# cython: infer_types=True # cython: profile=True # cython: cdivision=True # cython: boundscheck=False # coding: utf-8 from __future__ import unicode_literals, print_function from collections import Counter, OrderedDict import ujson import contextlib from libc.math cimport exp cimport cython cimport cython.parallel import cytoolz import dill import numpy.random cimport numpy as np from libcpp.vector cimport vector from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF from cpython.exc cimport PyErr_CheckSignals from libc.stdint cimport uint32_t, uint64_t from libc.string cimport memset, memcpy from libc.stdlib cimport malloc, calloc, free from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t from thinc.linear.avgtron cimport AveragedPerceptron from thinc.linalg cimport VecVec from thinc.structs cimport SparseArrayC, FeatureC, ExampleC from thinc.extra.eg cimport Example from thinc.extra.search cimport Beam from cymem.cymem cimport Pool, Address from murmurhash.mrmr cimport hash64 from preshed.maps cimport MapStruct from preshed.maps cimport map_get from thinc.api import layerize, chain, noop, clone from thinc.neural import Model, Affine, ReLu, Maxout from thinc.neural._classes.selu import SELU from thinc.neural._classes.layernorm import LayerNorm from thinc.neural.ops import NumpyOps, CupyOps from thinc.neural.util import get_array_module from .. import util from ..util import get_async, get_cuda_stream from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts from .._ml import Tok2Vec, doc2feats, rebatch, fine_tune from .._ml import Residual, drop_layer from ..compat import json_dumps from . import _parse_features from ._parse_features cimport CONTEXT_SIZE from ._parse_features cimport fill_context from .stateclass cimport StateClass from ._state cimport StateC from . import nonproj from .transition_system import OracleError from .transition_system cimport TransitionSystem, Transition from ..structs cimport TokenC from ..tokens.doc cimport Doc from ..strings cimport StringStore from ..gold cimport GoldParse from ..attrs cimport ID, TAG, DEP, ORTH, NORM, PREFIX, SUFFIX, TAG USE_FINE_TUNE = True def get_templates(*args, **kwargs): return [] USE_FTRL = True DEBUG = False def set_debug(val): global DEBUG DEBUG = val cdef class precompute_hiddens: '''Allow a model to be "primed" by pre-computing input features in bulk. This is used for the parser, where we want to take a batch of documents, and compute vectors for each (token, position) pair. These vectors can then be reused, especially for beam-search. Let's say we're using 12 features for each state, e.g. word at start of buffer, three words on stack, their children, etc. In the normal arc-eager system, a document of length N is processed in 2*N states. This means we'll create 2*N*12 feature vectors --- but if we pre-compute, we only need N*12 vector computations. The saving for beam-search is much better: if we have a beam of k, we'll normally make 2*N*12*K computations -- so we can save the factor k. This also gives a nice CPU/GPU division: we can do all our hard maths up front, packed into large multiplications, and do the hard-to-program parsing on the CPU. ''' cdef int nF, nO, nP cdef bint _is_synchronized cdef public object ops cdef np.ndarray _features cdef np.ndarray _cached cdef object _cuda_stream cdef object _bp_hiddens def __init__(self, batch_size, tokvecs, lower_model, cuda_stream=None, drop=0.): gpu_cached, bp_features = lower_model.begin_update(tokvecs, drop=drop) cdef np.ndarray cached if not isinstance(gpu_cached, numpy.ndarray): # Note the passing of cuda_stream here: it lets # cupy make the copy asynchronously. # We then have to block before first use. cached = gpu_cached.get(stream=cuda_stream) else: cached = gpu_cached self.nF = cached.shape[1] self.nO = cached.shape[2] self.nP = getattr(lower_model, 'nP', 1) self.ops = lower_model.ops self._is_synchronized = False self._cuda_stream = cuda_stream self._cached = cached self._bp_hiddens = bp_features cdef const float* get_feat_weights(self) except NULL: if not self._is_synchronized \ and self._cuda_stream is not None: self._cuda_stream.synchronize() self._is_synchronized = True return self._cached.data def __call__(self, X): return self.begin_update(X)[0] def begin_update(self, token_ids, drop=0.): cdef np.ndarray state_vector = numpy.zeros((token_ids.shape[0], self.nO*self.nP), dtype='f') # This is tricky, but (assuming GPU available); # - Input to forward on CPU # - Output from forward on CPU # - Input to backward on GPU! # - Output from backward on GPU bp_hiddens = self._bp_hiddens feat_weights = self.get_feat_weights() cdef int[:, ::1] ids = token_ids sum_state_features(state_vector.data, feat_weights, &ids[0,0], token_ids.shape[0], self.nF, self.nO*self.nP) state_vector, bp_nonlinearity = self._nonlinearity(state_vector) def backward(d_state_vector, sgd=None): if bp_nonlinearity is not None: d_state_vector = bp_nonlinearity(d_state_vector, sgd) # This will usually be on GPU if isinstance(d_state_vector, numpy.ndarray): d_state_vector = self.ops.xp.array(d_state_vector) d_tokens = bp_hiddens((d_state_vector, token_ids), sgd) return d_tokens return state_vector, backward def _nonlinearity(self, state_vector): if self.nP == 1: return state_vector, None state_vector = state_vector.reshape( (state_vector.shape[0], state_vector.shape[1]//self.nP, self.nP)) best, which = self.ops.maxout(state_vector) def backprop(d_best, sgd=None): return self.ops.backprop_maxout(d_best, which, self.nP) return best, backprop cdef void sum_state_features(float* output, const float* cached, const int* token_ids, int B, int F, int O) nogil: cdef int idx, b, f, i cdef const float* feature for b in range(B): for f in range(F): if token_ids[f] < 0: continue idx = token_ids[f] * F * O + f*O feature = &cached[idx] for i in range(O): output[i] += feature[i] output += O token_ids += F cdef void cpu_log_loss(float* d_scores, const float* costs, const int* is_valid, const float* scores, int O) nogil: """Do multi-label log loss""" cdef double max_, gmax, Z, gZ best = arg_max_if_gold(scores, costs, is_valid, O) guess = arg_max_if_valid(scores, is_valid, O) Z = 1e-10 gZ = 1e-10 max_ = scores[guess] gmax = scores[best] for i in range(O): if is_valid[i]: Z += exp(scores[i] - max_) if costs[i] <= costs[best]: gZ += exp(scores[i] - gmax) for i in range(O): if not is_valid[i]: d_scores[i] = 0. elif costs[i] <= costs[best]: d_scores[i] = (exp(scores[i]-max_) / Z) - (exp(scores[i]-gmax)/gZ) else: d_scores[i] = exp(scores[i]-max_) / Z cdef void cpu_regression_loss(float* d_scores, const float* costs, const int* is_valid, const float* scores, int O) nogil: cdef float eps = 2. best = arg_max_if_gold(scores, costs, is_valid, O) for i in range(O): if not is_valid[i]: d_scores[i] = 0. elif scores[i] < scores[best]: d_scores[i] = 0. else: # I doubt this is correct? # Looking for something like Huber loss diff = scores[i] - -costs[i] if diff > eps: d_scores[i] = eps elif diff < -eps: d_scores[i] = -eps else: d_scores[i] = diff cdef class Parser: """ Base class of the DependencyParser and EntityRecognizer. """ @classmethod def Model(cls, nr_class, token_vector_width=128, hidden_width=128, depth=1, **cfg): depth = util.env_opt('parser_hidden_depth', depth) token_vector_width = util.env_opt('token_vector_width', token_vector_width) hidden_width = util.env_opt('hidden_width', hidden_width) parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2) embed_size = util.env_opt('embed_size', 7500) tensors = fine_tune(Tok2Vec(token_vector_width, embed_size, preprocess=doc2feats())) if parser_maxout_pieces == 1: lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class, nF=cls.nr_feature, nI=token_vector_width) else: lower = PrecomputableMaxouts(hidden_width if depth >= 1 else nr_class, nF=cls.nr_feature, nP=parser_maxout_pieces, nI=token_vector_width) with Model.use_device('cpu'): upper = chain( clone(drop_layer(Residual(Maxout(hidden_width))), (depth-1)), zero_init(Affine(nr_class, drop_factor=0.0)) ) # TODO: This is an unfortunate hack atm! # Used to set input dimensions in network. lower.begin_training(lower.ops.allocate((500, token_vector_width))) upper.begin_training(upper.ops.allocate((500, hidden_width))) cfg = { 'nr_class': nr_class, 'depth': depth, 'token_vector_width': token_vector_width, 'hidden_width': hidden_width, 'maxout_pieces': parser_maxout_pieces } return (tensors, lower, upper), cfg def __init__(self, Vocab vocab, moves=True, model=True, **cfg): """ Create a Parser. Arguments: vocab (Vocab): The vocabulary object. Must be shared with documents to be processed. The value is set to the .vocab attribute. moves (TransitionSystem): Defines how the parse-state is created, updated and evaluated. The value is set to the .moves attribute unless True (default), in which case a new instance is created with Parser.Moves(). model (object): Defines how the parse-state is created, updated and evaluated. The value is set to the .model attribute unless True (default), in which case a new instance is created with Parser.Model(). **cfg: Arbitrary configuration parameters. Set to the .cfg attribute """ self.vocab = vocab if moves is True: self.moves = self.TransitionSystem(self.vocab.strings, {}) else: self.moves = moves self.cfg = cfg if 'actions' in self.cfg: for action, labels in self.cfg.get('actions', {}).items(): for label in labels: self.moves.add_action(action, label) self.model = model def __reduce__(self): return (Parser, (self.vocab, self.moves, self.model), None, None) def __call__(self, Doc doc, beam_width=None, beam_density=None): """ Apply the parser or entity recognizer, setting the annotations onto the Doc object. Arguments: doc (Doc): The document to be processed. Returns: None """ if beam_width is None: beam_width = self.cfg.get('beam_width', 1) if beam_density is None: beam_density = self.cfg.get('beam_density', 0.001) cdef Beam beam if beam_width == 1: states = self.parse_batch([doc], [doc.tensor]) self.set_annotations([doc], states) return doc else: beam = self.beam_parse([doc], [doc.tensor], beam_width=beam_width, beam_density=beam_density)[0] output = self.moves.get_beam_annot(beam) state = beam.at(0) self.set_annotations([doc], [state]) _cleanup(beam) return output def pipe(self, docs, int batch_size=1000, int n_threads=2, beam_width=1, beam_density=0.001): """ Process a stream of documents. Arguments: stream: The sequence of documents to process. batch_size (int): The number of documents to accumulate into a working set. n_threads (int): The number of threads with which to work on the buffer in parallel. Yields (Doc): Documents, in order. """ cdef Doc doc for docs in cytoolz.partition_all(batch_size, docs): docs = list(docs) tokvecs = [doc.tensor for doc in docs] if beam_width == 1: parse_states = self.parse_batch(docs, tokvecs) else: parse_states = self.beam_parse(docs, tokvecs, beam_width=beam_width, beam_density=beam_density) self.set_annotations(docs, parse_states) yield from docs def parse_batch(self, docs, tokvecses): cdef: precompute_hiddens state2vec StateClass state Pool mem const float* feat_weights StateC* st vector[StateC*] next_step, this_step int nr_class, nr_feat, nr_piece, nr_dim, nr_state if isinstance(docs, Doc): docs = [docs] if isinstance(tokvecses, np.ndarray): tokvecses = [tokvecses] tokvecs = self.model[0].ops.flatten(tokvecses) if USE_FINE_TUNE: tokvecs += self.model[0].ops.flatten(self.model[0]((docs, tokvecses))) nr_state = len(docs) nr_class = self.moves.n_moves nr_dim = tokvecs.shape[1] nr_feat = self.nr_feature cuda_stream = get_cuda_stream() state2vec, vec2scores = self.get_batch_model(nr_state, tokvecs, cuda_stream, 0.0) nr_piece = state2vec.nP states = self.moves.init_batch(docs) for state in states: if not state.c.is_final(): next_step.push_back(state.c) feat_weights = state2vec.get_feat_weights() cdef int i cdef np.ndarray token_ids = numpy.zeros((nr_state, nr_feat), dtype='i') cdef np.ndarray is_valid = numpy.zeros((nr_state, nr_class), dtype='i') cdef np.ndarray scores c_token_ids = token_ids.data c_is_valid = is_valid.data while not next_step.empty(): for i in range(next_step.size()): st = next_step[i] st.set_context_tokens(&c_token_ids[i*nr_feat], nr_feat) self.moves.set_valid(&c_is_valid[i*nr_class], st) vectors = state2vec(token_ids[:next_step.size()]) scores = vec2scores(vectors) c_scores = scores.data for i in range(next_step.size()): st = next_step[i] guess = arg_max_if_valid( &c_scores[i*nr_class], &c_is_valid[i*nr_class], nr_class) action = self.moves.c[guess] action.do(st, action.label) this_step, next_step = next_step, this_step next_step.clear() for st in this_step: if not st.is_final(): next_step.push_back(st) return states def beam_parse(self, docs, tokvecses, int beam_width=8, float beam_density=0.001): cdef Beam beam cdef np.ndarray scores cdef Doc doc cdef int nr_class = self.moves.n_moves cdef StateClass stcls, output tokvecs = self.model[0].ops.flatten(tokvecses) if USE_FINE_TUNE: tokvecs += self.model[0].ops.flatten(self.model[0]((docs, tokvecses))) cuda_stream = get_cuda_stream() state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs, cuda_stream, 0.0) beams = [] cdef int offset = 0 for doc in docs: beam = Beam(nr_class, beam_width, min_density=beam_density) beam.initialize(self.moves.init_beam_state, doc.length, doc.c) for i in range(beam.width): stcls = beam.at(i) stcls.c.offset = offset offset += len(doc) beam.check_done(_check_final_state, NULL) while not beam.is_done: states = [] for i in range(beam.size): stcls = beam.at(i) states.append(stcls) token_ids = self.get_token_ids(states) vectors = state2vec(token_ids) scores = vec2scores(vectors) for i in range(beam.size): stcls = beam.at(i) if not stcls.is_final(): self.moves.set_valid(beam.is_valid[i], stcls.c) for j in range(nr_class): beam.scores[i][j] = scores[i, j] beam.advance(_transition_state, _hash_state, self.moves.c) beam.check_done(_check_final_state, NULL) beams.append(beam) return beams def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None): if losses is not None and self.name not in losses: losses[self.name] = 0. docs, tokvec_lists = docs_tokvecs tokvecs = self.model[0].ops.flatten(tokvec_lists) if isinstance(docs, Doc) and isinstance(golds, GoldParse): docs = [docs] golds = [golds] if USE_FINE_TUNE: my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop) my_tokvecs = self.model[0].ops.flatten(my_tokvecs) tokvecs += my_tokvecs cuda_stream = get_cuda_stream() states, golds, max_steps = self._init_gold_batch(docs, golds) state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream, 0.0) todo = [(s, g) for (s, g) in zip(states, golds) if not s.is_final() and g is not None] if not todo: return None backprops = [] d_tokvecs = state2vec.ops.allocate(tokvecs.shape) cdef float loss = 0. n_steps = 0 while todo: states, golds = zip(*todo) token_ids = self.get_token_ids(states) vector, bp_vector = state2vec.begin_update(token_ids, drop=0.0) if drop != 0: mask = vec2scores.ops.get_dropout_mask(vector.shape, drop) vector *= mask scores, bp_scores = vec2scores.begin_update(vector, drop=drop) d_scores = self.get_batch_loss(states, golds, scores) d_vector = bp_scores(d_scores, sgd=sgd) if drop != 0: d_vector *= mask if isinstance(self.model[0].ops, CupyOps) \ and not isinstance(token_ids, state2vec.ops.xp.ndarray): # Move token_ids and d_vector to GPU, asynchronously backprops.append(( get_async(cuda_stream, token_ids), get_async(cuda_stream, d_vector), bp_vector )) else: backprops.append((token_ids, d_vector, bp_vector)) self.transition_batch(states, scores) todo = [st for st in todo if not st[0].is_final()] if losses is not None: losses[self.name] += (d_scores**2).sum() n_steps += 1 if n_steps >= max_steps: break self._make_updates(d_tokvecs, backprops, sgd, cuda_stream) d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs]) if USE_FINE_TUNE: bp_my_tokvecs(d_tokvecs, sgd=sgd) return d_tokvecs def _init_gold_batch(self, whole_docs, whole_golds): """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_states = self.moves.init_batch(whole_docs) max_length = max(5, min(50, 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) 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 _make_updates(self, d_tokvecs, backprops, sgd, cuda_stream=None): # Tells CUDA to block, so our async copies complete. if cuda_stream is not None: cuda_stream.synchronize() xp = get_array_module(d_tokvecs) for ids, d_vector, bp_vector in backprops: d_state_features = bp_vector(d_vector, sgd=sgd) active_feats = ids * (ids >= 0) active_feats = active_feats.reshape((ids.shape[0], ids.shape[1], 1)) if hasattr(xp, 'scatter_add'): xp.scatter_add(d_tokvecs, ids, d_state_features * active_feats) else: xp.add.at(d_tokvecs, ids, d_state_features * active_feats) @property def move_names(self): names = [] for i in range(self.moves.n_moves): name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label) names.append(name) return names def get_batch_model(self, batch_size, tokvecs, stream, dropout): _, lower, upper = self.model state2vec = precompute_hiddens(batch_size, tokvecs, lower, stream, drop=dropout) return state2vec, upper nr_feature = 13 def get_token_ids(self, states): cdef StateClass state cdef int n_tokens = self.nr_feature cdef np.ndarray ids = numpy.zeros((len(states), n_tokens), dtype='i', order='C') c_ids = ids.data for i, state in enumerate(states): if not state.is_final(): state.c.set_context_tokens(c_ids, n_tokens) c_ids += ids.shape[1] return ids def transition_batch(self, states, float[:, ::1] scores): cdef StateClass state cdef int[500] is_valid # TODO: Unhack cdef float* c_scores = &scores[0, 0] for state in states: self.moves.set_valid(is_valid, state.c) guess = arg_max_if_valid(c_scores, is_valid, scores.shape[1]) action = self.moves.c[guess] action.do(state.c, action.label) c_scores += scores.shape[1] def get_batch_loss(self, states, golds, float[:, ::1] scores): cdef StateClass state cdef GoldParse gold cdef Pool mem = Pool() cdef int i is_valid = mem.alloc(self.moves.n_moves, sizeof(int)) costs = 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 = d_scores.data 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) cpu_log_loss(c_d_scores, costs, is_valid, &scores[i, 0], d_scores.shape[1]) c_d_scores += d_scores.shape[1] return d_scores def set_annotations(self, docs, states): cdef StateClass state cdef Doc doc for state, doc in zip(states, docs): self.moves.finalize_state(state.c) for i in range(doc.length): doc.c[i] = state.c._sent[i] self.moves.finalize_doc(doc) def add_label(self, label): for action in self.moves.action_types: added = self.moves.add_action(action, label) if added: # Important that the labels be stored as a list! We need the # order, or the model goes out of synch self.cfg.setdefault('extra_labels', []).append(label) def begin_training(self, gold_tuples, **cfg): if 'model' in cfg: self.model = cfg['model'] gold_tuples = nonproj.preprocess_training_data(gold_tuples) actions = self.moves.get_actions(gold_parses=gold_tuples) for action, labels in actions.items(): for label in labels: self.moves.add_action(action, label) if self.model is True: self.model, cfg = self.Model(self.moves.n_moves, **cfg) self.cfg.update(cfg) def preprocess_gold(self, docs_golds): for doc, gold in docs_golds: yield doc, gold def use_params(self, params): # Can't decorate cdef class :(. Workaround. with self.model[0].use_params(params): with self.model[1].use_params(params): yield def to_disk(self, path, **exclude): serializers = { 'tok2vec_model': lambda p: p.open('wb').write( self.model[0].to_bytes()), 'lower_model': lambda p: p.open('wb').write( self.model[1].to_bytes()), 'upper_model': lambda p: p.open('wb').write( self.model[2].to_bytes()), 'vocab': lambda p: self.vocab.to_disk(p), 'moves': lambda p: self.moves.to_disk(p, strings=False), 'cfg': lambda p: p.open('w').write(json_dumps(self.cfg)) } util.to_disk(path, serializers, exclude) def from_disk(self, path, **exclude): deserializers = { 'vocab': lambda p: self.vocab.from_disk(p), 'moves': lambda p: self.moves.from_disk(p, strings=False), 'cfg': lambda p: self.cfg.update(ujson.load(p.open())), 'model': lambda p: None } util.from_disk(path, deserializers, exclude) if 'model' not in exclude: path = util.ensure_path(path) if self.model is True: self.model, cfg = self.Model(**self.cfg) else: cfg = {} with (path / 'tok2vec_model').open('rb') as file_: bytes_data = file_.read() self.model[0].from_bytes(bytes_data) with (path / 'lower_model').open('rb') as file_: bytes_data = file_.read() self.model[1].from_bytes(bytes_data) with (path / 'upper_model').open('rb') as file_: bytes_data = file_.read() self.model[2].from_bytes(bytes_data) self.cfg.update(cfg) return self def to_bytes(self, **exclude): serializers = OrderedDict(( ('tok2vec_model', lambda: self.model[0].to_bytes()), ('lower_model', lambda: self.model[1].to_bytes()), ('upper_model', lambda: self.model[2].to_bytes()), ('vocab', lambda: self.vocab.to_bytes()), ('moves', lambda: self.moves.to_bytes(strings=False)), ('cfg', lambda: ujson.dumps(self.cfg)) )) if 'model' in exclude: exclude['tok2vec_model'] = True exclude['lower_model'] = True exclude['upper_model'] = True exclude.pop('model') return util.to_bytes(serializers, exclude) 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(ujson.loads(b))), ('tok2vec_model', lambda b: None), ('lower_model', lambda b: None), ('upper_model', lambda b: None) )) msg = util.from_bytes(bytes_data, deserializers, exclude) if 'model' not in exclude: if self.model is True: self.model, cfg = self.Model(self.moves.n_moves) else: cfg = {} if 'tok2vec_model' in msg: self.model[0].from_bytes(msg['tok2vec_model']) if 'lower_model' in msg: self.model[1].from_bytes(msg['lower_model']) if 'upper_model' in msg: self.model[2].from_bytes(msg['upper_model']) self.cfg.update(cfg) return self 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, const int* is_valid, int n) nogil: # Find minimum cost cdef float cost = 1 for i in range(n): if is_valid[i] and costs[i] < cost: cost = costs[i] # Now find best-scoring with that cost cdef int best = -1 for i in range(n): if costs[i] <= cost and is_valid[i]: if best == -1 or scores[i] > scores[best]: best = i return best cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil: cdef int best = -1 for i in range(n): if is_valid[i] >= 1: if best == -1 or scores[i] > scores[best]: best = i return best cdef int arg_maxout_if_valid(const weight_t* scores, const int* is_valid, int n, int nP) nogil: cdef int best = -1 cdef float best_score = 0 for i in range(n): if is_valid[i] >= 1: for j in range(nP): if best == -1 or scores[i*nP+j] > best_score: best = i best_score = scores[i*nP+j] 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 # These are passed as callbacks to thinc.search.Beam cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1: dest = _dest src = _src moves = _moves dest.clone(src) moves[clas].do(dest.c, moves[clas].label) cdef int _check_final_state(void* _state, void* extra_args) except -1: return (_state).is_final() def _cleanup(Beam beam): for i in range(beam.width): Py_XDECREF(beam._states[i].content) Py_XDECREF(beam._parents[i].content) cdef hash_t _hash_state(void* _state, void* _) except 0: state = _state if state.c.is_final(): return 1 else: return state.c.hash()