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