221 lines
9.9 KiB
Python
221 lines
9.9 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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import contextlib
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import logging
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from typing import Dict, Generator, List, Optional
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
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from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
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from pytorch_lightning.plugins.precision import PrecisionPlugin
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from pytorch_lightning.strategies.ddp import DDPStrategy
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.utilities import _FAIRSCALE_FULLY_SHARDED_AVAILABLE
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from pytorch_lightning.utilities.enums import PrecisionType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.optimizer import optimizers_to_device
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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if _FAIRSCALE_FULLY_SHARDED_AVAILABLE:
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from fairscale.nn import default_auto_wrap_policy, enable_wrap
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from fairscale.nn.data_parallel import FullyShardedDataParallel
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log = logging.getLogger(__name__)
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class DDPFullyShardedStrategy(DDPStrategy):
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strategy_name = "ddp_fully_sharded"
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def __init__(
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self,
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accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
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cpu_offload: bool = False,
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flatten_parameters: bool = True,
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reshard_after_forward: bool = True,
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move_grads_to_cpu: Optional[bool] = None,
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fp32_reduce_scatter: Optional[bool] = None,
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compute_dtype: Optional[torch.dtype] = None,
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bucket_cap_mb: int = 25,
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min_num_params: int = 1e8,
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state_dict_to_cpu: bool = True,
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parallel_devices: Optional[List[torch.device]] = None,
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cluster_environment: Optional[ClusterEnvironment] = None,
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checkpoint_io: Optional[CheckpointIO] = None,
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precision_plugin: Optional[PrecisionPlugin] = None,
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process_group_backend: Optional[str] = None,
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):
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"""Plugin for Fully Sharded Data Parallel provided by FairScale.
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Full Sharded Training shards the entire model across all available GPUs, allowing you to scale model
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size, whilst using efficient communication to reduce overhead. In practice, this means we can remain
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at parity with PyTorch DDP, whilst scaling our model sizes dramatically. The technique is similar
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to ZeRO-Stage 3 but has been built for upstreaming to PyTorch.
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`For more information: https://fairscale.readthedocs.io/en/latest/api/nn/fsdp.html`.
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.. warning:: ``FullyShardedPlugin`` is in beta and subject to change.
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Defaults have been set and options have been exposed, but may require configuration
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based on your level of memory/speed efficiency. We suggest having a look at this PR for more information.
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`https://github.com/facebookresearch/fairscale/pull/413`
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Many of the helpful doc strings below came from the original FairScale documentation:
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`https://fairscale.readthedocs.io/en/latest/api/nn/fsdp.html`
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Arguments:
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cpu_offload: Offload FP32 params to CPU. Only usable in precision=16 mode.
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(Default: False).
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move_grads_to_cpu: Moves gradient shards to CPU after reduction.
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Only disable if using CPU based optimizers
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(Default to ``cpu_offload``).
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flatten_parameters: Flattens parameter into single contiguous tensor for speed efficiency
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(Default: True).
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reshard_after_forward: Reshard parameters after the forward pass, which saves memory but slows
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down training. This is only relevant when resharding individual layers.
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(Default: True).
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fp32_reduce_scatter: Reduce-Scatter gradients in FP32. Only relevant in mixed precision
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(Default: None).
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compute_dtype: dtype for full parameters for computation. Default to torch.float32,
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unless using mixed precision, in which case defaults to torch.float16.
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(Default: None).
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bucket_cap_mb: bucket parameters so that gradient reduction
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can potentially overlap with backward computation.
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bucket_cap_mb controls the bucket size in MegaBytes (MB).
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Buckets are sub-divided based on world_size,
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so the max shard size is roughly bucket_cap_mb / world_size.
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Values <= 0 disable bucketing.
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(Default: 25).
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min_num_params: Number of parameters to wrap when using FairScale ``auto_wrap``.
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(Default: 1e8)
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state_dict_to_cpu: Whether to return parameters (returned by :func:`state_dict`) on CPU device.
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If ``False``, this will default to ``compute_device``.
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(Default: True).
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"""
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super().__init__(
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accelerator=accelerator,
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parallel_devices=parallel_devices,
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cluster_environment=cluster_environment,
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checkpoint_io=checkpoint_io,
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precision_plugin=precision_plugin,
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process_group_backend=process_group_backend,
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)
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self.cpu_offload = cpu_offload
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self.move_grads_to_cpu = move_grads_to_cpu
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self.flatten_parameters = flatten_parameters
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self.reshard_after_forward = reshard_after_forward
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self.fp32_reduce_scatter = fp32_reduce_scatter
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self.compute_dtype = compute_dtype
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self.bucket_cap_mb = bucket_cap_mb
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self.min_num_params = min_num_params
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self.state_dict_device = torch.device("cpu") if state_dict_to_cpu else None
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self._process_group = None
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@property
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def process_group(self):
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if self._process_group is None:
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self._process_group = torch.distributed.new_group()
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return self._process_group
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def setup_distributed(self) -> None:
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if not self.root_device.type == "cuda":
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raise MisconfigurationException(
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"You selected strategy to be `ddp_fully_sharded`, but GPU is not available."
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)
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super().setup_distributed()
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def setup(self, trainer: "pl.Trainer") -> None:
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self.accelerator.setup(trainer)
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if trainer.state.fn == TrainerFn.FITTING and self._layer_sync:
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self.model = self._layer_sync.apply(self.model)
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self.configure_ddp()
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self.barrier()
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self.setup_optimizers(trainer)
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optimizers_to_device(self.optimizers, self.root_device)
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self.setup_precision_plugin()
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@contextlib.contextmanager
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def model_sharded_context(self) -> Generator:
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log.detail(f"{self.__class__.__name__}: entered model_sharded_context.")
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precision = self.precision_plugin.precision
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def wrap_policy(*args, **kwargs):
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return default_auto_wrap_policy(*args, **kwargs, min_num_params=self.min_num_params)
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with enable_wrap(
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wrapper_cls=FullyShardedDataParallel,
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auto_wrap_policy=wrap_policy,
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process_group=self.process_group,
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cpu_offload=self.cpu_offload,
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move_grads_to_cpu=self.move_grads_to_cpu,
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flatten_parameters=self.flatten_parameters,
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mixed_precision=(precision == PrecisionType.MIXED),
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reshard_after_forward=self.reshard_after_forward,
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fp32_reduce_scatter=self.fp32_reduce_scatter,
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compute_dtype=self.compute_dtype,
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bucket_cap_mb=self.bucket_cap_mb,
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state_dict_device=self.state_dict_device,
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):
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yield
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log.detail(f"{self.__class__.__name__}: exiting model_sharded_context.")
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def configure_ddp(self) -> None:
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log.detail(f"{self.__class__.__name__}: configuring FSDP... (cpu_offload: [{self.cpu_offload}])")
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if not self.cpu_offload:
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# When using CPU Offload, FSDP will manage the CUDA movement for us.
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# Note: this would be problematic for large model (which could not fit in one GPU)
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# as FSDP module.to(device) would first summon all parameters
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# (TODO: need to figure out solution)
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self.model_to_device()
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def model_to_device(self) -> None:
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log.detail(f"{self.__class__.__name__}: moving model to device [{self.root_device}]...")
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# ensure we update the device type in the lightning module
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self.lightning_module.to(self.root_device)
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def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
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with self.precision_plugin.train_step_context():
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return self.model.training_step(*args, **kwargs)
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def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
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with self.precision_plugin.val_step_context():
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return self.model.validation_step(*args, **kwargs)
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def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
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with self.precision_plugin.test_step_context():
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return self.model.test_step(*args, **kwargs)
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def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
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with self.precision_plugin.predict_step_context():
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return self.model.predict_step(*args, **kwargs)
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def post_training_step(self):
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pass
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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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"fsdp", cls, description="Fully sharded training with checkpointing the full state dict."
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)
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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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