ref: decoupled ddp2 (#3816)
This commit is contained in:
William Falcon
GitHub
parent
62320632d4
commit
0838c6bfce
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@ -15,10 +15,14 @@
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import os
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import torch
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import torch.distributed as torch_distrib
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.core.step_result import Result
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from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
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from pytorch_lightning.distributed.dist import LightningDistributed
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from pytorch_lightning import _logger as log
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from pytorch_lightning.accelerators.base_backend import Accelerator
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from pytorch_lightning.utilities import AMPType
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from pytorch_lightning.utilities.distributed import rank_zero_only
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try:
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from hydra.utils import to_absolute_path, get_original_cwd
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@ -29,15 +33,15 @@ else:
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HYDRA_AVAILABLE = True
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class DDP2Backend(DDPBase):
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class DDP2Backend(Accelerator):
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def __init__(self, trainer):
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super().__init__(trainer)
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self.task_idx = None
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self.dist = LightningDistributed()
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def setup(self, model):
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self._resolve_task_idx()
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self.trainer.model = model
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def _resolve_task_idx(self):
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@ -53,7 +57,27 @@ class DDP2Backend(DDPBase):
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def train(self):
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model = self.trainer.model
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self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model)
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return self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
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def training_step(self, args):
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if self.trainer.amp_backend == AMPType.NATIVE:
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with torch.cuda.amp.autocast():
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output = self.trainer.model(*args)
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else:
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output = self.trainer.model(*args)
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return output
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def validation_step(self, args):
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output = self.training_step(args)
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return output
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def test_step(self, args):
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output = self.training_step(args)
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return output
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def barrier(self, name: str = None):
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if torch_distrib.is_initialized():
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torch_distrib.barrier()
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def training_step_end(self, output):
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if isinstance(output, Result):
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@ -75,21 +99,89 @@ class DDP2Backend(DDPBase):
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self.trainer.global_rank = self.trainer.node_rank
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self.trainer.world_size = self.trainer.num_nodes
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def model_to_device(self, model, process_idx, is_master):
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gpu_idx = process_idx
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def broadcast(self, obj, src=0):
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return self.dist.broadcast(obj)
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# when using ddp, the master process (proc 0) continues running as the main one
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# this means that the local rank will always be 0
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# (even if cuda visible devices has other visible gpus)
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# this means that the master process needs to pull the 0th visible index as the device number
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if is_master:
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available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
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gpu_idx = int(available_gpus[self.trainer.local_rank])
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self.trainer.root_gpu = gpu_idx
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def model_to_device(self, model, process_idx):
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self.trainer.root_gpu = process_idx
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torch.cuda.set_device(self.trainer.root_gpu)
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model.cuda(self.trainer.root_gpu)
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def get_device_ids(self):
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device_ids = self.trainer.data_parallel_device_ids
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return device_ids
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def ddp_train(self, process_idx, mp_queue, model):
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"""
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Entry point for ddp
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Args:
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process_idx:
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mp_queue: multiprocessing queue
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model:
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Returns:
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"""
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# show progressbar only on progress_rank 0
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if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
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self.trainer.progress_bar_callback.disable()
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# determine which process we are and world size
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self.set_world_ranks(process_idx)
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# set warning rank
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rank_zero_only.rank = self.trainer.global_rank
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# set up server using proc 0's ip address
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# try to init for 20 times at max in case ports are taken
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# where to store ip_table
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model.trainer = self.trainer
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model.init_ddp_connection(
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self.trainer.global_rank,
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self.trainer.world_size,
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self.trainer.is_slurm_managing_tasks
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)
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# call setup after the ddp process has connected
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self.trainer.call_setup_hook(model)
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# on world_size=0 let everyone know training is starting
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if self.trainer.is_global_zero and not torch.distributed.is_initialized():
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log.info('-' * 100)
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log.info(f'distributed_backend={self.trainer.distributed_backend}')
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log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
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log.info('-' * 100)
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# call sync_bn before .cuda(), configure_apex and configure_ddp
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if self.trainer.sync_batchnorm:
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model = model.configure_sync_batchnorm(model)
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# move the model to the correct device
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self.model_to_device(model, process_idx)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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self.setup_optimizers(model)
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# set model properties before going into wrapper
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self.trainer.model_connector.copy_trainer_model_properties(model)
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# 16-bit
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model = self.trainer.precision_connector.connect(model)
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# device ids change depending on the DDP setup
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device_ids = self.get_device_ids()
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# allow user to configure ddp
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model = model.configure_ddp(model, device_ids)
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# set up training routine
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self.trainer.train_loop.setup_training(model)
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# train or test
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results = self.train_or_test()
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# clean up memory
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torch.cuda.empty_cache()
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return results
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@ -1,193 +0,0 @@
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# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License
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import os
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import re
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import torch
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import torch.distributed as torch_distrib
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import torch.distributed as dist
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from pytorch_lightning import _logger as log
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from pytorch_lightning.accelerators.base_backend import Accelerator
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from pytorch_lightning.utilities import AMPType
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from pytorch_lightning.utilities.cloud_io import atomic_save
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from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
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from pytorch_lightning.utilities.seed import seed_everything
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from pytorch_lightning.distributed.dist import LightningDistributed
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try:
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from hydra.core.hydra_config import HydraConfig
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from hydra.utils import get_original_cwd, to_absolute_path
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except ImportError:
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HYDRA_AVAILABLE = False
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else:
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HYDRA_AVAILABLE = True
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class DDPBase(Accelerator):
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def __init__(self, trainer):
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super().__init__(trainer)
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self.dist = LightningDistributed()
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def training_step(self, args):
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if self.trainer.amp_backend == AMPType.NATIVE:
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with torch.cuda.amp.autocast():
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output = self.trainer.model(*args)
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else:
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output = self.trainer.model(*args)
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return output
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def validation_step(self, args):
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output = self.training_step(args)
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return output
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def test_step(self, args):
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output = self.training_step(args)
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return output
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def barrier(self, name: str = None):
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if torch_distrib.is_initialized():
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torch_distrib.barrier()
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def early_stopping_should_stop(self, pl_module):
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stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
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dist.all_reduce(stop, op=dist.reduce_op.SUM)
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dist.barrier()
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should_stop = stop == self.trainer.world_size
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return should_stop
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def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
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if self.trainer.distributed_backend not in ("ddp_spawn", "ddp_cpu", "tpu"):
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return
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# track the best model path
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best_model_path = None
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if self.trainer.checkpoint_callback is not None:
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best_model_path = self.trainer.checkpoint_callback.best_model_path
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if self.trainer.global_rank == 0 and mp_queue is not None:
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rank_zero_warn('cleaning up ddp environment...')
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# todo, pass complete checkpoint as state dictionary
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mp_queue.put(best_model_path)
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mp_queue.put(results)
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# save the last weights
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last_path = None
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if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
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last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
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atomic_save(model.state_dict(), last_path)
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mp_queue.put(last_path)
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def ddp_train_tmp(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
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"""
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Entry point for ddp
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Args:
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process_idx:
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mp_queue: multiprocessing queue
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model:
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Returns:
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"""
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seed = os.environ.get("PL_GLOBAL_SEED")
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if seed is not None:
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seed_everything(int(seed))
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# offset the process id if requested
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process_idx = process_idx + proc_offset
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# show progressbar only on progress_rank 0
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if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
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self.trainer.progress_bar_callback.disable()
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# determine which process we are and world size
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self.set_world_ranks(process_idx)
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# set warning rank
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rank_zero_only.rank = self.trainer.global_rank
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# set up server using proc 0's ip address
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# try to init for 20 times at max in case ports are taken
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# where to store ip_table
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model.trainer = self.trainer
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model.init_ddp_connection(
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self.trainer.global_rank,
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self.trainer.world_size,
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self.trainer.is_slurm_managing_tasks
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)
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# call setup after the ddp process has connected
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self.trainer.call_setup_hook(model)
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# on world_size=0 let everyone know training is starting
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if self.trainer.is_global_zero and not torch.distributed.is_initialized():
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log.info('-' * 100)
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log.info(f'distributed_backend={self.trainer.distributed_backend}')
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log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
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log.info('-' * 100)
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# call sync_bn before .cuda(), configure_apex and configure_ddp
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if self.trainer.sync_batchnorm:
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model = model.configure_sync_batchnorm(model)
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# move the model to the correct device
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self.model_to_device(model, process_idx, is_master)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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self.setup_optimizers(model)
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# set model properties before going into wrapper
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self.trainer.model_connector.copy_trainer_model_properties(model)
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# 16-bit
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model = self.trainer.precision_connector.connect(model)
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# device ids change depending on the DDP setup
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device_ids = self.get_device_ids()
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# allow user to configure ddp
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model = model.configure_ddp(model, device_ids)
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# set up training routine
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self.trainer.train_loop.setup_training(model)
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# train or test
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results = self.train_or_test()
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# get original model
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model = self.trainer.get_model()
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# persist info in ddp_spawn
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self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
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# clean up memory
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torch.cuda.empty_cache()
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if self.trainer.global_rank == 0:
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return results
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def broadcast(self, obj, src=0):
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return self.dist.broadcast(obj)
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def set_world_ranks(self, process_idx):
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raise NotImplementedError('to create a ddp backend, please implement set_world_ranks')
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def model_to_device(self, model, process_idx, is_master):
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raise NotImplementedError('to create a ddp backend, please implement model_to_device')
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def get_device_ids(self):
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raise NotImplementedError('to create a ddp backend, please implement get_device_ids')
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