTR: "c"}) + ref_graph.add_edge("0", "1") + ref_graph.add_edge("1", "2") + ref_graph.add_edge("2", "3") + + pattern = GraphPattern() + node_1 = pattern.add_node(**{GraphPattern.METATYPE_ATTR: "a", GraphPattern.PATTERN_NODE_TO_EXCLUDE: True}) + node_2 = pattern.add_node(**{GraphPattern.METATYPE_ATTR: "b"}) + node_3 = pattern.add_node(**{GraphPattern.METATYPE_ATTR: "c"}) + pattern.add_edge(node_1, node_2) + pattern.add_edge(node_2, node_3) + + matches = find_subgraphs_matching_pattern(ref_graph, pattern) + if not match: + assert not matches + else: + assert matches == [["2", "3"]] diff --git a/tests/common/test_pattern_manager.py b/tests/common/test_pattern_manager.py new file mode 100644 index 00000000000..3b05edafc8f --- /dev/null +++ b/tests/common/test_pattern_manager.py @@ -0,0 +1,72 @@ +# 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 enum import Enum + +from nncf.common.graph.patterns.manager import PatternsManager +from nncf.common.graph.patterns.patterns import PatternDesc +from nncf.common.utils.registry import Registry +from nncf.parameters import ModelType +from nncf.parameters import TargetDevice + +TEST_DEVICE_PATTERN_REGISTRY = Registry("TEST_PATTERNS_REGISTRY") + + +# pylint: disable=protected-access + + +class DevicePatterns(Enum): + CPU_PATTERN = PatternDesc("CPU_PATTERN", devices=[TargetDevice.CPU]) + GPU_PATTERN = PatternDesc("GPU_PATTERN", devices=[TargetDevice.GPU]) + COMMON_PATTERN = PatternDesc("COMMON_PATTERN") + + +TEST_DEVICE_PATTERN_REGISTRY.register(DevicePatterns.CPU_PATTERN)(None) +TEST_DEVICE_PATTERN_REGISTRY.register(DevicePatterns.GPU_PATTERN)(None) +TEST_DEVICE_PATTERN_REGISTRY.register(DevicePatterns.COMMON_PATTERN)(None) + + +def test_pattern_filter_device(): + manager = PatternsManager() + filtered_patterns = manager._filter_patterns( + TEST_DEVICE_PATTERN_REGISTRY.registry_dict, device=TargetDevice.CPU, model_type=None + ) + assert len(filtered_patterns) == 2 + assert DevicePatterns.CPU_PATTERN in filtered_patterns + assert DevicePatterns.COMMON_PATTERN in filtered_patterns + + +TEST_MODEL_TYPE_PATTERN_REGISTRY = Registry("TEST_PATTERNS_REGISTRY") + + +class ModelTypePatterns(Enum): + TRANSFORMER_PATTERN = PatternDesc("TRANSFORMER_PATTERN", model_types=[ModelType.TRANSFORMER]) + COMMON_PATTERN = PatternDesc("COMMON_PATTERN") + + +TEST_MODEL_TYPE_PATTERN_REGISTRY.register(ModelTypePatterns.TRANSFORMER_PATTERN)(None) +TEST_MODEL_TYPE_PATTERN_REGISTRY.register(ModelTypePatterns.COMMON_PATTERN)(None) + + +def test_pattern_filter_model_type(): + manager = PatternsManager() + filtered_patterns = manager._filter_patterns( + TEST_MODEL_TYPE_PATTERN_REGISTRY.registry_dict, device=None, model_type=None + ) + assert len(filtered_patterns) == 1 + assert ModelTypePatterns.COMMON_PATTERN in filtered_patterns + + filtered_patterns = manager._filter_patterns( + TEST_MODEL_TYPE_PATTERN_REGISTRY.registry_dict, device=None, model_type=ModelType.TRANSFORMER + ) + assert len(filtered_patterns) == 2 + assert ModelTypePatterns.COMMON_PATTERN in filtered_patterns + assert ModelTypePatterns.TRANSFORMER_PATTERN in filtered_patterns diff --git a/tests/onnx/test_pattern_manager.py b/tests/onnx/test_pattern_manager.py index e06ae77e46d..e5f5010641c 100644 --- a/tests/onnx/test_pattern_manager.py +++ b/tests/onnx/test_pattern_manager.py @@ -23,7 +23,6 @@ HWFusedPatternNames.BATCH_INDEX: "Not relevant for ONNX.", HWFusedPatternNames.NORMALIZE_L2_MULTIPLY: "Not relevant for ONNX.", HWFusedPatternNames.LINEAR_WITH_BIAS: "Linear layers contains biases in ONNX.", - HWFusedPatternNames.SE_BLOCK: "Not relevant for ONNX.", HWFusedPatternNames.SOFTMAX_DIV: "Not relevant for ONNX.", HWFusedPatternNames.HSWISH_ACTIVATION_V2: "Is already covered by HSWISH_ACTIVATION for ONNX.", HWFusedPatternNames.SOFTMAX: "Not relevant for ONNX.", @@ -51,6 +50,7 @@ IGNORING_IGNORED_PATTERN_REASONS = { IgnoredPatternNames.FC_BN_HSWISH_ACTIVATION: "Not relevant for ONNX.", IgnoredPatternNames.EQUAL_LOGICALNOT: "Not relevant for ONNX.", + IgnoredPatternNames.SE_BLOCK: "Not relevant for ONNX.", } diff --git a/tests/openvino/native/data/2023.1/reference_graphs/quantized/SeBlockModel.dot b/tests/openvino/native/data/2023.1/reference_graphs/quantized/SeBlockModel.dot index 0f7dbb5f542..2553a125010 100644 --- a/tests/openvino/native/data/2023.1/reference_graphs/quantized/SeBlockModel.dot +++ b/tests/openvino/native/data/2023.1/reference_graphs/quantized/SeBlockModel.dot @@ -5,7 +5,7 @@ strict digraph { "3 Add0" [id=3, type=Add]; "4 Add0/fq_output_0" [id=4, type=FakeQuantize]; "5 Mul" [id=5, type=Multiply]; -"6 ReduceMean_6" [id=6, type=ReduceMean]; +"6 ReduceMean_31402" [id=6, type=ReduceMean]; "7 Mul/fq_output_0" [id=7, type=FakeQuantize]; "8 Conv" [id=8, type=Convolution]; "9 MatMul" [id=9, type=MatMul]; @@ -15,44 +15,49 @@ strict digraph { "13 Conv2" [id=13, type=Convolution]; "14 Add2" [id=14, type=Add]; "15 Sigmoid" [id=15, type=Sigmoid]; -"16 MatMul/fq_weights_1" [id=16, type=FakeQuantize]; -"17 Constant_2193" [id=17, type=Constant]; -"18 Constant_2192" [id=18, type=Constant]; -"19 Constant_2191" [id=19, type=Constant]; -"20 Constant_2190" [id=20, type=Constant]; -"21 Constant_18" [id=21, type=Constant]; -"22 Constant_2183" [id=22, type=Constant]; -"23 Constant_2182" [id=23, type=Constant]; -"24 Constant_2181" [id=24, type=Constant]; -"25 Constant_2180" [id=25, type=Constant]; -"26 Constant_14" [id=26, type=Constant]; -"27 Constant_12" [id=27, type=Constant]; -"28 Constant_9" [id=28, type=Constant]; -"29 Constant_7" [id=29, type=Constant]; -"30 Constant_5" [id=30, type=Constant]; -"31 Constant_2173" [id=31, type=Constant]; -"32 Constant_2172" [id=32, type=Constant]; -"33 Constant_2171" [id=33, type=Constant]; -"34 Constant_2170" [id=34, type=Constant]; -"35 Constant_3" [id=35, type=Constant]; -"36 Conv0/fq_weights_1" [id=36, type=FakeQuantize]; -"37 Constant_2188" [id=37, type=Constant]; -"38 Constant_2187" [id=38, type=Constant]; -"39 Constant_2186" [id=39, type=Constant]; -"40 Constant_2185" [id=40, type=Constant]; -"41 Constant_1" [id=41, type=Constant]; -"42 Constant_2178" [id=42, type=Constant]; -"43 Constant_2177" [id=43, type=Constant]; -"44 Constant_2176" [id=44, type=Constant]; -"45 Constant_2175" [id=45, type=Constant]; +"16 Sigmoid/fq_output_0" [id=16, type=FakeQuantize]; +"17 MatMul/fq_weights_1" [id=17, type=FakeQuantize]; +"18 Constant_33914" [id=18, type=Constant]; +"19 Constant_33913" [id=19, type=Constant]; +"20 Constant_33912" [id=20, type=Constant]; +"21 Constant_33911" [id=21, type=Constant]; +"22 Constant_31414" [id=22, type=Constant]; +"23 Constant_33909" [id=23, type=Constant]; +"24 Constant_33908" [id=24, type=Constant]; +"25 Constant_33907" [id=25, type=Constant]; +"26 Constant_33906" [id=26, type=Constant]; +"27 Constant_33904" [id=27, type=Constant]; +"28 Constant_33903" [id=28, type=Constant]; +"29 Constant_33902" [id=29, type=Constant]; +"30 Constant_33901" [id=30, type=Constant]; +"31 Constant_31410" [id=31, type=Constant]; +"32 Constant_31408" [id=32, type=Constant]; +"33 Constant_31405" [id=33, type=Constant]; +"34 Constant_31403" [id=34, type=Constant]; +"35 Constant_31401" [id=35, type=Constant]; +"36 Constant_33899" [id=36, type=Constant]; +"37 Constant_33898" [id=37, type=Constant]; +"38 Constant_33897" [id=38, type=Constant]; +"39 Constant_33896" [id=39, type=Constant]; +"40 Constant_31399" [id=40, type=Constant]; +"41 Conv0/fq_weights_1" [id=41, type=FakeQuantize]; +"42 Constant_33894" [id=42, type=Constant]; +"43 Constant_33893" [id=43, type=Constant]; +"44 Constant_33892" [id=44, type=Constant]; +"45 Constant_33891" [id=45, type=Constant]; +"46 Constant_31397" [id=46, type=Constant]; +"47 Constant_33889" [id=47, type=Constant]; +"48 Constant_33888" [id=48, type=Constant]; +"49 Constant_33887" [id=49, type=Constant]; +"50 Constant_33886" [id=50, type=Constant]; "0 Input" -> "1 Input/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; "1 Input/fq_output_0" -> "2 Conv0" [label="[1, 3, 5, 6]", style=solid]; "2 Conv0" -> "3 Add0" [label="[1, 3, 5, 6]", style=solid]; "3 Add0" -> "4 Add0/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; "4 Add0/fq_output_0" -> "5 Mul" [label="[1, 3, 5, 6]", style=solid]; -"4 Add0/fq_output_0" -> "6 ReduceMean_6" [label="[1, 3, 5, 6]", style=solid]; +"4 Add0/fq_output_0" -> "6 ReduceMean_31402" [label="[1, 3, 5, 6]", style=solid]; "5 Mul" -> "7 Mul/fq_output_0" [label="[1, 3, 5, 6]", style=solid]; -"6 ReduceMean_6" -> "8 Conv" [label="[1, 3, 1, 1]", style=solid]; +"6 ReduceMean_31402" -> "8 Conv" [label="[1, 3, 1, 1]", style=solid]; "7 Mul/fq_output_0" -> "9 MatMul" [label="[1, 3, 5, 6]", style=solid]; "8 Conv" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; "9 MatMul" -> "11 Result" [label="[1, 3, 5, 5]", style=solid]; @@ -60,35 +65,40 @@ strict digraph { "12 Relu" -> "13 Conv2" [label="[1, 5, 1, 1]", style=solid]; "13 Conv2" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; "14 Add2" -> "15 Sigmoid" [label="[1, 3, 1, 1]", style=solid]; -"15 Sigmoid" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; -"16 MatMul/fq_weights_1" -> "9 MatMul" [label="[1, 3, 6, 5]", style=solid]; -"17 Constant_2193" -> "16 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"18 Constant_2192" -> "16 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"19 Constant_2191" -> "16 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"20 Constant_2190" -> "16 MatMul/fq_weights_1" [label="[1, 1, 1, 1]", style=solid]; -"21 Constant_18" -> "16 MatMul/fq_weights_1" [label="[1, 3, 6, 5]", style=solid]; -"22 Constant_2183" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"23 Constant_2182" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"24 Constant_2181" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"25 Constant_2180" -> "7 Mul/fq_output_0" [label="[]", style=solid]; -"26 Constant_14" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; -"27 Constant_12" -> "13 Conv2" [label="[3, 5, 1, 1]", style=solid]; -"28 Constant_9" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; -"29 Constant_7" -> "8 Conv" [label="[5, 3, 1, 1]", style=solid]; -"30 Constant_5" -> "6 ReduceMean_6" [label="[2]", style=dashed]; -"31 Constant_2173" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"32 Constant_2172" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"33 Constant_2171" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"34 Constant_2170" -> "4 Add0/fq_output_0" [label="[]", style=solid]; -"35 Constant_3" -> "3 Add0" [label="[1, 3, 1, 1]", style=solid]; -"36 Conv0/fq_weights_1" -> "2 Conv0" [label="[3, 3, 1, 1]", style=solid]; -"37 Constant_2188" -> "36 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"38 Constant_2187" -> "36 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"39 Constant_2186" -> "36 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"40 Constant_2185" -> "36 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; -"41 Constant_1" -> "36 Conv0/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; -"42 Constant_2178" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"43 Constant_2177" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"44 Constant_2176" -> "1 Input/fq_output_0" [label="[]", style=solid]; -"45 Constant_2175" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"15 Sigmoid" -> "16 Sigmoid/fq_output_0" [label="[1, 3, 1, 1]", style=solid]; +"16 Sigmoid/fq_output_0" -> "5 Mul" [label="[1, 3, 1, 1]", style=solid]; +"17 MatMul/fq_weights_1" -> "9 MatMul" [label="[6, 5]", style=solid]; +"18 Constant_33914" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"19 Constant_33913" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"20 Constant_33912" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"21 Constant_33911" -> "17 MatMul/fq_weights_1" [label="[1, 5]", style=solid]; +"22 Constant_31414" -> "17 MatMul/fq_weights_1" [label="[6, 5]", style=solid]; +"23 Constant_33909" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"24 Constant_33908" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"25 Constant_33907" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"26 Constant_33906" -> "7 Mul/fq_output_0" [label="[]", style=solid]; +"27 Constant_33904" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"28 Constant_33903" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"29 Constant_33902" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"30 Constant_33901" -> "16 Sigmoid/fq_output_0" [label="[]", style=solid]; +"31 Constant_31410" -> "14 Add2" [label="[1, 3, 1, 1]", style=solid]; +"32 Constant_31408" -> "13 Conv2" [label="[3, 5, 1, 1]", style=solid]; +"33 Constant_31405" -> "10 Add" [label="[1, 5, 1, 1]", style=solid]; +"34 Constant_31403" -> "8 Conv" [label="[5, 3, 1, 1]", style=solid]; +"35 Constant_31401" -> "6 ReduceMean_31402" [label="[2]", style=dashed]; +"36 Constant_33899" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"37 Constant_33898" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"38 Constant_33897" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"39 Constant_33896" -> "4 Add0/fq_output_0" [label="[]", style=solid]; +"40 Constant_31399" -> "3 Add0" [label="[1, 3, 1, 1]", style=solid]; +"41 Conv0/fq_weights_1" -> "2 Conv0" [label="[3, 3, 1, 1]", style=solid]; +"42 Constant_33894" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"43 Constant_33893" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"44 Constant_33892" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"45 Constant_33891" -> "41 Conv0/fq_weights_1" [label="[3, 1, 1, 1]", style=solid]; +"46 Constant_31397" -> "41 Conv0/fq_weights_1" [label="[3, 3, 1, 1]", style=solid]; +"47 Constant_33889" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"48 Constant_33888" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"49 Constant_33887" -> "1 Input/fq_output_0" [label="[]", style=solid]; +"50 Constant_33886" -> "1 Input/fq_output_0" [label="[]", style=solid]; } diff --git a/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_mixed.json b/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_mixed.json index 01a0d229a04..cd816f7b3be 100644 --- a/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_mixed.json +++ b/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_mixed.json @@ -43,6 +43,12 @@ "output_low": 0.0, "output_high": 2.4107847213745117 }, + "Sigmoid/fq_output_0": { + "input_low": 0.0, + "input_high": 0.9998180866241455, + "output_low": 0.0, + "output_high": 0.9998180866241455 + }, "Add0/fq_output_0": { "input_low": 0.0, "input_high": 2.4112234115600586, diff --git a/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_performance.json b/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_performance.json index 01a0d229a04..cd816f7b3be 100644 --- a/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_performance.json +++ b/tests/openvino/native/data/2023.1/reference_scales/SeBlockModel_performance.json @@ -43,6 +43,12 @@ "output_low": 0.0, "output_high": 2.4107847213745117 }, + "Sigmoid/fq_output_0": { + "input_low": 0.0, + "input_high": 0.9998180866241455, + "output_low": 0.0, + "output_high": 0.9998180866241455 + }, "Add0/fq_output_0": { "input_low": 0.0, "input_high": 2.4112234115600586, diff --git a/tests/post_training/reference_data.yaml b/tests/post_training/reference_data.yaml index 250bfa3546b..ce3be340e0a 100644 --- a/tests/post_training/reference_data.yaml +++ b/tests/post_training/reference_data.yaml @@ -104,13 +104,13 @@ timm/efficientnet_b0_backend_OLD_TORCH: metric_value: 0.75538 metric_value_fp32: 0.77292 timm/efficientnet_b0_backend_TORCH: - metric_value: 0.76476 + metric_value: 0.7680 metric_value_fp32: 0.77292 timm/efficientnet_b0_backend_ONNX: metric_value: 0.76582 metric_value_fp32: 0.77292 timm/efficientnet_b0_backend_OV: - metric_value: 0.76796 + metric_value: 0.7688 metric_value_fp32: 0.77292 timm/efficientnet_b0_backend_POT: metric_value: 0.76866 diff --git a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/mobilenet_v3_small.dot b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/mobilenet_v3_small.dot index c922641d1e1..41264e4a2b3 100644 --- a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/mobilenet_v3_small.dot +++ b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/mobilenet_v3_small.dot @@ -12,285 +12,249 @@ strict digraph { "10 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[0]/ReLU[2]/relu__0" [id=10, type=relu_]; "11 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[0]/ReLU[2]/SymmetricQuantizer/symmetric_quantize_0" [id=11, type=symmetric_quantize]; "12 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/adaptive_avg_pool2d_0" [id=12, type=adaptive_avg_pool2d]; -"13 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/SymmetricQuantizer/symmetric_quantize_0" [id=13, type=symmetric_quantize]; -"14 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/NNCFConv2d[fc1]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=14, type=symmetric_quantize]; -"15 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/NNCFConv2d[fc1]/conv2d_0" [id=15, type=conv2d]; -"16 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/ReLU[relu]/relu__0" [id=16, type=relu_]; -"17 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/ReLU[relu]/SymmetricQuantizer/symmetric_quantize_0" [id=17, type=symmetric_quantize]; -"18 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/NNCFConv2d[fc2]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=18, type=symmetric_quantize]; -"19 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/NNCFConv2d[fc2]/conv2d_0" [id=19, type=conv2d]; -"20 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/hardsigmoid_0" [id=20, type=hardsigmoid]; -"21 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/SymmetricQuantizer/symmetric_quantize_1" [id=21, type=symmetric_quantize]; -"22 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/__mul___0" [id=22, type=__mul__]; -"23 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/SqueezeExcitation[1]/SymmetricQuantizer/symmetric_quantize_2" [id=23, type=symmetric_quantize]; -"24 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[2]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=24, type=symmetric_quantize]; -"25 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[2]/NNCFConv2d[0]/conv2d_0" [id=25, type=conv2d]; -"26 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[2]/NNCFBatchNorm2d[1]/batch_norm_0" [id=26, type=batch_norm]; -"27 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[2]/NNCFBatchNorm2d[1]/SymmetricQuantizer/symmetric_quantize_0" [id=27, type=symmetric_quantize]; -"28 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[0]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=28, type=symmetric_quantize]; -"29 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[0]/NNCFConv2d[0]/conv2d_0" [id=29, type=conv2d]; -"30 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[0]/NNCFBatchNorm2d[1]/batch_norm_0" [id=30, type=batch_norm]; -"31 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[0]/ReLU[2]/relu__0" [id=31, type=relu_]; -"32 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[0]/ReLU[2]/SymmetricQuantizer/symmetric_quantize_0" [id=32, type=symmetric_quantize]; -"33 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[1]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=33, type=symmetric_quantize]; -"34 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[1]/NNCFConv2d[0]/conv2d_0" [id=34, type=conv2d]; -"35 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[1]/NNCFBatchNorm2d[1]/batch_norm_0" [id=35, type=batch_norm]; -"36 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[1]/ReLU[2]/relu__0" [id=36, type=relu_]; -"37 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[1]/ReLU[2]/SymmetricQuantizer/symmetric_quantize_0" [id=37, type=symmetric_quantize]; -"38 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[2]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=38, type=symmetric_quantize]; -"39 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[2]/NNCFConv2d[0]/conv2d_0" [id=39, type=conv2d]; -"40 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[2]/NNCFBatchNorm2d[1]/batch_norm_0" [id=40, type=batch_norm]; -"41 MobileNetV3/Sequential[features]/InvertedResidual[2]/Sequential[block]/ConvBNActivation[2]/NNCFBatchNorm2d[1]/SymmetricQuantizer/symmetric_quantize_0" [id=41, type=symmetric_quantize]; -"42 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[0]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=42, type=symmetric_quantize]; -"43 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[0]/NNCFConv2d[0]/conv2d_0" [id=43, type=conv2d]; -"44 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[0]/NNCFBatchNorm2d[1]/batch_norm_0" [id=44, type=batch_norm]; -"45 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[0]/ReLU[2]/relu__0" [id=45, type=relu_]; -"46 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[0]/ReLU[2]/SymmetricQuantizer/symmetric_quantize_0" [id=46, type=symmetric_quantize]; -"47 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[1]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=47, type=symmetric_quantize]; -"48 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[1]/NNCFConv2d[0]/conv2d_0" [id=48, type=conv2d]; -"49 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[1]/NNCFBatchNorm2d[1]/batch_norm_0" [id=49, type=batch_norm]; -"50 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[1]/ReLU[2]/relu__0" [id=50, type=relu_]; -"51 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[1]/ReLU[2]/SymmetricQuantizer/symmetric_quantize_0" [id=51, type=symmetric_quantize]; -"52 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[2]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=52, type=symmetric_quantize]; -"53 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[2]/NNCFConv2d[0]/conv2d_0" [id=53, type=conv2d]; -"54 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[2]/NNCFBatchNorm2d[1]/batch_norm_0" [id=54, type=batch_norm]; -"55 MobileNetV3/Sequential[features]/InvertedResidual[3]/Sequential[block]/ConvBNActivation[2]/NNCFBatchNorm2d[1]/SymmetricQuantizer/symmetric_quantize_0" [id=55, type=symmetric_quantize]; -"56 MobileNetV3/Sequential[features]/InvertedResidual[3]/__iadd___0" [id=56, type=__iadd__]; -"57 MobileNetV3/Sequential[features]/InvertedResidual[3]/SymmetricQuantizer/symmetric_quantize_0" [id=57, type=symmetric_quantize]; -"58 MobileNetV3/Sequential[features]/InvertedResidual[4]/Sequential[block]/ConvBNActivation[0]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=58, type=symmetric_quantize]; -"59 MobileNetV3/Sequential[features]/InvertedResidual[4]/Sequential[block]/ConvBNActivation[0]/NNCFConv2d[0]/conv2d_0" [id=59, type=conv2d]; -"60 MobileNetV3/Sequential[features]/InvertedResidual[4]/Sequential[block]/ConvBNActivation[0]/NNCFBatchNorm2d[1]/batch_norm_0" [id=60, type=batch_norm]; -"61 MobileNetV3/Sequential[features]/InvertedResidual[4]/Sequential[block]/ConvBNActivation[0]/Hardswish[2]/hardswish_0" [id=61, type=hardswish]; -"62 MobileNetV3/Sequential[features]/InvertedResidual[4]/Sequential[block]/ConvBNActivation[0]/Hardswish[2]/SymmetricQuantizer/symmetric_quantize_0" [id=62, type=symmetric_quantize]; -"63 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MobileNetV3/Sequential[classifier]/NNCFLinear[3]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=253, type=symmetric_quantize]; +"254 MobileNetV3/Sequential[classifier]/NNCFLinear[3]/linear_0" [id=254, type=linear]; +"255 /nncf_model_output_0" [id=255, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "3 MobileNetV3/Sequential[features]/ConvBNActivation[0]/NNCFConv2d[0]/conv2d_0"; "2 MobileNetV3/Sequential[features]/ConvBNActivation[0]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "3 MobileNetV3/Sequential[features]/ConvBNActivation[0]/NNCFConv2d[0]/conv2d_0"; @@ -303,298 +267,262 @@ strict digraph { "9 MobileNetV3/Sequential[features]/InvertedResidual[1]/Sequential[block]/ConvBNActivation[0]/NNCFBatchNorm2d[1]/batch_norm_0" -> "10 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MobileNetV3/Sequential[features]/InvertedResidual[11]/Sequential[block]/ConvBNActivation[3]/NNCFConv2d[0]/conv2d_0"; +"235 MobileNetV3/Sequential[features]/InvertedResidual[11]/Sequential[block]/ConvBNActivation[3]/NNCFConv2d[0]/conv2d_0" -> "236 MobileNetV3/Sequential[features]/InvertedResidual[11]/Sequential[block]/ConvBNActivation[3]/NNCFBatchNorm2d[1]/batch_norm_0"; +"236 MobileNetV3/Sequential[features]/InvertedResidual[11]/Sequential[block]/ConvBNActivation[3]/NNCFBatchNorm2d[1]/batch_norm_0" -> "237 MobileNetV3/Sequential[features]/InvertedResidual[11]/Sequential[block]/ConvBNActivation[3]/NNCFBatchNorm2d[1]/SymmetricQuantizer/symmetric_quantize_0"; +"237 MobileNetV3/Sequential[features]/InvertedResidual[11]/Sequential[block]/ConvBNActivation[3]/NNCFBatchNorm2d[1]/SymmetricQuantizer/symmetric_quantize_0" -> "238 MobileNetV3/Sequential[features]/InvertedResidual[11]/__iadd___0"; +"238 MobileNetV3/Sequential[features]/InvertedResidual[11]/__iadd___0" -> "239 MobileNetV3/Sequential[features]/InvertedResidual[11]/SymmetricQuantizer/symmetric_quantize_0"; +"239 MobileNetV3/Sequential[features]/InvertedResidual[11]/SymmetricQuantizer/symmetric_quantize_0" -> "241 MobileNetV3/Sequential[features]/ConvBNActivation[12]/NNCFConv2d[0]/conv2d_0"; +"240 MobileNetV3/Sequential[features]/ConvBNActivation[12]/NNCFConv2d[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "241 MobileNetV3/Sequential[features]/ConvBNActivation[12]/NNCFConv2d[0]/conv2d_0"; +"241 MobileNetV3/Sequential[features]/ConvBNActivation[12]/NNCFConv2d[0]/conv2d_0" -> "242 MobileNetV3/Sequential[features]/ConvBNActivation[12]/NNCFBatchNorm2d[1]/batch_norm_0"; +"242 MobileNetV3/Sequential[features]/ConvBNActivation[12]/NNCFBatchNorm2d[1]/batch_norm_0" -> "243 MobileNetV3/Sequential[features]/ConvBNActivation[12]/Hardswish[2]/hardswish_0"; +"243 MobileNetV3/Sequential[features]/ConvBNActivation[12]/Hardswish[2]/hardswish_0" -> "244 MobileNetV3/Sequential[features]/ConvBNActivation[12]/Hardswish[2]/SymmetricQuantizer/symmetric_quantize_0"; +"244 MobileNetV3/Sequential[features]/ConvBNActivation[12]/Hardswish[2]/SymmetricQuantizer/symmetric_quantize_0" -> "245 MobileNetV3/AdaptiveAvgPool2d[avgpool]/adaptive_avg_pool2d_0"; +"245 MobileNetV3/AdaptiveAvgPool2d[avgpool]/adaptive_avg_pool2d_0" -> "246 MobileNetV3/AdaptiveAvgPool2d[avgpool]/SymmetricQuantizer/symmetric_quantize_0"; +"246 MobileNetV3/AdaptiveAvgPool2d[avgpool]/SymmetricQuantizer/symmetric_quantize_0" -> "247 MobileNetV3/flatten_0"; +"247 MobileNetV3/flatten_0" -> "249 MobileNetV3/Sequential[classifier]/NNCFLinear[0]/linear_0"; +"248 MobileNetV3/Sequential[classifier]/NNCFLinear[0]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "249 MobileNetV3/Sequential[classifier]/NNCFLinear[0]/linear_0"; +"249 MobileNetV3/Sequential[classifier]/NNCFLinear[0]/linear_0" -> "250 MobileNetV3/Sequential[classifier]/Hardswish[1]/hardswish_0"; +"250 MobileNetV3/Sequential[classifier]/Hardswish[1]/hardswish_0" -> "251 MobileNetV3/Sequential[classifier]/Hardswish[1]/SymmetricQuantizer/symmetric_quantize_0"; +"251 MobileNetV3/Sequential[classifier]/Hardswish[1]/SymmetricQuantizer/symmetric_quantize_0" -> "252 MobileNetV3/Sequential[classifier]/Dropout[2]/dropout_0"; +"252 MobileNetV3/Sequential[classifier]/Dropout[2]/dropout_0" -> "254 MobileNetV3/Sequential[classifier]/NNCFLinear[3]/linear_0"; +"253 MobileNetV3/Sequential[classifier]/NNCFLinear[3]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "254 MobileNetV3/Sequential[classifier]/NNCFLinear[3]/linear_0"; +"254 MobileNetV3/Sequential[classifier]/NNCFLinear[3]/linear_0" -> "255 /nncf_model_output_0"; } diff --git a/tests/torch/test_pattern_manager.py b/tests/torch/test_pattern_manager.py index 0be8fba0e9e..4db3841fc76 100644 --- a/tests/torch/test_pattern_manager.py +++ b/tests/torch/test_pattern_manager.py @@ -22,7 +22,6 @@ HWFusedPatternNames.MVN_SCALE_SHIFT: "Not relevant for Torch.", HWFusedPatternNames.NORMALIZE_L2_MULTIPLY: "Not relevant for Torch.", HWFusedPatternNames.SCALE_SHIFT: "Not relevant for Torch.", - HWFusedPatternNames.SE_BLOCK: "Not relevant for Torch.", HWFusedPatternNames.SOFTMAX_DIV: "Not relevant for Torch.", HWFusedPatternNames.HSWISH_ACTIVATION: "Not relevant for Torch.", HWFusedPatternNames.HSWISH_ACTIVATION_V2: "Not relevant for Torch.",