conv1d fix

backends/xnnpack/passes/conv1d_unsqueeze_pass.py

@@ -63,11 +63,23 @@ def call(self, graph_module: torch.fx.GraphModule):
                     kernel_param_4d = torch.nn.Parameter(
                         data=kernel_param_3d.data.contiguous().unsqueeze(dim=-1)
                     )
-                    setattr(
-                        kernel_node.graph.owning_module,
-                        kernel_node.target,
-                        kernel_param_4d,
-                    )
+
+                    if torch._export.utils.is_param(self.exported_program, kernel_node):
+                        parameter_name = (
+                            self.exported_program.graph_signature.inputs_to_parameters[
+                                kernel_node.name
+                            ]
+                        )
+                        self.exported_program.state_dict[
+                            parameter_name
+                        ] = kernel_param_4d
+                        kernel_node.meta["val"] = kernel_param_4d.data.contiguous()
+                    else:
+                        setattr(
+                            kernel_node.graph.owning_module,
+                            kernel_node.target,
+                            kernel_param_4d,
+                        )
 
                     # (b) Extend stride, padding, and dilation for extra dim
                     node.args = (

backends/xnnpack/test/TARGETS

@@ -111,6 +111,7 @@ python_unittest(
     name = "test_xnnpack_ops",
     srcs = [
         "ops/add.py",
+        "ops/conv1d.py",
     ],
     deps = [
         "//caffe2:torch",

backends/xnnpack/test/ops/conv1d.py

@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+
+from executorch.backends.xnnpack.test.tester import Tester
+
+
+class TestConv1d(unittest.TestCase):
+    class Conv1d(torch.nn.Module):
+        def __init__(self):
+            groups = 1
+            stride = [2]
+            padding = [1]
+            dilation = [1]
+            in_channels = 2
+            out_channels = 1
+            kernel_size = (3,)
+
+            super().__init__()
+
+            self.conv1d = torch.nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                groups=groups,
+                dilation=dilation,
+                bias=True,
+            )
+
+        def forward(self, x):
+            return self.conv1d(x)
+
+    def test_conv1d(self):
+        inputs = (torch.randn(1, 2, 4),)
+        (
+            Tester(self.Conv1d(), inputs)
+            .export()
+            .check_count({"torch.ops.aten.convolution.default": 1})
+            .to_edge()
+            .check_count(
+                {"executorch_exir_dialects_edge__ops_aten_convolution_default": 1}
+            )
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_convolution_default"])
+            .check_count({"torch.ops.executorch_call_delegate": 1})
+            .to_executorch()
+            .serialize()
+            .run_method()
+            .compare_outputs()
+        )

examples/backend/xnnpack_examples.py

@@ -82,7 +82,7 @@
     edge = export_to_edge(
         model,
         example_inputs,
-        capture_config=CaptureConfig(enable_aot=True, _unlift=True),
+        capture_config=CaptureConfig(enable_aot=True),
         edge_compile_config=EdgeCompileConfig(
             _check_ir_validity=False if args.quantize else True,
         ),