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Fix infinite loop bug

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This commit is contained in:
Matthew Honnibal 2017-05-08 07:54:26 -05:00
parent 94e86ae00a
commit 50ddc9fc45
1 changed files with 26 additions and 37 deletions
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63
spacy/syntax/parser.pyx
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@ -114,7 +114,7 @@ cdef class Parser:
def __reduce__(self):
return (Parser, (self.vocab, self.moves, self.model), None, None)
def build_model(self, width=64, nr_vector=1000, nF=1, nB=1, nS=1, nL=1, nR=1, **_):
def build_model(self, width=128, nr_vector=1000, nF=1, nB=1, nS=1, nL=1, nR=1, **_):
nr_context_tokens = StateClass.nr_context_tokens(nF, nB, nS, nL, nR)
state2vec = build_state2vec(nr_context_tokens, width, nr_vector)
#state2vec = build_debug_state2vec(width, nr_vector)
@ -175,17 +175,13 @@ cdef class Parser:
tokvecs = [d.tensor for d in docs]
all_states = list(states)
todo = zip(states, tokvecs)
i = 0
while todo:
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
if not todo:
break
states, tokvecs = zip(*todo)
scores, _ = self._begin_update(states, tokvecs)
for state, guess in zip(states, scores.argmax(axis=1)):
action = self.moves.c[guess]
action.do(state.c, action.label)
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
i += 1
if i >= 10000:
break
self._transition_batch(states, docs, scores)
for state, doc in zip(all_states, docs):
self.moves.finalize_state(state.c)
for i in range(doc.length):
@ -208,7 +204,6 @@ cdef class Parser:
features = self._get_features(states, tokvecs, attr_names)
self.model.begin_training(features)
def update(self, docs, golds, drop=0., sgd=None):
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
return self.update([docs], [golds], drop=drop)
@ -222,12 +217,17 @@ cdef class Parser:
todo = zip(states, tokvecs, golds, d_tokens)
assert len(states) == len(todo)
losses = []
i = 0
while todo:
# Get unfinished states (and their matching gold and token gradients)
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
if not todo:
break
states, tokvecs, golds, d_tokens = zip(*todo)
scores, finish_update = self._begin_update(states, tokvecs)
token_ids, batch_token_grads = finish_update(golds, sgd=sgd, losses=losses,
force_gold=False)
batch_token_grads *= (token_ids >= 0).reshape((token_ids.shape[0], token_ids.shape[1], 1))
token_ids *= token_ids >= 0
if hasattr(self.model.ops.xp, 'scatter_add'):
for i, tok_ids in enumerate(token_ids):
self.model.ops.xp.scatter_add(d_tokens[i],
@ -236,14 +236,7 @@ cdef class Parser:
for i, tok_ids in enumerate(token_ids):
self.model.ops.xp.add.at(d_tokens[i],
tok_ids, batch_token_grads[i])
self._transition_batch(states, scores)
# Get unfinished states (and their matching gold and token gradients)
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
i += 1
if i >= 10000:
break
self._transition_batch(states, docs, scores)
return output, sum(losses)
def _begin_update(self, states, tokvecs, drop=0.):
@ -293,39 +286,35 @@ cdef class Parser:
tokvecs = self.model.ops.allocate((len(states), n_tokens, vector_length), dtype='f')
for i, state in enumerate(states):
state.set_context_tokens(cpu_tokens[i], nF, nB, nS, nL, nR)
#state.set_attributes(features[i], tokens[i], attr_names)
gpu_tokens = self.model.ops.xp.array(cpu_tokens)
for i in range(len(states)):
tokvecs[i] = all_tokvecs[i][gpu_tokens[i]]
tokvecs *= (gpu_tokens >= 0).reshape((gpu_tokens.shape[0], gpu_tokens.shape[1], 1))
return (gpu_tokens, self.model.ops.asarray(features), tokvecs)
for j, tok_i in enumerate(cpu_tokens[i]):
if tok_i >= 0:
tokvecs[i, j] = all_tokvecs[i][tok_i]
return (cpu_tokens, self.model.ops.asarray(features), tokvecs)
def _validate_batch(self, is_valid, states):
def _validate_batch(self, int[:, ::1] is_valid, states):
cdef StateClass state
cdef int i
cdef int[:, :] is_valid_cpu = is_valid.get()
for i, state in enumerate(states):
self.moves.set_valid(&is_valid_cpu[i, 0], state.c)
is_valid.set(numpy.asarray(is_valid_cpu))
self.moves.set_valid(&is_valid[i, 0], state.c)
def _cost_batch(self, costs, is_valid,
def _cost_batch(self, float[:, ::1] costs, int[:, ::1] is_valid,
states, golds):
cdef int i
cdef StateClass state
cdef GoldParse gold
cdef int[:, :] is_valid_cpu = is_valid.get()
cdef weight_t[:, :] costs_cpu = costs.get()
for i, (state, gold) in enumerate(zip(states, golds)):
self.moves.set_costs(&is_valid_cpu[i, 0], &costs_cpu[i, 0], state, gold)
is_valid.set(numpy.asarray(is_valid_cpu))
costs.set(numpy.asarray(costs_cpu))
self.moves.set_costs(&is_valid[i, 0], &costs[i, 0], state, gold)
def _transition_batch(self, states, scores):
def _transition_batch(self, states, docs, scores):
cdef StateClass state
cdef int guess
for state, guess in zip(states, scores.argmax(axis=1)):
for state, doc, guess in zip(states, docs, scores.argmax(axis=1)):
action = self.moves.c[guess]
orths = [t.lex.orth for t in state.c._sent[:state.c.length]]
words = [doc.vocab.strings[w] for w in orths]
if not action.is_valid(state.c, action.label):
ValueError("Invalid action", scores)
action.do(state.c, action.label)
def _set_gradient(self, gradients, scores, is_valid, costs):