Add unit tests for old lowering flow for op_cat.py

Summary:
The team moved to leveraging a new API which allows them to improve the reliability of our lowering infra. Lowering here refers to converting a PyTorch model that's recognizable by the underlying hardware. This diff makes sure there are still unit tests for the older APIs.

(done as a bootcamp task, all comments welcome!)

Reviewed By: mcr229

Differential Revision: D65914291

Author: suchir1

backends/xnnpack/test/ops/cat.py

@@ -36,39 +36,45 @@ def forward(self, arg1, arg2, arg3, arg4, arg5):
             return x + x  # Quantize by propagation.
 
     def _test_cat(self, module, inputs, cat_num=1, quant=False, quant_ops=2):
-        tester = Tester(module, inputs)
-
-        if quant:
-            tester.quantize()
-
-        tester.export().check_count({"torch.ops.aten.cat": 1})
-        tester.dump_artifact()
-
-        if quant:
-            # Expect multiple quantize ops - one per input, cat, and add.
-            tester.check_node_count(
-                {
-                    # Q/DQ pair for each input and quantized op. For most tests, there are
-                    # two quantized ops - cat and add.
-                    torch.ops.quantized_decomposed.quantize_per_tensor.default: (
-                        cat_num + quant_ops
-                    )
-                }
+        for legacy_mode in (True, False):
+            tester = Tester(module, inputs)
+
+            if quant:
+                tester.quantize()
+
+            tester.export().check_count({"torch.ops.aten.cat": 1})
+            tester.dump_artifact()
+
+            if quant:
+                # Expect multiple quantize ops - one per input, cat, and add.
+                tester.check_node_count(
+                    {
+                        # Q/DQ pair for each input and quantized op. For most tests, there are
+                        # two quantized ops - cat and add.
+                        torch.ops.quantized_decomposed.quantize_per_tensor.default: (
+                            cat_num + quant_ops
+                        )
+                    }
+                )
+
+
+            if legacy_mode:
+                tester.to_edge()
+                tester.partition()
+            else:
+                tester.to_edge_transform_and_lower()
+
+            if quant:
+                tester.check_not(["torch.ops.quantized_decomposed"])
+
+            (
+                tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+                .check_not(["executorch_exir_dialects_edge__ops_aten_cat"])
+                .to_executorch()
+                .serialize()
+                .run_method_and_compare_outputs()
             )
 
-        tester.to_edge_transform_and_lower()
-
-        if quant:
-            tester.check_not(["torch.ops.quantized_decomposed"])
-
-        (
-            tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
-            .check_not(["executorch_exir_dialects_edge__ops_aten_cat"])
-            .to_executorch()
-            .serialize()
-            .run_method_and_compare_outputs()
-        )
-
     def test_fp16_cat2(self):
         """
         Using Clamp2 because fp16 add is done in fp32 ATM. Need to fix that first.
@@ -155,6 +161,26 @@ def test_fp32_cat_unsupported(self):
             .check_count({"executorch_exir_dialects_edge__ops_aten_cat": 1})
         )
 
+    def test_fp32_cat_unsupported_legacy_mode(self):
+        """
+        XNNPACK only supports concatenating up to 4 values, so it should not delegate here.
+        """
+        inputs = (
+            torch.randn(1, 2, 3),
+            torch.randn(3, 2, 3),
+            torch.randn(2, 2, 3),
+            torch.randn(5, 2, 3),
+            torch.randn(1, 2, 3),
+        )
+        (
+            Tester(self.Cat5(), inputs)
+            .export()
+            .check_count({"torch.ops.aten.cat": 1})
+            .to_edge()
+            .partition()
+            .check_count({"executorch_exir_dialects_edge__ops_aten_cat": 1})
+        )
+
     class CatNegativeDim(torch.nn.Module):
         def __init__(self):
             super().__init__()