ref: unify slurm and TE under backendPlugin 5/n" (#4582)

* ref: unify slurm and TE under backendPlugin 4/n

* ref: unify slurm and TE under backendPlugin 5/n

(cherry picked from commit 3ba48d3)

Author: williamFalcon

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -11,22 +11,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License
-
 import os
 
 import torch
 import torch.distributed as torch_distrib
 
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.core.step_result import Result
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning import _logger as log
-from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.utilities import AMPType
-from pytorch_lightning.utilities.distributed import rank_zero_only
+from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
 from torch.nn.parallel import DistributedDataParallel
-from typing import List, Optional
+from typing import List, Optional, Union, Any
 
 try:
     from hydra.utils import to_absolute_path, get_original_cwd
@@ -203,3 +201,9 @@ def configure_sync_batchnorm(self, model: LightningModule) -> LightningModule:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)
 
         return model
+
+    def sync_tensor(self,
+                    tensor: Union[torch.Tensor],
+                    group: Optional[Any] = None,
+                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
+        return sync_ddp_if_available(tensor, group, reduce_op)

pytorch_lightning/accelerators/ddp_cpu_hpc_accelerator.py

@@ -11,21 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License
-import os
-from typing import Any, List, Optional, Union
-
-import torch
-import torch.distributed as torch_distrib
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel
-
-from pytorch_lightning import _logger as log
-from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
-from pytorch_lightning.core.lightning import LightningModule
-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.distributed.dist import LightningDistributed
+from pytorch_lightning.accelerators.ddp_hpc_accelerator import DDPHPCAccelerator
 
 
 try:
@@ -37,167 +23,15 @@
     HYDRA_AVAILABLE = True
 
 
-class DDPCPUHPCAccelerator(Accelerator):
+class DDPCPUHPCAccelerator(DDPHPCAccelerator):
 
     def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
         super().__init__(trainer, cluster_environment, ddp_plugin)
-        self.task_idx = None
-        self._has_spawned_children = False
-        self.dist = LightningDistributed()
         self.nickname = 'ddp_cpu'
 
-    def setup(self, model):
-        self.trainer.model = model
-        self.task_idx = self.cluster_environment.local_rank()
-
-    def train(self):
-        model = self.trainer.model
-        self.ddp_train(process_idx=self.task_idx, model=model)
-
-    def set_world_ranks(self, process_idx):
-        self.trainer.local_rank = process_idx
-        self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
-        self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
-
     def model_to_device(self, model, process_idx):
         model.cpu()
 
     def get_device_ids(self):
         device_ids = None
         return device_ids
-
-    def training_step(self, args):
-        if self.trainer.amp_backend == AMPType.NATIVE:
-            with torch.cuda.amp.autocast():
-                output = self.trainer.model(*args)
-        else:
-            output = self.trainer.model(*args)
-        return output
-
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def barrier(self, name: Optional[str] = None):
-        if torch_distrib.is_initialized():
-            torch_distrib.barrier()
-
-    def early_stopping_should_stop(self, pl_module):
-        stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
-        dist.all_reduce(stop, op=dist.reduce_op.SUM)
-        dist.barrier()
-        should_stop = stop == self.trainer.world_size
-        return should_stop
-
-    def broadcast(self, obj, src=0):
-        return self.dist.broadcast(obj)
-
-    def ddp_train(self, process_idx, model):
-        """
-        Entry point for ddp
-
-        Args:
-            process_idx:
-            mp_queue: multiprocessing queue
-            model:
-
-        Returns:
-            Dict with evaluation results
-
-        """
-        # determine which process we are and world size
-        self.set_world_ranks(process_idx)
-
-        # toggle prog bar
-        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
-        rank_zero_only.rank = self.trainer.global_rank
-
-        # set up server using proc 0's ip address
-        # try to init for 20 times at max in case ports are taken
-        # where to store ip_table
-        model.trainer = self.trainer
-        self.init_ddp_connection(
-            self.trainer.global_rank,
-            self.trainer.world_size,
-            self.trainer.is_slurm_managing_tasks
-        )
-
-        # call setup after the ddp process has connected
-        self.trainer.call_setup_hook(model)
-
-        # on world_size=0 let everyone know training is starting
-        if self.trainer.is_global_zero and not torch.distributed.is_initialized():
-            log.info('-' * 100)
-            log.info(f'distributed_backend={self.trainer.distributed_backend} (TORCH_ELASTIC)')
-            log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
-            log.info('-' * 100)
-
-        # call sync_bn before .cuda(), configure_apex and configure_ddp
-        if self.trainer.sync_batchnorm:
-            model = self.configure_sync_batchnorm(model)
-
-        # move the model to the correct device
-        self.model_to_device(model, process_idx)
-
-        # CHOOSE OPTIMIZER
-        # allow for lr schedulers as well
-        self.setup_optimizers(model)
-
-        # set model properties before going into wrapper
-        self.trainer.model_connector.copy_trainer_model_properties(model)
-
-        # 16-bit
-        model = self.trainer.precision_connector.connect(model)
-
-        # device ids change depending on the DDP setup
-        device_ids = self.get_device_ids()
-
-        # allow user to configure ddp
-        model = self.configure_ddp(model, device_ids)
-
-        # set up training routine
-        self.trainer.train_loop.setup_training(model)
-
-        # train or test
-        results = self.train_or_test()
-
-        # clean up memory
-        torch.cuda.empty_cache()
-
-        return results
-
-    def configure_ddp(
-        self, model: LightningModule, device_ids: List[int]
-    ) -> DistributedDataParallel:
-        model = self.ddp_plugin.configure_ddp(model, device_ids)
-        return model
-
-    def configure_sync_batchnorm(self, model: LightningModule) -> LightningModule:
-        """
-        Add global batchnorm for a model spread across multiple GPUs and nodes.
-
-        Override to synchronize batchnorm between specific process groups instead
-        of the whole world or use a different sync_bn like `apex`'s version.
-
-        Args:
-            model: pointer to current :class:`LightningModule`.
-
-        Return:
-            LightningModule with batchnorm layers synchronized between process groups
-        """
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)
-
-        return model
-
-    def sync_tensor(self,
-                    tensor: Union[torch.Tensor],
-                    group: Optional[Any] = None,
-                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
-        return sync_ddp_if_available(tensor, group, reduce_op)

pytorch_lightning/accelerators/ddp_hpc_accelerator.py

@@ -136,7 +136,7 @@ def ddp_train(self, process_idx, model):
         # on world_size=0 let everyone know training is starting
         if self.trainer.is_global_zero and not torch.distributed.is_initialized():
             log.info('-' * 100)
-            log.info(f'distributed_backend={self.trainer.distributed_backend} (on SLURM)')
+            log.info(f'distributed_backend={self.trainer.distributed_backend}')
             log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
             log.info('-' * 100)