ref: separate te from ddp (#3810)

* ref: separate te from ddp

* ref: separate te from ddp

* ref: separate te from ddp

Author: williamFalcon

pytorch_lightning/accelerators/__init__.py

@@ -7,3 +7,4 @@
 from pytorch_lightning.accelerators.gpu_backend import GPUBackend
 from pytorch_lightning.accelerators.tpu_backend import TPUBackend
 from pytorch_lightning.accelerators.horovod_backend import HorovodBackend
+from pytorch_lightning.accelerators.ddp_torchelastic_backend import DDPTorchElasticBackend

pytorch_lightning/accelerators/accelerator_connector.py

@@ -145,7 +145,7 @@ def select_accelerator(self):
             accelerator_backend = accelerators.DDPBackend(self.trainer, mode='slurm_ddp')
 
         elif use_torchelastic_ddp:
-            accelerator_backend = accelerators.DDPBackend(self.trainer, mode='torchelastic_ddp')
+            accelerator_backend = accelerators.DDPTorchElasticBackend(self.trainer)
 
         elif use_ddp_spawn:
             accelerator_backend = accelerators.DDPSpawnBackend(self.trainer, nprocs=self.trainer.num_processes)

pytorch_lightning/accelerators/ddp_torchelastic_backend.py

@@ -0,0 +1,175 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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
+import torch.distributed as dist
+
+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
+    from hydra.core.hydra_config import HydraConfig
+except ImportError:
+    HYDRA_AVAILABLE = False
+else:
+    HYDRA_AVAILABLE = True
+
+
+# -------------------------------------------
+# !!!!!!!!!!!!!! NOTE !!!!!!!!!!!!!!!!!!!!!!
+# TEMP CLASS WHILE WE DECOUPLE SLURM FROM DDP
+# !!!!!!!!!!!!!! NOTE !!!!!!!!!!!!!!!!!!!!!!
+# -------------------------------------------
+class DDPTorchElasticBackend(Accelerator):
+
+    def __init__(self, trainer):
+        super().__init__(trainer)
+        self.task_idx = None
+        self._has_spawned_children = False
+        self.dist = LightningDistributed()
+
+    def setup(self, model):
+        self.trainer.model = model
+        self.task_idx = int(os.environ['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):
+        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.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):
+        """
+        Entry point for ddp
+
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
+
+        Returns:
+
+        """
+        # 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:
+            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
+        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} (on SLURM)')
+            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