mirror of
https://github.com/explosion/spaCy.git
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465 lines
19 KiB
Cython
465 lines
19 KiB
Cython
# cython: infer_types=True
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# cython: cdivision=True
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# cython: boundscheck=False
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# coding: utf-8
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from __future__ import unicode_literals, print_function
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from collections import OrderedDict
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import ujson
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import json
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import numpy
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cimport cython.parallel
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import cytoolz
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import numpy.random
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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.typedefs cimport weight_t, class_t, hash_t
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from thinc.extra.search cimport Beam
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from thinc.api import chain, clone
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from thinc.v2v import Model, Maxout, Affine
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from thinc.misc import LayerNorm
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from thinc.neural.ops import CupyOps
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from thinc.neural.util import get_array_module
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from thinc.linalg cimport Vec, VecVec
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from thinc cimport openblas
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from ._parser_model cimport resize_activations, 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 ._parser_model import ParserModel
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from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
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from .._ml import link_vectors_to_models, create_default_optimizer
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from ..compat import json_dumps, copy_array
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from ..tokens.doc cimport Doc
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from ..gold cimport GoldParse
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from ..errors import Errors, TempErrors
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from .. import util
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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 . 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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@classmethod
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def Model(cls, nr_class, **cfg):
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depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
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if depth != 1:
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raise ValueError(TempErrors.T004.format(value=depth))
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parser_maxout_pieces = util.env_opt('parser_maxout_pieces',
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cfg.get('maxout_pieces', 2))
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token_vector_width = util.env_opt('token_vector_width',
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cfg.get('token_vector_width', 128))
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hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 128))
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embed_size = util.env_opt('embed_size', cfg.get('embed_size', 5000))
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pretrained_vectors = cfg.get('pretrained_vectors', None)
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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pretrained_vectors=pretrained_vectors)
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tok2vec = chain(tok2vec, flatten)
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lower = PrecomputableAffine(hidden_width,
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nF=cls.nr_feature, nI=token_vector_width,
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nP=parser_maxout_pieces)
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lower.nP = parser_maxout_pieces
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with Model.use_device('cpu'):
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upper = zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
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cfg = {
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'nr_class': nr_class,
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'hidden_depth': depth,
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'token_vector_width': token_vector_width,
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'hidden_width': hidden_width,
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'maxout_pieces': parser_maxout_pieces,
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'pretrained_vectors': pretrained_vectors,
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}
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return ParserModel(tok2vec, lower, upper), cfg
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def __init__(self, Vocab vocab, moves=True, model=True, **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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moves (TransitionSystem): Defines how the parse-state is created,
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updated and evaluated. The value is set to the .moves attribute
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unless True (default), in which case a new instance is created with
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`Parser.Moves()`.
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model (object): Defines how the parse-state is created, updated and
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evaluated. The value is set to the .model attribute. If set to True
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(default), a new instance will be created with `Parser.Model()`
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in parser.begin_training(), parser.from_disk() or parser.from_bytes().
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**cfg: Arbitrary configuration parameters. Set to the `.cfg` attribute
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"""
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self.vocab = vocab
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if moves is True:
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self.moves = self.TransitionSystem(self.vocab.strings)
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else:
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self.moves = moves
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if 'beam_width' not in cfg:
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cfg['beam_width'] = util.env_opt('beam_width', 1)
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if 'beam_density' not in cfg:
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cfg['beam_density'] = util.env_opt('beam_density', 0.0)
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cfg.setdefault('cnn_maxout_pieces', 3)
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self.cfg = cfg
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self.model = model
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self._multitasks = []
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def __reduce__(self):
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return (Parser, (self.vocab, self.moves, self.model), None, None)
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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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names.append(name)
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return names
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nr_feature = 8
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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 None if self.model in (None, True, False) else self.model.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 self.model not in (True, False, None) and resized:
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self.model.resize_output(self.moves.n_moves)
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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_gold_tuples, 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, docs_golds):
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for doc, gold in docs_golds:
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yield doc, gold
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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, beam_density=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.get('beam_width', 1)
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if beam_density is None:
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beam_density = self.cfg.get('beam_density', 0.0)
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states = self.predict([doc])
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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=2,
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beam_width=None, beam_density=None):
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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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n_threads (int): The number of threads with which to work on the buffer
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in parallel.
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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.get('beam_width', 1)
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if beam_density is None:
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beam_density = self.cfg.get('beam_density', 0.0)
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cdef Doc doc
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for batch in cytoolz.partition_all(batch_size, docs):
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batch_in_order = list(batch)
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by_length = sorted(batch_in_order, key=lambda doc: len(doc))
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for subbatch in cytoolz.partition_all(8, by_length):
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subbatch = list(subbatch)
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parse_states = self.predict(subbatch,
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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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for doc in batch_in_order:
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yield doc
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def predict(self, docs, beam_width=1, beam_density=0.):
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if isinstance(docs, Doc):
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docs = [docs]
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cdef vector[StateC*] states
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cdef StateClass state
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state_objs = self.moves.init_batch(docs)
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for state in state_objs:
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states.push_back(state.c)
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# Prepare the stepwise model, and get the callback for finishing the batch
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model = self.model(docs)
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weights = get_c_weights(model)
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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 state_objs
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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
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memset(&activations, 0, sizeof(activations))
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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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def set_annotations(self, docs, states, tensors=None):
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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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for doc in docs:
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hook(doc)
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def transition_batch(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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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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cdef int[500] is_valid # TODO: Unhack
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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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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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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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if isinstance(docs, Doc) and isinstance(golds, GoldParse):
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docs = [docs]
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golds = [golds]
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if len(docs) != len(golds):
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raise ValueError(Errors.E077.format(value='update', n_docs=len(docs),
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n_golds=len(golds)))
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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(docs, golds, max_length=cut_gold)
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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, finish_update = self.model.begin_update(docs, drop=drop)
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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, drop=drop)
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d_scores = self.get_batch_loss(states, golds, scores, losses)
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backprop(d_scores, sgd=sgd)
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# Follow the predicted action
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self.transition_batch(states, scores)
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states_golds = [eg for eg in states_golds if not eg[0].is_final()]
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# Do the backprop
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finish_update(golds, sgd=sgd)
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return losses
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def _init_gold_batch(self, whole_docs, whole_golds, min_length=5, max_length=500):
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"""Make a square batch, of length equal to the shortest doc. A long
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doc will get multiple states. Let's say we have a doc of length 2*N,
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where N is the shortest doc. We'll make two states, one representing
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long_doc[:N], and another representing long_doc[N:]."""
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cdef:
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StateClass state
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Transition action
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whole_states = self.moves.init_batch(whole_docs)
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max_length = max(min_length, min(max_length, min([len(doc) for doc in whole_docs])))
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max_moves = 0
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states = []
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golds = []
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for doc, state, gold in zip(whole_docs, whole_states, whole_golds):
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gold = self.moves.preprocess_gold(gold)
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if gold is None:
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continue
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oracle_actions = self.moves.get_oracle_sequence(doc, gold)
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start = 0
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while start < len(doc):
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state = state.copy()
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n_moves = 0
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while state.B(0) < start and not state.is_final():
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action = self.moves.c[oracle_actions.pop(0)]
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action.do(state.c, action.label)
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state.c.push_hist(action.clas)
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n_moves += 1
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has_gold = self.moves.has_gold(gold, start=start,
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end=start+max_length)
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if not state.is_final() and has_gold:
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states.append(state)
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golds.append(gold)
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max_moves = max(max_moves, n_moves)
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start += min(max_length, len(doc)-start)
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max_moves = max(max_moves, len(oracle_actions))
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return states, golds, max_moves
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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 GoldParse gold
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cdef Pool mem = Pool()
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cdef int i
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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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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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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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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(self.model.ops,
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**self.cfg.get('optimizer', {}))
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def begin_training(self, get_gold_tuples, pipeline=None, sgd=None, **cfg):
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if 'model' in cfg:
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self.model = cfg['model']
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if not hasattr(get_gold_tuples, '__call__'):
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gold_tuples = get_gold_tuples
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get_gold_tuples = lambda: gold_tuples
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cfg.setdefault('min_action_freq', 30)
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actions = self.moves.get_actions(gold_parses=get_gold_tuples(),
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min_freq=cfg.get('min_action_freq', 30))
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self.moves.initialize_actions(actions)
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cfg.setdefault('token_vector_width', 128)
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if self.model is True:
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self.model, cfg = self.Model(self.moves.n_moves, **cfg)
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if sgd is None:
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sgd = self.create_optimizer()
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self.model.begin_training(
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self.model.ops.allocate((5, cfg['token_vector_width'])))
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if pipeline is not None:
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self.init_multitask_objectives(get_gold_tuples, pipeline, sgd=sgd, **cfg)
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link_vectors_to_models(self.vocab)
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else:
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if sgd is None:
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sgd = self.create_optimizer()
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self.model.begin_training(
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self.model.ops.allocate((5, cfg['token_vector_width'])))
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self.cfg.update(cfg)
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return sgd
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def to_disk(self, path, **exclude):
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serializers = {
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'model': lambda p: self.model.to_disk(p),
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'vocab': lambda p: self.vocab.to_disk(p),
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'moves': lambda p: self.moves.to_disk(p, strings=False),
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'cfg': lambda p: p.open('w').write(json_dumps(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):
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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, strings=False),
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'cfg': lambda p: self.cfg.update(util.read_json(p)),
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'model': lambda p: None
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}
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util.from_disk(path, deserializers, exclude)
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if 'model' not in exclude:
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path = util.ensure_path(path)
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if self.model is True:
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self.model, cfg = self.Model(**self.cfg)
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else:
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cfg = {}
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with (path / 'tok2vec_model').open('rb') as file_:
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bytes_data = file_.read()
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self.model.from_bytes(bytes_data)
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self.cfg.update(cfg)
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return self
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def to_bytes(self, **exclude):
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serializers = OrderedDict((
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('model', lambda: self.model.to_bytes()),
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('vocab', lambda: self.vocab.to_bytes()),
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('moves', lambda: self.moves.to_bytes(strings=False)),
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('cfg', lambda: json.dumps(self.cfg, indent=2, sort_keys=True))
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))
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return util.to_bytes(serializers, exclude)
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def from_bytes(self, bytes_data, **exclude):
|
|
deserializers = OrderedDict((
|
|
('vocab', lambda b: self.vocab.from_bytes(b)),
|
|
('moves', lambda b: self.moves.from_bytes(b, strings=False)),
|
|
('cfg', lambda b: self.cfg.update(json.loads(b))),
|
|
('model', lambda b: None)
|
|
))
|
|
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
# TODO: Remove this once we don't have to handle previous models
|
|
if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
|
|
self.cfg['pretrained_vectors'] = self.vocab.vectors.name
|
|
if self.model is True:
|
|
self.model, cfg = self.Model(**self.cfg)
|
|
else:
|
|
cfg = {}
|
|
if 'model' in msg:
|
|
self.model.from_bytes(msg['model'])
|
|
self.cfg.update(cfg)
|
|
return self
|