Source code for composer.core.state

# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0

"""The state of the trainer."""
from __future__ import annotations

import collections.abc
import logging
import textwrap
import warnings
from collections import OrderedDict
from contextlib import contextmanager
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Union, cast

import numpy as np
import torch
import torch.nn.modules.utils
from packaging import version
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import (FullOptimStateDictConfig, FullStateDictConfig,
                                                                ShardedOptimStateDictConfig, StateDictType)
from torch.nn.parallel import DistributedDataParallel
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
from torch.utils.data import DataLoader, Dataset
from torchmetrics import Metric

from composer.core.data_spec import DataSpec
from composer.core.event import Event
from composer.core.precision import Precision
from composer.core.serializable import Serializable
from composer.core.time import Time, Timestamp, TimeUnit
from composer.devices import Device
from composer.utils import (batch_get, batch_set, dist, ensure_tuple, get_composer_env_dict, is_model_deepspeed,
                            reproducibility)

if TYPE_CHECKING:
    import deepspeed

    from composer.core.algorithm import Algorithm
    from composer.core.callback import Callback
    from composer.core.evaluator import Evaluator
    from composer.core.passes import AlgorithmPass
    from composer.loggers import Logger
    from composer.profiler import Profiler

__all__ = ['State']

log = logging.getLogger(__name__)


@contextmanager
def fsdp_state_dict_type_context(module: torch.nn.Module, state_dict_type: str = 'full'):
    """Context manager for materializing or loading an fsdp module's state dict.

    Args:
        module (torch.nn.Module): The torch module that you want to call `state_dict()`
            or `load_state_dict()` on.
        state_dict_type (str, optional): which of the three state dict types you want to use.
            choices are ['full', 'sharded']. Defaults to 'full'.
            * 'full': the full, unsharded state dict materialized only on rank 0 with cpu_offload if necessary
            * 'sharded': the sharded, unflattened state_dict, where each rank only gets a single shard.
            See torch.distributed.fsdp.StateDictType for more info.

    Raises:
        NotImplementedError: if you specify a state_dict_type not in ['full', 'sharded'].
    """
    # Torch forgot to put ShardedStateDictConfig in torch/distributed/fsdp/__init__.py, so we
    # have to import it this way.
    from torch.distributed.fsdp.fully_sharded_data_parallel import ShardedStateDictConfig

    fsdp_state_dict_type = None
    state_dict_config = None
    optim_state_dict_config = None
    # Full is the full monolithic state dict materialized in memory on just rank 0
    # with offloading to cpu if necessary
    if state_dict_type == 'full':
        fsdp_state_dict_type = StateDictType.FULL_STATE_DICT
        state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
        optim_state_dict_config = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True)

    # Sharded is sharded state dict, but unflattened parameters (not useful for FSDP, but
    # useful if you plan to use the state dict outside of FSDP).
    elif state_dict_type == 'sharded':
        fsdp_state_dict_type = StateDictType.SHARDED_STATE_DICT
        state_dict_config = ShardedStateDictConfig()
        state_dict_config = ShardedStateDictConfig(offload_to_cpu=True)
        optim_state_dict_config = ShardedOptimStateDictConfig()

    else:
        raise NotImplementedError(f'No valid FSDP state_dict_type for {state_dict_type}')

    with FSDP.state_dict_type(module,
                              state_dict_type=fsdp_state_dict_type,
                              state_dict_config=state_dict_config,
                              optim_state_dict_config=optim_state_dict_config):
        yield


def fsdp_get_optim_state_dict(model: torch.nn.Module,
                              optim: torch.optim.Optimizer,
                              state_dict_type: str = 'full') -> Dict[str, Any]:
    """Materializes a given model's optimizer's state_dict.

    Args:
        model (torch.nn.Module): The model that the optimizer corresponds to.
        optim (torch.optim.Optimizer): The optimizer that you want a state dict for.
        state_dict_type (str, optional): which of the three state dict types you want to use.
            choices are ['full', 'sharded']. Defaults to 'full'.
            * 'full': the full, unsharded state dict materialized only on rank 0
            * 'sharded': the sharded, unflattened state_dict, where each rank only gets a single shard.

    Raises:
        NotImplementedError: if you specify a state_dict_type not in ['full', 'sharded'].

    Returns:
        Dict[str, Any]: The state_dict for the given optimizer.
    """
    with fsdp_state_dict_type_context(module=model, state_dict_type=state_dict_type):
        return FSDP.optim_state_dict(model, optim)  # type: ignore


def _legacy_optim_state_dict_to_load(
    optim_state_dict: Optional[Dict[str, Any]],
    model: torch.nn.Module,
    optim: torch.optim.Optimizer,
    state_dict_type: str = 'full',
):
    if state_dict_type == 'sharded':
        # Optimizer and optimizer state dict are already sharded, but not
        # flattened, so we flatten the state dict then load it.
        assert optim_state_dict is not None
        flattened_optim_state_dict = FSDP.flatten_sharded_optim_state_dict(sharded_optim_state_dict=optim_state_dict,
                                                                           model=model,
                                                                           optim=optim)
        return flattened_optim_state_dict
    else:  # fsdp_state_dict_type == 'full'
        # FSDP enabled, but fsdp_state_dict is set to 'full', so the state dict
        # is a full state dict and we must shard and flatten it first before loading it.
        sharded_optim_state_dict = FSDP.scatter_full_optim_state_dict(full_optim_state_dict=optim_state_dict,
                                                                      model=model)
        return sharded_optim_state_dict


def get_fsdp_sharded_optim_state_dict(full_optim_state_dict: Dict[str, Any], model: torch.nn.Module):
    from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
    log.debug(
        f'Scattering optimizer state dict with keys {full_optim_state_dict.keys()} and model of type {type(model)}')
    return FSDP.scatter_full_optim_state_dict(full_optim_state_dict=full_optim_state_dict, model=model)


def get_fsdp_full_optim_state_dict(model: torch.nn.Module, optim: torch.optim.Optimizer, rank0_only: bool = True):
    return FSDP.full_optim_state_dict(model=model, optim=optim, rank0_only=rank0_only)


def _ensure_backwards_compatible_checkpointing(state_dict: Dict[str, Any]):
    # v0.4.1 removed the leading underscores for the keys in the state_dict
    # It also renamed _is_model_ddp_wrapped to is_model_ddp
    state = {}
    for attribute_name, serialized_value in state_dict.items():
        if attribute_name == '_is_model_ddp_wrapped':
            attribute_name = 'is_model_ddp'
        if attribute_name.startswith('_'):
            attribute_name = attribute_name[1:]
        state[attribute_name] = serialized_value
    return state


_STATE_DICT_SERIALIZED_ATTRIBUTES = [
    # List of attributes that are serialized with state_dict
    # Only the attributes listed in state.serialized_attributes will actually be saved.
    'model',
    'optimizers',
    'schedulers',
    'algorithms',
    'callbacks',
    'scaler',
    'timestamp',
]


[docs]class State(Serializable): """The state of the trainer. Contains variables that the trainer tracks throughout the training loop. Note that all the necessary parts (i.e., :attr:`serialized_attributes`) of state are serialized when the trainer is checkpointed so that it can be used to restore the trainer and continue training from a checkpoint. :mod:`~composer.algorithms` are able to modify an instance of this class in-place. .. note:: An instance of this class is automatically constructed by the :class:`~.Trainer` constructor. A user need not instantiate this class. Args: model (torch.nn.Module): The model, typically as a subclass of :class:`~.ComposerModel`. rank_zero_seed (int): The seed used on the rank zero process. It is assumed that each rank's seed is ``rank_zero_seed + dist.get_global_rank()``. run_name (str): The name for this training run. device (Device): The device used by this process. The trainer moves the model and loaded data to this device. device_train_microbatch_size (int, optional): The microbatch size for each device during training. auto_microbatching (bool, optional): Whether automatic microbatching is enabled. train_dataloader (Iterable, optional): Dataloader used for training evaluators (Evaluator | Evaluators, optional): :class:`.Evaluator` used for evaluation. dataloader (Iterable, optional): The active DataLoader. dataloader_len (int | Time[int], optional): The number of batches per dataloader iteration (e.g. epoch). The trainer will yield the first ``dataloader_len`` batches per iteration. If ``-1`` (the default), the entire dataloader will be iterated over. dataloader_label (str, optional): The name for the dataloader. Required if ``dataloader`` is specified. (default: ``None``) By convention, the training dataloader is called ``'train'``. The evaluator dataloader is called ``'eval'``, or when multiple evaluators are used, the name of the evaluator. dataset_state (Dict[str, Any], optional): Mapping of dataset split to its iteration state for resumption. dataset_resumption (Dict[str, Any], optional): Mapping of dataset split to whether resumption is used. max_duration (str | Time, optional): The maximum duration to train for. (default: ``None``) precision (str | Precision): The numerical precision to use for training. See :class:`~.Precision` for the supported precisions. precision_config (Optional[Dict[str, Any]]): The config for FP8 scaling strategy. See parameters for `DelayedScaling <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html?highlight=delayedscaling#transformer_engine.common.recipe.DelayedScaling>`_. optimizers (torch.optim.Optimizer | Sequence[torch.optim.Optimizer], optional): The optimizer being used to train the model. Multiple optimizers are not currently supported. schedulers (LRScheduler | Sequence[LRScheduler], optional): The learning rate scheduler (can also be a list or tuple of schedulers). scaler (torch.cuda.amp.GradScaler, optional): The gradient scaler in use for mixed precision training. save_metrics (bool, optional): Whether to save metrics in state_dict. algorithms (Algorithm | Sequence[Algorithm], optional): The algorithms used for training. callbacks (Callback | Sequence[Callback], optional): The callbacks used for training. deepspeed_config (Dict[str, Any], optional): The configuration dictionary for deepspeed. fsdp_config (Dict[str, Any], optional): The configuration dictionary for FSDP. fsdp_auto_wrap (bool, optional): Whether to automatically wrap the model with FSDP. Attributes: batch (types.Batch): The batch. This will be the entire batch during the :attr:`.Event.AFTER_DATALOADER`, or a microbatch between :attr:`.Event.BATCH_START` and :attr:`.Event.BATCH_END`. device (Device): The device used by this process. The trainer moves the model and loaded data to this device. This can be used in callbacks and algorithms to move data onto the correct device. train_metrics (Dict[str, Metric]): The current train metrics, organized by metric name. ``train_metrics`` will be deep-copied to ensure that each evaluator updates only its ``train_metrics``. For example: >>> trainer = Trainer( ... ..., ... train_dataloader=train_dataloader, ... eval_dataloader=eval_dataloader, ... ) >>> trainer.fit() >>> trainer.state.train_metrics {'MulticlassAccuracy': MulticlassAccuracy()} eval_metrics (Dict[str, Dict[str, Metric]]): The current evaluation metrics, organized by dataloader label and then by metric name. If not using an :class:`.Evaluator`, the eval dataloader is labeled ``'eval'``. Otherwise, in the case of having multiple evaluation datasets, the evaluator label is used. See the `Multiple Datasets Documentation <https://docs.mosaicml.com/projects/composer/en/stable/trainer/evaluation.html#multiple-datasets>`_ for more information. ``eval_metrics`` will be deep-copied to ensure that each evaluator updates only its ``eval_metrics``. For example: >>> from composer.metrics import CrossEntropy >>> trainer = Trainer( ... ..., ... train_dataloader=train_dataloader, ... eval_dataloader=eval_dataloader, ... ) >>> trainer.fit() >>> trainer.state.eval_metrics {'eval': {'CrossEntropy': CrossEntropy(), 'MulticlassAccuracy': MulticlassAccuracy()}} Or, when using an :class:`.Evaluator` for multiple evaluation datasets: .. testsetup:: eval_1_dl = eval_dataloader eval_2_dl = eval_dataloader >>> from composer.core import Evaluator >>> trainer = Trainer( ... ..., ... train_dataloader=train_dataloader, ... eval_dataloader=[ ... Evaluator(label='eval1', dataloader=eval_1_dl, metric_names=['MulticlassAccuracy']), ... Evaluator(label='eval2', dataloader=eval_2_dl, metric_names=['MulticlassAccuracy']), ... ], ... ) >>> trainer.fit() >>> trainer.state.eval_metrics {'eval1': {'MulticlassAccuracy': MulticlassAccuracy()}, 'eval2': {'MulticlassAccuracy': MulticlassAccuracy()}} eval_timestamp (Timestamp): The timestamp for the current evaluation dataloader. This timestamp is reset before the dataloader is evaluated. The :attr:`~Timestamp.epoch` attribute for this timestamp is always ``0``. device_train_microbatch_size (int): The size of each train microbatch per device. loss (torch.Tensor | Sequence[torch.Tensor] | Dict[Any, torch.Tensor]): The most recently computed loss. model (torch.nn.Module): The training model. .. note:: When using DeepSpeed or multi-rank training, the model will be wrapped with :class:`~deepspeed.DeepSpeedEngine` or :class:`~torch.nn.parallel.DistributedDataParallel`, respectively. outputs (torch.Tensor | Sequence[torch.Tensor]): The most recently computed output from the model's forward pass. predict_timestamp (Timestamp): The timestamp for the current prediction dataloader. This timestamp is reset before the dataloader is used. The :attr:`~Timestamp.epoch` attribute for this timestamp is always ``0``. profiler (Profiler): The profiler (if profiling is enabled), or ``None`` if not profiling. rank_zero_seed (int): The seed of the rank zero process. run_name (str): The name for this training run. scaler (torch.cuda.amp.GradScaler): The gradient scaler if using mixed-precision training, or ``None`` if not using mixed-precision training. serialized_attributes (List[str]): The names of the attribute which are serialized in a checkpoint. By default, the following attributes are serialized: +-----------------------+-------------------------------------------------------------+ | Attribute | Description | +=======================+=============================================================+ | model | The model under training. | +-----------------------+-------------------------------------------------------------+ | optimizers | The optimizers being used to train the model. | +-----------------------+-------------------------------------------------------------+ | schedulers | The learning rate schedulers. | +-----------------------+-------------------------------------------------------------+ | algorithms | The algorithms used for training. | +-----------------------+-------------------------------------------------------------+ | callbacks | The callbacks used for training. | +-----------------------+-------------------------------------------------------------+ | scaler | The gradient scaler in use for mixed precision training. | +-----------------------+-------------------------------------------------------------+ | timestamp | The timestamp that tracks training loop progress. | +-----------------------+-------------------------------------------------------------+ | rank_zero_seed | The seed of the rank zero process. | +-----------------------+-------------------------------------------------------------+ | train_metrics | The current training metrics | +-----------------------+-------------------------------------------------------------+ | eval_metrics | The current evaluation metrics | +-----------------------+-------------------------------------------------------------+ | run_name | The run name for training. | +-----------------------+-------------------------------------------------------------+ | dataset_state | The dataset iteration state. | +-----------------------+-------------------------------------------------------------+ timestamp (Timestamp): The current training timestamp. """ def __init__( self, # model model: torch.nn.Module, # determinism rank_zero_seed: int, # run_name run_name: str, # device device: Device, # stopping conditions max_duration: Optional[Union[str, Time[int]]] = None, # data configurations device_train_microbatch_size: Optional[int] = None, auto_microbatching: bool = False, # dataloaders train_dataloader: Optional[Iterable] = None, evaluators: Optional[Union[Evaluator, Sequence[Evaluator]]] = None, # these track the current 'active' dataloader # depending on train, eval, or others dataloader: Optional[Iterable] = None, dataloader_label: Optional[str] = None, dataloader_len: Union[int, Time[int]] = -1, dataset_state: Optional[Dict[str, Any]] = None, dataset_resumption: Optional[Dict[str, Any]] = None, # precision precision: Union[str, Precision] = Precision.FP32, precision_config: Optional[Dict[str, Any]] = None, # optimizers optimizers: Optional[Union[Optimizer, Sequence[Optimizer]]] = None, # scaler scaler: Optional[torch.cuda.amp.grad_scaler.GradScaler] = None, # state_dict save_metrics: bool = False, # algorithms and callbacks algorithms: Optional[Union[Algorithm, Sequence[Algorithm]]] = None, callbacks: Optional[Union[Callback, Sequence[Callback]]] = None, # Distributed training configs deepspeed_config: Optional[Dict[str, Any]] = None, fsdp_config: Optional[Dict[str, Any]] = None, fsdp_auto_wrap: bool = True, ): self.rank_zero_seed = rank_zero_seed self.model = model self.run_name = run_name self.device = device self.device_train_microbatch_size = device_train_microbatch_size self.auto_microbatching = auto_microbatching self._dataloader_len = None self._dataloader = None self._dataloader_label = None self.set_dataloader(dataloader, dataloader_label, dataloader_len) self.dataset_state = dataset_state self.dataset_resumption = dataset_resumption or {} self._max_duration = None self.max_duration = max_duration self.save_metrics = save_metrics self._train_dataloader = train_dataloader self._evaluators = list(ensure_tuple(evaluators)) self.previous_timestamp: Optional[Timestamp] = None self.timestamp = Timestamp() self.eval_timestamp = Timestamp() self.predict_timestamp = Timestamp() self._precision = Precision(precision) self._precision_config = precision_config if optimizers is None: self._optimizers = [] else: self._optimizers = list(ensure_tuple(optimizers)) self._schedulers = [] self.scaler = scaler self._algorithms = list(ensure_tuple(algorithms)) self._callbacks = list(ensure_tuple(callbacks)) self.profiler: Optional[Profiler] = None self.deepspeed_config = deepspeed_config self.fsdp_config = fsdp_config self.fsdp_auto_wrap = fsdp_auto_wrap if self.load_fsdp_monolith_rank0_only: assert fsdp_config is not None error_message = '' if fsdp_config['use_orig_params'] == True: error_message += textwrap.dedent( "load_fsdp_monolith_rank0_only requires fsdp_config['use_orig_params'] to be False. " "Either set fsdp_config['use_orig_params'] = False or set load_fsdp_monolith_rank0_only = False. ") if fsdp_config['sync_module_states'] == False: error_message += textwrap.dedent( "load_fsdp_monolith_rank0_only requires fsdp_config['sync_module_states'] to be True. " "Either set fsdp_config['sync_module_states'] = True or set load_fsdp_monolith_rank0_only = False. " ) # Broadcast rank 0 meta check to all ranks so error can be raised on all ranks rank0_on_meta = 0 if dist.get_global_rank() == 0 and next(model.parameters()).device.type == 'meta': rank0_on_meta = 1 rank0_on_meta_tensor = self.device.tensor_to_device(torch.tensor([rank0_on_meta], dtype=torch.uint8)) dist.all_reduce(rank0_on_meta_tensor, reduce_operation='MAX') if rank0_on_meta_tensor.item() == 1: error_message += textwrap.dedent( 'load_fsdp_monolith_rank0_only requires the rank 0 model to be on cpu or gpu, ' 'but detected model device as meta. Either move the model to cpu or gpu, or set ' 'load_fsdp_monolith_rank0_only = False. ') if error_message != '': raise ValueError(error_message) self.sharded_ckpt_prefix_dir: Optional[str] = None if self.fsdp_config is not None: self.sharded_ckpt_prefix_dir = self.fsdp_config['sharded_ckpt_prefix_dir'] if self.fsdp_state_dict_type not in [None, 'full', 'sharded']: if self.fsdp_state_dict_type == 'local': raise ValueError('Composer and PyTorch no longer support saving or loading local state dicts. ' 'To upgrade an older checkpoint, use Composer version 0.18.1 and export as ' 'a monolithic checkpoint using a callback.') raise ValueError(f'fsdp_state_dict_type must be one of [None, "full", "sharded"], but got ' f'{self.fsdp_state_dict_type}') if self.fsdp_sharded_state_dict_enabled and self.save_metrics: # Sharded state dict breaks in many different ways with torchmetrics, due to both sharding # metric tensors and only sometimes flattening path names in state dict and _computed, so # saving metrics is not allowed with sharded state dict. raise ValueError( textwrap.dedent('Saving metrics is not allowed with sharded state dict as metric tensors will ' 'be sharded and break on load. If you wish to save metric state, set ' 'fsdp_config["state_dict_type"] = "full" to disable sharded checkpoints.')) # Set defaults for transient variables (to make pyright happy) self.batch: Any = None self.loss: Union[torch.Tensor, Sequence[torch.Tensor], Dict[Any, torch.Tensor]] = torch.Tensor() self.outputs: Union[torch.Tensor, Sequence[torch.Tensor]] = torch.Tensor() # These attributes will be serialized using .state_dict(), and loaded with .load_state_dict() # All other attributes will not be serialized. # For simplicity, omit the leading underscore for private attributes. # For example, even though the optimizers are stored on the state # as the "_optimizers" attribute, here we specify just "optimizers" self.serialized_attributes = [ 'model', 'optimizers', 'schedulers', 'algorithms', 'callbacks', 'scaler', 'timestamp', 'rank_zero_seed', 'train_metrics', 'eval_metrics', 'run_name', 'dataset_state', ] self.train_metrics: Optional[Dict[str, Metric]] = {} self.eval_metrics: Dict[str, Dict[str, Metric]] = {} self.train_metric_values: Dict[str, float] = {} self.eval_metric_values: Dict[str, float] = {} self.total_loss_dict: Dict[str, float] = {} def _dataset_of(self, dataloader: Optional[Union[Evaluator, DataSpec, DataLoader, Iterable]]) -> Optional[Dataset]: """Get the dataset contained by the given dataloader-like object. Args: dataloader (Evaluator | DataSpec | DataLoader | Iterable, optional): The dataloader, wrapped dataloader, or generic python iterable to get the dataset of, if applicable. Returns: Dataset: Its dataset, if there is one. """ from composer.core.evaluator import Evaluator # If it's None, no dataset for you. if dataloader is None: return None # An Evaluator is a dataloader wrapped with metrics. Unwrap its dataloader. if isinstance(dataloader, Evaluator): dataloader = dataloader.dataloader # A DataSpec is a dataloader wrapped with an on-device transform. Unwrap its dataloader. if isinstance(dataloader, DataSpec): dataloader = dataloader.dataloader # If what we now have is an actual DataLoader, return its dataset. If not, return None. if isinstance(dataloader, DataLoader): return dataloader.dataset else: return None @property def train_dataloader(self) -> Optional[Union[Iterable, DataLoader]]: """Get the train dataloader. Returns: Iterable | DataLoader, optional: The dataloader. """ return self._train_dataloader @train_dataloader.setter def train_dataloader(self, train_dataloader: Optional[Union[Iterable, DataLoader]]): """Set the train dataloader. Args: train_dataloader (Iterable | DataLoader, optional): The dataloader. """ self._train_dataloader = train_dataloader # Load dataset state from checkpoint when train_dataloader is set if self.dataset_state: dataset = self._dataset_of(self._train_dataloader) if hasattr(dataset, 'load_state_dict'): dataset.load_state_dict(self.dataset_state['train']) # pyright: ignore self.dataset_resumption['train'] = True self.dataset_state['train'] = None @property def seed(self): """The seed for the current rank.""" return self.rank_zero_seed + dist.get_global_rank() @property def max_duration(self): """The maximum training duration.""" return self._max_duration @max_duration.setter def max_duration(self, max_duration: Optional[Union[str, Time[int]]]): if max_duration is None: self._max_duration = None return if isinstance(max_duration, str): max_duration = cast(Time[int], Time.from_timestring(max_duration)) if max_duration.unit == TimeUnit.DURATION: raise ValueError('TimeUnit.DURATION is not allowed as a unit for max_duration') self._max_duration = max_duration
[docs] def get_elapsed_duration(self) -> Optional[Time[float]]: """Get the elapsed training duration. Returns: Optional[Time[float]]: The elapsed duration, in :attr:`TimeUnit.DURATION`. ``Time(0.0, TimeUnit.DURATION)`` represents the beginning of training and ``Time(1.0, TimeUnit.DURATION)`` represents a completed training process. Returns ``None`` if ``max_duration`` is None. """ if self.max_duration is None: return None return self.timestamp.get(self.max_duration.unit) / self.max_duration
[docs] def stop_training(self): """Gracefully stop training. The current batch of training will finish, and any scheduled evaluation, logging, and evaluation for that batch, as well as any epoch end events. """ self.max_duration = self.timestamp.batch
@property def optimizers(self): """The optimizers.""" return self._optimizers @optimizers.setter def optimizers(self, optimizers: Union[Optimizer, Sequence[Optimizer]]): self._optimizers[:] = ensure_tuple(optimizers) @property def schedulers(self): """The schedulers.""" return self._schedulers @schedulers.setter def schedulers(self, schedulers: Union[LRScheduler, Sequence[LRScheduler]]): self._schedulers[:] = ensure_tuple(schedulers)
[docs] def batch_get_item(self, key: Union[str, int, Callable, Any]) -> Any: """Gets element from batch either specified by key or user-specified function. See batch_get in `utils/batch_helpers.py` for examples. Args: key (str | int | Tuple[Callable, Callable] | Any, optional): A key to index into the batch or a user-specified function to do the extracting. A pair of callables is also supported for cases where a get and set function pair are both passed (like in Algorithms). The getter is assumed to be the first of the pair. Returns: The part of the batch specified by the key. This could be any type depending on what the batch is composed of. """ return batch_get(self.batch, key)
[docs] def batch_set_item(self, key: Union[str, int, Callable, Any], value: Any): """Sets the element specified by the key of the set_fn to the specified value. This is not an in-place operation, as for tuple-typed batches, a new batch object must be created to modify them. See batch_set in `utils/batch_helpers.py` for examples. Args: key (str | int | Tuple[Callable, Callable] | Any, optional): A key to index into the batch or a user-specified function to do the setting. A pair of callables is also supported for cases where a get and set function pair are both passed (like in Algorithms). The setter is assumed to be the second of the pair. value (Any): The value that batch[key] or batch.key gets set to or that the user-defined set function sets a part of the batch to. Returns: batch (Any): The updated batch with value set at key. """ self.batch = batch_set(self.batch, key=key, value=value)
@property def callbacks(self): """The callbacks.""" return self._callbacks @callbacks.setter def callbacks(self, callbacks: Sequence[Callback]): self._callbacks[:] = callbacks @property def algorithms(self): """The algorithms.""" return self._algorithms @algorithms.setter def algorithms(self, algorithms: Sequence[Algorithm]): self._algorithms[:] = algorithms @property def evaluators(self): """The evaluators.""" return self._evaluators @evaluators.setter def evaluators(self, evaluators: Union[Evaluator, Sequence[Evaluator]]): self._evaluators[:] = list(ensure_tuple(evaluators)) # Load dataset state from checkpoint when evaluators are set if self.dataset_state: state = self.dataset_state['eval'] for evaluator in self._evaluators: dataset = self._dataset_of(evaluator) if hasattr(dataset, 'load_state_dict') and evaluator.label in state: dataset.load_state_dict(state[evaluator.label]) # pyright: ignore del self.dataset_state['eval'] @property def deepspeed_enabled(self): """Indicates if deepspeed is enabled.""" return self.deepspeed_config is not None @property def fsdp_enabled(self): """Indicates if FSDP is enabled.""" for module in self.model.modules(): if isinstance(module, FSDP): return True return False @property def fsdp_state_dict_type(self): if not self.fsdp_enabled: return None if self.fsdp_config is not None: return self.fsdp_config['state_dict_type'] return 'full' @property def fsdp_sharded_state_dict_enabled(self): return self.fsdp_config is not None and self.fsdp_enabled and self.fsdp_state_dict_type == 'sharded' @property def fsdp_elastic_sharded_enabled(self): warnings.warn('state.fsdp_elastic_sharded_enabled is deprecated and will be removed v0.21.0', DeprecationWarning) return self.fsdp_sharded_state_dict_enabled @property def fsdp_device_mesh(self): if self.fsdp_enabled: if not hasattr(self.model, 'model'): return None return self.model.model._device_mesh else: return None @property def load_fsdp_monolith_rank0_only(self): return self.fsdp_config is not None and self.fsdp_auto_wrap and self.fsdp_config[ 'state_dict_type'] == 'full' and self.fsdp_config['load_monolith_rank0_only'] == True def _get_integrations_state_dict(self) -> Dict[str, Any]: """Gets a dictionary of information about integrations to store in the state dict. This metadata is used for loading things from state dict that need to be done outside of the normal Composer load path (e.g. HuggingFace model/tokenizer). """ from composer.models import HuggingFaceModel integrations = {} if isinstance(self.model, HuggingFaceModel): integrations['huggingface'] = self.model.get_metadata() elif self.is_model_ddp and isinstance(self.model.module, HuggingFaceModel): integrations['huggingface'] = self.model.module.get_metadata() return integrations def _get_state_metadata(self) -> Dict[str, Any]: """Gets a dictionary of metadata to store in the state dict. This metadata is used for checking compatibility between the current environment/setup and the environment/setup that was used for the checkpoint that is being loaded in """ metadata_dict = {} metadata_dict['composer_env_info'] = get_composer_env_dict() metadata_dict['torch_version'] = torch.__version__ metadata_dict['device'] = self.device.name metadata_dict['precision'] = self.precision.value metadata_dict['world_size'] = dist.get_world_size() metadata_dict['device_train_microbatch_size'] = self.device_train_microbatch_size if self._train_dataloader is not None and hasattr(self._train_dataloader, 'batch_size'): metadata_dict['train_dataloader_batch_size'] = self._train_dataloader.batch_size # type: ignore return metadata_dict def _dataset_state_dict(self) -> Dict[str, Any]: """Collect the state dict(s) of our train and eval dataset(s). Returns: Dict[str, Any]: The state dict(s). """ obj = { 'train': None, 'eval': {}, } dataset = self._dataset_of(self.train_dataloader) if hasattr(dataset, 'state_dict'): num_samples = int(self.timestamp.sample_in_epoch.value) obj['train'] = dataset.state_dict(num_samples, True) # pyright: ignore for evaluator in self.evaluators: dataset = self._dataset_of(evaluator) if hasattr(dataset, 'state_dict'): # Don't save eval sample because we do not checkpoint during eval. obj['eval'][evaluator.label] = dataset.state_dict(0, True) # pyright: ignore return obj
[docs] def get_model_state_dict(self) -> Dict[str, Any]: """Collect the state dict for the model. Returns: Dict[str, Any]: The state dict for the model. """ if version.parse(torch.__version__) > version.parse('2.2.9'): from torch.distributed.checkpoint.state_dict import StateDictOptions, get_model_state_dict if self.fsdp_state_dict_type not in [None, 'full', 'sharded']: raise NotImplementedError( textwrap.dedent(f'fsdp_state_dict_type={self.fsdp_state_dict_type} is not supported for ' f'torch version {{version.parse(torch.__version__)}} > 2.1.3. Please set ' 'fsdp_state_dict_type to None, "full", or "sharded".')) model_state_dict = get_model_state_dict( model=self.model, submodules=None, options=StateDictOptions( full_state_dict=self.fsdp_state_dict_type != 'sharded', cpu_offload=True, ), ) else: if self.fsdp_enabled and self.fsdp_state_dict_type is not None: with fsdp_state_dict_type_context(self.model, state_dict_type=self.fsdp_state_dict_type): model_state_dict = self.model.state_dict() else: model_state_dict = self.model.state_dict() # If model is DDP wrapped, do not save the `module.` prefix, as that is an implementation detail if self.is_model_ddp: torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(model_state_dict, 'module.') return model_state_dict
[docs] def get_optim_state_dict(self) -> Dict[str, Any]: """Collect the state dict for the optimizer. Returns: Dict[str, Any]: The state dict for the optimizer. """ if version.parse(torch.__version__) > version.parse('2.2.9'): from torch.distributed.checkpoint.state_dict import StateDictOptions, get_optimizer_state_dict if self.fsdp_state_dict_type not in [None, 'full', 'sharded']: raise NotImplementedError( textwrap.dedent(f'fsdp_state_dict_type={self.fsdp_state_dict_type} is not supported for ' f'torch version {{version.parse(torch.__version__)}} > 2.1.3. Please set ' 'fsdp_state_dict_type to None, "full", or "sharded".')) optimizer = ensure_tuple(self.optimizers)[0] optim_state_dict = get_optimizer_state_dict( model=self.model, optimizers=optimizer, submodules=None, options=StateDictOptions( full_state_dict=self.fsdp_state_dict_type != 'sharded', cpu_offload=True, ), ) return {type(optimizer).__qualname__: optim_state_dict} else: optimizer = ensure_tuple(self.optimizers)[0] if self.fsdp_enabled and self.fsdp_state_dict_type is not None: optim_state_dict = { type(optimizer).__qualname__: fsdp_get_optim_state_dict(self.model, optimizer, state_dict_type=self.fsdp_state_dict_type) } else: optim_state_dict = {type(optimizer).__qualname__: optimizer.state_dict()} return optim_state_dict
[docs] def state_dict(self) -> Dict[str, Any]: """Collect the state dicts of our serializable attributes. Returns: Dict[str, Any]: The state dict. """ state_dict = {} for attribute_name in self.serialized_attributes: attribute_value = getattr(self, attribute_name) if attribute_name == 'dataset_state': serialized_value = self._dataset_state_dict() elif attribute_name == 'model': serialized_value = self.get_model_state_dict() elif attribute_name == 'optimizers': serialized_value = self.get_optim_state_dict() elif attribute_name == 'algorithms': # Store as list to preserve order in which algorithms were applied serialized_value = [(type(obj).__qualname__, obj.state_dict()) for obj in ensure_tuple(attribute_value)] elif attribute_name in _STATE_DICT_SERIALIZED_ATTRIBUTES: serialized_value = {type(obj).__qualname__: obj.state_dict() for obj in ensure_tuple(attribute_value)} elif attribute_name == 'train_metrics': if self.save_metrics and attribute_value is not None: serialized_value = {} for k, v in attribute_value.items(): # No need to use __qualname__, we already know this corresponds to # a metric object when we deserialize. # Along with the rest of a Composer checkpoint, the state_dict() and _computed attributes of # a Torchmetrics object are enough information to recreate it upon serialization. We only serialize # the minimum metric information to maximize backwards compatibility --- old checkpoints # will continue to be compatible even if other Torchmetrics attributes have changed. # metric._computed stores the cached value of the previous metric computation # We need to serialize this because it cannot always be recomputed from the state dict. # See https://torchmetrics.readthedocs.io/en/stable/pages/implement.html#torchmetrics.Metric for more details v.persistent(mode=True) serialized_value[k] = { 'state_dict': v.state_dict(), '_computed': v._computed, } else: serialized_value = None elif attribute_name == 'eval_metrics': if self.save_metrics and attribute_value is not None: serialized_value = {} for eval_key, eval_metrics in attribute_value.items(): serialized_value[eval_key] = {} for k, v in eval_metrics.items(): v.persistent(mode=True) serialized_value[eval_key][k] = { 'state_dict': v.state_dict(), '_computed': v._computed, } else: serialized_value = None else: serialized_value = attribute_value if serialized_value is not None: state_dict[attribute_name] = serialized_value state_dict['integrations'] = self._get_integrations_state_dict() state_dict['metadata'] = self._get_state_metadata() return state_dict
def _apply_required_algorithms( self, state_dict: Dict[str, Any], logger: Logger, exclude_algorithms: Optional[List[str]] = None, algorithm_passes: Optional[List[AlgorithmPass]] = None, ): """Applies required algorithms which haven't been specified and aren't in the exclude list. Args: state_dict (Dict[str, Any]): State from checkpoint. logger (Logger): Logger to use. exclude_algorithms (List[str], optional): List of algorithm names to exclude. (default: ``None``) algorithm_passes (List[AlgorithmPass], optional): A list of algorithm passes to apply to autoloaded algorithms to sort them into the correct order. (default: ``None``) """ # Don't try to autoload on old checkpoints if not isinstance(state_dict['algorithms'], list): return import composer.algorithms as algorithms # type: ignore imports used in `eval(representation)` # Get repr of existing algorithms current_algos = {} for algo in self.algorithms: if algo.required_on_load(): if type(algo) not in current_algos: current_algos[type(algo)] = [] current_algos[type(algo)].append(algo.__repr__()) # Gather algorithms to apply missing_algos = set() missing_algo_names = [] missing_algo_reprs = [] for algo_name, serialized_value in state_dict['algorithms']: # Check if required algorithm if hasattr(algorithms, algo_name) and getattr(algorithms, algo_name).required_on_load(): # Check that algorithm is not explicitly excluded by user if exclude_algorithms is None or algo_name not in exclude_algorithms: try: algo = eval(f"algorithms.{serialized_value['repr']}") except: warnings.warn( textwrap.dedent( f"required_on_load algorithm {serialized_value['repr']} was enabled when training the " f'loaded checkpoint. Attempted to check its presence but recreating the algorithm ' "failed. This may be due to a change in the algorithm's API. If this required_on_load " 'algorithm is not properly specified, it may lead to unexpected behavior, including ' 'failing to load weights for some layers.')) continue # Raise warning if we are unable to safely autoapply if type(algo) in current_algos and not serialized_value['repr'] in current_algos[type(algo)]: warnings.warn( textwrap.dedent( f"required_on_load algorithm {serialized_value['repr']} was enabled when training the " f"loaded checkpoint but is now specified in the following forms: {', '.join(current_algos[type(algo)])}." 'Potential parameter discrepancies for this required_on_load algorithm may lead to ' 'unexpected behavior, including failing to load weights for some layers.')) # Otherwise, queue algorithm to be autoapplied elif type(algo) not in current_algos: missing_algos.add(algo) missing_algo_names.append(algo_name) missing_algo_reprs.append(serialized_value['repr']) self.algorithms.append(algo) # Reorder algorithms based on algorithm_passes from engine algo_list = self.algorithms if algorithm_passes is not None: for algo_pass in algorithm_passes: algo_list = algo_pass(algo_list, Event.INIT) # Raise ValueError if algorithm_passes order any checkpoint algorithm before an already # applied user specified algorithm encountered_ckpt_algo = False for algo in algo_list: if algo in missing_algos: encountered_ckpt_algo = True elif encountered_ckpt_algo: raise ValueError( textwrap.dedent('The following algorithms were enabled when training this checkpoint ' f'and are required to successfully load it: {missing_algo_reprs}. ' 'Attempted to autocreate and apply required algorithms, but at least one ' 'of the loaded algorithms was ordered before a user specified algorithm ' 'which has already been applied, preventing automatic application of ' 'algorithms. If you wish to use pretrained weights and reinitialize ' 'layers which have undergone surgery, the following algorithms may be ' 'excluded using `load_exclude_algorithms`, e.g. ' f'`load_exclude_algorithms=[{missing_algo_names}]`.')) try: for algo in missing_algos: # TODO: use compiled algorithm order if algo.match(Event.INIT, self): algo.apply(Event.INIT, self, logger) warnings.warn( textwrap.dedent( f'Automatically adding required_on_load algorithm {repr(algo)} to trainer, which was enabled ' 'when training the loaded checkpoint. If you wish to use pretrained weights and ignore ' f'required_on_load algorithms, which may result in some weights failing to load, include {type(algo).__qualname__} ' f"in `load_exclude_algorithms`, e.g. `load_exclude_algorithms=['{type(algo).__qualname__}']`.")) except Exception as e: raise ValueError( textwrap.dedent( 'The following algorithms were enabled when training this checkpoint ' f'and are required to successfully load it: {missing_algo_reprs}. ' 'Attempted to autocreate and apply required algorithms but an exception was ' 'encountered. If you wish to use pretrained weights and reinitialize layers which ' 'have undergone surgery, the following algorithms may be excluded using ' f'`load_exclude_algorithms`, e.g. `load_exclude_algorithms=[{missing_algo_names}]`.')) from e def _legacy_load_optim_state(self, state_dict: Dict[str, Any]): """Load the optimizer state. Args: state_dict (Dict[str, Any]): The state to load. """ serialized_value = state_dict['optimizers'] for optimizer in ensure_tuple(self.optimizers): # Broadcast compatibility check as monolith rank 0 only loads won't have optimizer on all ranks skip_optimizer_load = 1 if serialized_value is not None and type( optimizer).__qualname__ not in serialized_value else 0 skip_optimizer_load_tensor = self.device.tensor_to_device( torch.tensor([skip_optimizer_load], dtype=torch.uint8)) dist.all_reduce(skip_optimizer_load_tensor, reduce_operation='MAX') if skip_optimizer_load_tensor.item() == 1: warnings.warn( f'{type(optimizer).__qualname__} is not in the state_dict. Its state will not be restored.', category=UserWarning) continue optim_state_dict = serialized_value[type(optimizer).__qualname__] if serialized_value is not None else None if self.fsdp_enabled: assert self.fsdp_state_dict_type is not None # pyright log.debug(f'Loading FSDP optimizer with fsdp_state_dict_type={self.fsdp_state_dict_type}') # Loading FSDP monolith on rank 0 only requires FSDP.scatter_full_optim_state_dict # as the context manager does not seem to pass rank0_only=True for the optimizer config if self.load_fsdp_monolith_rank0_only: optim_state_dict = _legacy_optim_state_dict_to_load( optim_state_dict=optim_state_dict, model=self.model, optim=optimizer, state_dict_type=self.fsdp_state_dict_type, ) else: assert optim_state_dict is not None with fsdp_state_dict_type_context(module=self.model, state_dict_type=self.fsdp_state_dict_type): optim_state_dict = FSDP.optim_state_dict_to_load( # type: ignore optim_state_dict=optim_state_dict, model=self.model, optim=optimizer) assert optim_state_dict is not None optimizer.load_state_dict(optim_state_dict) else: assert optim_state_dict is not None log.debug(f'Loading optimizer state dict') optimizer.load_state_dict(optim_state_dict) def _load_dataset_state(self, obj: Dict[str, Any]) -> None: """Load the dataset state. Args: obj (Dict[str, Any]): The state to load. """ self.dataset_state = obj dataset = self._dataset_of(self.train_dataloader) if hasattr(dataset, 'load_state_dict'): dataset.load_state_dict(obj['train']) # pyright: ignore obj['train'] = None self.dataset_resumption['train'] = True for evaluator in self.evaluators: dataset = self._dataset_of(evaluator) if hasattr(dataset, 'load_state_dict') and evaluator.label in obj['eval']: dataset.load_state_dict(obj['eval'][evaluator.label]) # pyright: ignore del obj['eval'][evaluator.label] if 'eval' not in self.dataset_resumption: self.dataset_resumption['eval'] = {} # Note: We currently disable setting dataset_resumption for eval datasets, # which means they have one sample fetched in _spin_dataloaders before training # starts. This avoids "CUDA error: initialization error" -- its not clear why. # self.dataset_resumption['eval'][evaluator.label] = True
[docs] def load_model_state( self, state_dict: Dict[str, Any], logger: Logger, strict: bool, exclude_algorithms: Optional[List[str]] = None, algorithm_passes: Optional[List[AlgorithmPass]] = None, ): """Loads the model's state from a ``state_dict``. Args: state_dict (Dict[str, Any]): The state dict, generated from a previous call to :meth:`state_dict`. logger (Logger): The logger. strict (bool): Whether the keys (i.e., model parameter names) in the model state dict should perfectly match the keys in the model instance. exclude_algorithms (List[str], optional): List of algorithm names to exclude from autoloading. (default: ``None``) algorithm_passes (List[AlgorithmPass], optional): A list of algorithm passes to apply to autoloaded algorithms to sort them into the correct order. (default: ``None``) """ if 'algorithms' in state_dict: self._apply_required_algorithms(state_dict, logger, exclude_algorithms, algorithm_passes) if state_dict.get('is_model_ddp', False) and not self.is_model_ddp: # This check is for backwards compatibility, as pre-v0.6.0 checkpoints serialized the state # with the `module.` prefix torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(state_dict['model'], 'module.') # For FSDP monolith checkpoints, the model does not exist on ranks > 0 model_on_rank = state_dict['model'] is not None if model_on_rank: if version.parse(torch.__version__) > version.parse('2.2.9'): from torch.distributed.checkpoint.state_dict import StateDictOptions, set_model_state_dict set_model_state_dict( model=self.model, model_state_dict=state_dict['model'], options=StateDictOptions(strict=strict, cpu_offload=True), ) else: missing_keys, unexpected_keys = [], [] try: # Load model if it exists if self.fsdp_enabled and self.fsdp_state_dict_type is not None and not self.load_fsdp_monolith_rank0_only: log.debug( f'Loading model state dict with strict={strict} and FSDP state_dict_type={self.fsdp_state_dict_type}' ) with fsdp_state_dict_type_context(self.model, state_dict_type=self.fsdp_state_dict_type): missing_keys, unexpected_keys = self.model.load_state_dict(state_dict['model'], strict=strict) else: log.debug(f'Loading model state dict with strict={strict}') missing_keys, unexpected_keys = self.model.load_state_dict(state_dict['model'], strict=strict) except RuntimeError as e: if 'Missing key(s) in state_dict' in str(e) or 'Unexpected key(s) in state_dict' in str(e): raise RuntimeError( textwrap.dedent( 'Failed to load checkpoint due to missing or unexpected keys in state_dict. ' 'This is likely due to a change in the model architecture. If this is intentional, ' 'you can set load_strict_model_weights=False in the Trainer.')) from e else: raise e if len(missing_keys) > 0: log.warning(f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}") if len(unexpected_keys) > 0: log.warning(f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}") # If loading FSDP monolith checkpoint on rank 0 only, the model must be wrapped after loading if self.load_fsdp_monolith_rank0_only: assert self.fsdp_config is not None log.info('Wrapping model with FSDP after loading model_state.') from composer.trainer.dist_strategy import prepare_fsdp_module with reproducibility.seed_context(self.rank_zero_seed): prepare_fsdp_module(self.model, self.optimizers, self.fsdp_config, self.precision, self.device, self.auto_microbatching) log.debug('Finished wrapping model with FSDP.')
[docs] def load_optim_state(self, state_dict: Dict[str, Any], strict: bool = True): """Load the optimizer state. Args: state_dict (Dict[str, Any]): The state to load. strict (bool): Whether the keys (i.e., optimizer parameter names) in the optimizer state dict should perfectly match the keys in the optimizer instance. """ if version.parse(torch.__version__) > version.parse('2.2.9'): from torch.distributed.checkpoint.state_dict import StateDictOptions, set_optimizer_state_dict optimizer = self.optimizers[0] set_optimizer_state_dict( model=self.model, optimizers=optimizer, optim_state_dict=state_dict['optimizers'].get(type(optimizer).__qualname__, {}), options=StateDictOptions(strict=strict, cpu_offload=True), ) else: serialized_value = state_dict['optimizers'] for optimizer in ensure_tuple(self.optimizers): # Broadcast compatibility check as monolith rank 0 only loads won't have optimizer on all ranks skip_optimizer_load = 1 if serialized_value is not None and type( optimizer).__qualname__ not in serialized_value else 0 skip_optimizer_load_tensor = self.device.tensor_to_device( torch.tensor([skip_optimizer_load], dtype=torch.uint8)) dist.all_reduce(skip_optimizer_load_tensor, reduce_operation='MAX') if skip_optimizer_load_tensor.item() == 1: warnings.warn( f'{type(optimizer).__qualname__} is not in the state_dict. Its state will not be restored.', category=UserWarning) continue optim_state_dict = serialized_value[type( optimizer).__qualname__] if serialized_value is not None else None if self.fsdp_enabled: assert self.fsdp_state_dict_type is not None # pyright log.debug(f'Loading FSDP optimizer with fsdp_state_dict_type={self.fsdp_state_dict_type}') # Loading FSDP monolith on rank 0 only requires FSDP.scatter_full_optim_state_dict # as the context manager does not seem to pass rank0_only=True for the optimizer config if self.load_fsdp_monolith_rank0_only: optim_state_dict = _legacy_optim_state_dict_to_load( optim_state_dict=optim_state_dict, model=self.model, optim=optimizer, state_dict_type=self.fsdp_state_dict_type, ) else: assert optim_state_dict is not None with fsdp_state_dict_type_context(module=self.model, state_dict_type=self.fsdp_state_dict_type): optim_state_dict = FSDP.optim_state_dict_to_load( # type: ignore optim_state_dict=optim_state_dict, model=self.model, optim=optimizer) assert optim_state_dict is not None optimizer.load_state_dict(optim_state_dict) else: assert optim_state_dict is not None log.debug(f'Loading optimizer state dict') optimizer.load_state_dict(optim_state_dict)
[docs] def load_state_dict( self, state: Dict[str, Any], logger: Logger, strict: bool = False, exclude_algorithms: Optional[List[str]] = None, algorithm_passes: Optional[List[AlgorithmPass]] = None, ): """Loads the state. Args: state (Dict[str, Any]): object returned from call to :meth:`state_dict`. logger (Logger): The logger. strict (bool): whether the keys in the ``state["model"]`` should perfectly match the keys in the ``self.model``. Defaults to False. exclude_algorithms (List[str], optional): List of algorithm names to exclude from autoloading. (default: ``None``) algorithm_passes (List[AlgorithmPass], optional): A list of algorithm passes to apply to autoloaded algorithms to sort them into the correct order. (default: ``None``) """ state = _ensure_backwards_compatible_checkpointing(state) # Call load_model_state first since it applies required algorithms if 'model' in state: self.load_model_state( state, logger, strict=strict, exclude_algorithms=exclude_algorithms, algorithm_passes=algorithm_passes, ) for attribute_name in sorted(state.keys()): # Sort so all ranks load in the same order serialized_value = state[attribute_name] # Skip removed attributes as well as algorithms and model, which was already loaded if attribute_name not in self.serialized_attributes or attribute_name == 'model': continue # Integrations are extra information about other libraries (e.g. huggingface) and not attributes to be loaded here if attribute_name == 'integrations': continue # Skip metadata, which is not an attribute on State if attribute_name == 'metadata': continue log.debug(f'Loading {attribute_name} into state.') # Restructure algorithms serialized_value from list to dict if attribute_name == 'algorithms' and isinstance(serialized_value, list): serialized_value = dict(serialized_value) if attribute_name == 'dataset_state': self._load_dataset_state(serialized_value) elif attribute_name == 'optimizers': self.load_optim_state(state) elif attribute_name == 'train_metrics': # Get current metrics object and populate each metric present # in serialization with serialized data via load_state_dict() state_field_value = getattr(self, attribute_name) for metric_name in state_field_value.keys(): if metric_name not in serialized_value: continue # Increment _update_count so it is non-zero, preventing Torchmetrics from warning us when we call metric.compute() state_field_value[metric_name]._update_count += 1 if isinstance(serialized_value[metric_name], Metric): # For checkpoints saved using Composer <= 0.13.5 serialized_value[metric_name].persistent(mode=True) # Add new attr in torch2 serialized_value[metric_name]._state_dict_pre_hooks = OrderedDict() metric_state_dict = serialized_value[metric_name].state_dict() metric_computed_field = serialized_value[metric_name]._computed elif isinstance(serialized_value[metric_name], dict): # The metric tensor is saved as a numpy array, so that FSDP doesn't mistake it for a tensor to be sharded upon load. # So we have to cast it back to a torch tensor. # For checkpoints saved using Composer >= 0.14 metric_state_dict = serialized_value[metric_name]['state_dict'] metric_computed_field = serialized_value[metric_name]['_computed'] # Backwards compatible loading of torchmetrics from 0.16.0 which casted metric tensors to numpy if isinstance(metric_computed_field, np.ndarray): metric_computed_field = torch.from_numpy(metric_computed_field) metric_computed_device = serialized_value[metric_name].get('_computed_device', None) if metric_computed_device is not None: metric_computed_field = metric_computed_field.to(metric_computed_device) else: raise ValueError( 'Error while loading train metric. Train metric from serialization is neither a Torchmetrics Metric object nor a dictionary.' ) missing_keys, unexpected_keys = state_field_value[metric_name].load_state_dict(metric_state_dict, strict=False) state_field_value[metric_name]._computed = metric_computed_field state_field_value[metric_name].persistent(mode=True) self.device.module_to_device(state_field_value[metric_name]) if len(missing_keys) > 0: warnings.warn( f"While loading train metric: {metric_name}, missing these keys: {', '.join(missing_keys)}" ) if len(unexpected_keys) > 0: warnings.warn( f"While loading train metric: {metric_name}, found these unexpected keys: {', '.join(unexpected_keys)}" ) elif attribute_name == 'eval_metrics': # Get current metrics object and populate each metric present # in serialization with serialized data via load_state_dict() state_field_value = getattr(self, attribute_name) for eval_key in state_field_value.keys(): if eval_key not in serialized_value: continue for metric_name in state_field_value[eval_key].keys(): if metric_name not in serialized_value[eval_key]: continue # Increment _update_count so it is non-zero, preventing Torchmetrics from warning us when we call metric.compute() state_field_value[eval_key][metric_name]._update_count += 1 if isinstance(serialized_value[eval_key][metric_name], Metric): # For checkpoints saved using Composer <= 0.13.5 serialized_value[eval_key][metric_name].persistent(mode=True) # Add new attr in torch2 serialized_value[eval_key][metric_name]._state_dict_pre_hooks = OrderedDict() eval_metric_state_dict = serialized_value[eval_key][metric_name].state_dict() eval_metric_computed_field = serialized_value[eval_key][metric_name]._computed elif isinstance(serialized_value[eval_key][metric_name], dict): # The metric tensor is saved as a numpy array, so that FSDP doesn't mistake it for a tensor to be sharded upon load. # So we have to cast it back to a torch tensor. # For checkpoints saved using Composer >= 0.14 eval_metric_state_dict = serialized_value[eval_key][metric_name]['state_dict'] eval_metric_computed_field = serialized_value[eval_key][metric_name]['_computed'] # Backwards compatible loading of torchmetrics from 0.16.0 which casted metric tensors to numpy if isinstance(eval_metric_computed_field, np.ndarray): eval_metric_computed_field = torch.from_numpy(eval_metric_computed_field) eval_metric_computed_device = serialized_value[eval_key][metric_name].get( '_computed_device', None) if eval_metric_computed_device is not None: eval_metric_computed_field = eval_metric_computed_field.to( eval_metric_computed_device) else: raise ValueError( 'Error while loading evaluation metric. Evaluation metric from serialization is neither a Torchmetrics Metric object nor a dictionary.' ) missing_keys, unexpected_keys = state_field_value[eval_key][metric_name].load_state_dict( eval_metric_state_dict, strict=False) state_field_value[eval_key][metric_name]._computed = eval_metric_computed_field state_field_value[eval_key][metric_name].persistent(mode=True) self.device.module_to_device(state_field_value[eval_key][metric_name]) if len(missing_keys) > 0: warnings.warn( f"While loading evaluation metric: {metric_name} for eval dataloader {eval_key}, missing these keys: {', '.join(missing_keys)}" ) if len(unexpected_keys) > 0: warnings.warn( f"While loading evaluation metric: {metric_name} for eval dataloader {eval_key}, found these unexpected keys: {', '.join(unexpected_keys)}" ) elif attribute_name in _STATE_DICT_SERIALIZED_ATTRIBUTES: state_field_value = getattr(self, attribute_name) for target in ensure_tuple(state_field_value): if type(target).__qualname__ not in serialized_value: warnings.warn( f'{type(target).__qualname__} is not in the state_dict. Its state will not be restored.', category=UserWarning) continue source = serialized_value[type(target).__qualname__] target.load_state_dict(source) else: # direct serialization try: setattr(self, attribute_name, serialized_value) except AttributeError: # ignore AttributeError for properties that have getters but not setters. pass
@property def dataloader(self): """The active dataloader.""" return self._dataloader @property def dataloader_label(self): """The dataloader label for the active dataloader. By default, the training dataloader is called ``'train'``. The evaluator dataloader is called ``'eval'``, or when multiple evaluators are used, the name of the evaluator. However, the dataloader label can be explicitly specified in :meth:`.Trainer.fit` and :meth:`.Trainer.eval`. Returns: Optional[str]: The dataloader label, or None if no dataloader is set. """ return self._dataloader_label
[docs] def set_dataloader( self, dataloader: Optional[Iterable] = None, dataloader_label: Optional[str] = None, dataloader_len: Union[int, Time[int]] = -1, ): """Update the active dataloader and dataloader label. Args: dataloader (Iterable, optional): The dataloader. Defaults to None. dataloader_label (str, optional): The dataloader label. Must be ``None`` if and only if ``dataloader`` is None. Defaults to None. dataloader_len (int, int): The number of batches per dataloader iteration (e.g. epoch), as used by the trainer. Set to ``-1`` to iterate over the entire dataset. (Default: ``-1``.) """ if dataloader is None: dataloader_label = None else: if dataloader_label is None: raise ValueError('If the `dataloader` is specified, then `dataloader_label` must not be None.') self._dataloader = dataloader self._dataloader_label = dataloader_label if dataloader is not None: self.dataloader_len = dataloader_len # setting it to -1 will do a failsafe read of len(dataloader) else: self._dataloader_len = None
@property def dataloader_len(self): """The number of batches per dataloader iteration (e.g. epoch), as used by the trainer. .. note:: If not explicitly specified, this value is an approximation, as it depends on ``len(self.dataloader)``. See the :doc:`PyTorch DataLoader Documentation <torch:data>` for more information. Returns: Optional[Time[int]]: The number of batches per dataloader iteration (e.g. epoch), or None if no dataloader is defined or if the dataloader has an unknown length (e.g. streaming dataloaders). """ return self._dataloader_len @dataloader_len.setter def dataloader_len(self, num_batches: Union[int, Time[int]]): if isinstance(num_batches, int): num_batches = Time(num_batches, TimeUnit.BATCH) if self._dataloader is None: raise RuntimeError('`State.dataloader_len` cannot be set if the dataloader is not defined.') try: if isinstance(self._dataloader, collections.abc.Sized): dataloader_len = len(self._dataloader) else: dataloader_len = None except (TypeError, NotImplementedError): dataloader_len = None if dataloader_len is not None and num_batches >= 0 and int(num_batches) > dataloader_len: warnings.warn((f'DataloaderNumBatchesWarning: The dataloader_len ({int(num_batches)}) ' f'is greater than the length (i.e. number of batches) of the dataloader, which is ' f'{dataloader_len}. State.dataloader_len is thus being set to {dataloader_len}.')) self._dataloader_len = Time(dataloader_len, TimeUnit.BATCH) return if num_batches < 0: if dataloader_len is not None: # len(dataloader) is an approximation -- see https://pytorch.org/docs/stable/data.html. # However, in the worst case where additional last batches are dropped, this calculation should be # an over-estimate, leading to the entire dataloader still being iterated over. self._dataloader_len = Time(dataloader_len, TimeUnit.BATCH) else: # The dataloader length is unknown. self._dataloader_len = None return self._dataloader_len = num_batches @property def precision(self): """The numerical precision to use for training. See :class:`~.Precision` for the supported precisions. """ return self._precision @precision.setter def precision(self, precision: Union[str, Precision]): self._precision = Precision(precision) @property def precision_config(self): """The config for FP8 scaling strategy. See parameters for `DelayedScaling <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html?highlight=delayedscaling#transformer_engine.common.recipe.DelayedScaling>`_. """ return self._precision_config @property def is_model_ddp(self): """Whether :attr:`model` is an instance of a :class:`.DistributedDataParallel`.""" return isinstance(self.model, DistributedDataParallel) @property def deepspeed_model(self) -> deepspeed.DeepSpeedEngine: """Cast :attr:`model` to :class:`~deepspeed.DeepSpeedEngine`.""" if is_model_deepspeed(self.model): return cast('deepspeed.DeepSpeedEngine', self.model) raise TypeError('state.model is not a DeepSpeed model')