Reproducibility for TextCat and Tok2Vec (#6218)

* ensure fixed seed in HashEmbed layers

* forgot about the joys of python 2

spacy/_ml.py

@@ -654,10 +654,10 @@ def build_text_classifier(nr_class, width=64, **cfg):
             )
             return model
 
-        lower = HashEmbed(width, nr_vector, column=1)
-        prefix = HashEmbed(width // 2, nr_vector, column=2)
-        suffix = HashEmbed(width // 2, nr_vector, column=3)
-        shape = HashEmbed(width // 2, nr_vector, column=4)
+        lower = HashEmbed(width, nr_vector, column=1, seed=10)
+        prefix = HashEmbed(width // 2, nr_vector, column=2, seed=11)
+        suffix = HashEmbed(width // 2, nr_vector, column=3, seed=12)
+        shape = HashEmbed(width // 2, nr_vector, column=4, seed=13)
 
         trained_vectors = FeatureExtracter(
             [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]

spacy/ml/_legacy_tok2vec.py

@@ -27,16 +27,16 @@ def Tok2Vec(width, embed_size, **kwargs):
     bilstm_depth = kwargs.get("bilstm_depth", 0)
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
-        norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm")
+        norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm", seed=6)
         if subword_features:
             prefix = HashEmbed(
-                width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix"
+                width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix", seed=7
             )
             suffix = HashEmbed(
-                width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix"
+                width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix", seed=8
             )
             shape = HashEmbed(
-                width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape"
+                width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape", seed=9
             )
         else:
             prefix, suffix, shape = (None, None, None)

spacy/ml/tok2vec.py

@@ -42,16 +42,16 @@ def MultiHashEmbed(config):
     width = config["width"]
     rows = config["rows"]
 
-    norm = HashEmbed(width, rows, column=cols.index("NORM"), name="embed_norm")
+    norm = HashEmbed(width, rows, column=cols.index("NORM"), name="embed_norm", seed=1)
     if config["use_subwords"]:
         prefix = HashEmbed(
-            width, rows // 2, column=cols.index("PREFIX"), name="embed_prefix"
+            width, rows // 2, column=cols.index("PREFIX"), name="embed_prefix", seed=2
         )
         suffix = HashEmbed(
-            width, rows // 2, column=cols.index("SUFFIX"), name="embed_suffix"
+            width, rows // 2, column=cols.index("SUFFIX"), name="embed_suffix", seed=3
         )
         shape = HashEmbed(
-            width, rows // 2, column=cols.index("SHAPE"), name="embed_shape"
+            width, rows // 2, column=cols.index("SHAPE"), name="embed_shape", seed=4
         )
     if config.get("@pretrained_vectors"):
         glove = make_layer(config["@pretrained_vectors"])

spacy/tests/regression/test_issue6177.py

@@ -0,0 +1,35 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from spacy.lang.en import English
+from spacy.util import fix_random_seed
+
+
+def test_issue6177():
+    """Test that after fixing the random seed, the results of the pipeline are truly identical"""
+
+    # NOTE: no need to transform this code to v3 when 'master' is merged into 'develop'.
+    # A similar test exists already for v3: test_issue5551
+    # This is just a backport
+
+    results = []
+    for i in range(3):
+        fix_random_seed(0)
+        nlp = English()
+        example = (
+            "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g.",
+            {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}},
+        )
+        textcat = nlp.create_pipe("textcat")
+        nlp.add_pipe(textcat)
+        for label in set(example[1]["cats"]):
+            textcat.add_label(label)
+        nlp.begin_training()
+        # Store the result of each iteration
+        result = textcat.model.predict([nlp.make_doc(example[0])])
+        results.append(list(result[0]))
+
+    # All results should be the same because of the fixed seed
+    assert len(results) == 3
+    assert results[0] == results[1]
+    assert results[0] == results[2]