# coding: utf8 from __future__ import absolute_import, unicode_literals import random import itertools import warnings from thinc.extra import load_nlp import weakref import functools from collections import OrderedDict from contextlib import contextmanager from copy import copy, deepcopy from thinc.neural import Model import srsly import multiprocessing as mp from itertools import chain, cycle from .tokenizer import Tokenizer from .tokens.underscore import Underscore from .vocab import Vocab from .lemmatizer import Lemmatizer from .lookups import Lookups from .analysis import analyze_pipes, analyze_all_pipes, validate_attrs from .compat import izip, basestring_, is_python2, class_types from .gold import GoldParse from .scorer import Scorer from ._ml import link_vectors_to_models, create_default_optimizer from .attrs import IS_STOP, LANG, NORM from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES from .lang.punctuation import TOKENIZER_INFIXES from .lang.tokenizer_exceptions import TOKEN_MATCH, URL_MATCH from .lang.norm_exceptions import BASE_NORMS from .lang.tag_map import TAG_MAP from .tokens import Doc from .lang.lex_attrs import LEX_ATTRS, is_stop from .errors import Errors, Warnings from . import util from . import about ENABLE_PIPELINE_ANALYSIS = False class BaseDefaults(object): @classmethod def create_lemmatizer(cls, nlp=None, lookups=None): if lookups is None: lookups = cls.create_lookups(nlp=nlp) return Lemmatizer(lookups=lookups) @classmethod def create_lookups(cls, nlp=None): root = util.get_module_path(cls) filenames = {name: root / filename for name, filename in cls.resources} if LANG in cls.lex_attr_getters: lang = cls.lex_attr_getters[LANG](None) if lang in util.registry.lookups: filenames.update(util.registry.lookups.get(lang)) lookups = Lookups() for name, filename in filenames.items(): data = util.load_language_data(filename) lookups.add_table(name, data) return lookups @classmethod def create_vocab(cls, nlp=None): lookups = cls.create_lookups(nlp) lemmatizer = cls.create_lemmatizer(nlp, lookups=lookups) lex_attr_getters = dict(cls.lex_attr_getters) # This is messy, but it's the minimal working fix to Issue #639. lex_attr_getters[IS_STOP] = functools.partial(is_stop, stops=cls.stop_words) vocab = Vocab( lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map, lemmatizer=lemmatizer, lookups=lookups, ) vocab.lex_attr_getters[NORM] = util.add_lookups( vocab.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), BASE_NORMS, vocab.lookups.get_table("lexeme_norm"), ) for tag_str, exc in cls.morph_rules.items(): for orth_str, attrs in exc.items(): vocab.morphology.add_special_case(tag_str, orth_str, attrs) return vocab @classmethod def create_tokenizer(cls, nlp=None): rules = cls.tokenizer_exceptions token_match = cls.token_match url_match = cls.url_match prefix_search = ( util.compile_prefix_regex(cls.prefixes).search if cls.prefixes else None ) suffix_search = ( util.compile_suffix_regex(cls.suffixes).search if cls.suffixes else None ) infix_finditer = ( util.compile_infix_regex(cls.infixes).finditer if cls.infixes else None ) vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp) return Tokenizer( vocab, rules=rules, prefix_search=prefix_search, suffix_search=suffix_search, infix_finditer=infix_finditer, token_match=token_match, url_match=url_match, ) pipe_names = ["tagger", "parser", "ner"] token_match = TOKEN_MATCH url_match = URL_MATCH prefixes = tuple(TOKENIZER_PREFIXES) suffixes = tuple(TOKENIZER_SUFFIXES) infixes = tuple(TOKENIZER_INFIXES) tag_map = dict(TAG_MAP) tokenizer_exceptions = {} stop_words = set() morph_rules = {} lex_attr_getters = LEX_ATTRS syntax_iterators = {} resources = {} writing_system = {"direction": "ltr", "has_case": True, "has_letters": True} single_orth_variants = [] paired_orth_variants = [] class Language(object): """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 (unicode): Two-letter language ID, i.e. ISO code. DOCS: https://spacy.io/api/language """ Defaults = BaseDefaults lang = None factories = {"tokenizer": lambda nlp: nlp.Defaults.create_tokenizer(nlp)} def __init__( self, vocab=True, make_doc=True, max_length=10 ** 6, meta={}, **kwargs ): """Initialise a Language object. vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via `Language.Defaults.create_vocab`. make_doc (callable): A function that takes text and returns a `Doc` object. Usually a `Tokenizer`. 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 v2 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 characters (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. RETURNS (Language): The newly constructed object. """ user_factories = util.registry.factories.get_all() self.factories.update(user_factories) self._meta = dict(meta) self._path = None if vocab is True: factory = self.Defaults.create_vocab vocab = factory(self, **meta.get("vocab", {})) if vocab.vectors.name is None: vocab.vectors.name = meta.get("vectors", {}).get("name") 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 if make_doc is True: factory = self.Defaults.create_tokenizer make_doc = factory(self, **meta.get("tokenizer", {})) self.tokenizer = make_doc self.pipeline = [] self.max_length = max_length self._optimizer = None @property def path(self): return self._path @property def meta(self): 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", ">={}".format(about.__version__)) self._meta.setdefault("description", "") self._meta.setdefault("author", "") self._meta.setdefault("email", "") self._meta.setdefault("url", "") self._meta.setdefault("license", "") 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["pipeline"] = self.pipe_names self._meta["factories"] = self.pipe_factories self._meta["labels"] = self.pipe_labels return self._meta @meta.setter def meta(self, value): self._meta = value # Conveniences to access pipeline components # Shouldn't be used anymore! @property def tensorizer(self): return self.get_pipe("tensorizer") @property def tagger(self): return self.get_pipe("tagger") @property def parser(self): return self.get_pipe("parser") @property def entity(self): return self.get_pipe("ner") @property def linker(self): return self.get_pipe("entity_linker") @property def matcher(self): return self.get_pipe("matcher") @property def pipe_names(self): """Get names of available pipeline components. RETURNS (list): List of component name strings, in order. """ return [pipe_name for pipe_name, _ in self.pipeline] @property def pipe_factories(self): """Get the component factories for the available pipeline components. RETURNS (dict): Factory names, keyed by component names. """ factories = {} for pipe_name, pipe in self.pipeline: factories[pipe_name] = getattr(pipe, "factory", pipe_name) return factories @property def pipe_labels(self): """Get the labels set by the pipeline components, if available (if the component exposes a labels property). RETURNS (dict): Labels keyed by component name. """ labels = OrderedDict() for name, pipe in self.pipeline: if hasattr(pipe, "labels"): labels[name] = list(pipe.labels) return labels def get_pipe(self, name): """Get a pipeline component for a given component name. name (unicode): 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, name, config=dict()): """Create a pipeline component from a factory. name (unicode): Factory name to look up in `Language.factories`. config (dict): Configuration parameters to initialise component. RETURNS (callable): Pipeline component. DOCS: https://spacy.io/api/language#create_pipe """ if name not in self.factories: if name == "sbd": raise KeyError(Errors.E108.format(name=name)) else: raise KeyError(Errors.E002.format(name=name)) factory = self.factories[name] return factory(self, **config) def add_pipe( self, component, name=None, before=None, after=None, first=None, last=None ): """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". component (callable): The pipeline component. name (unicode): 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 (unicode): Component name to insert component directly before. after (unicode): Component name to insert component directly after. first (bool): Insert component first / not first in the pipeline. last (bool): Insert component last / not last in the pipeline. DOCS: https://spacy.io/api/language#add_pipe """ if not hasattr(component, "__call__"): msg = Errors.E003.format(component=repr(component), name=name) if isinstance(component, basestring_) and component in self.factories: msg += Errors.E004.format(component=component) raise ValueError(msg) if name is None: name = util.get_component_name(component) if name in self.pipe_names: raise ValueError(Errors.E007.format(name=name, opts=self.pipe_names)) if sum([bool(before), bool(after), bool(first), bool(last)]) >= 2: raise ValueError(Errors.E006) pipe_index = 0 pipe = (name, component) if last or not any([first, before, after]): pipe_index = len(self.pipeline) self.pipeline.append(pipe) elif first: self.pipeline.insert(0, pipe) elif before and before in self.pipe_names: pipe_index = self.pipe_names.index(before) self.pipeline.insert(self.pipe_names.index(before), pipe) elif after and after in self.pipe_names: pipe_index = self.pipe_names.index(after) + 1 self.pipeline.insert(self.pipe_names.index(after) + 1, pipe) else: raise ValueError( Errors.E001.format(name=before or after, opts=self.pipe_names) ) if ENABLE_PIPELINE_ANALYSIS: analyze_pipes(self.pipeline, name, component, pipe_index) def has_pipe(self, name): """Check if a component name is present in the pipeline. Equivalent to `name in nlp.pipe_names`. name (unicode): 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, component): """Replace a component in the pipeline. name (unicode): Name of the component to replace. component (callable): Pipeline component. 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 not hasattr(component, "__call__"): msg = Errors.E003.format(component=repr(component), name=name) if isinstance(component, basestring_) and component in self.factories: msg += Errors.E135.format(name=name) raise ValueError(msg) self.pipeline[self.pipe_names.index(name)] = (name, component) if ENABLE_PIPELINE_ANALYSIS: analyze_all_pipes(self.pipeline) def rename_pipe(self, old_name, new_name): """Rename a pipeline component. old_name (unicode): Name of the component to rename. new_name (unicode): 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]) def remove_pipe(self, name): """Remove a component from the pipeline. name (unicode): 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)) if ENABLE_PIPELINE_ANALYSIS: analyze_all_pipes(self.pipeline) return removed def __call__(self, text, disable=[], component_cfg=None): """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 (unicode): The text to be processed. disable (list): Names of the pipeline components to disable. component_cfg (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)) doc = proc(doc, **component_cfg.get(name, {})) if doc is None: raise ValueError(Errors.E005.format(name=name)) return doc def disable_pipes(self, *names): """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. DOCS: https://spacy.io/api/language#disable_pipes """ 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 make_doc(self, text): return self.tokenizer(text) def _format_docs_and_golds(self, docs, golds): """Format golds and docs before update models.""" expected_keys = ("words", "tags", "heads", "deps", "entities", "cats", "links") gold_objs = [] doc_objs = [] for doc, gold in zip(docs, golds): if isinstance(doc, basestring_): doc = self.make_doc(doc) if not isinstance(gold, GoldParse): unexpected = [k for k in gold if k not in expected_keys] if unexpected: err = Errors.E151.format(unexp=unexpected, exp=expected_keys) raise ValueError(err) gold = GoldParse(doc, **gold) doc_objs.append(doc) gold_objs.append(gold) return doc_objs, gold_objs def update(self, docs, golds, drop=0.0, sgd=None, losses=None, component_cfg=None): """Update the models in the pipeline. docs (iterable): A batch of `Doc` objects. golds (iterable): A batch of `GoldParse` objects. drop (float): The dropout rate. sgd (callable): An optimizer. losses (dict): Dictionary to update with the loss, keyed by component. component_cfg (dict): Config parameters for specific pipeline components, keyed by component name. DOCS: https://spacy.io/api/language#update """ if len(docs) != len(golds): raise IndexError(Errors.E009.format(n_docs=len(docs), n_golds=len(golds))) if len(docs) == 0: return if sgd is None: if self._optimizer is None: self._optimizer = create_default_optimizer(Model.ops) sgd = self._optimizer # Allow dict of args to GoldParse, instead of GoldParse objects. docs, golds = self._format_docs_and_golds(docs, golds) grads = {} def get_grads(W, dW, key=None): grads[key] = (W, dW) get_grads.alpha = sgd.alpha get_grads.b1 = sgd.b1 get_grads.b2 = sgd.b2 pipes = list(self.pipeline) random.shuffle(pipes) if component_cfg is None: component_cfg = {} for name, proc in pipes: if not hasattr(proc, "update"): continue grads = {} kwargs = component_cfg.get(name, {}) kwargs.setdefault("drop", drop) proc.update(docs, golds, sgd=get_grads, losses=losses, **kwargs) for key, (W, dW) in grads.items(): sgd(W, dW, key=key) def rehearse(self, docs, sgd=None, losses=None, config=None): """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. docs (iterable): A batch of `Doc` objects. drop (float): The dropout rate. sgd (callable): An optimizer. RETURNS (dict): Results from the update. EXAMPLE: >>> raw_text_batches = minibatch(raw_texts) >>> for labelled_batch in minibatch(zip(train_docs, train_golds)): >>> docs, golds = zip(*train_docs) >>> nlp.update(docs, golds) >>> raw_batch = [nlp.make_doc(text) for text in next(raw_text_batches)] >>> nlp.rehearse(raw_batch) """ # TODO: document if len(docs) == 0: return if sgd is None: if self._optimizer is None: self._optimizer = create_default_optimizer(Model.ops) sgd = self._optimizer docs = list(docs) for i, doc in enumerate(docs): if isinstance(doc, basestring_): docs[i] = self.make_doc(doc) pipes = list(self.pipeline) random.shuffle(pipes) if config is None: config = {} grads = {} def get_grads(W, dW, key=None): grads[key] = (W, dW) get_grads.alpha = sgd.alpha get_grads.b1 = sgd.b1 get_grads.b2 = sgd.b2 for name, proc in pipes: if not hasattr(proc, "rehearse"): continue grads = {} proc.rehearse(docs, sgd=get_grads, losses=losses, **config.get(name, {})) for key, (W, dW) in grads.items(): sgd(W, dW, key=key) return losses def preprocess_gold(self, docs_golds): """Can be called before training to pre-process gold data. By default, it handles nonprojectivity and adds missing tags to the tag map. docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects. YIELDS (tuple): Tuples of preprocessed `Doc` and `GoldParse` objects. """ for name, proc in self.pipeline: if hasattr(proc, "preprocess_gold"): docs_golds = proc.preprocess_gold(docs_golds) for doc, gold in docs_golds: yield doc, gold def begin_training(self, get_gold_tuples=None, sgd=None, component_cfg=None, **cfg): """Allocate models, pre-process training data and acquire a trainer and optimizer. Used as a contextmanager. get_gold_tuples (function): Function returning gold data component_cfg (dict): Config parameters for specific components. **cfg: Config parameters. RETURNS: An optimizer. DOCS: https://spacy.io/api/language#begin_training """ if get_gold_tuples is None: get_gold_tuples = lambda: [] # Populate vocab else: for _, annots_brackets in get_gold_tuples(): _ = annots_brackets.pop() for annots, _ in annots_brackets: for word in annots[1]: _ = self.vocab[word] # noqa: F841 if cfg.get("device", -1) >= 0: util.use_gpu(cfg["device"]) if self.vocab.vectors.data.shape[1] >= 1: self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data) link_vectors_to_models(self.vocab) if self.vocab.vectors.data.shape[1]: cfg["pretrained_vectors"] = self.vocab.vectors.name cfg["pretrained_dims"] = self.vocab.vectors.data.shape[1] if sgd is None: sgd = create_default_optimizer(Model.ops) self._optimizer = sgd if component_cfg is None: component_cfg = {} for name, proc in self.pipeline: if hasattr(proc, "begin_training"): kwargs = component_cfg.get(name, {}) kwargs.update(cfg) proc.begin_training( get_gold_tuples, pipeline=self.pipeline, sgd=self._optimizer, **kwargs ) return self._optimizer def resume_training(self, sgd=None, **cfg): """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 initialised "knowledge". To perform rehearsal, collect samples of text you want the models to retain performance on, and call nlp.rehearse() with a batch of Doc objects. """ if cfg.get("device", -1) >= 0: util.use_gpu(cfg["device"]) if self.vocab.vectors.data.shape[1] >= 1: self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data) link_vectors_to_models(self.vocab) if self.vocab.vectors.data.shape[1]: cfg["pretrained_vectors"] = self.vocab.vectors.name if sgd is None: sgd = create_default_optimizer(Model.ops) 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, docs_golds, verbose=False, batch_size=256, scorer=None, component_cfg=None ): """Evaluate a model's pipeline components. docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects. verbose (bool): Print debugging information. batch_size (int): Batch size to use. scorer (Scorer): Optional `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 scorer is None: scorer = Scorer(pipeline=self.pipeline) if component_cfg is None: component_cfg = {} docs, golds = zip(*docs_golds) docs = [ self.make_doc(doc) if isinstance(doc, basestring_) else doc for doc in docs ] golds = list(golds) 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) for doc, gold in zip(docs, golds): if not isinstance(gold, GoldParse): gold = GoldParse(doc, **gold) if verbose: print(doc) kwargs = component_cfg.get("scorer", {}) kwargs.setdefault("verbose", verbose) scorer.score(doc, gold, **kwargs) return scorer @contextmanager def use_params(self, params, **cfg): """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. **cfg: Config parameters. EXAMPLE: >>> with nlp.use_params(optimizer.averages): >>> nlp.to_disk('/tmp/checkpoint') """ contexts = [ pipe.use_params(params) for name, pipe in self.pipeline if hasattr(pipe, "use_params") ] # 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, as_tuples=False, n_threads=-1, batch_size=1000, disable=[], cleanup=False, component_cfg=None, n_process=1, ): """Process texts as a stream, and yield `Doc` objects in order. texts (iterator): 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): Names of the pipeline components to disable. cleanup (bool): If True, unneeded strings are freed to control memory use. Experimental. component_cfg (dict): An optional dictionary with extra keyword arguments for specific components. n_process (int): Number of processors to process texts, only supported in Python3. If -1, set `multiprocessing.cpu_count()`. YIELDS (Doc): Documents in the order of the original text. DOCS: https://spacy.io/api/language#pipe """ if is_python2 and n_process != 1: warnings.warn(Warnings.W023) n_process = 1 if n_threads != -1: warnings.warn(Warnings.W016, DeprecationWarning) 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 izip(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, pipes, n_process, batch_size): # 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(), load_nlp.VECTORS, ), ) 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 to_disk(self, path, exclude=tuple(), disable=None): """Save the current state to a directory. If a model is loaded, this will include the model. path (unicode or 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 """ if disable is not None: warnings.warn(Warnings.W014, DeprecationWarning) exclude = disable path = util.ensure_path(path) serializers = OrderedDict() serializers["tokenizer"] = lambda p: self.tokenizer.to_disk( p, exclude=["vocab"] ) serializers["meta.json"] = lambda p: srsly.write_json(p, self.meta) 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, exclude=tuple(), disable=None): """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 (unicode or 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): 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): if path.exists(): self.vocab.from_disk(path) _fix_pretrained_vectors_name(self) if disable is not None: warnings.warn(Warnings.W014, DeprecationWarning) exclude = disable path = util.ensure_path(path) deserializers = OrderedDict() 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 return self def to_bytes(self, exclude=tuple(), disable=None, **kwargs): """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 """ if disable is not None: warnings.warn(Warnings.W014, DeprecationWarning) exclude = disable serializers = OrderedDict() serializers["vocab"] = lambda: self.vocab.to_bytes() serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"]) serializers["meta.json"] = lambda: srsly.json_dumps( OrderedDict(sorted(self.meta.items())) ) 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"]) exclude = util.get_serialization_exclude(serializers, exclude, kwargs) return util.to_bytes(serializers, exclude) def from_bytes(self, bytes_data, exclude=tuple(), disable=None, **kwargs): """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) if disable is not None: warnings.warn(Warnings.W014, DeprecationWarning) exclude = disable deserializers = OrderedDict() 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"] ) exclude = util.get_serialization_exclude(deserializers, exclude, kwargs) util.from_bytes(bytes_data, deserializers, exclude) return self class component(object): """Decorator for pipeline components. Can decorate both function components and class components and will automatically register components in the Language.factories. If the component is a class and needs access to the nlp object or config parameters, it can expose a from_nlp classmethod that takes the nlp object and **cfg arguments and returns the initialized component. """ # NB: This decorator needs to live here, because it needs to write to # Language.factories. All other solutions would cause circular import. def __init__(self, name=None, assigns=tuple(), requires=tuple(), retokenizes=False): """Decorate a pipeline component. name (unicode): Default component and factory name. assigns (list): Attributes assigned by component, e.g. `["token.pos"]`. requires (list): Attributes required by component, e.g. `["token.dep"]`. retokenizes (bool): Whether the component changes the tokenization. """ self.name = name self.assigns = validate_attrs(assigns) self.requires = validate_attrs(requires) self.retokenizes = retokenizes def __call__(self, *args, **kwargs): obj = args[0] args = args[1:] factory_name = self.name or util.get_component_name(obj) obj.name = factory_name obj.factory = factory_name obj.assigns = self.assigns obj.requires = self.requires obj.retokenizes = self.retokenizes def factory(nlp, **cfg): if hasattr(obj, "from_nlp"): return obj.from_nlp(nlp, **cfg) elif isinstance(obj, class_types): return obj() return obj Language.factories[obj.factory] = factory return obj def _fix_pretrained_vectors_name(nlp): # 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 = "%s_%s.vectors" % (nlp.meta["lang"], nlp.meta["name"]) nlp.vocab.vectors.name = vectors_name else: raise ValueError(Errors.E092) if nlp.vocab.vectors.size != 0: link_vectors_to_models(nlp.vocab, skip_rank=True) 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, *names): 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) 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): """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[:] = [] def _pipe(docs, proc, kwargs): # We added some args for pipe that __call__ doesn't expect. kwargs = dict(kwargs) for arg in ["n_threads", "batch_size"]: if arg in kwargs: kwargs.pop(arg) for doc in docs: doc = proc(doc, **kwargs) yield doc def _apply_pipes(make_doc, pipes, receiver, sender, underscore_state, vectors): """Worker for Language.pipe 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): The data in the Underscore class of the parent vectors (dict): The global vectors data, copied from the parent """ Underscore.load_state(underscore_state) load_nlp.VECTORS = vectors 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, queues, chunk_size): self.data = iter(data) self.queues = iter(cycle(queues)) self.chunk_size = chunk_size self.count = 0 def send(self): """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): """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()