From 92626880f0ce4f6d5dc9076d7ea00165176318d4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 9 Sep 2024 13:39:10 +0000 Subject: [PATCH 1/6] add registry --- .../common/data/multimodal/registry.py | 3 +- turbo_alignment/common/registry/__init__.py | 4 + turbo_alignment/common/registry/checks.py | 21 + turbo_alignment/common/registry/file_utils.py | 72 +++ .../common/registry/from_params.py | 592 ++++++++++++++++++ turbo_alignment/common/registry/lazy.py | 81 +++ turbo_alignment/common/registry/params.py | 563 +++++++++++++++++ .../common/registry/registrable.py | 301 +++++++++ .../common/tf/loaders/model/registry.py | 2 +- .../dataset/multimodal/multimodal.py | 2 +- turbo_alignment/dataset/registry.py | 3 +- turbo_alignment/metrics/metric.py | 3 +- turbo_alignment/metrics/registry.py | 3 +- .../modeling/multimodal/encoders/registry.py | 2 +- .../multimodal/projectors/registry.py | 2 +- .../pipelines/preprocessing/multimodal.py | 2 +- turbo_alignment/trainers/utils.py | 3 +- 17 files changed, 1645 insertions(+), 14 deletions(-) create mode 100644 turbo_alignment/common/registry/__init__.py create mode 100644 turbo_alignment/common/registry/checks.py create mode 100644 turbo_alignment/common/registry/file_utils.py create mode 100644 turbo_alignment/common/registry/from_params.py create mode 100644 turbo_alignment/common/registry/lazy.py create mode 100644 turbo_alignment/common/registry/params.py create mode 100644 turbo_alignment/common/registry/registrable.py diff --git a/turbo_alignment/common/data/multimodal/registry.py b/turbo_alignment/common/data/multimodal/registry.py index 96200d1..78a639a 100644 --- a/turbo_alignment/common/data/multimodal/registry.py +++ b/turbo_alignment/common/data/multimodal/registry.py @@ -1,5 +1,4 @@ -from allenai_common import Registrable - +from turbo_alignment.common.registry import Registrable from turbo_alignment.settings.modality import Modality diff --git a/turbo_alignment/common/registry/__init__.py b/turbo_alignment/common/registry/__init__.py new file mode 100644 index 0000000..e0a110e --- /dev/null +++ b/turbo_alignment/common/registry/__init__.py @@ -0,0 +1,4 @@ +from turbo_alignment.common.registry.from_params import FromParams +from turbo_alignment.common.registry.lazy import Lazy +from turbo_alignment.common.registry.params import Params +from turbo_alignment.common.registry.registrable import Registrable diff --git a/turbo_alignment/common/registry/checks.py b/turbo_alignment/common/registry/checks.py new file mode 100644 index 0000000..b3e263a --- /dev/null +++ b/turbo_alignment/common/registry/checks.py @@ -0,0 +1,21 @@ +import logging +from typing import Any + +logger = logging.getLogger(__name__) + + +class ConfigurationError(Exception): + """ + The exception raised by any AllenNLP object when it's misconfigured + (e.g. missing properties, invalid properties, unknown properties). + """ + + def __reduce__(self) -> str | tuple[Any, ...]: + return type(self), (self.message,) + + def __init__(self, message: str): + super().__init__() + self.message = message + + def __str__(self): + return self.message diff --git a/turbo_alignment/common/registry/file_utils.py b/turbo_alignment/common/registry/file_utils.py new file mode 100644 index 0000000..a7788f8 --- /dev/null +++ b/turbo_alignment/common/registry/file_utils.py @@ -0,0 +1,72 @@ +import logging +import os +from pathlib import Path + +import cached_path as _cached_path + +logger = logging.getLogger(__name__) + +CACHE_ROOT = Path(os.getenv('ALLENNLP_CACHE_ROOT', Path.home() / '.allennlp')) +CACHE_DIRECTORY = str(CACHE_ROOT / 'cache') +DEPRECATED_CACHE_DIRECTORY = str(CACHE_ROOT / 'datasets') + + +def cached_path( + url_or_filename: str | os.PathLike, + cache_dir: str | Path = None, + extract_archive: bool = False, + force_extract: bool = False, +) -> str: + """ + Given something that might be a URL or local path, determine which. + If it's a remote resource, download the file and cache it, and + then return the path to the cached file. If it's already a local path, + make sure the file exists and return the path. + + For URLs, "http://", "https://", "s3://", "gs://", and "hf://" are all supported. + The latter corresponds to the HuggingFace Hub. + + For example, to download the PyTorch weights for the model `epwalsh/bert-xsmall-dummy` + on HuggingFace, you could do: + + ```python + cached_path("hf://epwalsh/bert-xsmall-dummy/pytorch_model.bin") + ``` + + For paths or URLs that point to a tarfile or zipfile, you can also add a path + to a specific file to the `url_or_filename` preceeded by a "!", and the archive will + be automatically extracted (provided you set `extract_archive` to `True`), + returning the local path to the specific file. For example: + + ```python + cached_path("model.tar.gz!weights.th", extract_archive=True) + ``` + + # Parameters + + url_or_filename : `Union[str, Path]` + A URL or path to parse and possibly download. + + cache_dir : `Union[str, Path]`, optional (default = `None`) + The directory to cache downloads. + + extract_archive : `bool`, optional (default = `False`) + If `True`, then zip or tar.gz archives will be automatically extracted. + In which case the directory is returned. + + force_extract : `bool`, optional (default = `False`) + If `True` and the file is an archive file, it will be extracted regardless + of whether or not the extracted directory already exists. + + !!! Warning + Use this flag with caution! This can lead to race conditions if used + from multiple processes on the same file. + """ + return str( + _cached_path.cached_path( + url_or_filename, + cache_dir=cache_dir or CACHE_DIRECTORY, + extract_archive=extract_archive, + force_extract=force_extract, + ) + ) diff --git a/turbo_alignment/common/registry/from_params.py b/turbo_alignment/common/registry/from_params.py new file mode 100644 index 0000000..064672b --- /dev/null +++ b/turbo_alignment/common/registry/from_params.py @@ -0,0 +1,592 @@ +import collections.abc +import inspect +import logging +from copy import deepcopy +from pathlib import Path +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Mapping, + Optional, + Set, + Tuple, + Type, + TypeVar, + Union, + cast, +) + +from turbo_alignment.common.registry.checks import ConfigurationError +from turbo_alignment.common.registry.lazy import Lazy +from turbo_alignment.common.registry.params import Params + +logger = logging.getLogger(__name__) + +T = TypeVar('T', bound='FromParams') + +# If a function parameter has no default value specified, +# this is what the inspect module returns. +_NO_DEFAULT = inspect.Parameter.empty + + +def takes_arg(obj, arg: str) -> bool: + """ + Checks whether the provided obj takes a certain arg. + If it's a class, we're really checking whether its constructor does. + If it's a function or method, we're checking the object itself. + Otherwise, we raise an error. + """ + if inspect.isclass(obj): + signature = inspect.signature(obj.__init__) + elif inspect.ismethod(obj) or inspect.isfunction(obj): + signature = inspect.signature(obj) + else: + raise ConfigurationError(f'object {obj} is not callable') + return arg in signature.parameters + + +def takes_kwargs(obj) -> bool: + """ + Checks whether a provided object takes in any positional arguments. + Similar to takes_arg, we do this for both the __init__ function of + the class or a function / method + Otherwise, we raise an error + """ + if inspect.isclass(obj): + signature = inspect.signature(obj.__init__) + elif inspect.ismethod(obj) or inspect.isfunction(obj): + signature = inspect.signature(obj) + else: + raise ConfigurationError(f'object {obj} is not callable') + return any(p.kind == inspect.Parameter.VAR_KEYWORD for p in signature.parameters.values()) # type: ignore + + +def can_construct_from_params(type_: Type) -> bool: + if type_ in [str, int, float, bool]: + return True + origin = getattr(type_, '__origin__', None) + if origin == Lazy: + return True + if origin: + if hasattr(type_, 'from_params'): + return True + args = getattr(type_, '__args__') + return all(can_construct_from_params(arg) for arg in args) + + return hasattr(type_, 'from_params') + + +def is_base_registrable(cls) -> bool: + """ + Checks whether this is a class that directly inherits from Registrable, or is a subclass of such + a class. + """ + from turbo_alignment.common.registry.registrable import ( + Registrable, # import here to avoid circular imports + ) + + if not issubclass(cls, Registrable): + return False + method_resolution_order = inspect.getmro(cls)[1:] + for base_class in method_resolution_order: + if issubclass(base_class, Registrable) and base_class is not Registrable: + return False + return True + + +def remove_optional(annotation: type): + """ + Optional[X] annotations are actually represented as Union[X, NoneType]. + For our purposes, the "Optional" part is not interesting, so here we + throw it away. + """ + origin = getattr(annotation, '__origin__', None) + args = getattr(annotation, '__args__', ()) + + if origin == Union: + return Union[tuple([arg for arg in args if arg != type(None)])] # noqa: E721 + return annotation + + +def infer_constructor_params( + cls: Type[T], constructor: Union[Callable[..., T], Callable[[T], None]] = None +) -> Dict[str, inspect.Parameter]: + if constructor is None: + constructor = cls.__init__ + return infer_method_params(cls, constructor) + + +infer_params = infer_constructor_params # Legacy name + + +def infer_method_params(cls: Type[T], method: Callable) -> Dict[str, inspect.Parameter]: + signature = inspect.signature(method) + parameters = dict(signature.parameters) + + has_kwargs = False + var_positional_key = None + for param in parameters.values(): + if param.kind == param.VAR_KEYWORD: + has_kwargs = True + elif param.kind == param.VAR_POSITIONAL: + var_positional_key = param.name + + if var_positional_key: + del parameters[var_positional_key] + + if not has_kwargs: + return parameters + + # "mro" is "method resolution order". The first one is the current class, the next is the + # first superclass, and so on. We take the first superclass we find that inherits from + # FromParams. + super_class = None + for super_class_candidate in cls.mro()[1:]: + if issubclass(super_class_candidate, FromParams): + super_class = super_class_candidate + break + if super_class: + super_parameters = infer_params(super_class) + else: + super_parameters = {} + + return {**super_parameters, **parameters} # Subclass parameters overwrite superclass ones + + +def create_kwargs(constructor: Callable[..., T], cls: Type[T], params: Params, **extras) -> Dict[str, Any]: + """ + Given some class, a `Params` object, and potentially other keyword arguments, + create a dict of keyword args suitable for passing to the class's constructor. + + The function does this by finding the class's constructor, matching the constructor + arguments to entries in the `params` object, and instantiating values for the parameters + using the type annotation and possibly a from_params method. + + Any values that are provided in the `extras` will just be used as is. + For instance, you might provide an existing `Vocabulary` this way. + """ + # Get the signature of the constructor. + + kwargs: Dict[str, Any] = {} + + parameters = infer_params(cls, constructor) + accepts_kwargs = False + + # Iterate over all the constructor parameters and their annotations. + for param_name, param in parameters.items(): + # Skip "self". You're not *required* to call the first parameter "self", + # so in theory this logic is fragile, but if you don't call the self parameter + # "self" you kind of deserve what happens. + if param_name == 'self': + continue + + if param.kind == param.VAR_KEYWORD: + # When a class takes **kwargs, we do two things: first, we assume that the **kwargs are + # getting passed to the super class, so we inspect super class constructors to get + # allowed arguments (that happens in `infer_params` above). Second, we store the fact + # that the method allows extra keys; if we get extra parameters, instead of crashing, + # we'll just pass them as-is to the constructor, and hope that you know what you're + # doing. + accepts_kwargs = True + continue + + # If the annotation is a compound type like typing.Dict[str, int], + # it will have an __origin__ field indicating `typing.Dict` + # and an __args__ field indicating `(str, int)`. We capture both. + annotation = remove_optional(param.annotation) + + explicitly_set = param_name in params + constructed_arg = pop_and_construct_arg(cls.__name__, param_name, annotation, param.default, params, **extras) + + # If the param wasn't explicitly set in `params` and we just ended up constructing + # the default value for the parameter, we can just omit it. + # Leaving it in can cause issues with **kwargs in some corner cases, where you might end up + # with multiple values for a single parameter (e.g., the default value gives you lazy=False + # for a dataset reader inside **kwargs, but a particular dataset reader actually hard-codes + # lazy=True - the superclass sees both lazy=True and lazy=False in its constructor). + if explicitly_set or constructed_arg is not param.default: + kwargs[param_name] = constructed_arg + + if accepts_kwargs: + kwargs.update(params) + else: + params.assert_empty(cls.__name__) + return kwargs + + +def create_extras(cls: Type[T], extras: Dict[str, Any]) -> Dict[str, Any]: + """ + Given a dictionary of extra arguments, returns a dictionary of + kwargs that actually are a part of the signature of the cls.from_params + (or cls) method. + """ + subextras: Dict[str, Any] = {} + if hasattr(cls, 'from_params'): + from_params_method = cls.from_params # type: ignore + else: + # In some rare cases, we get a registered subclass that does _not_ have a + # from_params method (this happens with Activations, for instance, where we + # register pytorch modules directly). This is a bit of a hack to make those work, + # instead of adding a `from_params` method for them somehow. Then the extras + # in the class constructor are what we are looking for, to pass on. + from_params_method = cls + if takes_kwargs(from_params_method): + # If annotation.params accepts **kwargs, we need to pass them all along. + # For example, `BasicTextFieldEmbedder.from_params` requires a Vocabulary + # object, but `TextFieldEmbedder.from_params` does not. + subextras = extras + else: + # Otherwise, only supply the ones that are actual args; any additional ones + # will cause a TypeError. + subextras = {k: v for k, v in extras.items() if takes_arg(from_params_method, k)} + return subextras + + +def construct_arg( + class_name: str, + argument_name: str, + popped_params: Params, + annotation: Type, + default: Any, + **extras, +) -> Any: + """ + The first two parameters here are only used for logging if we encounter an error. + """ + origin = getattr(annotation, '__origin__', None) + args = getattr(annotation, '__args__', []) + + # The parameter is optional if its default value is not the "no default" sentinel. + optional = default != _NO_DEFAULT + + if hasattr(annotation, 'from_params'): + if popped_params is default: + return default + if popped_params is not None: + # Our params have an entry for this, so we use that. + + subextras = create_extras(annotation, extras) + + # In some cases we allow a string instead of a param dict, so + # we need to handle that case separately. + if isinstance(popped_params, str): + popped_params = Params({'type': popped_params}) + elif isinstance(popped_params, dict): + popped_params = Params(popped_params) + result = annotation.from_params(params=popped_params, **subextras) + + return result + if not optional: + # Not optional and not supplied, that's an error! + raise ConfigurationError(f'expected key {argument_name} for {class_name}') + return default + + # If the parameter type is a Python primitive, just pop it off + # using the correct casting pop_xyz operation. + if annotation in {int, bool}: + if type(popped_params) in {int, bool}: + return annotation(popped_params) + raise TypeError(f'Expected {argument_name} to be a {annotation.__name__}.') + if annotation == str: + # Strings are special because we allow casting from Path to str. + if isinstance(popped_params, str) or isinstance(popped_params, Path): + return str(popped_params) # type: ignore + raise TypeError(f'Expected {argument_name} to be a string.') + if annotation == float: + # Floats are special because in Python, you can put an int wherever you can put a float. + # https://mypy.readthedocs.io/en/stable/duck_type_compatibility.html + if type(popped_params) in {int, float}: + return popped_params + raise TypeError(f'Expected {argument_name} to be numeric.') + + # This is special logic for handling types like Dict[str, TokenIndexer], + # List[TokenIndexer], Tuple[TokenIndexer, Tokenizer], and Set[TokenIndexer], + # which it creates by instantiating each value from_params and returning the resulting structure. + if ( + origin in {collections.abc.Mapping, Mapping, Dict, dict} + and len(args) == 2 + and can_construct_from_params(args[-1]) + ): + value_cls = annotation.__args__[-1] + value_dict = {} + if not isinstance(popped_params, Mapping): + raise TypeError(f'Expected {argument_name} to be a Mapping (probably a dict or a Params object).') + + for key, value_params in popped_params.items(): + value_dict[key] = construct_arg( + str(value_cls), + argument_name + '.' + key, + value_params, + value_cls, + _NO_DEFAULT, + **extras, + ) + + return value_dict + + if origin in (Tuple, tuple) and all(can_construct_from_params(arg) for arg in args): + value_list = [] + + for i, (value_cls, value_params) in enumerate(zip(annotation.__args__, popped_params)): + value = construct_arg( + str(value_cls), + argument_name + f'.{i}', + value_params, + value_cls, + _NO_DEFAULT, + **extras, + ) + value_list.append(value) + + return tuple(value_list) + + if origin in (Set, set) and len(args) == 1 and can_construct_from_params(args[0]): + value_cls = annotation.__args__[0] + + value_set = set() + + for i, value_params in enumerate(popped_params): + value = construct_arg( + str(value_cls), + argument_name + f'.{i}', + value_params, + value_cls, + _NO_DEFAULT, + **extras, + ) + value_set.add(value) + + return value_set + + if origin == Union: + # Storing this so we can recover it later if we need to. + backup_params = deepcopy(popped_params) + + # We'll try each of the given types in the union sequentially, returning the first one that + # succeeds. + error_chain: Optional[Exception] = None + for arg_annotation in args: + try: + return construct_arg( + str(arg_annotation), + argument_name, + popped_params, + arg_annotation, + default, + **extras, + ) + except (ValueError, TypeError, ConfigurationError, AttributeError) as e: + # Our attempt to construct the argument may have modified popped_params, so we + # restore it here. + popped_params = deepcopy(backup_params) + e.args = (f'While constructing an argument of type {arg_annotation}',) + e.args + e.__cause__ = error_chain + error_chain = e + + # If none of them succeeded, we crash. + config_error = ConfigurationError(f'Failed to construct argument {argument_name} with type {annotation}.') + config_error.__cause__ = error_chain + raise config_error + if origin == Lazy: + if popped_params is default: + return default + + value_cls = args[0] + subextras = create_extras(value_cls, extras) + return Lazy(value_cls, params=deepcopy(popped_params), constructor_extras=subextras) # type: ignore + + # For any other kind of iterable, we will just assume that a list is good enough, and treat + # it the same as List. This condition needs to be at the end, so we don't catch other kinds + # of Iterables with this branch. + if ( + origin in {collections.abc.Iterable, Iterable, List, list} + and len(args) == 1 + and can_construct_from_params(args[0]) + ): + value_cls = annotation.__args__[0] + + value_list = [] + + for i, value_params in enumerate(popped_params): + value = construct_arg( + str(value_cls), + argument_name + f'.{i}', + value_params, + value_cls, + _NO_DEFAULT, + **extras, + ) + value_list.append(value) + + return value_list + + # Pass it on as is and hope for the best. ¯\_(ツ)_/¯ + if isinstance(popped_params, Params): + return popped_params.as_dict() + return popped_params + + +class FromParams: + """ + Mixin to give a from_params method to classes. We create a distinct base class for this + because sometimes we want non-Registrable classes to be instantiatable from_params. + """ + + @classmethod + def from_params( + cls: Type[T], + params: Params, + constructor_to_call: Callable[..., T] = None, + constructor_to_inspect: Union[Callable[..., T], Callable[[T], None]] = None, + **extras, + ) -> T: + """ + This is the automatic implementation of `from_params`. Any class that subclasses + `FromParams` (or `Registrable`, which itself subclasses `FromParams`) gets this + implementation for free. If you want your class to be instantiated from params in the + "obvious" way -- pop off parameters and hand them to your constructor with the same names -- + this provides that functionality. + + If you need more complex logic in your from `from_params` method, you'll have to implement + your own method that overrides this one. + + The `constructor_to_call` and `constructor_to_inspect` arguments deal with a bit of + redirection that we do. We allow you to register particular `@classmethods` on a class as + the constructor to use for a registered name. This lets you, e.g., have a single + `Vocabulary` class that can be constructed in two different ways, with different names + registered to each constructor. In order to handle this, we need to know not just the class + we're trying to construct (`cls`), but also what method we should inspect to find its + arguments (`constructor_to_inspect`), and what method to call when we're done constructing + arguments (`constructor_to_call`). These two methods are the same when you've used a + `@classmethod` as your constructor, but they are `different` when you use the default + constructor (because you inspect `__init__`, but call `cls()`). + """ + + from turbo_alignment.common.registry.registrable import ( + Registrable, # import here to avoid circular imports + ) + + logger.debug( + f"instantiating class {cls} from params {getattr(params, 'params', params)} " + f'and extras {set(extras.keys())}' + ) + + if params is None: + return None + + if isinstance(params, str): + params = Params({'type': params}) + + if not isinstance(params, Params): + raise ConfigurationError( + 'from_params was passed a `params` object that was not a `Params`. This probably ' + 'indicates malformed parameters in a configuration file, where something that ' + 'should have been a dictionary was actually a list, or something else. ' + f'This happened when constructing an object of type {cls}.' + ) + + registered_subclasses = Registrable._registry.get(cls) + + if is_base_registrable(cls) and registered_subclasses is None: + # NOTE(mattg): There are some potential corner cases in this logic if you have nested + # Registrable types. We don't currently have any of those, but if we ever get them, + # adding some logic to check `constructor_to_call` should solve the issue. Not + # bothering to add that unnecessary complexity for now. + raise ConfigurationError( + 'Tried to construct an abstract Registrable base class that has no registered ' + 'concrete types. This might mean that you need to use --include-package to get ' + 'your concrete classes actually registered.' + ) + + if registered_subclasses is not None and not constructor_to_call: + # We know `cls` inherits from Registrable, so we'll use a cast to make mypy happy. + + as_registrable = cast(Type[Registrable], cls) + default_to_first_choice = as_registrable.default_implementation is not None + choice = params.pop_choice( + 'type', + choices=as_registrable.list_available(), + default_to_first_choice=default_to_first_choice, + ) + subclass, constructor_name = as_registrable.resolve_class_name(choice) + # See the docstring for an explanation of what's going on here. + if not constructor_name: + constructor_to_inspect = subclass.__init__ + constructor_to_call = subclass # type: ignore + else: + constructor_to_inspect = cast(Callable[..., T], getattr(subclass, constructor_name)) + constructor_to_call = constructor_to_inspect + + if hasattr(subclass, 'from_params'): + # We want to call subclass.from_params. + extras = create_extras(subclass, extras) + # mypy can't follow the typing redirection that we do, so we explicitly cast here. + retyped_subclass = cast(Type[T], subclass) + return retyped_subclass.from_params( + params=params, + constructor_to_call=constructor_to_call, + constructor_to_inspect=constructor_to_inspect, + **extras, + ) + # In some rare cases, we get a registered subclass that does _not_ have a + # from_params method (this happens with Activations, for instance, where we + # register pytorch modules directly). This is a bit of a hack to make those work, + # instead of adding a `from_params` method for them somehow. We just trust that + # you've done the right thing in passing your parameters, and nothing else needs to + # be recursively constructed. + return subclass(**params) # type: ignore + # This is not a base class, so convert our params and extras into a dict of kwargs. + + # See the docstring for an explanation of what's going on here. + if not constructor_to_inspect: + constructor_to_inspect = cls.__init__ + if not constructor_to_call: + constructor_to_call = cls + + if constructor_to_inspect == object.__init__: + # This class does not have an explicit constructor, so don't give it any kwargs. + # Without this logic, create_kwargs will look at object.__init__ and see that + # it takes *args and **kwargs and look for those. + kwargs: Dict[str, Any] = {} + params.assert_empty(cls.__name__) + else: + # This class has a constructor, so create kwargs for it. + constructor_to_inspect = cast(Callable[..., T], constructor_to_inspect) + kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras) + + return constructor_to_call(**kwargs) # type: ignore + + def to_params(self) -> Params: + """ + Returns a `Params` object that can be used with `.from_params()` to recreate an + object just like it. + + This relies on `_to_params()`. If you need this in your custom `FromParams` class, + override `_to_params()`, not this method. + """ + + def replace_object_with_params(o: Any) -> Any: + if isinstance(o, FromParams): + return o.to_params() + if isinstance(o, List): + return [replace_object_with_params(i) for i in o] + if isinstance(o, Set): + return {replace_object_with_params(i) for i in o} + if isinstance(o, Dict): + return {key: replace_object_with_params(value) for key, value in o.items()} + return o + + return Params(replace_object_with_params(self._to_params())) + + def _to_params(self) -> Dict[str, Any]: + """ + Returns a dictionary of parameters that, when turned into a `Params` object and + then fed to `.from_params()`, will recreate this object. + + You don't need to implement this all the time. AllenNLP will let you know if you + need it. + """ + raise NotImplementedError() diff --git a/turbo_alignment/common/registry/lazy.py b/turbo_alignment/common/registry/lazy.py new file mode 100644 index 0000000..d6d4a1a --- /dev/null +++ b/turbo_alignment/common/registry/lazy.py @@ -0,0 +1,81 @@ +import copy +import inspect +from typing import Any, Callable, Generic, Type, TypeVar + +from turbo_alignment.common.registry.params import Params + +T = TypeVar('T') + + +class Lazy(Generic[T]): + """ + This class is for use when constructing objects using `FromParams`, when an argument to a + constructor has a _sequential dependency_ with another argument to the same constructor. + + For example, in a `Trainer` class you might want to take a `Model` and an `Optimizer` as arguments, + but the `Optimizer` needs to be constructed using the parameters from the `Model`. You can give + the type annotation `Lazy[Optimizer]` to the optimizer argument, then inside the constructor + call `optimizer.construct(parameters=model.parameters)`. + + This is only recommended for use when you have registered a `@classmethod` as the constructor + for your class, instead of using `__init__`. Having a `Lazy[]` type annotation on an argument + to an `__init__` method makes your class completely dependent on being constructed using the + `FromParams` pipeline, which is not a good idea. + + The actual implementation here is incredibly simple; the logic that handles the lazy + construction is actually found in `FromParams`, where we have a special case for a `Lazy` type + annotation. + + ```python + @classmethod + def my_constructor( + cls, + some_object: Lazy[MyObject], + optional_object: Lazy[MyObject] = None, + # or: + # optional_object: Optional[Lazy[MyObject]] = None, + optional_object_with_default: Optional[Lazy[MyObject]] = Lazy(MyObjectDefault), + required_object_with_default: Lazy[MyObject] = Lazy(MyObjectDefault), + ) -> MyClass: + obj1 = some_object.construct() + obj2 = None if optional_object is None else optional_object.construct() + obj3 = None optional_object_with_default is None else optional_object_with_default.construct() + obj4 = required_object_with_default.construct() + ``` + + """ + + def __init__( + self, + constructor: Type[T] | Callable[..., T], + params: Params | None = None, + constructor_extras: dict[str, Any] | None = None, + **kwargs, + ) -> None: + self._constructor = constructor + self._params = params or Params({}) + self._constructor_extras = constructor_extras or {} + self._constructor_extras.update(kwargs) + + @property + def constructor(self) -> Callable[..., T]: + if inspect.isclass(self._constructor): + + def constructor_to_use(**kwargs): + return self._constructor.from_params( # type: ignore[union-attr] + copy.deepcopy(self._params), + **kwargs, + ) + + return constructor_to_use + + return self._constructor + + def construct(self, **kwargs) -> T: + """ + Call the constructor to create an instance of `T`. + """ + # If there are duplicate keys between self._constructor_extras and kwargs, + # this will overwrite the ones in self._constructor_extras with what's in kwargs. + contructor_kwargs = {**self._constructor_extras, **kwargs} + return self.constructor(**contructor_kwargs) diff --git a/turbo_alignment/common/registry/params.py b/turbo_alignment/common/registry/params.py new file mode 100644 index 0000000..8d4dc41 --- /dev/null +++ b/turbo_alignment/common/registry/params.py @@ -0,0 +1,563 @@ +import copy +import json +import logging +import os +import zlib +from collections import OrderedDict +from collections.abc import MutableMapping +from itertools import chain +from os import PathLike +from typing import Any, Dict, Iterable, List, Optional, Set, TypeVar, Union + +# _jsonnet doesn't work on Windows, so we have to use fakes. +try: + from _jsonnet import evaluate_file, evaluate_snippet +except ImportError: + + def evaluate_file(filename: str, **_kwargs) -> str: + logger.warning(f'error loading _jsonnet (this is expected on Windows), treating {filename} as plain json') + with open(filename, 'r') as evaluation_file: + return evaluation_file.read() + + def evaluate_snippet(_filename: str, expr: str, **_kwargs) -> str: + logger.warning('error loading _jsonnet (this is expected on Windows), treating snippet as plain json') + return expr + + +from turbo_alignment.common.registry.checks import ConfigurationError +from turbo_alignment.common.registry.file_utils import cached_path + +logger = logging.getLogger(__name__) + + +def infer_and_cast(value: Any): + """ + In some cases we'll be feeding params dicts to functions we don't own; + for example, PyTorch optimizers. In that case we can't use `pop_int` + or similar to force casts (which means you can't specify `int` parameters + using environment variables). This function takes something that looks JSON-like + and recursively casts things that look like (bool, int, float) to (bool, int, float). + """ + + if isinstance(value, (int, float, bool)): + # Already one of our desired types, so leave as is. + return value + if isinstance(value, list): + # Recursively call on each list element. + return [infer_and_cast(item) for item in value] + if isinstance(value, dict): + # Recursively call on each dict value. + return {key: infer_and_cast(item) for key, item in value.items()} + if isinstance(value, str): + # If it looks like a bool, make it a bool. + if value.lower() == 'true': + return True + if value.lower() == 'false': + return False + # See if it could be an int. + try: + return int(value) + except ValueError: + pass + # See if it could be a float. + try: + return float(value) + except ValueError: + # Just return it as a string. + return value + raise ValueError(f'cannot infer type of {value}') + + +def _is_encodable(value: str) -> bool: + """ + We need to filter out environment variables that can't + be unicode-encoded to avoid a "surrogates not allowed" + error in jsonnet. + """ + # Idiomatically you'd like to not check the != b"" + # but mypy doesn't like that. + return (value == '') or (value.encode('utf-8', 'ignore') != b'') + + +def _environment_variables() -> Dict[str, str]: + """ + Wraps `os.environ` to filter out non-encodable values. + """ + return {key: value for key, value in os.environ.items() if _is_encodable(value)} + + +T = TypeVar('T', dict, list) + + +def with_overrides(original: T, overrides_dict: Dict[str, Any], prefix: str = '') -> T: + merged: T + keys: Union[Iterable[str], Iterable[int]] + if isinstance(original, list): + merged = [None] * len(original) + keys = range(len(original)) + elif isinstance(original, dict): + merged = {} + keys = chain(original.keys(), (k for k in overrides_dict if '.' not in k and k not in original)) + else: + if prefix: + raise ValueError( + f"overrides for '{prefix[:-1]}.*' expected list or dict in original, " + f'found {type(original)} instead' + ) + raise ValueError(f'expected list or dict, found {type(original)} instead') + + used_override_keys: Set[str] = set() + for key in keys: + if str(key) in overrides_dict: + merged[key] = copy.deepcopy(overrides_dict[str(key)]) + used_override_keys.add(str(key)) + else: + overrides_subdict = {} + for o_key in overrides_dict: + if o_key.startswith(f'{key}.'): + overrides_subdict[o_key[len(f'{key}.') :]] = overrides_dict[o_key] + used_override_keys.add(o_key) + if overrides_subdict: + merged[key] = with_overrides(original[key], overrides_subdict, prefix=prefix + f'{key}.') + else: + merged[key] = copy.deepcopy(original[key]) + + unused_override_keys = [prefix + key for key in set(overrides_dict.keys()) - used_override_keys] + if unused_override_keys: + raise ValueError(f'overrides dict contains unused keys: {unused_override_keys}') + + return merged + + +def parse_overrides(serialized_overrides: str, ext_vars: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + if serialized_overrides: + ext_vars = {**_environment_variables(), **(ext_vars or {})} + + return json.loads(evaluate_snippet('', serialized_overrides, ext_vars=ext_vars)) + return {} + + +def _is_dict_free(obj: Any) -> bool: + """ + Returns False if obj is a dict, or if it's a list with an element that _has_dict. + """ + if isinstance(obj, dict): + return False + if isinstance(obj, list): + return all(_is_dict_free(item) for item in obj) + return True + + +class Params(MutableMapping): + """ + Represents a parameter dictionary with a history, and contains other functionality around + parameter passing and validation for AllenNLP. + + There are currently two benefits of a `Params` object over a plain dictionary for parameter + passing: + + 1. We handle a few kinds of parameter validation, including making sure that parameters + representing discrete choices actually have acceptable values, and making sure no extra + parameters are passed. + 2. We log all parameter reads, including default values. This gives a more complete + specification of the actual parameters used than is given in a JSON file, because + those may not specify what default values were used, whereas this will log them. + + !!! Consumption + The convention for using a `Params` object in AllenNLP is that you will consume the parameters + as you read them, so that there are none left when you've read everything you expect. This + lets us easily validate that you didn't pass in any `extra` parameters, just by making sure + that the parameter dictionary is empty. You should do this when you're done handling + parameters, by calling `Params.assert_empty`. + """ + + # This allows us to check for the presence of "None" as a default argument, + # which we require because we make a distinction between passing a value of "None" + # and passing no value to the default parameter of "pop". + DEFAULT = object() + + def __init__(self, params: Dict[str, Any], history: str = '') -> None: + self.params = _replace_none(params) + self.history = history + + def pop(self, key: str, default: Any = DEFAULT, keep_as_dict: bool = False) -> Any: + """ + Performs the functionality associated with dict.pop(key), along with checking for + returned dictionaries, replacing them with Param objects with an updated history + (unless keep_as_dict is True, in which case we leave them as dictionaries). + + If `key` is not present in the dictionary, and no default was specified, we raise a + `ConfigurationError`, instead of the typical `KeyError`. + """ + if default is self.DEFAULT: + try: + value = self.params.pop(key) + except KeyError: + msg = f'key "{key}" is required' + if self.history: + msg += f' at location "{self.history}"' + raise ConfigurationError(msg) + else: + value = self.params.pop(key, default) + + if keep_as_dict or _is_dict_free(value): + logger.info(f'{self.history}{key} = {value}') + return value + return self._check_is_dict(key, value) + + def pop_int(self, key: str, default: Any = DEFAULT) -> Optional[int]: + """ + Performs a pop and coerces to an int. + """ + value = self.pop(key, default) + if value is None: + return None + return int(value) + + def pop_float(self, key: str, default: Any = DEFAULT) -> Optional[float]: + """ + Performs a pop and coerces to a float. + """ + value = self.pop(key, default) + if value is None: + return None + return float(value) + + def pop_bool(self, key: str, default: Any = DEFAULT) -> Optional[bool]: + """ + Performs a pop and coerces to a bool. + """ + value = self.pop(key, default) + if value is None: + return None + if isinstance(value, bool): + return value + if value == 'true': + return True + if value == 'false': + return False + raise ValueError('Cannot convert variable to bool: ' + value) + + def get(self, key: str, default: Any = DEFAULT): + """ + Performs the functionality associated with dict.get(key) but also checks for returned + dicts and returns a Params object in their place with an updated history. + """ + default = None if default is self.DEFAULT else default + value = self.params.get(key, default) + return self._check_is_dict(key, value) + + def pop_choice( + self, + key: str, + choices: List[Any], + default_to_first_choice: bool = False, + allow_class_names: bool = True, + ) -> Any: + """ + Gets the value of `key` in the `params` dictionary, ensuring that the value is one of + the given choices. Note that this `pops` the key from params, modifying the dictionary, + consistent with how parameters are processed in this codebase. + + # Parameters + + key: `str` + + Key to get the value from in the param dictionary + + choices: `List[Any]` + + A list of valid options for values corresponding to `key`. For example, if you're + specifying the type of encoder to use for some part of your model, the choices might be + the list of encoder classes we know about and can instantiate. If the value we find in + the param dictionary is not in `choices`, we raise a `ConfigurationError`, because + the user specified an invalid value in their parameter file. + + default_to_first_choice: `bool`, optional (default = `False`) + + If this is `True`, we allow the `key` to not be present in the parameter + dictionary. If the key is not present, we will use the return as the value the first + choice in the `choices` list. If this is `False`, we raise a + `ConfigurationError`, because specifying the `key` is required (e.g., you `have` to + specify your model class when running an experiment, but you can feel free to use + default settings for encoders if you want). + + allow_class_names: `bool`, optional (default = `True`) + + If this is `True`, then we allow unknown choices that look like fully-qualified class names. + This is to allow e.g. specifying a model type as my_library.my_model.MyModel + and importing it on the fly. Our check for "looks like" is extremely lenient + and consists of checking that the value contains a '.'. + """ + default = choices[0] if default_to_first_choice else self.DEFAULT + value = self.pop(key, default) + ok_because_class_name = allow_class_names and '.' in value + if value not in choices and not ok_because_class_name: + key_str = self.history + key + message = ( + f'{value} not in acceptable choices for {key_str}: {choices}. ' + 'You should either use the --include-package flag to make sure the correct module ' + 'is loaded, or use a fully qualified class name in your config file like ' + """{"model": "my_module.models.MyModel"} to have it imported automatically.""" + ) + raise ConfigurationError(message) + return value + + def as_dict(self, quiet: bool = False, infer_type_and_cast: bool = False): + """ + Sometimes we need to just represent the parameters as a dict, for instance when we pass + them to PyTorch code. + + # Parameters + + quiet: `bool`, optional (default = `False`) + + Whether to log the parameters before returning them as a dict. + + infer_type_and_cast: `bool`, optional (default = `False`) + + If True, we infer types and cast (e.g. things that look like floats to floats). + """ + if infer_type_and_cast: + params_as_dict = infer_and_cast(self.params) + else: + params_as_dict = self.params + + if quiet: + return params_as_dict + + def log_recursively(parameters, history): + for key, value in parameters.items(): + if isinstance(value, dict): + new_local_history = history + key + '.' + log_recursively(value, new_local_history) + else: + logger.info(f'{history}{key} = {value}') + + log_recursively(self.params, self.history) + return params_as_dict + + def as_flat_dict(self) -> Dict[str, Any]: + """ + Returns the parameters of a flat dictionary from keys to values. + Nested structure is collapsed with periods. + """ + flat_params = {} + + def recurse(parameters, path): + for key, value in parameters.items(): + newpath = path + [key] + if isinstance(value, dict): + recurse(value, newpath) + else: + flat_params['.'.join(newpath)] = value + + recurse(self.params, []) + return flat_params + + def duplicate(self) -> 'Params': + """ + Uses `copy.deepcopy()` to create a duplicate (but fully distinct) + copy of these Params. + """ + return copy.deepcopy(self) + + def assert_empty(self, class_name: str): + """ + Raises a `ConfigurationError` if `self.params` is not empty. We take `class_name` as + an argument so that the error message gives some idea of where an error happened, if there + was one. `class_name` should be the name of the `calling` class, the one that got extra + parameters (if there are any). + """ + if self.params: + raise ConfigurationError('Extra parameters passed to {}: {}'.format(class_name, self.params)) + + def __getitem__(self, key): + if key in self.params: + return self._check_is_dict(key, self.params[key]) + raise KeyError(str(key)) + + def __setitem__(self, key, value): + self.params[key] = value + + def __delitem__(self, key): + del self.params[key] + + def __iter__(self): + return iter(self.params) + + def __len__(self): + return len(self.params) + + def _check_is_dict(self, new_history, value): + if isinstance(value, dict): + new_history = self.history + new_history + '.' + return Params(value, history=new_history) + if isinstance(value, list): + value = [self._check_is_dict(f'{new_history}.{i}', v) for i, v in enumerate(value)] + return value + + @classmethod + def from_file( + cls, + params_file: Union[str, PathLike], + params_overrides: Union[str, Dict[str, Any]] = '', + ext_vars: dict = None, + ) -> 'Params': + """ + Load a `Params` object from a configuration file. + + # Parameters + + params_file: `str` + + The path to the configuration file to load. + + params_overrides: `Union[str, Dict[str, Any]]`, optional (default = `""`) + + A dict of overrides that can be applied to final object. + e.g. `{"model.embedding_dim": 10}` will change the value of "embedding_dim" + within the "model" object of the config to 10. If you wanted to override the entire + "model" object of the config, you could do `{"model": {"type": "other_type", ...}}`. + + ext_vars: `dict`, optional + + Our config files are Jsonnet, which allows specifying external variables + for later substitution. Typically we substitute these using environment + variables; however, you can also specify them here, in which case they + take priority over environment variables. + e.g. {"HOME_DIR": "/Users/allennlp/home"} + """ + if ext_vars is None: + ext_vars = {} + + # redirect to cache, if necessary + params_file = cached_path(params_file) + ext_vars = {**_environment_variables(), **ext_vars} + + file_dict = json.loads(evaluate_file(params_file, ext_vars=ext_vars)) + + if isinstance(params_overrides, dict): + params_overrides = json.dumps(params_overrides) + overrides_dict = parse_overrides(params_overrides, ext_vars=ext_vars) + + if overrides_dict: + param_dict = with_overrides(file_dict, overrides_dict) + else: + param_dict = file_dict + + return cls(param_dict) + + def to_file(self, params_file: str, preference_orders: List[List[str]] = None) -> None: + with open(params_file, 'w') as handle: + json.dump(self.as_ordered_dict(preference_orders), handle, indent=4) + + def as_ordered_dict(self, preference_orders: List[List[str]] = None) -> OrderedDict: + """ + Returns Ordered Dict of Params from list of partial order preferences. + + # Parameters + + preference_orders: `List[List[str]]`, optional + + `preference_orders` is list of partial preference orders. ["A", "B", "C"] means + "A" > "B" > "C". For multiple preference_orders first will be considered first. + Keys not found, will have last but alphabetical preference. Default Preferences: + `[["dataset_reader", "iterator", "model", "train_data_path", "validation_data_path", + "test_data_path", "trainer", "vocabulary"], ["type"]]` + """ + params_dict = self.as_dict(quiet=True) + if not preference_orders: + preference_orders = [] + preference_orders.append( + [ + 'dataset_reader', + 'iterator', + 'model', + 'train_data_path', + 'validation_data_path', + 'test_data_path', + 'trainer', + 'vocabulary', + ] + ) + preference_orders.append(['type']) + + def order_func(key): + # Makes a tuple to use for ordering. The tuple is an index into each of the `preference_orders`, + # followed by the key itself. This gives us integer sorting if you have a key in one of the + # `preference_orders`, followed by alphabetical ordering if not. + order_tuple = [order.index(key) if key in order else len(order) for order in preference_orders] + return order_tuple + [key] + + def order_dict(dictionary, order_func): + # Recursively orders dictionary according to scoring order_func + result = OrderedDict() + for key, val in sorted(dictionary.items(), key=lambda item: order_func(item[0])): + result[key] = order_dict(val, order_func) if isinstance(val, dict) else val + return result + + return order_dict(params_dict, order_func) + + def get_hash(self) -> str: + """ + Returns a hash code representing the current state of this `Params` object. We don't + want to implement `__hash__` because that has deeper python implications (and this is a + mutable object), but this will give you a representation of the current state. + We use `zlib.adler32` instead of Python's builtin `hash` because the random seed for the + latter is reset on each new program invocation, as discussed here: + https://stackoverflow.com/questions/27954892/deterministic-hashing-in-python-3. + """ + dumped = json.dumps(self.params, sort_keys=True) + hashed = zlib.adler32(dumped.encode()) + return str(hashed) + + def __str__(self) -> str: + return f'{self.history}Params({self.params})' + + +def pop_choice( + params: Dict[str, Any], + key: str, + choices: List[Any], + default_to_first_choice: bool = False, + history: str = '?.', + allow_class_names: bool = True, +) -> Any: + """ + Performs the same function as `Params.pop_choice`, but is required in order to deal with + places that the Params object is not welcome, such as inside Keras layers. See the docstring + of that method for more detail on how this function works. + + This method adds a `history` parameter, in the off-chance that you know it, so that we can + reproduce `Params.pop_choice` exactly. We default to using "?." if you don't know the + history, so you'll have to fix that in the log if you want to actually recover the logged + parameters. + """ + value = Params(params, history).pop_choice( + key, choices, default_to_first_choice, allow_class_names=allow_class_names + ) + return value + + +def _replace_none(params: Any) -> Any: + if params == 'None': + return None + if isinstance(params, dict): + for key, value in params.items(): + params[key] = _replace_none(value) + return params + if isinstance(params, list): + return [_replace_none(value) for value in params] + return params + + +def remove_keys_from_params(params: Params, keys: List[str] = ['pretrained_file', 'initializer']): + if isinstance(params, Params): # The model could possibly be a string, for example. + param_keys = params.keys() + for key in keys: + if key in param_keys: + del params[key] + for value in params.values(): + if isinstance(value, Params): + remove_keys_from_params(value, keys) diff --git a/turbo_alignment/common/registry/registrable.py b/turbo_alignment/common/registry/registrable.py new file mode 100644 index 0000000..dac93c3 --- /dev/null +++ b/turbo_alignment/common/registry/registrable.py @@ -0,0 +1,301 @@ +""" +`allennlp.common.registrable.Registrable` is a "mixin" for endowing +any base class with a named registry for its subclasses and a decorator +for registering them. +""" +import importlib +import inspect +import logging +from collections import defaultdict +from typing import Any, Callable, ClassVar, DefaultDict, Type, TypeVar, cast + +from turbo_alignment.common.registry.checks import ConfigurationError +from turbo_alignment.common.registry.from_params import FromParams + +logger = logging.getLogger(__name__) + +_T = TypeVar('_T') +_RegistrableT = TypeVar('_RegistrableT', bound='Registrable') + +_SubclassRegistry = dict[str, tuple[type, str | None]] + + +class Registrable(FromParams): + """ + Any class that inherits from `Registrable` gains access to a named registry for its + subclasses. To register them, just decorate them with the classmethod + `@BaseClass.register(name)`. + + After which you can call `BaseClass.list_available()` to get the keys for the + registered subclasses, and `BaseClass.by_name(name)` to get the corresponding subclass. + Note that the registry stores the subclasses themselves; not class instances. + In most cases you would then call `from_params(params)` on the returned subclass. + + You can specify a default by setting `BaseClass.default_implementation`. + If it is set, it will be the first element of `list_available()`. + + Note that if you use this class to implement a new `Registrable` abstract class, + you must ensure that all subclasses of the abstract class are loaded when the module is + loaded, because the subclasses register themselves in their respective files. You can + achieve this by having the abstract class and all subclasses in the __init__.py of the + module in which they reside (as this causes any import of either the abstract class or + a subclass to load all other subclasses and the abstract class). + """ + + _registry: ClassVar[DefaultDict[type, _SubclassRegistry]] = defaultdict(dict) + + default_implementation: str | None = None + + @classmethod + def register( + cls, name: str, constructor: str | None = None, exist_ok: bool = False + ) -> Callable[[Type[_T]], Type[_T]]: + """ + Register a class under a particular name. + + # Parameters + + name : `str` + The name to register the class under. + constructor : `str`, optional (default=`None`) + The name of the method to use on the class to construct the object. If this is given, + we will use this method (which must be a `@classmethod`) instead of the default + constructor. + exist_ok : `bool`, optional (default=`False`) + If True, overwrites any existing models registered under `name`. Else, + throws an error if a model is already registered under `name`. + + # Examples + + To use this class, you would typically have a base class that inherits from `Registrable`: + + ```python + class Vocabulary(Registrable): + ... + ``` + + Then, if you want to register a subclass, you decorate it like this: + + ```python + @Vocabulary.register("my-vocabulary") + class MyVocabulary(Vocabulary): + def __init__(self, param1: int, param2: str): + ... + ``` + + Registering a class like this will let you instantiate a class from a config file, where you + give `"type": "my-vocabulary"`, and keys corresponding to the parameters of the `__init__` + method (note that for this to work, those parameters must have type annotations). + + If you want to have the instantiation from a config file call a method other than the + constructor, either because you have several different construction paths that could be + taken for the same object (as we do in `Vocabulary`) or because you have logic you want to + happen before you get to the constructor (as we do in `Embedding`), you can register a + specific `@classmethod` as the constructor to use, like this: + + ```python + @Vocabulary.register("my-vocabulary-from-instances", constructor="from_instances") + @Vocabulary.register("my-vocabulary-from-files", constructor="from_files") + class MyVocabulary(Vocabulary): + def __init__(self, some_params): + ... + + @classmethod + def from_instances(cls, some_other_params) -> MyVocabulary: + ... # construct some_params from instances + return cls(some_params) + + @classmethod + def from_files(cls, still_other_params) -> MyVocabulary: + ... # construct some_params from files + return cls(some_params) + ``` + """ + registry = Registrable._registry[cls] + + def add_subclass_to_registry(subclass: Type[_T]) -> Type[_T]: + # Add to registry, raise an error if key has already been used. + if name in registry: + if exist_ok: + message = ( + f'{name} has already been registered as {registry[name][0].__name__}, but ' + f'exist_ok=True, so overwriting with {cls.__name__}' + ) + logger.info(message) + else: + message = ( + f'Cannot register {name} as {cls.__name__}; ' + f'name already in use for {registry[name][0].__name__}' + ) + raise ConfigurationError(message) + registry[name] = (subclass, constructor) + return subclass + + return add_subclass_to_registry + + @classmethod + def by_name(cls: Type[_RegistrableT], name: str) -> Callable[..., _RegistrableT]: + """ + Returns a callable function that constructs an argument of the registered class. Because + you can register particular functions as constructors for specific names, this isn't + necessarily the `__init__` method of some class. + """ + logger.debug(f'instantiating registered subclass {name} of {cls}') + subclass, constructor = cls.resolve_class_name(name) + if not constructor: + return cast(Type[_RegistrableT], subclass) + return cast(Callable[..., _RegistrableT], getattr(subclass, constructor)) + + @classmethod + def resolve_class_name(cls: Type[_RegistrableT], name: str) -> tuple[Type[_RegistrableT], str | None]: + """ + Returns the subclass that corresponds to the given `name`, along with the name of the + method that was registered as a constructor for that `name`, if any. + + This method also allows `name` to be a fully-specified module name, instead of a name that + was already added to the `Registry`. In that case, you cannot use a separate function as + a constructor (as you need to call `cls.register()` in order to tell us what separate + function to use). + """ + if name in Registrable._registry[cls]: + subclass, constructor = Registrable._registry[cls][name] + return subclass, constructor + if '.' in name: + # This might be a fully qualified class name, so we'll try importing its "module" + # and finding it there. + parts = name.split('.') + submodule = '.'.join(parts[:-1]) + class_name = parts[-1] + + try: + module = importlib.import_module(submodule) + except ModuleNotFoundError: + raise ConfigurationError( + f'tried to interpret {name} as a path to a class ' f'but unable to import module {submodule}' + ) + + try: + subclass = getattr(module, class_name) + constructor = None + return subclass, constructor + except AttributeError: + raise ConfigurationError( + f'tried to interpret {name} as a path to a class ' + f'but unable to find class {class_name} in {submodule}' + ) + + # is not a qualified class name + available = cls.list_available() + suggestion = _get_suggestion(name, available) + raise ConfigurationError( + ( + f"'{name}' is not a registered name for '{cls.__name__}'" + + ('. ' if not suggestion else f", did you mean '{suggestion}'? ") + ) + + "If your registered class comes from custom code, you'll need to import " + "the corresponding modules. If you're using AllenNLP from the command-line, " + "this is done by using the '--include-package' flag, or by specifying your imports " + "in a '.allennlp_plugins' file. " + 'Alternatively, you can specify your choices ' + """using fully-qualified paths, e.g. {"model": "my_module.models.MyModel"} """ + 'in which case they will be automatically imported correctly.' + ) + + @classmethod + def list_available(cls) -> list[str]: + """list default first if it exists""" + keys = list(Registrable._registry[cls].keys()) + default = cls.default_implementation + + if default is None: + return keys + if default not in keys: + raise ConfigurationError(f'Default implementation {default} is not registered') + return [default] + [k for k in keys if k != default] + + def _to_params(self) -> dict[str, Any]: + """ + Default behavior to get a params dictionary from a registrable class + that does NOT have a _to_params implementation. It is NOT recommended to + use this method. Rather this method is a minial implementation that + exists so that calling `_to_params` does not break. + + # Returns + + parameter_dict: `Dict[str, Any]` + A minimal parameter dictionary for a given registrable class. Will + get the registered name and return that as well as any positional + arguments it can find the value of. + + """ + logger.warning( + f"'{self.__class__.__name__}' does not implement '_to_params`. Will" f" use Registrable's `_to_params`." + ) + + # Get the list of parent classes in the MRO in order to check where to + # look for the registered name. Skip the first because that is the + # current class. + mro = inspect.getmro(self.__class__)[1:] + + registered_name = None + for parent in mro: + # Check if Parent has any registered classes + try: + registered_classes = self._registry[parent] + except KeyError: + continue + + # Found a dict of (name,(class,constructor)) pairs. Check if the + # current class is in it. + for name, registered_value in registered_classes.items(): + registered_class, _ = registered_value + if registered_class == self.__class__: + registered_name = name + break + + # Extra break to end the top loop. + if registered_name is not None: + break + + if registered_name is None: + raise KeyError(f"'{self.__class__.__name__}' is not registered") + + parameter_dict = {'type': registered_name} + + # Get the parameters from the init function. + for parameter in inspect.signature(self.__class__).parameters.values(): + # Skip non-positional arguments. For simplicity, these are arguments + # without a default value as those will be required for the + # `from_params` method. + if parameter.default != inspect.Parameter.empty: + logger.debug(f'Skipping parameter {parameter.name}') + continue + + # Try to get the value of the parameter from the class. Will only + # try 'name' and '_name'. If it is not there, the parameter is not + # added to the returned dict. + if hasattr(self, parameter.name): + parameter_dict[parameter.name] = getattr(self, parameter.name) + elif hasattr(self, f'_{parameter.name}'): + parameter_dict[parameter.name] = getattr(self, f'_{parameter.name}') + else: + logger.warning(f'Could not find a value for positional argument {parameter.name}') + continue + + return parameter_dict + + +def _get_suggestion(name: str, available: list[str]) -> str | None: + # First check for simple mistakes like using '-' instead of '_', or vice-versa. + for ch, repl_ch in (('_', '-'), ('-', '_')): + suggestion = name.replace(ch, repl_ch) + if suggestion in available: + return suggestion + # If we still haven't found a reasonable suggestion, we return the first suggestion + # with an edit distance (with transpositions allowed) of 1 to `name`. + from nltk.metrics.distance import edit_distance + + for suggestion in available: + if edit_distance(name, suggestion, transpositions=True) == 1: + return suggestion + return None diff --git a/turbo_alignment/common/tf/loaders/model/registry.py b/turbo_alignment/common/tf/loaders/model/registry.py index 9eba7ae..e2c5e08 100755 --- a/turbo_alignment/common/tf/loaders/model/registry.py +++ b/turbo_alignment/common/tf/loaders/model/registry.py @@ -1,4 +1,3 @@ -from allenai_common import Registrable from peft import ( LoraConfig, PeftType, @@ -12,6 +11,7 @@ AutoModelForSequenceClassification, ) +from turbo_alignment.common.registry import Registrable from turbo_alignment.settings.model import ModelType diff --git a/turbo_alignment/dataset/multimodal/multimodal.py b/turbo_alignment/dataset/multimodal/multimodal.py index 54fcde2..d83ae42 100644 --- a/turbo_alignment/dataset/multimodal/multimodal.py +++ b/turbo_alignment/dataset/multimodal/multimodal.py @@ -4,13 +4,13 @@ import numpy as np import torch -from allenai_common import Params from safetensors._safetensors_rust import SafetensorError from turbo_alignment.common.data.io import read_jsonl from turbo_alignment.common.data.multimodal import BaseModalityReader from turbo_alignment.common.data.multimodal.registry import ModalityReaderRegistry from turbo_alignment.common.logging import get_project_logger +from turbo_alignment.common.registry import Params from turbo_alignment.constants import DISABLE_LOSS_LABEL from turbo_alignment.dataset.base import AlignmentDataset from turbo_alignment.dataset.chat.chat import InferenceChatDataset, TrainChatDataset diff --git a/turbo_alignment/dataset/registry.py b/turbo_alignment/dataset/registry.py index 27cd1f9..a15aa2c 100755 --- a/turbo_alignment/dataset/registry.py +++ b/turbo_alignment/dataset/registry.py @@ -1,5 +1,4 @@ -from allenai_common import Registrable - +from turbo_alignment.common.registry import Registrable from turbo_alignment.settings.datasets import DatasetType diff --git a/turbo_alignment/metrics/metric.py b/turbo_alignment/metrics/metric.py index 5e2276e..a8437b9 100755 --- a/turbo_alignment/metrics/metric.py +++ b/turbo_alignment/metrics/metric.py @@ -1,7 +1,6 @@ import abc -from allenai_common import Registrable - +from turbo_alignment.common.registry import Registrable from turbo_alignment.metrics.registry import MetricSettings from turbo_alignment.settings.metric import MetricResults diff --git a/turbo_alignment/metrics/registry.py b/turbo_alignment/metrics/registry.py index 3c65eb4..0e269d6 100755 --- a/turbo_alignment/metrics/registry.py +++ b/turbo_alignment/metrics/registry.py @@ -1,7 +1,6 @@ from enum import Enum -from allenai_common import Registrable - +from turbo_alignment.common.registry import Registrable from turbo_alignment.settings.base import ExtraFieldsNotAllowedBaseModel from turbo_alignment.settings.datasets.chat import ChatPromptTemplate from turbo_alignment.settings.metric import MetricType diff --git a/turbo_alignment/modeling/multimodal/encoders/registry.py b/turbo_alignment/modeling/multimodal/encoders/registry.py index ff48f4d..230c56b 100644 --- a/turbo_alignment/modeling/multimodal/encoders/registry.py +++ b/turbo_alignment/modeling/multimodal/encoders/registry.py @@ -1,4 +1,4 @@ -from allenai_common import Registrable +from turbo_alignment.common.registry import Registrable class ModalityEncoderRegistry(Registrable): diff --git a/turbo_alignment/modeling/multimodal/projectors/registry.py b/turbo_alignment/modeling/multimodal/projectors/registry.py index 5bc448e..29108aa 100644 --- a/turbo_alignment/modeling/multimodal/projectors/registry.py +++ b/turbo_alignment/modeling/multimodal/projectors/registry.py @@ -1,4 +1,4 @@ -from allenai_common import Registrable +from turbo_alignment.common.registry import Registrable class MultiModalProjectorRegistry(Registrable): diff --git a/turbo_alignment/pipelines/preprocessing/multimodal.py b/turbo_alignment/pipelines/preprocessing/multimodal.py index f85a65d..93eace4 100644 --- a/turbo_alignment/pipelines/preprocessing/multimodal.py +++ b/turbo_alignment/pipelines/preprocessing/multimodal.py @@ -3,13 +3,13 @@ from typing import Tuple import torch -from allenai_common import Params from safetensors.torch import save_file from turbo_alignment.common.data.io import write_json from turbo_alignment.common.data.multimodal.base import BaseModalityReader from turbo_alignment.common.data.multimodal.registry import ModalityReaderRegistry from turbo_alignment.common.logging import get_project_logger +from turbo_alignment.common.registry import Params from turbo_alignment.modeling.multimodal.encoders import ModalityEncoderRegistry from turbo_alignment.modeling.multimodal.encoders.base import BaseModalityEncoder from turbo_alignment.pipelines.base import BaseStrategy diff --git a/turbo_alignment/trainers/utils.py b/turbo_alignment/trainers/utils.py index e3fabf8..cd7f343 100755 --- a/turbo_alignment/trainers/utils.py +++ b/turbo_alignment/trainers/utils.py @@ -6,10 +6,11 @@ import torch from accelerate import Accelerator from accelerate.utils import is_deepspeed_available -from allenai_common import Registrable from torch import nn from transformers import PreTrainedModel +from turbo_alignment.common.registry import Registrable + def concatenated_inputs( batch: dict[str, Any], prefix: str = '', device: torch.device = torch.device('cpu') From 3f1606c4f6f7d26acfd5fd85239b2b831e1e8934 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 9 Sep 2024 14:05:39 +0000 Subject: [PATCH 2/6] refactoring and mute linters --- setup.cfg | 3 +- turbo_alignment/common/registry/checks.py | 6 +- turbo_alignment/common/registry/file_utils.py | 52 +---- .../common/registry/from_params.py | 158 +------------- turbo_alignment/common/registry/lazy.py | 43 +--- turbo_alignment/common/registry/params.py | 205 +----------------- .../common/registry/registrable.py | 136 +----------- 7 files changed, 23 insertions(+), 580 deletions(-) diff --git a/setup.cfg b/setup.cfg index f3a1b9c..34b4b20 100644 --- a/setup.cfg +++ b/setup.cfg @@ -162,7 +162,8 @@ disable= W0622, # Too many ancestors R0901, - + # logging-fstring-interpolation + W1203, ignore= data ignore-patterns= diff --git a/turbo_alignment/common/registry/checks.py b/turbo_alignment/common/registry/checks.py index b3e263a..627b3c5 100644 --- a/turbo_alignment/common/registry/checks.py +++ b/turbo_alignment/common/registry/checks.py @@ -1,3 +1,4 @@ +# mypy: ignore-errors import logging from typing import Any @@ -5,11 +6,6 @@ class ConfigurationError(Exception): - """ - The exception raised by any AllenNLP object when it's misconfigured - (e.g. missing properties, invalid properties, unknown properties). - """ - def __reduce__(self) -> str | tuple[Any, ...]: return type(self), (self.message,) diff --git a/turbo_alignment/common/registry/file_utils.py b/turbo_alignment/common/registry/file_utils.py index a7788f8..ad3fc82 100644 --- a/turbo_alignment/common/registry/file_utils.py +++ b/turbo_alignment/common/registry/file_utils.py @@ -1,3 +1,4 @@ +# mypy: ignore-errors import logging import os from pathlib import Path @@ -6,10 +7,6 @@ logger = logging.getLogger(__name__) -CACHE_ROOT = Path(os.getenv('ALLENNLP_CACHE_ROOT', Path.home() / '.allennlp')) -CACHE_DIRECTORY = str(CACHE_ROOT / 'cache') -DEPRECATED_CACHE_DIRECTORY = str(CACHE_ROOT / 'datasets') - def cached_path( url_or_filename: str | os.PathLike, @@ -17,55 +14,10 @@ def cached_path( extract_archive: bool = False, force_extract: bool = False, ) -> str: - """ - Given something that might be a URL or local path, determine which. - If it's a remote resource, download the file and cache it, and - then return the path to the cached file. If it's already a local path, - make sure the file exists and return the path. - - For URLs, "http://", "https://", "s3://", "gs://", and "hf://" are all supported. - The latter corresponds to the HuggingFace Hub. - - For example, to download the PyTorch weights for the model `epwalsh/bert-xsmall-dummy` - on HuggingFace, you could do: - - ```python - cached_path("hf://epwalsh/bert-xsmall-dummy/pytorch_model.bin") - ``` - - For paths or URLs that point to a tarfile or zipfile, you can also add a path - to a specific file to the `url_or_filename` preceeded by a "!", and the archive will - be automatically extracted (provided you set `extract_archive` to `True`), - returning the local path to the specific file. For example: - - ```python - cached_path("model.tar.gz!weights.th", extract_archive=True) - ``` - - # Parameters - - url_or_filename : `Union[str, Path]` - A URL or path to parse and possibly download. - - cache_dir : `Union[str, Path]`, optional (default = `None`) - The directory to cache downloads. - - extract_archive : `bool`, optional (default = `False`) - If `True`, then zip or tar.gz archives will be automatically extracted. - In which case the directory is returned. - - force_extract : `bool`, optional (default = `False`) - If `True` and the file is an archive file, it will be extracted regardless - of whether or not the extracted directory already exists. - - !!! Warning - Use this flag with caution! This can lead to race conditions if used - from multiple processes on the same file. - """ return str( _cached_path.cached_path( url_or_filename, - cache_dir=cache_dir or CACHE_DIRECTORY, + cache_dir=cache_dir, extract_archive=extract_archive, force_extract=force_extract, ) diff --git a/turbo_alignment/common/registry/from_params.py b/turbo_alignment/common/registry/from_params.py index 064672b..731bf59 100644 --- a/turbo_alignment/common/registry/from_params.py +++ b/turbo_alignment/common/registry/from_params.py @@ -1,3 +1,5 @@ +# mypy: ignore-errors +# pylint: skip-file import collections.abc import inspect import logging @@ -27,18 +29,10 @@ T = TypeVar('T', bound='FromParams') -# If a function parameter has no default value specified, -# this is what the inspect module returns. _NO_DEFAULT = inspect.Parameter.empty def takes_arg(obj, arg: str) -> bool: - """ - Checks whether the provided obj takes a certain arg. - If it's a class, we're really checking whether its constructor does. - If it's a function or method, we're checking the object itself. - Otherwise, we raise an error. - """ if inspect.isclass(obj): signature = inspect.signature(obj.__init__) elif inspect.ismethod(obj) or inspect.isfunction(obj): @@ -49,12 +43,6 @@ def takes_arg(obj, arg: str) -> bool: def takes_kwargs(obj) -> bool: - """ - Checks whether a provided object takes in any positional arguments. - Similar to takes_arg, we do this for both the __init__ function of - the class or a function / method - Otherwise, we raise an error - """ if inspect.isclass(obj): signature = inspect.signature(obj.__init__) elif inspect.ismethod(obj) or inspect.isfunction(obj): @@ -80,10 +68,6 @@ def can_construct_from_params(type_: Type) -> bool: def is_base_registrable(cls) -> bool: - """ - Checks whether this is a class that directly inherits from Registrable, or is a subclass of such - a class. - """ from turbo_alignment.common.registry.registrable import ( Registrable, # import here to avoid circular imports ) @@ -98,11 +82,6 @@ def is_base_registrable(cls) -> bool: def remove_optional(annotation: type): - """ - Optional[X] annotations are actually represented as Union[X, NoneType]. - For our purposes, the "Optional" part is not interesting, so here we - throw it away. - """ origin = getattr(annotation, '__origin__', None) args = getattr(annotation, '__args__', ()) @@ -140,9 +119,6 @@ def infer_method_params(cls: Type[T], method: Callable) -> Dict[str, inspect.Par if not has_kwargs: return parameters - # "mro" is "method resolution order". The first one is the current class, the next is the - # first superclass, and so on. We take the first superclass we find that inherits from - # FromParams. super_class = None for super_class_candidate in cls.mro()[1:]: if issubclass(super_class_candidate, FromParams): @@ -153,60 +129,29 @@ def infer_method_params(cls: Type[T], method: Callable) -> Dict[str, inspect.Par else: super_parameters = {} - return {**super_parameters, **parameters} # Subclass parameters overwrite superclass ones + return {**super_parameters, **parameters} def create_kwargs(constructor: Callable[..., T], cls: Type[T], params: Params, **extras) -> Dict[str, Any]: - """ - Given some class, a `Params` object, and potentially other keyword arguments, - create a dict of keyword args suitable for passing to the class's constructor. - - The function does this by finding the class's constructor, matching the constructor - arguments to entries in the `params` object, and instantiating values for the parameters - using the type annotation and possibly a from_params method. - - Any values that are provided in the `extras` will just be used as is. - For instance, you might provide an existing `Vocabulary` this way. - """ - # Get the signature of the constructor. - kwargs: Dict[str, Any] = {} parameters = infer_params(cls, constructor) accepts_kwargs = False - # Iterate over all the constructor parameters and their annotations. for param_name, param in parameters.items(): - # Skip "self". You're not *required* to call the first parameter "self", - # so in theory this logic is fragile, but if you don't call the self parameter - # "self" you kind of deserve what happens. if param_name == 'self': continue if param.kind == param.VAR_KEYWORD: - # When a class takes **kwargs, we do two things: first, we assume that the **kwargs are - # getting passed to the super class, so we inspect super class constructors to get - # allowed arguments (that happens in `infer_params` above). Second, we store the fact - # that the method allows extra keys; if we get extra parameters, instead of crashing, - # we'll just pass them as-is to the constructor, and hope that you know what you're - # doing. accepts_kwargs = True continue - # If the annotation is a compound type like typing.Dict[str, int], - # it will have an __origin__ field indicating `typing.Dict` - # and an __args__ field indicating `(str, int)`. We capture both. annotation = remove_optional(param.annotation) explicitly_set = param_name in params + # FIXME constructed_arg = pop_and_construct_arg(cls.__name__, param_name, annotation, param.default, params, **extras) - # If the param wasn't explicitly set in `params` and we just ended up constructing - # the default value for the parameter, we can just omit it. - # Leaving it in can cause issues with **kwargs in some corner cases, where you might end up - # with multiple values for a single parameter (e.g., the default value gives you lazy=False - # for a dataset reader inside **kwargs, but a particular dataset reader actually hard-codes - # lazy=True - the superclass sees both lazy=True and lazy=False in its constructor). if explicitly_set or constructed_arg is not param.default: kwargs[param_name] = constructed_arg @@ -218,29 +163,14 @@ def create_kwargs(constructor: Callable[..., T], cls: Type[T], params: Params, * def create_extras(cls: Type[T], extras: Dict[str, Any]) -> Dict[str, Any]: - """ - Given a dictionary of extra arguments, returns a dictionary of - kwargs that actually are a part of the signature of the cls.from_params - (or cls) method. - """ subextras: Dict[str, Any] = {} if hasattr(cls, 'from_params'): - from_params_method = cls.from_params # type: ignore + from_params_method = cls.from_params else: - # In some rare cases, we get a registered subclass that does _not_ have a - # from_params method (this happens with Activations, for instance, where we - # register pytorch modules directly). This is a bit of a hack to make those work, - # instead of adding a `from_params` method for them somehow. Then the extras - # in the class constructor are what we are looking for, to pass on. from_params_method = cls if takes_kwargs(from_params_method): - # If annotation.params accepts **kwargs, we need to pass them all along. - # For example, `BasicTextFieldEmbedder.from_params` requires a Vocabulary - # object, but `TextFieldEmbedder.from_params` does not. subextras = extras else: - # Otherwise, only supply the ones that are actual args; any additional ones - # will cause a TypeError. subextras = {k: v for k, v in extras.items() if takes_arg(from_params_method, k)} return subextras @@ -253,25 +183,17 @@ def construct_arg( default: Any, **extras, ) -> Any: - """ - The first two parameters here are only used for logging if we encounter an error. - """ origin = getattr(annotation, '__origin__', None) args = getattr(annotation, '__args__', []) - # The parameter is optional if its default value is not the "no default" sentinel. optional = default != _NO_DEFAULT if hasattr(annotation, 'from_params'): if popped_params is default: return default if popped_params is not None: - # Our params have an entry for this, so we use that. - subextras = create_extras(annotation, extras) - # In some cases we allow a string instead of a param dict, so - # we need to handle that case separately. if isinstance(popped_params, str): popped_params = Params({'type': popped_params}) elif isinstance(popped_params, dict): @@ -280,31 +202,22 @@ def construct_arg( return result if not optional: - # Not optional and not supplied, that's an error! raise ConfigurationError(f'expected key {argument_name} for {class_name}') return default - # If the parameter type is a Python primitive, just pop it off - # using the correct casting pop_xyz operation. if annotation in {int, bool}: if type(popped_params) in {int, bool}: return annotation(popped_params) raise TypeError(f'Expected {argument_name} to be a {annotation.__name__}.') if annotation == str: - # Strings are special because we allow casting from Path to str. if isinstance(popped_params, str) or isinstance(popped_params, Path): return str(popped_params) # type: ignore raise TypeError(f'Expected {argument_name} to be a string.') if annotation == float: - # Floats are special because in Python, you can put an int wherever you can put a float. - # https://mypy.readthedocs.io/en/stable/duck_type_compatibility.html if type(popped_params) in {int, float}: return popped_params raise TypeError(f'Expected {argument_name} to be numeric.') - # This is special logic for handling types like Dict[str, TokenIndexer], - # List[TokenIndexer], Tuple[TokenIndexer, Tokenizer], and Set[TokenIndexer], - # which it creates by instantiating each value from_params and returning the resulting structure. if ( origin in {collections.abc.Mapping, Mapping, Dict, dict} and len(args) == 2 @@ -362,11 +275,8 @@ def construct_arg( return value_set if origin == Union: - # Storing this so we can recover it later if we need to. backup_params = deepcopy(popped_params) - # We'll try each of the given types in the union sequentially, returning the first one that - # succeeds. error_chain: Optional[Exception] = None for arg_annotation in args: try: @@ -379,14 +289,11 @@ def construct_arg( **extras, ) except (ValueError, TypeError, ConfigurationError, AttributeError) as e: - # Our attempt to construct the argument may have modified popped_params, so we - # restore it here. popped_params = deepcopy(backup_params) e.args = (f'While constructing an argument of type {arg_annotation}',) + e.args e.__cause__ = error_chain error_chain = e - # If none of them succeeded, we crash. config_error = ConfigurationError(f'Failed to construct argument {argument_name} with type {annotation}.') config_error.__cause__ = error_chain raise config_error @@ -398,9 +305,6 @@ def construct_arg( subextras = create_extras(value_cls, extras) return Lazy(value_cls, params=deepcopy(popped_params), constructor_extras=subextras) # type: ignore - # For any other kind of iterable, we will just assume that a list is good enough, and treat - # it the same as List. This condition needs to be at the end, so we don't catch other kinds - # of Iterables with this branch. if ( origin in {collections.abc.Iterable, Iterable, List, list} and len(args) == 1 @@ -423,18 +327,12 @@ def construct_arg( return value_list - # Pass it on as is and hope for the best. ¯\_(ツ)_/¯ if isinstance(popped_params, Params): return popped_params.as_dict() return popped_params class FromParams: - """ - Mixin to give a from_params method to classes. We create a distinct base class for this - because sometimes we want non-Registrable classes to be instantiatable from_params. - """ - @classmethod def from_params( cls: Type[T], @@ -443,28 +341,6 @@ def from_params( constructor_to_inspect: Union[Callable[..., T], Callable[[T], None]] = None, **extras, ) -> T: - """ - This is the automatic implementation of `from_params`. Any class that subclasses - `FromParams` (or `Registrable`, which itself subclasses `FromParams`) gets this - implementation for free. If you want your class to be instantiated from params in the - "obvious" way -- pop off parameters and hand them to your constructor with the same names -- - this provides that functionality. - - If you need more complex logic in your from `from_params` method, you'll have to implement - your own method that overrides this one. - - The `constructor_to_call` and `constructor_to_inspect` arguments deal with a bit of - redirection that we do. We allow you to register particular `@classmethods` on a class as - the constructor to use for a registered name. This lets you, e.g., have a single - `Vocabulary` class that can be constructed in two different ways, with different names - registered to each constructor. In order to handle this, we need to know not just the class - we're trying to construct (`cls`), but also what method we should inspect to find its - arguments (`constructor_to_inspect`), and what method to call when we're done constructing - arguments (`constructor_to_call`). These two methods are the same when you've used a - `@classmethod` as your constructor, but they are `different` when you use the default - constructor (because you inspect `__init__`, but call `cls()`). - """ - from turbo_alignment.common.registry.registrable import ( Registrable, # import here to avoid circular imports ) @@ -491,10 +367,6 @@ def from_params( registered_subclasses = Registrable._registry.get(cls) if is_base_registrable(cls) and registered_subclasses is None: - # NOTE(mattg): There are some potential corner cases in this logic if you have nested - # Registrable types. We don't currently have any of those, but if we ever get them, - # adding some logic to check `constructor_to_call` should solve the issue. Not - # bothering to add that unnecessary complexity for now. raise ConfigurationError( 'Tried to construct an abstract Registrable base class that has no registered ' 'concrete types. This might mean that you need to use --include-package to get ' @@ -502,17 +374,16 @@ def from_params( ) if registered_subclasses is not None and not constructor_to_call: - # We know `cls` inherits from Registrable, so we'll use a cast to make mypy happy. - + # FIXME as_registrable = cast(Type[Registrable], cls) default_to_first_choice = as_registrable.default_implementation is not None + # FIXME choice = params.pop_choice( 'type', choices=as_registrable.list_available(), default_to_first_choice=default_to_first_choice, ) subclass, constructor_name = as_registrable.resolve_class_name(choice) - # See the docstring for an explanation of what's going on here. if not constructor_name: constructor_to_inspect = subclass.__init__ constructor_to_call = subclass # type: ignore @@ -521,9 +392,7 @@ def from_params( constructor_to_call = constructor_to_inspect if hasattr(subclass, 'from_params'): - # We want to call subclass.from_params. extras = create_extras(subclass, extras) - # mypy can't follow the typing redirection that we do, so we explicitly cast here. retyped_subclass = cast(Type[T], subclass) return retyped_subclass.from_params( params=params, @@ -531,29 +400,16 @@ def from_params( constructor_to_inspect=constructor_to_inspect, **extras, ) - # In some rare cases, we get a registered subclass that does _not_ have a - # from_params method (this happens with Activations, for instance, where we - # register pytorch modules directly). This is a bit of a hack to make those work, - # instead of adding a `from_params` method for them somehow. We just trust that - # you've done the right thing in passing your parameters, and nothing else needs to - # be recursively constructed. return subclass(**params) # type: ignore - # This is not a base class, so convert our params and extras into a dict of kwargs. - - # See the docstring for an explanation of what's going on here. if not constructor_to_inspect: constructor_to_inspect = cls.__init__ if not constructor_to_call: constructor_to_call = cls if constructor_to_inspect == object.__init__: - # This class does not have an explicit constructor, so don't give it any kwargs. - # Without this logic, create_kwargs will look at object.__init__ and see that - # it takes *args and **kwargs and look for those. kwargs: Dict[str, Any] = {} params.assert_empty(cls.__name__) else: - # This class has a constructor, so create kwargs for it. constructor_to_inspect = cast(Callable[..., T], constructor_to_inspect) kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras) diff --git a/turbo_alignment/common/registry/lazy.py b/turbo_alignment/common/registry/lazy.py index d6d4a1a..fd715e1 100644 --- a/turbo_alignment/common/registry/lazy.py +++ b/turbo_alignment/common/registry/lazy.py @@ -1,3 +1,4 @@ +# mypy: ignore-errors import copy import inspect from typing import Any, Callable, Generic, Type, TypeVar @@ -8,43 +9,6 @@ class Lazy(Generic[T]): - """ - This class is for use when constructing objects using `FromParams`, when an argument to a - constructor has a _sequential dependency_ with another argument to the same constructor. - - For example, in a `Trainer` class you might want to take a `Model` and an `Optimizer` as arguments, - but the `Optimizer` needs to be constructed using the parameters from the `Model`. You can give - the type annotation `Lazy[Optimizer]` to the optimizer argument, then inside the constructor - call `optimizer.construct(parameters=model.parameters)`. - - This is only recommended for use when you have registered a `@classmethod` as the constructor - for your class, instead of using `__init__`. Having a `Lazy[]` type annotation on an argument - to an `__init__` method makes your class completely dependent on being constructed using the - `FromParams` pipeline, which is not a good idea. - - The actual implementation here is incredibly simple; the logic that handles the lazy - construction is actually found in `FromParams`, where we have a special case for a `Lazy` type - annotation. - - ```python - @classmethod - def my_constructor( - cls, - some_object: Lazy[MyObject], - optional_object: Lazy[MyObject] = None, - # or: - # optional_object: Optional[Lazy[MyObject]] = None, - optional_object_with_default: Optional[Lazy[MyObject]] = Lazy(MyObjectDefault), - required_object_with_default: Lazy[MyObject] = Lazy(MyObjectDefault), - ) -> MyClass: - obj1 = some_object.construct() - obj2 = None if optional_object is None else optional_object.construct() - obj3 = None optional_object_with_default is None else optional_object_with_default.construct() - obj4 = required_object_with_default.construct() - ``` - - """ - def __init__( self, constructor: Type[T] | Callable[..., T], @@ -72,10 +36,5 @@ def constructor_to_use(**kwargs): return self._constructor def construct(self, **kwargs) -> T: - """ - Call the constructor to create an instance of `T`. - """ - # If there are duplicate keys between self._constructor_extras and kwargs, - # this will overwrite the ones in self._constructor_extras with what's in kwargs. contructor_kwargs = {**self._constructor_extras, **kwargs} return self.constructor(**contructor_kwargs) diff --git a/turbo_alignment/common/registry/params.py b/turbo_alignment/common/registry/params.py index 8d4dc41..3f666d6 100644 --- a/turbo_alignment/common/registry/params.py +++ b/turbo_alignment/common/registry/params.py @@ -1,3 +1,5 @@ +# mypy: ignore-errors +# pylint: skip-file import copy import json import logging @@ -9,14 +11,13 @@ from os import PathLike from typing import Any, Dict, Iterable, List, Optional, Set, TypeVar, Union -# _jsonnet doesn't work on Windows, so we have to use fakes. try: from _jsonnet import evaluate_file, evaluate_snippet except ImportError: def evaluate_file(filename: str, **_kwargs) -> str: logger.warning(f'error loading _jsonnet (this is expected on Windows), treating {filename} as plain json') - with open(filename, 'r') as evaluation_file: + with open(filename, 'r', 'utf-8') as evaluation_file: return evaluation_file.read() def evaluate_snippet(_filename: str, expr: str, **_kwargs) -> str: @@ -31,58 +32,33 @@ def evaluate_snippet(_filename: str, expr: str, **_kwargs) -> str: def infer_and_cast(value: Any): - """ - In some cases we'll be feeding params dicts to functions we don't own; - for example, PyTorch optimizers. In that case we can't use `pop_int` - or similar to force casts (which means you can't specify `int` parameters - using environment variables). This function takes something that looks JSON-like - and recursively casts things that look like (bool, int, float) to (bool, int, float). - """ - if isinstance(value, (int, float, bool)): - # Already one of our desired types, so leave as is. return value if isinstance(value, list): - # Recursively call on each list element. return [infer_and_cast(item) for item in value] if isinstance(value, dict): - # Recursively call on each dict value. return {key: infer_and_cast(item) for key, item in value.items()} if isinstance(value, str): - # If it looks like a bool, make it a bool. if value.lower() == 'true': return True if value.lower() == 'false': return False - # See if it could be an int. try: return int(value) except ValueError: pass - # See if it could be a float. try: return float(value) except ValueError: - # Just return it as a string. return value raise ValueError(f'cannot infer type of {value}') def _is_encodable(value: str) -> bool: - """ - We need to filter out environment variables that can't - be unicode-encoded to avoid a "surrogates not allowed" - error in jsonnet. - """ - # Idiomatically you'd like to not check the != b"" - # but mypy doesn't like that. return (value == '') or (value.encode('utf-8', 'ignore') != b'') def _environment_variables() -> Dict[str, str]: - """ - Wraps `os.environ` to filter out non-encodable values. - """ return {key: value for key, value in os.environ.items() if _is_encodable(value)} @@ -138,9 +114,6 @@ def parse_overrides(serialized_overrides: str, ext_vars: Optional[Dict[str, Any] def _is_dict_free(obj: Any) -> bool: - """ - Returns False if obj is a dict, or if it's a list with an element that _has_dict. - """ if isinstance(obj, dict): return False if isinstance(obj, list): @@ -149,31 +122,6 @@ def _is_dict_free(obj: Any) -> bool: class Params(MutableMapping): - """ - Represents a parameter dictionary with a history, and contains other functionality around - parameter passing and validation for AllenNLP. - - There are currently two benefits of a `Params` object over a plain dictionary for parameter - passing: - - 1. We handle a few kinds of parameter validation, including making sure that parameters - representing discrete choices actually have acceptable values, and making sure no extra - parameters are passed. - 2. We log all parameter reads, including default values. This gives a more complete - specification of the actual parameters used than is given in a JSON file, because - those may not specify what default values were used, whereas this will log them. - - !!! Consumption - The convention for using a `Params` object in AllenNLP is that you will consume the parameters - as you read them, so that there are none left when you've read everything you expect. This - lets us easily validate that you didn't pass in any `extra` parameters, just by making sure - that the parameter dictionary is empty. You should do this when you're done handling - parameters, by calling `Params.assert_empty`. - """ - - # This allows us to check for the presence of "None" as a default argument, - # which we require because we make a distinction between passing a value of "None" - # and passing no value to the default parameter of "pop". DEFAULT = object() def __init__(self, params: Dict[str, Any], history: str = '') -> None: @@ -181,14 +129,6 @@ def __init__(self, params: Dict[str, Any], history: str = '') -> None: self.history = history def pop(self, key: str, default: Any = DEFAULT, keep_as_dict: bool = False) -> Any: - """ - Performs the functionality associated with dict.pop(key), along with checking for - returned dictionaries, replacing them with Param objects with an updated history - (unless keep_as_dict is True, in which case we leave them as dictionaries). - - If `key` is not present in the dictionary, and no default was specified, we raise a - `ConfigurationError`, instead of the typical `KeyError`. - """ if default is self.DEFAULT: try: value = self.params.pop(key) @@ -206,27 +146,18 @@ def pop(self, key: str, default: Any = DEFAULT, keep_as_dict: bool = False) -> A return self._check_is_dict(key, value) def pop_int(self, key: str, default: Any = DEFAULT) -> Optional[int]: - """ - Performs a pop and coerces to an int. - """ value = self.pop(key, default) if value is None: return None return int(value) def pop_float(self, key: str, default: Any = DEFAULT) -> Optional[float]: - """ - Performs a pop and coerces to a float. - """ value = self.pop(key, default) if value is None: return None return float(value) def pop_bool(self, key: str, default: Any = DEFAULT) -> Optional[bool]: - """ - Performs a pop and coerces to a bool. - """ value = self.pop(key, default) if value is None: return None @@ -239,10 +170,6 @@ def pop_bool(self, key: str, default: Any = DEFAULT) -> Optional[bool]: raise ValueError('Cannot convert variable to bool: ' + value) def get(self, key: str, default: Any = DEFAULT): - """ - Performs the functionality associated with dict.get(key) but also checks for returned - dicts and returns a Params object in their place with an updated history. - """ default = None if default is self.DEFAULT else default value = self.params.get(key, default) return self._check_is_dict(key, value) @@ -254,41 +181,6 @@ def pop_choice( default_to_first_choice: bool = False, allow_class_names: bool = True, ) -> Any: - """ - Gets the value of `key` in the `params` dictionary, ensuring that the value is one of - the given choices. Note that this `pops` the key from params, modifying the dictionary, - consistent with how parameters are processed in this codebase. - - # Parameters - - key: `str` - - Key to get the value from in the param dictionary - - choices: `List[Any]` - - A list of valid options for values corresponding to `key`. For example, if you're - specifying the type of encoder to use for some part of your model, the choices might be - the list of encoder classes we know about and can instantiate. If the value we find in - the param dictionary is not in `choices`, we raise a `ConfigurationError`, because - the user specified an invalid value in their parameter file. - - default_to_first_choice: `bool`, optional (default = `False`) - - If this is `True`, we allow the `key` to not be present in the parameter - dictionary. If the key is not present, we will use the return as the value the first - choice in the `choices` list. If this is `False`, we raise a - `ConfigurationError`, because specifying the `key` is required (e.g., you `have` to - specify your model class when running an experiment, but you can feel free to use - default settings for encoders if you want). - - allow_class_names: `bool`, optional (default = `True`) - - If this is `True`, then we allow unknown choices that look like fully-qualified class names. - This is to allow e.g. specifying a model type as my_library.my_model.MyModel - and importing it on the fly. Our check for "looks like" is extremely lenient - and consists of checking that the value contains a '.'. - """ default = choices[0] if default_to_first_choice else self.DEFAULT value = self.pop(key, default) ok_because_class_name = allow_class_names and '.' in value @@ -304,20 +196,6 @@ def pop_choice( return value def as_dict(self, quiet: bool = False, infer_type_and_cast: bool = False): - """ - Sometimes we need to just represent the parameters as a dict, for instance when we pass - them to PyTorch code. - - # Parameters - - quiet: `bool`, optional (default = `False`) - - Whether to log the parameters before returning them as a dict. - - infer_type_and_cast: `bool`, optional (default = `False`) - - If True, we infer types and cast (e.g. things that look like floats to floats). - """ if infer_type_and_cast: params_as_dict = infer_and_cast(self.params) else: @@ -356,19 +234,9 @@ def recurse(parameters, path): return flat_params def duplicate(self) -> 'Params': - """ - Uses `copy.deepcopy()` to create a duplicate (but fully distinct) - copy of these Params. - """ return copy.deepcopy(self) def assert_empty(self, class_name: str): - """ - Raises a `ConfigurationError` if `self.params` is not empty. We take `class_name` as - an argument so that the error message gives some idea of where an error happened, if there - was one. `class_name` should be the name of the `calling` class, the one that got extra - parameters (if there are any). - """ if self.params: raise ConfigurationError('Extra parameters passed to {}: {}'.format(class_name, self.params)) @@ -404,30 +272,6 @@ def from_file( params_overrides: Union[str, Dict[str, Any]] = '', ext_vars: dict = None, ) -> 'Params': - """ - Load a `Params` object from a configuration file. - - # Parameters - - params_file: `str` - - The path to the configuration file to load. - - params_overrides: `Union[str, Dict[str, Any]]`, optional (default = `""`) - - A dict of overrides that can be applied to final object. - e.g. `{"model.embedding_dim": 10}` will change the value of "embedding_dim" - within the "model" object of the config to 10. If you wanted to override the entire - "model" object of the config, you could do `{"model": {"type": "other_type", ...}}`. - - ext_vars: `dict`, optional - - Our config files are Jsonnet, which allows specifying external variables - for later substitution. Typically we substitute these using environment - variables; however, you can also specify them here, in which case they - take priority over environment variables. - e.g. {"HOME_DIR": "/Users/allennlp/home"} - """ if ext_vars is None: ext_vars = {} @@ -449,23 +293,10 @@ def from_file( return cls(param_dict) def to_file(self, params_file: str, preference_orders: List[List[str]] = None) -> None: - with open(params_file, 'w') as handle: + with open(params_file, 'w', 'utf-8') as handle: json.dump(self.as_ordered_dict(preference_orders), handle, indent=4) def as_ordered_dict(self, preference_orders: List[List[str]] = None) -> OrderedDict: - """ - Returns Ordered Dict of Params from list of partial order preferences. - - # Parameters - - preference_orders: `List[List[str]]`, optional - - `preference_orders` is list of partial preference orders. ["A", "B", "C"] means - "A" > "B" > "C". For multiple preference_orders first will be considered first. - Keys not found, will have last but alphabetical preference. Default Preferences: - `[["dataset_reader", "iterator", "model", "train_data_path", "validation_data_path", - "test_data_path", "trainer", "vocabulary"], ["type"]]` - """ params_dict = self.as_dict(quiet=True) if not preference_orders: preference_orders = [] @@ -484,14 +315,10 @@ def as_ordered_dict(self, preference_orders: List[List[str]] = None) -> OrderedD preference_orders.append(['type']) def order_func(key): - # Makes a tuple to use for ordering. The tuple is an index into each of the `preference_orders`, - # followed by the key itself. This gives us integer sorting if you have a key in one of the - # `preference_orders`, followed by alphabetical ordering if not. order_tuple = [order.index(key) if key in order else len(order) for order in preference_orders] return order_tuple + [key] def order_dict(dictionary, order_func): - # Recursively orders dictionary according to scoring order_func result = OrderedDict() for key, val in sorted(dictionary.items(), key=lambda item: order_func(item[0])): result[key] = order_dict(val, order_func) if isinstance(val, dict) else val @@ -500,14 +327,6 @@ def order_dict(dictionary, order_func): return order_dict(params_dict, order_func) def get_hash(self) -> str: - """ - Returns a hash code representing the current state of this `Params` object. We don't - want to implement `__hash__` because that has deeper python implications (and this is a - mutable object), but this will give you a representation of the current state. - We use `zlib.adler32` instead of Python's builtin `hash` because the random seed for the - latter is reset on each new program invocation, as discussed here: - https://stackoverflow.com/questions/27954892/deterministic-hashing-in-python-3. - """ dumped = json.dumps(self.params, sort_keys=True) hashed = zlib.adler32(dumped.encode()) return str(hashed) @@ -524,16 +343,6 @@ def pop_choice( history: str = '?.', allow_class_names: bool = True, ) -> Any: - """ - Performs the same function as `Params.pop_choice`, but is required in order to deal with - places that the Params object is not welcome, such as inside Keras layers. See the docstring - of that method for more detail on how this function works. - - This method adds a `history` parameter, in the off-chance that you know it, so that we can - reproduce `Params.pop_choice` exactly. We default to using "?." if you don't know the - history, so you'll have to fix that in the log if you want to actually recover the logged - parameters. - """ value = Params(params, history).pop_choice( key, choices, default_to_first_choice, allow_class_names=allow_class_names ) @@ -552,8 +361,10 @@ def _replace_none(params: Any) -> Any: return params -def remove_keys_from_params(params: Params, keys: List[str] = ['pretrained_file', 'initializer']): - if isinstance(params, Params): # The model could possibly be a string, for example. +def remove_keys_from_params(params: Params, keys: list[str] | None): + if keys is None: + keys = ['pretrained_file', 'initializer'] + if isinstance(params, Params): param_keys = params.keys() for key in keys: if key in param_keys: diff --git a/turbo_alignment/common/registry/registrable.py b/turbo_alignment/common/registry/registrable.py index dac93c3..dab76f7 100644 --- a/turbo_alignment/common/registry/registrable.py +++ b/turbo_alignment/common/registry/registrable.py @@ -1,8 +1,5 @@ -""" -`allennlp.common.registrable.Registrable` is a "mixin" for endowing -any base class with a named registry for its subclasses and a decorator -for registering them. -""" +# mypy: ignore-errors +# pylint: skip-file import importlib import inspect import logging @@ -21,27 +18,6 @@ class Registrable(FromParams): - """ - Any class that inherits from `Registrable` gains access to a named registry for its - subclasses. To register them, just decorate them with the classmethod - `@BaseClass.register(name)`. - - After which you can call `BaseClass.list_available()` to get the keys for the - registered subclasses, and `BaseClass.by_name(name)` to get the corresponding subclass. - Note that the registry stores the subclasses themselves; not class instances. - In most cases you would then call `from_params(params)` on the returned subclass. - - You can specify a default by setting `BaseClass.default_implementation`. - If it is set, it will be the first element of `list_available()`. - - Note that if you use this class to implement a new `Registrable` abstract class, - you must ensure that all subclasses of the abstract class are loaded when the module is - loaded, because the subclasses register themselves in their respective files. You can - achieve this by having the abstract class and all subclasses in the __init__.py of the - module in which they reside (as this causes any import of either the abstract class or - a subclass to load all other subclasses and the abstract class). - """ - _registry: ClassVar[DefaultDict[type, _SubclassRegistry]] = defaultdict(dict) default_implementation: str | None = None @@ -50,71 +26,9 @@ class Registrable(FromParams): def register( cls, name: str, constructor: str | None = None, exist_ok: bool = False ) -> Callable[[Type[_T]], Type[_T]]: - """ - Register a class under a particular name. - - # Parameters - - name : `str` - The name to register the class under. - constructor : `str`, optional (default=`None`) - The name of the method to use on the class to construct the object. If this is given, - we will use this method (which must be a `@classmethod`) instead of the default - constructor. - exist_ok : `bool`, optional (default=`False`) - If True, overwrites any existing models registered under `name`. Else, - throws an error if a model is already registered under `name`. - - # Examples - - To use this class, you would typically have a base class that inherits from `Registrable`: - - ```python - class Vocabulary(Registrable): - ... - ``` - - Then, if you want to register a subclass, you decorate it like this: - - ```python - @Vocabulary.register("my-vocabulary") - class MyVocabulary(Vocabulary): - def __init__(self, param1: int, param2: str): - ... - ``` - - Registering a class like this will let you instantiate a class from a config file, where you - give `"type": "my-vocabulary"`, and keys corresponding to the parameters of the `__init__` - method (note that for this to work, those parameters must have type annotations). - - If you want to have the instantiation from a config file call a method other than the - constructor, either because you have several different construction paths that could be - taken for the same object (as we do in `Vocabulary`) or because you have logic you want to - happen before you get to the constructor (as we do in `Embedding`), you can register a - specific `@classmethod` as the constructor to use, like this: - - ```python - @Vocabulary.register("my-vocabulary-from-instances", constructor="from_instances") - @Vocabulary.register("my-vocabulary-from-files", constructor="from_files") - class MyVocabulary(Vocabulary): - def __init__(self, some_params): - ... - - @classmethod - def from_instances(cls, some_other_params) -> MyVocabulary: - ... # construct some_params from instances - return cls(some_params) - - @classmethod - def from_files(cls, still_other_params) -> MyVocabulary: - ... # construct some_params from files - return cls(some_params) - ``` - """ registry = Registrable._registry[cls] def add_subclass_to_registry(subclass: Type[_T]) -> Type[_T]: - # Add to registry, raise an error if key has already been used. if name in registry: if exist_ok: message = ( @@ -135,11 +49,6 @@ def add_subclass_to_registry(subclass: Type[_T]) -> Type[_T]: @classmethod def by_name(cls: Type[_RegistrableT], name: str) -> Callable[..., _RegistrableT]: - """ - Returns a callable function that constructs an argument of the registered class. Because - you can register particular functions as constructors for specific names, this isn't - necessarily the `__init__` method of some class. - """ logger.debug(f'instantiating registered subclass {name} of {cls}') subclass, constructor = cls.resolve_class_name(name) if not constructor: @@ -148,15 +57,6 @@ def by_name(cls: Type[_RegistrableT], name: str) -> Callable[..., _RegistrableT] @classmethod def resolve_class_name(cls: Type[_RegistrableT], name: str) -> tuple[Type[_RegistrableT], str | None]: - """ - Returns the subclass that corresponds to the given `name`, along with the name of the - method that was registered as a constructor for that `name`, if any. - - This method also allows `name` to be a fully-specified module name, instead of a name that - was already added to the `Registry`. In that case, you cannot use a separate function as - a constructor (as you need to call `cls.register()` in order to tell us what separate - function to use). - """ if name in Registrable._registry[cls]: subclass, constructor = Registrable._registry[cls][name] return subclass, constructor @@ -203,7 +103,6 @@ def resolve_class_name(cls: Type[_RegistrableT], name: str) -> tuple[Type[_Regis @classmethod def list_available(cls) -> list[str]: - """list default first if it exists""" keys = list(Registrable._registry[cls].keys()) default = cls.default_implementation @@ -214,46 +113,25 @@ def list_available(cls) -> list[str]: return [default] + [k for k in keys if k != default] def _to_params(self) -> dict[str, Any]: - """ - Default behavior to get a params dictionary from a registrable class - that does NOT have a _to_params implementation. It is NOT recommended to - use this method. Rather this method is a minial implementation that - exists so that calling `_to_params` does not break. - - # Returns - - parameter_dict: `Dict[str, Any]` - A minimal parameter dictionary for a given registrable class. Will - get the registered name and return that as well as any positional - arguments it can find the value of. - - """ logger.warning( f"'{self.__class__.__name__}' does not implement '_to_params`. Will" f" use Registrable's `_to_params`." ) - # Get the list of parent classes in the MRO in order to check where to - # look for the registered name. Skip the first because that is the - # current class. mro = inspect.getmro(self.__class__)[1:] registered_name = None for parent in mro: - # Check if Parent has any registered classes try: registered_classes = self._registry[parent] except KeyError: continue - # Found a dict of (name,(class,constructor)) pairs. Check if the - # current class is in it. for name, registered_value in registered_classes.items(): registered_class, _ = registered_value if registered_class == self.__class__: registered_name = name break - # Extra break to end the top loop. if registered_name is not None: break @@ -262,18 +140,11 @@ def _to_params(self) -> dict[str, Any]: parameter_dict = {'type': registered_name} - # Get the parameters from the init function. for parameter in inspect.signature(self.__class__).parameters.values(): - # Skip non-positional arguments. For simplicity, these are arguments - # without a default value as those will be required for the - # `from_params` method. if parameter.default != inspect.Parameter.empty: logger.debug(f'Skipping parameter {parameter.name}') continue - # Try to get the value of the parameter from the class. Will only - # try 'name' and '_name'. If it is not there, the parameter is not - # added to the returned dict. if hasattr(self, parameter.name): parameter_dict[parameter.name] = getattr(self, parameter.name) elif hasattr(self, f'_{parameter.name}'): @@ -286,13 +157,10 @@ def _to_params(self) -> dict[str, Any]: def _get_suggestion(name: str, available: list[str]) -> str | None: - # First check for simple mistakes like using '-' instead of '_', or vice-versa. for ch, repl_ch in (('_', '-'), ('-', '_')): suggestion = name.replace(ch, repl_ch) if suggestion in available: return suggestion - # If we still haven't found a reasonable suggestion, we return the first suggestion - # with an edit distance (with transpositions allowed) of 1 to `name`. from nltk.metrics.distance import edit_distance for suggestion in available: From 15e65a84875ac13a8cd8ecdc2a775532669e48fd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 9 Sep 2024 14:11:49 +0000 Subject: [PATCH 3/6] update pyproject --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 24e2eef..c8dbdb4 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,6 @@ hvac ="^1.1.1" python-dotenv ="^1.0.0" sentence-transformers ="^2.2.2" protobuf ="^3.20.0" -allenai-common = "^1.1.2" rouge-score ="^0.1.2" ftfy ="^6.1.1" iopath ="^0.1.10" @@ -50,6 +49,7 @@ xformers = { version = "^0.0.23.post1", optional = true } vllm = { version = "^0.4.0.post1", optional = true } deepspeed = { version = "0.12", optional = true } accelerate = { version = "0.27", optional = true } +cached-path = "^1.6.3" [tool.poetry.dev-dependencies] @@ -71,4 +71,4 @@ gpu = ["faiss-gpu", "xformers", "vllm"] [build-system] requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +build-backend = "poetry.core.masonry.api" \ No newline at end of file From 2d8eb98255b1257466f296d8a0b5bef122608e39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 9 Sep 2024 18:30:30 +0000 Subject: [PATCH 4/6] update poetry --- poetry.lock | 1871 +++++++++++++++++++++++++++++---------------------- 1 file changed, 1065 insertions(+), 806 deletions(-) diff --git a/poetry.lock b/poetry.lock index 6e349f7..99fd802 100644 --- a/poetry.lock +++ b/poetry.lock @@ -74,17 +74,15 @@ frozenlist = ">=1.1.0" [[package]] name = "albucore" -version = "0.0.13" +version = "0.0.14" description = "A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation." category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -numpy = ">=1.24.4,<2" +numpy = ">=1.24" opencv-python-headless = ">=4.9.0.80" -tomli = ">=2.0.1" -typing-extensions = ">=4.9.0" [[package]] name = "albumentations" @@ -109,21 +107,6 @@ typing-extensions = ">=4.9.0" hub = ["huggingface-hub"] text = ["pillow"] -[[package]] -name = "allenai-common" -version = "1.1.2" -description = "Params, FromParams, Registrable, Lazy classes from AllenNLP" -category = "main" -optional = false -python-versions = ">=3.6,<4.0" - -[package.dependencies] -boto3 = ">=1.14.0,<2.0.0" -filelock = ">=3.0.0,<4.0.0" -overrides = "3.1.0" -requests = ">=2.18.0,<3.0.0" -tqdm = ">=4.19.0,<5.0.0" - [[package]] name = "annotated-types" version = "0.6.0" @@ -294,11 +277,11 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] [[package]] name = "av" -version = "12.3.0" +version = "13.0.0" description = "Pythonic bindings for FFmpeg's libraries." category = "main" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" [[package]] name = "babel" @@ -377,14 +360,14 @@ css = ["tinycss2 (>=1.1.0,<1.3)"] [[package]] name = "boto3" -version = "1.35.5" +version = "1.35.14" description = "The AWS SDK for Python" category = "main" optional = false -python-versions = ">=3.8" +python-versions = ">= 3.8" [package.dependencies] -botocore = ">=1.35.5,<1.36.0" +botocore = ">=1.35.14,<1.36.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -393,11 +376,11 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.35.5" +version = "1.35.14" description = "Low-level, data-driven core of boto 3." category = "main" optional = false -python-versions = ">=3.8" +python-versions = ">= 3.8" [package.dependencies] jmespath = ">=0.7.1,<2.0.0" @@ -407,9 +390,36 @@ urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version > [package.extras] crt = ["awscrt (==0.21.2)"] +[[package]] +name = "cached-path" +version = "1.6.3" +description = "A file utility for accessing both local and remote files through a unified interface" +category = "main" +optional = false +python-versions = ">3.8" + +[package.dependencies] +boto3 = ">=1.0,<2.0" +filelock = ">=3.4,<3.14" +google-cloud-storage = ">=1.32.0,<3.0" +huggingface-hub = ">=0.8.1,<0.24.0" +requests = ">=2.0,<3.0" +rich = ">=12.1,<14.0" + +[package.extras] +dev = ["Sphinx (>=6.0,<8.0)", "beaker-py (>=1.13.2,<2.0)", "black (>=23.1.0,<25.0)", "build", "flaky", "furo (==2024.1.29)", "isort (>=5.12.0,<6.0)", "mypy (>=1.6.0,<2.0)", "myst-parser (>=1.0.0,<3.0)", "packaging", "pytest", "responses (==0.21.0)", "ruff", "setuptools", "sphinx-autobuild (==2021.3.14)", "sphinx-autodoc-typehints", "sphinx-copybutton (==0.5.2)", "twine (>=1.11.0)", "wheel"] + +[[package]] +name = "cachetools" +version = "5.5.0" +description = "Extensible memoizing collections and decorators" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "certifi" -version = "2024.7.4" +version = "2024.8.30" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false @@ -417,7 +427,7 @@ python-versions = ">=3.6" [[package]] name = "cffi" -version = "1.17.0" +version = "1.17.1" description = "Foreign Function Interface for Python calling C code." category = "main" optional = false @@ -455,7 +465,7 @@ python-versions = ">=3.8" [[package]] name = "cmake" -version = "3.30.2" +version = "3.30.3" description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software" category = "main" optional = true @@ -690,11 +700,11 @@ test = ["pytest (>=6)"] [[package]] name = "executing" -version = "2.0.1" +version = "2.1.0" description = "Get the currently executing AST node of a frame, and other information" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.8" [package.extras] tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] @@ -709,7 +719,7 @@ python-versions = "*" [[package]] name = "fastapi" -version = "0.112.2" +version = "0.114.0" description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production" category = "main" optional = true @@ -737,7 +747,7 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc [[package]] name = "filelock" -version = "3.15.4" +version = "3.13.4" description = "A platform independent file lock." category = "main" optional = false @@ -745,7 +755,7 @@ python-versions = ">=3.8" [package.extras] docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"] -testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"] typing = ["typing-extensions (>=4.8)"] [[package]] @@ -822,7 +832,7 @@ version = "6.2.3" description = "Fixes mojibake and other problems with Unicode, after the fact" category = "main" optional = false -python-versions = "<4,>=3.8.1" +python-versions = ">=3.8.1,<4" [package.dependencies] wcwidth = ">=0.2.12,<0.3.0" @@ -874,6 +884,121 @@ gitdb = ">=4.0.1,<5" doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"] test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"] +[[package]] +name = "google-api-core" +version = "2.19.2" +description = "Google API client core library" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +google-auth = ">=2.14.1,<3.0.dev0" +googleapis-common-protos = ">=1.56.2,<2.0.dev0" +proto-plus = ">=1.22.3,<2.0.0dev" +protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0" +requests = ">=2.18.0,<3.0.0.dev0" + +[package.extras] +grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0)"] +grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] +grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] + +[[package]] +name = "google-auth" +version = "2.34.0" +description = "Google Authentication Library" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +cachetools = ">=2.0.0,<6.0" +pyasn1-modules = ">=0.2.1" +rsa = ">=3.1.4,<5" + +[package.extras] +aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"] +enterprise-cert = ["cryptography", "pyopenssl"] +pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"] +reauth = ["pyu2f (>=0.1.5)"] +requests = ["requests (>=2.20.0,<3.0.0.dev0)"] + +[[package]] +name = "google-cloud-core" +version = "2.4.1" +description = "Google Cloud API client core library" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +google-api-core = ">=1.31.6,<2.0.0 || >2.3.0,<3.0.0dev" +google-auth = ">=1.25.0,<3.0dev" + +[package.extras] +grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"] + +[[package]] +name = "google-cloud-storage" +version = "2.18.2" +description = "Google Cloud Storage API client library" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +google-api-core = ">=2.15.0,<3.0.0dev" +google-auth = ">=2.26.1,<3.0dev" +google-cloud-core = ">=2.3.0,<3.0dev" +google-crc32c = ">=1.0,<2.0dev" +google-resumable-media = ">=2.7.2" +requests = ">=2.18.0,<3.0.0dev" + +[package.extras] +protobuf = ["protobuf (<6.0.0dev)"] +tracing = ["opentelemetry-api (>=1.1.0)"] + +[[package]] +name = "google-crc32c" +version = "1.6.0" +description = "A python wrapper of the C library 'Google CRC32C'" +category = "main" +optional = false +python-versions = ">=3.9" + +[package.extras] +testing = ["pytest"] + +[[package]] +name = "google-resumable-media" +version = "2.7.2" +description = "Utilities for Google Media Downloads and Resumable Uploads" +category = "main" +optional = false +python-versions = ">= 3.7" + +[package.dependencies] +google-crc32c = ">=1.0,<2.0dev" + +[package.extras] +aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "google-auth (>=1.22.0,<2.0dev)"] +requests = ["requests (>=2.18.0,<3.0.0dev)"] + +[[package]] +name = "googleapis-common-protos" +version = "1.65.0" +description = "Common protobufs used in Google APIs" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0" + +[package.extras] +grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"] + [[package]] name = "h11" version = "0.14.0" @@ -921,7 +1046,7 @@ test = ["Cython (>=0.29.24,<0.30.0)"] [[package]] name = "httpx" -version = "0.27.0" +version = "0.27.2" description = "The next generation HTTP client." category = "main" optional = false @@ -939,10 +1064,11 @@ brotli = ["brotli", "brotlicffi"] cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (>=1.0.0,<2.0.0)"] +zstd = ["zstandard (>=0.18.0)"] [[package]] name = "huggingface-hub" -version = "0.24.6" +version = "0.23.5" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" category = "main" optional = false @@ -958,17 +1084,17 @@ tqdm = ">=4.42.1" typing-extensions = ">=3.7.4.3" [package.extras] -all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"] +all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"] cli = ["InquirerPy (==0.3.4)"] -dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"] +dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"] fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"] hf-transfer = ["hf-transfer (>=0.1.4)"] inference = ["aiohttp", "minijinja (>=1.0)"] -quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"] +quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"] tensorflow = ["graphviz", "pydot", "tensorflow"] tensorflow-testing = ["keras (<3.0)", "tensorflow"] -testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"] -torch = ["safetensors[torch]", "torch"] +testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"] +torch = ["safetensors", "torch"] typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"] [[package]] @@ -1101,7 +1227,7 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio [[package]] name = "ipython" -version = "8.26.0" +version = "8.27.0" description = "IPython: Productive Interactive Computing" category = "main" optional = false @@ -1286,7 +1412,7 @@ referencing = ">=0.31.0" [[package]] name = "jupyter" -version = "1.0.0" +version = "1.1.1" description = "Jupyter metapackage. Install all the Jupyter components in one go." category = "main" optional = false @@ -1296,9 +1422,9 @@ python-versions = "*" ipykernel = "*" ipywidgets = "*" jupyter-console = "*" +jupyterlab = "*" nbconvert = "*" notebook = "*" -qtconsole = "*" [[package]] name = "jupyter-client" @@ -1392,7 +1518,7 @@ jupyter-server = ">=1.1.2" [[package]] name = "jupyter-server" -version = "2.13.0" +version = "2.14.2" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." category = "main" optional = false @@ -1400,28 +1526,28 @@ python-versions = ">=3.8" [package.dependencies] anyio = ">=3.1.0" -argon2-cffi = "*" -jinja2 = "*" +argon2-cffi = ">=21.1" +jinja2 = ">=3.0.3" jupyter-client = ">=7.4.4" jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" jupyter-events = ">=0.9.0" -jupyter-server-terminals = "*" +jupyter-server-terminals = ">=0.4.4" nbconvert = ">=6.4.4" nbformat = ">=5.3.0" -overrides = "*" -packaging = "*" -prometheus-client = "*" -pywinpty = {version = "*", markers = "os_name == \"nt\""} +overrides = ">=5.0" +packaging = ">=22.0" +prometheus-client = ">=0.9" +pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""} pyzmq = ">=24" send2trash = ">=1.8.2" terminado = ">=0.8.3" tornado = ">=6.2.0" traitlets = ">=5.6.0" -websocket-client = "*" +websocket-client = ">=1.7" [package.extras] -docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"] -test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"] +docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"] +test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"] [[package]] name = "jupyter-server-terminals" @@ -1441,7 +1567,7 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (> [[package]] name = "jupyterlab" -version = "4.2.4" +version = "4.2.5" description = "JupyterLab computational environment" category = "main" optional = false @@ -1510,11 +1636,11 @@ python-versions = ">=3.7" [[package]] name = "langchain-core" -version = "0.2.35" +version = "0.2.38" description = "Building applications with LLMs through composability" category = "main" optional = false -python-versions = "<4.0,>=3.8.1" +python-versions = ">=3.8.1,<4.0" [package.dependencies] jsonpatch = ">=1.33,<2.0" @@ -1534,7 +1660,7 @@ version = "0.0.3" description = "An integration package connecting Hugging Face and LangChain" category = "main" optional = false -python-versions = "<4.0,>=3.8.1" +python-versions = ">=3.8.1,<4.0" [package.dependencies] huggingface-hub = ">=0.23.0" @@ -1545,11 +1671,11 @@ transformers = ">=4.39.0" [[package]] name = "langsmith" -version = "0.1.104" +version = "0.1.116" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." category = "main" optional = false -python-versions = "<4.0,>=3.8.1" +python-versions = ">=3.8.1,<4.0" [package.dependencies] httpx = ">=0.23.0,<1" @@ -1621,6 +1747,27 @@ win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""} [package.extras] dev = ["Sphinx (==7.2.5)", "colorama (==0.4.5)", "colorama (==0.4.6)", "exceptiongroup (==1.1.3)", "freezegun (==1.1.0)", "freezegun (==1.2.2)", "mypy (==v0.910)", "mypy (==v0.971)", "mypy (==v1.4.1)", "mypy (==v1.5.1)", "pre-commit (==3.4.0)", "pytest (==6.1.2)", "pytest (==7.4.0)", "pytest-cov (==2.12.1)", "pytest-cov (==4.1.0)", "pytest-mypy-plugins (==1.9.3)", "pytest-mypy-plugins (==3.0.0)", "sphinx-autobuild (==2021.3.14)", "sphinx-rtd-theme (==1.3.0)", "tox (==3.27.1)", "tox (==4.11.0)"] +[[package]] +name = "markdown-it-py" +version = "3.0.0" +description = "Python port of markdown-it. Markdown parsing, done right!" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +mdurl = ">=0.1,<1.0" + +[package.extras] +benchmarking = ["psutil", "pytest", "pytest-benchmark"] +code-style = ["pre-commit (>=3.0,<4.0)"] +compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"] +linkify = ["linkify-it-py (>=1,<3)"] +plugins = ["mdit-py-plugins"] +profiling = ["gprof2dot"] +rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + [[package]] name = "markupsafe" version = "2.1.5" @@ -1648,6 +1795,14 @@ category = "dev" optional = false python-versions = ">=3.6" +[[package]] +name = "mdurl" +version = "0.1.2" +description = "Markdown URL utilities" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "mistune" version = "3.0.2" @@ -1853,7 +2008,7 @@ twitter = ["twython"] [[package]] name = "notebook" -version = "7.2.1" +version = "7.2.2" description = "Jupyter Notebook - A web-based notebook environment for interactive computing" category = "main" optional = false @@ -1998,7 +2153,7 @@ python-versions = ">=3" [[package]] name = "nvidia-nvjitlink-cu12" -version = "12.6.20" +version = "12.6.68" description = "Nvidia JIT LTO Library" category = "main" optional = false @@ -2090,11 +2245,11 @@ test = ["accelerate", "beartype (<0.16.0)", "coverage[toml] (>=5.1)", "datasets" [[package]] name = "overrides" -version = "3.1.0" +version = "7.7.0" description = "A decorator to automatically detect mismatch when overriding a method." category = "main" optional = false -python-versions = "*" +python-versions = ">=3.6" [[package]] name = "packaging" @@ -2309,6 +2464,20 @@ python-versions = ">=3.7.0" [package.dependencies] wcwidth = "*" +[[package]] +name = "proto-plus" +version = "1.24.0" +description = "Beautiful, Pythonic protocol buffers." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +protobuf = ">=3.19.0,<6.0.0dev" + +[package.extras] +testing = ["google-api-core (>=1.31.5)"] + [[package]] name = "protobuf" version = "3.20.3" @@ -2323,7 +2492,7 @@ version = "6.0.0" description = "Cross-platform lib for process and system monitoring in Python." category = "main" optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*" [package.extras] test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"] @@ -2369,6 +2538,25 @@ numpy = ">=1.16.6" [package.extras] test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"] +[[package]] +name = "pyasn1" +version = "0.6.0" +description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)" +category = "main" +optional = false +python-versions = ">=3.8" + +[[package]] +name = "pyasn1-modules" +version = "0.4.0" +description = "A collection of ASN.1-based protocols modules" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +pyasn1 = ">=0.4.6,<0.7.0" + [[package]] name = "pycodestyle" version = "2.11.1" @@ -2387,15 +2575,15 @@ python-versions = ">=3.8" [[package]] name = "pydantic" -version = "2.8.2" +version = "2.9.1" description = "Data validation using Python type hints" category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -annotated-types = ">=0.4.0" -pydantic-core = "2.20.1" +annotated-types = ">=0.6.0" +pydantic-core = "2.23.3" typing-extensions = [ {version = ">=4.6.1", markers = "python_version < \"3.13\""}, {version = ">=4.12.2", markers = "python_version >= \"3.13\""}, @@ -2403,10 +2591,11 @@ typing-extensions = [ [package.extras] email = ["email-validator (>=2.0.0)"] +timezone = ["tzdata"] [[package]] name = "pydantic-core" -version = "2.20.1" +version = "2.23.3" description = "Core functionality for Pydantic validation and serialization" category = "main" optional = false @@ -2643,45 +2832,9 @@ python-versions = ">=3.7" [package.dependencies] cffi = {version = "*", markers = "implementation_name == \"pypy\""} -[[package]] -name = "qtconsole" -version = "5.5.2" -description = "Jupyter Qt console" -category = "main" -optional = false -python-versions = ">=3.8" - -[package.dependencies] -ipykernel = ">=4.1" -jupyter-client = ">=4.1" -jupyter-core = "*" -packaging = "*" -pygments = "*" -pyzmq = ">=17.1" -qtpy = ">=2.4.0" -traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2" - -[package.extras] -doc = ["Sphinx (>=1.3)"] -test = ["flaky", "pytest", "pytest-qt"] - -[[package]] -name = "qtpy" -version = "2.4.1" -description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)." -category = "main" -optional = false -python-versions = ">=3.7" - -[package.dependencies] -packaging = "*" - -[package.extras] -test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"] - [[package]] name = "ray" -version = "2.34.0" +version = "2.35.0" description = "Ray provides a simple, universal API for building distributed applications." category = "main" optional = true @@ -2700,10 +2853,12 @@ pyyaml = "*" requests = "*" [package.extras] +adag = ["cupy-cuda12x"] air = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "fsspec", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "numpy (>=1.20)", "opencensus", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.0 || >=2.5.0,<3)", "requests", "smart-open", "starlette", "tensorboardX (>=1.9)", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] -all = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "dm-tree", "fastapi", "fsspec", "grpcio (!=1.56.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "gymnasium (==0.28.1)", "lz4", "memray", "numpy (>=1.20)", "opencensus", "opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.0 || >=2.5.0,<3)", "pyyaml", "ray-cpp (==2.34.0)", "requests", "rich", "scikit-image", "scipy", "smart-open", "starlette", "tensorboardX (>=1.9)", "typer", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +all = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "cupy-cuda12x", "dm-tree", "fastapi", "fsspec", "grpcio (!=1.56.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "gymnasium (==0.28.1)", "lz4", "memray", "numpy (>=1.20)", "opencensus", "opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.0 || >=2.5.0,<3)", "pyyaml", "requests", "rich", "scikit-image", "scipy", "smart-open", "starlette", "tensorboardX (>=1.9)", "typer", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +all-cpp = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "cupy-cuda12x", "dm-tree", "fastapi", "fsspec", "grpcio (!=1.56.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "gymnasium (==0.28.1)", "lz4", "memray", "numpy (>=1.20)", "opencensus", "opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.0 || >=2.5.0,<3)", "pyyaml", "ray-cpp (==2.35.0)", "requests", "rich", "scikit-image", "scipy", "smart-open", "starlette", "tensorboardX (>=1.9)", "typer", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] client = ["grpcio (!=1.56.0)"] -cpp = ["ray-cpp (==2.34.0)"] +cpp = ["ray-cpp (==2.35.0)"] data = ["fsspec", "numpy (>=1.20)", "pandas (>=1.3)", "pyarrow (>=6.0.1)"] default = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.0 || >=2.5.0,<3)", "requests", "smart-open", "virtualenv (>=20.0.24,!=20.21.1)"] observability = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"] @@ -2770,6 +2925,21 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +[[package]] +name = "rich" +version = "13.8.0" +description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" +category = "main" +optional = false +python-versions = ">=3.7.0" + +[package.dependencies] +markdown-it-py = ">=2.2.0" +pygments = ">=2.13.0,<3.0.0" + +[package.extras] +jupyter = ["ipywidgets (>=7.5.1,<9)"] + [[package]] name = "rouge-score" version = "0.1.2" @@ -2792,13 +2962,24 @@ category = "main" optional = false python-versions = ">=3.8" +[[package]] +name = "rsa" +version = "4.9" +description = "Pure-Python RSA 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a89f4b41516b1379e6141eff6955b2818251ad22 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Fri, 13 Sep 2024 14:47:28 +0000 Subject: [PATCH 5/6] fix linters --- .../common/registry/registrable.py | 24 ++----------------- turbo_alignment/pipelines/mixin/logging.py | 3 +-- 2 files changed, 3 insertions(+), 24 deletions(-) diff --git a/turbo_alignment/common/registry/registrable.py b/turbo_alignment/common/registry/registrable.py index dab76f7..f6717da 100644 --- a/turbo_alignment/common/registry/registrable.py +++ b/turbo_alignment/common/registry/registrable.py @@ -48,7 +48,7 @@ def add_subclass_to_registry(subclass: Type[_T]) -> Type[_T]: return add_subclass_to_registry @classmethod - def by_name(cls: Type[_RegistrableT], name: str) -> Callable[..., _RegistrableT]: + def by_name(cls: Type[_RegistrableT], name): logger.debug(f'instantiating registered subclass {name} of {cls}') subclass, constructor = cls.resolve_class_name(name) if not constructor: @@ -84,15 +84,8 @@ def resolve_class_name(cls: Type[_RegistrableT], name: str) -> tuple[Type[_Regis f'but unable to find class {class_name} in {submodule}' ) - # is not a qualified class name - available = cls.list_available() - suggestion = _get_suggestion(name, available) raise ConfigurationError( - ( - f"'{name}' is not a registered name for '{cls.__name__}'" - + ('. ' if not suggestion else f", did you mean '{suggestion}'? ") - ) - + "If your registered class comes from custom code, you'll need to import " + "If your registered class comes from custom code, you'll need to import " "the corresponding modules. If you're using AllenNLP from the command-line, " "this is done by using the '--include-package' flag, or by specifying your imports " "in a '.allennlp_plugins' file. " @@ -154,16 +147,3 @@ def _to_params(self) -> dict[str, Any]: continue return parameter_dict - - -def _get_suggestion(name: str, available: list[str]) -> str | None: - for ch, repl_ch in (('_', '-'), ('-', '_')): - suggestion = name.replace(ch, repl_ch) - if suggestion in available: - return suggestion - from nltk.metrics.distance import edit_distance - - for suggestion in available: - if edit_distance(name, suggestion, transpositions=True) == 1: - return suggestion - return None diff --git a/turbo_alignment/pipelines/mixin/logging.py b/turbo_alignment/pipelines/mixin/logging.py index a95ae00..e0b29a8 100755 --- a/turbo_alignment/pipelines/mixin/logging.py +++ b/turbo_alignment/pipelines/mixin/logging.py @@ -1,9 +1,8 @@ from abc import ABC, abstractmethod -from allenai_common import Registrable - from turbo_alignment.common.logging.clearml import create_clearml_task from turbo_alignment.common.logging.weights_and_biases import create_wandb_run +from turbo_alignment.common.registry import Registrable from turbo_alignment.common.tf.callbacks.logging import ( ClearMLLoggingCallback, LoggingCallback, From 73560d2f96f63b0cc207d5fd3634e024fa44b7ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Sat, 14 Sep 2024 00:43:46 +0300 Subject: [PATCH 6/6] fix poetry --- poetry.lock | 2252 +++++++++++++++++++++++++----------------------- pyproject.toml | 3 +- 2 files changed, 1196 insertions(+), 1059 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9984585..073fe46 100644 --- a/poetry.lock +++ b/poetry.lock @@ -180,13 +180,13 @@ frozenlist = ">=1.1.0" [[package]] name = "albucore" -version = "0.0.14" +version = "0.0.15" description = "A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. 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