mirror of
https://github.com/explosion/spaCy.git
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523 lines
19 KiB
Cython
523 lines
19 KiB
Cython
"""
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MALT-style dependency parser
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"""
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# coding: utf-8
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# cython: infer_types=True
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from __future__ import unicode_literals
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from collections import Counter
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import ujson
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cimport cython
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cimport cython.parallel
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import numpy.random
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from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
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from cpython.exc cimport PyErr_CheckSignals
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from libc.stdint cimport uint32_t, uint64_t
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from libc.string cimport memset, memcpy
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from libc.stdlib cimport malloc, calloc, free
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from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.linalg cimport VecVec
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from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
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from thinc.extra.eg cimport Example
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from cymem.cymem cimport Pool, Address
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from murmurhash.mrmr cimport hash64
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from preshed.maps cimport MapStruct
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from preshed.maps cimport map_get
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from numpy import exp
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from . import _parse_features
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from ._parse_features cimport CONTEXT_SIZE
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from ._parse_features cimport fill_context
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from .stateclass cimport StateClass
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from ._state cimport StateC
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from .nonproj import PseudoProjectivity
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from .transition_system import OracleError
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from .transition_system cimport TransitionSystem, Transition
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from ..structs cimport TokenC
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from ..tokens.doc cimport Doc
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from ..strings cimport StringStore
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from ..gold cimport GoldParse
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from ..attrs cimport TAG, DEP
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from .._ml import build_parser_state2vec, build_model
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from .._ml import build_state2vec, build_model
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from .._ml import build_debug_state2vec, build_debug_model
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USE_FTRL = True
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DEBUG = False
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def set_debug(val):
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global DEBUG
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DEBUG = val
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def get_templates(*args, **kwargs):
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return []
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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 load(cls, path, Vocab vocab, TransitionSystem=None, require=False, **cfg):
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"""
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Load the statistical model from the supplied path.
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Arguments:
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path (Path):
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The path to load from.
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vocab (Vocab):
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The vocabulary. Must be shared by the documents to be processed.
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require (bool):
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Whether to raise an error if the files are not found.
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Returns (Parser):
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The newly constructed object.
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"""
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with (path / 'config.json').open() as file_:
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cfg = ujson.load(file_)
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self = cls(vocab, TransitionSystem=TransitionSystem, model=None, **cfg)
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if (path / 'model').exists():
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self.model.load(str(path / 'model'))
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elif require:
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raise IOError(
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"Required file %s/model not found when loading" % str(path))
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return self
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def __init__(self, Vocab vocab, TransitionSystem=None, model=None, **cfg):
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"""
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Create a Parser.
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Arguments:
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vocab (Vocab):
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The vocabulary object. Must be shared with documents to be processed.
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model (thinc Model):
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The statistical model.
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Returns (Parser):
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The newly constructed object.
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"""
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if TransitionSystem is None:
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TransitionSystem = self.TransitionSystem
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self.vocab = vocab
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cfg['actions'] = TransitionSystem.get_actions(**cfg)
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self.moves = TransitionSystem(vocab.strings, cfg['actions'])
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if model is None:
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model = self.build_model(**cfg)
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self.model = model
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self.cfg = cfg
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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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def build_model(self, width=64, nr_vector=1000, nF=1, nB=1, nS=1, nL=1, nR=1, **_):
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nr_context_tokens = StateClass.nr_context_tokens(nF, nB, nS, nL, nR)
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state2vec = build_state2vec(nr_context_tokens, width, nr_vector)
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#state2vec = build_debug_state2vec(width, nr_vector)
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model = build_debug_model(state2vec, width*2, 2, self.moves.n_moves)
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return model
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def __call__(self, Doc tokens):
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"""
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Apply the parser or entity recognizer, setting the annotations onto the Doc object.
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Arguments:
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doc (Doc): The document to be processed.
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Returns:
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None
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"""
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self.parse_batch([tokens])
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self.moves.finalize_doc(tokens)
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def pipe(self, stream, int batch_size=1000, int n_threads=2):
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"""
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Process a stream of documents.
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Arguments:
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stream: The sequence of documents to process.
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batch_size (int):
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The number of documents to accumulate into a working set.
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n_threads (int):
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The number of threads with which to work on the buffer in parallel.
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Yields (Doc): Documents, in order.
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"""
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cdef Pool mem = Pool()
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cdef int* lengths = <int*>mem.alloc(batch_size, sizeof(int))
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cdef Doc doc
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cdef int i
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cdef int nr_feat = self.model.nr_feat
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cdef int status
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queue = []
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for doc in stream:
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queue.append(doc)
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if len(queue) == batch_size:
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self.parse_batch(queue)
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for doc in queue:
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self.moves.finalize_doc(doc)
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yield doc
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queue = []
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if queue:
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self.parse_batch(queue)
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for doc in queue:
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self.moves.finalize_doc(doc)
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yield doc
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def parse_batch(self, docs):
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states = self._init_states(docs)
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nr_class = self.moves.n_moves
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cdef Doc doc
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cdef StateClass state
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cdef int guess
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tokvecs = [d.tensor for d in docs]
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all_states = list(states)
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todo = zip(states, tokvecs)
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i = 0
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while todo:
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states, tokvecs = zip(*todo)
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scores, _ = self._begin_update(states, tokvecs)
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for state, guess in zip(states, scores.argmax(axis=1)):
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action = self.moves.c[guess]
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action.do(state.c, action.label)
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todo = filter(lambda sp: not sp[0].py_is_final(), todo)
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i += 1
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if i >= 10000:
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break
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for state, doc in zip(all_states, docs):
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self.moves.finalize_state(state.c)
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for i in range(doc.length):
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doc.c[i] = state.c._sent[i]
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def begin_training(self, docs, golds):
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for gold in golds:
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self.moves.preprocess_gold(gold)
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states = self._init_states(docs)
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tokvecs = [d.tensor for d in docs]
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d_tokens = [self.model.ops.allocate(d.tensor.shape) for d in docs]
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nr_class = self.moves.n_moves
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costs = self.model.ops.allocate((len(docs), nr_class), dtype='f')
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gradients = self.model.ops.allocate((len(docs), nr_class), dtype='f')
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is_valid = self.model.ops.allocate((len(docs), nr_class), dtype='i')
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attr_names = numpy.zeros((2,), dtype='i')
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attr_names[0] = TAG
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attr_names[1] = DEP
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features = self._get_features(states, tokvecs, attr_names)
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self.model.begin_training(features)
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def update(self, docs, golds, drop=0., sgd=None):
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if isinstance(docs, Doc) and isinstance(golds, GoldParse):
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return self.update([docs], [golds], drop=drop)
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for gold in golds:
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self.moves.preprocess_gold(gold)
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states = self._init_states(docs)
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tokvecs = [d.tensor for d in docs]
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d_tokens = [self.model.ops.allocate(d.tensor.shape) for d in docs]
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nr_class = self.moves.n_moves
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output = list(d_tokens)
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todo = zip(states, tokvecs, golds, d_tokens)
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assert len(states) == len(todo)
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losses = []
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i = 0
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while todo:
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states, tokvecs, golds, d_tokens = zip(*todo)
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scores, finish_update = self._begin_update(states, tokvecs)
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token_ids, batch_token_grads = finish_update(golds, sgd=sgd, losses=losses,
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force_gold=False)
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if hasattr(self.model.ops.xp, 'scatter_add'):
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for i, tok_ids in enumerate(token_ids):
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self.model.ops.xp.scatter_add(d_tokens[i],
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tok_ids, batch_token_grads[i])
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else:
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for i, tok_ids in enumerate(token_ids):
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self.model.ops.xp.add.at(d_tokens[i],
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tok_ids, batch_token_grads[i])
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self._transition_batch(states, scores)
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# Get unfinished states (and their matching gold and token gradients)
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todo = filter(lambda sp: not sp[0].py_is_final(), todo)
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i += 1
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if i >= 10000:
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break
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return output, sum(losses)
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def _begin_update(self, states, tokvecs, drop=0.):
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nr_class = self.moves.n_moves
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attr_names = numpy.zeros((2,), dtype='i')
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attr_names[0] = TAG
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attr_names[1] = DEP
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features = self._get_features(states, tokvecs, attr_names)
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scores, finish_update = self.model.begin_update(features, drop=drop)
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assert scores.shape[0] == len(states), (len(states), scores.shape)
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assert len(scores.shape) == 2
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is_valid = self.model.ops.allocate((len(states), nr_class), dtype='i')
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self._validate_batch(is_valid, states)
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softmaxed = self.model.ops.softmax(scores)
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softmaxed *= is_valid
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softmaxed /= softmaxed.sum(axis=1).reshape((softmaxed.shape[0], 1))
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def backward(golds, sgd=None, losses=[], force_gold=False):
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nonlocal softmaxed
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costs = self.model.ops.allocate((len(states), nr_class), dtype='f')
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d_scores = self.model.ops.allocate((len(states), nr_class), dtype='f')
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self._cost_batch(costs, is_valid, states, golds)
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self._set_gradient(d_scores, scores, is_valid, costs)
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losses.append(self.model.ops.xp.abs(d_scores).sum())
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if force_gold:
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softmaxed *= costs <= 0
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return finish_update(d_scores, sgd=sgd)
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return softmaxed, backward
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def _init_states(self, docs):
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states = []
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cdef Doc doc
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cdef StateClass state
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for i, doc in enumerate(docs):
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state = StateClass.init(doc.c, doc.length)
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self.moves.initialize_state(state.c)
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states.append(state)
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return states
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def _get_features(self, states, all_tokvecs, attr_names,
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nF=1, nB=0, nS=2, nL=2, nR=2):
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n_tokens = states[0].nr_context_tokens(nF, nB, nS, nL, nR)
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vector_length = all_tokvecs[0].shape[1]
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cpu_tokens = numpy.zeros((len(states), n_tokens), dtype='int32')
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features = numpy.zeros((len(states), n_tokens, attr_names.shape[0]), dtype='uint64')
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tokvecs = self.model.ops.allocate((len(states), n_tokens, vector_length), dtype='f')
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for i, state in enumerate(states):
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state.set_context_tokens(cpu_tokens[i], nF, nB, nS, nL, nR)
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#state.set_attributes(features[i], tokens[i], attr_names)
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gpu_tokens = self.model.ops.xp.array(cpu_tokens)
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for i in range(len(states)):
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tokvecs[i] = all_tokvecs[i][gpu_tokens[i]]
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tokvecs *= (gpu_tokens >= 0).reshape((gpu_tokens.shape[0], gpu_tokens.shape[1], 1))
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return (gpu_tokens, self.model.ops.asarray(features), tokvecs)
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def _validate_batch(self, is_valid, states):
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cdef StateClass state
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cdef int i
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cdef int[:, :] is_valid_cpu = is_valid.get()
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for i, state in enumerate(states):
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self.moves.set_valid(&is_valid_cpu[i, 0], state.c)
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is_valid.set(numpy.asarray(is_valid_cpu))
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def _cost_batch(self, costs, is_valid,
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states, golds):
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cdef int i
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cdef StateClass state
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cdef GoldParse gold
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cdef int[:, :] is_valid_cpu = is_valid.get()
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cdef weight_t[:, :] costs_cpu = costs.get()
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for i, (state, gold) in enumerate(zip(states, golds)):
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self.moves.set_costs(&is_valid_cpu[i, 0], &costs_cpu[i, 0], state, gold)
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is_valid.set(numpy.asarray(is_valid_cpu))
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costs.set(numpy.asarray(costs_cpu))
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def _transition_batch(self, states, scores):
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cdef StateClass state
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cdef int guess
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for state, guess in zip(states, scores.argmax(axis=1)):
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action = self.moves.c[guess]
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action.do(state.c, action.label)
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def _set_gradient(self, gradients, scores, is_valid, costs):
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"""Do multi-label log loss"""
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cdef double Z, gZ, max_, g_max
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n = gradients.shape[0]
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scores = scores * is_valid
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g_scores = scores * is_valid * (costs <= 0.)
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exps = self.model.ops.xp.exp(scores - scores.max(axis=1).reshape((n, 1)))
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exps *= is_valid
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g_exps = self.model.ops.xp.exp(g_scores - g_scores.max(axis=1).reshape((n, 1)))
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g_exps *= costs <= 0.
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g_exps *= is_valid
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gradients[:] = exps / exps.sum(axis=1).reshape((n, 1))
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gradients -= g_exps / g_exps.sum(axis=1).reshape((n, 1))
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def step_through(self, Doc doc, GoldParse gold=None):
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"""
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Set up a stepwise state, to introspect and control the transition sequence.
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Arguments:
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doc (Doc): The document to step through.
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gold (GoldParse): Optional gold parse
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Returns (StepwiseState):
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A state object, to step through the annotation process.
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"""
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return StepwiseState(self, doc, gold=gold)
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def from_transition_sequence(self, Doc doc, sequence):
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"""Control the annotations on a document by specifying a transition sequence
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to follow.
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Arguments:
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doc (Doc): The document to annotate.
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sequence: A sequence of action names, as unicode strings.
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Returns: None
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"""
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with self.step_through(doc) as stepwise:
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for transition in sequence:
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stepwise.transition(transition)
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def add_label(self, label):
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# Doesn't set label into serializer -- subclasses override it to do that.
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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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# Important that the labels be stored as a list! We need the
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# order, or the model goes out of synch
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self.cfg.setdefault('extra_labels', []).append(label)
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cdef int dropout(FeatureC* feats, int nr_feat, float prob) except -1:
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if prob <= 0 or prob >= 1.:
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return 0
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cdef double[::1] py_probs = numpy.random.uniform(0., 1., nr_feat)
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cdef double* probs = &py_probs[0]
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for i in range(nr_feat):
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if probs[i] >= prob:
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feats[i].value /= prob
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else:
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feats[i].value = 0.
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cdef class StepwiseState:
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cdef readonly StateClass stcls
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cdef readonly Example eg
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cdef readonly Doc doc
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cdef readonly GoldParse gold
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cdef readonly Parser parser
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def __init__(self, Parser parser, Doc doc, GoldParse gold=None):
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self.parser = parser
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self.doc = doc
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if gold is not None:
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self.gold = gold
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self.parser.moves.preprocess_gold(self.gold)
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else:
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self.gold = GoldParse(doc)
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self.stcls = StateClass.init(doc.c, doc.length)
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self.parser.moves.initialize_state(self.stcls.c)
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self.eg = Example(
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nr_class=self.parser.moves.n_moves,
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nr_atom=CONTEXT_SIZE,
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nr_feat=self.parser.model.nr_feat)
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def __enter__(self):
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return self
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def __exit__(self, type, value, traceback):
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self.finish()
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@property
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def is_final(self):
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return self.stcls.is_final()
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@property
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def stack(self):
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return self.stcls.stack
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@property
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def queue(self):
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return self.stcls.queue
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@property
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def heads(self):
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return [self.stcls.H(i) for i in range(self.stcls.c.length)]
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@property
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def deps(self):
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return [self.doc.vocab.strings[self.stcls.c._sent[i].dep]
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for i in range(self.stcls.c.length)]
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@property
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def costs(self):
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"""
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Find the action-costs for the current state.
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"""
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if not self.gold:
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raise ValueError("Can't set costs: No GoldParse provided")
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self.parser.moves.set_costs(self.eg.c.is_valid, self.eg.c.costs,
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self.stcls, self.gold)
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costs = {}
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for i in range(self.parser.moves.n_moves):
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if not self.eg.c.is_valid[i]:
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continue
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transition = self.parser.moves.c[i]
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name = self.parser.moves.move_name(transition.move, transition.label)
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costs[name] = self.eg.c.costs[i]
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return costs
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def predict(self):
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self.eg.reset()
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#self.eg.c.nr_feat = self.parser.model.set_featuresC(self.eg.c.atoms, self.eg.c.features,
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# self.stcls.c)
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self.parser.moves.set_valid(self.eg.c.is_valid, self.stcls.c)
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#self.parser.model.set_scoresC(self.eg.c.scores,
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# self.eg.c.features, self.eg.c.nr_feat)
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cdef Transition action = self.parser.moves.c[self.eg.guess]
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return self.parser.moves.move_name(action.move, action.label)
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def transition(self, action_name=None):
|
|
if action_name is None:
|
|
action_name = self.predict()
|
|
moves = {'S': 0, 'D': 1, 'L': 2, 'R': 3}
|
|
if action_name == '_':
|
|
action_name = self.predict()
|
|
action = self.parser.moves.lookup_transition(action_name)
|
|
elif action_name == 'L' or action_name == 'R':
|
|
self.predict()
|
|
move = moves[action_name]
|
|
clas = _arg_max_clas(self.eg.c.scores, move, self.parser.moves.c,
|
|
self.eg.c.nr_class)
|
|
action = self.parser.moves.c[clas]
|
|
else:
|
|
action = self.parser.moves.lookup_transition(action_name)
|
|
action.do(self.stcls.c, action.label)
|
|
|
|
def finish(self):
|
|
if self.stcls.is_final():
|
|
self.parser.moves.finalize_state(self.stcls.c)
|
|
self.doc.set_parse(self.stcls.c._sent)
|
|
self.parser.moves.finalize_doc(self.doc)
|
|
|
|
|
|
class ParserStateError(ValueError):
|
|
def __init__(self, doc):
|
|
ValueError.__init__(self,
|
|
"Error analysing doc -- no valid actions available. This should "
|
|
"never happen, so please report the error on the issue tracker. "
|
|
"Here's the thread to do so --- reopen it if it's closed:\n"
|
|
"https://github.com/spacy-io/spaCy/issues/429\n"
|
|
"Please include the text that the parser failed on, which is:\n"
|
|
"%s" % repr(doc.text))
|
|
|
|
cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, int n) nogil:
|
|
cdef int best = -1
|
|
for i in range(n):
|
|
if costs[i] <= 0:
|
|
if best == -1 or scores[i] > scores[best]:
|
|
best = i
|
|
return best
|
|
|
|
|
|
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
|
|
int nr_class) except -1:
|
|
cdef weight_t score = 0
|
|
cdef int mode = -1
|
|
cdef int i
|
|
for i in range(nr_class):
|
|
if actions[i].move == move and (mode == -1 or scores[i] >= score):
|
|
mode = i
|
|
score = scores[i]
|
|
return mode
|