Bring back support for update_with_oracle_cut_size

This option was available in the pre-refactor parser, but was never
implemented in the refactored parser. This option cuts transition
sequences that are longer than `update_with_oracle_cut` size into
separate sequences that have at most `update_with_oracle_cut`
transitions. The oracle (gold standard) transition sequence is used to
determine the cuts and the initial states for the additional sequences.

Applying this cut makes the batches more homogeneous in the transition
sequence lengths, making forward passes (and as a consequence training)
much faster.

Training time 1000 steps on de_core_news_lg:

- Before this change: 149s
- After this change: 68s
- Pre-refactor parser: 81s

spacy/ml/tb_framework.pyx

@@ -148,32 +148,74 @@ def init(
     # model = _lsuv_init(model)
     return model
 
-InWithoutActions = Tuple[List[Doc], TransitionSystem]
-InWithActions = Tuple[List[Doc], TransitionSystem, List[Ints1d]]
-InT = TypeVar("InT", InWithoutActions, InWithActions)
 
-def forward(model, docs_moves: InT, is_train: bool):
+class TransitionModelInputs:
-    if len(docs_moves) == 2:
+    """
-        docs, moves = docs_moves
+    Input to transition model.
-        actions = None
+    """
-    else:
+
-        docs, moves, actions = docs_moves
+    # dataclass annotation is not yet supported in Cython 0.29.x,
+    # so, we'll do something close to it.
+
+    actions: Optional[List[Ints1d]]
+    docs: List[Doc]
+    max_moves: int
+    moves: TransitionSystem
+    states: Optional[List[State]]
+
+    __slots__ = [
+        "actions",
+        "docs",
+        "max_moves",
+        "moves",
+        "states",
+    ]
+
+    def __init__(self, docs: List[Doc], moves: TransitionSystem,
+        actions: Optional[List[Ints1d]]=None, max_moves: int=0,
+        states: Optional[List[State]]=None):
+        """
+        actions (Optional[List[Ints1d]]): actions to apply for each Doc.
+        docs (List[Doc]): Docs to predict transition sequences for.
+        max_moves: (Optional[int]): the maximum number of moves to apply,
+            values less than 1 will apply moves to states until they are
+            final states.
+        moves (TransitionSystem): the transition system to use when predicting
+            the transition sequences.
+        states (Optional[List[States]]): the initial states to predict the
+            transition sequences for. When absent, the initial states are
+            initialized from the provided Docs.
+        """
+        self.actions = actions
+        self.docs = docs
+        self.moves = moves
+        self.max_moves = max_moves
+        self.states = states
+
+
+def forward(model, inputs: TransitionModelInputs, is_train: bool):
+    docs = inputs.docs
+    moves = inputs.moves
+    actions = inputs.actions
 
     beam_width = model.attrs["beam_width"]
     hidden_pad = model.get_param("hidden_pad")
     tok2vec = model.get_ref("tok2vec")
 
-    states = moves.init_batch(docs)
+    states = moves.init_batch(docs) if inputs.states is None else inputs.states
     tokvecs, backprop_tok2vec = tok2vec(docs, is_train)
     tokvecs = model.ops.xp.vstack((tokvecs, hidden_pad))
     feats, backprop_feats = _forward_precomputable_affine(model, tokvecs, is_train)
     seen_mask = _get_seen_mask(model)
 
-    # Fixme: support actions in forward_cpu
     if beam_width == 1 and not is_train and isinstance(model.ops, NumpyOps):
+        # Note: max_moves is only used during training, so we don't need to
+        #       pass it to the greedy inference path.
         return _forward_greedy_cpu(model, moves, states, feats, seen_mask, actions=actions)
     else:
-        return _forward_fallback(model, moves, states, tokvecs, backprop_tok2vec, feats, backprop_feats, seen_mask, is_train, actions=actions)
+        return _forward_fallback(model, moves, states, tokvecs, backprop_tok2vec,
+            feats, backprop_feats, seen_mask, is_train, actions=actions,
+            max_moves=inputs.max_moves)
 
 
 def _forward_greedy_cpu(model: Model, TransitionSystem moves, states: List[StateClass], np.ndarray feats,
@@ -230,7 +272,7 @@ cdef list _parse_batch(CBlas cblas, TransitionSystem moves, StateC** states,
 
 
 def _forward_fallback(model: Model, moves: TransitionSystem, states: List[StateClass], tokvecs, backprop_tok2vec, feats, backprop_feats, seen_mask, is_train: bool,
-                  actions: Optional[List[Ints1d]]=None):
+        actions: Optional[List[Ints1d]]=None, max_moves=0):
     nF = model.get_dim("nF")
     output = model.get_ref("output")
     hidden_b = model.get_param("hidden_b")
@@ -253,6 +295,7 @@ def _forward_fallback(model: Model, moves: TransitionSystem, states: List[StateC
             moves, states, None, width=beam_width, density=beam_density
         )
     arange = ops.xp.arange(nF)
+    n_moves = 0
     while not batch.is_done:
         ids = numpy.zeros((len(batch.get_unfinished_states()), nF), dtype="i")
         for i, state in enumerate(batch.get_unfinished_states()):
@@ -281,6 +324,9 @@ def _forward_fallback(model: Model, moves: TransitionSystem, states: List[StateC
             all_ids.append(ids)
             all_statevecs.append(statevecs)
             all_which.append(which)
+        if max_moves >= 1 and n_moves >= max_moves:
+            break
+        n_moves += 1
 
     def backprop_parser(d_states_d_scores):
         ids = ops.xp.vstack(all_ids)

spacy/pipeline/transition_parser.pyx

@@ -19,6 +19,7 @@ import numpy.random
 import numpy
 import warnings
 
+from ..ml.tb_framework import TransitionModelInputs
 from ._parser_internals.stateclass cimport StateC, StateClass
 from ._parser_internals.search cimport Beam
 from ..tokens.doc cimport Doc
@@ -26,7 +27,7 @@ from .trainable_pipe cimport TrainablePipe
 from ._parser_internals cimport _beam_utils
 from ._parser_internals import _beam_utils
 from ..vocab cimport Vocab
-from ._parser_internals.transition_system cimport TransitionSystem
+from ._parser_internals.transition_system cimport Transition, TransitionSystem
 from ..typedefs cimport weight_t
 
 from ..training import validate_examples, validate_get_examples
@@ -254,20 +255,23 @@ class Parser(TrainablePipe):
             result = self.moves.init_batch(docs)
             return result
         with _change_attrs(self.model, beam_width=self.cfg["beam_width"], beam_density=self.cfg["beam_density"]):
-            states_or_beams, _ = self.model.predict((docs, self.moves))
+            inputs = TransitionModelInputs(docs=docs, moves=self.moves)
+            states_or_beams, _ = self.model.predict(inputs)
         return states_or_beams
 
     def greedy_parse(self, docs, drop=0.):
         self._resize()
         self._ensure_labels_are_added(docs)
         with _change_attrs(self.model, beam_width=1):
-            states, _ = self.model.predict((docs, self.moves))
+            inputs = TransitionModelInputs(docs=docs, moves=self.moves)
+            states, _ = self.model.predict(inputs)
         return states
 
     def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
         self._ensure_labels_are_added(docs)
         with _change_attrs(self.model, beam_width=self.cfg["beam_width"], beam_density=self.cfg["beam_density"]):
-            beams, _ = self.model.predict((docs, self.moves))
+            inputs = TransitionModelInputs(docs=docs, moves=self.moves)
+            beams, _ = self.model.predict(inputs)
         return beams
 
     def set_annotations(self, docs, states_or_beams):
@@ -298,11 +302,27 @@ class Parser(TrainablePipe):
             return losses
         set_dropout_rate(self.model, drop)
         docs = [eg.x for eg in examples if len(eg.x)]
-        (states, scores), backprop_scores = self.model.begin_update((docs, self.moves))
+
+        max_moves = self.cfg["update_with_oracle_cut_size"]
+        if max_moves >= 1:
+            # Chop sequences into lengths of this many words, to make the
+            # batch uniform length.
+            max_moves = int(random.uniform(max_moves // 2, max_moves * 2))
+            init_states, gold_states, _ = self._init_gold_batch(
+                examples,
+                max_length=max_moves
+            )
+        else:
+            init_states, gold_states, _ = self.moves.init_gold_batch(examples)
+
+        inputs = TransitionModelInputs(docs=docs, moves=self.moves,
+            max_moves=max_moves, states=[state.copy() for state in init_states])
+        (pred_states, scores), backprop_scores = self.model.begin_update(inputs)
         if sum(s.shape[0] for s in scores) == 0:
             return losses
-        d_scores = self.get_loss((states, scores), examples)
+        d_scores = self.get_loss((gold_states, init_states, pred_states, scores),
-        backprop_scores((states, d_scores))
+            examples, max_moves)
+        backprop_scores((pred_states, d_scores))
         if sgd not in (None, False):
             self.finish_update(sgd)
         losses[self.name] += float((d_scores**2).sum())
@@ -312,12 +332,15 @@ class Parser(TrainablePipe):
         del backprop_scores
         return losses
 
-    def get_loss(self, states_scores, examples):
+    def get_loss(self, states_scores, examples, max_moves):
-        states, scores = states_scores
+        gold_states, init_states, pred_states, scores = states_scores
         scores = self.model.ops.xp.vstack(scores)
         costs = self._get_costs_from_histories(
             examples,
-            [list(state.history) for state in states]
+            gold_states,
+            init_states,
+            [list(state.history) for state in pred_states],
+            max_moves
         )
         xp = get_array_module(scores)
         best_costs = costs.min(axis=1, keepdims=True)
@@ -335,7 +358,7 @@ class Parser(TrainablePipe):
         d_scores -= (costs <= best_costs) * (exp_gscores / gZ)
         return d_scores
 
-    def _get_costs_from_histories(self, examples, histories):
+    def _get_costs_from_histories(self, examples, gold_states, init_states, histories, max_moves):
         cdef TransitionSystem moves = self.moves
         cdef StateClass state
         cdef int clas
@@ -345,16 +368,12 @@ class Parser(TrainablePipe):
         cdef Pool mem = Pool()
         cdef np.ndarray costs_i
         is_valid = <int*>mem.alloc(nO, sizeof(int))
-        states = moves.init_batch([eg.x for eg in examples])
+        batch = list(zip(init_states, histories, gold_states))
-        batch = []
+        n_moves = 0
-        for eg, s, h in zip(examples, states, histories):
-            if not s.is_final():
-                gold = moves.init_gold(s, eg)
-                batch.append((eg, s, h, gold))
         output = []
         while batch:
             costs = numpy.zeros((len(batch), nO), dtype="f")
-            for i, (eg, state, history, gold) in enumerate(batch):
+            for i, (state, history, gold) in enumerate(batch):
                 costs_i = costs[i]
                 clas = history.pop(0)
                 moves.set_costs(is_valid, <weight_t*>costs_i.data, state.c, gold)
@@ -362,7 +381,11 @@ class Parser(TrainablePipe):
                 action.do(state.c, action.label)
                 state.c.history.push_back(clas)
             output.append(costs)
-            batch = [(eg, s, h, g) for eg, s, h, g in batch if len(h) != 0]
+            batch = [(s, h, g) for s, h, g in batch if len(h) != 0]
+            if max_moves >= 1 and n_moves >= max_moves:
+                break
+            n_moves += 1
+
         return self.model.ops.xp.vstack(output)
 
     def rehearse(self, examples, sgd=None, losses=None, **cfg):
@@ -384,9 +407,11 @@ class Parser(TrainablePipe):
         # Prepare the stepwise model, and get the callback for finishing the batch
         set_dropout_rate(self._rehearsal_model, 0.0)
         set_dropout_rate(self.model, 0.0)
-        (student_states, student_scores), backprop_scores = self.model.begin_update((docs, self.moves))
+        student_inputs = TransitionModelInputs(docs=docs, moves=self.moves)
+        (student_states, student_scores), backprop_scores = self.model.begin_update(student_inputs)
         actions = states2actions(student_states)
-        _, teacher_scores = self._rehearsal_model.predict((docs, self.moves, actions))
+        teacher_inputs = TransitionModelInputs(docs=docs, moves=self.moves, actions=actions)
+        _, teacher_scores = self._rehearsal_model.predict(teacher_inputs)
 
         teacher_scores = self.model.ops.xp.vstack(teacher_scores)
         student_scores = self.model.ops.xp.vstack(student_scores)
@@ -502,6 +527,49 @@ class Parser(TrainablePipe):
                     raise ValueError(Errors.E149) from None
         return self
 
+    def _init_gold_batch(self, examples, max_length):
+        """Make a square batch, of length equal to the shortest transition
+        sequence or a cap. A long
+        doc will get multiple states. Let's say we have a doc of length 2*N,
+        where N is the shortest doc. We'll make two states, one representing
+        long_doc[:N], and another representing long_doc[N:]."""
+        cdef:
+            StateClass start_state
+            StateClass state
+            Transition action
+            TransitionSystem moves = self.moves
+        all_states = moves.init_batch([eg.predicted for eg in examples])
+        states = []
+        golds = []
+        to_cut = []
+        for state, eg in zip(all_states, examples):
+            if moves.has_gold(eg) and not state.is_final():
+                gold = moves.init_gold(state, eg)
+                if len(eg.x) < max_length:
+                    states.append(state)
+                    golds.append(gold)
+                else:
+                    oracle_actions = moves.get_oracle_sequence_from_state(
+                        state.copy(), gold)
+                    to_cut.append((eg, state, gold, oracle_actions))
+        if not to_cut:
+            return states, golds, 0
+        cdef int clas
+        for eg, state, gold, oracle_actions in to_cut:
+            for i in range(0, len(oracle_actions), max_length):
+                start_state = state.copy()
+                for clas in oracle_actions[i:i+max_length]:
+                    action = moves.c[clas]
+                    action.do(state.c, action.label)
+                    if state.is_final():
+                        break
+                if moves.has_gold(eg, start_state.B(0), state.B(0)):
+                    states.append(start_state)
+                    golds.append(gold)
+                if state.is_final():
+                    break
+        return states, golds, max_length
+
 
 @contextlib.contextmanager
 def _change_attrs(model, **kwargs):