ref: decoupled ddp2 (#3816)

pytorch_lightning/accelerators/ddp2_backend.py

@@ -15,10 +15,14 @@
 import os
 
 import torch
-
+import torch.distributed as torch_distrib
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.core.step_result import Result
-from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
+from pytorch_lightning.distributed.dist import LightningDistributed
+from pytorch_lightning import _logger as log
+from pytorch_lightning.accelerators.base_backend import Accelerator
+from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities.distributed import rank_zero_only
 
 try:
     from hydra.utils import to_absolute_path, get_original_cwd
@@ -29,15 +33,15 @@
     HYDRA_AVAILABLE = True
 
 
-class DDP2Backend(DDPBase):
+class DDP2Backend(Accelerator):
 
     def __init__(self, trainer):
         super().__init__(trainer)
         self.task_idx = None
+        self.dist = LightningDistributed()
 
     def setup(self, model):
         self._resolve_task_idx()
-
         self.trainer.model = model
 
     def _resolve_task_idx(self):
@@ -53,7 +57,27 @@ def _resolve_task_idx(self):
 
     def train(self):
         model = self.trainer.model
-        self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model)
+        return self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
+
+    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 training_step_end(self, output):
         if isinstance(output, Result):
@@ -75,21 +99,89 @@ def set_world_ranks(self, process_idx):
         self.trainer.global_rank = self.trainer.node_rank
         self.trainer.world_size = self.trainer.num_nodes
 
-    def model_to_device(self, model, process_idx, is_master):
-        gpu_idx = process_idx
+    def broadcast(self, obj, src=0):
+        return self.dist.broadcast(obj)
 
-        # when using ddp, the master process (proc 0) continues running as the main one
-        # this means that the local rank will always be 0
-        # (even if cuda visible devices has other visible gpus)
-        # this means that the master process needs to pull the 0th visible index as the device number
-        if is_master:
-            available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
-            gpu_idx = int(available_gpus[self.trainer.local_rank])
-
-        self.trainer.root_gpu = gpu_idx
+    def model_to_device(self, model, process_idx):
+        self.trainer.root_gpu = process_idx
         torch.cuda.set_device(self.trainer.root_gpu)
         model.cuda(self.trainer.root_gpu)
 
     def get_device_ids(self):
         device_ids = self.trainer.data_parallel_device_ids
         return device_ids
+
+    def ddp_train(self, process_idx, mp_queue, model):
+        """
+        Entry point for ddp
+
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
+
+        Returns:
+
+        """
+        # 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)
+
+        # 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()
+        return results