260 lines
8.9 KiB
Python
260 lines
8.9 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 contextmanager
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from typing import Any, Optional, Union
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import torch
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from torch.optim import Optimizer
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from pytorch_lightning.cluster_environments import ClusterEnvironment
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
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from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
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from pytorch_lightning.utilities.apply_func import move_data_to_device
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from pytorch_lightning.utilities.parsing import AttributeDict
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if torch.distributed.is_available():
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from torch.distributed import ReduceOp
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else:
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class ReduceOp:
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SUM = None
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class Accelerator(object):
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def __init__(self,
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trainer: Optional = None,
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cluster_environment: Optional[ClusterEnvironment] = None,
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ddp_plugin: Optional[DDPPlugin] = None):
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self.trainer = trainer
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self.nickname = None
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self.cluster_environment = cluster_environment
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self.dist = AttributeDict(rank=0, device=None)
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self.ddp_plugin = ddp_plugin
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if trainer is not None:
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self.train_loop = self.trainer.train
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self.validation_loop = self.trainer.run_evaluation
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self.test_loop = self.trainer.run_evaluation
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def setup(self, model):
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pass
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def teardown(self):
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# Ensure if necessary all processes are finished
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self.barrier()
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def barrier(self, name: Optional[str] = None):
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pass
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def broadcast(self, obj, src=0):
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return obj
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def train_or_test(self):
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if self.trainer.testing:
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results = self.trainer.run_test()
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else:
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results = self.trainer.train()
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return results
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def batch_to_device(self, batch: Any, device: torch.device):
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model = self.trainer.get_model()
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if model is not None:
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return model.transfer_batch_to_device(batch, device)
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return move_data_to_device(batch, device)
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def training_step_end(self, output):
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return output
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def test_step_end(self, output):
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return output
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def validation_step_end(self, output):
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return output
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def process_dataloader(self, dataloader):
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return dataloader
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def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
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automatic_optimization = self.trainer.train_loop.automatic_optimization
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if not automatic_optimization and self.ddp_plugin is not None:
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# Manually prepare for reduce as user calling backwards manually
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self.ddp_plugin.on_before_manual_backward(self.trainer.model, closure_loss)
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if self.trainer.precision == 16:
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closure_loss = self.trainer.precision_connector.backend.backward(
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closure_loss, optimizer, opt_idx, *args, **kwargs
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)
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else:
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# do backward pass
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model = self.trainer.get_model()
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model.backward(closure_loss, optimizer, opt_idx, *args, **kwargs)
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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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if not automatic_optimization and self.ddp_plugin is not None:
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# Manually prepare for reduce as user calling backwards manually
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self.ddp_plugin.on_after_manual_backward(self.trainer.model)
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return closure_loss
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def clip_gradients(self, optimizer, clip_val=None):
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# use the trainer's clip val if none passed
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grad_clip_val = self.trainer.gradient_clip_val
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if clip_val is not None:
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grad_clip_val = clip_val
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grad_clip_val = float(grad_clip_val)
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if grad_clip_val <= 0:
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return
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self._clip_gradients(optimizer, grad_clip_val)
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def _clip_gradients(self, optimizer: Optimizer, grad_clip_val: Union[float, int], norm_type: float = 2.0):
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if self.trainer.amp_backend:
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self.trainer.precision_connector.backend.clip_gradients(grad_clip_val, optimizer, norm_type)
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else:
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model = self.trainer.get_model()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=grad_clip_val, norm_type=norm_type)
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def on_train_epoch_end(self, outputs):
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pass
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def on_train_end(self):
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pass
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def early_stopping_should_stop(self, pl_module):
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return self.trainer.should_stop
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def setup_optimizers(self, model):
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if self.trainer.testing:
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return
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optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
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self.trainer.optimizers = optimizers
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self.trainer.lr_schedulers = lr_schedulers
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self.trainer.optimizer_frequencies = optimizer_frequencies
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def init_ddp_connection(
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self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True
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) -> None:
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self.ddp_plugin.init_ddp_connection(
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self.trainer,
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self.cluster_environment,
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global_rank,
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world_size,
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is_slurm_managing_tasks,
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)
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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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"""
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Function to reduce a tensor from several distributed processes to one aggregated tensor.
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Args:
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tensor: the tensor to sync and reduce
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group: the process group to gather results from. Defaults to all processes (world)
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reduce_op: the reduction operation. Defaults to sum.
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Can also be a string of 'avg', 'mean' to calculate the mean during reduction.
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Return:
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reduced value
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"""
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raise NotImplementedError()
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def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
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"""
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Function to gather a tensor from several distributed processes
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Args:
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tensor: tensor of shape (batch, ...)
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group: the process group to gather results from. Defaults to all processes (world)
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sync_grads: flag that allows users to synchronize gradients for all_gather op
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Return:
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A tensor of shape (world_size, batch, ...)
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"""
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raise NotImplementedError()
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def optimizer_state(self, optimizer: Optimizer) -> dict:
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"""
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Returns state of an optimizer. Allows for syncing/collating optimizer state from processes in custom
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plugins.
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Return:
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Optimizer state dict
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"""
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if self.ddp_plugin:
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return self.ddp_plugin.optimizer_state(optimizer)
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return optimizer.state_dict()
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def get_reference_model(self, model) -> LightningModule:
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"""
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Override to modify returning base :class:`LightningModule`
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when accessing variable and functions if the accelerator has wrapped the model.
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Example::
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ref_model = accelerator.get_reference_model(model)
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ref_model.training_step(...)
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Args:
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model: Accelerator model.
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Returns: Reference :class:`LightningModule`.
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"""
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return model
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def __getstate__(self):
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return {
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'trainer': self.trainer,
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'nickname': self.nickname,
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'cluster_environment': self.cluster_environment,
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'dist': self.dist,
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'ddp_plugin': self.ddp_plugin
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}
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def __setstate__(self, d):
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self.trainer = d['trainer']
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self.nickname = d['nickname']
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self.cluster_environment = d['cluster_environment']
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self.dist = d['dist']
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self.ddp_plugin = d['ddp_plugin']
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def on_save(self, checkpoint):
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return checkpoint
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@property
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def rpc_enabled(self):
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return self.ddp_plugin is not None and isinstance(self.ddp_plugin, RPCPlugin)
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@property
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def distributed_sampler_kwargs(self):
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raise NotImplementedError
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@property
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def require_distributed_sampler(self):
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raise NotImplementedError
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@contextmanager
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def block_ddp_plugin_sync_behaviour(self):
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"""
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Blocks ddp sync gradients behaviour on backwards pass.
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This is useful for skipping sync when accumulating gradients, reducing communication overhead
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Returns: context manager with sync behaviour off
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"""
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cm = self.ddp_plugin.block_backward_sync(self.trainer.model) if self.ddp_plugin else None
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yield cm
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