Improve correctness of minibatching
@@ -427,7 +427,7 @@ cdef class Parser:
cuda_stream = get_cuda_stream()
- states, golds, max_length = self._init_gold_batch(docs, golds)
+ states, golds, max_steps = self._init_gold_batch(docs, golds)
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
0.0)
todo = [(s, g) for (s, g) in zip(states, golds)
@@ -438,6 +438,7 @@ cdef class Parser:
backprops = []
d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
cdef float loss = 0.
+ n_steps = 0
while todo:
states, golds = zip(*todo)
@@ -467,7 +468,8 @@ cdef class Parser:
todo = [st for st in todo if not st[0].is_final()]
if losses is not None:
losses[self.name] += (d_scores**2).sum()
- if len(backprops) >= (max_length * 2):
+ n_steps += 1
+ if n_steps >= max_steps:
break
self._make_updates(d_tokvecs,
backprops, sgd, cuda_stream)
@@ -482,7 +484,8 @@ cdef class Parser:
StateClass state
Transition action
whole_states = self.moves.init_batch(whole_docs)
- max_length = max(5, min(20, min([len(doc) for doc in whole_docs])))
+ max_length = max(5, min(50, 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):
@@ -493,16 +496,20 @@ cdef class Parser:
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)
+ 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)
- return states, golds, max_length
+ max_moves = max(max_moves, len(oracle_actions))
+ return states, golds, max_moves
def _make_updates(self, d_tokvecs, backprops, sgd, cuda_stream=None):
# Tells CUDA to block, so our async copies complete.
