mirror of
https://github.com/explosion/spaCy.git
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555 lines
22 KiB
Cython
555 lines
22 KiB
Cython
# cython: infer_types=True, cdivision=True, boundscheck=False
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from __future__ import print_function
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from cymem.cymem cimport Pool
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cimport numpy as np
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from itertools import islice
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from libcpp.vector cimport vector
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from libc.string cimport memset
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from libc.stdlib cimport calloc, free
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import srsly
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from ._parser_internals.stateclass cimport StateClass
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from ..ml.parser_model cimport alloc_activations, free_activations
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from ..ml.parser_model cimport predict_states, arg_max_if_valid
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from ..ml.parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
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from ..ml.parser_model cimport get_c_weights, get_c_sizes
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from ..tokens.doc cimport Doc
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from ..errors import Errors, Warnings
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from .. import util
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from ..util import create_default_optimizer
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from thinc.api import set_dropout_rate
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import numpy.random
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import numpy
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import warnings
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cdef class Parser(Pipe):
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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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multitasks=tuple(),
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min_action_freq,
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learn_tokens,
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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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**cfg: Configuration parameters. Set to the `.cfg` attribute.
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If it doesn't include a value for 'moves', a new instance is
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created with `self.TransitionSystem()`. This defines how the
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parse-state is created, updated and evaluated.
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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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}
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if moves is None:
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# defined by EntityRecognizer as a BiluoPushDown
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moves = self.TransitionSystem(self.vocab.strings)
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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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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 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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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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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 __call__(self, Doc doc):
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"""Apply the parser or entity recognizer, setting the annotations onto
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the `Doc` object.
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doc (Doc): The document to be processed.
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"""
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states = self.predict([doc])
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self.set_annotations([doc], states)
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return doc
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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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YIELDS (Doc): Documents, in order.
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"""
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cdef Doc doc
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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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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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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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self._resize()
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return result
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return self.greedy_parse(docs, drop=0.0)
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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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set_dropout_rate(self.model, drop)
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batch = 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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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(&states[0],
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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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cdef void _parseC(self, 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(&activations,
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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):
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cdef StateClass state
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cdef Doc doc
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for i, (state, doc) in enumerate(zip(states, docs)):
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self.moves.finalize_state(state.c)
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for j in range(doc.length):
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doc.c[j] = state.c._sent[j]
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self.moves.finalize_doc(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
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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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states[i].push_hist(guess)
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free(is_valid)
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def update(self, examples, *, drop=0., set_annotations=False, 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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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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# Prepare the stepwise model, and get the callback for finishing the batch
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model, backprop_tok2vec = self.model.begin_update(
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[eg.predicted for eg in examples])
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if self.cfg["update_with_oracle_cut_size"] >= 1:
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# Chop sequences into lengths of this many transitions, to make the
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# batch uniform length.
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# We used to randomize this, but it's not clear that actually helps?
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cut_size = self.cfg["update_with_oracle_cut_size"]
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states, golds, max_steps = self._init_gold_batch(
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examples,
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max_length=cut_size
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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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max_steps = max([len(eg.x) for eg in examples])
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if not states:
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return losses
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all_states = list(states)
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states_golds = list(zip(states, golds))
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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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backprop_tok2vec(golds)
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if sgd not in (None, False):
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self.model.finish_update(sgd)
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if set_annotations:
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docs = [eg.predicted for eg in examples]
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self.set_annotations(docs, all_states)
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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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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]
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# If all weights for an output are 0 in the original model, don't
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# supervise that output. This allows us to add classes.
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loss += (d_scores**2).sum()
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backprop(d_scores, sgd=sgd)
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# Follow the predicted action
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self.transition_states(states, guesses)
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states = [state for state in states if not state.is_final()]
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n_scores += d_scores.size
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# Do the backprop
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backprop_tok2vec(docs)
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if sgd is not None:
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self.model.finish_update(sgd)
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losses[self.name] += loss / n_scores
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del backprop
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del backprop_tok2vec
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model.clear_memory()
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tutor.clear_memory()
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del model
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del tutor
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return losses
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def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
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cdef StateClass state
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cdef Pool mem = Pool()
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cdef int i
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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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assert self.moves.n_moves > 0
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is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
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costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
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cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
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dtype='f', order='C')
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c_d_scores = <float*>d_scores.data
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unseen_classes = self.model.attrs["unseen_classes"]
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for i, (state, gold) in enumerate(zip(states, golds)):
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memset(is_valid, 0, self.moves.n_moves * sizeof(int))
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memset(costs, 0, self.moves.n_moves * sizeof(float))
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self.moves.set_costs(is_valid, costs, state, gold)
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for j in range(self.moves.n_moves):
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if costs[j] <= 0.0 and j in unseen_classes:
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unseen_classes.remove(j)
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cpu_log_loss(c_d_scores,
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costs, is_valid, &scores[i, 0], d_scores.shape[1])
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c_d_scores += d_scores.shape[1]
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# Note that we don't normalize this. See comment in update() for why.
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if losses is not None:
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losses.setdefault(self.name, 0.)
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losses[self.name] += (d_scores**2).sum()
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return d_scores
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def create_optimizer(self):
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return create_default_optimizer()
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def set_output(self, nO):
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self.model.attrs["resize_output"](self.model, nO)
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def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
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self.cfg.update(kwargs)
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lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
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if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
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langs = ", ".join(util.LEXEME_NORM_LANGS)
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warnings.warn(Warnings.W033.format(model="parser or NER", langs=langs))
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if not hasattr(get_examples, '__call__'):
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gold_tuples = get_examples
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get_examples = lambda: gold_tuples
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actions = self.moves.get_actions(
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examples=get_examples(),
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min_freq=self.cfg['min_action_freq'],
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learn_tokens=self.cfg["learn_tokens"]
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)
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for action, labels in self.moves.labels.items():
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actions.setdefault(action, {})
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for label, freq in labels.items():
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if label not in actions[action]:
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actions[action][label] = freq
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self.moves.initialize_actions(actions)
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# make sure we resize so we have an appropriate upper layer
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self._resize()
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if sgd is None:
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sgd = self.create_optimizer()
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doc_sample = []
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for example in islice(get_examples(), 10):
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doc_sample.append(example.predicted)
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if pipeline is not None:
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for name, component in pipeline:
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if component is self:
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break
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if hasattr(component, "pipe"):
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doc_sample = list(component.pipe(doc_sample, batch_size=8))
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else:
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doc_sample = [component(doc) for doc in doc_sample]
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if doc_sample:
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self.model.initialize(doc_sample)
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else:
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self.model.initialize()
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if pipeline is not None:
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self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
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return sgd
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def to_disk(self, path, exclude=tuple()):
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serializers = {
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'model': lambda p: (self.model.to_disk(p) if self.model is not True else True),
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'vocab': lambda p: self.vocab.to_disk(p),
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'moves': lambda p: self.moves.to_disk(p, exclude=["strings"]),
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'cfg': lambda p: srsly.write_json(p, self.cfg)
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}
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util.to_disk(path, serializers, exclude)
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def from_disk(self, path, exclude=tuple()):
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deserializers = {
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'vocab': lambda p: self.vocab.from_disk(p),
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'moves': lambda p: self.moves.from_disk(p, exclude=["strings"]),
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'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) from None
|
|
return self
|
|
|
|
def to_bytes(self, exclude=tuple()):
|
|
serializers = {
|
|
"model": lambda: (self.model.to_bytes()),
|
|
"vocab": lambda: self.vocab.to_bytes(),
|
|
"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),
|
|
"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, min_length=5, max_length=500):
|
|
"""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 = []
|
|
kept = []
|
|
max_length_seen = 0
|
|
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)
|
|
kept.append((eg, state, gold, oracle_actions))
|
|
min_length = min(min_length, len(oracle_actions))
|
|
max_length_seen = max(max_length, len(oracle_actions))
|
|
if not kept:
|
|
return states, golds, 0
|
|
max_length = max(min_length, min(max_length, max_length_seen))
|
|
cdef int clas
|
|
max_moves = 0
|
|
for eg, state, gold, oracle_actions in kept:
|
|
for i in range(0, len(oracle_actions), max_length):
|
|
start_state = state.copy()
|
|
n_moves = 0
|
|
for clas in oracle_actions[i:i+max_length]:
|
|
action = self.moves.c[clas]
|
|
action.do(state.c, action.label)
|
|
state.c.push_hist(action.clas)
|
|
n_moves += 1
|
|
if state.is_final():
|
|
break
|
|
max_moves = max(max_moves, n_moves)
|
|
if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
|
|
states.append(start_state)
|
|
golds.append(gold)
|
|
max_moves = max(max_moves, n_moves)
|
|
if state.is_final():
|
|
break
|
|
return states, golds, max_moves
|