Use GraphBuilder in memory passes unit tests. # 1 (pytorch#11265)

Summary:

Use GraphBuilder in memory passes unit tests.

Differential Revision: D75467583

Author: eigen-k

backends/cadence/aot/TARGETS

@@ -433,6 +433,7 @@ python_unittest(
     srcs = [
         "tests/test_memory_passes.py",
     ],
+    supports_static_listing = False,
     typing = True,
     deps = [
         ":compiler",
@@ -441,7 +442,9 @@ python_unittest(
         ":pass_utils",
         "//caffe2:torch",
         "//executorch/exir:memory",
+        "fbsource//third-party/pypi/parameterized:parameterized",
         "//executorch/exir/dialects:lib",
+        "//executorch/backends/cadence/aot:graph_builder",
         "//executorch/exir/tests:models",
     ],
 )

backends/cadence/aot/memory_constraints.py

@@ -350,14 +350,28 @@ def is_slice_view(self, node: torch.fx.Node) -> bool:
     def is_cat_along_outermost_dim(
         self, graph_module: torch.fx.GraphModule, cat_node: torch.fx.Node
     ) -> bool:
+        assert len(cat_node.args) > 0
+        cat_tensors = cat_node.args[0]
+        if not isinstance(cat_tensors, Sequence) or not all(
+            isinstance(t, torch.fx.Node) for t in cat_tensors
+        ):
+            raise ValueError("cat_tensors must be a sequence of torch.fx.Node objects.")
+
+        if len(cat_node.args) > 1:
+            cat_dim = cat_node.args[1]
+        else:
+            cat_dim = cat_node.kwargs.get("dim", None)
+        if not isinstance(cat_dim, int):
+            raise ValueError("cat_dim must be an integer.")
+
         # If the cat op has default dim, then the concat dim is 0
-        if len(cat_node.args) == 1 or cat_node.args[1] == 0:
+        if len(cat_tensors) == 1 or cat_dim == 0:
             return True
-        # Get the concatenation dimension and concatenated tensors
-        (cat_tensors, cat_dim) = cast(
-            tuple[Sequence[torch.fx.Node], int], cat_node.args
-        )
+
+        # Make sure all dimes before cat_dim are 1.
         for tensor in cat_tensors:
+            if not isinstance(tensor, torch.fx.Node):
+                continue
             shape = get_shape(graph_module, tensor)
             if shape is None or not all(dim == 1 for dim in shape[0:cat_dim]):
                 return False