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Use nogil in parser, and set L1 to 0.0 by default
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c61c501406
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@ -21,4 +21,4 @@ cdef class Parser:
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cdef readonly TransitionSystem moves
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cdef readonly object cfg
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cdef int parseC(self, TokenC* tokens, int length, int nr_feat, int nr_class) with gil
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cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil
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@ -84,15 +84,17 @@ cdef class ParserModel(AveragedPerceptron):
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self.time += 1
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best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
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guess = eg.guess
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cdef weight_t loss = 0.0
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if guess == best:
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return loss
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for clas in [guess, best]:
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loss += (-eg.c.costs[clas] - eg.c.scores[clas]) ** 2
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d_loss = eg.c.scores[clas] - -eg.c.costs[clas]
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for feat in eg.c.features[:eg.c.nr_feat]:
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self.update_weight_ftrl(feat.key, clas, feat.value * d_loss)
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return loss
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if guess == best or best == -1:
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return 0.0
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for feat in eg.c.features[:eg.c.nr_feat]:
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self.update_weight(feat.key, guess, feat.value * eg.c.costs[guess])
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self.update_weight(feat.key, best, -feat.value * eg.c.costs[guess])
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#for clas in [guess, best]:
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# loss += (-eg.c.costs[clas] - eg.c.scores[clas]) ** 2
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# d_loss = eg.c.scores[clas] - -eg.c.costs[clas]
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# for feat in eg.c.features[:eg.c.nr_feat]:
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# self.update_weight_ftrl(feat.key, clas, feat.value * d_loss)
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return eg.c.costs[guess]
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def update_from_histories(self, TransitionSystem moves, Doc doc, histories, weight_t min_grad=0.0):
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cdef Pool mem = Pool()
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@ -175,7 +177,7 @@ cdef class Parser:
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elif 'features' not in cfg:
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cfg['features'] = self.feature_templates
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self.model = ParserModel(cfg['features'])
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self.model.l1_penalty = cfg.get('L1', 1e-8)
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self.model.l1_penalty = cfg.get('L1', 0.0)
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self.model.learn_rate = cfg.get('learn_rate', 0.001)
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self.cfg = cfg
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@ -193,7 +195,7 @@ cdef class Parser:
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"""
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cdef int nr_feat = self.model.nr_feat
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with nogil:
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status = self.parseC(tokens.c, tokens.length, nr_feat, self.moves.n_moves)
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status = self.parseC(tokens.c, tokens.length, nr_feat)
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# Check for KeyboardInterrupt etc. Untested
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PyErr_CheckSignals()
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if status != 0:
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@ -226,7 +228,7 @@ cdef class Parser:
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if len(queue) == batch_size:
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with nogil:
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for i in cython.parallel.prange(batch_size, num_threads=n_threads):
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status = self.parseC(doc_ptr[i], lengths[i], nr_feat, self.moves.n_moves)
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status = self.parseC(doc_ptr[i], lengths[i], nr_feat)
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if status != 0:
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with gil:
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raise ParserStateError(queue[i])
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@ -238,7 +240,7 @@ cdef class Parser:
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batch_size = len(queue)
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with nogil:
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for i in cython.parallel.prange(batch_size, num_threads=n_threads):
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status = self.parseC(doc_ptr[i], lengths[i], nr_feat, self.moves.n_moves)
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status = self.parseC(doc_ptr[i], lengths[i], nr_feat)
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if status != 0:
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with gil:
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raise ParserStateError(queue[i])
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@ -247,10 +249,11 @@ cdef class Parser:
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self.moves.finalize_doc(doc)
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yield doc
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cdef int parseC(self, TokenC* tokens, int length, int nr_feat, int nr_class) with gil:
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cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil:
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state = new StateC(tokens, length)
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# NB: This can change self.moves.n_moves!
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self.moves.initialize_state(state)
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nr_class = self.moves.n_moves
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cdef ExampleC eg
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eg.nr_feat = nr_feat
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@ -267,10 +270,10 @@ cdef class Parser:
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self.model.set_scoresC(eg.scores, eg.features, eg.nr_feat)
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guess = VecVec.arg_max_if_true(eg.scores, eg.is_valid, eg.nr_class)
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if guess < 0:
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return 1
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action = self.moves.c[guess]
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if not eg.is_valid[guess]:
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return 1
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action.do(state, action.label)
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memset(eg.scores, 0, sizeof(eg.scores[0]) * eg.nr_class)
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