type checking for xnnpack

Differential Revision: D68350539

Pull Request resolved: pytorch#7741

Author: lucylq

backends/xnnpack/_passes/tag_implicit_q_dq_pass.py

@@ -90,7 +90,9 @@ def is_supported_quant_op(self, node: torch.fx.Node) -> bool:
 
         # Weight and Input should both be quantized
         if op_name == exir_ops.edge.aten.convolution.default.name():
-            return is_dequant(node.args[1])
+            if isinstance(node.args[1], torch.fx.Node):
+                # pyre-ignore Incompatible parameter type [6]: is_dequant expects Node
+                return is_dequant(node.args[1])
 
         return op_name in SUPPORTED_IMPLICIT_Q_DQ_OP_NAMES_SET
 

backends/xnnpack/operators/node_visitor.py

@@ -560,11 +560,11 @@ def get_serialized_buffer_index(
             # which should be used for depthwise/transpose convolution weights for XNNPACK
             shape = const_val.shape
             const_val = const_val.reshape(
-                (groups, const_val.shape[0] // groups) + const_val.shape[1:]
+                (groups, const_val.shape[0] // groups) + tuple(const_val.shape[1:])
             )
             const_val = const_val.permute((0, 2, 1) + tuple(range(3, const_val.dim())))
             const_val = const_val.reshape(
-                (shape[1] * groups, shape[0] // groups) + shape[2:]
+                (shape[1] * groups, shape[0] // groups) + tuple(shape[2:])
             ).contiguous()
 
         if convert_to_nhwc: