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Fix beam training

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This commit is contained in:
Matthew Honnibal 2017-08-13 18:02:05 -05:00
parent 6a42cc16ff
commit 0ae045256d
2 changed files with 45 additions and 22 deletions
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59
spacy/syntax/_beam_utils.pyx
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@ -57,7 +57,7 @@ cdef class ParserBeam(object):
for state in states:
beam = Beam(self.moves.n_moves, width, density)
beam.initialize(self.moves.init_beam_state, state.c.length, state.c._sent)
for i in range(beam.size):
for i in range(beam.width):
st = <StateClass>beam.at(i)
st.c.offset = state.c.offset
self.beams.append(beam)
@ -81,7 +81,7 @@ cdef class ParserBeam(object):
def advance(self, scores, follow_gold=False):
cdef Beam beam
for i, beam in enumerate(self.beams):
if beam.is_done:
if beam.is_done or not scores[i].size:
continue
self._set_scores(beam, scores[i])
if self.golds is not None:
@ -92,6 +92,12 @@ cdef class ParserBeam(object):
else:
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
beam.check_done(_check_final_state, NULL)
if beam.is_done:
for j in range(beam.size):
if is_gold(<StateClass>beam.at(j), self.golds[i], self.moves.strings):
beam._states[j].loss = 0.0
elif beam._states[j].loss == 0.0:
beam._states[j].loss = 1.0
def _set_scores(self, Beam beam, float[:, ::1] scores):
cdef float* c_scores = &scores[0, 0]
@ -152,32 +158,49 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
width=width, density=density)
gbeam = ParserBeam(moves, states, golds,
width=width, density=0.0)
cdef StateClass state
beam_maps = []
backprops = []
violns = [MaxViolation() for _ in range(len(states))]
for t in range(max_steps):
# The beam maps let us find the right row in the flattened scores
# arrays for each state. States are identified by (example id, history).
# We keep a different beam map for each step (since we'll have a flat
# scores array for each step). The beam map will let us take the per-state
# losses, and compute the gradient for each (step, state, class).
beam_maps.append({})
# Gather all states from the two beams in a list. Some stats may occur
# in both beams. To figure out which beam each state belonged to,
# we keep two lists of indices, p_indices and g_indices
states, p_indices, g_indices = get_states(pbeam, gbeam, beam_maps[-1], nr_update)
if not states:
break
# Now that we have our flat list of states, feed them through the model
token_ids = get_token_ids(states, nr_feature)
vectors, bp_vectors = state2vec.begin_update(token_ids, drop=drop)
scores, bp_scores = vec2scores.begin_update(vectors, drop=drop)
# Store the callbacks for the backward pass
backprops.append((token_ids, bp_vectors, bp_scores))
# Unpack the flat scores into lists for the two beams. The indices arrays
# tell us which example and state the scores-row refers to.
p_scores = [numpy.ascontiguousarray(scores[indices], dtype='f') for indices in p_indices]
g_scores = [numpy.ascontiguousarray(scores[indices], dtype='f') for indices in g_indices]
# Now advance the states in the beams. The gold beam is contrained to
# to follow only gold analyses.
pbeam.advance(p_scores)
gbeam.advance(g_scores, follow_gold=True)
# Track the "maximum violation", to use in the update.
for i, violn in enumerate(violns):
violn.check_crf(pbeam[i], gbeam[i])
histories = [(v.p_hist + v.g_hist) for v in violns]
losses = [(v.p_probs + v.g_probs) for v in violns]
# Only make updates if we have non-gold states
histories = [((v.p_hist + v.g_hist) if v.p_hist else []) for v in violns]
losses = [((v.p_probs + v.g_probs) if v.p_probs else []) for v in violns]
states_d_scores = get_gradient(moves.n_moves, beam_maps,
histories, losses)
assert len(states_d_scores) == len(backprops), (len(states_d_scores), len(backprops))
return states_d_scores, backprops
@ -187,17 +210,20 @@ def get_states(pbeams, gbeams, beam_map, nr_update):
p_indices = []
g_indices = []
cdef Beam pbeam, gbeam
assert len(pbeams) == len(gbeams)
for eg_id, (pbeam, gbeam) in enumerate(zip(pbeams, gbeams)):
p_indices.append([])
g_indices.append([])
if pbeam.loss > 0 and pbeam.min_score > gbeam.score:
continue
for i in range(pbeam.size):
state = <StateClass>pbeam.at(i)
if not state.is_final():
key = tuple([eg_id] + pbeam.histories[i])
seen[key] = len(states)
p_indices[-1].append(len(states))
states.append(<StateClass>pbeam.at(i))
states.append(state)
beam_map.update(seen)
g_indices.append([])
for i in range(gbeam.size):
state = <StateClass>gbeam.at(i)
if not state.is_final():
@ -207,10 +233,10 @@ def get_states(pbeams, gbeams, beam_map, nr_update):
else:
g_indices[-1].append(len(states))
beam_map[key] = len(states)
states.append(<StateClass>gbeam.at(i))
p_indices = [numpy.asarray(idx, dtype='i') for idx in p_indices]
g_indices = [numpy.asarray(idx, dtype='i') for idx in g_indices]
return states, p_indices, g_indices
states.append(state)
p_idx = [numpy.asarray(idx, dtype='i') for idx in p_indices]
g_idx = [numpy.asarray(idx, dtype='i') for idx in g_indices]
return states, p_idx, g_idx
def get_gradient(nr_class, beam_maps, histories, losses):
@ -230,20 +256,17 @@ def get_gradient(nr_class, beam_maps, histories, losses):
nr_step = len(beam_maps)
grads = []
for beam_map in beam_maps:
grads.append(numpy.zeros((max(beam_map.values())+1, nr_class), dtype='f'))
if beam_map:
grads.append(numpy.zeros((max(beam_map.values())+1, nr_class), dtype='f'))
assert len(histories) == len(losses)
for eg_id, hists in enumerate(histories):
for loss, hist in zip(losses[eg_id], hists):
key = tuple([eg_id])
for j, clas in enumerate(hist):
try:
i = beam_maps[j][key]
except:
print(sorted(beam_maps[j].items()))
raise
i = beam_maps[j][key]
# In step j, at state i action clas
# resulted in loss
grads[j][i, clas] += loss
grads[j][i, clas] += loss / len(histories)
key = key + tuple([clas])
return grads

8
spacy/syntax/nn_parser.pyx
View File

@ -557,20 +557,20 @@ cdef class Parser:
my_tokvecs = self.model[0].ops.flatten(my_tokvecs)
tokvecs += my_tokvecs
states, golds, max_moves = self._init_gold_batch(docs, golds)
states = self.moves.init_batch(docs)
for gold in golds:
self.moves.preprocess_gold(gold)
cuda_stream = get_cuda_stream()
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream, 0.0)
states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, max_moves,
states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, 500,
states, tokvecs, golds,
state2vec, vec2scores,
drop, sgd, losses,
width=8)
backprop_lower = []
for i, d_scores in enumerate(states_d_scores):
if d_scores is None:
continue
if losses is not None:
losses[self.name] += (d_scores**2).sum()
ids, bp_vectors, bp_scores = backprops[i]