File size: 5,093 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
# Copyright The Lightning AI 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.
from abc import ABC, abstractmethod
from collections.abc import Generator
from contextlib import contextmanager
from typing import Any, Optional
import torch
from torch import Tensor
from typing_extensions import override
import pytorch_lightning as pl
from lightning_fabric.plugins import CheckpointIO, ClusterEnvironment
from lightning_fabric.utilities.distributed import ReduceOp, _all_gather_ddp_if_available
from pytorch_lightning.plugins import LayerSync
from pytorch_lightning.plugins.precision import Precision
from pytorch_lightning.strategies.strategy import Strategy
class ParallelStrategy(Strategy, ABC):
"""Strategy for training with multiple processes in parallel."""
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[list[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[Precision] = None,
):
super().__init__(accelerator=accelerator, checkpoint_io=checkpoint_io, precision_plugin=precision_plugin)
self.parallel_devices = parallel_devices
self.cluster_environment: Optional[ClusterEnvironment] = cluster_environment
self._layer_sync: Optional[LayerSync] = None
@property
@abstractmethod
@override
def root_device(self) -> torch.device:
"""Return the root device."""
@property
def global_rank(self) -> int:
return self.cluster_environment.global_rank() if self.cluster_environment is not None else 0
@property
def local_rank(self) -> int:
return self.cluster_environment.local_rank() if self.cluster_environment is not None else 0
@property
def node_rank(self) -> int:
return self.cluster_environment.node_rank() if self.cluster_environment is not None else 0
@property
def world_size(self) -> int:
return self.cluster_environment.world_size() if self.cluster_environment is not None else 1
@property
@override
def is_global_zero(self) -> bool:
return self.global_rank == 0
@property
def parallel_devices(self) -> Optional[list[torch.device]]:
return self._parallel_devices
@parallel_devices.setter
def parallel_devices(self, parallel_devices: Optional[list[torch.device]]) -> None:
self._parallel_devices = parallel_devices
@property
def distributed_sampler_kwargs(self) -> dict[str, Any]:
return {
"num_replicas": len(self.parallel_devices) if self.parallel_devices is not None else 0,
"rank": self.global_rank,
}
@override
def all_gather(self, tensor: Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> Tensor:
"""Perform a all_gather on all processes."""
return _all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
@override
def reduce_boolean_decision(self, decision: bool, all: bool = True) -> bool:
"""Reduces a boolean decision over distributed processes. By default is analogous to ``all`` from the standard
library, returning ``True`` only if all input decisions evaluate to ``True``. If ``all`` is set to ``False``,
it behaves like ``any`` instead.
Args:
decision: A single input decision.
all: Whether to logically emulate ``all`` or ``any``. Defaults to True.
Returns:
bool: The reduced boolean decision.
"""
decision = torch.tensor(int(decision), device=self.root_device)
decision = self.reduce(
decision,
reduce_op=ReduceOp.SUM, # type: ignore[arg-type]
)
decision = bool(decision == self.world_size) if all else bool(decision)
return decision
@contextmanager
def block_backward_sync(self) -> Generator:
"""Blocks ddp sync gradients behaviour on backwards pass.
This is useful for skipping sync when accumulating gradients, reducing communication overhead
Returns: context manager with sync behaviour off
"""
if isinstance(self.model, pl.utilities.types.DistributedDataParallel):
with self.model.no_sync():
yield None
else:
yield None
@override
def teardown(self) -> None:
assert self.cluster_environment is not None
self.cluster_environment.teardown()
super().teardown()
|