mirror of https://github.com/explosion/spaCy.git
294 lines
11 KiB
Cython
294 lines
11 KiB
Cython
# cython: infer_types=True
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# cython: profile=True
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cimport numpy as np
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import numpy
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from cpython.ref cimport PyObject, Py_XDECREF
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from thinc.extra.search cimport Beam
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from thinc.extra.search import MaxViolation
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from thinc.typedefs cimport hash_t, class_t
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from thinc.extra.search cimport MaxViolation
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from .transition_system cimport TransitionSystem, Transition
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from ..gold cimport GoldParse
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from ..errors import Errors
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from .stateclass cimport StateC, StateClass
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# These are passed as callbacks to thinc.search.Beam
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cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
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dest = <StateC*>_dest
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src = <StateC*>_src
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moves = <const Transition*>_moves
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dest.clone(src)
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moves[clas].do(dest, moves[clas].label)
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dest.push_hist(clas)
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cdef int _check_final_state(void* _state, void* extra_args) except -1:
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state = <StateC*>_state
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return state.is_final()
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cdef hash_t _hash_state(void* _state, void* _) except 0:
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state = <StateC*>_state
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if state.is_final():
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return 1
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else:
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return state.hash()
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cdef class ParserBeam(object):
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cdef public TransitionSystem moves
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cdef public object states
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cdef public object golds
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cdef public object beams
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cdef public object dones
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def __init__(self, TransitionSystem moves, states, golds,
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int width, float density):
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self.moves = moves
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self.states = states
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self.golds = golds
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self.beams = []
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cdef Beam beam
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cdef StateClass state
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cdef StateC* st
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for state in states:
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beam = Beam(self.moves.n_moves, width, density)
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beam.initialize(self.moves.init_beam_state, state.c.length,
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state.c._sent)
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for i in range(beam.width):
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st = <StateC*>beam.at(i)
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st.offset = state.c.offset
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self.beams.append(beam)
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self.dones = [False] * len(self.beams)
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@property
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def is_done(self):
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return all(b.is_done or self.dones[i]
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for i, b in enumerate(self.beams))
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def __getitem__(self, i):
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return self.beams[i]
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def __len__(self):
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return len(self.beams)
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def advance(self, scores, follow_gold=False):
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cdef Beam beam
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for i, beam in enumerate(self.beams):
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if beam.is_done or not scores[i].size or self.dones[i]:
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continue
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self._set_scores(beam, scores[i])
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if self.golds is not None:
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self._set_costs(beam, self.golds[i], follow_gold=follow_gold)
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beam.advance(_transition_state, NULL, <void*>self.moves.c)
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beam.check_done(_check_final_state, NULL)
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# This handles the non-monotonic stuff for the parser.
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if beam.is_done and self.golds is not None:
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for j in range(beam.size):
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state = StateClass.borrow(<StateC*>beam.at(j))
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if state.is_final():
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try:
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if self.moves.is_gold_parse(state, self.golds[i]):
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beam._states[j].loss = 0.0
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elif beam._states[j].loss == 0.0:
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beam._states[j].loss = 1.0
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except NotImplementedError:
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break
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def _set_scores(self, Beam beam, float[:, ::1] scores):
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cdef float* c_scores = &scores[0, 0]
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cdef int nr_state = min(scores.shape[0], beam.size)
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cdef int nr_class = scores.shape[1]
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for i in range(nr_state):
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state = <StateC*>beam.at(i)
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if not state.is_final():
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for j in range(nr_class):
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beam.scores[i][j] = c_scores[i * nr_class + j]
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self.moves.set_valid(beam.is_valid[i], state)
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else:
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for j in range(beam.nr_class):
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beam.scores[i][j] = 0
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beam.costs[i][j] = 0
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def _set_costs(self, Beam beam, GoldParse gold, int follow_gold=False):
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for i in range(beam.size):
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state = StateClass.borrow(<StateC*>beam.at(i))
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if not state.is_final():
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self.moves.set_costs(beam.is_valid[i], beam.costs[i],
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state, gold)
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if follow_gold:
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for j in range(beam.nr_class):
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if beam.costs[i][j] >= 1:
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beam.is_valid[i][j] = 0
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def get_token_ids(states, int n_tokens):
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cdef StateClass state
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cdef np.ndarray ids = numpy.zeros((len(states), n_tokens),
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dtype='int32', order='C')
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c_ids = <int*>ids.data
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for i, state in enumerate(states):
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if not state.is_final():
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state.c.set_context_tokens(c_ids, n_tokens)
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else:
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ids[i] = -1
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c_ids += ids.shape[1]
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return ids
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nr_update = 0
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def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
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states, golds,
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state2vec, vec2scores,
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int width, float density, int hist_feats,
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losses=None, drop=0.):
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global nr_update
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cdef MaxViolation violn
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nr_update += 1
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pbeam = ParserBeam(moves, states, golds,
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width=width, density=density)
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gbeam = ParserBeam(moves, states, golds,
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width=width, density=density)
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cdef StateClass state
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beam_maps = []
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backprops = []
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violns = [MaxViolation() for _ in range(len(states))]
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for t in range(max_steps):
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if pbeam.is_done and gbeam.is_done:
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break
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# The beam maps let us find the right row in the flattened scores
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# arrays for each state. States are identified by (example id,
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# history). We keep a different beam map for each step (since we'll
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# have a flat scores array for each step). The beam map will let us
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# take the per-state losses, and compute the gradient for each (step,
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# state, class).
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beam_maps.append({})
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# Gather all states from the two beams in a list. Some stats may occur
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# in both beams. To figure out which beam each state belonged to,
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# we keep two lists of indices, p_indices and g_indices
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states, p_indices, g_indices = get_states(pbeam, gbeam, beam_maps[-1],
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nr_update)
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if not states:
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break
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# Now that we have our flat list of states, feed them through the model
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token_ids = get_token_ids(states, nr_feature)
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vectors, bp_vectors = state2vec.begin_update(token_ids, drop=drop)
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if hist_feats:
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hists = numpy.asarray([st.history[:hist_feats] for st in states],
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dtype='i')
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scores, bp_scores = vec2scores.begin_update((vectors, hists),
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drop=drop)
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else:
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scores, bp_scores = vec2scores.begin_update(vectors, drop=drop)
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# Store the callbacks for the backward pass
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backprops.append((token_ids, bp_vectors, bp_scores))
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# Unpack the flat scores into lists for the two beams. The indices arrays
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# tell us which example and state the scores-row refers to.
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p_scores = [numpy.ascontiguousarray(scores[indices], dtype='f')
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for indices in p_indices]
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g_scores = [numpy.ascontiguousarray(scores[indices], dtype='f')
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for indices in g_indices]
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# Now advance the states in the beams. The gold beam is contrained to
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# to follow only gold analyses.
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pbeam.advance(p_scores)
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gbeam.advance(g_scores, follow_gold=True)
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# Track the "maximum violation", to use in the update.
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for i, violn in enumerate(violns):
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violn.check_crf(pbeam[i], gbeam[i])
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histories = []
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losses = []
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for violn in violns:
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if violn.p_hist:
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histories.append(violn.p_hist + violn.g_hist)
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losses.append(violn.p_probs + violn.g_probs)
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else:
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histories.append([])
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losses.append([])
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states_d_scores = get_gradient(moves.n_moves, beam_maps, histories, losses)
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beams = list(pbeam.beams) + list(gbeam.beams)
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return states_d_scores, backprops[:len(states_d_scores)], beams
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def get_states(pbeams, gbeams, beam_map, nr_update):
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seen = {}
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states = []
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p_indices = []
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g_indices = []
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cdef Beam pbeam, gbeam
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if len(pbeams) != len(gbeams):
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raise ValueError(Errors.E079.format(pbeams=len(pbeams), gbeams=len(gbeams)))
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for eg_id, (pbeam, gbeam) in enumerate(zip(pbeams, gbeams)):
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p_indices.append([])
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g_indices.append([])
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for i in range(pbeam.size):
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state = StateClass.borrow(<StateC*>pbeam.at(i))
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if not state.is_final():
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key = tuple([eg_id] + pbeam.histories[i])
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if key in seen:
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raise ValueError(Errors.E080.format(key=key))
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seen[key] = len(states)
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p_indices[-1].append(len(states))
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states.append(state)
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beam_map.update(seen)
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for i in range(gbeam.size):
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state = StateClass.borrow(<StateC*>gbeam.at(i))
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if not state.is_final():
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key = tuple([eg_id] + gbeam.histories[i])
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if key in seen:
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g_indices[-1].append(seen[key])
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else:
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g_indices[-1].append(len(states))
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beam_map[key] = len(states)
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states.append(state)
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p_idx = [numpy.asarray(idx, dtype='i') for idx in p_indices]
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g_idx = [numpy.asarray(idx, dtype='i') for idx in g_indices]
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return states, p_idx, g_idx
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def get_gradient(nr_class, beam_maps, histories, losses):
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"""The global model assigns a loss to each parse. The beam scores
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are additive, so the same gradient is applied to each action
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in the history. This gives the gradient of a single *action*
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for a beam state -- so we have "the gradient of loss for taking
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action i given history H."
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Histories: Each hitory is a list of actions
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Each candidate has a history
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Each beam has multiple candidates
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Each batch has multiple beams
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So history is list of lists of lists of ints
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"""
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nr_step = len(beam_maps)
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grads = []
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nr_step = 0
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for eg_id, hists in enumerate(histories):
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for loss, hist in zip(losses[eg_id], hists):
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if loss != 0.0 and not numpy.isnan(loss):
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nr_step = max(nr_step, len(hist))
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for i in range(nr_step):
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grads.append(numpy.zeros((max(beam_maps[i].values())+1, nr_class),
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dtype='f'))
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if len(histories) != len(losses):
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raise ValueError(Errors.E081.format(n_hist=len(histories), losses=len(losses)))
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for eg_id, hists in enumerate(histories):
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for loss, hist in zip(losses[eg_id], hists):
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if loss == 0.0 or numpy.isnan(loss):
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continue
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key = tuple([eg_id])
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# Adjust loss for length
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avg_loss = loss / len(hist)
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loss += avg_loss * (nr_step - len(hist))
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for j, clas in enumerate(hist):
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i = beam_maps[j][key]
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# In step j, at state i action clas
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# resulted in loss
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grads[j][i, clas] += loss
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key = key + tuple([clas])
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return grads
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