SE block HW fusing pattern is presented

nncf/torch/hardware/fused_patterns.py

@@ -320,3 +320,112 @@ def create_h_sigmoid_act() -> GraphPattern:
     main_pattern.add_pattern_alternative(pattern)
 
     return main_pattern
+
+
+@PT_HW_FUSED_PATTERNS.register(HWFusedPatternNames.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"],
+    }
+
+    main_pattern = GraphPattern()
+
+    pattern = GraphPattern()
+
+    any_node = pattern.add_node(label="ANY", 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(label="SIGMOID", type="sigmoid")
+    multiply_node = pattern.add_node(label="MUL", type="__mul__")
+
+    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)
+    main_pattern.add_pattern_alternative(pattern)
+
+    pattern = GraphPattern()
+
+    any_node = pattern.add_node(label="ANY", 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(label="SIGMOID", type="sigmoid")
+    multiply_node = pattern.add_node(label="MUL", type="__mul__")
+
+    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)
+    main_pattern.add_pattern_alternative(pattern)
+
+    RESHAPE_NODES = {
+        GraphPattern.LABEL_ATTR: "RESHAPE",
+        GraphPattern.METATYPE_ATTR: ["reshape", "view", "flatten", "unsqueeze"],
+    }
+
+    pattern = GraphPattern()
+
+    any_node = pattern.add_node(label="ANY", 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(label="SIGMOID", type="sigmoid")
+    reshape_node_2 = pattern.add_node(**RESHAPE_NODES)
+    multiply_node = pattern.add_node(label="MUL", type="__mul__")
+
+    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)
+    main_pattern.add_pattern_alternative(pattern)
+
+    pattern = GraphPattern()
+
+    any_node = pattern.add_node(label="ANY", 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(label="SIGMOID", type="sigmoid")
+    reshape_node_2 = pattern.add_node(**RESHAPE_NODES)
+    multiply_node = pattern.add_node(label="MUL", type="__mul__")
+
+    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)
+    main_pattern.add_pattern_alternative(pattern)
+
+    return main_pattern

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.",