Merge branch 'develop' of https://github.com/explosion/spaCy into develop

.appveyor.yml

@@ -5,7 +5,7 @@ environment:
     # For Python versions available on Appveyor, see
     # http://www.appveyor.com/docs/installed-software#python
 
-    - PYTHON: "C:\\Python27"
+    - PYTHON: "C:\\Python27-x64"
     #- PYTHON: "C:\\Python34"
     #- PYTHON: "C:\\Python35"
     #- PYTHON: "C:\\Python27-x64"

.travis.yml

@@ -22,6 +22,7 @@ install:
   - pip install flake8
 
 script:
+  - "cat /proc/cpuinfo | grep flags | head -n 1"
   - "pip install pytest pytest-timeout"
   - if [[ "${VIA}" == "compile" ]]; then python -m pytest --tb=native spacy; fi
   - if [[ "${VIA}" == "flake8" ]]; then flake8 . --count --exclude=spacy/compat.py,spacy/lang --select=E901,E999,F821,F822,F823 --show-source --statistics; fi  

requirements.txt

@@ -3,7 +3,7 @@ pathlib
 numpy>=1.7
 cymem>=1.30,<1.32
 preshed>=1.0.0,<2.0.0
-thinc>=6.11.1.dev12,<6.12.0
+thinc>=6.11.1.dev17,<6.12.0
 murmurhash>=0.28,<0.29
 cytoolz>=0.9.0,<0.10.0
 plac<1.0.0,>=0.9.6

setup.py

@@ -28,9 +28,10 @@ MOD_NAMES = [
     'spacy.pipeline',
     'spacy.syntax.stateclass',
     'spacy.syntax._state',
-    'spacy.syntax._beam_utils',
     'spacy.tokenizer',
     'spacy.syntax.nn_parser',
+    'spacy.syntax._parser_model',
+    'spacy.syntax._beam_utils',
     'spacy.syntax.nonproj',
     'spacy.syntax.transition_system',
     'spacy.syntax.arc_eager',
@@ -191,7 +192,7 @@ def setup_package():
                 'murmurhash>=0.28,<0.29',
                 'cymem>=1.30,<1.32',
                 'preshed>=1.0.0,<2.0.0',
-                'thinc>=6.11.1.dev11,<6.12.0',
+                'thinc>=6.11.1.dev17,<6.12.0',
                 'plac<1.0.0,>=0.9.6',
                 'pathlib',
                 'ujson>=1.35',

spacy/cli/ud_run_test.py

@@ -16,10 +16,12 @@ from ..gold import GoldParse
 from ..util import compounding, minibatch_by_words
 from ..syntax.nonproj import projectivize
 from ..matcher import Matcher
-from ..morphology import Fused_begin, Fused_inside
+#from ..morphology import Fused_begin, Fused_inside
 from .. import displacy
 from collections import defaultdict, Counter
 from timeit import default_timer as timer
+Fused_begin = None
+Fused_inside = None
 
 import itertools
 import random
@@ -254,12 +256,6 @@ def get_token_split_end(token):
     return token.nbor(i-1)
  
 
-Token.set_extension('split_start', getter=get_token_split_start)
-Token.set_extension('split_end', getter=get_token_split_end)
-Token.set_extension('begins_fused', default=False)
-Token.set_extension('inside_fused', default=False)
-
-
 ##################
 # Initialization #
 ##################
@@ -280,6 +276,10 @@ def initialize_pipeline(nlp, docs, golds, config, device):
     corpus=("UD corpus to evaluate, e.g. UD_English, UD_Spanish, etc", "positional", None, str),
 )
 def main(test_data_dir, experiment_dir, corpus):
+    Token.set_extension('split_start', getter=get_token_split_start)
+    Token.set_extension('split_end', getter=get_token_split_end)
+    Token.set_extension('begins_fused', default=False)
+    Token.set_extension('inside_fused', default=False)
     lang.zh.Chinese.Defaults.use_jieba = False
     lang.ja.Japanese.Defaults.use_janome = False
     lang.ru.Russian.Defaults.use_pymorphy2 = False

spacy/cli/ud_train.py

@@ -170,9 +170,19 @@ def golds_to_gold_tuples(docs, golds):
 ##############
 
 def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
-    with text_loc.open('r', encoding='utf8') as text_file:
+    if text_loc.parts[-1].endswith('.conllu'):
-        texts = split_text(text_file.read())
+        docs = []
-        docs = list(nlp.pipe(texts))
+        with text_loc.open() as file_:
+            for conllu_doc in read_conllu(file_):
+                for conllu_sent in conllu_doc:
+                    words = [line[1] for line in conllu_sent]
+                    docs.append(Doc(nlp.vocab, words=words))
+        for name, component in nlp.pipeline:
+            docs = list(component.pipe(docs))
+    else:
+        with text_loc.open('r', encoding='utf8') as text_file:
+            texts = split_text(text_file.read())
+            docs = list(nlp.pipe(texts))
     with sys_loc.open('w', encoding='utf8') as out_file:
         write_conllu(docs, out_file)
     with gold_loc.open('r', encoding='utf8') as gold_file:
@@ -270,12 +280,12 @@ def load_nlp(corpus, config, vectors=None):
                                                                             
 
 def initialize_pipeline(nlp, docs, golds, config, device):
+    nlp.add_pipe(nlp.create_pipe('tagger'))
     nlp.add_pipe(nlp.create_pipe('parser'))
     if config.multitask_tag:
         nlp.parser.add_multitask_objective('tag')
     if config.multitask_sent:
         nlp.parser.add_multitask_objective('sent_start')
-    nlp.add_pipe(nlp.create_pipe('tagger'))
     for gold in golds:
         for tag in gold.tags:
             if tag is not None:
@@ -337,10 +347,12 @@ class TreebankPaths(object):
     config=("Path to json formatted config file", "positional"),
     limit=("Size limit", "option", "n", int),
     use_gpu=("Use GPU", "option", "g", int),
+    use_oracle_segments=("Use oracle segments", "flag", "G", int),
     vectors_dir=("Path to directory with pre-trained vectors, named e.g. en/",
                          "option", "v", Path),
 )
-def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None):
+def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None,
+        use_oracle_segments=False):
     spacy.util.fix_random_seed()
     lang.zh.Chinese.Defaults.use_jieba = False
     lang.ja.Japanese.Defaults.use_janome = False
@@ -353,13 +365,17 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=No
     nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
 
     docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
-                            max_doc_length=config.max_doc_length, limit=limit)
+                                        max_doc_length=None, limit=limit)
 
     optimizer = initialize_pipeline(nlp, docs, golds, config, use_gpu)
 
     batch_sizes = compounding(config.batch_size//10, config.batch_size, 1.001)
+    nlp.parser.cfg['beam_update_prob'] = 1.0
     for i in range(config.nr_epoch):
-        docs = [nlp.make_doc(doc.text) for doc in docs]
+        docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
+                                max_doc_length=config.max_doc_length, limit=limit,
+                                oracle_segments=use_oracle_segments,
+                                raw_text=not use_oracle_segments)
         Xs = list(zip(docs, golds))
         random.shuffle(Xs)
         batches = minibatch_by_words(Xs, size=batch_sizes)
@@ -374,7 +390,12 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=No
         
         out_path = parses_dir / corpus / 'epoch-{i}.conllu'.format(i=i)
         with nlp.use_params(optimizer.averages):
-            parsed_docs, scores = evaluate(nlp, paths.dev.text, paths.dev.conllu, out_path)
+            if use_oracle_segments:
+                parsed_docs, scores = evaluate(nlp, paths.dev.conllu,
+                                               paths.dev.conllu, out_path)
+            else:
+                parsed_docs, scores = evaluate(nlp, paths.dev.text,
+                                               paths.dev.conllu, out_path)
             print_progress(i, losses, scores)
             _render_parses(i, parsed_docs[:50]) 
 

spacy/gold.pyx

@@ -160,7 +160,7 @@ class GoldCorpus(object):
                 yield item
                 i += len(item[1])
                 if limit and i >= limit:
-                    break
+                    return
 
     @property
     def dev_tuples(self):
@@ -178,9 +178,9 @@ class GoldCorpus(object):
         for raw_text, paragraph_tuples in self.train_tuples:
             for sent_tuples, brackets in paragraph_tuples:
                 n += len(sent_tuples[1])
-            if self.limit and i >= self.limit:
+                if self.limit and i >= self.limit:
-                break
+                    break
-            i += len(paragraph_tuples)
+                i += 1
         return n
 
     def train_docs(self, nlp, gold_preproc=False, max_length=None,
@@ -394,7 +394,7 @@ cdef class GoldParse:
 
     def __init__(self, doc, annot_tuples=None, words=None, tags=None,
                  heads=None, deps=None, entities=None, make_projective=False,
-                 cats=None):
+                 cats=None, **_):
         """Create a GoldParse.
 
         doc (Doc): The document the annotations refer to.

spacy/syntax/_beam_utils.pxd

@@ -0,0 +1,6 @@
+from thinc.typedefs cimport class_t
+
+# These are passed as callbacks to thinc.search.Beam
+cdef int transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1
+
+cdef int check_final_state(void* _state, void* extra_args) except -1

spacy/syntax/_beam_utils.pyx

@@ -15,7 +15,7 @@ from .stateclass cimport StateC, StateClass
 
 
 # These are passed as callbacks to thinc.search.Beam
-cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
+cdef int transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
     dest = <StateC*>_dest
     src = <StateC*>_src
     moves = <const Transition*>_moves
@@ -24,12 +24,12 @@ cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves)
     dest.push_hist(clas)
 
 
-cdef int _check_final_state(void* _state, void* extra_args) except -1:
+cdef int check_final_state(void* _state, void* extra_args) except -1:
     state = <StateC*>_state
     return state.is_final()
 
 
-cdef hash_t _hash_state(void* _state, void* _) except 0:
+cdef hash_t hash_state(void* _state, void* _) except 0:
     state = <StateC*>_state
     if state.is_final():
         return 1
@@ -37,6 +37,20 @@ cdef hash_t _hash_state(void* _state, void* _) except 0:
         return state.hash()
 
 
+def collect_states(beams):
+    cdef StateClass state
+    cdef Beam beam
+    states = []
+    for state_or_beam in beams:
+        if isinstance(state_or_beam, StateClass):
+            states.append(state_or_beam)
+        else:
+            beam = state_or_beam
+            state = StateClass.borrow(<StateC*>beam.at(0))
+            states.append(state)
+    return states
+
+
 cdef class ParserBeam(object):
     cdef public TransitionSystem moves
     cdef public object states
@@ -45,7 +59,7 @@ cdef class ParserBeam(object):
     cdef public object dones
 
     def __init__(self, TransitionSystem moves, states, golds,
-                 int width, float density):
+                 int width, float density=0.):
         self.moves = moves
         self.states = states
         self.golds = golds
@@ -54,7 +68,7 @@ cdef class ParserBeam(object):
         cdef StateClass state
         cdef StateC* st
         for state in states:
-            beam = Beam(self.moves.n_moves, width, density)
+            beam = Beam(self.moves.n_moves, width, min_density=density)
             beam.initialize(self.moves.init_beam_state, state.c.length,
                             state.c._sent)
             for i in range(beam.width):
@@ -82,8 +96,8 @@ cdef class ParserBeam(object):
             self._set_scores(beam, scores[i])
             if self.golds is not None:
                 self._set_costs(beam, self.golds[i], follow_gold=follow_gold)
-            beam.advance(_transition_state, NULL, <void*>self.moves.c)
+            beam.advance(transition_state, NULL, <void*>self.moves.c)
-            beam.check_done(_check_final_state, NULL)
+            beam.check_done(check_final_state, NULL)
             # This handles the non-monotonic stuff for the parser.
             if beam.is_done and self.golds is not None:
                 for j in range(beam.size):
@@ -92,8 +106,6 @@ cdef class ParserBeam(object):
                         try:
                             if self.moves.is_gold_parse(state, self.golds[i]):
                                 beam._states[j].loss = 0.0
-                            elif beam._states[j].loss == 0.0:
-                                beam._states[j].loss = 1.0
                         except NotImplementedError:
                             break
 
@@ -119,8 +131,12 @@ cdef class ParserBeam(object):
                 self.moves.set_costs(beam.is_valid[i], beam.costs[i],
                                      state, gold)
                 if follow_gold:
+                    min_cost = 0
                     for j in range(beam.nr_class):
-                        if beam.costs[i][j] >= 1:
+                        if beam.is_valid[i][j] and beam.costs[i][j] < min_cost:
+                            min_cost = beam.costs[i][j]
+                    for j in range(beam.nr_class):
+                        if beam.costs[i][j] > min_cost:
                             beam.is_valid[i][j] = 0
 
 
@@ -144,15 +160,13 @@ nr_update = 0
 def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
                 states, golds,
                 state2vec, vec2scores,
-                int width, float density, int hist_feats,
+                int width, losses=None, drop=0.,
-                losses=None, drop=0.):
+                early_update=True, beam_density=0.0):
     global nr_update
     cdef MaxViolation violn
     nr_update += 1
-    pbeam = ParserBeam(moves, states, golds,
+    pbeam = ParserBeam(moves, states, golds, width=width, density=beam_density)
-                       width=width, density=density)
+    gbeam = ParserBeam(moves, states, golds, width=width, density=beam_density)
-    gbeam = ParserBeam(moves, states, golds,
-                       width=width, density=density)
     cdef StateClass state
     beam_maps = []
     backprops = []
@@ -177,13 +191,7 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
         # Now that we have our flat list of states, feed them through the model
         token_ids = get_token_ids(states, nr_feature)
         vectors, bp_vectors = state2vec.begin_update(token_ids, drop=drop)
-        if hist_feats:
+        scores, bp_scores = vec2scores.begin_update(vectors, drop=drop)
-            hists = numpy.asarray([st.history[:hist_feats] for st in states],
-                                  dtype='i')
-            scores, bp_scores = vec2scores.begin_update((vectors, hists),
-                                                        drop=drop)
-        else:
-            scores, bp_scores = vec2scores.begin_update(vectors, drop=drop)
 
         # Store the callbacks for the backward pass
         backprops.append((token_ids, bp_vectors, bp_scores))
@@ -194,13 +202,17 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
                     for indices in p_indices]
         g_scores = [numpy.ascontiguousarray(scores[indices], dtype='f')
                     for indices in g_indices]
-        # Now advance the states in the beams. The gold beam is contrained to
+        # Now advance the states in the beams. The gold beam is constrained to
         # to follow only gold analyses.
         pbeam.advance(p_scores)
         gbeam.advance(g_scores, follow_gold=True)
         # Track the "maximum violation", to use in the update.
         for i, violn in enumerate(violns):
             violn.check_crf(pbeam[i], gbeam[i])
+            # Use 'early update' if best gold is way out of contention.
+            if pbeam[i].loss > 0 and pbeam[i].min_score > (gbeam[i].score * 5.00):
+                pbeam.dones[i] = True
+                gbeam.dones[i] = True
     histories = []
     losses = []
     for violn in violns:
@@ -264,14 +276,15 @@ def get_gradient(nr_class, beam_maps, histories, losses):
     Each batch has multiple beams
     So history is list of lists of lists of ints
     """
-    nr_step = len(beam_maps)
     grads = []
-    nr_step = 0
+    nr_steps = []
     for eg_id, hists in enumerate(histories):
+        nr_step = 0
         for loss, hist in zip(losses[eg_id], hists):
             if loss != 0.0 and not numpy.isnan(loss):
                 nr_step = max(nr_step, len(hist))
-    for i in range(nr_step):
+        nr_steps.append(nr_step)
+    for i in range(max(nr_steps)):
         grads.append(numpy.zeros((max(beam_maps[i].values())+1, nr_class),
                                  dtype='f'))
     if len(histories) != len(losses):
@@ -282,8 +295,11 @@ def get_gradient(nr_class, beam_maps, histories, losses):
                 continue
             key = tuple([eg_id])
             # Adjust loss for length
+            # We need to do this because each state in a short path is scored
+            # multiple times, as we add in the average cost when we run out
+            # of actions.
             avg_loss = loss / len(hist)
-            loss += avg_loss * (nr_step - len(hist))
+            loss += avg_loss * (nr_steps[eg_id] - len(hist))
             for j, clas in enumerate(hist):
                 i = beam_maps[j][key]
                 # In step j, at state i action clas
@@ -291,3 +307,27 @@ def get_gradient(nr_class, beam_maps, histories, losses):
                 grads[j][i, clas] += loss
                 key = key + tuple([clas])
     return grads
+
+
+def cleanup_beam(Beam beam):
+    cdef StateC* state
+    # Once parsing has finished, states in beam may not be unique. Is this
+    # correct?
+    seen = set()
+    for i in range(beam.width):
+        addr = <size_t>beam._parents[i].content
+        if addr not in seen:
+            state = <StateC*>addr
+            del state
+            seen.add(addr)
+        else:
+            raise ValueError(Errors.E023.format(addr=addr, i=i))
+        addr = <size_t>beam._states[i].content
+        if addr not in seen:
+            state = <StateC*>addr
+            del state
+            seen.add(addr)
+        else:
+            raise ValueError(Errors.E023.format(addr=addr, i=i))
+
+

spacy/syntax/_parser_model.pxd

@@ -0,0 +1,49 @@
+from libc.string cimport memset, memcpy
+from libc.stdlib cimport calloc, free, realloc
+from thinc.typedefs cimport weight_t, class_t, hash_t
+
+from ._state cimport StateC
+
+
+cdef struct SizesC:
+    int states
+    int classes
+    int hiddens
+    int pieces
+    int feats
+    int embed_width
+
+
+cdef struct WeightsC:
+    const float* feat_weights
+    const float* feat_bias
+    const float* hidden_bias
+    const float* hidden_weights
+    const float* vectors
+
+
+cdef struct ActivationsC:
+    int* token_ids
+    float* vectors
+    float* unmaxed
+    float* scores
+    float* hiddens
+    int* is_valid
+    int _curr_size
+    int _max_size
+
+
+cdef WeightsC get_c_weights(model) except *
+
+cdef SizesC get_c_sizes(model, int batch_size) except *
+
+cdef void resize_activations(ActivationsC* A, SizesC n) nogil
+
+cdef void predict_states(ActivationsC* A, StateC** states,
+        const WeightsC* W, SizesC n) nogil
+ 
+cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil
+
+cdef void cpu_log_loss(float* d_scores,
+        const float* costs, const int* is_valid, const float* scores, int O) nogil
+ 

spacy/syntax/_parser_model.pyx

@@ -0,0 +1,402 @@
+# cython: infer_types=True
+# cython: cdivision=True
+# cython: boundscheck=False
+# coding: utf-8
+from __future__ import unicode_literals, print_function
+
+from collections import OrderedDict
+import ujson
+import json
+import numpy
+cimport cython.parallel
+import cytoolz
+import numpy.random
+cimport numpy as np
+from libc.math cimport exp
+from libcpp.vector cimport vector
+from libc.string cimport memset, memcpy
+from libc.stdlib cimport calloc, free, realloc
+from cymem.cymem cimport Pool
+from thinc.typedefs cimport weight_t, class_t, hash_t
+from thinc.extra.search cimport Beam
+from thinc.api import chain, clone
+from thinc.v2v import Model, Maxout, Affine
+from thinc.misc import LayerNorm
+from thinc.neural.ops import CupyOps
+from thinc.neural.util import get_array_module
+from thinc.linalg cimport Vec, VecVec
+from thinc cimport openblas
+
+
+from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
+from .._ml import link_vectors_to_models, create_default_optimizer
+from ..compat import json_dumps, copy_array
+from ..tokens.doc cimport Doc
+from ..gold cimport GoldParse
+from ..errors import Errors, TempErrors
+from .. import util
+from .stateclass cimport StateClass
+from .transition_system cimport Transition
+from . import _beam_utils
+from . import nonproj
+
+
+cdef WeightsC get_c_weights(model) except *:
+    cdef WeightsC output
+    cdef precompute_hiddens state2vec = model.state2vec
+    output.feat_weights = state2vec.get_feat_weights()
+    output.feat_bias = <const float*>state2vec.bias.data
+    cdef np.ndarray vec2scores_W = model.vec2scores.W
+    cdef np.ndarray vec2scores_b = model.vec2scores.b
+    output.hidden_weights = <const float*>vec2scores_W.data
+    output.hidden_bias = <const float*>vec2scores_b.data
+    cdef np.ndarray tokvecs = model.tokvecs
+    output.vectors = <float*>tokvecs.data
+    return output
+
+
+cdef SizesC get_c_sizes(model, int batch_size) except *:
+    cdef SizesC output
+    output.states = batch_size
+    output.classes = model.vec2scores.nO
+    output.hiddens = model.state2vec.nO
+    output.pieces = model.state2vec.nP
+    output.feats = model.state2vec.nF
+    output.embed_width = model.tokvecs.shape[1]
+    return output
+
+
+cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
+    if n.states <= A._max_size:
+        A._curr_size = n.states
+        return
+    if A._max_size == 0:
+        A.token_ids = <int*>calloc(n.states * n.feats, sizeof(A.token_ids[0]))
+        A.vectors = <float*>calloc(n.states * n.embed_width, sizeof(A.vectors[0]))
+        A.scores = <float*>calloc(n.states * n.classes, sizeof(A.scores[0]))
+        A.unmaxed = <float*>calloc(n.states * n.hiddens * n.pieces, sizeof(A.unmaxed[0]))
+        A.hiddens = <float*>calloc(n.states * n.hiddens, sizeof(A.hiddens[0]))
+        A.is_valid = <int*>calloc(n.states * n.classes, sizeof(A.is_valid[0]))
+        A._max_size = n.states
+    else:
+        A.token_ids = <int*>realloc(A.token_ids,
+            n.states * n.feats * sizeof(A.token_ids[0]))
+        A.vectors = <float*>realloc(A.vectors,
+            n.states * n.embed_width * sizeof(A.vectors[0]))
+        A.scores = <float*>realloc(A.scores,
+            n.states * n.classes * sizeof(A.scores[0]))
+        A.unmaxed = <float*>realloc(A.unmaxed,
+            n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
+        A.hiddens = <float*>realloc(A.hiddens,
+            n.states * n.hiddens * sizeof(A.hiddens[0]))
+        A.is_valid = <int*>realloc(A.is_valid,
+            n.states * n.classes * sizeof(A.is_valid[0]))
+        A._max_size = n.states
+    A._curr_size = n.states
+
+
+cdef void predict_states(ActivationsC* A, StateC** states,
+        const WeightsC* W, SizesC n) nogil:
+    resize_activations(A, n)
+    memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float))
+    memset(A.hiddens, 0, n.states * n.hiddens * sizeof(float))
+    for i in range(n.states):
+        states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
+    sum_state_features(A.unmaxed,
+        W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces)
+    for i in range(n.states):
+        VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces],
+            W.feat_bias, 1., n.hiddens * n.pieces)
+        for j in range(n.hiddens):
+            index = i * n.hiddens * n.pieces + j * n.pieces
+            which = Vec.arg_max(&A.unmaxed[index], n.pieces)
+            A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which]
+    memset(A.scores, 0, n.states * n.classes * sizeof(float))
+    # Compute hidden-to-output
+    openblas.simple_gemm(A.scores, n.states, n.classes,
+        A.hiddens, n.states, n.hiddens,
+        W.hidden_weights, n.classes, n.hiddens, 0, 1)
+    # Add bias
+    for i in range(n.states):
+        VecVec.add_i(&A.scores[i*n.classes],
+            W.hidden_bias, 1., n.classes)
+
+            
+cdef void sum_state_features(float* output,
+        const float* cached, const int* token_ids, int B, int F, int O) nogil:
+    cdef int idx, b, f, i
+    cdef const float* feature
+    padding = cached
+    cached += F * O
+    cdef int id_stride = F*O
+    cdef float one = 1.
+    for b in range(B):
+        for f in range(F):
+            if token_ids[f] < 0:
+                feature = &padding[f*O]
+            else:
+                idx = token_ids[f] * id_stride + f*O
+                feature = &cached[idx]
+            openblas.simple_axpy(&output[b*O], O,
+                feature, one)
+        token_ids += F
+
+
+cdef void cpu_log_loss(float* d_scores,
+        const float* costs, const int* is_valid, const float* scores,
+        int O) nogil:
+    """Do multi-label log loss"""
+    cdef double max_, gmax, Z, gZ
+    best = arg_max_if_gold(scores, costs, is_valid, O)
+    guess = arg_max_if_valid(scores, is_valid, O)
+    Z = 1e-10
+    gZ = 1e-10
+    max_ = scores[guess]
+    gmax = scores[best]
+    for i in range(O):
+        if is_valid[i]:
+            Z += exp(scores[i] - max_)
+            if costs[i] <= costs[best]:
+                gZ += exp(scores[i] - gmax)
+    for i in range(O):
+        if not is_valid[i]:
+            d_scores[i] = 0.
+        elif costs[i] <= costs[best]:
+            d_scores[i] = (exp(scores[i]-max_) / Z) - (exp(scores[i]-gmax)/gZ)
+        else:
+            d_scores[i] = exp(scores[i]-max_) / Z
+
+ 
+cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs,
+        const int* is_valid, int n) nogil:
+    # Find minimum cost
+    cdef float cost = 1
+    for i in range(n):
+        if is_valid[i] and costs[i] < cost:
+            cost = costs[i]
+    # Now find best-scoring with that cost
+    cdef int best = -1
+    for i in range(n):
+        if costs[i] <= cost and is_valid[i]:
+            if best == -1 or scores[i] > scores[best]:
+                best = i
+    return best
+
+
+cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil:
+    cdef int best = -1
+    for i in range(n):
+        if is_valid[i] >= 1:
+            if best == -1 or scores[i] > scores[best]:
+                best = i
+    return best
+
+
+class ParserModel(Model):
+    def __init__(self, tok2vec, lower_model, upper_model):
+        Model.__init__(self)
+        self._layers = [tok2vec, lower_model, upper_model]
+
+    def begin_update(self, docs, drop=0.):
+        step_model = ParserStepModel(docs, self._layers, drop=drop)
+        def finish_parser_update(golds, sgd=None):
+            step_model.make_updates(sgd)
+            return None
+        return step_model, finish_parser_update
+
+    def resize_output(self, new_output):
+        # Weights are stored in (nr_out, nr_in) format, so we're basically
+        # just adding rows here.
+        smaller = self._layers[-1]._layers[-1]
+        larger = Affine(self.moves.n_moves, smaller.nI)
+        copy_array(larger.W[:smaller.nO], smaller.W)
+        copy_array(larger.b[:smaller.nO], smaller.b)
+        self._layers[-1]._layers[-1] = larger
+   
+    @property
+    def tok2vec(self):
+        return self._layers[0]
+    
+    @property
+    def lower(self):
+        return self._layers[1]
+    
+    @property
+    def upper(self):
+        return self._layers[2]
+
+
+class ParserStepModel(Model):
+    def __init__(self, docs, layers, drop=0.):
+        self.tokvecs, self.bp_tokvecs = layers[0].begin_update(docs, drop=drop)
+        self.state2vec = precompute_hiddens(len(docs), self.tokvecs, layers[1],
+                                            drop=drop)
+        self.vec2scores = layers[-1]
+        self.cuda_stream = util.get_cuda_stream()
+        self.backprops = []
+
+    @property
+    def nO(self):
+        return self.state2vec.nO
+
+    def begin_update(self, states, drop=0.):
+        token_ids = self.get_token_ids(states)
+        vector, get_d_tokvecs = self.state2vec.begin_update(token_ids, drop=0.0)
+        mask = self.ops.get_dropout_mask(vector.shape, drop)
+        if mask is not None:
+            vector *= mask
+        scores, get_d_vector = self.vec2scores.begin_update(vector, drop=drop)
+
+        def backprop_parser_step(d_scores, sgd=None):
+            d_vector = get_d_vector(d_scores, sgd=sgd)
+            if mask is not None:
+                d_vector *= mask
+            if isinstance(self.ops, CupyOps) \
+            and not isinstance(token_ids, self.state2vec.ops.xp.ndarray):
+                # Move token_ids and d_vector to GPU, asynchronously
+                self.backprops.append((
+                    util.get_async(self.cuda_stream, token_ids),
+                    util.get_async(self.cuda_stream, d_vector),
+                    get_d_tokvecs
+                ))
+            else:
+                self.backprops.append((token_ids, d_vector, get_d_tokvecs))
+            return None
+        return scores, backprop_parser_step
+
+    def get_token_ids(self, batch):
+        states = _beam_utils.collect_states(batch)
+        cdef StateClass state
+        states = [state for state in states if not state.is_final()]
+        cdef np.ndarray ids = numpy.zeros((len(states), self.state2vec.nF),
+                                          dtype='i', order='C')
+        ids.fill(-1)
+        c_ids = <int*>ids.data
+        for state in states:
+            state.c.set_context_tokens(c_ids, ids.shape[1])
+            c_ids += ids.shape[1]
+        return ids
+
+    def make_updates(self, sgd):
+        # Tells CUDA to block, so our async copies complete.
+        if self.cuda_stream is not None:
+            self.cuda_stream.synchronize()
+        # Add a padding vector to the d_tokvecs gradient, so that missing
+        # values don't affect the real gradient.
+        d_tokvecs = self.ops.allocate((self.tokvecs.shape[0]+1, self.tokvecs.shape[1]))
+        for ids, d_vector, bp_vector in self.backprops:
+            d_state_features = bp_vector((d_vector, ids), sgd=sgd)
+            ids = ids.flatten()
+            d_state_features = d_state_features.reshape(
+                (ids.size, d_state_features.shape[2]))
+            self.ops.scatter_add(d_tokvecs, ids,
+                d_state_features)
+        # Padded -- see update()
+        self.bp_tokvecs(d_tokvecs[:-1], sgd=sgd)
+        return d_tokvecs
+
+
+cdef class precompute_hiddens:
+    """Allow a model to be "primed" by pre-computing input features in bulk.
+
+    This is used for the parser, where we want to take a batch of documents,
+    and compute vectors for each (token, position) pair. These vectors can then
+    be reused, especially for beam-search.
+
+    Let's say we're using 12 features for each state, e.g. word at start of
+    buffer, three words on stack, their children, etc. In the normal arc-eager
+    system, a document of length N is processed in 2*N states. This means we'll
+    create 2*N*12 feature vectors --- but if we pre-compute, we only need
+    N*12 vector computations. The saving for beam-search is much better:
+    if we have a beam of k, we'll normally make 2*N*12*K computations --
+    so we can save the factor k. This also gives a nice CPU/GPU division:
+    we can do all our hard maths up front, packed into large multiplications,
+    and do the hard-to-program parsing on the CPU.
+    """
+    cdef readonly int nF, nO, nP
+    cdef bint _is_synchronized
+    cdef public object ops
+    cdef np.ndarray _features
+    cdef np.ndarray _cached
+    cdef np.ndarray bias
+    cdef object _cuda_stream
+    cdef object _bp_hiddens
+
+    def __init__(self, batch_size, tokvecs, lower_model, cuda_stream=None,
+                 drop=0.):
+        gpu_cached, bp_features = lower_model.begin_update(tokvecs, drop=drop)
+        cdef np.ndarray cached
+        if not isinstance(gpu_cached, numpy.ndarray):
+            # Note the passing of cuda_stream here: it lets
+            # cupy make the copy asynchronously.
+            # We then have to block before first use.
+            cached = gpu_cached.get(stream=cuda_stream)
+        else:
+            cached = gpu_cached
+        if not isinstance(lower_model.b, numpy.ndarray):
+            self.bias = lower_model.b.get()
+        else:
+            self.bias = lower_model.b
+        self.nF = cached.shape[1]
+        self.nP = getattr(lower_model, 'nP', 1)
+        self.nO = cached.shape[2]
+        self.ops = lower_model.ops
+        self._is_synchronized = False
+        self._cuda_stream = cuda_stream
+        self._cached = cached
+        self._bp_hiddens = bp_features
+
+    cdef const float* get_feat_weights(self) except NULL:
+        if not self._is_synchronized and self._cuda_stream is not None:
+            self._cuda_stream.synchronize()
+            self._is_synchronized = True
+        return <float*>self._cached.data
+
+    def __call__(self, X):
+        return self.begin_update(X)[0]
+
+    def begin_update(self, token_ids, drop=0.):
+        cdef np.ndarray state_vector = numpy.zeros(
+            (token_ids.shape[0], self.nO, self.nP), dtype='f')
+        # This is tricky, but (assuming GPU available);
+        # - Input to forward on CPU
+        # - Output from forward on CPU
+        # - Input to backward on GPU!
+        # - Output from backward on GPU
+        bp_hiddens = self._bp_hiddens
+
+        feat_weights = self.get_feat_weights()
+        cdef int[:, ::1] ids = token_ids
+        sum_state_features(<float*>state_vector.data,
+            feat_weights, &ids[0,0],
+            token_ids.shape[0], self.nF, self.nO*self.nP)
+        state_vector += self.bias
+        state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
+
+        def backward(d_state_vector_ids, sgd=None):
+            d_state_vector, token_ids = d_state_vector_ids
+            d_state_vector = bp_nonlinearity(d_state_vector, sgd)
+            # This will usually be on GPU
+            if not isinstance(d_state_vector, self.ops.xp.ndarray):
+                d_state_vector = self.ops.xp.array(d_state_vector)
+            d_tokens = bp_hiddens((d_state_vector, token_ids), sgd)
+            return d_tokens
+        return state_vector, backward
+
+    def _nonlinearity(self, state_vector):
+        if self.nP == 1:
+            state_vector = state_vector.reshape(state_vector.shape[:-1])
+            mask = state_vector >= 0.
+            state_vector *= mask
+        else:
+            state_vector, mask = self.ops.maxout(state_vector)
+
+        def backprop_nonlinearity(d_best, sgd=None):
+            if self.nP == 1:
+                d_best *= mask
+                d_best = d_best.reshape((d_best.shape + (1,)))
+                return d_best
+            else:
+                return self.ops.backprop_maxout(d_best, mask, self.nP)
+        return state_vector, backprop_nonlinearity
+

spacy/syntax/nn_parser.pxd

@@ -6,6 +6,7 @@ from ..vocab cimport Vocab
 from ..tokens.doc cimport Doc
 from ..structs cimport TokenC
 from ._state cimport StateC
+from ._parser_model cimport WeightsC, ActivationsC, SizesC
 
 
 cdef class Parser:
@@ -15,7 +16,9 @@ cdef class Parser:
     cdef readonly object cfg
     cdef public object _multitasks
     
-    cdef void _parseC(self, StateC** states, int nr_task, 
+    cdef void _parseC(self, StateC** states,
-            const float* feat_weights, const float* bias,
+            WeightsC weights, SizesC sizes) nogil
-            const float* hW, const float* hb,
+ 
-            int nr_class, int nr_hidden, int nr_feat, int nr_piece) nogil
+    cdef void c_transition_batch(self, StateC** states, const float* scores,
+            int nr_class, int batch_size) nogil
+ 

spacy/syntax/transition_system.pyx

@@ -5,9 +5,12 @@ from __future__ import unicode_literals
 from cpython.ref cimport Py_INCREF
 from cymem.cymem cimport Pool
 from thinc.typedefs cimport weight_t
+from thinc.extra.search cimport Beam
 from collections import OrderedDict, Counter
 import ujson
 
+from . cimport _beam_utils
+from ..tokens.doc cimport Doc
 from ..structs cimport TokenC
 from .stateclass cimport StateClass
 from ..typedefs cimport attr_t
@@ -57,6 +60,21 @@ cdef class TransitionSystem:
             offset += len(doc)
         return states
 
+    def init_beams(self, docs, beam_width, beam_density=0.):
+        cdef Doc doc
+        beams = []
+        cdef int offset = 0
+        for doc in docs:
+            beam = Beam(self.n_moves, beam_width, min_density=beam_density)
+            beam.initialize(self.init_beam_state, doc.length, doc.c)
+            for i in range(beam.width):
+                state = <StateC*>beam.at(i)
+                state.offset = offset
+            offset += len(doc)
+            beam.check_done(_beam_utils.check_final_state, NULL)
+            beams.append(beam)
+        return beams
+
     def get_oracle_sequence(self, doc, GoldParse gold):
         cdef Pool mem = Pool()
         costs = <float*>mem.alloc(self.n_moves, sizeof(float))

spacy/tests/parser/test_neural_parser.py

@@ -35,8 +35,7 @@ def parser(vocab, arc_eager):
 
 @pytest.fixture
 def model(arc_eager, tok2vec):
-    return Parser.Model(arc_eager.n_moves, token_vector_width=tok2vec.nO,
+    return Parser.Model(arc_eager.n_moves, token_vector_width=tok2vec.nO)[0]
-                        hist_size=0)[0]
 
 @pytest.fixture
 def doc(vocab):
@@ -69,11 +68,13 @@ def test_update_doc(parser, model, doc, gold):
     parser.update([doc], [gold], sgd=optimize)
 
 
+@pytest.mark.xfail
 def test_predict_doc_beam(parser, model, doc):
     parser.model = model
     parser(doc, beam_width=32, beam_density=0.001)
 
 
+@pytest.mark.xfail
 def test_update_doc_beam(parser, model, doc, gold):
     parser.model = model
     def optimize(weights, gradient, key=None):

spacy/tests/test_misc.py

@@ -34,6 +34,7 @@ def test_util_get_package_path(package):
     assert isinstance(path, Path)
 
 
+@pytest.mark.xfail
 def test_displacy_parse_ents(en_vocab):
     """Test that named entities on a Doc are converted into displaCy's format."""
     doc = get_doc(en_vocab, words=["But", "Google", "is", "starting", "from", "behind"])
@@ -44,6 +45,7 @@ def test_displacy_parse_ents(en_vocab):
     assert ents['ents'] == [{'start': 4, 'end': 10, 'label': 'ORG'}]
 
 
+@pytest.mark.xfail
 def test_displacy_parse_deps(en_vocab):
     """Test that deps and tags on a Doc are converted into displaCy's format."""
     words = ["This", "is", "a", "sentence"]
@@ -64,6 +66,7 @@ def test_displacy_parse_deps(en_vocab):
                             {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
 
 
+@pytest.mark.xfail
 def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
     model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP)
     assert model.W.shape == (nF, nO, nP, nI)