mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
689 lines
27 KiB
Cython
689 lines
27 KiB
Cython
# cython: infer_types=True, cdivision=True, boundscheck=False, binding=True
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from __future__ import print_function
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cimport numpy as np
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from cymem.cymem cimport Pool
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from itertools import islice
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from libc.stdlib cimport calloc, free
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from libc.string cimport memcpy, memset
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from libcpp.vector cimport vector
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import random
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import srsly
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from thinc.api import CupyOps, NumpyOps, get_ops, set_dropout_rate
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from thinc.extra.search cimport Beam
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import warnings
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import numpy
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import numpy.random
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from ..ml.parser_model cimport (
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ActivationsC,
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SizesC,
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WeightsC,
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alloc_activations,
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arg_max_if_valid,
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cpu_log_loss,
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free_activations,
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get_c_sizes,
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get_c_weights,
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predict_states,
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)
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from ..tokens.doc cimport Doc
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from ._parser_internals.stateclass cimport StateClass
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from .trainable_pipe import TrainablePipe
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from ._parser_internals cimport _beam_utils
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from .. import util
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from ..errors import Errors, Warnings
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from ..training import validate_examples, validate_get_examples
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from ._parser_internals import _beam_utils
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NUMPY_OPS = NumpyOps()
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cdef class Parser(TrainablePipe):
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"""
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Base class of the DependencyParser and EntityRecognizer.
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"""
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def __init__(
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self,
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Vocab vocab,
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model,
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name="base_parser",
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moves=None,
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*,
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update_with_oracle_cut_size,
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min_action_freq,
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learn_tokens,
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beam_width=1,
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beam_density=0.0,
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beam_update_prob=0.0,
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multitasks=tuple(),
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incorrect_spans_key=None,
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scorer=None,
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):
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"""Create a Parser.
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vocab (Vocab): The vocabulary object. Must be shared with documents
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to be processed. The value is set to the `.vocab` attribute.
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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name (str): The name of the pipeline component
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moves (Optional[TransitionSystem]): This defines how the parse-state is created,
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updated and evaluated. If 'moves' is None, a new instance is
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created with `self.TransitionSystem()`. Defaults to `None`.
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update_with_oracle_cut_size (int): During training, cut long sequences into
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shorter segments by creating intermediate states based on the gold-standard
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history. The model is not very sensitive to this parameter, so you usually
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won't need to change it. 100 is a good default.
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min_action_freq (int): The minimum frequency of labelled actions to retain.
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Rarer labelled actions have their label backed-off to "dep". While this
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primarily affects the label accuracy, it can also affect the attachment
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structure, as the labels are used to represent the pseudo-projectivity
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transformation.
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learn_tokens (bool): Whether to learn to merge subtokens that are split
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relative to the gold standard. Experimental.
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beam_width (int): The number of candidate analyses to maintain.
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beam_density (float): The minimum ratio between the scores of the first and
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last candidates in the beam. This allows the parser to avoid exploring
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candidates that are too far behind. This is mostly intended to improve
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efficiency, but it can also improve accuracy as deeper search is not
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always better.
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beam_update_prob (float): The chance of making a beam update, instead of a
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greedy update. Greedy updates are an approximation for the beam updates,
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and are faster to compute.
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multitasks: additional multi-tasking components. Experimental.
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incorrect_spans_key (Optional[str]): Identifies spans that are known
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to be incorrect entity annotations. The incorrect entity annotations
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can be stored in the span group, under this key.
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scorer (Optional[Callable]): The scoring method. Defaults to None.
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"""
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self.vocab = vocab
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self.name = name
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cfg = {
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"moves": moves,
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"update_with_oracle_cut_size": update_with_oracle_cut_size,
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"multitasks": list(multitasks),
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"min_action_freq": min_action_freq,
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"learn_tokens": learn_tokens,
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"beam_width": beam_width,
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"beam_density": beam_density,
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"beam_update_prob": beam_update_prob,
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"incorrect_spans_key": incorrect_spans_key
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}
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if moves is None:
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# EntityRecognizer -> BiluoPushDown
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# DependencyParser -> ArcEager
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moves = self.TransitionSystem(
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self.vocab.strings,
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incorrect_spans_key=incorrect_spans_key
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)
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self.moves = moves
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self.model = model
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if self.moves.n_moves != 0:
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self.set_output(self.moves.n_moves)
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self.cfg = cfg
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self._multitasks = []
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for multitask in cfg["multitasks"]:
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self.add_multitask_objective(multitask)
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self._rehearsal_model = None
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self.scorer = scorer
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def __getnewargs_ex__(self):
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"""This allows pickling the Parser and its keyword-only init arguments"""
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args = (self.vocab, self.model, self.name, self.moves)
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return args, self.cfg
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@property
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def move_names(self):
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names = []
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for i in range(self.moves.n_moves):
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name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
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# Explicitly removing the internal "U-" token used for blocking entities
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if name != "U-":
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names.append(name)
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return names
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@property
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def labels(self):
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class_names = [self.moves.get_class_name(i) for i in range(self.moves.n_moves)]
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return class_names
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@property
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def label_data(self):
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return self.moves.labels
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@property
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def tok2vec(self):
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"""Return the embedding and convolutional layer of the model."""
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return self.model.get_ref("tok2vec")
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@property
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def postprocesses(self):
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# Available for subclasses, e.g. to deprojectivize
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return []
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@property
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def incorrect_spans_key(self):
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return self.cfg["incorrect_spans_key"]
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def add_label(self, label):
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resized = False
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for action in self.moves.action_types:
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added = self.moves.add_action(action, label)
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if added:
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resized = True
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if resized:
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self._resize()
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self.vocab.strings.add(label)
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return 1
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return 0
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def _ensure_labels_are_added(self, docs):
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"""Ensure that all labels for a batch of docs are added."""
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resized = False
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labels = set()
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for doc in docs:
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labels.update(self.moves.get_doc_labels(doc))
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for label in labels:
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for action in self.moves.action_types:
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added = self.moves.add_action(action, label)
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if added:
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self.vocab.strings.add(label)
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resized = True
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if resized:
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self._resize()
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return 1
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return 0
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def _resize(self):
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self.model.attrs["resize_output"](self.model, self.moves.n_moves)
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if self._rehearsal_model not in (True, False, None):
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self._rehearsal_model.attrs["resize_output"](
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self._rehearsal_model, self.moves.n_moves
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)
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def add_multitask_objective(self, target):
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# Defined in subclasses, to avoid circular import
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raise NotImplementedError
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def init_multitask_objectives(self, get_examples, pipeline, **cfg):
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"""Setup models for secondary objectives, to benefit from multi-task
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learning. This method is intended to be overridden by subclasses.
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For instance, the dependency parser can benefit from sharing
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an input representation with a label prediction model. These auxiliary
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models are discarded after training.
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"""
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pass
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def use_params(self, params):
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# Can't decorate cdef class :(. Workaround.
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with self.model.use_params(params):
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yield
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def pipe(self, docs, *, int batch_size=256):
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"""Process a stream of documents.
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stream: The sequence of documents to process.
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batch_size (int): Number of documents to accumulate into a working set.
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error_handler (Callable[[str, List[Doc], Exception], Any]): Function that
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deals with a failing batch of documents. The default function just reraises
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the exception.
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YIELDS (Doc): Documents, in order.
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"""
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cdef Doc doc
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error_handler = self.get_error_handler()
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for batch in util.minibatch(docs, size=batch_size):
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batch_in_order = list(batch)
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try:
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by_length = sorted(batch, key=lambda doc: len(doc))
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for subbatch in util.minibatch(by_length, size=max(batch_size//4, 2)):
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subbatch = list(subbatch)
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parse_states = self.predict(subbatch)
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self.set_annotations(subbatch, parse_states)
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yield from batch_in_order
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except Exception as e:
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error_handler(self.name, self, batch_in_order, e)
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def predict(self, docs):
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if isinstance(docs, Doc):
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docs = [docs]
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if not any(len(doc) for doc in docs):
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result = self.moves.init_batch(docs)
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return result
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if self.cfg["beam_width"] == 1:
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return self.greedy_parse(docs, drop=0.0)
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else:
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return self.beam_parse(
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docs,
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drop=0.0,
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beam_width=self.cfg["beam_width"],
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beam_density=self.cfg["beam_density"]
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)
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def greedy_parse(self, docs, drop=0.):
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cdef vector[StateC*] states
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cdef StateClass state
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ops = self.model.ops
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cdef CBlas cblas
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if isinstance(ops, CupyOps):
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cblas = NUMPY_OPS.cblas()
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else:
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cblas = ops.cblas()
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self._ensure_labels_are_added(docs)
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set_dropout_rate(self.model, drop)
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batch = self.moves.init_batch(docs)
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model = self.model.predict(docs)
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weights = get_c_weights(model)
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for state in batch:
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if not state.is_final():
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states.push_back(state.c)
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sizes = get_c_sizes(model, states.size())
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with nogil:
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self._parseC(cblas, &states[0], weights, sizes)
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model.clear_memory()
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del model
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return batch
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def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
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cdef Beam beam
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cdef Doc doc
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self._ensure_labels_are_added(docs)
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batch = _beam_utils.BeamBatch(
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self.moves,
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self.moves.init_batch(docs),
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None,
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beam_width,
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density=beam_density
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)
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model = self.model.predict(docs)
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while not batch.is_done:
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states = batch.get_unfinished_states()
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if not states:
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break
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scores = model.predict(states)
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batch.advance(scores)
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model.clear_memory()
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del model
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return list(batch)
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cdef void _parseC(self, CBlas cblas, StateC** states,
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WeightsC weights, SizesC sizes) nogil:
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cdef int i, j
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cdef vector[StateC*] unfinished
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cdef ActivationsC activations = alloc_activations(sizes)
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while sizes.states >= 1:
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predict_states(cblas, &activations, states, &weights, sizes)
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# Validate actions, argmax, take action.
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self.c_transition_batch(states,
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activations.scores, sizes.classes, sizes.states)
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for i in range(sizes.states):
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if not states[i].is_final():
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unfinished.push_back(states[i])
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for i in range(unfinished.size()):
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states[i] = unfinished[i]
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sizes.states = unfinished.size()
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unfinished.clear()
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free_activations(&activations)
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def set_annotations(self, docs, states_or_beams):
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cdef StateClass state
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cdef Beam beam
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cdef Doc doc
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states = _beam_utils.collect_states(states_or_beams, docs)
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for i, (state, doc) in enumerate(zip(states, docs)):
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self.moves.set_annotations(state, doc)
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for hook in self.postprocesses:
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hook(doc)
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def transition_states(self, states, float[:, ::1] scores):
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cdef StateClass state
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cdef float* c_scores = &scores[0, 0]
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cdef vector[StateC*] c_states
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for state in states:
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c_states.push_back(state.c)
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self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0])
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return [state for state in states if not state.c.is_final()]
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cdef void c_transition_batch(self, StateC** states, const float* scores,
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int nr_class, int batch_size) nogil:
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# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
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with gil:
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assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
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is_valid = <int*>calloc(self.moves.n_moves, sizeof(int))
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cdef int i, guess
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cdef Transition action
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for i in range(batch_size):
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self.moves.set_valid(is_valid, states[i])
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guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
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if guess == -1:
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# This shouldn't happen, but it's hard to raise an error here,
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# and we don't want to infinite loop. So, force to end state.
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states[i].force_final()
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else:
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action = self.moves.c[guess]
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action.do(states[i], action.label)
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free(is_valid)
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def update(self, examples, *, drop=0., sgd=None, losses=None):
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cdef StateClass state
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if losses is None:
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losses = {}
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losses.setdefault(self.name, 0.)
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validate_examples(examples, "Parser.update")
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self._ensure_labels_are_added(
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[eg.x for eg in examples] + [eg.y for eg in examples]
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)
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for multitask in self._multitasks:
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multitask.update(examples, drop=drop, sgd=sgd)
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n_examples = len([eg for eg in examples if self.moves.has_gold(eg)])
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if n_examples == 0:
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return losses
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set_dropout_rate(self.model, drop)
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# The probability we use beam update, instead of falling back to
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# a greedy update
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beam_update_prob = self.cfg["beam_update_prob"]
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if self.cfg['beam_width'] >= 2 and numpy.random.random() < beam_update_prob:
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return self.update_beam(
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examples,
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beam_width=self.cfg["beam_width"],
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sgd=sgd,
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losses=losses,
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beam_density=self.cfg["beam_density"]
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)
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max_moves = self.cfg["update_with_oracle_cut_size"]
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if max_moves >= 1:
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# Chop sequences into lengths of this many words, to make the
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# batch uniform length.
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max_moves = int(random.uniform(max_moves // 2, max_moves * 2))
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states, golds, _ = self._init_gold_batch(
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examples,
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max_length=max_moves
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)
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else:
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states, golds, _ = self.moves.init_gold_batch(examples)
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if not states:
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return losses
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model, backprop_tok2vec = self.model.begin_update([eg.x for eg in examples])
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all_states = list(states)
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states_golds = list(zip(states, golds))
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n_moves = 0
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while states_golds:
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states, golds = zip(*states_golds)
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scores, backprop = model.begin_update(states)
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d_scores = self.get_batch_loss(states, golds, scores, losses)
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# Note that the gradient isn't normalized by the batch size
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# here, because our "samples" are really the states...But we
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# can't normalize by the number of states either, as then we'd
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# be getting smaller gradients for states in long sequences.
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backprop(d_scores)
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# Follow the predicted action
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self.transition_states(states, scores)
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states_golds = [(s, g) for (s, g) in zip(states, golds) if not s.is_final()]
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if max_moves >= 1 and n_moves >= max_moves:
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break
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n_moves += 1
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backprop_tok2vec(golds)
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if sgd not in (None, False):
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self.finish_update(sgd)
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# Ugh, this is annoying. If we're working on GPU, we want to free the
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# memory ASAP. It seems that Python doesn't necessarily get around to
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# removing these in time if we don't explicitly delete? It's confusing.
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del backprop
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del backprop_tok2vec
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model.clear_memory()
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del model
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return losses
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def rehearse(self, examples, sgd=None, losses=None, **cfg):
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"""Perform a "rehearsal" update, to prevent catastrophic forgetting."""
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if losses is None:
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losses = {}
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for multitask in self._multitasks:
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if hasattr(multitask, 'rehearse'):
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multitask.rehearse(examples, losses=losses, sgd=sgd)
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if self._rehearsal_model is None:
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return None
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losses.setdefault(self.name, 0.)
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validate_examples(examples, "Parser.rehearse")
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docs = [eg.predicted for eg in examples]
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states = self.moves.init_batch(docs)
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# This is pretty dirty, but the NER can resize itself in init_batch,
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# if labels are missing. We therefore have to check whether we need to
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# expand our model output.
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self._resize()
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# Prepare the stepwise model, and get the callback for finishing the batch
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set_dropout_rate(self._rehearsal_model, 0.0)
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set_dropout_rate(self.model, 0.0)
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tutor, _ = self._rehearsal_model.begin_update(docs)
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model, backprop_tok2vec = self.model.begin_update(docs)
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n_scores = 0.
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loss = 0.
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while states:
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targets, _ = tutor.begin_update(states)
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guesses, backprop = model.begin_update(states)
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d_scores = (guesses - targets) / targets.shape[0]
|
|
# If all weights for an output are 0 in the original model, don't
|
|
# supervise that output. This allows us to add classes.
|
|
loss += (d_scores**2).sum()
|
|
backprop(d_scores)
|
|
# Follow the predicted action
|
|
self.transition_states(states, guesses)
|
|
states = [state for state in states if not state.is_final()]
|
|
n_scores += d_scores.size
|
|
# Do the backprop
|
|
backprop_tok2vec(docs)
|
|
if sgd is not None:
|
|
self.finish_update(sgd)
|
|
losses[self.name] += loss / n_scores
|
|
del backprop
|
|
del backprop_tok2vec
|
|
model.clear_memory()
|
|
tutor.clear_memory()
|
|
del model
|
|
del tutor
|
|
return losses
|
|
|
|
def update_beam(self, examples, *, beam_width,
|
|
drop=0., sgd=None, losses=None, beam_density=0.0):
|
|
states, golds, _ = self.moves.init_gold_batch(examples)
|
|
if not states:
|
|
return losses
|
|
# Prepare the stepwise model, and get the callback for finishing the batch
|
|
model, backprop_tok2vec = self.model.begin_update(
|
|
[eg.predicted for eg in examples])
|
|
loss = _beam_utils.update_beam(
|
|
self.moves,
|
|
states,
|
|
golds,
|
|
model,
|
|
beam_width,
|
|
beam_density=beam_density,
|
|
)
|
|
losses[self.name] += loss
|
|
backprop_tok2vec(golds)
|
|
if sgd is not None:
|
|
self.finish_update(sgd)
|
|
|
|
def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
|
|
cdef StateClass state
|
|
cdef Pool mem = Pool()
|
|
cdef int i
|
|
|
|
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
|
|
assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
|
|
|
|
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
|
|
costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
|
|
cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
|
|
dtype='f', order='C')
|
|
c_d_scores = <float*>d_scores.data
|
|
unseen_classes = self.model.attrs["unseen_classes"]
|
|
for i, (state, gold) in enumerate(zip(states, golds)):
|
|
memset(is_valid, 0, self.moves.n_moves * sizeof(int))
|
|
memset(costs, 0, self.moves.n_moves * sizeof(float))
|
|
self.moves.set_costs(is_valid, costs, state.c, gold)
|
|
for j in range(self.moves.n_moves):
|
|
if costs[j] <= 0.0 and j in unseen_classes:
|
|
unseen_classes.remove(j)
|
|
cpu_log_loss(c_d_scores,
|
|
costs, is_valid, &scores[i, 0], d_scores.shape[1])
|
|
c_d_scores += d_scores.shape[1]
|
|
# Note that we don't normalize this. See comment in update() for why.
|
|
if losses is not None:
|
|
losses.setdefault(self.name, 0.)
|
|
losses[self.name] += (d_scores**2).sum()
|
|
return d_scores
|
|
|
|
def set_output(self, nO):
|
|
self.model.attrs["resize_output"](self.model, nO)
|
|
|
|
def initialize(self, get_examples, nlp=None, labels=None):
|
|
validate_get_examples(get_examples, "Parser.initialize")
|
|
util.check_lexeme_norms(self.vocab, "parser or NER")
|
|
if labels is not None:
|
|
actions = dict(labels)
|
|
else:
|
|
actions = self.moves.get_actions(
|
|
examples=get_examples(),
|
|
min_freq=self.cfg['min_action_freq'],
|
|
learn_tokens=self.cfg["learn_tokens"]
|
|
)
|
|
for action, labels in self.moves.labels.items():
|
|
actions.setdefault(action, {})
|
|
for label, freq in labels.items():
|
|
if label not in actions[action]:
|
|
actions[action][label] = freq
|
|
self.moves.initialize_actions(actions)
|
|
# make sure we resize so we have an appropriate upper layer
|
|
self._resize()
|
|
doc_sample = []
|
|
if nlp is not None:
|
|
for name, component in nlp.pipeline:
|
|
if component is self:
|
|
break
|
|
# non-trainable components may have a pipe() implementation that refers to dummy
|
|
# predict and set_annotations methods
|
|
if hasattr(component, "pipe"):
|
|
doc_sample = list(component.pipe(doc_sample, batch_size=8))
|
|
else:
|
|
doc_sample = [component(doc) for doc in doc_sample]
|
|
if not doc_sample:
|
|
for example in islice(get_examples(), 10):
|
|
doc_sample.append(example.predicted)
|
|
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
|
|
self.model.initialize(doc_sample)
|
|
if nlp is not None:
|
|
self.init_multitask_objectives(get_examples, nlp.pipeline)
|
|
|
|
def to_disk(self, path, exclude=tuple()):
|
|
serializers = {
|
|
"model": lambda p: (self.model.to_disk(p) if self.model is not True else True),
|
|
"vocab": lambda p: self.vocab.to_disk(p, exclude=exclude),
|
|
"moves": lambda p: self.moves.to_disk(p, exclude=["strings"]),
|
|
"cfg": lambda p: srsly.write_json(p, self.cfg)
|
|
}
|
|
util.to_disk(path, serializers, exclude)
|
|
|
|
def from_disk(self, path, exclude=tuple()):
|
|
deserializers = {
|
|
"vocab": lambda p: self.vocab.from_disk(p, exclude=exclude),
|
|
"moves": lambda p: self.moves.from_disk(p, exclude=["strings"]),
|
|
"cfg": lambda p: self.cfg.update(srsly.read_json(p)),
|
|
"model": lambda p: None,
|
|
}
|
|
util.from_disk(path, deserializers, exclude)
|
|
if "model" not in exclude:
|
|
path = util.ensure_path(path)
|
|
with (path / "model").open("rb") as file_:
|
|
bytes_data = file_.read()
|
|
try:
|
|
self._resize()
|
|
self.model.from_bytes(bytes_data)
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149)
|
|
return self
|
|
|
|
def to_bytes(self, exclude=tuple()):
|
|
serializers = {
|
|
"model": lambda: (self.model.to_bytes()),
|
|
"vocab": lambda: self.vocab.to_bytes(exclude=exclude),
|
|
"moves": lambda: self.moves.to_bytes(exclude=["strings"]),
|
|
"cfg": lambda: srsly.json_dumps(self.cfg, indent=2, sort_keys=True)
|
|
}
|
|
return util.to_bytes(serializers, exclude)
|
|
|
|
def from_bytes(self, bytes_data, exclude=tuple()):
|
|
deserializers = {
|
|
"vocab": lambda b: self.vocab.from_bytes(b, exclude=exclude),
|
|
"moves": lambda b: self.moves.from_bytes(b, exclude=["strings"]),
|
|
"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
|
|
"model": lambda b: None,
|
|
}
|
|
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
if 'model' in msg:
|
|
try:
|
|
self.model.from_bytes(msg['model'])
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149) from None
|
|
return self
|
|
|
|
def _init_gold_batch(self, examples, max_length):
|
|
"""Make a square batch, of length equal to the shortest transition
|
|
sequence or a cap. A long
|
|
doc will get multiple states. Let's say we have a doc of length 2*N,
|
|
where N is the shortest doc. We'll make two states, one representing
|
|
long_doc[:N], and another representing long_doc[N:]."""
|
|
cdef:
|
|
StateClass start_state
|
|
StateClass state
|
|
Transition action
|
|
all_states = self.moves.init_batch([eg.predicted for eg in examples])
|
|
states = []
|
|
golds = []
|
|
to_cut = []
|
|
for state, eg in zip(all_states, examples):
|
|
if self.moves.has_gold(eg) and not state.is_final():
|
|
gold = self.moves.init_gold(state, eg)
|
|
if len(eg.x) < max_length:
|
|
states.append(state)
|
|
golds.append(gold)
|
|
else:
|
|
oracle_actions = self.moves.get_oracle_sequence_from_state(
|
|
state.copy(), gold)
|
|
to_cut.append((eg, state, gold, oracle_actions))
|
|
if not to_cut:
|
|
return states, golds, 0
|
|
cdef int clas
|
|
for eg, state, gold, oracle_actions in to_cut:
|
|
for i in range(0, len(oracle_actions), max_length):
|
|
start_state = state.copy()
|
|
for clas in oracle_actions[i:i+max_length]:
|
|
action = self.moves.c[clas]
|
|
action.do(state.c, action.label)
|
|
if state.is_final():
|
|
break
|
|
if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
|
|
states.append(start_state)
|
|
golds.append(gold)
|
|
if state.is_final():
|
|
break
|
|
return states, golds, max_length
|