ref: unify slurm and TE under backendPlugin 1/n

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -46,19 +46,8 @@ def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
         self.nickname = 'ddp2'
 
     def setup(self, model):
-        self._resolve_task_idx()
         self.trainer.model = model
-
-    def _resolve_task_idx(self):
-        if self.trainer.is_slurm_managing_tasks:
-            self.task_idx = int(os.environ['SLURM_LOCALID'])
-        else:
-            # torchelastic or general non_slurm ddp2
-            try:
-                self.task_idx = int(os.environ['LOCAL_RANK'])
-            except Exception as exp:
-                m = 'ddp2 only works in SLURM or via torchelastic with the WORLD_SIZE, LOCAL_RANK, GROUP_RANK flags'
-                raise MisconfigurationException(m) from exp
+        self.task_idx = self.cluster_environment.local_rank()
 
     def train(self):
         model = self.trainer.model

pytorch_lightning/accelerators/ddp_cpu_slurm_accelerator.py

@@ -53,7 +53,7 @@ def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
 
     def setup(self, model):
         self.trainer.model = model
-        self.task_idx = int(os.environ['SLURM_LOCALID'])
+        self.task_idx = self.cluster_environment.local_rank()
 
     def train(self):
         model = self.trainer.model
@@ -118,7 +118,7 @@ def ddp_train(self, process_idx, model):
         self.set_world_ranks(process_idx)
 
         # toggle prog bar
-        if self.trainer.global_rank == 0 and self.trainer.progress_bar_callback is not None:
+        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
             self.trainer.progress_bar_callback.disable()
 
         # set warning rank

pytorch_lightning/accelerators/ddp_cpu_torchelastic_accelerator.py

@@ -52,7 +52,7 @@ def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
 
     def setup(self, model):
         self.trainer.model = model
-        self.task_idx = int(os.environ['LOCAL_RANK'])
+        self.task_idx = self.cluster_environment.local_rank()
 
     def train(self):
         model = self.trainer.model
@@ -117,7 +117,7 @@ def ddp_train(self, process_idx, model):
         self.set_world_ranks(process_idx)
 
         # toggle prog bar
-        if self.trainer.global_rank == 0 and self.trainer.progress_bar_callback is not None:
+        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
             self.trainer.progress_bar_callback.disable()
 
         # set warning rank

pytorch_lightning/accelerators/ddp_slurm_accelerator.py

@@ -25,7 +25,6 @@
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
-from pytorch_lightning.utilities.seed import seed_everything
 
 try:
     from hydra.utils import to_absolute_path, get_original_cwd
@@ -52,7 +51,7 @@ def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
 
     def setup(self, model):
         self.trainer.model = model
-        self.task_idx = int(os.environ['SLURM_LOCALID'])
+        self.task_idx = self.cluster_environment.local_rank()
 
     def train(self):
         model = self.trainer.model
@@ -88,7 +87,7 @@ def test_step(self, args):
         output = self.training_step(args)
         return output
 
-    def barrier(self, name: str = None):
+    def barrier(self, name: Optional[str] = None):
         if torch_distrib.is_initialized():
             torch_distrib.barrier()
 
@@ -115,15 +114,11 @@ def ddp_train(self, process_idx, model):
             Dict with evaluation results
 
         """
-        seed = os.environ.get("PL_GLOBAL_SEED")
-        if seed is not None:
-            seed_everything(int(seed))
-
         # determine which process we are and world size
         self.set_world_ranks(process_idx)
 
         # toggle prog bar
-        if self.trainer.global_rank != 0 and self.trainer.progress_bar_callback is not None:
+        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
             self.trainer.progress_bar_callback.disable()
 
         # set warning rank

pytorch_lightning/accelerators/ddp_torchelastic_accelerator.py

@@ -24,8 +24,7 @@
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.utilities import AMPType
-from pytorch_lightning.utilities.distributed import rank_zero_only
-from pytorch_lightning.utilities.distributed import sync_ddp_if_available
+from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
 
 
 try:
@@ -53,7 +52,7 @@ def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
 
     def setup(self, model):
         self.trainer.model = model
-        self.task_idx = int(os.environ['LOCAL_RANK'])
+        self.task_idx = self.cluster_environment.local_rank()
 
     def train(self):
         model = self.trainer.model
@@ -120,7 +119,7 @@ def ddp_train(self, process_idx, model):
         self.set_world_ranks(process_idx)
 
         # toggle prog bar
-        if self.trainer.global_rank == 0 and self.trainer.progress_bar_callback is not None:
+        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
             self.trainer.progress_bar_callback.disable()
 
         # set warning rank

pytorch_lightning/cluster_environments/cluster_environment.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
 class ClusterEnvironment:
 
     def __init__(self):
@@ -25,3 +26,6 @@ def master_port(self):
 
     def world_size(self):
         return self._world_size
+
+    def local_rank(self):
+        pass

pytorch_lightning/cluster_environments/slurm_environment.py

@@ -67,6 +67,9 @@ def master_port(self):
     def world_size(self):
         return self._world_size
 
+    def local_rank(self):
+        return int(os.environ['SLURM_LOCALID'])
+
     def _resolve_root_node_address(self, root_node):
         if '[' in root_node:
             name, numbers = root_node.split('[', maxsplit=1)

pytorch_lightning/cluster_environments/torchelastic_environment.py

@@ -46,3 +46,6 @@ def master_port(self):
 
     def world_size(self):
         return os.environ.get('WORLD_SIZE')
+
+    def local_rank(self):
+        return int(os.environ['LOCAL_RANK'])

tests/backends/test_accelerator_connector.py

@@ -104,7 +104,7 @@ def on_fit_start(self, trainer, pl_module):
     "SLURM_NTASKS": "2",
     "SLURM_JOB_NAME": "SOME_NAME",
     "SLURM_NODEID": "0",
-    "SLURM_LOCALID": "0"
+    "SLURM_LOCALID": "10"
 })
 @mock.patch('torch.cuda.device_count', return_value=2)
 def test_accelerator_choice_ddp_slurm(tmpdir):
@@ -113,6 +113,8 @@ def on_fit_start(self, trainer, pl_module):
             assert trainer.use_ddp
             assert isinstance(trainer.accelerator_backend, accelerators.DDPSLURMAccelerator)
             assert isinstance(trainer.accelerator_backend.cluster_environment, SLURMEnvironment)
+            assert trainer.accelerator_backend.task_idx == 10
+            assert trainer.accelerator_backend.cluster_environment.local_rank() == trainer.accelerator_backend.task_idx
             raise SystemExit()
 
     model = BoringModel()
@@ -133,7 +135,7 @@ def on_fit_start(self, trainer, pl_module):
     "SLURM_JOB_NAME": "SOME_NAME",
     "SLURM_NODEID": "0",
     "LOCAL_RANK": "0",
-    "SLURM_LOCALID": "0"
+    "SLURM_LOCALID": "10"
 })
 @mock.patch('torch.cuda.device_count', return_value=2)
 def test_accelerator_choice_ddp2_slurm(tmpdir):
@@ -142,6 +144,9 @@ def on_fit_start(self, trainer, pl_module):
             assert trainer.use_ddp2
             assert isinstance(trainer.accelerator_backend, accelerators.DDP2Accelerator)
             assert isinstance(trainer.accelerator_backend.cluster_environment, SLURMEnvironment)
+            assert trainer.accelerator_backend.task_idx == 10
+            assert trainer.accelerator_backend.cluster_environment.local_rank() == trainer.accelerator_backend.task_idx
+
             raise SystemExit()
 
     model = BoringModel()
@@ -159,7 +164,7 @@ def on_fit_start(self, trainer, pl_module):
 @mock.patch.dict(os.environ, {
     "CUDA_VISIBLE_DEVICES": "0,1",
     "WORLD_SIZE": "2",
-    "LOCAL_RANK": "0",
+    "LOCAL_RANK": "10",
     "NODE_RANK": "0"
 })
 @mock.patch('torch.cuda.device_count', return_value=2)
@@ -169,6 +174,8 @@ def on_fit_start(self, trainer, pl_module):
             assert trainer.use_ddp
             assert isinstance(trainer.accelerator_backend, accelerators.DDPTorchElasticAccelerator)
             assert isinstance(trainer.accelerator_backend.cluster_environment, TorchElasticEnvironment)
+            assert trainer.accelerator_backend.task_idx == 10
+            assert trainer.accelerator_backend.cluster_environment.local_rank() == trainer.accelerator_backend.task_idx
             raise SystemExit()
 
     model = BoringModel()
@@ -186,7 +193,7 @@ def on_fit_start(self, trainer, pl_module):
 @mock.patch.dict(os.environ, {
     "CUDA_VISIBLE_DEVICES": "0,1",
     "WORLD_SIZE": "2",
-    "LOCAL_RANK": "0",
+    "LOCAL_RANK": "10",
     "NODE_RANK": "0"
 })
 @mock.patch('torch.cuda.device_count', return_value=2)
@@ -196,6 +203,8 @@ def on_fit_start(self, trainer, pl_module):
             assert trainer.use_ddp2
             assert isinstance(trainer.accelerator_backend, accelerators.DDP2Accelerator)
             assert isinstance(trainer.accelerator_backend.cluster_environment, TorchElasticEnvironment)
+            assert trainer.accelerator_backend.task_idx == 10
+            assert trainer.accelerator_backend.cluster_environment.local_rank() == trainer.accelerator_backend.task_idx
             raise SystemExit()
 
     model = BoringModel()
@@ -212,7 +221,7 @@ def on_fit_start(self, trainer, pl_module):
 
 @mock.patch.dict(os.environ, {
     "WORLD_SIZE": "1",
-    "LOCAL_RANK": "0",
+    "LOCAL_RANK": "10",
     "NODE_RANK": "0"
 })
 @mock.patch('torch.cuda.device_count', return_value=0)
@@ -222,6 +231,9 @@ def on_fit_start(self, trainer, pl_module):
             assert trainer.use_ddp
             assert isinstance(trainer.accelerator_backend, accelerators.DDPCPUTorchElasticAccelerator)
             assert isinstance(trainer.accelerator_backend.cluster_environment, TorchElasticEnvironment)
+            assert trainer.accelerator_backend.task_idx == 10
+            assert trainer.accelerator_backend.cluster_environment.local_rank() == trainer.accelerator_backend.task_idx
+
             raise SystemExit()
 
     model = BoringModel()