skip some test cases in test_compile.py when python is 3.14+

Author: chenhao388

test/test_compile.py

@@ -7,6 +7,7 @@
 import importlib.util
 import inspect
 import platform
+import sys
 from pathlib import Path
 from typing import Any, Callable
 
@@ -60,6 +61,9 @@
     npu_device_count = torch.npu.device_count()
 
 
+@pytest.mark.skipif(
+    sys.version_info > (3, 14), reason="torch.compile is not supported on python 3.14+ "
+)
 def test_vmap_compile():
     # Since we monkey patch vmap we need to make sure compile is happy with it
     def func(x, y):
@@ -76,6 +80,9 @@ def func(x, y):
 @pytest.mark.skipif(
     TORCH_VERSION < version.parse("2.4.0"), reason="requires torch>=2.4"
 )
+@pytest.mark.skipif(
+    sys.version_info > (3, 14), reason="torch.compile is not supported on python 3.14+ "
+)
 @pytest.mark.parametrize("mode", [None, "reduce-overhead"])
 class TestTD:
     def test_tensor_output(self, mode):
@@ -271,7 +278,7 @@ def make_td_with_names(data):
         make_td_with_names_c(data_dict)
 
     @pytest.mark.skipif(
-        not torch.cuda.is_available() and not is_npu_available(), reason="cuda or npu required to test device casting"
+        not torch.cuda.is_available(), reason="cuda required to test device casting"
     )
     @pytest.mark.parametrize("has_device", [True, False])
     def test_to(self, has_device, mode):
@@ -292,6 +299,9 @@ def test_to_device(td):
         assert td_device_c.batch_size == td.batch_size
         assert td_device_c.device == torch.device(device)
 
+    @pytest.mark.skipif(
+        is_npu_available(), reason="torch.device in torch.compile is not supported on NPU currently."
+    )
     def test_lock(self, mode):
         def locked_op(td):
             # Adding stuff uses cache, check that this doesn't break
@@ -366,6 +376,9 @@ class MyClass:
 @pytest.mark.skipif(
     TORCH_VERSION < version.parse("2.4.0"), reason="requires torch>=2.4"
 )
+@pytest.mark.skipif(
+    sys.version_info > (3, 14), reason="torch.compile is not supported on python 3.14+ "
+)
 @pytest.mark.parametrize("mode", [None, "reduce-overhead"])
 class TestTC:
     def test_tc_tensor_output(self, mode):
@@ -558,7 +571,7 @@ def clone(td: TensorDict):
             assert clone_c(data).a.b is data.a.b
 
     @pytest.mark.skipif(
-        not torch.cuda.is_available() and not is_npu_available(), reason="cuda or npu required to test device casting"
+        not torch.cuda.is_available(), reason="cuda required to test device casting"
     )
     @pytest.mark.parametrize("has_device", [True, False])
     def test_tc_to(self, has_device, mode):
@@ -579,6 +592,9 @@ def test_to_device(tc):
         assert tc_device_c.batch_size == data.batch_size
         assert tc_device_c.device == torch.device(device)
 
+    @pytest.mark.skipif(
+        is_npu_available(), reason="torch.device in torch.compile is not supported on NPU currently."
+    )
     def test_tc_lock(self, mode):
         def locked_op(tc):
             # Adding stuff uses cache, check that this doesn't break
@@ -630,6 +646,9 @@ def func_c_mytd():
 @pytest.mark.skipif(
     TORCH_VERSION < version.parse("2.4.0"), reason="requires torch>=2.4"
 )
+@pytest.mark.skipif(
+    sys.version_info > (3, 14), reason="torch.compile is not supported on python 3.14+ "
+)
 @pytest.mark.parametrize("mode", [None, "reduce-overhead"])
 class TestNN:
     def test_func(self, mode):
@@ -734,6 +753,9 @@ def test_prob_module_with_kwargs(self, mode):
 @pytest.mark.skipif(
     TORCH_VERSION <= version.parse("2.4.0"), reason="requires torch>2.4"
 )
+@pytest.mark.skipif(
+    sys.version_info > (3, 14), reason="torch.compile is not supported on python 3.14+ "
+)
 @pytest.mark.parametrize("mode", [None, "reduce-overhead"])
 class TestFunctional:
     def test_functional_error(self, mode):
@@ -1032,6 +1054,9 @@ def to_numpy(tensor):
     (TORCH_VERSION <= version.parse("2.7.0")) and _IS_OSX,
     reason="requires torch>=2.7 ons OSX",
 )
+@pytest.mark.skipif(
+    sys.version_info > (3, 14), reason="torch.compile is not supported on python 3.14+ "
+)
 @pytest.mark.parametrize("compiled", [False, True])
 class TestCudaGraphs:
     @pytest.fixture(scope="class", autouse=True)
@@ -1251,7 +1276,7 @@ def test_state_dict(self, compiled):
         torch.testing.assert_close(y1, y2)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available() and not is_npu_available(), reason="cuda or npu is not available")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda is not available")
 class TestCompileNontensor:
     # Same issue with the decorator @tensorclass version
     @pytest.fixture(scope="class")