Fix logic and add test for apex check, rename file, add DDP launcher tests

benchmarks/test_sharded_parity.py

@@ -10,6 +10,7 @@ from torch.utils.data.distributed import DistributedSampler
 from pytorch_lightning import Trainer, seed_everything
 from pytorch_lightning.plugins.sharded_plugin import DDPShardedPlugin
 from pytorch_lightning.utilities import FAIRSCALE_AVAILABLE, NATIVE_AMP_AVALAIBLE
+from tests.backends.launcher import DDPLauncher
 from tests.base.boring_model import BoringModel, RandomDataset
 
 
@@ -55,6 +56,26 @@ def test_ddp_sharded_plugin_correctness_amp_multi_gpu():
     run_sharded_correctness(gpus=2, precision=16, accelerator='ddp_spawn')
 
 
+@pytest.mark.skipif(not FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
+@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+@pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
+                    reason="test should be run outside of pytest")
+@DDPLauncher.run("--distributed_backend ddp --gpus 2 --precision 32")
+def test_ddp_sharded_plugin_correctness_amp_multi_gpu_ddp(tmpdir, args=None):
+    run_sharded_correctness(gpus=args.gpus, precision=args.precision, accelerator=args.distributed_backend)
+
+
+@pytest.mark.skipif(not FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
+@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+@pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
+                    reason="test should be run outside of pytest")
+@DDPLauncher.run("--distributed_backend ddp --gpus 2  --precision 16")
+def test_ddp_sharded_plugin_correctness_amp_multi_gpu_ddp(tmpdir, args=None):
+    run_sharded_correctness(gpus=args.gpus, precision=args.precision, accelerator=args.distributed_backend)
+
+
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 @pytest.mark.skipif(platform.system() == "Windows",
                     reason="Distributed training is not supported on Windows")

pytorch_lightning/trainer/connectors/precision_connector.py

@@ -73,8 +73,8 @@ class PrecisionConnector:
                 rank_zero_warn('You have asked for Apex AMP but you have not installed it yet.'
                                ' Install apex first using this guide: https://github.com/NVIDIA/apex#linux')
             elif using_sharded_plugin:
-                rank_zero_warn(
-                    'Sharded Plugin is not supported with Apex AMP, please using native AMP for 16 bit precision.')
+                raise MisconfigurationException('Sharded Plugin is not supported with Apex AMP, '
+                                                'please using native AMP for 16 bit precision.')
             else:
                 log.info('Using APEX 16bit precision.')
                 self.trainer.amp_backend = AMPType.APEX

tests/plugins/test_sharded_plugin.py

@@ -9,7 +9,8 @@ from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.plugins.sharded_native_amp_plugin import ShardedNativeAMPPlugin
 from pytorch_lightning.plugins.sharded_plugin import DDPShardedPlugin, FAIRSCALE_AVAILABLE
-from pytorch_lightning.utilities import NATIVE_AMP_AVALAIBLE
+from pytorch_lightning.utilities import NATIVE_AMP_AVALAIBLE, APEX_AVAILABLE
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base.boring_model import BoringModel
 
 
@@ -54,6 +55,26 @@ def test_ddp_choice_sharded(tmpdir, ddp_backend, gpus, num_processes):
         trainer.fit(model)
 
 
+@pytest.mark.skipif(not APEX_AVAILABLE, reason="test requires apex")
+@pytest.mark.skipif(not FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
+def test_invalid_apex_sharded(tmpdir):
+    """
+        Test to ensure that we raise an error when we try to use apex and sharded
+    """
+
+    model = BoringModel()
+    with pytest.raises(MisconfigurationException, match='Sharded Plugin is not supported with Apex AMP'):
+        trainer = Trainer(
+            fast_dev_run=True,
+            distributed_backend='ddp_spawn',
+            plugins=[DDPShardedPlugin()],
+            precision=16,
+            amp_backend='apex'
+        )
+
+        trainer.fit(model)
+
+
 @mock.patch.dict(
     os.environ,
     {