cython fixes and cleanup

spacy/matcher/phrasematcher.pyx

@@ -158,7 +158,6 @@ cdef class PhraseMatcher:
         del self._callbacks[key]
         del self._docs[key]
 
-
     def _add_from_arrays(self, key, specs, *, on_match=None):
         """Add a preprocessed list of specs, with an optional callback.
 
@@ -194,7 +193,6 @@ cdef class PhraseMatcher:
                 result = internal_node
             map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
 
-
     def add(self, key, docs, *, on_match=None):
         """Add a match-rule to the phrase-matcher. A match-rule consists of: an ID
         key, a list of one or more patterns, and (optionally) an on_match callback.

spacy/ml/tb_framework.pyx

@@ -1,5 +1,5 @@
 # cython: infer_types=True, cdivision=True, boundscheck=False
-from typing import Any, List, Optional, Tuple, TypeVar, cast
+from typing import Any, List, Optional, Tuple, cast
 
 from libc.stdlib cimport calloc, free, realloc
 from libc.string cimport memcpy, memset
@@ -23,7 +23,7 @@ from thinc.api import (
 
 from thinc.backends.cblas cimport CBlas, saxpy, sgemm
 
-from thinc.types import Floats1d, Floats2d, Floats3d, Floats4d, Ints1d, Ints2d
+from thinc.types import Floats2d, Floats3d, Floats4d, Ints1d, Ints2d
 
 from ..errors import Errors
 from ..pipeline._parser_internals import _beam_utils
@@ -136,7 +136,7 @@ def init(
     Y: Optional[Tuple[List[State], List[Floats2d]]] = None,
 ):
     if X is not None:
-        docs, moves = X
+        docs, _ = X
         model.get_ref("tok2vec").initialize(X=docs)
     else:
         model.get_ref("tok2vec").initialize()
@@ -145,7 +145,7 @@ def init(
         current_nO = model.maybe_get_dim("nO")
         if current_nO is None or current_nO != inferred_nO:
             model.attrs["resize_output"](model, inferred_nO)
-    nO = model.get_dim("nO")
+    # nO = model.get_dim("nO")
     nP = model.get_dim("nP")
     nH = model.get_dim("nH")
     nI = model.get_dim("nI")
@@ -194,7 +194,8 @@ class TransitionModelInputs:
         moves: TransitionSystem,
         actions: Optional[List[Ints1d]] = None,
         max_moves: int = 0,
-        states: Optional[List[State]]=None):
+        states: Optional[List[State]] = None,
+    ):
         """
         actions (Optional[List[Ints1d]]): actions to apply for each Doc.
         docs (List[Doc]): Docs to predict transition sequences for.
@@ -257,9 +258,10 @@ def _forward_greedy_cpu(model: Model, TransitionSystem moves, states: List[State
 
     return (states, scores), backprop
 
+
 cdef list _parse_batch(CBlas cblas, TransitionSystem moves, StateC** states,
                        WeightsC weights, SizesC sizes, actions: Optional[List[Ints1d]]=None):
-    cdef int i, j
+    cdef int i
     cdef vector[StateC *] unfinished
     cdef ActivationsC activations = _alloc_activations(sizes)
     cdef np.ndarray step_scores
@@ -371,7 +373,7 @@ def _forward_fallback(
             for clas in set(model.attrs["unseen_classes"]):
                 if (d_scores[:, clas] < 0).any():
                     model.attrs["unseen_classes"].remove(clas)
-        d_scores *= seen_mask == False
+        d_scores *= seen_mask == False  # no-cython-lint
         # Calculate the gradients for the parameters of the output layer.
         # The weight gemm is (nS, nO) @ (nS, nH).T
         output.inc_grad("b", d_scores.sum(axis=0))
@@ -617,12 +619,12 @@ cdef void _predict_states(CBlas cblas, ActivationsC* A, float* scores, StateC**
                 scores[i*n.classes+j] = min_
 
 
-cdef void _sum_state_features(CBlas cblas, float* output,
+cdef void _sum_state_features(CBlas cblas, float* output, const float* cached,
-        const float* cached, const int* token_ids, SizesC n) nogil:
+                              const int* token_ids, SizesC n) nogil:
-    cdef int idx, b, f, i
+    cdef int idx, b, f
     cdef const float* feature
     cdef int B = n.states
-    cdef int O = n.hiddens * n.pieces
+    cdef int O = n.hiddens * n.pieces  # no-cython-lint
     cdef int F = n.feats
     cdef int T = n.tokens
     padding = cached + (T * F * O)
@@ -637,4 +639,3 @@ cdef void _sum_state_features(CBlas cblas, float* output,
                 feature = &cached[idx]
             saxpy(cblas)(O, one, <const float*>feature, 1, &output[b*O], 1)
         token_ids += F
-

spacy/morphology.pyx

@@ -80,7 +80,6 @@ cdef class Morphology:
         out.sort(key=lambda x: x[0])
         return dict(out)
 
-
     def _normalized_feat_dict_to_str(self, feats: Dict[str, str]) -> str:
         norm_feats_string = self.FEATURE_SEP.join([
             self.FIELD_SEP.join([field, self.VALUE_SEP.join(values) if isinstance(values, list) else values])
@@ -88,7 +87,6 @@ cdef class Morphology:
             ])
         return norm_feats_string or self.EMPTY_MORPH
 
-
     cdef hash_t _add(self, features):
         """Insert a morphological analysis in the morphology table, if not
         already present. The morphological analysis may be provided in the UD

spacy/pipeline/_parser_internals/ner.pyx

@@ -1,5 +1,4 @@
 from cymem.cymem cimport Pool
-from libc.stdint cimport int32_t
 from libcpp.memory cimport shared_ptr
 from libcpp.vector cimport vector
 

spacy/pipeline/_parser_internals/search.pxd

@@ -58,7 +58,6 @@ cdef class Beam:
                      void* extra_args) except -1
     cdef int check_done(self, finish_func_t finish_func, void* extra_args) except -1
 
-
     cdef inline void set_cell(self, int i, int j, weight_t score, int is_valid, weight_t cost) nogil:
         self.scores[i][j] = score
         self.is_valid[i][j] = is_valid

spacy/pipeline/_parser_internals/search.pyx

@@ -1,11 +1,8 @@
 # cython: profile=True, experimental_cpp_class_def=True, cdivision=True, infer_types=True
 cimport cython
-from libc.math cimport exp, log
-from libc.string cimport memcpy, memset
-
-import math
-
 from cymem.cymem cimport Pool
+from libc.math cimport exp
+from libc.string cimport memcpy, memset
 from preshed.maps cimport PreshMap
 
 
@@ -70,7 +67,7 @@ cdef class Beam:
             self.costs[i][j] = costs[j]
 
     cdef int set_table(self, weight_t** scores, int** is_valid, weight_t** costs) except -1:
-        cdef int i, j
+        cdef int i
         for i in range(self.width):
             memcpy(self.scores[i], scores[i], sizeof(weight_t) * self.nr_class)
             memcpy(self.is_valid[i], is_valid[i], sizeof(bint) * self.nr_class)
@@ -176,7 +173,6 @@ cdef class Beam:
         beam-width, and n is the number of classes.
         """
         cdef Entry entry
-        cdef weight_t score
         cdef _State* s
         cdef int i, j, move_id
         assert self.size >= 1

spacy/pipeline/_parser_internals/transition_system.pyx

@@ -319,4 +319,3 @@ cdef void c_transition_batch(TransitionSystem moves, StateC** states, const floa
             action.do(states[i], action.label)
             states[i].history.push_back(guess)
     free(is_valid)
-

spacy/pipeline/morphologizer.pyx

@@ -1,8 +1,7 @@
 # cython: infer_types=True, profile=True, binding=True
 from itertools import islice
-from typing import Callable, Dict, Iterable, List, Optional, Union
+from typing import Callable, Dict, Iterable, Optional, Union
 
-import srsly
 from thinc.api import Config, Model
 from thinc.legacy import LegacySequenceCategoricalCrossentropy
 

spacy/pipeline/pipe.pyx

@@ -1,12 +1,11 @@
 # cython: infer_types=True, profile=True, binding=True
-import warnings
 from typing import Callable, Dict, Iterable, Iterator, Tuple, Union
 
 import srsly
 
 from ..tokens.doc cimport Doc
 
-from ..errors import Errors, Warnings
+from ..errors import Errors
 from ..language import Language
 from ..training import Example
 from ..util import raise_error

spacy/pipeline/trainable_pipe.pyx

@@ -1,5 +1,4 @@
 # cython: infer_types=True, profile=True, binding=True
-import warnings
 from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple
 
 import srsly
@@ -8,7 +7,7 @@ from thinc.api import Model, Optimizer, set_dropout_rate
 from ..tokens.doc cimport Doc
 
 from .. import util
-from ..errors import Errors, Warnings
+from ..errors import Errors
 from ..language import Language
 from ..training import Example, validate_distillation_examples, validate_examples
 from ..vocab import Vocab
@@ -56,14 +55,14 @@ cdef class TrainablePipe(Pipe):
         except Exception as e:
             error_handler(self.name, self, [doc], e)
 
-
     def distill(self,
                 teacher_pipe: Optional["TrainablePipe"],
                 examples: Iterable["Example"],
                 *,
                 drop: float = 0.0,
                 sgd: Optional[Optimizer] = None,
-               losses: Optional[Dict[str, float]]=None) -> Dict[str, float]:
+                losses: Optional[Dict[str, float]] = None
+                ) -> Dict[str, float]:
         """Train a pipe (the student) on the predictions of another pipe
         (the teacher). The student is typically trained on the probability
         distribution of the teacher, but details may differ per pipe.

spacy/pipeline/transition_parser.pyx

@@ -227,7 +227,8 @@ class Parser(TrainablePipe):
                 *,
                 drop: float = 0.0,
                 sgd: Optional[Optimizer] = None,
-               losses: Optional[Dict[str, float]]=None):
+                losses: Optional[Dict[str, float]] = None
+                ):
         """Train a pipe (the student) on the predictions of another pipe
         (the teacher). The student is trained on the transition probabilities
         of the teacher.
@@ -277,7 +278,9 @@ class Parser(TrainablePipe):
         # teacher's distributions.
 
         student_inputs = TransitionModelInputs(docs=student_docs,
-            states=[state.copy() for state in states], moves=self.moves, max_moves=max_moves)
+                                               states=[state.copy() for state in states],
+                                               moves=self.moves,
+                                               max_moves=max_moves)
         (student_states, student_scores), backprop_scores = self.model.begin_update(student_inputs)
         actions = _states_diff_to_actions(states, student_states)
         teacher_inputs = TransitionModelInputs(docs=[eg.reference for eg in examples],
@@ -294,7 +297,6 @@ class Parser(TrainablePipe):
 
         return losses
 
-
     def get_teacher_student_loss(
             self, teacher_scores: List[Floats2d], student_scores: List[Floats2d],
             normalize: bool = False,
@@ -436,8 +438,10 @@ class Parser(TrainablePipe):
         else:
             init_states, gold_states, _ = self.moves.init_gold_batch(examples)
 
-        inputs = TransitionModelInputs(docs=docs, moves=self.moves,
+        inputs = TransitionModelInputs(docs=docs,
-            max_moves=max_moves, states=[state.copy() for state in init_states])
+                                       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
@@ -483,9 +487,7 @@ class Parser(TrainablePipe):
         cdef TransitionSystem moves = self.moves
         cdef StateClass state
         cdef int clas
-        cdef int nF = self.model.get_dim("nF")
         cdef int nO = moves.n_moves
-        cdef int nS = sum([len(history) for history in histories])
         cdef Pool mem = Pool()
         cdef np.ndarray costs_i
         is_valid = <int*>mem.alloc(nO, sizeof(int))
@@ -552,8 +554,8 @@ class Parser(TrainablePipe):
 
         return losses
 
-    def update_beam(self, examples, *, beam_width,
+    def update_beam(self, examples, *, beam_width, drop=0.,
-            drop=0., sgd=None, losses=None, beam_density=0.0):
+                    sgd=None, losses=None, beam_density=0.0):
         raise NotImplementedError
 
     def set_output(self, nO):
@@ -678,7 +680,8 @@ class Parser(TrainablePipe):
             return states
 
         # Parse the states that are too long with the teacher's parsing model.
-        teacher_inputs = TransitionModelInputs(docs=docs, moves=moves,
+        teacher_inputs = TransitionModelInputs(docs=docs,
+                                               moves=moves,
                                                states=[state.copy() for state in to_cut])
         (teacher_states, _) = teacher_pipe.model.predict(teacher_inputs)
 
@@ -778,6 +781,7 @@ def _states_to_actions(states: List[StateClass]) -> List[Ints1d]:
 
     return actions
 
+
 def _states_diff_to_actions(
     before_states: List[StateClass],
     after_states: List[StateClass]
@@ -798,7 +802,8 @@ def _states_diff_to_actions(
         c_state_before = before_state.c
         c_state_after = after_state.c
 
-        assert equal(c_state_before.history.begin(), c_state_before.history.end(),
+        assert equal(c_state_before.history.begin(),
+                     c_state_before.history.end(),
                      c_state_after.history.begin())
 
     actions = []

spacy/strings.pyx

@@ -1,7 +1,6 @@
 # cython: infer_types=True
-from typing import Any, Callable, Iterable, Iterator, List, Optional, Tuple, Union
+from typing import Iterable, Iterator, List, Optional, Tuple, Union
 
-cimport cython
 from libc.stdint cimport uint32_t
 from libc.string cimport memcpy
 from murmurhash.mrmr cimport hash64
@@ -243,7 +242,6 @@ cdef class StringStore:
         cdef int n_length_bytes
         cdef int i
         cdef Utf8Str* string = <Utf8Str*>self.mem.alloc(1, sizeof(Utf8Str))
-        cdef uint32_t ulength = length
         if length < sizeof(string.s):
             string.s[0] = <unsigned char>length
             memcpy(&string.s[1], chars, length)
@@ -301,7 +299,7 @@ cpdef hash_t get_string_id(object string_or_hash) except -1:
 
     try:
         return hash_string(string_or_hash)
-    except:
+    except:   # no-cython-lint
         if _try_coerce_to_hash(string_or_hash, &str_hash):
             # Coerce the integral key to the expected primitive hash type.
             # This ensures that custom/overloaded "primitive" data types
@@ -318,6 +316,5 @@ cdef inline bint _try_coerce_to_hash(object key, hash_t* out_hash):
     try:
         out_hash[0] = key
         return True
-    except:
+    except:  # no-cython-lint
         return False
-

spacy/tests/parser/_search.pyx

@@ -2,7 +2,7 @@
 from cymem.cymem cimport Pool
 
 from spacy.pipeline._parser_internals.search cimport Beam, MaxViolation
-from spacy.typedefs cimport class_t, weight_t
+from spacy.typedefs cimport class_t
 
 import pytest
 
@@ -42,6 +42,7 @@ cdef int destroy(Pool mem, void* state, void* extra_args) except -1:
     state = <TestState*>state
     mem.free(state)
 
+
 @cytest
 @pytest.mark.parametrize("nr_class,beam_width",
                          [
@@ -56,10 +57,12 @@ def test_init(nr_class, beam_width):
     assert b.width == beam_width
     assert b.nr_class == nr_class
 
+
 @cytest
 def test_init_violn():
     MaxViolation()
 
+
 @cytest
 @pytest.mark.parametrize("nr_class,beam_width,length",
                          [

spacy/tokens/morphanalysis.pyx

@@ -1,5 +1,4 @@
 cimport numpy as np
-from libc.string cimport memset
 
 from ..errors import Errors
 from ..morphology import Morphology

spacy/tokens/span.pyx

@@ -225,8 +225,8 @@ cdef class Span:
 
     @property
     def _(self):
-        cdef SpanC* span_c = self.span_c()
         """Custom extension attributes registered via `set_extension`."""
+        cdef SpanC* span_c = self.span_c()
         return Underscore(Underscore.span_extensions, self,
                           start=span_c.start_char, end=span_c.end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
 
@@ -933,7 +933,6 @@ cdef class Span:
             self.id_ = ent_id_
 
 
-
 cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
     # Don't allow spaces to be the root, if there are
     # better candidates