# 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.
import argparse
import json
import logging
import os
import platform
from collections import OrderedDict
from contextlib import contextmanager
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Mapping, Optional, Tuple, Union
import torch
from torch.nn import Module
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler, ReduceLROnPlateau
from typing_extensions import override
import lightning.pytorch as pl
from lightning.fabric.plugins import ClusterEnvironment
from lightning.fabric.strategies import _StrategyRegistry
from lightning.fabric.strategies.deepspeed import (
_DEEPSPEED_AVAILABLE,
_format_precision_config,
_validate_checkpoint_directory,
_validate_device_index_selection,
)
from lightning.fabric.utilities.optimizer import _optimizers_to_device
from lightning.fabric.utilities.seed import reset_seed
from lightning.fabric.utilities.types import _PATH
from lightning.pytorch.accelerators.cuda import CUDAAccelerator
from lightning.pytorch.core.optimizer import _init_optimizers_and_lr_schedulers
from lightning.pytorch.plugins.precision import Precision
from lightning.pytorch.strategies.ddp import DDPStrategy
from lightning.pytorch.trainer.states import TrainerFn
from lightning.pytorch.utilities import GradClipAlgorithmType
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.model_helpers import is_overridden
from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_info, rank_zero_warn
from lightning.pytorch.utilities.types import LRSchedulerConfig
log = logging.getLogger(__name__)
warning_cache = WarningCache()
if TYPE_CHECKING:
import deepspeed
def remove_module_hooks(model: torch.nn.Module) -> None:
# todo (tchaton) awaiting this feature to move upstream to DeepSpeed
for module in model.modules():
module._backward_hooks = OrderedDict()
module._is_full_backward_hook = None
module._forward_hooks = OrderedDict()
module._forward_pre_hooks = OrderedDict()
module._state_dict_hooks = OrderedDict()
module._load_state_dict_pre_hooks = OrderedDict()
[docs]class DeepSpeedStrategy(DDPStrategy):
strategy_name = "deepspeed"
DEEPSPEED_ENV_VAR = "PL_DEEPSPEED_CONFIG_PATH"
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
zero_optimization: bool = True,
stage: int = 2,
remote_device: Optional[str] = None,
offload_optimizer: bool = False,
offload_parameters: bool = False,
offload_params_device: str = "cpu",
nvme_path: str = "/local_nvme",
params_buffer_count: int = 5,
params_buffer_size: int = 100_000_000,
max_in_cpu: int = 1_000_000_000,
offload_optimizer_device: str = "cpu",
optimizer_buffer_count: int = 4,
block_size: int = 1048576,
queue_depth: int = 8,
single_submit: bool = False,
overlap_events: bool = True,
thread_count: int = 1,
pin_memory: bool = False,
sub_group_size: int = 1_000_000_000_000,
contiguous_gradients: bool = True,
overlap_comm: bool = True,
allgather_partitions: bool = True,
reduce_scatter: bool = True,
allgather_bucket_size: int = 200_000_000,
reduce_bucket_size: int = 200_000_000,
zero_allow_untested_optimizer: bool = True,
logging_batch_size_per_gpu: Union[str, int] = "auto",
config: Optional[Union[_PATH, Dict[str, Any]]] = None,
logging_level: int = logging.WARN,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
loss_scale: float = 0,
initial_scale_power: int = 16,
loss_scale_window: int = 1000,
hysteresis: int = 2,
min_loss_scale: int = 1,
partition_activations: bool = False,
cpu_checkpointing: bool = False,
contiguous_memory_optimization: bool = False,
synchronize_checkpoint_boundary: bool = False,
load_full_weights: bool = False,
precision_plugin: Optional[Precision] = None,
process_group_backend: Optional[str] = None,
) -> None:
"""Provides capabilities to run training using the DeepSpeed library, with training optimizations for large
billion parameter models. `For more information: https://pytorch-
lightning.readthedocs.io/en/stable/advanced/model_parallel.html#deepspeed`.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Defaults have been set to enable ZeRO-Offload and some have been taken from the link below.
These defaults have been set generally, but may require tuning for optimum performance based on your model size.
`For more information: https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training`.
Arguments:
zero_optimization: Enable ZeRO optimization. This is compatible with either `precision="16-mixed"` or
`precision="bf16-mixed"`.
stage: Different stages of the ZeRO Optimizer. 0 is disabled,
1 is optimizer state partitioning, 2 is optimizer+gradient state partitioning,
3 is optimizer+gradient_parameter partitioning using the infinity engine.
remote_device: Device to instantiate the model on initially (``cpu`` or ``nvme``). Defaults to GPU.
offload_optimizer: Enable offloading optimizer memory and computation to CPU or NVMe
based on ``offload_optimizer_device``.
offload_parameters: When using ZeRO Stage 3, Enable offloading parameter memory and computation
to CPU or NVMe based on ``offload_params_device``.
offload_params_device: When offloading parameters choose the device to offload to, ``cpu`` or ``nvme``.
offload_optimizer_device: When offloading optimizer state choose the device to offload to,
``cpu`` or ``nvme``.
params_buffer_count: Number of buffers in buffer pool for
parameter offloading when ``offload_params_device`` is ``nvme``.
params_buffer_size: Size of buffers in buffer pool for parameter offloading
when ``offload_params_device`` is ``nvme``.
max_in_cpu: Number of parameter elements to maintain in CPU memory when offloading to NVMe is enabled.
nvme_path: Filesystem path for NVMe device for optimizer/parameter state offloading.
optimizer_buffer_count: Number of buffers in buffer pool for optimizer state offloading
when ``offload_optimizer_device`` is set to to ``nvme``.
This should be at least the number of states maintained per parameter by the optimizer.
For example, Adam optimizer has 4 states (parameter, gradient, momentum, and variance).
block_size: When using NVMe Offloading, the I/O block size in bytes.
queue_depth: When using NVMe Offloading, the I/O queue depth.
single_submit: When using NVMe Offloading,
submit requests to storage device as multiple individual requests,
as opposed to one block of requests.
overlap_events: When using NVMe Offloading,
submit requests to storage device in an overlapped fashion
without waiting for completion of earlier requests.
thread_count: When using NVMe Offloading,
Intra-request parallelism for each read/write submitted by a user thread.
pin_memory: When using ZeRO stage 3, pin optimizer state memory on CPU.
This could boost throughput at the cost of extra memory overhead.
sub_group_size: When using ZeRO stage 3, defines the number of parameters
within a sub group to offload at a time.
Smaller numbers require more communication, but improve memory efficiency.
contiguous_gradients: Copies gradients to a continuous buffer as they are produced.
Avoids memory fragmentation during backwards. Useful when training large models.
overlap_comm: Overlap the reduction (synchronization) of gradients with the backwards computation.
This is a speed optimization when training across multiple GPUs/machines.
allgather_partitions: All gather updated parameters at the end of training step,
instead of using a series of broadcast collectives.
reduce_scatter: Use reduce/scatter instead of allreduce to average gradients.
allgather_bucket_size: Number of elements to allgather at once.
Used to limit the memory required for larger model sizes, with a tradeoff with speed.
reduce_bucket_size: Number of elements to reduce at once.
Used to limit the memory required for larger model sizes, with a tradeoff with speed.
zero_allow_untested_optimizer: Allow untested optimizers to be used with ZeRO. Currently only Adam is a
DeepSpeed supported optimizer when using ZeRO.
logging_batch_size_per_gpu: Config used in DeepSpeed to calculate verbose timing for logging
on a per sample per second basis (only displayed if logging=logging.INFO).
If set to "auto", the strategy tries to infer this from
the train DataLoader's BatchSampler, else defaults to 1.
To obtain accurate logs when using datasets that do not support batch samplers,
set this to the actual per gpu batch size (trainer.batch_size).
config: Pass in a deepspeed formatted config dict,
or path to a deepspeed config: https://www.deepspeed.ai/docs/config-json.
All defaults will be ignored if a config is passed in.
logging_level: Set logging level for deepspeed.
loss_scale: Loss scaling value for FP16 training.
0.0 results in dynamic loss scaling, otherwise static.
initial_scale_power: Power of the initial dynamic loss scale value. Loss scale is computed
by ``2^initial_scale_power``.
loss_scale_window: Window in which to raise/lower the dynamic FP16 loss scaling value.
hysteresis: FP16 Delay shift in Dynamic Loss scaling.
min_loss_scale: The minimum FP16 dynamic loss scaling value.
partition_activations: Enables partition activation when used with ZeRO stage 3 and model parallelism.
Still requires you to wrap your forward functions in deepspeed.checkpointing.checkpoint.
See `deepspeed tutorial
<https://www.deepspeed.ai/tutorials/megatron/#deepspeed-activation-checkpoints-optional>`_.
cpu_checkpointing: Offloads partitioned activations to CPU if ``partition_activations`` is enabled.
contiguous_memory_optimization: Copies partitioned activations so that they are contiguous in memory.
Not supported by all models.
synchronize_checkpoint_boundary: Insert :func:`torch.cuda.synchronize` at each checkpoint boundary.
load_full_weights: True when loading a single checkpoint file containing the model state dict
when using ZeRO Stage 3. This differs from the DeepSpeed checkpoint which contains shards
per worker.
"""
if not _DEEPSPEED_AVAILABLE:
raise MisconfigurationException(
"To use the `DeepSpeedStrategy`, you must have DeepSpeed installed."
" Install it by running `pip install -U deepspeed`."
)
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=cluster_environment,
precision_plugin=precision_plugin,
process_group_backend=process_group_backend,
)
self.config = self._load_config(config)
if self.config is None:
# User has not overridden config, set defaults
self.config = self._create_default_config(
zero_optimization,
zero_allow_untested_optimizer,
logging_batch_size_per_gpu,
offload_optimizer=offload_optimizer,
offload_parameters=offload_parameters,
nvme_path=nvme_path,
offload_params_device=offload_params_device,
params_buffer_count=params_buffer_count,
params_buffer_size=params_buffer_size,
max_in_cpu=max_in_cpu,
pin_memory=pin_memory,
offload_optimizer_device=offload_optimizer_device,
optimizer_buffer_count=optimizer_buffer_count,
block_size=block_size,
queue_depth=queue_depth,
single_submit=single_submit,
overlap_events=overlap_events,
thread_count=thread_count,
partition_activations=partition_activations,
cpu_checkpointing=cpu_checkpointing,
contiguous_memory_optimization=contiguous_memory_optimization,
synchronize_checkpoint_boundary=synchronize_checkpoint_boundary,
stage=stage,
contiguous_gradients=contiguous_gradients,
overlap_comm=overlap_comm,
allgather_partitions=allgather_partitions,
reduce_scatter=reduce_scatter,
allgather_bucket_size=allgather_bucket_size,
reduce_bucket_size=reduce_bucket_size,
sub_group_size=sub_group_size,
)
import deepspeed
self._config_initialized = False
deepspeed.utils.logging.logger.setLevel(logging_level)
self.remote_device = remote_device
self.load_full_weights = load_full_weights
# default FP16 parameters.
self.loss_scale = loss_scale
self.initial_scale_power = initial_scale_power
self.loss_scale_window = loss_scale_window
self.hysteresis = hysteresis
self.min_loss_scale = min_loss_scale
[docs] @override
def setup_environment(self) -> None:
if not isinstance(self.accelerator, CUDAAccelerator):
raise RuntimeError(
f"The DeepSpeed strategy is only supported on CUDA GPUs but `{self.accelerator.__class__.__name__}`"
" is used."
)
super().setup_environment()
@override
def setup_distributed(self) -> None:
assert self.parallel_devices is not None
_validate_device_index_selection(self.parallel_devices)
reset_seed()
self.set_world_ranks()
self._init_deepspeed_distributed()
[docs] @override
def setup(self, trainer: "pl.Trainer") -> None:
self._init_config_if_needed()
assert self.accelerator is not None
self.accelerator.setup(trainer)
assert self.model is not None
self.model = self.precision_plugin.convert_module(self.model)
self.model = self._setup_model(self.model)
if trainer.state.fn == TrainerFn.FITTING:
self.setup_optimizers(trainer)
self.setup_precision_plugin()
if trainer.state.fn == TrainerFn.FITTING:
_optimizers_to_device(self.optimizers, self.root_device)
self.init_deepspeed()
self.barrier()
def _init_deepspeed_distributed(self) -> None:
import deepspeed
assert self.cluster_environment is not None
if platform.system() != "Windows":
# do not set env variables on windows, allow deepspeed to control setup
self._set_node_environment_variables()
log.info(
"initializing deepspeed distributed: "
f"GLOBAL_RANK: {self.global_rank}, "
f"MEMBER: {self.global_rank + 1}/{self.world_size}"
)
self._process_group_backend = self._get_process_group_backend()
deepspeed.init_distributed(self._process_group_backend, distributed_port=self.cluster_environment.main_port)
def _set_node_environment_variables(self) -> None:
assert self.cluster_environment is not None
os.environ["MASTER_ADDR"] = self.cluster_environment.main_address
os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port)
os.environ["RANK"] = str(self.global_rank)
os.environ["WORLD_SIZE"] = str(self.world_size)
os.environ["LOCAL_RANK"] = str(self.local_rank)
@property
@override
def restore_checkpoint_after_setup(self) -> bool:
return True
@override
def _setup_model_and_optimizers(
self, model: Module, optimizers: List[Optimizer]
) -> Tuple["deepspeed.DeepSpeedEngine", List[Optimizer]]:
"""Setup a model and multiple optimizers together.
Currently only a single optimizer is supported.
Return:
The model wrapped into a :class:`deepspeed.DeepSpeedEngine` and a list with a single
deepspeed optimizer.
"""
if len(optimizers) != 1:
raise ValueError(
f"Currently only one optimizer is supported with DeepSpeed."
f" Got {len(optimizers)} optimizers instead."
)
# train_micro_batch_size_per_gpu is used for throughput logging purposes
# normally we set this to the batch size, but it is not available here unless the user provides it
# as part of the config
assert self.config is not None
self.config.setdefault("train_micro_batch_size_per_gpu", 1)
self.model, optimizer = self._setup_model_and_optimizer(model, optimizers[0])
self._set_deepspeed_activation_checkpointing()
return self.model, [optimizer]
def _setup_model_and_optimizer(
self,
model: Module,
optimizer: Optional[Optimizer],
lr_scheduler: Optional[Union[LRScheduler, ReduceLROnPlateau]] = None,
) -> Tuple["deepspeed.DeepSpeedEngine", Optimizer]:
"""Initialize one model and one optimizer with an optional learning rate scheduler.
This calls ``deepspeed.initialize`` internally.
"""
import deepspeed
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
deepspeed_engine, deepspeed_optimizer, _, _ = deepspeed.initialize(
args=argparse.Namespace(device_rank=self.root_device.index),
config=self.config,
model=model,
model_parameters=model_parameters,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
dist_init_required=False,
)
return deepspeed_engine, deepspeed_optimizer
def init_deepspeed(self) -> None:
assert self.lightning_module is not None
# deepspeed handles gradient clipping internally
if is_overridden("configure_gradient_clipping", self.lightning_module, pl.LightningModule):
rank_zero_warn(
"Since DeepSpeed handles gradient clipping internally, the default"
" `LightningModule.configure_gradient_clipping` implementation will not actually clip gradients."
" The hook will still be called. Consider setting"
" `Trainer(gradient_clip_val=..., gradient_clip_algorithm='norm')`"
" which will use the internal mechanism."
)
if self.lightning_module.trainer.gradient_clip_algorithm == GradClipAlgorithmType.VALUE:
raise MisconfigurationException("DeepSpeed does not support clipping gradients by value.")
assert isinstance(self.model, pl.LightningModule)
if self.lightning_module.trainer and self.lightning_module.trainer.training:
self._initialize_deepspeed_train(self.model)
else:
self._initialize_deepspeed_inference(self.model)
def _init_optimizers(self) -> Tuple[Optimizer, Optional[LRSchedulerConfig]]:
assert self.lightning_module is not None
optimizers, lr_schedulers = _init_optimizers_and_lr_schedulers(self.lightning_module)
if len(optimizers) > 1 or len(lr_schedulers) > 1:
raise MisconfigurationException(
"DeepSpeed currently only supports single optimizer, single optional scheduler."
)
return optimizers[0], lr_schedulers[0] if lr_schedulers else None
@property
def zero_stage_3(self) -> bool:
assert isinstance(self.config, dict)
zero_optimization = self.config.get("zero_optimization")
return zero_optimization is not None and zero_optimization.get("stage") == 3
def _initialize_deepspeed_train(self, model: Module) -> None:
optimizer, scheduler = None, None
assert isinstance(self.config, dict)
if "optimizer" in self.config:
rank_zero_info(
"You have specified an optimizer and/or scheduler within the DeepSpeed config."
" It is recommended to define it in `LightningModule.configure_optimizers`."
)
lr_scheduler = None
else:
(
optimizer,
lr_scheduler,
) = self._init_optimizers()
if lr_scheduler is not None:
scheduler = lr_scheduler.scheduler
model, deepspeed_optimizer = self._setup_model_and_optimizer(model, optimizer, scheduler)
self._set_deepspeed_activation_checkpointing()
# although we set these here, deepspeed manages the specific optimizer logic
self.optimizers = [deepspeed_optimizer]
deepspeed_scheduler = model.lr_scheduler
if deepspeed_scheduler is not None:
# disable deepspeed lr scheduling as lightning manages scheduling
model.lr_scheduler = None
if lr_scheduler is None:
lr_scheduler = LRSchedulerConfig(deepspeed_scheduler, interval="step")
else:
lr_scheduler.scheduler = deepspeed_scheduler
self.lr_scheduler_configs = [lr_scheduler]
self.model = model
[docs] @contextmanager
@override
def tensor_init_context(self, empty_init: Optional[bool] = None) -> Generator[None, None, None]:
if self.zero_stage_3:
if empty_init is False:
raise NotImplementedError(
f"`{empty_init=}` is not a valid choice with `DeepSpeedStrategy` when ZeRO stage 3 is enabled."
)
yield
return
with super().tensor_init_context(empty_init=empty_init):
yield
[docs] @contextmanager
@override
def model_sharded_context(self) -> Generator[None, None, None]:
import deepspeed
self._init_config_if_needed()
with deepspeed.zero.Init(
enabled=self.zero_stage_3,
remote_device=self.remote_device,
config_dict_or_path=self.config,
):
yield
def _set_deepspeed_activation_checkpointing(self) -> None:
import deepspeed
assert isinstance(self.config, dict)
if self.config.get("activation_checkpointing"):
checkpoint_config = self.config["activation_checkpointing"]
deepspeed.checkpointing.configure(
mpu_=None,
partition_activations=checkpoint_config.get("partition_activations"),
contiguous_checkpointing=checkpoint_config.get("contiguous_memory_optimization"),
checkpoint_in_cpu=checkpoint_config.get("cpu_checkpointing"),
profile=checkpoint_config.get("profile"),
)
def _initialize_deepspeed_inference(self, model: Module) -> None:
import deepspeed
assert isinstance(self.config, dict)
# todo: this is required for DeepSpeed throughput timers
inference_config = {"train_micro_batch_size_per_gpu": 1}
if "fp16" in self.config:
inference_config.update({"fp16": self.config["fp16"]})
if "bf16" in self.config:
inference_config.update({"bf16": self.config["bf16"]})
if self.zero_stage_3:
inference_config.update({
"zero_allow_untested_optimizer": self.config["zero_allow_untested_optimizer"],
"zero_optimization": self.config["zero_optimization"],
})
# Remove all module hooks before initializing new model
remove_module_hooks(model)
model, _, _, _ = deepspeed.initialize(
args=argparse.Namespace(device_rank=self.root_device.index),
config=inference_config,
model=model,
optimizer=None,
lr_scheduler=None,
model_parameters=[],
dist_init_required=False,
)
self.model = model
@property
@override
def distributed_sampler_kwargs(self) -> Dict[str, int]:
return {"num_replicas": self.world_size, "rank": self.global_rank}
[docs] @override
def setup_optimizers(self, trainer: "pl.Trainer") -> None:
"""Creates optimizers and schedulers.
Args:
trainer: the Trainer, these optimizers should be connected to
"""
# Skip initializing optimizers here as DeepSpeed handles optimizers via config.
# User may have specified config options instead in configure_optimizers, but this is handled
# via `_initialize_deepspeed_train`
# empty optimizers, schedulers
self.optimizers = []
self.lr_scheduler_configs = []
def _setup_model(self, model: Module) -> Module: # type: ignore[override]
return model
@property
@override
def handles_gradient_accumulation(self) -> bool:
"""Whether the strategy handles gradient accumulation internally."""
return True
@property
def deepspeed_engine(self) -> "deepspeed.DeepSpeedEngine":
return self.model
@property
def _multi_device(self) -> bool:
return self.num_processes > 1 or self.num_nodes > 1
[docs] @override
def save_checkpoint(self, checkpoint: Dict, filepath: _PATH, storage_options: Optional[Any] = None) -> None:
"""Save model/training states as a checkpoint file through state-dump and file-write.
Args:
checkpoint: The checkpoint state dictionary
filepath: write-target file's path
storage_options: not used for ``DeepSpeedStrategy`` as ``CheckpointIO`` is not used
Raises:
TypeError:
If ``storage_options`` arg is passed in
"""
# broadcast the filepath from rank 0 to ensure all the states are saved in a common filepath
filepath = self.broadcast(filepath)
if storage_options is not None:
raise TypeError(
"`Trainer.save_checkpoint(..., storage_options=...)` with `storage_options` arg"
f" is not supported for `{self.__class__.__name__}` as `CheckpointIO` is not used."
)
if self.zero_stage_3 and self._multi_device and self.is_global_zero:
warning_cache.warn(
"When saving the DeepSpeed Stage 3 checkpoint, "
"each worker will save a shard of the checkpoint within a directory. "
"If a single file is required after training, "
"see https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#"
"deepspeed-zero-stage-3-single-file for instructions."
)
# Use deepspeed's internal checkpointing function to handle partitioned weights across processes
# dump states as a checkpoint dictionary object
_exclude_keys = ["state_dict", "optimizer_states"]
checkpoint = {k: v for k, v in checkpoint.items() if k not in _exclude_keys}
self.deepspeed_engine.save_checkpoint(filepath, client_state=checkpoint, tag="checkpoint")
@override
def load_checkpoint(self, checkpoint_path: _PATH) -> Dict[str, Any]:
if self.load_full_weights and self.zero_stage_3:
# Broadcast to ensure we load from the rank 0 checkpoint
# This doesn't have to be the case when using deepspeed sharded checkpointing
checkpoint_path = self.broadcast(checkpoint_path)
return super().load_checkpoint(checkpoint_path)
_validate_checkpoint_directory(checkpoint_path)
# Rely on deepspeed to load the checkpoint and necessary information
assert self.lightning_module is not None
from lightning.pytorch.trainer.states import TrainerFn
is_fitting = self.lightning_module.trainer.state.fn == TrainerFn.FITTING
_, client_state = self.deepspeed_engine.load_checkpoint(
checkpoint_path,
load_optimizer_states=is_fitting,
load_lr_scheduler_states=False,
load_module_strict=self.lightning_module.strict_loading,
)
if client_state is None:
raise MisconfigurationException(
"DeepSpeed was unable to load the checkpoint. Ensure you passed in a DeepSpeed compatible checkpoint "
"or a single checkpoint file with `Trainer(strategy=DeepSpeedStrategy(load_full_weights=True))`."
)
return client_state
@property
@override
def lightning_restore_optimizer(self) -> bool:
assert self.lightning_module is not None
# managed by DeepSpeed
if self.load_full_weights and self.zero_stage_3 and self.lightning_module.trainer.state.fn == TrainerFn.FITTING:
rank_zero_warn(
"A single checkpoint file has been given. This means optimizer states cannot be restored."
" If you'd like to restore these states, you must provide a path to the originally saved DeepSpeed"
" checkpoint. When using ZeRO 3, the original path should be a directory."
)
return False
@override
def load_model_state_dict(self, checkpoint: Mapping[str, Any], strict: bool = True) -> None:
# override to do nothing, deepspeed engine already loaded the weights in `load_checkpoint()`
if self.load_full_weights and self.zero_stage_3:
self.model_to_device()
self._restore_zero_state(checkpoint, strict=strict)
def _restore_zero_state(self, ckpt: Mapping[str, Any], strict: bool) -> None:
"""Overrides the normal load_state_dict behaviour in PyTorch to ensure we gather parameters that may be sharded
across processes before loading the state dictionary when using ZeRO stage 3. This is then automatically synced
across processes.
Args:
ckpt: The ckpt file.
"""
import deepspeed
assert self.lightning_module is not None
def load(module: torch.nn.Module, prefix: str = "") -> None:
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
state_dict = ckpt["state_dict"]
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, "_metadata", None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
# because zero3 puts placeholders in model params, this context
# manager gathers (unpartitions) the params of the current layer, then loads from
# the state dict and then re-partitions them again
with deepspeed.zero.GatheredParameters(list(module.parameters(recurse=False)), modifier_rank=0):
if self.is_global_zero:
module._load_from_state_dict(
state_dict=state_dict,
prefix=prefix,
local_metadata=local_metadata,
strict=strict,
missing_keys=missing_keys,
unexpected_keys=unexpected_keys,
error_msgs=error_msgs,
)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
load(self.lightning_module, prefix="")
@override
def load_optimizer_state_dict(self, checkpoint: Mapping[str, Any]) -> None:
# Override to do nothing, the deepspeed engine already loaded the states in `load_checkpoint()`
pass
@classmethod
@override
def register_strategies(cls, strategy_registry: _StrategyRegistry) -> None:
strategy_registry.register("deepspeed", cls, description="Default DeepSpeed Strategy")
strategy_registry.register("deepspeed_stage_1", cls, description="DeepSpeed with ZeRO Stage 1 enabled", stage=1)
strategy_registry.register("deepspeed_stage_2", cls, description="DeepSpeed with ZeRO Stage 2 enabled", stage=2)
strategy_registry.register(
"deepspeed_stage_2_offload",
cls,
description="DeepSpeed ZeRO Stage 2 and CPU Offload",
stage=2,
offload_optimizer=True,
)
strategy_registry.register("deepspeed_stage_3", cls, description="DeepSpeed ZeRO Stage 3", stage=3)
strategy_registry.register(
"deepspeed_stage_3_offload",
cls,
description="DeepSpeed ZeRO Stage 3 and CPU Offload",
stage=3,
offload_optimizer=True,
offload_parameters=True,
)
strategy_registry.register(
"deepspeed_stage_3_offload_nvme",
cls,
description="DeepSpeed ZeRO Stage 3 and NVMe Offload",
stage=3,
offload_optimizer=True,
offload_parameters=True,
remote_device="nvme",
offload_params_device="nvme",
offload_optimizer_device="nvme",
)
def _load_config(self, config: Optional[Union[_PATH, Dict[str, Any]]]) -> Optional[Dict[str, Any]]:
if config is None and self.DEEPSPEED_ENV_VAR in os.environ:
rank_zero_info(f"Loading DeepSpeed config from set {self.DEEPSPEED_ENV_VAR} environment variable")
config = os.environ[self.DEEPSPEED_ENV_VAR]
if isinstance(config, (str, Path)):
if not os.path.isfile(config):
raise MisconfigurationException(
f"You passed in a path to a DeepSpeed config but the path does not exist: {config}"
)
with open(config) as f:
config = json.load(f)
assert isinstance(config, dict) or config is None
return config
def _init_config_if_needed(self) -> None:
if not self._config_initialized:
self._format_config()
self._config_initialized = True
def _format_config(self) -> None:
if self.config is None:
raise MisconfigurationException(
"To use DeepSpeed you must pass in a DeepSpeed config dict, or a path to a JSON config."
" See: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed"
)
self._format_batch_size_and_grad_accum_config()
_format_precision_config(
config=self.config,
precision=self.precision_plugin.precision,
loss_scale=self.loss_scale,
loss_scale_window=self.loss_scale_window,
min_loss_scale=self.min_loss_scale,
initial_scale_power=self.initial_scale_power,
hysteresis=self.hysteresis,
)
def _create_default_config(
self,
zero_optimization: bool,
zero_allow_untested_optimizer: bool,
logging_batch_size_per_gpu: Union[str, int],
partition_activations: bool,
cpu_checkpointing: bool,
contiguous_memory_optimization: bool,
synchronize_checkpoint_boundary: bool,
offload_optimizer: bool,
offload_parameters: bool,
nvme_path: str,
offload_params_device: str,
params_buffer_count: int,
params_buffer_size: int,
max_in_cpu: int,
offload_optimizer_device: str,
optimizer_buffer_count: int,
pin_memory: bool,
block_size: int,
queue_depth: int,
single_submit: bool,
overlap_events: bool,
thread_count: int,
**zero_kwargs: Any,
) -> Dict:
cfg = {
"activation_checkpointing": {
"partition_activations": partition_activations,
"cpu_checkpointing": cpu_checkpointing,
"contiguous_memory_optimization": contiguous_memory_optimization,
"synchronize_checkpoint_boundary": synchronize_checkpoint_boundary,
},
"aio": {
"block_size": block_size,
"queue_depth": queue_depth,
"single_submit": single_submit,
"overlap_events": overlap_events,
"thread_count": thread_count,
},
}
if zero_optimization:
zero_config = zero_kwargs
if offload_optimizer:
zero_config["offload_optimizer"] = {
"device": offload_optimizer_device,
"nvme_path": nvme_path,
"buffer_count": optimizer_buffer_count,
"pin_memory": pin_memory,
}
if offload_parameters:
zero_config["offload_param"] = {
"device": offload_params_device,
"nvme_path": nvme_path,
"buffer_count": params_buffer_count,
"buffer_size": params_buffer_size,
"max_in_cpu": max_in_cpu,
"pin_memory": pin_memory,
}
cfg = {
"zero_allow_untested_optimizer": zero_allow_untested_optimizer,
"zero_optimization": zero_config,
**cfg,
}
if logging_batch_size_per_gpu != "auto":
cfg = {"train_micro_batch_size_per_gpu": logging_batch_size_per_gpu, **cfg}
return cfg
def _format_batch_size_and_grad_accum_config(self) -> None:
# TODO: Using Fabric, we do not support these variables within the config
assert isinstance(self.config, dict)
if self.lightning_module is None:
return
if "gradient_accumulation_steps" in self.config:
raise MisconfigurationException(
"Do not set `gradient_accumulation_steps` in the DeepSpeed config"
" as this will be set with the `accumulate_grad_batches` argument passed via the Lightning Trainer."
)
self.config["gradient_accumulation_steps"] = self.lightning_module.trainer.accumulate_grad_batches
if "train_micro_batch_size_per_gpu" not in self.config:
batch_size = self._auto_select_batch_size()
self.config["train_micro_batch_size_per_gpu"] = batch_size
if "gradient_clipping" not in self.config:
self.config["gradient_clipping"] = self.lightning_module.trainer.gradient_clip_val or 0.0
def _auto_select_batch_size(self) -> int:
# train_micro_batch_size_per_gpu is used for throughput logging purposes
# by default we try to use the batch size of the loader
assert self.lightning_module is not None
batch_size = 1
data_source = self.lightning_module.trainer.fit_loop._data_source
if data_source.is_defined():
train_dataloader = data_source.dataloader()
if hasattr(train_dataloader, "batch_sampler"):
batch_size = train_dataloader.batch_sampler.batch_size
return batch_size
