[xnnpack] Fix pyre issues for deconv

pytorch · Jan 17, 2025 · 4f62baf · 4f62baf
1 parent 9f47380
commit 4f62baf
Show file tree

Hide file tree

Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/backends/xnnpack/_passes/tag_implicit_q_dq_pass.py b/backends/xnnpack/_passes/tag_implicit_q_dq_pass.py
@@ -90,7 +90,7 @@ 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])
+            return is_dequant(cast(torch.fx.Node, node.args[1]))
 
         return op_name in SUPPORTED_IMPLICIT_Q_DQ_OP_NAMES_SET
 

diff --git a/backends/xnnpack/operators/node_visitor.py b/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: