Drop torch.compile fullgraph test (#19166)

Author: carmocca

tests/tests_fabric/test_fabric.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-import sys
 from re import escape
 from unittest import mock
 from unittest.mock import ANY, MagicMock, Mock, PropertyMock, call
@@ -35,7 +34,6 @@
 )
 from lightning.fabric.strategies.strategy import _Sharded
 from lightning.fabric.utilities.exceptions import MisconfigurationException
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
 from lightning.fabric.utilities.seed import pl_worker_init_function, seed_everything
 from lightning.fabric.utilities.warnings import PossibleUserWarning
 from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer
@@ -1204,40 +1202,3 @@ def test_verify_launch_called():
     fabric.launch()
     assert fabric._launched
     fabric._validate_launched()
-
-
-@pytest.mark.skipif(sys.platform == "darwin" and not _TORCH_GREATER_EQUAL_2_1, reason="Fix for MacOS in PyTorch 2.1")
-@RunIf(dynamo=True)
-@pytest.mark.parametrize(
-    "kwargs",
-    [
-        {},
-        pytest.param({"precision": "16-true"}, marks=pytest.mark.xfail(raises=RuntimeError, match="Unsupported")),
-        pytest.param({"precision": "64-true"}, marks=pytest.mark.xfail(raises=RuntimeError, match="Unsupported")),
-    ],
-)
-def test_fabric_with_torchdynamo_fullgraph(kwargs):
-    class MyModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.l = torch.nn.Linear(10, 10)
-
-        def forward(self, x):
-            # forward gets compiled
-            assert torch._dynamo.is_compiling()
-            return self.l(x)
-
-    def fn(model, x):
-        assert torch._dynamo.is_compiling()
-        a = x * 10
-        return model(a)
-
-    fabric = Fabric(devices=1, accelerator="cpu", **kwargs)
-    model = MyModel()
-    fmodel = fabric.setup(model)
-    # we are compiling a function that calls model.forward() inside
-    cfn = torch.compile(fn, fullgraph=True)
-    x = torch.randn(10, 10, device=fabric.device)
-    # pass the fabric wrapped model to the compiled function, so that it gets compiled too
-    out = cfn(fmodel, x)
-    assert isinstance(out, torch.Tensor)