Update Conditions of Getting min-max during TF MatMul Requantize (#1660)

zehao-intel · web-flow · commit d07175c39cd7 · 2024-03-12T14:56:50.000+08:00
Signed-off-by: zehao-intel &lt;zehao.huang@intel.com&gt;
diff --git a/neural_compressor/adaptor/tf_utils/graph_rewriter/int8/fuse_matmul_requantize.py b/neural_compressor/adaptor/tf_utils/graph_rewriter/int8/fuse_matmul_requantize.py
@@ -263,10 +263,14 @@ def do_transformation(self):
 
             weight_node = self.graph_info[new_node.input[1]].node
             bias_node = self.graph_info[new_node.input[2]].node
-            max_input_node = self.graph_info[last_node.input[-1]].node
-            min_input_node = self.graph_info[last_node.input[-2]].node
 
-            if max_input_node.op == "Enter":  # pragma: no cover
+            max_input_node = None
+            min_input_node = None
+            if last_node.op.find("Requantize") != -1 or last_node.op.find("QuantizeV2") != -1:
+                max_input_node = self.graph_info[last_node.input[-1]].node
+                min_input_node = self.graph_info[last_node.input[-2]].node
+
+            if max_input_node and max_input_node.op == "Enter":  # pragma: no cover
                 min_input_parent_name = Helper.node_name_from_input(min_input_node.input[0])
                 max_input_parent_name = Helper.node_name_from_input(max_input_node.input[0])
                 min_input_parent_node = self.graph_info[min_input_parent_name].node
