Remove unnecessary PYTORCH_TEST_WITH_SLOW env (#5631)

* Remove unnecessary PYTORCH_TEST_WITH_SLOW env

* Completely remove skip

Author: datumbox

.circleci/unittest/linux/scripts/run_test.sh

@@ -5,6 +5,5 @@ set -e
 eval "$(./conda/bin/conda shell.bash hook)"
 conda activate ./env
 
-export PYTORCH_TEST_WITH_SLOW='1'
 python -m torch.utils.collect_env
 pytest --junitxml=test-results/junit.xml -v --durations 20

.circleci/unittest/windows/scripts/run_test.sh

@@ -8,6 +8,5 @@ conda activate ./env
 this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 source "$this_dir/set_cuda_envs.sh"
 
-export PYTORCH_TEST_WITH_SLOW='1'
 python -m torch.utils.collect_env
 pytest --junitxml=test-results/junit.xml -v --durations 20

test/test_models.py

@@ -118,7 +118,7 @@ def _assert_expected(output, name, prec=None, atol=None, rtol=None):
         torch.testing.assert_close(output, expected, rtol=rtol, atol=atol, check_dtype=False)
 
 
-def _check_jit_scriptable(nn_module, args, unwrapper=None, skip=False, eager_out=None):
+def _check_jit_scriptable(nn_module, args, unwrapper=None, eager_out=None):
     """Check that a nn.Module's results in TorchScript match eager and that it can be exported"""
 
     def get_export_import_copy(m):
@@ -129,20 +129,6 @@ def get_export_import_copy(m):
             imported = torch.jit.load(path)
         return imported
 
-    TEST_WITH_SLOW = os.getenv("PYTORCH_TEST_WITH_SLOW", "0") == "1"
-    if not TEST_WITH_SLOW or skip:
-        # TorchScript is not enabled, skip these tests
-        msg = (
-            f"The check_jit_scriptable test for {nn_module.__class__.__name__} was skipped. "
-            "This test checks if the module's results in TorchScript "
-            "match eager and that it can be exported. To run these "
-            "tests make sure you set the environment variable "
-            "PYTORCH_TEST_WITH_SLOW=1 and that the test is not "
-            "manually skipped."
-        )
-        warnings.warn(msg, RuntimeWarning)
-        return None
-
     sm = torch.jit.script(nn_module)
 
     if eager_out is None: