Fix backward compatibility issue: loading FastTextKeyedVectors using KeyedVectors (missing attribute compatible_hash)

gensim/models/keyedvectors.py

@@ -1512,6 +1512,15 @@ def get_keras_embedding(self, train_embeddings=False):
         )
         return layer
 
+    @classmethod
+    def load(cls, fname_or_handle, **kwargs):
+        model = super(WordEmbeddingsKeyedVectors, cls).load(fname_or_handle, **kwargs)
+        if isinstance(model, FastTextKeyedVectors):
+            if not hasattr(model, 'compatible_hash'):
+                model.compatible_hash = False
+
+        return model
+
 
 KeyedVectors = Word2VecKeyedVectors  # alias for backward compatibility
 
@@ -1974,6 +1983,14 @@ def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash):
         self.num_ngram_vectors = 0
         self.compatible_hash = compatible_hash
 
+    @classmethod
+    def load(cls, fname_or_handle, **kwargs):
+        model = super(WordEmbeddingsKeyedVectors, cls).load(fname_or_handle, **kwargs)
+        if not hasattr(model, 'compatible_hash'):
+            model.compatible_hash = False
+
+        return model
+
     @property
     @deprecated("Attribute will be removed in 4.0.0, use self.vectors_vocab instead")
     def syn0_vocab(self):

gensim/test/test_keyedvectors.py

@@ -15,7 +15,8 @@
 import numpy as np
 
 from gensim.corpora import Dictionary
-from gensim.models import KeyedVectors as EuclideanKeyedVectors, WordEmbeddingSimilarityIndex
+from gensim.models.keyedvectors import KeyedVectors as EuclideanKeyedVectors, WordEmbeddingSimilarityIndex, \
+    FastTextKeyedVectors
 from gensim.test.utils import datapath
 
 import gensim.models.keyedvectors
@@ -279,6 +280,19 @@ def test_set_item(self):
         for ent, vector in zip(entities, vectors):
             self.assertTrue(np.allclose(self.vectors[ent], vector))
 
+    def test_ft_kv_backward_compat_w_360(self):
+        kv = EuclideanKeyedVectors.load(datapath("ft_kv_3.6.0.model.gz"))
+        ft_kv = FastTextKeyedVectors.load(datapath("ft_kv_3.6.0.model.gz"))
+
+        expected = ['trees', 'survey', 'system', 'graph', 'interface']
+        actual = [word for (word, similarity) in kv.most_similar("human", topn=5)]
+
+        self.assertEqual(actual, expected)
+
+        actual = [word for (word, similarity) in ft_kv.most_similar("human", topn=5)]
+
+        self.assertEqual(actual, expected)
+
 
 class L2NormTest(unittest.TestCase):
     def test(self):