ref: merge backends x/n

pytorch_lightning/accelerators/__init__.py

@@ -2,6 +2,7 @@
 from pytorch_lightning.accelerators.ddp2_backend import DDP2Backend
 from pytorch_lightning.accelerators.ddp_backend import DDPBackend
 from pytorch_lightning.accelerators.ddp_spawn_backend import DDPSpawnBackend
+from pytorch_lightning.accelerators.ddp_cpu_spawn_backend import DDPCPUSpawnBackend
 from pytorch_lightning.accelerators.dp_backend import DataParallelBackend
 from pytorch_lightning.accelerators.gpu_backend import GPUBackend
 from pytorch_lightning.accelerators.tpu_backend import TPUBackend

pytorch_lightning/accelerators/accelerator_connector.py

@@ -129,7 +129,8 @@ def select_accelerator(self):
         te_flags_passed = 'WORLD_SIZE' in os.environ and ('GROUP_RANK' in os.environ or 'NODE_RANK' in os.environ)
         use_torchelastic_ddp = self.trainer.use_ddp and te_flags_passed
 
-        use_ddp_spawn = self.trainer.use_ddp and self.trainer.distributed_backend in ['ddp_cpu', 'ddp_spawn']
+        use_ddp_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == 'ddp_spawn'
+        use_ddp_cpu_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == 'ddp_cpu'
 
         # choose the appropriate accelerator backend
         if self.trainer.use_ddp2:
@@ -144,6 +145,9 @@ def select_accelerator(self):
         elif use_ddp_spawn:
             accelerator_backend = accelerators.DDPSpawnBackend(self.trainer, nprocs=self.trainer.num_processes)
 
+        elif use_ddp_cpu_spawn:
+            accelerator_backend = accelerators.DDPCPUSpawnBackend(self.trainer, nprocs=self.trainer.num_processes)
+
         elif self.trainer.distributed_backend == 'ddp':
             accelerator_backend = accelerators.DDPBackend(self.trainer, mode='ddp')
 

pytorch_lightning/accelerators/ddp_base_backend.py

@@ -20,7 +20,8 @@
 import torch.distributed as torch_distrib
 import torch.distributed as dist
 from pytorch_lightning.utilities.cloud_io import atomic_save
-from pytorch_lightning.utilities.distributed import rank_zero_warn
+from pytorch_lightning.utilities.distributed import rank_zero_warn, rank_zero_only
+from pytorch_lightning import _logger as log
 
 try:
     from hydra.utils import to_absolute_path, get_original_cwd
@@ -88,3 +89,102 @@ def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
                 last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
                 atomic_save(model.state_dict(), last_path)
             mp_queue.put(last_path)
+
+    def ddp_train_tmp(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
+        """
+        Entry point for ddp
+
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
+
+        Returns:
+
+        """
+        # 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:
+            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
+        optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
+        self.trainer.optimizers = optimizers
+        self.trainer.lr_schedulers = lr_schedulers
+        self.trainer.optimizer_frequencies = optimizer_frequencies
+
+        # set model properties before going into wrapper
+        self.trainer.model_connector.copy_trainer_model_properties(model)
+
+        # AMP -
+        # run through amp wrapper before going to distributed DP
+        if self.trainer.amp_backend == AMPType.APEX:
+            model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
+            self.trainer.optimizers = optimizers
+            self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
+
+        # 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):
+        raise NotImplementedError('to create a ddp backend, please implement set_world_ranks')
+
+    def model_to_device(self, model, process_idx):
+        raise NotImplementedError('to create a ddp backend, please implement model_to_device')
+
+    def get_device_ids(self):
+        raise NotImplementedError('to create a ddp backend, please implement get_device_ids')

pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py

@@ -0,0 +1,82 @@
+# 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.multiprocessing as mp
+
+from pytorch_lightning.utilities.distributed import find_free_network_port
+from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
+
+try:
+    from apex import amp
+except ImportError:
+    amp = None
+
+
+class DDPCPUSpawnBackend(DDPBase):
+
+    def __init__(self, trainer, nprocs):
+        super().__init__(trainer)
+        self.mp_queue = None
+        self.nprocs = nprocs
+
+    def setup(self, model):
+        os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
+
+        # pass in a state q
+        smp = mp.get_context('spawn')
+        self.mp_queue = smp.SimpleQueue()
+
+        self.trainer.model = model
+
+    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,))
+
+        # restore main state with best weights
+        best_path = self.mp_queue.get()
+        results = self.mp_queue.get()
+        last_path = self.mp_queue.get()
+
+        # recover the weights of the processes trained in the children
+        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
+
+        # 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 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):
+        pass
+
+    def get_device_ids(self):
+        device_ids = None
+        return device_ids

pytorch_lightning/accelerators/ddp_spawn_backend.py

@@ -16,9 +16,7 @@
 import torch
 import torch.multiprocessing as mp
 
-from pytorch_lightning import _logger as log
-from pytorch_lightning.utilities import AMPType
-from pytorch_lightning.utilities.distributed import rank_zero_only, find_free_network_port
+from pytorch_lightning.utilities.distributed import find_free_network_port
 from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
 
 try:
@@ -47,7 +45,7 @@ def train(self):
         model = self.trainer.model
 
         # train in children process
-        mp.spawn(self.ddp_train, nprocs=self.nprocs, args=(self.mp_queue, model,))
+        mp.spawn(self.ddp_train_tmp, nprocs=self.nprocs, args=(self.mp_queue, model,))
 
         # restore main state with best weights
         best_path = self.mp_queue.get()
@@ -71,107 +69,17 @@ def __recover_child_process_weights(self, model, best_path, last_path):
 
         self.trainer.model = model
 
-    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
-        if self.trainer.use_ddp:
-            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
-
-        elif self.trainer.use_ddp2:
-            self.trainer.local_rank = self.trainer.node_rank
-            self.trainer.global_rank = self.trainer.node_rank
-            self.trainer.world_size = self.trainer.num_nodes
-
-        # 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:
-            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)
-
-        # MODEL
-        # copy model to each gpu
-        if self.trainer.on_gpu:
-            gpu_idx = process_idx
-            self.trainer.root_gpu = gpu_idx
-            torch.cuda.set_device(self.trainer.root_gpu)
-            model.cuda(self.trainer.root_gpu)
-
-        # CHOOSE OPTIMIZER
-        # allow for lr schedulers as well
-        optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
-        self.trainer.optimizers = optimizers
-        self.trainer.lr_schedulers = lr_schedulers
-        self.trainer.optimizer_frequencies = optimizer_frequencies
-
-        # set model properties before going into wrapper
-        self.trainer.model_connector.copy_trainer_model_properties(model)
-
-        # AMP -
-        # run through amp wrapper before going to distributed DP
-        if self.trainer.amp_backend == AMPType.APEX:
-            model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
-            self.trainer.optimizers = optimizers
-            self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
-
-        # DDP2 uses all GPUs on the machine
-        if self.trainer.distributed_backend == 'ddp' or self.trainer.distributed_backend == 'ddp_spawn':
-            device_ids = [self.trainer.root_gpu]
-        elif self.trainer.use_ddp2:
-            device_ids = self.trainer.data_parallel_device_ids
-        else:  # includes ddp_cpu
-            device_ids = None
-
-        # 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
+        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):
+        gpu_idx = process_idx
+        self.trainer.root_gpu = gpu_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