ref: part 7 of #3733

pytorch_lightning/accelerators/ddp_spawn_backend.py

@@ -12,20 +12,39 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 import os
+import re
 
 import torch
 import torch.multiprocessing as mp
-
+import torch.distributed as torch_distrib
+import torch.distributed as dist
+
+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.cloud_io import atomic_save
+from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities.seed import seed_everything
+from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.utilities.distributed import find_free_network_port
-from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
 
 
-class DDPSpawnBackend(DDPBase):
+try:
+    from hydra.core.hydra_config import HydraConfig
+    from hydra.utils import get_original_cwd, to_absolute_path
+except ImportError:
+    HYDRA_AVAILABLE = False
+else:
+    HYDRA_AVAILABLE = True
+
+
+class DDPSpawnBackend(Accelerator):
 
     def __init__(self, trainer, nprocs):
         super().__init__(trainer)
         self.mp_queue = None
         self.nprocs = nprocs
+        self.dist = LightningDistributed()
 
     def setup(self, model):
         os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
@@ -40,7 +59,7 @@ def train(self):
         model = self.trainer.model
 
         # train in children process
-        mp.spawn(self.ddp_train_tmp, nprocs=self.nprocs, args=(self.mp_queue, model,))
+        mp.spawn(self.ddp_train, nprocs=self.nprocs, args=(self.mp_queue, model,))
 
         # restore main state with best weights
         best_path = self.mp_queue.get()
@@ -51,18 +70,92 @@ def train(self):
         self.__recover_child_process_weights(model, best_path, last_path)
         return results
 
-    def __recover_child_process_weights(self, model, best_path, last_path):
-        # transfer back the best path to the trainer
-        if self.trainer.checkpoint_callback:
-            self.trainer.checkpoint_callback.best_model_path = best_path
-        # todo, pass also best score
+    def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
+        """
+        Entry point for ddp
 
-        # load last weights
-        if last_path is not None and not self.trainer.testing:
-            ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
-            model.load_state_dict(ckpt)
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
 
-        self.trainer.model = 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()
+
+        # get original model
+        model = self.trainer.get_model()
+
+        # persist info in ddp_spawn
+        self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
+
+        # clean up memory
+        torch.cuda.empty_cache()
 
     def set_world_ranks(self, process_idx):
         self.trainer.local_rank = process_idx
@@ -78,3 +171,64 @@ 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 __recover_child_process_weights(self, model, best_path, last_path):
+        # transfer back the best path to the trainer
+        if self.trainer.checkpoint_callback:
+            self.trainer.checkpoint_callback.best_model_path = best_path
+        # todo, pass also best score
+
+        # load last weights
+        if last_path is not None and not self.trainer.testing:
+            ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
+            model.load_state_dict(ckpt)
+
+        self.trainer.model = model
+
+    def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
+        best_model_path = None
+        if self.trainer.checkpoint_callback is not None:
+            best_model_path = self.trainer.checkpoint_callback.best_model_path
+
+        if self.trainer.global_rank == 0 and mp_queue is not None:
+            rank_zero_warn('cleaning up ddp environment...')
+            # todo, pass complete checkpoint as state dictionary
+            mp_queue.put(best_model_path)
+            mp_queue.put(results)
+
+            # save the last weights
+            last_path = None
+            if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
+                last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
+                atomic_save(model.state_dict(), last_path)
+            mp_queue.put(last_path)