from typing import Optional, Any, Dict, Callable, Iterable, Union, List, Pattern from typing import Tuple, Iterator from dataclasses import dataclass import random import itertools import weakref import functools from collections import Iterable as IterableInstance from contextlib import contextmanager from copy import copy, deepcopy from pathlib import Path import warnings from thinc.api import get_current_ops, Config, require_gpu, Optimizer import srsly import multiprocessing as mp from itertools import chain, cycle from timeit import default_timer as timer from .tokens.underscore import Underscore from .vocab import Vocab, create_vocab from .pipe_analysis import analyze_pipes, analyze_all_pipes, validate_attrs from .gold import Example from .scorer import Scorer from .util import link_vectors_to_models, create_default_optimizer, registry from .util import SimpleFrozenDict, combine_score_weights from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES from .lang.punctuation import TOKENIZER_INFIXES from .tokens import Doc from .lookups import load_lookups from .tokenizer import Tokenizer from .lemmatizer import Lemmatizer from .errors import Errors, Warnings from .schemas import ConfigSchema from .git_info import GIT_VERSION from . import util from . import about # TODO: integrate pipeline analyis ENABLE_PIPELINE_ANALYSIS = False # This is the base config will all settings (training etc.) DEFAULT_CONFIG_PATH = Path(__file__).parent / "default_config.cfg" DEFAULT_CONFIG = Config().from_disk(DEFAULT_CONFIG_PATH) class BaseDefaults: """Language data defaults, available via Language.Defaults. Can be overwritten by language subclasses by defining their own subclasses of Language.Defaults. """ config: Config = Config() tokenizer_exceptions: Dict[str, List[dict]] = BASE_EXCEPTIONS prefixes: Optional[List[Union[str, Pattern]]] = TOKENIZER_PREFIXES suffixes: Optional[List[Union[str, Pattern]]] = TOKENIZER_SUFFIXES infixes: Optional[List[Union[str, Pattern]]] = TOKENIZER_INFIXES token_match: Optional[Pattern] = None url_match: Optional[Pattern] = URL_MATCH syntax_iterators: Dict[str, Callable] = {} lex_attr_getters: Dict[int, Callable[[str], Any]] = {} stop_words = set() writing_system = {"direction": "ltr", "has_case": True, "has_letters": True} @registry.tokenizers("spacy.Tokenizer.v1") def create_tokenizer() -> Callable[["Language"], Tokenizer]: """Registered function to create a tokenizer. Returns a factory that takes the nlp object and returns a Tokenizer instance using the language detaults. """ def tokenizer_factory(nlp: "Language") -> Tokenizer: prefixes = nlp.Defaults.prefixes suffixes = nlp.Defaults.suffixes infixes = nlp.Defaults.infixes prefix_search = util.compile_prefix_regex(prefixes).search if prefixes else None suffix_search = util.compile_suffix_regex(suffixes).search if suffixes else None infix_finditer = util.compile_infix_regex(infixes).finditer if infixes else None return Tokenizer( nlp.vocab, rules=nlp.Defaults.tokenizer_exceptions, prefix_search=prefix_search, suffix_search=suffix_search, infix_finditer=infix_finditer, token_match=nlp.Defaults.token_match, url_match=nlp.Defaults.url_match, ) return tokenizer_factory @registry.lemmatizers("spacy.Lemmatizer.v1") def create_lemmatizer() -> Callable[["Language"], "Lemmatizer"]: """Registered function to create a lemmatizer. Returns a factory that takes the nlp object and returns a Lemmatizer instance with data loaded in from spacy-lookups-data, if the package is installed. """ # TODO: Will be replaced when the lemmatizer becomes a pipeline component tables = ["lemma_lookup", "lemma_rules", "lemma_exc", "lemma_index"] def lemmatizer_factory(nlp: "Language") -> "Lemmatizer": lookups = load_lookups(lang=nlp.lang, tables=tables, strict=False) return Lemmatizer(lookups=lookups) return lemmatizer_factory class Language: """A text-processing pipeline. Usually you'll load this once per process, and pass the instance around your application. Defaults (class): Settings, data and factory methods for creating the `nlp` object and processing pipeline. lang (str): Two-letter language ID, i.e. ISO code. DOCS: https://spacy.io/api/language """ Defaults = BaseDefaults lang: str = None default_config = DEFAULT_CONFIG factories = SimpleFrozenDict(error=Errors.E957) _factory_meta: Dict[str, "FactoryMeta"] = {} # meta by factory def __init__( self, vocab: Union[Vocab, bool] = True, *, max_length: int = 10 ** 6, meta: Dict[str, Any] = {}, create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None, create_lemmatizer: Optional[Callable[["Language"], Callable]] = None, **kwargs, ) -> None: """Initialise a Language object. vocab (Vocab): A `Vocab` object. If `True`, a vocab is created. meta (dict): Custom meta data for the Language class. Is written to by models to add model meta data. max_length (int): Maximum number of characters in a single text. The current models may run out memory on extremely long texts, due to large internal allocations. You should segment these texts into meaningful units, e.g. paragraphs, subsections etc, before passing them to spaCy. Default maximum length is 1,000,000 charas (1mb). As a rule of thumb, if all pipeline components are enabled, spaCy's default models currently requires roughly 1GB of temporary memory per 100,000 characters in one text. create_tokenizer (Callable): Function that takes the nlp object and returns a tokenizer. create_lemmatizer (Callable): Function that takes the nlp object and returns a lemmatizer. DOCS: https://spacy.io/api/language#init """ # We're only calling this to import all factories provided via entry # points. The factory decorator applied to these functions takes care # of the rest. util.registry._entry_point_factories.get_all() self._config = util.deep_merge_configs(self.default_config, DEFAULT_CONFIG) self._meta = dict(meta) self._path = None self._optimizer = None # Component meta and configs are only needed on the instance self._pipe_meta: Dict[str, "FactoryMeta"] = {} # meta by component self._pipe_configs: Dict[str, Config] = {} # config by component if vocab is True: vectors_name = meta.get("vectors", {}).get("name") if not create_lemmatizer: lemma_cfg = {"lemmatizer": self._config["nlp"]["lemmatizer"]} create_lemmatizer = registry.make_from_config(lemma_cfg)["lemmatizer"] vocab = create_vocab( self.lang, self.Defaults, lemmatizer=create_lemmatizer(self), vectors_name=vectors_name, load_data=self._config["nlp"]["load_vocab_data"], ) else: if (self.lang and vocab.lang) and (self.lang != vocab.lang): raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang)) self.vocab: Vocab = vocab if self.lang is None: self.lang = self.vocab.lang self.pipeline = [] self.max_length = max_length self.resolved = {} # Create the default tokenizer from the default config if not create_tokenizer: tokenizer_cfg = {"tokenizer": self._config["nlp"]["tokenizer"]} create_tokenizer = registry.make_from_config(tokenizer_cfg)["tokenizer"] self.tokenizer = create_tokenizer(self) def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) cls.default_config = util.deep_merge_configs( cls.Defaults.config, DEFAULT_CONFIG ) cls.default_config["nlp"]["lang"] = cls.lang @property def path(self): return self._path @property def meta(self) -> Dict[str, Any]: """Custom meta data of the language class. If a model is loaded, this includes details from the model's meta.json. RETURNS (Dict[str, Any]): The meta. DOCS: https://spacy.io/api/language#meta """ spacy_version = util.get_model_version_range(about.__version__) if self.vocab.lang: self._meta.setdefault("lang", self.vocab.lang) else: self._meta.setdefault("lang", self.lang) self._meta.setdefault("name", "model") self._meta.setdefault("version", "0.0.0") self._meta.setdefault("spacy_version", spacy_version) self._meta.setdefault("description", "") self._meta.setdefault("author", "") self._meta.setdefault("email", "") self._meta.setdefault("url", "") self._meta.setdefault("license", "") self._meta.setdefault("spacy_git_version", GIT_VERSION) self._meta["vectors"] = { "width": self.vocab.vectors_length, "vectors": len(self.vocab.vectors), "keys": self.vocab.vectors.n_keys, "name": self.vocab.vectors.name, } self._meta["labels"] = self.pipe_labels # TODO: Adding this back to prevent breaking people's code etc., but # we should consider removing it self._meta["pipeline"] = self.pipe_names return self._meta @meta.setter def meta(self, value: Dict[str, Any]) -> None: self._meta = value @property def config(self) -> Config: """Trainable config for the current language instance. Includes the current pipeline components, as well as default training config. RETURNS (thinc.api.Config): The config. DOCS: https://spacy.io/api/language#config """ self._config.setdefault("nlp", {}) self._config.setdefault("training", {}) self._config["nlp"]["lang"] = self.lang # We're storing the filled config for each pipeline component and so # we can populate the config again later pipeline = {} score_weights = [] for pipe_name in self.pipe_names: pipe_meta = self.get_pipe_meta(pipe_name) pipe_config = self.get_pipe_config(pipe_name) pipeline[pipe_name] = {"factory": pipe_meta.factory, **pipe_config} if pipe_meta.default_score_weights: score_weights.append(pipe_meta.default_score_weights) self._config["nlp"]["pipeline"] = self.pipe_names self._config["components"] = pipeline self._config["training"]["score_weights"] = combine_score_weights(score_weights) if not srsly.is_json_serializable(self._config): raise ValueError(Errors.E961.format(config=self._config)) return self._config @config.setter def config(self, value: Config) -> None: self._config = value @property def factory_names(self) -> List[str]: """Get names of all available factories. RETURNS (List[str]): The factory names. """ return list(self.factories.keys()) @property def pipe_names(self) -> List[str]: """Get names of available pipeline components. RETURNS (List[str]): List of component name strings, in order. """ return [pipe_name for pipe_name, _ in self.pipeline] @property def pipe_factories(self) -> Dict[str, str]: """Get the component factories for the available pipeline components. RETURNS (Dict[str, str]): Factory names, keyed by component names. """ factories = {} for pipe_name, pipe in self.pipeline: factories[pipe_name] = self.get_pipe_meta(pipe_name).factory return factories @property def pipe_labels(self) -> Dict[str, List[str]]: """Get the labels set by the pipeline components, if available (if the component exposes a labels property). RETURNS (Dict[str, List[str]]): Labels keyed by component name. """ labels = {} for name, pipe in self.pipeline: if hasattr(pipe, "labels"): labels[name] = list(pipe.labels) return labels @classmethod def has_factory(cls, name: str) -> bool: """RETURNS (bool): Whether a factory of that name is registered.""" internal_name = cls.get_factory_name(name) return name in registry.factories or internal_name in registry.factories @classmethod def get_factory_name(cls, name: str) -> str: """Get the internal factory name based on the language subclass. name (str): The factory name. RETURNS (str): The internal factory name. """ if cls.lang is None: return name return f"{cls.lang}.{name}" @classmethod def get_factory_meta(cls, name: str) -> "FactoryMeta": """Get the meta information for a given factory name. name (str): The component factory name. RETURNS (FactoryMeta): The meta for the given factory name. """ internal_name = cls.get_factory_name(name) if internal_name in cls._factory_meta: return cls._factory_meta[internal_name] if name in cls._factory_meta: return cls._factory_meta[name] raise ValueError(Errors.E967.format(meta="factory", name=name)) @classmethod def set_factory_meta(cls, name: str, value: "FactoryMeta") -> None: """Set the meta information for a given factory name. name (str): The component factory name. value (FactoryMeta): The meta to set. """ cls._factory_meta[cls.get_factory_name(name)] = value def get_pipe_meta(self, name: str) -> "FactoryMeta": """Get the meta information for a given component name. name (str): The component name. RETURNS (FactoryMeta): The meta for the given component name. """ if name not in self._pipe_meta: raise ValueError(Errors.E967.format(meta="component", name=name)) return self._pipe_meta[name] def get_pipe_config(self, name: str) -> Config: """Get the config used to create a pipeline component. name (str): The component name. RETURNS (Config): The config used to create the pipeline component. """ if name not in self._pipe_configs: raise ValueError(Errors.E960.format(name=name)) pipe_config = self._pipe_configs[name] pipe_config.pop("nlp", None) pipe_config.pop("name", None) return pipe_config @classmethod def factory( cls, name: str, *, default_config: Dict[str, Any] = SimpleFrozenDict(), assigns: Iterable[str] = tuple(), requires: Iterable[str] = tuple(), retokenizes: bool = False, scores: Iterable[str] = tuple(), default_score_weights: Dict[str, float] = SimpleFrozenDict(), func: Optional[Callable] = None, ) -> Callable: """Register a new pipeline component factory. Can be used as a decorator on a function or classmethod, or called as a function with the factory provided as the func keyword argument. To create a component and add it to the pipeline, you can use nlp.add_pipe(name). name (str): The name of the component factory. default_config (Dict[str, Any]): Default configuration, describing the default values of the factory arguments. assigns (Iterable[str]): Doc/Token attributes assigned by this component, e.g. "token.ent_id". Used for pipeline analyis. requires (Iterable[str]): Doc/Token attributes required by this component, e.g. "token.ent_id". Used for pipeline analyis. retokenizes (bool): Whether the component changes the tokenization. Used for pipeline analysis. scores (Iterable[str]): All scores set by the component if it's trainable, e.g. ["ents_f", "ents_r", "ents_p"]. default_score_weights (Dict[str, float]): The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to 1.0 per component and will be combined and normalized for the whole pipeline. func (Optional[Callable]): Factory function if not used as a decorator. DOCS: https://spacy.io/api/language#factory """ if not isinstance(name, str): raise ValueError(Errors.E963.format(decorator="factory")) if not isinstance(default_config, dict): err = Errors.E962.format( style="default config", name=name, cfg_type=type(default_config) ) raise ValueError(err) internal_name = cls.get_factory_name(name) if internal_name in registry.factories: # We only check for the internal name here – it's okay if it's a # subclass and the base class has a factory of the same name raise ValueError(Errors.E004.format(name=name)) def add_factory(factory_func: Callable) -> Callable: arg_names = util.get_arg_names(factory_func) if "nlp" not in arg_names or "name" not in arg_names: raise ValueError(Errors.E964.format(name=name)) # Officially register the factory so we can later call # registry.make_from_config and refer to it in the config as # @factories = "spacy.Language.xyz". We use the class name here so # different classes can have different factories. registry.factories.register(internal_name, func=factory_func) factory_meta = FactoryMeta( factory=name, default_config=default_config, assigns=validate_attrs(assigns), requires=validate_attrs(requires), scores=scores, default_score_weights=default_score_weights, retokenizes=retokenizes, ) cls.set_factory_meta(name, factory_meta) # We're overwriting the class attr with a frozen dict to handle # backwards-compat (writing to Language.factories directly). This # wouldn't work with an instance property and just produce a # confusing error – here we can show a custom error cls.factories = SimpleFrozenDict( registry.factories.get_all(), error=Errors.E957 ) return factory_func if func is not None: # Support non-decorator use cases return add_factory(func) return add_factory @classmethod def component( cls, name: Optional[str] = None, *, assigns: Iterable[str] = tuple(), requires: Iterable[str] = tuple(), retokenizes: bool = False, scores: Iterable[str] = tuple(), default_score_weights: Dict[str, float] = SimpleFrozenDict(), func: Optional[Callable[[Doc], Doc]] = None, ) -> Callable: """Register a new pipeline component. Can be used for stateless function components that don't require a separate factory. Can be used as a decorator on a function or classmethod, or called as a function with the factory provided as the func keyword argument. To create a component and add it to the pipeline, you can use nlp.add_pipe(name). name (str): The name of the component factory. assigns (Iterable[str]): Doc/Token attributes assigned by this component, e.g. "token.ent_id". Used for pipeline analyis. requires (Iterable[str]): Doc/Token attributes required by this component, e.g. "token.ent_id". Used for pipeline analyis. retokenizes (bool): Whether the component changes the tokenization. Used for pipeline analysis. scores (Iterable[str]): All scores set by the component if it's trainable, e.g. ["ents_f", "ents_r", "ents_p"]. default_score_weights (Dict[str, float]): The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to 1.0 per component and will be combined and normalized for the whole pipeline. func (Optional[Callable]): Factory function if not used as a decorator. DOCS: https://spacy.io/api/language#component """ if name is not None and not isinstance(name, str): raise ValueError(Errors.E963.format(decorator="component")) component_name = name if name is not None else util.get_object_name(func) def add_component(component_func: Callable[[Doc], Doc]) -> Callable: if isinstance(func, type): # function is a class raise ValueError(Errors.E965.format(name=component_name)) def factory_func(nlp: cls, name: str) -> Callable[[Doc], Doc]: return component_func cls.factory( component_name, assigns=assigns, requires=requires, retokenizes=retokenizes, scores=scores, default_score_weights=default_score_weights, func=factory_func, ) return component_func if func is not None: # Support non-decorator use cases return add_component(func) return add_component def get_pipe(self, name: str) -> Callable[[Doc], Doc]: """Get a pipeline component for a given component name. name (str): Name of pipeline component to get. RETURNS (callable): The pipeline component. DOCS: https://spacy.io/api/language#get_pipe """ for pipe_name, component in self.pipeline: if pipe_name == name: return component raise KeyError(Errors.E001.format(name=name, opts=self.pipe_names)) def create_pipe( self, factory_name: str, name: Optional[str] = None, *, config: Optional[Dict[str, Any]] = SimpleFrozenDict(), overrides: Optional[Dict[str, Any]] = SimpleFrozenDict(), validate: bool = True, ) -> Callable[[Doc], Doc]: """Create a pipeline component. Mostly used internally. To create and add a component to the pipeline, you can use nlp.add_pipe. factory_name (str): Name of component factory. name (Optional[str]): Optional name to assign to component instance. Defaults to factory name if not set. config (Optional[Dict[str, Any]]): Config parameters to use for this component. Will be merged with default config, if available. overrides (Optional[Dict[str, Any]]): Config overrides, typically passed in via the CLI. validate (bool): Whether to validate the component config against the arguments and types expected by the factory. RETURNS (Callable[[Doc], Doc]): The pipeline component. DOCS: https://spacy.io/api/language#create_pipe """ name = name if name is not None else factory_name if not isinstance(config, dict): err = Errors.E962.format(style="config", name=name, cfg_type=type(config)) raise ValueError(err) if not srsly.is_json_serializable(config): raise ValueError(Errors.E961.format(config=config)) if not self.has_factory(factory_name): err = Errors.E002.format( name=factory_name, opts=", ".join(self.factory_names), method="create_pipe", lang=util.get_object_name(self), lang_code=self.lang, ) raise ValueError(err) pipe_meta = self.get_factory_meta(factory_name) config = config or {} # This is unideal, but the alternative would mean you always need to # specify the full config settings, which is not really viable. if pipe_meta.default_config: config = util.deep_merge_configs(config, pipe_meta.default_config) # We need to create a top-level key because Thinc doesn't allow resolving # top-level references to registered functions. Also gives nicer errors. # The name allows components to know their pipe name and use it in the # losses etc. (even if multiple instances of the same factory are used) internal_name = self.get_factory_name(factory_name) # If the language-specific factory doesn't exist, try again with the # not-specific name if internal_name not in registry.factories: internal_name = factory_name config = {"nlp": self, "name": name, **config, "@factories": internal_name} cfg = {factory_name: config} # We're calling the internal _fill here to avoid constructing the # registered functions twice # TODO: customize validation to make it more readable / relate it to # pipeline component and why it failed, explain default config resolved, filled = registry.resolve(cfg, validate=validate, overrides=overrides) filled = filled[factory_name] filled["factory"] = factory_name filled.pop("@factories", None) self._pipe_configs[name] = filled return resolved[factory_name] def add_pipe( self, factory_name: str, name: Optional[str] = None, *, before: Optional[Union[str, int]] = None, after: Optional[Union[str, int]] = None, first: Optional[bool] = None, last: Optional[bool] = None, config: Optional[Dict[str, Any]] = SimpleFrozenDict(), overrides: Optional[Dict[str, Any]] = SimpleFrozenDict(), validate: bool = True, ) -> Callable[[Doc], Doc]: """Add a component to the processing pipeline. Valid components are callables that take a `Doc` object, modify it and return it. Only one of before/after/first/last can be set. Default behaviour is "last". factory_name (str): Name of the component factory. name (str): Name of pipeline component. Overwrites existing component.name attribute if available. If no name is set and the component exposes no name attribute, component.__name__ is used. An error is raised if a name already exists in the pipeline. before (Union[str, int]): Name or index of the component to insert new component directly before. after (Union[str, int]): Name or index of the component to insert new component directly after. first (bool): If True, insert component first in the pipeline. last (bool): If True, insert component last in the pipeline. config (Optional[Dict[str, Any]]): Config parameters to use for this component. Will be merged with default config, if available. overrides (Optional[Dict[str, Any]]): Config overrides, typically passed in via the CLI. validate (bool): Whether to validate the component config against the arguments and types expected by the factory. RETURNS (Callable[[Doc], Doc]): The pipeline component. DOCS: https://spacy.io/api/language#add_pipe """ if not isinstance(factory_name, str): bad_val = repr(factory_name) err = Errors.E966.format(component=bad_val, name=name) raise ValueError(err) if not self.has_factory(factory_name): err = Errors.E002.format( name=factory_name, opts=", ".join(self.factory_names), method="add_pipe", lang=util.get_object_name(self), lang_code=self.lang, ) name = name if name is not None else factory_name if name in self.pipe_names: raise ValueError(Errors.E007.format(name=name, opts=self.pipe_names)) pipe_component = self.create_pipe( factory_name, name=name, config=config, overrides=overrides, validate=validate, ) pipe_index = self._get_pipe_index(before, after, first, last) self._pipe_meta[name] = self.get_factory_meta(factory_name) self.pipeline.insert(pipe_index, (name, pipe_component)) if ENABLE_PIPELINE_ANALYSIS: analyze_pipes(self, name, pipe_index) return pipe_component def _get_pipe_index( self, before: Optional[Union[str, int]] = None, after: Optional[Union[str, int]] = None, first: Optional[bool] = None, last: Optional[bool] = None, ) -> int: """Determine where to insert a pipeline component based on the before/ after/first/last values. before (str): Name or index of the component to insert directly before. after (str): Name or index of component to insert directly after. first (bool): If True, insert component first in the pipeline. last (bool): If True, insert component last in the pipeline. RETURNS (int): The index of the new pipeline component. """ all_args = {"before": before, "after": after, "first": first, "last": last} if sum(arg is not None for arg in [before, after, first, last]) >= 2: raise ValueError(Errors.E006.format(args=all_args, opts=self.pipe_names)) if last or not any(value is not None for value in [first, before, after]): return len(self.pipeline) elif first: return 0 elif isinstance(before, str): if before not in self.pipe_names: raise ValueError(Errors.E001.format(name=before, opts=self.pipe_names)) return self.pipe_names.index(before) elif isinstance(after, str): if after not in self.pipe_names: raise ValueError(Errors.E001.format(name=after, opts=self.pipe_names)) return self.pipe_names.index(after) + 1 # We're only accepting indices referring to components that exist # (can't just do isinstance here because bools are instance of int, too) elif type(before) == int: if before >= len(self.pipeline) or before < 0: err = Errors.E959.format(dir="before", idx=before, opts=self.pipe_names) raise ValueError(err) return before elif type(after) == int: if after >= len(self.pipeline) or after < 0: err = Errors.E959.format(dir="after", idx=after, opts=self.pipe_names) raise ValueError(err) return after + 1 raise ValueError(Errors.E006.format(args=all_args, opts=self.pipe_names)) def has_pipe(self, name: str) -> bool: """Check if a component name is present in the pipeline. Equivalent to `name in nlp.pipe_names`. name (str): Name of the component. RETURNS (bool): Whether a component of the name exists in the pipeline. DOCS: https://spacy.io/api/language#has_pipe """ return name in self.pipe_names def replace_pipe( self, name: str, factory_name: str, *, config: Dict[str, Any] = SimpleFrozenDict(), validate: bool = True, ) -> None: """Replace a component in the pipeline. name (str): Name of the component to replace. factory_name (str): Factory name of replacement component. config (Optional[Dict[str, Any]]): Config parameters to use for this component. Will be merged with default config, if available. validate (bool): Whether to validate the component config against the arguments and types expected by the factory. DOCS: https://spacy.io/api/language#replace_pipe """ if name not in self.pipe_names: raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names)) if hasattr(factory_name, "__call__"): err = Errors.E968.format(component=repr(factory_name), name=name) raise ValueError(err) # We need to delegate to Language.add_pipe here instead of just writing # to Language.pipeline to make sure the configs are handled correctly pipe_index = self.pipe_names.index(name) self.remove_pipe(name) if not len(self.pipeline): # we have no components to insert before/after self.add_pipe(factory_name, name=name) else: self.add_pipe(factory_name, name=name, before=pipe_index) if ENABLE_PIPELINE_ANALYSIS: analyze_all_pipes(self) def rename_pipe(self, old_name: str, new_name: str) -> None: """Rename a pipeline component. old_name (str): Name of the component to rename. new_name (str): New name of the component. DOCS: https://spacy.io/api/language#rename_pipe """ if old_name not in self.pipe_names: raise ValueError(Errors.E001.format(name=old_name, opts=self.pipe_names)) if new_name in self.pipe_names: raise ValueError(Errors.E007.format(name=new_name, opts=self.pipe_names)) i = self.pipe_names.index(old_name) self.pipeline[i] = (new_name, self.pipeline[i][1]) self._pipe_meta[new_name] = self._pipe_meta.pop(old_name) self._pipe_configs[new_name] = self._pipe_configs.pop(old_name) def remove_pipe(self, name: str) -> Tuple[str, Callable[[Doc], Doc]]: """Remove a component from the pipeline. name (str): Name of the component to remove. RETURNS (tuple): A `(name, component)` tuple of the removed component. DOCS: https://spacy.io/api/language#remove_pipe """ if name not in self.pipe_names: raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names)) removed = self.pipeline.pop(self.pipe_names.index(name)) # We're only removing the component itself from the metas/configs here # because factory may be used for something else self._pipe_meta.pop(name) self._pipe_configs.pop(name) if ENABLE_PIPELINE_ANALYSIS: analyze_all_pipes(self) return removed def __call__( self, text: str, *, disable: Iterable[str] = tuple(), component_cfg: Optional[Dict[str, Dict[str, Any]]] = None, ) -> Doc: """Apply the pipeline to some text. The text can span multiple sentences, and can contain arbitrary whitespace. Alignment into the original string is preserved. text (str): The text to be processed. disable (list): Names of the pipeline components to disable. component_cfg (Dict[str, dict]): An optional dictionary with extra keyword arguments for specific components. RETURNS (Doc): A container for accessing the annotations. DOCS: https://spacy.io/api/language#call """ if len(text) > self.max_length: raise ValueError( Errors.E088.format(length=len(text), max_length=self.max_length) ) doc = self.make_doc(text) if component_cfg is None: component_cfg = {} for name, proc in self.pipeline: if name in disable: continue if not hasattr(proc, "__call__"): raise ValueError(Errors.E003.format(component=type(proc), name=name)) try: doc = proc(doc, **component_cfg.get(name, {})) except KeyError: raise ValueError(Errors.E109.format(name=name)) if doc is None: raise ValueError(Errors.E005.format(name=name)) return doc def disable_pipes(self, *names) -> "DisabledPipes": """Disable one or more pipeline components. If used as a context manager, the pipeline will be restored to the initial state at the end of the block. Otherwise, a DisabledPipes object is returned, that has a `.restore()` method you can use to undo your changes. This method has been deprecated since 3.0 """ warnings.warn(Warnings.W096, DeprecationWarning) if len(names) == 1 and isinstance(names[0], (list, tuple)): names = names[0] # support list of names instead of spread return DisabledPipes(self, names) def select_pipes( self, *, disable: Optional[Union[str, Iterable[str]]] = None, enable: Optional[Union[str, Iterable[str]]] = None, ) -> "DisabledPipes": """Disable one or more pipeline components. If used as a context manager, the pipeline will be restored to the initial state at the end of the block. Otherwise, a DisabledPipes object is returned, that has a `.restore()` method you can use to undo your changes. disable (str or iterable): The name(s) of the pipes to disable enable (str or iterable): The name(s) of the pipes to enable - all others will be disabled DOCS: https://spacy.io/api/language#select_pipes """ if enable is None and disable is None: raise ValueError(Errors.E991) if disable is not None and isinstance(disable, str): disable = [disable] if enable is not None: if isinstance(enable, str): enable = [enable] to_disable = [pipe for pipe in self.pipe_names if pipe not in enable] # raise an error if the enable and disable keywords are not consistent if disable is not None and disable != to_disable: raise ValueError( Errors.E992.format( enable=enable, disable=disable, names=self.pipe_names ) ) disable = to_disable return DisabledPipes(self, disable) def make_doc(self, text: str) -> Doc: """Turn a text into a Doc object. text (str): The text to process. RETURNS (Doc): The processed doc. """ return self.tokenizer(text) def update( self, examples: Iterable[Example], _: Optional[Any] = None, *, drop: float = 0.0, sgd: Optional[Optimizer] = None, losses: Optional[Dict[str, float]] = None, component_cfg: Optional[Dict[str, Dict[str, Any]]] = None, ): """Update the models in the pipeline. examples (Iterable[Example]): A batch of examples _: Should not be set - serves to catch backwards-incompatible scripts. drop (float): The dropout rate. sgd (Optimizer): An optimizer. losses (Dict[str, float]): Dictionary to update with the loss, keyed by component. component_cfg (Dict[str, Dict]): Config parameters for specific pipeline components, keyed by component name. RETURNS (Dict[str, float]): The updated losses dictionary DOCS: https://spacy.io/api/language#update """ if _ is not None: raise ValueError(Errors.E989) if losses is None: losses = {} if len(examples) == 0: return losses if not isinstance(examples, IterableInstance): raise TypeError( Errors.E978.format( name="language", method="update", types=type(examples) ) ) wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)]) if wrong_types: raise TypeError( Errors.E978.format(name="language", method="update", types=wrong_types) ) if sgd is None: if self._optimizer is None: self._optimizer = create_default_optimizer() sgd = self._optimizer if component_cfg is None: component_cfg = {} for i, (name, proc) in enumerate(self.pipeline): component_cfg.setdefault(name, {}) component_cfg[name].setdefault("drop", drop) component_cfg[name].setdefault("set_annotations", False) for name, proc in self.pipeline: if not hasattr(proc, "update"): continue proc.update(examples, sgd=None, losses=losses, **component_cfg[name]) if sgd not in (None, False): for name, proc in self.pipeline: if hasattr(proc, "model"): proc.model.finish_update(sgd) return losses def rehearse( self, examples: Iterable[Example], *, sgd: Optional[Optimizer] = None, losses: Optional[Dict[str, float]] = None, component_cfg: Optional[Dict[str, Dict[str, Any]]] = None, ) -> Dict[str, float]: """Make a "rehearsal" update to the models in the pipeline, to prevent forgetting. Rehearsal updates run an initial copy of the model over some data, and update the model so its current predictions are more like the initial ones. This is useful for keeping a pretrained model on-track, even if you're updating it with a smaller set of examples. examples (Iterable[Example]): A batch of `Example` objects. sgd (Optional[Optimizer]): An optimizer. component_cfg (Dict[str, Dict]): Config parameters for specific pipeline components, keyed by component name. RETURNS (dict): Results from the update. EXAMPLE: >>> raw_text_batches = minibatch(raw_texts) >>> for labelled_batch in minibatch(examples): >>> nlp.update(labelled_batch) >>> raw_batch = [Example.from_dict(nlp.make_doc(text), {}) for text in next(raw_text_batches)] >>> nlp.rehearse(raw_batch) DOCS: https://spacy.io/api/language#rehearse """ if len(examples) == 0: return if not isinstance(examples, IterableInstance): raise TypeError( Errors.E978.format( name="language", method="rehearse", types=type(examples) ) ) wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)]) if wrong_types: raise TypeError( Errors.E978.format( name="language", method="rehearse", types=wrong_types ) ) if sgd is None: if self._optimizer is None: self._optimizer = create_default_optimizer() sgd = self._optimizer pipes = list(self.pipeline) random.shuffle(pipes) if component_cfg is None: component_cfg = {} grads = {} def get_grads(W, dW, key=None): grads[key] = (W, dW) get_grads.learn_rate = sgd.learn_rate get_grads.b1 = sgd.b1 get_grads.b2 = sgd.b2 for name, proc in pipes: if not hasattr(proc, "rehearse"): continue grads = {} proc.rehearse( examples, sgd=get_grads, losses=losses, **component_cfg.get(name, {}) ) for key, (W, dW) in grads.items(): sgd(W, dW, key=key) return losses def begin_training( self, get_examples: Optional[Callable[[], Iterable[Example]]] = None, *, sgd: Optional[Optimizer] = None, device: int = -1, ) -> Optimizer: """Initialize the pipe for training, using data examples if available. get_examples (Callable[[], Iterable[Example]]): Optional function that returns gold-standard Example objects. sgd (thinc.api.Optimizer): Optional optimizer. Will be created with create_optimizer if it doesn't exist. RETURNS (thinc.api.Optimizer): The optimizer. DOCS: https://spacy.io/api/language#begin_training """ # TODO: throw warning when get_gold_tuples is provided instead of get_examples if get_examples is None: get_examples = lambda: [] else: # Populate vocab for example in get_examples(): for word in [t.text for t in example.reference]: _ = self.vocab[word] # noqa: F841 if device >= 0: # TODO: do we need this here? require_gpu(device) if self.vocab.vectors.data.shape[1] >= 1: ops = get_current_ops() self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data) link_vectors_to_models(self.vocab) if sgd is None: sgd = create_default_optimizer() self._optimizer = sgd for name, proc in self.pipeline: if hasattr(proc, "begin_training"): proc.begin_training( get_examples, pipeline=self.pipeline, sgd=self._optimizer ) self._link_components() return self._optimizer def resume_training( self, *, sgd: Optional[Optimizer] = None, device: int = -1 ) -> Optimizer: """Continue training a pretrained model. Create and return an optimizer, and initialize "rehearsal" for any pipeline component that has a .rehearse() method. Rehearsal is used to prevent models from "forgetting" their initialized "knowledge". To perform rehearsal, collect samples of text you want the models to retain performance on, and call nlp.rehearse() with a batch of Example objects. sgd (Optional[Optimizer]): An optimizer. RETURNS (Optimizer): The optimizer. DOCS: https://spacy.io/api/language#resume_training """ if device >= 0: # TODO: do we need this here? require_gpu(device) ops = get_current_ops() if self.vocab.vectors.data.shape[1] >= 1: self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data) link_vectors_to_models(self.vocab) if sgd is None: sgd = create_default_optimizer() self._optimizer = sgd for name, proc in self.pipeline: if hasattr(proc, "_rehearsal_model"): proc._rehearsal_model = deepcopy(proc.model) return self._optimizer def evaluate( self, examples: Iterable[Example], *, verbose: bool = False, batch_size: int = 256, scorer: Optional[Scorer] = None, component_cfg: Optional[Dict[str, Dict[str, Any]]] = None, ) -> Dict[str, Union[float, dict]]: """Evaluate a model's pipeline components. examples (Iterable[Example]): `Example` objects. verbose (bool): Print debugging information. batch_size (int): Batch size to use. scorer (Optional[Scorer]): Scorer to use. If not passed in, a new one will be created. component_cfg (dict): An optional dictionary with extra keyword arguments for specific components. RETURNS (Scorer): The scorer containing the evaluation results. DOCS: https://spacy.io/api/language#evaluate """ if not isinstance(examples, IterableInstance): err = Errors.E978.format( name="language", method="evaluate", types=type(examples) ) raise TypeError(err) wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)]) if wrong_types: err = Errors.E978.format( name="language", method="evaluate", types=wrong_types ) raise TypeError(err) if component_cfg is None: component_cfg = {} if scorer is None: kwargs = component_cfg.get("scorer", {}) kwargs.setdefault("verbose", verbose) kwargs.setdefault("nlp", self) scorer = Scorer(**kwargs) texts = [eg.reference.text for eg in examples] docs = [eg.predicted for eg in examples] start_time = timer() # tokenize the texts only for timing purposes if not hasattr(self.tokenizer, "pipe"): _ = [self.tokenizer(text) for text in texts] else: _ = list(self.tokenizer.pipe(texts)) for name, pipe in self.pipeline: kwargs = component_cfg.get(name, {}) kwargs.setdefault("batch_size", batch_size) if not hasattr(pipe, "pipe"): docs = _pipe(docs, pipe, kwargs) else: docs = pipe.pipe(docs, **kwargs) # iterate over the final generator if len(self.pipeline): docs = list(docs) end_time = timer() for i, (doc, eg) in enumerate(zip(docs, examples)): if verbose: print(doc) eg.predicted = doc results = scorer.score(examples) n_words = sum(len(eg.predicted) for eg in examples) results["speed"] = n_words / (end_time - start_time) return results @contextmanager def use_params(self, params: dict): """Replace weights of models in the pipeline with those provided in the params dictionary. Can be used as a contextmanager, in which case, models go back to their original weights after the block. params (dict): A dictionary of parameters keyed by model ID. EXAMPLE: >>> with nlp.use_params(optimizer.averages): >>> nlp.to_disk("/tmp/checkpoint") DOCS: https://spacy.io/api/language#use_params """ contexts = [ pipe.use_params(params) for name, pipe in self.pipeline if hasattr(pipe, "use_params") and hasattr(pipe, "model") ] # TODO: Having trouble with contextlib # Workaround: these aren't actually context managers atm. for context in contexts: try: next(context) except StopIteration: pass yield for context in contexts: try: next(context) except StopIteration: pass def pipe( self, texts: Iterable[str], *, as_tuples: bool = False, batch_size: int = 1000, disable: Iterable[str] = tuple(), cleanup: bool = False, component_cfg: Optional[Dict[str, Dict[str, Any]]] = None, n_process: int = 1, ): """Process texts as a stream, and yield `Doc` objects in order. texts (Iterable[str]): A sequence of texts to process. as_tuples (bool): If set to True, inputs should be a sequence of (text, context) tuples. Output will then be a sequence of (doc, context) tuples. Defaults to False. batch_size (int): The number of texts to buffer. disable (List[str]): Names of the pipeline components to disable. cleanup (bool): If True, unneeded strings are freed to control memory use. Experimental. component_cfg (Dict[str, Dict]): An optional dictionary with extra keyword arguments for specific components. n_process (int): Number of processors to process texts. If -1, set `multiprocessing.cpu_count()`. YIELDS (Doc): Documents in the order of the original text. DOCS: https://spacy.io/api/language#pipe """ if n_process == -1: n_process = mp.cpu_count() if as_tuples: text_context1, text_context2 = itertools.tee(texts) texts = (tc[0] for tc in text_context1) contexts = (tc[1] for tc in text_context2) docs = self.pipe( texts, batch_size=batch_size, disable=disable, n_process=n_process, component_cfg=component_cfg, ) for doc, context in zip(docs, contexts): yield (doc, context) return if component_cfg is None: component_cfg = {} pipes = ( [] ) # contains functools.partial objects to easily create multiprocess worker. for name, proc in self.pipeline: if name in disable: continue kwargs = component_cfg.get(name, {}) # Allow component_cfg to overwrite the top-level kwargs. kwargs.setdefault("batch_size", batch_size) if hasattr(proc, "pipe"): f = functools.partial(proc.pipe, **kwargs) else: # Apply the function, but yield the doc f = functools.partial(_pipe, proc=proc, kwargs=kwargs) pipes.append(f) if n_process != 1: docs = self._multiprocessing_pipe(texts, pipes, n_process, batch_size) else: # if n_process == 1, no processes are forked. docs = (self.make_doc(text) for text in texts) for pipe in pipes: docs = pipe(docs) # Track weakrefs of "recent" documents, so that we can see when they # expire from memory. When they do, we know we don't need old strings. # This way, we avoid maintaining an unbounded growth in string entries # in the string store. recent_refs = weakref.WeakSet() old_refs = weakref.WeakSet() # Keep track of the original string data, so that if we flush old strings, # we can recover the original ones. However, we only want to do this if we're # really adding strings, to save up-front costs. original_strings_data = None nr_seen = 0 for doc in docs: yield doc if cleanup: recent_refs.add(doc) if nr_seen < 10000: old_refs.add(doc) nr_seen += 1 elif len(old_refs) == 0: old_refs, recent_refs = recent_refs, old_refs if original_strings_data is None: original_strings_data = list(self.vocab.strings) else: keys, strings = self.vocab.strings._cleanup_stale_strings( original_strings_data ) self.vocab._reset_cache(keys, strings) self.tokenizer._reset_cache(keys) nr_seen = 0 def _multiprocessing_pipe( self, texts: Iterable[str], pipes: Iterable[Callable[[Doc], Doc]], n_process: int, batch_size: int, ) -> None: # raw_texts is used later to stop iteration. texts, raw_texts = itertools.tee(texts) # for sending texts to worker texts_q = [mp.Queue() for _ in range(n_process)] # for receiving byte-encoded docs from worker bytedocs_recv_ch, bytedocs_send_ch = zip( *[mp.Pipe(False) for _ in range(n_process)] ) batch_texts = util.minibatch(texts, batch_size) # Sender sends texts to the workers. # This is necessary to properly handle infinite length of texts. # (In this case, all data cannot be sent to the workers at once) sender = _Sender(batch_texts, texts_q, chunk_size=n_process) # send twice to make process busy sender.send() sender.send() procs = [ mp.Process( target=_apply_pipes, args=(self.make_doc, pipes, rch, sch, Underscore.get_state()), ) for rch, sch in zip(texts_q, bytedocs_send_ch) ] for proc in procs: proc.start() # Cycle channels not to break the order of docs. # The received object is a batch of byte-encoded docs, so flatten them with chain.from_iterable. byte_docs = chain.from_iterable(recv.recv() for recv in cycle(bytedocs_recv_ch)) docs = (Doc(self.vocab).from_bytes(byte_doc) for byte_doc in byte_docs) try: for i, (_, doc) in enumerate(zip(raw_texts, docs), 1): yield doc if i % batch_size == 0: # tell `sender` that one batch was consumed. sender.step() finally: for proc in procs: proc.terminate() def _link_components(self) -> None: """Register 'listeners' within pipeline components, to allow them to effectively share weights. """ for i, (name1, proc1) in enumerate(self.pipeline): if hasattr(proc1, "find_listeners"): for name2, proc2 in self.pipeline[i:]: if hasattr(proc2, "model"): proc1.find_listeners(proc2.model) @classmethod def from_config( cls, config: Union[Dict[str, Any], Config] = {}, *, disable: Iterable[str] = tuple(), overrides: Dict[str, Any] = {}, auto_fill: bool = True, validate: bool = True, ) -> "Language": """Create the nlp object from a loaded config. Will set up the tokenizer and language data, add pipeline components etc. If no config is provided, the default config of the given language is used. config (Dict[str, Any] / Config): The loaded config. disable (Iterable[str]): List of pipeline component names to disable. auto_fill (bool): Automatically fill in missing values in config based on defaults and function argument annotations. validate (bool): Validate the component config and arguments against the types expected by the factory. RETURNS (Language): The initialized Language class. DOCS: https://spacy.io/api/language#from_config """ if auto_fill: config = util.deep_merge_configs(config, cls.default_config) if "nlp" not in config: raise ValueError(Errors.E985.format(config=config)) config_lang = config["nlp"]["lang"] if cls.lang is not None and config_lang is not None and config_lang != cls.lang: raise ValueError( Errors.E958.format( bad_lang_code=config["nlp"]["lang"], lang_code=cls.lang, lang=util.get_object_name(cls), ) ) config["nlp"]["lang"] = cls.lang # This isn't very elegant, but we remove the [components] block here to prevent # it from getting resolved (causes problems because we expect to pass in # the nlp and name args for each component). If we're auto-filling, we're # using the nlp.config with all defaults. config = util.copy_config(config) orig_pipeline = config.pop("components", {}) config["components"] = {} non_pipe_overrides, pipe_overrides = _get_config_overrides(overrides) resolved, filled = registry.resolve( config, validate=validate, schema=ConfigSchema, overrides=non_pipe_overrides ) filled["components"] = orig_pipeline config["components"] = orig_pipeline create_tokenizer = resolved["nlp"]["tokenizer"] create_lemmatizer = resolved["nlp"]["lemmatizer"] nlp = cls( create_tokenizer=create_tokenizer, create_lemmatizer=create_lemmatizer, ) pipeline = config.get("components", {}) for pipe_name in config["nlp"]["pipeline"]: if pipe_name not in pipeline: opts = ", ".join(pipeline.keys()) raise ValueError(Errors.E956.format(name=pipe_name, opts=opts)) pipe_cfg = util.copy_config(pipeline[pipe_name]) if pipe_name not in disable: if "factory" not in pipe_cfg: err = Errors.E984.format(name=pipe_name, config=pipe_cfg) raise ValueError(err) factory = pipe_cfg.pop("factory") # The pipe name (key in the config) here is the unique name of the # component, not necessarily the factory nlp.add_pipe( factory, name=pipe_name, config=pipe_cfg, overrides=pipe_overrides, validate=validate, ) nlp.config = filled if auto_fill else config nlp.resolved = resolved return nlp def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = tuple()) -> None: """Save the current state to a directory. If a model is loaded, this will include the model. path (str / Path): Path to a directory, which will be created if it doesn't exist. exclude (list): Names of components or serialization fields to exclude. DOCS: https://spacy.io/api/language#to_disk """ path = util.ensure_path(path) serializers = {} serializers["tokenizer"] = lambda p: self.tokenizer.to_disk( p, exclude=["vocab"] ) serializers["meta.json"] = lambda p: srsly.write_json(p, self.meta) serializers["config.cfg"] = lambda p: self.config.to_disk(p) for name, proc in self.pipeline: if not hasattr(proc, "name"): continue if name in exclude: continue if not hasattr(proc, "to_disk"): continue serializers[name] = lambda p, proc=proc: proc.to_disk(p, exclude=["vocab"]) serializers["vocab"] = lambda p: self.vocab.to_disk(p) util.to_disk(path, serializers, exclude) def from_disk( self, path: Union[str, Path], exclude: Iterable[str] = tuple() ) -> "Language": """Loads state from a directory. Modifies the object in place and returns it. If the saved `Language` object contains a model, the model will be loaded. path (str / Path): A path to a directory. exclude (list): Names of components or serialization fields to exclude. RETURNS (Language): The modified `Language` object. DOCS: https://spacy.io/api/language#from_disk """ def deserialize_meta(path: Path) -> None: if path.exists(): data = srsly.read_json(path) self.meta.update(data) # self.meta always overrides meta["vectors"] with the metadata # from self.vocab.vectors, so set the name directly self.vocab.vectors.name = data.get("vectors", {}).get("name") def deserialize_vocab(path: Path) -> None: if path.exists(): self.vocab.from_disk(path) _fix_pretrained_vectors_name(self) path = util.ensure_path(path) deserializers = {} if Path(path / "config.cfg").exists(): deserializers["config.cfg"] = lambda p: self.config.from_disk(p) deserializers["meta.json"] = deserialize_meta deserializers["vocab"] = deserialize_vocab deserializers["tokenizer"] = lambda p: self.tokenizer.from_disk( p, exclude=["vocab"] ) for name, proc in self.pipeline: if name in exclude: continue if not hasattr(proc, "from_disk"): continue deserializers[name] = lambda p, proc=proc: proc.from_disk( p, exclude=["vocab"] ) if not (path / "vocab").exists() and "vocab" not in exclude: # Convert to list here in case exclude is (default) tuple exclude = list(exclude) + ["vocab"] util.from_disk(path, deserializers, exclude) self._path = path self._link_components() return self def to_bytes(self, exclude: Iterable[str] = tuple()) -> bytes: """Serialize the current state to a binary string. exclude (list): Names of components or serialization fields to exclude. RETURNS (bytes): The serialized form of the `Language` object. DOCS: https://spacy.io/api/language#to_bytes """ serializers = {} serializers["vocab"] = lambda: self.vocab.to_bytes() serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"]) serializers["meta.json"] = lambda: srsly.json_dumps(self.meta) serializers["config.cfg"] = lambda: self.config.to_bytes() for name, proc in self.pipeline: if name in exclude: continue if not hasattr(proc, "to_bytes"): continue serializers[name] = lambda proc=proc: proc.to_bytes(exclude=["vocab"]) return util.to_bytes(serializers, exclude) def from_bytes( self, bytes_data: bytes, exclude: Iterable[str] = tuple() ) -> "Language": """Load state from a binary string. bytes_data (bytes): The data to load from. exclude (list): Names of components or serialization fields to exclude. RETURNS (Language): The `Language` object. DOCS: https://spacy.io/api/language#from_bytes """ def deserialize_meta(b): data = srsly.json_loads(b) self.meta.update(data) # self.meta always overrides meta["vectors"] with the metadata # from self.vocab.vectors, so set the name directly self.vocab.vectors.name = data.get("vectors", {}).get("name") def deserialize_vocab(b): self.vocab.from_bytes(b) _fix_pretrained_vectors_name(self) deserializers = {} deserializers["config.cfg"] = lambda b: self.config.from_bytes(b) deserializers["meta.json"] = deserialize_meta deserializers["vocab"] = deserialize_vocab deserializers["tokenizer"] = lambda b: self.tokenizer.from_bytes( b, exclude=["vocab"] ) for name, proc in self.pipeline: if name in exclude: continue if not hasattr(proc, "from_bytes"): continue deserializers[name] = lambda b, proc=proc: proc.from_bytes( b, exclude=["vocab"] ) util.from_bytes(bytes_data, deserializers, exclude) self._link_components() return self @dataclass class FactoryMeta: """Dataclass containing information about a component and its defaults provided by the @Language.component or @Language.factory decorator. It's created whenever a component is defined and stored on the Language class for each component instance and factory instance. """ factory: str default_config: Optional[Dict[str, Any]] = None # noqa: E704 assigns: Iterable[str] = tuple() requires: Iterable[str] = tuple() retokenizes: bool = False scores: Iterable[str] = tuple() default_score_weights: Optional[Dict[str, float]] = None # noqa: E704 def _get_config_overrides( items: Dict[str, Any], prefix: str = "components" ) -> Tuple[Dict[str, Any], Dict[str, Any]]: prefix = f"{prefix}." non_pipe = {k: v for k, v in items.items() if not k.startswith(prefix)} pipe = {k.replace(prefix, ""): v for k, v in items.items() if k.startswith(prefix)} return non_pipe, pipe def _fix_pretrained_vectors_name(nlp: Language) -> None: # TODO: Replace this once we handle vectors consistently as static # data if "vectors" in nlp.meta and "name" in nlp.meta["vectors"]: nlp.vocab.vectors.name = nlp.meta["vectors"]["name"] elif not nlp.vocab.vectors.size: nlp.vocab.vectors.name = None elif "name" in nlp.meta and "lang" in nlp.meta: vectors_name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors" nlp.vocab.vectors.name = vectors_name else: raise ValueError(Errors.E092) if nlp.vocab.vectors.size != 0: link_vectors_to_models(nlp.vocab) for name, proc in nlp.pipeline: if not hasattr(proc, "cfg"): continue proc.cfg.setdefault("deprecation_fixes", {}) proc.cfg["deprecation_fixes"]["vectors_name"] = nlp.vocab.vectors.name class DisabledPipes(list): """Manager for temporary pipeline disabling.""" def __init__(self, nlp: Language, names: List[str]) -> None: self.nlp = nlp self.names = names # Important! Not deep copy -- we just want the container (but we also # want to support people providing arbitrarily typed nlp.pipeline # objects.) self.original_pipeline = copy(nlp.pipeline) self.metas = {name: nlp.get_pipe_meta(name) for name in names} self.configs = {name: nlp.get_pipe_config(name) for name in names} list.__init__(self) self.extend(nlp.remove_pipe(name) for name in names) def __enter__(self): return self def __exit__(self, *args): self.restore() def restore(self) -> None: """Restore the pipeline to its state when DisabledPipes was created.""" current, self.nlp.pipeline = self.nlp.pipeline, self.original_pipeline unexpected = [name for name, pipe in current if not self.nlp.has_pipe(name)] if unexpected: # Don't change the pipeline if we're raising an error. self.nlp.pipeline = current raise ValueError(Errors.E008.format(names=unexpected)) self.nlp._pipe_meta.update(self.metas) self.nlp._pipe_configs.update(self.configs) self[:] = [] def _pipe( examples: Iterable[Example], proc: Callable[[Doc], Doc], kwargs: Dict[str, Any] ) -> Iterator[Example]: # We added some args for pipe that __call__ doesn't expect. kwargs = dict(kwargs) for arg in ["batch_size"]: if arg in kwargs: kwargs.pop(arg) for eg in examples: eg = proc(eg, **kwargs) yield eg def _apply_pipes( make_doc: Callable[[str], Doc], pipes: Iterable[Callable[[Doc], Doc]], receiver, sender, underscore_state: Tuple[dict, dict, dict], ) -> None: """Worker for Language.pipe make_doc (Callable[[str,] Doc]): Function to create Doc from text. pipes (Iterable[Callable[[Doc], Doc]]): The components to apply. receiver (multiprocessing.Connection): Pipe to receive text. Usually created by `multiprocessing.Pipe()` sender (multiprocessing.Connection): Pipe to send doc. Usually created by `multiprocessing.Pipe()` underscore_state (Tuple[dict, dict, dict]): The data in the Underscore class of the parent. """ Underscore.load_state(underscore_state) while True: texts = receiver.get() docs = (make_doc(text) for text in texts) for pipe in pipes: docs = pipe(docs) # Connection does not accept unpickable objects, so send list. sender.send([doc.to_bytes() for doc in docs]) class _Sender: """Util for sending data to multiprocessing workers in Language.pipe""" def __init__( self, data: Iterable[Any], queues: List[mp.Queue], chunk_size: int ) -> None: self.data = iter(data) self.queues = iter(cycle(queues)) self.chunk_size = chunk_size self.count = 0 def send(self) -> None: """Send chunk_size items from self.data to channels.""" for item, q in itertools.islice( zip(self.data, cycle(self.queues)), self.chunk_size ): # cycle channels so that distribute the texts evenly q.put(item) def step(self) -> None: """Tell sender that comsumed one item. Data is sent to the workers after every chunk_size calls. """ self.count += 1 if self.count >= self.chunk_size: self.count = 0 self.send()