Reset Dynamo Before Export for all XNNPACK Tests (#9561)

https://github.com/pytorch/executorch/actions/runs/14047575373/job/39331644423

There seems to be some CI issues with:
```
torch._dynamo.exc.FailOnRecompileLimitHit: recompile_limit reached with one_graph=True. Excessive recompilations can degrade performance due to the compilation overhead of each recompilation. To monitor recompilations, enable TORCH_LOGS=recompiles. If recompilations are expected, consider increasing
```

To help resolve this we reset dynamo at setup for all unittests. Let's
see if this helps

Author: mcr229

backends/xnnpack/test/models/deeplab_v3.py

@@ -23,6 +23,9 @@ def forward(self, *args):
 
 
 class TestDeepLabV3(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     dl3 = DL3Wrapper()
     dl3 = dl3.eval()
     model_inputs = (torch.randn(1, 3, 224, 224),)

backends/xnnpack/test/models/edsr.py

@@ -14,6 +14,9 @@
 
 
 class TestEDSR(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     edsr = edsr_r16f64(2, False).eval()  # noqa
     model_inputs = (torch.randn(1, 3, 224, 224),)
 

backends/xnnpack/test/models/emformer_rnnt.py

@@ -13,6 +13,9 @@
 
 
 class TestEmformerModel(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     class EmformerRnnt(torch.nn.Module):
         def __init__(self):
             super().__init__()

backends/xnnpack/test/models/inception_v3.py

@@ -13,6 +13,9 @@
 
 
 class TestInceptionV3(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     ic3 = models.inception_v3(weights="IMAGENET1K_V1").eval()  # noqa
     model_inputs = (torch.randn(1, 3, 224, 224),)
 

backends/xnnpack/test/models/inception_v4.py

@@ -12,6 +12,9 @@
 
 
 class TestInceptionV4(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     ic4 = inception_v4(pretrained=False).eval()
     model_inputs = (torch.randn(3, 299, 299).unsqueeze(0),)
 

backends/xnnpack/test/models/llama2_et_example.py

@@ -13,6 +13,9 @@
 
 
 class TestLlama2ETExample(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     def test_f32(self):
         self._test()
 

backends/xnnpack/test/models/mobilebert.py

@@ -12,6 +12,9 @@
 
 
 class TestMobilebert(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     # pyre-ignore
     mobilebert = MobileBertModel(MobileBertConfig()).eval()
     example_inputs = (torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]),)

backends/xnnpack/test/models/mobilenet_v2.py

@@ -14,6 +14,9 @@
 
 
 class TestMobileNetV2(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     mv2 = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights)
     mv2 = mv2.eval()
     model_inputs = (torch.randn(1, 3, 224, 224),)

backends/xnnpack/test/models/mobilenet_v3.py

@@ -13,6 +13,9 @@
 
 
 class TestMobileNetV3(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     mv3 = models.mobilenetv3.mobilenet_v3_small(pretrained=True)
     mv3 = mv3.eval()
     model_inputs = (torch.randn(1, 3, 224, 224),)

backends/xnnpack/test/models/resnet.py

@@ -13,6 +13,9 @@
 
 
 class TestResNet18(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
     inputs = (torch.randn(1, 3, 224, 224),)
     dynamic_shapes = (
         {