Add channel-last unit test for conv1d (#14337)

Summary:

As titled.

Differential Revision: D82552951

Author: mcremon-meta

backends/cadence/aot/export_example.py

@@ -39,6 +39,7 @@
 def export_model(
     model: nn.Module,
     example_inputs: Tuple[Any, ...],
+    opt_level: int = 1,
     file_name: str = "CadenceDemoModel",
     working_dir: Optional[str] = None,
 ):
@@ -99,14 +100,15 @@ def export_model(
 def export_and_run_model(
     model: nn.Module,
     example_inputs: Tuple[Any, ...],
+    opt_level: int = 1,
     file_name: str = "CadenceDemoModel",
     eps_error: float = 1e-1,
     eps_warn: float = 1e-5,
 ):
     # create work directory for outputs and model binary
     working_dir = tempfile.mkdtemp(dir="/tmp")
     logging.debug(f"Created work directory {working_dir}")
-    exec_prog = export_model(model, example_inputs, file_name, working_dir)
+    exec_prog = export_model(model, example_inputs, opt_level, file_name, working_dir)
     ref_outputs = model(*example_inputs)
     runtime.run_and_compare(
         executorch_prog=exec_prog,

examples/cadence/operators/test_quantized_conv1d_op.py

@@ -35,7 +35,7 @@
         depthwise,
         bias,
         channel_last,
-    ) = [(1, 8, 33), 8, 16, 3, 2, 4, 3, False, True, False]
+    ) = [(1, 2, 4), 2, 8, 1, 1, 0, 1, False, False, False]
 
     class QuantizedConv(torch.nn.Module):
         def __init__(self):
@@ -60,3 +60,10 @@ def forward(self, x: torch.Tensor):
     example_inputs = (torch.randn(cast(Sequence[int], shape)),)
 
     export_and_run_model(model, example_inputs)
+
+    model = QuantizedConv()
+    model.eval()
+
+    example_inputs = (torch.randn(cast(Sequence[int], shape)),)
+
+    export_and_run_model(model, example_inputs, opt_level=3)