Add support for pattern

Signed-off-by: gcunhase <[email protected]>

Author: gcunhase

modelopt/onnx/quantization/graph_utils.py

@@ -201,6 +201,8 @@ def _get_backbone(root: Node):
             ["BatchNormalization", "BiasAdd", conv_type],
             ["Relu", "BatchNormalization", "BiasAdd", conv_type],
             ["MaxPool", "Relu", "BatchNormalization", "BiasAdd", conv_type],
+            ["Mul", "Sigmoid", "BatchNormalization", conv_type],
+            ["Mul", "Sigmoid", "BatchNormalization", "BiasAdd", conv_type],
         ]
     for idx, path_type in enumerate(fusible_linear_path_types):
         if has_path_type(node, graph, path_type, is_forward=False, wild_card_types=[]):

tests/_test_utils/onnx_quantization/lib_test_models.py

@@ -575,9 +575,15 @@ def build_conv_batchnorm_sig_mul_model():
 
     # Create the ONNX graph with the nodes
     nodes = [
+        helper.make_node(
+            op_type="Relu",
+            inputs=["input_0"],
+            outputs=["relu0_relu/Relu:0"],
+            name="relu0_relu/Relu",
+        ),
         helper.make_node(
             op_type="Conv",
-            inputs=["input_0", "weights_1"],
+            inputs=["relu0_relu/Relu:0", "weights_1"],
             outputs=["conv1_conv/Conv2D:0"],
             name="conv1_conv/Conv2D",
             dilations=[1, 1],
@@ -606,7 +612,7 @@ def build_conv_batchnorm_sig_mul_model():
         ),
         helper.make_node(
             op_type="Add",
-            inputs=["input_0", "mul1_mul/Mul:0"],
+            inputs=["relu0_relu/Relu:0", "mul1_mul/Mul:0"],
             outputs=["add1_add/Add:0"],
             name="add1_add/Add",
         ),