# 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 ujson import json import numpy cimport cython.parallel import cytoolz import numpy.random cimport numpy as np 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 from thinc.linalg cimport MatVec, VecVec from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten from .._ml import link_vectors_to_models, create_default_optimizer from ..compat import json_dumps, copy_array from ..tokens.doc cimport Doc from ..gold cimport GoldParse from .. import util from .stateclass cimport StateClass from ._state cimport StateC from .transition_system cimport Transition from . import _beam_utils, nonproj def get_templates(*args, **kwargs): return [] 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 np.ndarray bias 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 if not isinstance(lower_model.b, numpy.ndarray): self.bias = lower_model.b.get() else: self.bias = lower_model.b self.nF = cached.shape[1] self.nP = getattr(lower_model, 'nP', 1) self.nO = cached.shape[2] 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 += self.bias state_vector, bp_nonlinearity = self._nonlinearity(state_vector) def backward(d_state_vector_ids, sgd=None): d_state_vector, token_ids = d_state_vector_ids d_state_vector = bp_nonlinearity(d_state_vector, sgd) # This will usually be on GPU if not isinstance(d_state_vector, self.ops.xp.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: state_vector = state_vector.reshape(state_vector.shape[:-1]) mask = state_vector >= 0. state_vector *= mask else: state_vector, mask = self.ops.maxout(state_vector) def backprop_nonlinearity(d_best, sgd=None): if self.nP == 1: d_best *= mask d_best = d_best.reshape((d_best.shape + (1,))) return d_best else: return self.ops.backprop_maxout(d_best, mask, self.nP) return state_vector, backprop_nonlinearity 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 padding = cached cached += F * O for b in range(B): for f in range(F): if token_ids[f] < 0: feature = &padding[f*O] else: idx = token_ids[f] * F * O + f*O feature = &cached[idx] VecVec.add_i(output, feature, 1., O) 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 def _collect_states(beams): cdef StateClass state cdef Beam beam states = [] for beam in beams: state = StateClass.borrow(beam.at(0)) states.append(state) return states 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)) if depth != 1: raise ValueError("Currently parser depth is hard-coded to 1.") 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', 128)) hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 200)) embed_size = util.env_opt('embed_size', cfg.get('embed_size', 7000)) hist_size = util.env_opt('history_feats', cfg.get('hist_size', 0)) hist_width = util.env_opt('history_width', cfg.get('hist_width', 0)) if hist_size != 0: raise ValueError("Currently history size is hard-coded to 0") if hist_width != 0: raise ValueError("Currently history width is hard-coded to 0") tok2vec = Tok2Vec(token_vector_width, embed_size, pretrained_dims=cfg.get('pretrained_dims', 0)) tok2vec = chain(tok2vec, flatten) 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 = chain( clone(Maxout(hidden_width, hidden_width), depth-1), zero_init(Affine(nr_class, hidden_width, drop_factor=0.0)) ) cfg = { 'nr_class': nr_class, 'hidden_depth': depth, 'token_vector_width': token_vector_width, 'hidden_width': hidden_width, 'maxout_pieces': parser_maxout_pieces, 'hist_size': hist_size, 'hist_width': hist_width } return (tok2vec, lower, upper), cfg def create_optimizer(self): return create_default_optimizer(self.model[0].ops, **self.cfg.get('optimizer', {})) 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 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 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 'pretrained_dims' not in cfg: cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1] cfg.setdefault('cnn_maxout_pieces', 3) 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 self._multitasks = [] 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. doc (Doc): The document to be processed. """ 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', 0.0) cdef Beam beam if beam_width == 1: states, tokvecs = self.parse_batch([doc]) self.set_annotations([doc], states, tensors=tokvecs) return doc else: beams, tokvecs = self.beam_parse([doc], beam_width=beam_width, beam_density=beam_density) beam = beams[0] output = self.moves.get_beam_annot(beam) state = StateClass.borrow(beam.at(0)) self.set_annotations([doc], [state], tensors=tokvecs) _cleanup(beam) return output def pipe(self, docs, int batch_size=256, int n_threads=2, beam_width=None, beam_density=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. n_threads (int): The number of threads with which to work on the buffer in parallel. YIELDS (Doc): Documents, in order. """ 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', 0.0) cdef Doc doc for batch in cytoolz.partition_all(batch_size, docs): batch_in_order = list(batch) by_length = sorted(batch_in_order, key=lambda doc: len(doc)) batch_beams = [] for subbatch in cytoolz.partition_all(8, by_length): subbatch = list(subbatch) if beam_width == 1: parse_states, tokvecs = self.parse_batch(subbatch) beams = [] else: beams, tokvecs = self.beam_parse(subbatch, beam_width=beam_width, beam_density=beam_density) parse_states = _collect_states(beams) self.set_annotations(subbatch, parse_states, tensors=None) for beam in beams: _cleanup(beam) for doc in batch_in_order: yield doc def parse_batch(self, docs): cdef: precompute_hiddens state2vec Pool mem const float* feat_weights StateC* st StateClass stcls vector[StateC*] states int guess, nr_class, nr_feat, nr_piece, nr_dim, nr_state, nr_step int j if isinstance(docs, Doc): docs = [docs] cuda_stream = util.get_cuda_stream() (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model( docs, cuda_stream, 0.0) nr_state = len(docs) nr_class = self.moves.n_moves nr_dim = tokvecs.shape[1] nr_feat = self.nr_feature nr_piece = state2vec.nP state_objs = self.moves.init_batch(docs) for stcls in state_objs: if not stcls.c.is_final(): states.push_back(stcls.c) feat_weights = state2vec.get_feat_weights() cdef int i cdef np.ndarray hidden_weights = numpy.ascontiguousarray( vec2scores._layers[-1].W.T) cdef np.ndarray hidden_bias = vec2scores._layers[-1].b hW = hidden_weights.data hb = hidden_bias.data bias = state2vec.bias.data cdef int nr_hidden = hidden_weights.shape[0] cdef int nr_task = states.size() with nogil: self._parseC(&states[0], nr_task, feat_weights, bias, hW, hb, nr_class, nr_hidden, nr_feat, nr_piece) PyErr_CheckSignals() tokvecs = self.model[0].ops.unflatten(tokvecs, [len(doc) for doc in docs]) return state_objs, tokvecs cdef void _parseC(self, StateC** states, int nr_task, const float* feat_weights, const float* bias, const float* hW, const float* hb, int nr_class, int nr_hidden, int nr_feat, int nr_piece) nogil: token_ids = calloc(nr_feat, sizeof(int)) is_valid = calloc(nr_class, sizeof(int)) vectors = calloc(nr_hidden * nr_task, sizeof(float)) unmaxed = calloc(nr_hidden * nr_piece, sizeof(float)) scores = calloc(nr_class*nr_task, sizeof(float)) if not (token_ids and is_valid and vectors and scores): with gil: PyErr_SetFromErrno(MemoryError) PyErr_CheckSignals() cdef int nr_todo = nr_task cdef int i, j cdef vector[StateC*] unfinished while nr_todo >= 1: memset(vectors, 0, nr_todo * nr_hidden * sizeof(float)) memset(scores, 0, nr_todo * nr_class * sizeof(float)) for i in range(nr_todo): state = states[i] state.set_context_tokens(token_ids, nr_feat) memset(unmaxed, 0, nr_hidden * nr_piece * sizeof(float)) sum_state_features(unmaxed, feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece) VecVec.add_i(unmaxed, bias, 1., nr_hidden*nr_piece) state_vector = &vectors[i*nr_hidden] for j in range(nr_hidden): index = j * nr_piece which = Vec.arg_max(&unmaxed[index], nr_piece) state_vector[j] = unmaxed[index + which] # Compute hidden-to-output # TODO: These methods in Thinc are confusing at the moment, and # quite backwards. But this currently does what we need. MatVec.batch_T_dot(scores, hW, vectors, nr_class, nr_hidden, nr_todo) # Add bias for i in range(nr_todo): VecVec.add_i(&scores[i*nr_class], hb, 1., nr_class) # Validate actions, argmax, take action. for i in range(nr_todo): state = states[i] self.moves.set_valid(is_valid, state) guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class) action = self.moves.c[guess] action.do(state, action.label) state.push_hist(guess) if not state.is_final(): unfinished.push_back(state) for i in range(unfinished.size()): states[i] = unfinished[i] nr_todo = unfinished.size() unfinished.clear() free(token_ids) free(is_valid) free(vectors) free(unmaxed) free(scores) def beam_parse(self, docs, int beam_width=3, float beam_density=0.001, float drop=0.): cdef Beam beam cdef np.ndarray scores cdef Doc doc cdef int nr_class = self.moves.n_moves cuda_stream = util.get_cuda_stream() (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model( docs, cuda_stream, drop) cdef int offset = 0 cdef int j = 0 cdef int k beams = [] 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): state = beam.at(i) state.offset = offset offset += len(doc) beam.check_done(_check_final_state, NULL) beams.append(beam) cdef np.ndarray token_ids token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature), dtype='i', order='C') todo = [beam for beam in beams if not beam.is_done] cdef int* c_ids cdef int nr_feature = self.nr_feature cdef int n_states while todo: todo = [beam for beam in beams if not beam.is_done] 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, _ = state2vec.begin_update(token_ids[:n_states], drop) scores, _ = vec2scores.begin_update(vectors, drop=drop) c_scores = scores.data for beam in todo: 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, nr_class * sizeof(float)) c_scores += nr_class beam.advance(_transition_state, NULL, self.moves.c) beam.check_done(_check_final_state, NULL) tokvecs = self.model[0].ops.unflatten(tokvecs, [len(doc) for doc in docs]) return beams, tokvecs def update(self, docs, golds, drop=0., sgd=None, losses=None): if not any(self.moves.has_gold(gold) for gold in golds): return None assert len(docs) == len(golds) if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.0: return self.update_beam(docs, golds, self.cfg['beam_width'], self.cfg['beam_density'], drop=drop, sgd=sgd, losses=losses) if losses is not None and self.name not in losses: losses[self.name] = 0. if isinstance(docs, Doc) and isinstance(golds, GoldParse): docs = [docs] golds = [golds] for multitask in self._multitasks: multitask.update(docs, golds, drop=drop, sgd=sgd) cuda_stream = util.get_cuda_stream() # 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) (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, drop) 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 = [] # Add a padding vector to the d_tokvecs gradient, so that missing # values don't affect the real gradient. d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1])) 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 hists = numpy.asarray([st.history for st in states], dtype='i') if self.cfg.get('hist_size', 0): scores, bp_scores = vec2scores.begin_update((vector, hists), drop=drop) else: scores, bp_scores = vec2scores.begin_update(vector, drop=drop) d_scores = self.get_batch_loss(states, golds, scores) d_scores /= len(docs) 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(( util.get_async(cuda_stream, token_ids), util.get_async(cuda_stream, d_vector), bp_vector )) else: backprops.append((token_ids, d_vector, bp_vector)) self.transition_batch(states, scores) todo = [(st, gold) for (st, gold) in todo if not st.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, bp_tokvecs, backprops, sgd, cuda_stream) def update_beam(self, docs, golds, width=None, density=None, drop=0., sgd=None, losses=None): if not any(self.moves.has_gold(gold) for gold in golds): return None if not golds: return None if width is None: width = self.cfg.get('beam_width', 2) if density is None: density = self.cfg.get('beam_density', 0.0) if losses is not None and self.name not in losses: losses[self.name] = 0. lengths = [len(d) for d in docs] assert min(lengths) >= 1 states = self.moves.init_batch(docs) for gold in golds: self.moves.preprocess_gold(gold) cuda_stream = util.get_cuda_stream() (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model( docs, cuda_stream, drop) states_d_scores, backprops, beams = _beam_utils.update_beam( self.moves, self.nr_feature, 500, states, golds, state2vec, vec2scores, width, density, self.cfg.get('hist_size', 0), drop=drop, losses=losses) backprop_lower = [] cdef float batch_size = len(docs) for i, d_scores in enumerate(states_d_scores): d_scores /= batch_size if losses is not None: losses[self.name] += (d_scores**2).sum() ids, bp_vectors, bp_scores = backprops[i] d_vector = bp_scores(d_scores, sgd=sgd) if isinstance(self.model[0].ops, CupyOps) \ and not isinstance(ids, state2vec.ops.xp.ndarray): backprop_lower.append(( util.get_async(cuda_stream, ids), util.get_async(cuda_stream, d_vector), bp_vectors)) else: backprop_lower.append((ids, d_vector, bp_vectors)) # Add a padding vector to the d_tokvecs gradient, so that missing # values don't affect the real gradient. d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1])) self._make_updates(d_tokvecs, bp_tokvecs, backprop_lower, sgd, cuda_stream) cdef Beam beam for beam in beams: _cleanup(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 _make_updates(self, d_tokvecs, bp_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, ids), sgd=sgd) ids = ids.flatten() d_state_features = d_state_features.reshape( (ids.size, d_state_features.shape[2])) self.model[0].ops.scatter_add(d_tokvecs, ids, d_state_features) # Padded -- see update() bp_tokvecs(d_tokvecs[:-1], sgd=sgd) @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, docs, stream, dropout): tok2vec, lower, upper = self.model tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout) state2vec = precompute_hiddens(len(docs), tokvecs, lower, stream, drop=0.0) return (tokvecs, bp_tokvecs), 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] state.c.push_hist(guess) 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, tensors=None): cdef StateClass state cdef Doc doc 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] if tensors is not None: if isinstance(doc.tensor, numpy.ndarray) \ and not isinstance(tensors[i], numpy.ndarray): doc.extend_tensor(tensors[i].get()) else: doc.extend_tensor(tensors[i]) self.moves.finalize_doc(doc) for hook in self.postprocesses: for doc in docs: hook(doc) @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.''' if self.model in (None, True, False): return None else: return self.model[0] @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: # 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) resized = True if self.model not in (True, False, None) and resized: # Weights are stored in (nr_out, nr_in) format, so we're basically # just adding rows here. smaller = self.model[-1]._layers[-1] larger = Affine(self.moves.n_moves, smaller.nI) copy_array(larger.W[:smaller.nO], smaller.W) copy_array(larger.b[:smaller.nO], smaller.b) self.model[-1]._layers[-1] = larger def begin_training(self, gold_tuples, pipeline=None, sgd=None, **cfg): if 'model' in cfg: self.model = cfg['model'] gold_tuples = nonproj.preprocess_training_data(gold_tuples, label_freq_cutoff=30) 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) cfg.setdefault('token_vector_width', 128) if self.model is True: cfg['pretrained_dims'] = self.vocab.vectors_length self.model, cfg = self.Model(self.moves.n_moves, **cfg) if sgd is None: sgd = self.create_optimizer() self.model[1].begin_training( self.model[1].ops.allocate((5, cfg['token_vector_width']))) if pipeline is not None: self.init_multitask_objectives(gold_tuples, pipeline, sgd=sgd, **cfg) link_vectors_to_models(self.vocab) else: if sgd is None: sgd = self.create_optimizer() self.model[1].begin_training( self.model[1].ops.allocate((5, cfg['token_vector_width']))) self.cfg.update(cfg) return sgd def add_multitask_objective(self, target): # Defined in subclasses, to avoid circular import raise NotImplementedError def init_multitask_objectives(self, 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[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(util.read_json(p)), '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.cfg.setdefault('pretrained_dims', self.vocab.vectors_length) 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: json.dumps(self.cfg, indent=2, sort_keys=True)) )) 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(json.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.cfg) cfg['pretrained_dims'] = self.vocab.vectors_length else: cfg = {} cfg['pretrained_dims'] = self.vocab.vectors_length 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 # 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, moves[clas].label) dest.push_hist(clas) cdef int _check_final_state(void* _state, void* extra_args) except -1: state = _state return state.is_final() def _cleanup(Beam beam): cdef StateC* state # Once parsing has finished, states in beam may not be unique. Is this # correct? seen = set() for i in range(beam.width): addr = beam._parents[i].content if addr not in seen: state = addr del state seen.add(addr) else: print(i, addr) print(seen) raise Exception addr = beam._states[i].content if addr not in seen: state = addr del state seen.add(addr) else: print(i, addr) print(seen) raise Exception