Arm backend: Wrap modules in test_nn_modules (pytorch#15297)

This has been observed to mitigate a compilation error when quantizing.

Signed-off-by: Erik Lundell <[email protected]>

Author: Erik-Lundell

backends/arm/test/models/test_nn_modules.py

@@ -17,32 +17,91 @@
 - Transformer
 """
 
+from typing import Callable
+
 import torch
 from executorch.backends.arm.test.common import parametrize
 from executorch.backends.arm.test.tester.test_pipeline import (
     TosaPipelineFP,
     TosaPipelineINT,
 )
 
+
+def make_module_wrapper(
+    name: str, module_factory: Callable[[], torch.nn.Module]
+) -> torch.nn.Module:
+    class ModuleWrapper(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self._module = module_factory()
+
+        def forward(self, *args, **kwargs):
+            return self._module(*args, **kwargs)
+
+    ModuleWrapper.__name__ = name
+    ModuleWrapper.__qualname__ = name
+    return ModuleWrapper()
+
+
 example_input = torch.rand(1, 6, 16, 16)
 
 module_tests = [
-    (torch.nn.Embedding(10, 10), (torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]]),)),
-    (torch.nn.LeakyReLU(), (example_input,)),
-    (torch.nn.BatchNorm1d(16), (torch.rand(6, 16, 16),)),
-    (torch.nn.AdaptiveAvgPool2d((12, 12)), (example_input,)),
-    (torch.nn.ConvTranspose2d(6, 3, 2), (example_input,)),
-    (torch.nn.GRU(10, 20, 2), (torch.randn(5, 3, 10), torch.randn(2, 3, 20))),
-    (torch.nn.GroupNorm(2, 6), (example_input,)),
-    (torch.nn.InstanceNorm2d(16), (example_input,)),
-    (torch.nn.PReLU(), (example_input,)),
     (
-        torch.nn.Transformer(
-            d_model=64,
-            nhead=1,
-            num_encoder_layers=1,
-            num_decoder_layers=1,
-            dtype=torch.float32,
+        make_module_wrapper(
+            "EmbeddingModule",
+            lambda: torch.nn.Embedding(10, 10),
+        ),
+        (torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]]),),
+    ),
+    (
+        make_module_wrapper("LeakyReLUModule", torch.nn.LeakyReLU),
+        (example_input,),
+    ),
+    (
+        make_module_wrapper("BatchNorm1dModule", lambda: torch.nn.BatchNorm1d(16)),
+        (torch.rand(6, 16, 16),),
+    ),
+    (
+        make_module_wrapper(
+            "AdaptiveAvgPool2dModule",
+            lambda: torch.nn.AdaptiveAvgPool2d((12, 12)),
+        ),
+        (example_input,),
+    ),
+    (
+        make_module_wrapper(
+            "ConvTranspose2dModule", lambda: torch.nn.ConvTranspose2d(6, 3, 2)
+        ),
+        (example_input,),
+    ),
+    (
+        make_module_wrapper("GRUModule", lambda: torch.nn.GRU(10, 20, 2)),
+        (torch.randn(5, 3, 10), torch.randn(2, 3, 20)),
+    ),
+    (
+        make_module_wrapper("GroupNormModule", lambda: torch.nn.GroupNorm(2, 6)),
+        (example_input,),
+    ),
+    (
+        make_module_wrapper(
+            "InstanceNorm2dModule", lambda: torch.nn.InstanceNorm2d(16)
+        ),
+        (example_input,),
+    ),
+    (
+        make_module_wrapper("PReLUModule", torch.nn.PReLU),
+        (example_input,),
+    ),
+    (
+        make_module_wrapper(
+            "TransformerModule",
+            lambda: torch.nn.Transformer(
+                d_model=64,
+                nhead=1,
+                num_encoder_layers=1,
+                num_decoder_layers=1,
+                dtype=torch.float32,
+            ),
         ),
         (torch.rand((10, 32, 64)), torch.rand((20, 32, 64))),
     ),
@@ -78,9 +137,9 @@ def test_nn_Modules_FP(test_data):
     "test_data",
     test_parameters,
     xfails={
-        "GRU": "RuntimeError: Node aten_linear_default with op <EdgeOpOverload: aten.linear[...]> was not decomposed or delegated.",
-        "PReLU": "RuntimeError: mul(): functions with out=... arguments don't support automatic differentiation, but one of the arguments requires grad.",
-        "Transformer": "AssertionError: Output 0 does not match reference output.",
+        "GRUModule": "RuntimeError: Node aten_linear_default with op <EdgeOpOverload: aten.linear[...]> was not decomposed or delegated.",
+        "PReLUModule": "RuntimeError: mul(): functions with out=... arguments don't support automatic differentiation, but one of the arguments requires grad.",
+        "TransformerModule": "AssertionError: Output 0 does not match reference output.",
     },
 )
 def test_nn_Modules_INT(test_data):