85 lines
2.8 KiB
Python
85 lines
2.8 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 typing import Dict
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import torch
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from pytorch_lightning.strategies.ddp import DDPStrategy
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from pytorch_lightning.utilities.apply_func import apply_to_collection
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from pytorch_lightning.utilities.types import _METRIC_COLLECTION
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class DDP2Strategy(DDPStrategy):
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"""DDP2 behaves like DP in one node, but synchronization across nodes behaves like in DDP."""
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strategy_name = "ddp2"
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@property
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def global_rank(self) -> int:
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return self.node_rank
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@property
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def world_size(self) -> int:
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return self.num_nodes
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def reduce(self, collection: _METRIC_COLLECTION, *args, **kwargs) -> _METRIC_COLLECTION:
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"""Reduces a collection of tensors from all processes. It can be applied to just a single tensor. In DDP2,
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the reduction here is only across local devices within the node.
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Args:
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collection: The collection of tensors to sync and reduce.
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*args: ignored for DDP2
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**kwargs: ignored for DDP2
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Return:
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Reduced tensor values or the same value if it was not or did not contain a tensor.
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"""
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def mean(t: torch.Tensor) -> torch.Tensor:
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original_dtype = t.dtype
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return t.float().mean().to(original_dtype)
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return apply_to_collection(collection, torch.Tensor, mean)
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@property
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def root_device(self):
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return self.parallel_devices[0]
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def model_to_device(self):
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# no need to do anything when model is wrapped in torch.nn.DataParallel
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pass
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@property
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def distributed_sampler_kwargs(self):
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distributed_sampler_kwargs = dict(num_replicas=self.num_nodes, rank=self.global_rank)
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return distributed_sampler_kwargs
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@property
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def _is_single_process_single_device(self) -> bool:
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return False
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def set_world_ranks(self) -> None:
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if self.cluster_environment is None:
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return
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self.cluster_environment.set_global_rank(self.node_rank)
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self.cluster_environment.set_world_size(self.num_nodes)
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@classmethod
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def register_strategies(cls, strategy_registry: Dict) -> None:
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strategy_registry.register(
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cls.strategy_name,
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cls,
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description=f"{cls.__class__.__name__}",
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)
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