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spaCy/spacy/language.py

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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 validate_attrs, analyze_pipes, print_pipe_analysis
from .gold import Example
from .scorer import Scorer
from .util import 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 .errors import Errors, Warnings
from .schemas import ConfigSchema
from .git_info import GIT_VERSION
from . import util
from . import about
# 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
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,
**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.
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")
vocab = create_vocab(
self.lang,
self.Defaults,
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 analyze_pipes(
self,
*,
keys: List[str] = ["assigns", "requires", "scores", "retokenizes"],
pretty: bool = False,
) -> Optional[Dict[str, Any]]:
"""Analyze the current pipeline components, print a summary of what
they assign or require and check that all requirements are met.
keys (List[str]): The meta values to display in the table. Corresponds
to values in FactoryMeta, defined by @Language.factory decorator.
pretty (bool): Pretty-print the results.
RETURNS (dict): The data.
"""
analysis = analyze_pipes(self, keys=keys)
if pretty:
print_pipe_analysis(analysis, keys=keys)
return analysis
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(),
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.
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)
filled = filled[factory_name]
filled["factory"] = factory_name
filled.pop("@factories", None)
self._pipe_configs[name] = filled
return resolved[factory_name]
def create_pipe_from_source(
self, source_name: str, source: "Language", *, name: str,
) -> Tuple[Callable[[Doc], Doc], str]:
"""Create a pipeline component by copying it from an existing model.
source_name (str): Name of the component in the source pipeline.
source (Language): The source nlp object to copy from.
name (str): Optional alternative name to use in current pipeline.
RETURNS (Tuple[Callable, str]): The component and its factory name.
"""
# TODO: handle errors and mismatches (vectors etc.)
if not isinstance(source, self.__class__):
raise ValueError(Errors.E945.format(name=source_name, source=type(source)))
if not source.has_pipe(source_name):
raise KeyError(
Errors.E944.format(
name=source_name,
model=f"{source.meta['lang']}_{source.meta['name']}",
opts=", ".join(source.pipe_names),
)
)
pipe = source.get_pipe(source_name)
pipe_config = util.copy_config(source.config["components"][source_name])
self._pipe_configs[name] = pipe_config
return pipe, pipe_config["factory"]
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,
source: Optional["Language"] = None,
config: 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.
source (Language): Optional loaded nlp object to copy the pipeline
component from.
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.
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)
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))
if source is not None:
# We're loading the component from a model. After loading the
# component, we know its real factory name
pipe_component, factory_name = self.create_pipe_from_source(
factory_name, source, name=name
)
else:
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,
)
pipe_component = self.create_pipe(
factory_name, name=name, config=config, 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))
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) or pipe_index == len(self.pipeline):
# we have no components to insert before/after, or we're replacing the last component
self.add_pipe(factory_name, name=name)
else:
self.add_pipe(factory_name, name=name, before=pipe_index)
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)
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)) from None
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,
exclude: Iterable[str] = tuple(),
):
"""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.
exclude (Iterable[str]): Names of components that shouldn't be updated.
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 name in exclude or 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 name not in exclude and 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,
exclude: Iterable[str] = tuple(),
) -> 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.
exclude (Iterable[str]): Names of components that shouldn't be updated.
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 name in exclude or 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)
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)
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,
scorer_cfg: Optional[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.
scorer_cfg (dict): An optional dictionary with extra keyword arguments
for the scorer.
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_cfg is None:
scorer_cfg = {}
if scorer is None:
kwargs = dict(scorer_cfg)
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] # noqa: F841
else:
_ = list(self.tokenizer.pipe(texts)) # noqa: F841
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] = {},
*,
vocab: Union[Vocab, bool] = True,
disable: Iterable[str] = tuple(),
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.
vocab (Vocab): A Vocab object. If True, a vocab is created.
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"] = {}
resolved, filled = registry.resolve(
config, validate=validate, schema=ConfigSchema
)
filled["components"] = orig_pipeline
config["components"] = orig_pipeline
create_tokenizer = resolved["nlp"]["tokenizer"]
before_creation = resolved["nlp"]["before_creation"]
after_creation = resolved["nlp"]["after_creation"]
after_pipeline_creation = resolved["nlp"]["after_pipeline_creation"]
lang_cls = cls
if before_creation is not None:
lang_cls = before_creation(cls)
if (
not isinstance(lang_cls, type)
or not issubclass(lang_cls, cls)
or lang_cls is not cls
):
raise ValueError(Errors.E943.format(value=type(lang_cls)))
nlp = lang_cls(
vocab=vocab,
create_tokenizer=create_tokenizer,
)
if after_creation is not None:
nlp = after_creation(nlp)
if not isinstance(nlp, cls):
raise ValueError(Errors.E942.format(name="creation", value=type(nlp)))
# Note that we don't load vectors here, instead they get loaded explicitly
# inside stuff like the spacy train function. If we loaded them here,
# then we would load them twice at runtime: once when we make from config,
# and then again when we load from disk.
pipeline = config.get("components", {})
# If components are loaded from a source (existing models), we cache
# them here so they're only loaded once
source_nlps = {}
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 and "source" not in pipe_cfg:
err = Errors.E984.format(name=pipe_name, config=pipe_cfg)
raise ValueError(err)
if "factory" in pipe_cfg:
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, validate=validate,
)
else:
model = pipe_cfg["source"]
if model not in source_nlps:
# We only need the components here and we need to init
# model with the same vocab as the current nlp object
source_nlps[model] = util.load_model(
model, vocab=nlp.vocab, disable=["vocab", "tokenizer"]
)
source_name = pipe_cfg.get("component", pipe_name)
nlp.add_pipe(source_name, source=source_nlps[model], name=pipe_name)
nlp.config = filled if auto_fill else config
nlp.resolved = resolved
if after_pipeline_creation is not None:
nlp = after_pipeline_creation(nlp)
if not isinstance(nlp, cls):
raise ValueError(
Errors.E942.format(name="pipeline_creation", value=type(nlp))
)
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 _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)
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()
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