# cython: infer_types=True # cython: cdivision=True # cython: boundscheck=False # coding: utf-8 from __future__ import unicode_literals, print_function from collections import OrderedDict import numpy cimport cython.parallel import numpy.random cimport numpy as np from itertools import islice from cpython.ref cimport PyObject, Py_XDECREF from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno from libc.math cimport exp from libcpp.vector cimport vector from libc.string cimport memset, memcpy from libc.stdlib cimport calloc, free from cymem.cymem cimport Pool from thinc.typedefs cimport weight_t, class_t, hash_t from thinc.extra.search cimport Beam from thinc.api import chain, clone from thinc.v2v import Model, Maxout, Affine from thinc.misc import LayerNorm from thinc.neural.ops import CupyOps from thinc.neural.util import get_array_module from thinc.linalg cimport Vec, VecVec import srsly from ._parser_model cimport alloc_activations, free_activations from ._parser_model cimport predict_states, arg_max_if_valid from ._parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss from ._parser_model cimport get_c_weights, get_c_sizes from ._parser_model import ParserModel from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten from .._ml import link_vectors_to_models, create_default_optimizer from ..compat import copy_array from ..tokens.doc cimport Doc from ..gold cimport GoldParse from ..errors import Errors, TempErrors from .. import util from .stateclass cimport StateClass from ._state cimport StateC from .transition_system cimport Transition from . cimport _beam_utils from . import _beam_utils from . import nonproj cdef class Parser: """ Base class of the DependencyParser and EntityRecognizer. """ @classmethod def Model(cls, nr_class, **cfg): depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1)) subword_features = util.env_opt('subword_features', cfg.get('subword_features', True)) conv_depth = util.env_opt('conv_depth', cfg.get('conv_depth', 4)) bilstm_depth = util.env_opt('bilstm_depth', cfg.get('bilstm_depth', 0)) if depth != 1: raise ValueError(TempErrors.T004.format(value=depth)) parser_maxout_pieces = util.env_opt('parser_maxout_pieces', cfg.get('maxout_pieces', 2)) token_vector_width = util.env_opt('token_vector_width', cfg.get('token_vector_width', 96)) hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 64)) embed_size = util.env_opt('embed_size', cfg.get('embed_size', 2000)) pretrained_vectors = cfg.get('pretrained_vectors', None) tok2vec = Tok2Vec(token_vector_width, embed_size, conv_depth=conv_depth, subword_features=subword_features, pretrained_vectors=pretrained_vectors, bilstm_depth=bilstm_depth) tok2vec = chain(tok2vec, flatten) tok2vec.nO = token_vector_width lower = PrecomputableAffine(hidden_width, nF=cls.nr_feature, nI=token_vector_width, nP=parser_maxout_pieces) lower.nP = parser_maxout_pieces with Model.use_device('cpu'): upper = Affine(nr_class, hidden_width, drop_factor=0.0) upper.W *= 0 cfg = { 'nr_class': nr_class, 'hidden_depth': depth, 'token_vector_width': token_vector_width, 'hidden_width': hidden_width, 'maxout_pieces': parser_maxout_pieces, 'pretrained_vectors': pretrained_vectors, 'bilstm_depth': bilstm_depth } return ParserModel(tok2vec, lower, upper), cfg name = 'base_parser' def __init__(self, Vocab vocab, moves=True, model=True, **cfg): """Create a Parser. 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. If set to True (default), a new instance will be created with `Parser.Model()` in parser.begin_training(), parser.from_disk() or parser.from_bytes(). **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 if 'beam_width' not in cfg: cfg['beam_width'] = util.env_opt('beam_width', 1) if 'beam_density' not in cfg: cfg['beam_density'] = util.env_opt('beam_density', 0.0) if 'beam_update_prob' not in cfg: cfg['beam_update_prob'] = util.env_opt('beam_update_prob', 1.0) cfg.setdefault('cnn_maxout_pieces', 3) self.cfg = cfg self.model = model self._multitasks = [] self._rehearsal_model = None def __reduce__(self): return (Parser, (self.vocab, self.moves, self.model), None, None) @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) # Explicitly removing the internal "U-" token used for blocking entities if name != "U-": names.append(name) return names nr_feature = 8 @property def labels(self): class_names = [self.moves.get_class_name(i) for i in range(self.moves.n_moves)] return class_names @property def tok2vec(self): '''Return the embedding and convolutional layer of the model.''' return None if self.model in (None, True, False) else self.model.tok2vec @property def postprocesses(self): # Available for subclasses, e.g. to deprojectivize return [] def add_label(self, label): resized = False for action in self.moves.action_types: added = self.moves.add_action(action, label) if added: resized = True if resized: self._resize() def _resize(self): if "nr_class" in self.cfg: self.cfg["nr_class"] = self.moves.n_moves if self.model not in (True, False, None): self.model.resize_output(self.moves.n_moves) if self._rehearsal_model not in (True, False, None): self._rehearsal_model.resize_output(self.moves.n_moves) def add_multitask_objective(self, target): # Defined in subclasses, to avoid circular import raise NotImplementedError def init_multitask_objectives(self, get_gold_tuples, pipeline, **cfg): '''Setup models for secondary objectives, to benefit from multi-task learning. This method is intended to be overridden by subclasses. For instance, the dependency parser can benefit from sharing an input representation with a label prediction model. These auxiliary models are discarded after training. ''' pass 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.use_params(params): yield def __call__(self, Doc doc, beam_width=None): """Apply the parser or entity recognizer, setting the annotations onto the `Doc` object. doc (Doc): The document to be processed. """ if beam_width is None: beam_width = self.cfg.get('beam_width', 1) beam_density = self.cfg.get('beam_density', 0.) states = self.predict([doc], beam_width=beam_width, beam_density=beam_density) self.set_annotations([doc], states, tensors=None) return doc def pipe(self, docs, int batch_size=256, int n_threads=-1, beam_width=None): """Process a stream of documents. stream: The sequence of documents to process. batch_size (int): Number of documents to accumulate into a working set. YIELDS (Doc): Documents, in order. """ if beam_width is None: beam_width = self.cfg.get('beam_width', 1) beam_density = self.cfg.get('beam_density', 0.) cdef Doc doc for batch in util.minibatch(docs, size=batch_size): batch_in_order = list(batch) by_length = sorted(batch_in_order, key=lambda doc: len(doc)) for subbatch in util.minibatch(by_length, size=max(batch_size//4, 2)): subbatch = list(subbatch) parse_states = self.predict(subbatch, beam_width=beam_width, beam_density=beam_density) self.set_annotations(subbatch, parse_states, tensors=None) for doc in batch_in_order: yield doc def require_model(self): """Raise an error if the component's model is not initialized.""" if getattr(self, 'model', None) in (None, True, False): raise ValueError(Errors.E109.format(name=self.name)) def predict(self, docs, beam_width=1, beam_density=0.0, drop=0.): self.require_model() if isinstance(docs, Doc): docs = [docs] if not any(len(doc) for doc in docs): result = self.moves.init_batch(docs) self._resize() return result if beam_width < 2: return self.greedy_parse(docs, drop=drop) else: return self.beam_parse(docs, beam_width=beam_width, beam_density=beam_density, drop=drop) def greedy_parse(self, docs, drop=0.): cdef vector[StateC*] states cdef StateClass state batch = self.moves.init_batch(docs) # This is pretty dirty, but the NER can resize itself in init_batch, # if labels are missing. We therefore have to check whether we need to # expand our model output. self._resize() model = self.model(docs) weights = get_c_weights(model) for state in batch: if not state.is_final(): states.push_back(state.c) sizes = get_c_sizes(model, states.size()) with nogil: self._parseC(&states[0], weights, sizes) return batch def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.): cdef Beam beam cdef Doc doc cdef np.ndarray token_ids beams = self.moves.init_beams(docs, beam_width, beam_density=beam_density) # This is pretty dirty, but the NER can resize itself in init_batch, # if labels are missing. We therefore have to check whether we need to # expand our model output. self._resize() model = self.model(docs) token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature), dtype='i', order='C') cdef int* c_ids cdef int nr_feature = self.nr_feature cdef int n_states model = self.model(docs) todo = [beam for beam in beams if not beam.is_done] while todo: token_ids.fill(-1) c_ids = token_ids.data n_states = 0 for beam in todo: for i in range(beam.size): state = beam.at(i) # This way we avoid having to score finalized states # We do have to take care to keep indexes aligned, though if not state.is_final(): state.set_context_tokens(c_ids, nr_feature) c_ids += nr_feature n_states += 1 if n_states == 0: break vectors = model.state2vec(token_ids[:n_states]) scores = model.vec2scores(vectors) todo = self.transition_beams(todo, scores) return beams cdef void _parseC(self, StateC** states, WeightsC weights, SizesC sizes) nogil: cdef int i, j cdef vector[StateC*] unfinished cdef ActivationsC activations = alloc_activations(sizes) while sizes.states >= 1: predict_states(&activations, states, &weights, sizes) # Validate actions, argmax, take action. self.c_transition_batch(states, activations.scores, sizes.classes, sizes.states) for i in range(sizes.states): if not states[i].is_final(): unfinished.push_back(states[i]) for i in range(unfinished.size()): states[i] = unfinished[i] sizes.states = unfinished.size() unfinished.clear() free_activations(&activations) def set_annotations(self, docs, states_or_beams, tensors=None): cdef StateClass state cdef Beam beam cdef Doc doc states = [] beams = [] for state_or_beam in states_or_beams: if isinstance(state_or_beam, StateClass): states.append(state_or_beam) else: beam = state_or_beam state = StateClass.borrow(beam.at(0)) states.append(state) beams.append(beam) for i, (state, doc) in enumerate(zip(states, docs)): self.moves.finalize_state(state.c) for j in range(doc.length): doc.c[j] = state.c._sent[j] self.moves.finalize_doc(doc) for hook in self.postprocesses: hook(doc) for beam in beams: _beam_utils.cleanup_beam(beam) def transition_states(self, states, float[:, ::1] scores): cdef StateClass state cdef float* c_scores = &scores[0, 0] cdef vector[StateC*] c_states for state in states: c_states.push_back(state.c) self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0]) return [state for state in states if not state.c.is_final()] cdef void c_transition_batch(self, StateC** states, const float* scores, int nr_class, int batch_size) nogil: # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc with gil: assert self.moves.n_moves > 0 is_valid = calloc(self.moves.n_moves, sizeof(int)) cdef int i, guess cdef Transition action for i in range(batch_size): self.moves.set_valid(is_valid, states[i]) guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class) if guess == -1: # This shouldn't happen, but it's hard to raise an error here, # and we don't want to infinite loop. So, force to end state. states[i].force_final() else: action = self.moves.c[guess] action.do(states[i], action.label) states[i].push_hist(guess) free(is_valid) def transition_beams(self, beams, float[:, ::1] scores): cdef Beam beam cdef float* c_scores = &scores[0, 0] for beam in beams: for i in range(beam.size): state = beam.at(i) if not state.is_final(): self.moves.set_valid(beam.is_valid[i], state) memcpy(beam.scores[i], c_scores, scores.shape[1] * sizeof(float)) c_scores += scores.shape[1] beam.advance(_beam_utils.transition_state, _beam_utils.hash_state, self.moves.c) beam.check_done(_beam_utils.check_final_state, NULL) return [b for b in beams if not b.is_done] def update(self, docs, golds, drop=0., sgd=None, losses=None): self.require_model() if isinstance(docs, Doc) and isinstance(golds, GoldParse): docs = [docs] golds = [golds] if len(docs) != len(golds): raise ValueError(Errors.E077.format(value='update', n_docs=len(docs), n_golds=len(golds))) if losses is None: losses = {} losses.setdefault(self.name, 0.) for multitask in self._multitasks: multitask.update(docs, golds, drop=drop, sgd=sgd) # The probability we use beam update, instead of falling back to # a greedy update beam_update_prob = self.cfg.get('beam_update_prob', 0.5) if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() < beam_update_prob: return self.update_beam(docs, golds, self.cfg.get('beam_width', 1), drop=drop, sgd=sgd, losses=losses, beam_density=self.cfg.get('beam_density', 0.001)) # Chop sequences into lengths of this many transitions, to make the # batch uniform length. cut_gold = numpy.random.choice(range(20, 100)) states, golds, max_steps = self._init_gold_batch(docs, golds, max_length=cut_gold) states_golds = [(s, g) for (s, g) in zip(states, golds) if not s.is_final() and g is not None] # Prepare the stepwise model, and get the callback for finishing the batch model, finish_update = self.model.begin_update(docs, drop=drop) for _ in range(max_steps): if not states_golds: break states, golds = zip(*states_golds) scores, backprop = model.begin_update(states, drop=drop) d_scores = self.get_batch_loss(states, golds, scores, losses) backprop(d_scores, sgd=sgd) # Follow the predicted action self.transition_states(states, scores) states_golds = [eg for eg in states_golds if not eg[0].is_final()] # Do the backprop finish_update(golds, sgd=sgd) return losses def rehearse(self, docs, sgd=None, losses=None, **cfg): """Perform a "rehearsal" update, to prevent catastrophic forgetting.""" if isinstance(docs, Doc): docs = [docs] if losses is None: losses = {} for multitask in self._multitasks: if hasattr(multitask, 'rehearse'): multitask.rehearse(docs, losses=losses, sgd=sgd) if self._rehearsal_model is None: return None losses.setdefault(self.name, 0.) states = self.moves.init_batch(docs) # This is pretty dirty, but the NER can resize itself in init_batch, # if labels are missing. We therefore have to check whether we need to # expand our model output. self._resize() # Prepare the stepwise model, and get the callback for finishing the batch tutor, _ = self._rehearsal_model.begin_update(docs, drop=0.0) model, finish_update = self.model.begin_update(docs, drop=0.0) n_scores = 0. loss = 0. while states: targets, _ = tutor.begin_update(states, drop=0.) guesses, backprop = model.begin_update(states, drop=0.) d_scores = (guesses - targets) / targets.shape[0] # If all weights for an output are 0 in the original model, don't # supervise that output. This allows us to add classes. loss += (d_scores**2).sum() backprop(d_scores, sgd=sgd) # Follow the predicted action self.transition_states(states, guesses) states = [state for state in states if not state.is_final()] n_scores += d_scores.size # Do the backprop finish_update(docs, sgd=sgd) losses[self.name] += loss / n_scores return losses def update_beam(self, docs, golds, width, drop=0., sgd=None, losses=None, beam_density=0.0): lengths = [len(d) for d in docs] states = self.moves.init_batch(docs) for gold in golds: self.moves.preprocess_gold(gold) model, finish_update = self.model.begin_update(docs, drop=drop) states_d_scores, backprops, beams = _beam_utils.update_beam( self.moves, self.nr_feature, 10000, states, golds, model.state2vec, model.vec2scores, width, drop=drop, losses=losses, beam_density=beam_density) for i, d_scores in enumerate(states_d_scores): losses[self.name] += (d_scores**2).mean() ids, bp_vectors, bp_scores = backprops[i] d_vector = bp_scores(d_scores, sgd=sgd) if isinstance(model.ops, CupyOps) \ and not isinstance(ids, model.state2vec.ops.xp.ndarray): model.backprops.append(( util.get_async(model.cuda_stream, ids), util.get_async(model.cuda_stream, d_vector), bp_vectors)) else: model.backprops.append((ids, d_vector, bp_vectors)) model.make_updates(sgd) cdef Beam beam for beam in beams: _beam_utils.cleanup_beam(beam) def _init_gold_batch(self, whole_docs, whole_golds, min_length=5, max_length=500): """Make a square batch, of length equal to the shortest doc. A long doc will get multiple states. Let's say we have a doc of length 2*N, where N is the shortest doc. We'll make two states, one representing long_doc[:N], and another representing long_doc[N:].""" cdef: StateClass state Transition action whole_states = self.moves.init_batch(whole_docs) max_length = max(min_length, min(max_length, min([len(doc) for doc in whole_docs]))) max_moves = 0 states = [] golds = [] for doc, state, gold in zip(whole_docs, whole_states, whole_golds): gold = self.moves.preprocess_gold(gold) if gold is None: continue oracle_actions = self.moves.get_oracle_sequence(doc, gold) start = 0 while start < len(doc): state = state.copy() n_moves = 0 while state.B(0) < start and not state.is_final(): action = self.moves.c[oracle_actions.pop(0)] action.do(state.c, action.label) state.c.push_hist(action.clas) n_moves += 1 has_gold = self.moves.has_gold(gold, start=start, end=start+max_length) if not state.is_final() and has_gold: states.append(state) golds.append(gold) max_moves = max(max_moves, n_moves) start += min(max_length, len(doc)-start) max_moves = max(max_moves, len(oracle_actions)) return states, golds, max_moves def get_batch_loss(self, states, golds, float[:, ::1] scores, losses): cdef StateClass state cdef GoldParse gold cdef Pool mem = Pool() cdef int i # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc assert self.moves.n_moves > 0 is_valid = 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) for j in range(self.moves.n_moves): if costs[j] <= 0.0 and j in self.model.unseen_classes: self.model.unseen_classes.remove(j) cpu_log_loss(c_d_scores, costs, is_valid, &scores[i, 0], d_scores.shape[1]) c_d_scores += d_scores.shape[1] if losses is not None: losses.setdefault(self.name, 0.) losses[self.name] += (d_scores**2).sum() return d_scores def create_optimizer(self): return create_default_optimizer(self.model.ops, **self.cfg.get('optimizer', {})) def begin_training(self, get_gold_tuples, pipeline=None, sgd=None, **cfg): if 'model' in cfg: self.model = cfg['model'] if not hasattr(get_gold_tuples, '__call__'): gold_tuples = get_gold_tuples get_gold_tuples = lambda: gold_tuples cfg.setdefault('min_action_freq', 30) actions = self.moves.get_actions(gold_parses=get_gold_tuples(), min_freq=cfg.get('min_action_freq', 30), learn_tokens=self.cfg.get("learn_tokens", False)) for action, labels in self.moves.labels.items(): actions.setdefault(action, {}) for label, freq in labels.items(): if label not in actions[action]: actions[action][label] = freq self.moves.initialize_actions(actions) cfg.setdefault('token_vector_width', 96) if self.model is True: self.model, cfg = self.Model(self.moves.n_moves, **cfg) if sgd is None: sgd = self.create_optimizer() doc_sample = [] gold_sample = [] for raw_text, annots_brackets in islice(get_gold_tuples(), 1000): _ = annots_brackets.pop() for annots, brackets in annots_brackets: ids, words, tags, heads, deps, ents = annots doc_sample.append(Doc(self.vocab, words=words)) gold_sample.append(GoldParse(doc_sample[-1], words=words, tags=tags, heads=heads, deps=deps, ents=ents)) self.model.begin_training(doc_sample, gold_sample) if pipeline is not None: self.init_multitask_objectives(get_gold_tuples, pipeline, sgd=sgd, **cfg) link_vectors_to_models(self.vocab) else: if sgd is None: sgd = self.create_optimizer() self.model.begin_training([]) self.cfg.update(cfg) return sgd def to_disk(self, path, exclude=tuple(), **kwargs): serializers = { 'model': lambda p: (self.model.to_disk(p) if self.model is not True else True), 'vocab': lambda p: self.vocab.to_disk(p), 'moves': lambda p: self.moves.to_disk(p, exclude=["strings"]), 'cfg': lambda p: srsly.write_json(p, self.cfg) } exclude = util.get_serialization_exclude(serializers, exclude, kwargs) util.to_disk(path, serializers, exclude) def from_disk(self, path, exclude=tuple(), **kwargs): deserializers = { 'vocab': lambda p: self.vocab.from_disk(p), 'moves': lambda p: self.moves.from_disk(p, exclude=["strings"]), 'cfg': lambda p: self.cfg.update(srsly.read_json(p)), 'model': lambda p: None } exclude = util.get_serialization_exclude(deserializers, exclude, kwargs) util.from_disk(path, deserializers, exclude) if 'model' not in exclude: path = util.ensure_path(path) if self.model is True: self.model, cfg = self.Model(**self.cfg) else: cfg = {} with (path / 'model').open('rb') as file_: bytes_data = file_.read() try: self.model.from_bytes(bytes_data) except AttributeError: raise ValueError(Errors.E149) self.cfg.update(cfg) return self def to_bytes(self, exclude=tuple(), **kwargs): serializers = OrderedDict(( ('model', lambda: (self.model.to_bytes() if self.model is not True else True)), ('vocab', lambda: self.vocab.to_bytes()), ('moves', lambda: self.moves.to_bytes(exclude=["strings"])), ('cfg', lambda: srsly.json_dumps(self.cfg, indent=2, sort_keys=True)) )) exclude = util.get_serialization_exclude(serializers, exclude, kwargs) return util.to_bytes(serializers, exclude) def from_bytes(self, bytes_data, exclude=tuple(), **kwargs): deserializers = OrderedDict(( ('vocab', lambda b: self.vocab.from_bytes(b)), ('moves', lambda b: self.moves.from_bytes(b, exclude=["strings"])), ('cfg', lambda b: self.cfg.update(srsly.json_loads(b))), ('model', lambda b: None) )) exclude = util.get_serialization_exclude(deserializers, exclude, kwargs) msg = util.from_bytes(bytes_data, deserializers, exclude) if 'model' not in exclude: # 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: try: self.model.from_bytes(msg['model']) except AttributeError: raise ValueError(Errors.E149) self.cfg.update(cfg) return self