ref: part 8 of #3733

pytorch_lightning/accelerators/ddp_backend.py

@@ -21,9 +21,17 @@
 
 import numpy as np
 import torch
+import torch.distributed as torch_distrib
+import torch.distributed as dist
 
 from pytorch_lightning.utilities.distributed import find_free_network_port
-from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
+from pytorch_lightning.accelerators.base_backend import Accelerator
+from pytorch_lightning import _logger as log
+from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from pytorch_lightning.utilities.seed import seed_everything
+from pytorch_lightning.distributed.dist import LightningDistributed
+
 
 try:
     from hydra.utils import to_absolute_path, get_original_cwd
@@ -34,13 +42,14 @@
     HYDRA_AVAILABLE = True
 
 
-class DDPBackend(DDPBase):
+class DDPBackend(Accelerator):
 
     def __init__(self, trainer, mode: str = 'ddp'):
         super().__init__(trainer)
         self.task_idx = None
         self._has_spawned_children = False
         self.mode = mode
+        self.dist = LightningDistributed()
 
     def setup(self, model):
         if self.mode == 'ddp':
@@ -130,11 +139,11 @@ def __ddp_script_mode_setup(self):
     def train(self):
         model = self.trainer.model
         if self.mode == 'ddp':
-            results = self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model, is_master=True)
+            results = self.ddp_train(process_idx=self.task_idx, model=model, is_master=True)
             del os.environ['WORLD_SIZE']
             return results
         else:
-            self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model)
+            self.ddp_train(process_idx=self.task_idx, model=model)
 
     def _check_can_spawn_children(self):
         if self._has_spawned_children:
@@ -168,3 +177,117 @@ def model_to_device(self, model, process_idx, is_master):
     def get_device_ids(self):
         device_ids = [self.trainer.root_gpu]
         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: 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, is_master=False, proc_offset=0):
+        """
+        Entry point for ddp
+
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
+
+        Returns:
+
+        """
+        seed = os.environ.get("PL_GLOBAL_SEED")
+        if seed is not None:
+            seed_everything(int(seed))
+
+        # offset the process id if requested
+        process_idx = process_idx + proc_offset
+
+        # show progressbar only on progress_rank 0
+        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()
+
+        # determine which process we are and world size
+        self.set_world_ranks(process_idx)
+
+        # 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
+        model.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}')
+            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 = model.configure_sync_batchnorm(model)
+
+        # move the model to the correct device
+        self.model_to_device(model, process_idx, is_master)
+
+        # 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 = model.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()
+
+        if self.trainer.global_rank == 0:
+            return results