Fix pickle for ngram suggester (#12486)

spacy/pipeline/spancat.py

@@ -1,5 +1,6 @@
 from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast, Union
 from dataclasses import dataclass
+from functools import partial
 from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
 from thinc.api import Optimizer
 from thinc.types import Ragged, Ints2d, Floats2d
@@ -82,13 +83,9 @@ class Suggester(Protocol):
         ...
 
 
-@registry.misc("spacy.ngram_suggester.v1")
-def build_ngram_suggester(sizes: List[int]) -> Suggester:
-    """Suggest all spans of the given lengths. Spans are returned as a ragged
-    array of integers. The array has two columns, indicating the start and end
-    position."""
-
-    def ngram_suggester(docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged:
+def ngram_suggester(
+    docs: Iterable[Doc], sizes: List[int], *, ops: Optional[Ops] = None
+) -> Ragged:
     if ops is None:
         ops = get_current_ops()
     spans = []
@@ -114,7 +111,14 @@ def build_ngram_suggester(sizes: List[int]) -> Suggester:
     assert output.dataXd.ndim == 2
     return output
 
-    return ngram_suggester
+
+@registry.misc("spacy.ngram_suggester.v1")
+def build_ngram_suggester(sizes: List[int]) -> Suggester:
+    """Suggest all spans of the given lengths. Spans are returned as a ragged
+    array of integers. The array has two columns, indicating the start and end
+    position."""
+
+    return partial(ngram_suggester, sizes=sizes)
 
 
 @registry.misc("spacy.ngram_range_suggester.v1")

spacy/tests/pipeline/test_spancat.py

@@ -1,7 +1,7 @@
 import pytest
 import numpy
 from numpy.testing import assert_array_equal, assert_almost_equal
-from thinc.api import get_current_ops, Ragged
+from thinc.api import get_current_ops, NumpyOps, Ragged
 
 from spacy import util
 from spacy.lang.en import English
@@ -577,3 +577,21 @@ def test_set_candidates(name):
     assert len(docs[0].spans["candidates"]) == 9
     assert docs[0].spans["candidates"][0].text == "Just"
     assert docs[0].spans["candidates"][4].text == "Just a"
+
+
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+@pytest.mark.parametrize("n_process", [1, 2])
+def test_spancat_multiprocessing(name, n_process):
+    if isinstance(get_current_ops, NumpyOps) or n_process < 2:
+        nlp = Language()
+        spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
+        train_examples = make_examples(nlp)
+        nlp.initialize(get_examples=lambda: train_examples)
+        texts = [
+            "Just a sentence.",
+            "I like London and Berlin",
+            "I like Berlin",
+            "I eat ham.",
+        ]
+        docs = list(nlp.pipe(texts, n_process=n_process))
+        assert len(docs) == len(texts)