211 lines
7.6 KiB
Python
211 lines
7.6 KiB
Python
# 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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from contextlib import ExitStack
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from typing import Any, Optional, Union
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import torch
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from torch.optim.lr_scheduler import _LRScheduler
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from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
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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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import horovod.torch as hvd
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except (ModuleNotFoundError, ImportError):
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HOROVOD_AVAILABLE = False
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else:
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HOROVOD_AVAILABLE = True
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class HorovodAccelerator(Accelerator):
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amp_backend: AMPType
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def __init__(self, trainer, cluster_environment=None):
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"""
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Runs training using horovod
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Example::
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# default
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trainer = Trainer(accelerator=HorovodAccelerator())
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"""
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super().__init__(trainer, cluster_environment)
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self.nickname = 'horovod'
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def setup(self, model):
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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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if torch.cuda.is_available() and self.trainer.on_gpu:
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# Horovod: pin GPU to local rank
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assert self.trainer.root_gpu == hvd.local_rank()
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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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# avoid duplicating progress bar
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if hvd.rank() != 0 and self.trainer.progress_bar_callback is not None:
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self.trainer.progress_bar_callback.disable()
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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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# Horovod: scale the learning rate by the number of workers to account for
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# increased total batch size
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for optimizer in self.trainer.optimizers:
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for param_group in optimizer.param_groups:
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param_group['lr'] *= hvd.size()
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# Horovod: adjust base LR used by schedulers to match scaled optimizer initial LR
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for scheduler in self.trainer.lr_schedulers:
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scheduler = scheduler['scheduler']
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if isinstance(scheduler, _LRScheduler):
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scheduler.base_lrs = [lr * hvd.size() for lr in scheduler.base_lrs]
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# Horovod: broadcast parameters & optimizer state to ensure consistent initialization
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hvd.broadcast_parameters(model.state_dict(), root_rank=0)
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for optimizer in self.trainer.optimizers:
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hvd.broadcast_optimizer_state(optimizer, root_rank=0)
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def _filter_named_parameters(model, optimizer):
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opt_params = set([p for group in optimizer.param_groups for p in group.get('params', [])])
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return [(name, p) for name, p in model.named_parameters() if p in opt_params]
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# Horovod: wrap optimizers to perform gradient aggregation via allreduce
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self.trainer.optimizers = [
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hvd.DistributedOptimizer(optimizer, named_parameters=_filter_named_parameters(model, optimizer))
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for optimizer in self.trainer.optimizers
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]
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# 16-bit
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model = self.trainer.precision_connector.connect(model)
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# Update logger rank info from Horovod to avoid race conditions from different ranks
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# creating directories / writing files in the same locations.
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self.trainer.global_rank = hvd.rank()
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rank_zero_only.rank = self.trainer.global_rank
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self.trainer.model = model
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def train(self):
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with ExitStack() as stack:
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for optimizer in self.trainer.optimizers:
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# Synchronization will be performed explicitly following backward()
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stack.enter_context(optimizer.skip_synchronize())
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# set up training routine
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self.trainer.train_loop.setup_training(self.trainer.model)
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# train or test
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results = self.train_or_test()
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# Make sure all workers have finished training before returning to the user
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hvd.join()
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return results
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def training_step(self, args):
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if self.trainer.on_gpu:
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batch = args[0]
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batch = self.batch_to_device(batch, hvd.local_rank())
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args[0] = batch
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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.training_step(*args)
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else:
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output = self.trainer.model.training_step(*args)
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return output
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def validation_step(self, args):
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if self.trainer.on_gpu:
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batch = args[0]
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batch = self.batch_to_device(batch, hvd.local_rank())
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args[0] = batch
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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.validation_step(*args)
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else:
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output = self.trainer.model.validation_step(*args)
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return output
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def test_step(self, args):
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if self.trainer.on_gpu:
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batch = args[0]
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batch = self.batch_to_device(batch, hvd.local_rank())
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args[0] = batch
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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.test_step(*args)
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else:
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output = self.trainer.model.test_step(*args)
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return output
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def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
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super().backward(closure_loss, optimizer, opt_idx, *args, **kwargs)
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optimizer.synchronize()
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def on_train_epoch_end(self, outputs):
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hvd.join(hvd.local_rank() if self.trainer.on_gpu else -1)
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def barrier(self, name: Optional[str] = None):
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hvd.join()
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def broadcast(self, obj, src=0):
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obj = hvd.broadcast_object(obj, src)
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return obj
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def gather_all_tensors(self, result: Union[torch.Tensor], group: Optional[Any] = None):
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if group is not None:
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raise ValueError(
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"Horovod does not support allgather using a subcommunicator at this time. "
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"Unset `group`."
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)
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if len(result.shape) == 0:
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# Convert scalars to single dimension tensors
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result = result.reshape(1)
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# sync and gather all
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hvd.join()
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gathered = hvd.allgather(result)
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gathered_result = list(gathered.split(1, dim=0))
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return gathered_result
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def sync_tensor(self,
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tensor: Union[torch.Tensor],
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group: Optional[Any] = None,
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reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
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if group is not None:
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raise ValueError(
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"Horovod does not support allreduce using a subcommunicator at this time. "
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"Unset `group`."
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)
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if reduce_op is None or reduce_op == "sum":
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reduce_op = hvd.Sum
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elif isinstance(reduce_op, str) and reduce_op in ("avg", "mean"):
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reduce_op = hvd.Average
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else:
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raise ValueError(f"unrecognized `reduce_op`: {reduce_op}")
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# sync all processes before reduction
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hvd.join()
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return hvd.allreduce(tensor, op=reduce_op)
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