Merge branch 'master' into spacy.io

spacy/tests/regression/test_issue3540.py

@@ -0,0 +1,44 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from spacy.tokens import Doc
+
+import numpy as np
+
+
+def test_issue3540(en_vocab):
+
+    words = ["I", "live", "in", "NewYork", "right", "now"]
+    tensor = np.asarray([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1], [4.0, 4.1], [5.0, 5.1], [6.0, 6.1]], dtype="f")
+    doc = Doc(en_vocab, words=words)
+    doc.tensor = tensor
+
+    gold_text = ["I", "live", "in", "NewYork", "right", "now"]
+    assert [token.text for token in doc] == gold_text
+
+    gold_lemma = ["I", "live", "in", "NewYork", "right", "now"]
+    assert [token.lemma_ for token in doc] == gold_lemma
+
+    vectors_1 = [token.vector for token in doc]
+    assert len(vectors_1) == len(doc)
+
+    with doc.retokenize() as retokenizer:
+        heads = [(doc[3], 1), doc[2]]
+        attrs = {"POS": ["PROPN", "PROPN"], "DEP": ["pobj", "compound"]}
+        retokenizer.split(doc[3], [u"New", u"York"], heads=heads, attrs=attrs)
+
+    gold_text = ["I", "live", "in", "New", "York", "right", "now"]
+    assert [token.text for token in doc] == gold_text
+
+    gold_lemma = ["I", "live", "in", "New", "York", "right", "now"]
+    assert [token.lemma_ for token in doc] == gold_lemma
+
+    vectors_2 = [token.vector for token in doc]
+    assert len(vectors_2) == len(doc)
+
+    assert vectors_1[0].tolist() == vectors_2[0].tolist()
+    assert vectors_1[1].tolist() == vectors_2[1].tolist()
+    assert vectors_1[2].tolist() == vectors_2[2].tolist()
+
+    assert vectors_1[4].tolist() == vectors_2[5].tolist()
+    assert vectors_1[5].tolist() == vectors_2[6].tolist()

spacy/tokens/_retokenize.pyx

@@ -404,14 +404,24 @@ def _split(Doc doc, int token_index, orths, heads, attrs):
         doc._realloc(doc.length * 2)
     # Move tokens after the split to create space for the new tokens
     doc.length = len(doc) + nb_subtokens -1
+    to_process_tensor = (doc.tensor is not None and doc.tensor.size != 0)
+    if to_process_tensor:
+        xp = get_array_module(doc.tensor)
+        doc.tensor = xp.append(doc.tensor, xp.zeros((nb_subtokens,doc.tensor.shape[1]), dtype="float32"), axis=0)
     for token_to_move in range(doc.length - 1, token_index, -1):
         doc.c[token_to_move + nb_subtokens - 1] = doc.c[token_to_move]
+        if to_process_tensor:
+            doc.tensor[token_to_move + nb_subtokens - 1] = doc.tensor[token_to_move]
     # Host the tokens in the newly created space
     cdef int idx_offset = 0
     for i, orth in enumerate(orths):
         token = &doc.c[token_index + i]
         lex = doc.vocab.get(doc.mem, orth)
         token.lex = lex
+        token.lemma = 0  # reset lemma
+        if to_process_tensor:
+            # setting the tensors of the split tokens to array of zeros
+            doc.tensor[token_index + i] = xp.zeros((1,doc.tensor.shape[1]), dtype="float32")
         # Update the character offset of the subtokens
         if i != 0:
             token.idx = orig_token.idx + idx_offset