mirror of https://github.com/explosion/spaCy.git
713 lines
29 KiB
Cython
713 lines
29 KiB
Cython
# cython: infer_types=True, cdivision=True, boundscheck=False
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cimport cython.parallel
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cimport numpy as np
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from cpython.ref cimport PyObject, Py_XDECREF
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from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
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from libc.math cimport exp
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from libcpp.vector cimport vector
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from libc.string cimport memset, memcpy
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from libc.stdlib cimport calloc, free
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from cymem.cymem cimport Pool
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from thinc.extra.search cimport Beam
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from thinc.backends.linalg cimport Vec, VecVec
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from thinc.api import chain, clone, Linear, list2array, NumpyOps, CupyOps, use_ops
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from thinc.api import get_array_module, zero_init, set_dropout_rate
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from itertools import islice
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import srsly
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import numpy.random
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import numpy
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import warnings
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from ..tokens.doc cimport Doc
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from ..gold cimport GoldParse
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from ..typedefs cimport weight_t, class_t, hash_t
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from ._parser_model cimport alloc_activations, free_activations
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from ._parser_model cimport predict_states, arg_max_if_valid
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from ._parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
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from ._parser_model cimport get_c_weights, get_c_sizes
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from .stateclass cimport StateClass
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from ._state cimport StateC
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from .transition_system cimport Transition
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from . cimport _beam_utils
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from ..gold import Example
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from ..util import link_vectors_to_models, create_default_optimizer, registry
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from ..compat import copy_array
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from ..errors import Errors, Warnings
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from .. import util
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from . import _beam_utils
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from . import nonproj
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cdef class Parser:
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"""
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Base class of the DependencyParser and EntityRecognizer.
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"""
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name = 'base_parser'
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def __init__(self, Vocab vocab, model, **cfg):
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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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moves = cfg.get("moves", None)
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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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cfg.setdefault('min_action_freq', 30)
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cfg.setdefault('learn_tokens', False)
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cfg.setdefault('beam_width', 1)
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cfg.setdefault('beam_update_prob', 1.0) # or 0.5 (both defaults were previously used)
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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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self._rehearsal_model = None
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@classmethod
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def from_nlp(cls, nlp, model, **cfg):
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return cls(nlp.vocab, model, **cfg)
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def __reduce__(self):
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return (Parser, (self.vocab, self.model), self.moves)
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def __getstate__(self):
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return self.moves
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def __setstate__(self, moves):
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self.moves = moves
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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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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 preprocess_gold(self, examples):
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for ex in examples:
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yield ex
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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, beam_width=None):
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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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if beam_width is None:
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beam_width = self.cfg['beam_width']
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beam_density = self.cfg.get('beam_density', 0.)
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states = self.predict([doc], beam_width=beam_width,
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beam_density=beam_density)
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self.set_annotations([doc], states, tensors=None)
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return doc
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def pipe(self, docs, int batch_size=256, int n_threads=-1, beam_width=None,
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as_example=False):
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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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if beam_width is None:
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beam_width = self.cfg['beam_width']
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beam_density = self.cfg.get('beam_density', 0.)
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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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docs = [self._get_doc(ex) for ex in batch_in_order]
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by_length = sorted(docs, 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, beam_width=beam_width,
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beam_density=beam_density)
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self.set_annotations(subbatch, parse_states, tensors=None)
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if as_example:
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annotated_examples = []
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for ex, doc in zip(batch_in_order, docs):
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ex.doc = doc
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annotated_examples.append(ex)
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yield from annotated_examples
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else:
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yield from batch_in_order
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def predict(self, docs, beam_width=1, beam_density=0.0, drop=0.):
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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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if beam_width < 2:
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return self.greedy_parse(docs, drop=drop)
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else:
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return self.beam_parse(docs, beam_width=beam_width,
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beam_density=beam_density, drop=drop)
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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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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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cdef np.ndarray token_ids
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set_dropout_rate(self.model, drop)
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beams = self.moves.init_beams(docs, beam_width, beam_density=beam_density)
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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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cdef int nr_feature = self.model.get_ref("lower").get_dim("nF")
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model = self.model.predict(docs)
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token_ids = numpy.zeros((len(docs) * beam_width, nr_feature),
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dtype='i', order='C')
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cdef int* c_ids
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cdef int n_states
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model = self.model.predict(docs)
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todo = [beam for beam in beams if not beam.is_done]
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while todo:
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token_ids.fill(-1)
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c_ids = <int*>token_ids.data
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n_states = 0
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for beam in todo:
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for i in range(beam.size):
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state = <StateC*>beam.at(i)
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# This way we avoid having to score finalized states
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# We do have to take care to keep indexes aligned, though
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if not state.is_final():
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state.set_context_tokens(c_ids, nr_feature)
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c_ids += nr_feature
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n_states += 1
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if n_states == 0:
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break
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vectors = model.state2vec.predict(token_ids[:n_states])
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scores = model.vec2scores.predict(vectors)
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todo = self.transition_beams(todo, scores)
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return beams
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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_or_beams, tensors=None):
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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 = []
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beams = []
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for state_or_beam in states_or_beams:
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if isinstance(state_or_beam, StateClass):
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states.append(state_or_beam)
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else:
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beam = state_or_beam
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state = StateClass.borrow(<StateC*>beam.at(0))
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states.append(state)
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beams.append(beam)
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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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for beam in beams:
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_beam_utils.cleanup_beam(beam)
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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 transition_beams(self, beams, float[:, ::1] scores):
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cdef Beam beam
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cdef float* c_scores = &scores[0, 0]
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for beam in beams:
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for i in range(beam.size):
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state = <StateC*>beam.at(i)
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if not state.is_final():
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self.moves.set_valid(beam.is_valid[i], state)
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memcpy(beam.scores[i], c_scores, scores.shape[1] * sizeof(float))
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c_scores += scores.shape[1]
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beam.advance(_beam_utils.transition_state, _beam_utils.hash_state, <void*>self.moves.c)
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beam.check_done(_beam_utils.check_final_state, NULL)
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return [b for b in beams if not b.is_done]
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def update(self, examples, drop=0., set_annotations=False, sgd=None, losses=None):
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examples = Example.to_example_objects(examples)
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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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# 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(examples, self.cfg['beam_width'],
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drop=drop, sgd=sgd, losses=losses, set_annotations=set_annotations,
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beam_density=self.cfg.get('beam_density', 0.001))
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set_dropout_rate(self.model, drop)
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cut_gold = True
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if cut_gold:
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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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cut_gold = numpy.random.choice(range(20, 100))
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states, golds, max_steps = self._init_gold_batch(examples, max_length=cut_gold)
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else:
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states, golds, max_steps = self._init_gold_batch_no_cut(examples)
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states_golds = [(s, g) for (s, g) in zip(states, golds)
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if not s.is_final() and g is not None]
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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([ex.doc for ex in examples])
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all_states = list(states)
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for _ in range(max_steps):
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if not states_golds:
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break
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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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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 = [eg for eg in states_golds if not eg[0].is_final()]
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backprop_tok2vec(golds)
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if sgd is not None:
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self.model.finish_update(sgd)
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if set_annotations:
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docs = [ex.doc for ex in examples]
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self.set_annotations(docs, all_states)
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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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examples = Example.to_example_objects(examples)
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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 = [ex.doc for ex 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, finish_update = 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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finish_update(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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return losses
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def update_beam(self, examples, width, drop=0., sgd=None, losses=None,
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set_annotations=False, beam_density=0.0):
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examples = Example.to_example_objects(examples)
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docs = [ex.doc for ex in examples]
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golds = [ex.gold for ex in examples]
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new_golds = []
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lengths = [len(d) for d in docs]
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states = self.moves.init_batch(docs)
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for gold in golds:
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self.moves.preprocess_gold(gold)
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new_golds.append(gold)
|
|
set_dropout_rate(self.model, drop)
|
|
model, backprop_tok2vec = self.model.begin_update(docs)
|
|
states_d_scores, backprops, beams = _beam_utils.update_beam(
|
|
self.moves,
|
|
self.model.get_ref("lower").get_dim("nF"),
|
|
10000,
|
|
states,
|
|
golds,
|
|
model.state2vec,
|
|
model.vec2scores,
|
|
width,
|
|
losses=losses,
|
|
beam_density=beam_density
|
|
)
|
|
for i, d_scores in enumerate(states_d_scores):
|
|
losses[self.name] += (d_scores**2).mean()
|
|
ids, bp_vectors, bp_scores = backprops[i]
|
|
d_vector = bp_scores(d_scores)
|
|
if isinstance(model.ops, CupyOps) \
|
|
and not isinstance(ids, model.state2vec.ops.xp.ndarray):
|
|
model.backprops.append((
|
|
util.get_async(model.cuda_stream, ids),
|
|
util.get_async(model.cuda_stream, d_vector),
|
|
bp_vectors))
|
|
else:
|
|
model.backprops.append((ids, d_vector, bp_vectors))
|
|
backprop_tok2vec(golds)
|
|
if sgd is not None:
|
|
self.model.finish_update(sgd)
|
|
if set_annotations:
|
|
self.set_annotations(docs, beams)
|
|
cdef Beam beam
|
|
for beam in beams:
|
|
_beam_utils.cleanup_beam(beam)
|
|
|
|
def get_gradients(self):
|
|
"""Get non-zero gradients of the model's parameters, as a dictionary
|
|
keyed by the parameter ID. The values are (weights, gradients) tuples.
|
|
"""
|
|
gradients = {}
|
|
queue = [self.model]
|
|
seen = set()
|
|
for node in queue:
|
|
if node.id in seen:
|
|
continue
|
|
seen.add(node.id)
|
|
if hasattr(node, "_mem") and node._mem.gradient.any():
|
|
gradients[node.id] = [node._mem.weights, node._mem.gradient]
|
|
if hasattr(node, "_layers"):
|
|
queue.extend(node._layers)
|
|
return gradients
|
|
|
|
def _init_gold_batch_no_cut(self, whole_examples):
|
|
states = self.moves.init_batch([eg.doc for eg in whole_examples])
|
|
good_docs = []
|
|
good_golds = []
|
|
good_states = []
|
|
for i, eg in enumerate(whole_examples):
|
|
doc = eg.doc
|
|
gold = self.moves.preprocess_gold(eg.gold)
|
|
if gold is not None and self.moves.has_gold(gold):
|
|
good_docs.append(doc)
|
|
good_golds.append(gold)
|
|
good_states.append(states[i])
|
|
n_moves = []
|
|
for doc, gold in zip(good_docs, good_golds):
|
|
oracle_actions = self.moves.get_oracle_sequence(doc, gold)
|
|
n_moves.append(len(oracle_actions))
|
|
return good_states, good_golds, max(n_moves, default=0) * 2
|
|
|
|
def _init_gold_batch(self, whole_examples, min_length=5, max_length=500):
|
|
"""Make a square batch, of length equal to the shortest doc. 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 state
|
|
Transition action
|
|
whole_docs = [ex.doc for ex in whole_examples]
|
|
whole_golds = [ex.gold for ex in whole_examples]
|
|
whole_states = self.moves.init_batch(whole_docs)
|
|
max_length = max(min_length, min(max_length, min([len(doc) for doc in whole_docs])))
|
|
max_moves = 0
|
|
states = []
|
|
golds = []
|
|
for doc, state, gold in zip(whole_docs, whole_states, whole_golds):
|
|
gold = self.moves.preprocess_gold(gold)
|
|
if gold is None:
|
|
continue
|
|
oracle_actions = self.moves.get_oracle_sequence(doc, gold)
|
|
start = 0
|
|
while start < len(doc):
|
|
state = state.copy()
|
|
n_moves = 0
|
|
while state.B(0) < start and not state.is_final():
|
|
action = self.moves.c[oracle_actions.pop(0)]
|
|
action.do(state.c, action.label)
|
|
state.c.push_hist(action.clas)
|
|
n_moves += 1
|
|
has_gold = self.moves.has_gold(gold, start=start,
|
|
end=start+max_length)
|
|
if not state.is_final() and has_gold:
|
|
states.append(state)
|
|
golds.append(gold)
|
|
max_moves = max(max_moves, n_moves)
|
|
start += min(max_length, len(doc)-start)
|
|
max_moves = max(max_moves, len(oracle_actions))
|
|
return states, golds, max_moves
|
|
|
|
def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
|
|
cdef StateClass state
|
|
cdef GoldParse gold
|
|
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
|
|
|
|
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, 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]
|
|
if len(states):
|
|
d_scores /= len(states)
|
|
if losses is not None:
|
|
losses.setdefault(self.name, 0.)
|
|
losses[self.name] += (d_scores**2).sum()
|
|
return d_scores
|
|
|
|
def create_optimizer(self):
|
|
return create_default_optimizer()
|
|
|
|
def set_output(self, nO):
|
|
self.model.attrs["resize_output"](self.model, nO)
|
|
|
|
def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
|
|
self.cfg.update(kwargs)
|
|
if not hasattr(get_examples, '__call__'):
|
|
gold_tuples = get_examples
|
|
get_examples = lambda: gold_tuples
|
|
actions = self.moves.get_actions(gold_parses=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()
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
doc_sample = []
|
|
gold_sample = []
|
|
for example in islice(get_examples(), 10):
|
|
parses = example.get_gold_parses(merge=False, vocab=self.vocab)
|
|
for doc, gold in parses:
|
|
if len(doc):
|
|
doc_sample.append(doc)
|
|
gold_sample.append(gold)
|
|
|
|
if pipeline is not None:
|
|
for name, component in pipeline:
|
|
if component is self:
|
|
break
|
|
if hasattr(component, "pipe"):
|
|
doc_sample = list(component.pipe(doc_sample))
|
|
else:
|
|
doc_sample = [component(doc) for doc in doc_sample]
|
|
if doc_sample:
|
|
self.model.initialize(doc_sample)
|
|
else:
|
|
self.model.initialize()
|
|
if pipeline is not None:
|
|
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
|
|
link_vectors_to_models(self.vocab)
|
|
return sgd
|
|
|
|
def _get_doc(self, example):
|
|
""" Use this method if the `example` can be both a Doc or an Example """
|
|
if isinstance(example, Doc):
|
|
return example
|
|
return example.doc
|
|
|
|
def to_disk(self, path, exclude=tuple(), **kwargs):
|
|
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),
|
|
'moves': lambda p: self.moves.to_disk(p, exclude=["strings"]),
|
|
'cfg': lambda p: srsly.write_json(p, self.cfg)
|
|
}
|
|
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
|
|
util.to_disk(path, serializers, exclude)
|
|
|
|
def from_disk(self, path, exclude=tuple(), **kwargs):
|
|
deserializers = {
|
|
'vocab': lambda p: self.vocab.from_disk(p),
|
|
'moves': lambda p: self.moves.from_disk(p, exclude=["strings"]),
|
|
'cfg': lambda p: self.cfg.update(srsly.read_json(p)),
|
|
'model': lambda p: None,
|
|
}
|
|
exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
|
|
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(), **kwargs):
|
|
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)
|
|
}
|
|
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
|
|
return util.to_bytes(serializers, exclude)
|
|
|
|
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
|
|
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,
|
|
}
|
|
exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
|
|
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)
|
|
return self
|