# 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 functools import inspect import os from collections import OrderedDict from contextlib import contextmanager from functools import partial from typing import Any, Callable, Dict, Generator, Iterable, Optional, Tuple, Type, Union from lightning_utilities.core.inheritance import get_all_subclasses from torch.utils.data import BatchSampler, DataLoader, Dataset, IterableDataset, Sampler from lightning_fabric.utilities.enums import LightningEnum from lightning_fabric.utilities.exceptions import MisconfigurationException from lightning_fabric.utilities.rank_zero import rank_zero_warn from lightning_fabric.utilities.seed import pl_worker_init_function class _WrapAttrTag(LightningEnum): SET = "set" DEL = "del" def __call__(self, *args: Any) -> None: fn: Union[Callable[[object, str], None], Callable[[object, str, Any], None]] if self == self.SET: fn = setattr else: fn = delattr return fn(*args) def has_iterable_dataset(dataloader: DataLoader) -> bool: return hasattr(dataloader, "dataset") and isinstance(dataloader.dataset, IterableDataset) def has_len(dataloader: Union[DataLoader, Iterable, Dataset]) -> bool: """Checks if a given Dataloader has ``__len__`` method implemented i.e. if it is a finite dataloader or infinite dataloader.""" try: # try getting the length if len(dataloader) == 0: # type: ignore [arg-type] rank_zero_warn( f"`{dataloader.__class__.__name__}` returned 0 length. Please make sure this was your intention." ) has_len = True except (TypeError, NotImplementedError): has_len = False if has_len and isinstance(dataloader, DataLoader) and has_iterable_dataset(dataloader): rank_zero_warn( "Your `IterableDataset` has `__len__` defined." " In combination with multi-process data loading (when num_workers > 1)," " `__len__` could be inaccurate if each worker is not configured independently" " to avoid having duplicate data." ) return has_len def _update_dataloader(dataloader: DataLoader, sampler: Union[Sampler, Iterable]) -> DataLoader: dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, sampler) dataloader = _reinstantiate_wrapped_cls(dataloader, *dl_args, **dl_kwargs) return dataloader def _get_dataloader_init_args_and_kwargs( dataloader: DataLoader, sampler: Union[Sampler, Iterable], disallow_batch_sampler: bool = False, ) -> Tuple[Tuple[Any], Dict[str, Any]]: if not isinstance(dataloader, DataLoader): raise ValueError(f"The dataloader {dataloader} needs to subclass `torch.utils.data.DataLoader`") was_wrapped = hasattr(dataloader, "__pl_saved_args") if was_wrapped: dl_args = dataloader.__pl_saved_args dl_kwargs = dataloader.__pl_saved_kwargs arg_names = dataloader.__pl_saved_arg_names original_dataset = dataloader.__dataset # we have this saved from _wrap_init else: # get the dataloader instance attributes attrs = {k: v for k, v in vars(dataloader).items() if not k.startswith("_")} # We cannot be 100% sure the class sets dataset argument. Let's set it to None to be safe # and hope we can get it from the instance attributes original_dataset = None # not part of `vars` attrs["multiprocessing_context"] = dataloader.multiprocessing_context arg_names = () # get the dataloader instance `__init__` parameters params = dict(inspect.signature(dataloader.__init__).parameters) # type: ignore[misc] has_variadic_kwargs = any(p.kind is p.VAR_KEYWORD for p in params.values()) if has_variadic_kwargs: # if the signature takes **kwargs, assume they will be passed down with `super().__init__(**kwargs)` if was_wrapped: # if the dataloader was wrapped in a hook, only take arguments with default values # and assume user passes their kwargs correctly params.update( {k: v for k, v in inspect.signature(DataLoader.__init__).parameters.items() if v.default is not v.empty} ) else: params.update(inspect.signature(DataLoader.__init__).parameters) params.pop("self", None) if not was_wrapped: # keep only the params whose default is different to the current attr value non_defaults = {name for name, p in params.items() if name in attrs and p.default is not attrs[name]} # add `dataset` as it might have been replaced with `*args` non_defaults.add("dataset") # kwargs to re-construct the dataloader dl_kwargs = {k: v for k, v in attrs.items() if k in non_defaults} dl_args = () dataset = dl_kwargs.get("dataset", original_dataset) if isinstance(dataset, IterableDataset): dl_kwargs["batch_sampler"] = None dl_kwargs["sampler"] = None else: dl_kwargs.update(_dataloader_init_kwargs_resolve_sampler(dataloader, sampler, disallow_batch_sampler)) required_args = { p.name for p in params.values() if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) and p.default is p.empty and p.name not in dl_kwargs and p.name not in arg_names } # the dataloader has required args which we could not extract from the existing attributes if required_args: sorted_required_args = sorted(required_args) dataloader_cls_name = dataloader.__class__.__name__ missing_args_message = ", ".join(f"`self.{arg_name}`" for arg_name in sorted_required_args) raise MisconfigurationException( f"Trying to inject custom `Sampler` into the `{dataloader_cls_name}` instance. " "This would fail as some of the `__init__` arguments are not available as instance attributes. " f"The missing attributes are {sorted_required_args}. If you instantiate your `{dataloader_cls_name}` " "inside a `*_dataloader` hook of your module, we will do this for you." f" Otherwise, define {missing_args_message} inside your `__init__`." ) if not has_variadic_kwargs: # the dataloader signature does not allow keyword arguments that need to be passed missing_kwargs = (set(dl_kwargs) | set(arg_names)) - params.keys() if missing_kwargs: sorted_missing_kwargs = sorted(missing_kwargs) dataloader_cls_name = dataloader.__class__.__name__ raise TypeError( f"Trying to inject parameters into the `{dataloader_cls_name}` instance. " "This would fail as it doesn't expose all its attributes in the `__init__` signature. " f"The missing arguments are {sorted_missing_kwargs}. HINT: If you wrote the `{dataloader_cls_name}` " "class, add the `__init__` arguments or allow passing `**kwargs`" ) return dl_args, dl_kwargs def _dataloader_init_kwargs_resolve_sampler( dataloader: DataLoader, sampler: Union[Sampler, Iterable], disallow_batch_sampler: bool = False, ) -> Dict[str, Any]: """This function is used to handle the sampler, batch_sampler arguments associated within a DataLoader for its re-instantiation.""" batch_sampler = getattr(dataloader, "batch_sampler") if batch_sampler is not None: if disallow_batch_sampler: # Check that we don't have a PyTorch default batch sampler that was instantiated in DataLoader __init__ if not ( type(batch_sampler) is BatchSampler and batch_sampler.sampler == sampler and dataloader.batch_size == batch_sampler.batch_size ): raise MisconfigurationException( "It is not possible to have a batch sampler in your dataloader, " "when running on multiple IPU devices." ) elif type(batch_sampler) is not BatchSampler: batch_sampler_cls = type(batch_sampler) if hasattr(batch_sampler, "__pl_saved_args"): args = batch_sampler.__pl_saved_args kwargs = batch_sampler.__pl_saved_kwargs default_kwargs = batch_sampler.__pl_saved_default_kwargs arg_names = batch_sampler.__pl_saved_arg_names success, args, kwargs = _replace_value_in_saved_args( "sampler", sampler, args, kwargs, default_kwargs, arg_names ) if not success: raise TypeError( "Trying to inject a modified sampler into the batch sampler; however, it seems the class " f"`{batch_sampler_cls.__qualname__}` does not have an argument called `sampler.` To mitigate " "this, expose an argument `sampler` in the `__init__` method of your custom class." ) batch_sampler = _reinstantiate_wrapped_cls(batch_sampler, *args, **kwargs) else: try: batch_sampler = batch_sampler_cls( sampler, batch_size=batch_sampler.batch_size, drop_last=batch_sampler.drop_last, ) except TypeError as e: import re match = re.match(r".*__init__\(\) (got multiple values)|(missing \d required)", str(e)) if not match: # an unexpected `TypeError`, continue failure raise # There could either be too few or too many arguments. Customizing the message based on this doesn't # make much sense since our MisconfigurationException is going to be raised from the original one. raise TypeError( "We tried to re-instantiate your custom batch sampler and failed. " "To mitigate this, either follow the API of `BatchSampler` or instantiate " "your custom batch sampler inside `*_dataloader` hooks of your module." ) from e return { "sampler": None, "shuffle": False, "batch_sampler": batch_sampler, "batch_size": 1, "drop_last": False, } return {"sampler": sampler, "shuffle": False, "batch_sampler": None} def _auto_add_worker_init_fn(dataloader: DataLoader, rank: int) -> None: if int(os.environ.get("PL_SEED_WORKERS", 0)) and dataloader.worker_init_fn is None: dataloader.worker_init_fn = partial(pl_worker_init_function, rank=rank) def _reinstantiate_wrapped_cls(orig_object: Any, *args: Any, explicit_cls: Optional[Type] = None, **kwargs: Any) -> Any: constructor = type(orig_object) if explicit_cls is None else explicit_cls try: result = constructor(*args, **kwargs) except TypeError as e: # improve exception message due to an incorrect implementation of the `DataLoader` where multiple subclass # `__init__` arguments map to one `DataLoader.__init__` argument import re match = re.match(r".*__init__\(\) got multiple values .* '(\w+)'", str(e)) if not match: # an unexpected `TypeError`, continue failure raise argument = match.groups()[0] message = ( f"The {constructor.__name__} implementation has an error where more than one `__init__` argument" f" can be passed to its parent's `{argument}=...` `__init__` argument. This is likely caused by allowing" f" passing both a custom argument that will map to the `{argument}` argument as well as `**kwargs`." f" `kwargs` should be filtered to make sure they don't contain the `{argument}` key." " This argument was automatically passed to your object by PyTorch Lightning." ) raise MisconfigurationException(message) from e attrs_record = getattr(orig_object, "__pl_attrs_record", list()) for args, fn in attrs_record: fn(result, *args) return result def _wrap_init_method(init: Callable, store_explicit_arg: Optional[str] = None) -> Callable: """Wraps the ``__init__`` method of classes (currently :class:`~torch.utils.data.DataLoader` and :class:`~torch.utils.data.BatchSampler`) in order to enable re-instantiation of custom subclasses.""" @functools.wraps(init) def wrapper(obj: Any, *args: Any, **kwargs: Any) -> None: # We need to inspect `init`, as inspecting `obj.__init__` # can lead to inspecting the wrong function with multiple inheritance old_inside_init = getattr(obj, "__pl_inside_init", False) object.__setattr__(obj, "__pl_inside_init", True) params = inspect.signature(init).parameters parameters_defaults = OrderedDict( (param.name, param.default) for param in params.values() if param.name != "self" and param.kind not in (param.VAR_POSITIONAL, param.VAR_KEYWORD) ) param_names = tuple(parameters_defaults)[: len(args)] default_kwargs = { name: value for name, value in parameters_defaults.items() if name not in kwargs and name not in param_names and value != inspect.Parameter.empty } if not hasattr(obj, "__pl_saved_args"): object.__setattr__(obj, "__pl_saved_args", args) object.__setattr__(obj, "__pl_saved_kwargs", kwargs) object.__setattr__(obj, "__pl_saved_arg_names", param_names) object.__setattr__(obj, "__pl_saved_default_kwargs", default_kwargs) # We want to use the latest possible value for explicit argument (i.e. ideally what gets passed to base class) # so that we can be sure, that it will not get changed anymore. # That is why we are setting this in every `__init__` if store_explicit_arg is not None: if store_explicit_arg in param_names: object.__setattr__(obj, f"__{store_explicit_arg}", args[param_names.index(store_explicit_arg)]) elif store_explicit_arg in kwargs: object.__setattr__(obj, f"__{store_explicit_arg}", kwargs[store_explicit_arg]) init(obj, *args, **kwargs) object.__setattr__(obj, "__pl_inside_init", old_inside_init) return wrapper def _wrap_attr_method(method: Callable, tag: _WrapAttrTag) -> Callable: """Wraps the ``__setattr__`` or ``__delattr__`` method of classes (currently :class:`~torch.utils.data.DataLoader` and :class:`~torch.utils.data.BatchSampler`) in order to enable re- instantiation of custom subclasses.""" @functools.wraps(method) def wrapper(obj: Any, *args: Any) -> None: # First, let's find out if we're the first in inheritance chain calling the patched method. name, *_ = args prev_call_name, prev_call_method = getattr(obj, "__pl_current_call", (None, "method")) first_call = not (prev_call_name == name and prev_call_method == tag) # Then mark the current called method object.__setattr__(obj, "__pl_current_call", (name, tag)) # call original method method(obj, *args) if first_call and not getattr(obj, "__pl_inside_init", True): # and save the value it was called with to the internal list, # if we're outside of __init__ and the original call did not fail and we're the first call attrs_record = getattr(obj, "__pl_attrs_record", list()) attrs_record.append((args, tag)) object.__setattr__(obj, "__pl_attrs_record", attrs_record) object.__setattr__(obj, "__pl_current_call", (prev_call_name, prev_call_method)) return wrapper @contextmanager def _replace_dunder_methods(base_cls: Type, store_explicit_arg: Optional[str] = None) -> Generator[None, None, None]: """This context manager is used to add support for re-instantiation of custom (subclasses) of `base_cls`. It patches the ``__init__``, ``__setattr__`` and ``__delattr__`` methods. """ classes = get_all_subclasses(base_cls) | {base_cls} for cls in classes: # Check that __init__ belongs to the class # https://stackoverflow.com/a/5253424 if "__init__" in cls.__dict__: cls.__old__init__ = cls.__init__ cls.__init__ = _wrap_init_method(cls.__init__, store_explicit_arg) # we want at least one setattr/delattr in the chain to be patched and it can happen, that none of the subclasses # implement `__setattr__`/`__delattr__`. Therefore, we are always patching the `base_cls` for patch_fn_name, tag in (("__setattr__", _WrapAttrTag.SET), ("__delattr__", _WrapAttrTag.DEL)): if patch_fn_name in cls.__dict__ or cls is base_cls: saved_name = f"__old{patch_fn_name}" setattr(cls, saved_name, getattr(cls, patch_fn_name)) setattr(cls, patch_fn_name, _wrap_attr_method(getattr(cls, patch_fn_name), tag)) yield for cls in classes: for patched_name in ("__setattr__", "__delattr__", "__init__"): # Check that __old__{init,setattr,delattr} belongs to the class # https://stackoverflow.com/a/5253424 if f"__old{patched_name}" in cls.__dict__: setattr(cls, patched_name, getattr(cls, f"__old{patched_name}")) delattr(cls, f"__old{patched_name}") def _replace_value_in_saved_args( replace_key: str, replace_value: Any, args: Tuple[Any, ...], kwargs: Dict[str, Any], default_kwargs: Dict[str, Any], arg_names: Tuple[str, ...], ) -> Tuple[bool, Tuple[Any, ...], Dict[str, Any]]: """Tries to replace an argument value in a saved list of args and kwargs. Returns a tuple indicating success of the operation and modified saved args and kwargs """ if replace_key in arg_names: replace_index = arg_names.index(replace_key) args = args[:replace_index] + (replace_value,) + args[replace_index + 1 :] return True, args, kwargs elif replace_key in kwargs or replace_key in default_kwargs: kwargs[replace_key] = replace_value return True, args, kwargs return False, args, kwargs