Add detokenize testing for model tokenizers

keras_nlp/models/albert/albert_tokenizer_test.py

@@ -28,14 +28,14 @@ def setUp(self):
                 self.get_test_data_dir(), "albert_test_vocab.spm"
             )
         }
-        self.input_data = ["the quick brown fox.", "the earth is round."]
+        self.input_data = ["the quick brown fox", "the earth is round"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=AlbertTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[5, 10, 6, 1], [5, 7, 9, 1]],
+            expected_output=[[5, 10, 6, 8], [5, 7, 9, 11]],
         )
 
     def test_errors_missing_special_tokens(self):

keras_nlp/models/bart/bart_tokenizer_test.py

@@ -37,7 +37,12 @@ def test_tokenizer_basics(self):
             cls=BartTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
+            # TODO: </s> should not get tokenized as <s>
             expected_output=[[0, 4, 5, 6, 4, 7, 0, 1], [4, 5, 4, 7]],
+            expected_detokenize_output=[
+                "<s> airplane at airport<s><pad>",
+                " airplane airport",
+            ],
         )
 
     def test_errors_missing_special_tokens(self):

keras_nlp/models/bert/bert_tokenizer_test.py

@@ -24,14 +24,14 @@ def setUp(self):
         self.vocab += ["THE", "QUICK", "BROWN", "FOX"]
         self.vocab += ["the", "quick", "brown", "fox"]
         self.init_kwargs = {"vocabulary": self.vocab}
-        self.input_data = ["THE QUICK BROWN FOX.", "THE FOX."]
+        self.input_data = ["THE QUICK BROWN FOX", "THE FOX"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=BertTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[5, 6, 7, 8, 1], [5, 8, 1]],
+            expected_output=[[5, 6, 7, 8], [5, 8]],
         )
 
     def test_lowercase(self):

keras_nlp/models/deberta_v3/deberta_v3_tokenizer_test.py

@@ -28,14 +28,14 @@ def setUp(self):
         )
         self.tokenizer = DebertaV3Tokenizer(proto=proto)
         self.init_kwargs = {"proto": proto}
-        self.input_data = ["the quick brown fox.", "the earth is round."]
+        self.input_data = ["the quick brown fox", "the earth is round"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=DebertaV3Tokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[5, 10, 6, 3], [5, 7, 9, 3]],
+            expected_output=[[5, 10, 6, 8], [5, 7, 9, 11]],
         )
 
     def test_errors_missing_special_tokens(self):

keras_nlp/models/distil_bert/distil_bert_tokenizer_test.py

@@ -26,14 +26,14 @@ def setUp(self):
         self.vocab += ["THE", "QUICK", "BROWN", "FOX"]
         self.vocab += ["the", "quick", "brown", "fox"]
         self.init_kwargs = {"vocabulary": self.vocab}
-        self.input_data = ["THE QUICK BROWN FOX.", "THE FOX."]
+        self.input_data = ["THE QUICK BROWN FOX", "THE FOX"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=DistilBertTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[5, 6, 7, 8, 1], [5, 8, 1]],
+            expected_output=[[5, 6, 7, 8], [5, 8]],
         )
 
     def test_lowercase(self):

keras_nlp/models/f_net/f_net_tokenizer_test.py

@@ -28,14 +28,14 @@ def setUp(self):
                 self.get_test_data_dir(), "f_net_test_vocab.spm"
             )
         }
-        self.input_data = ["the quick brown fox.", "the earth is round."]
+        self.input_data = ["the quick brown fox", "the earth is round"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=FNetTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[5, 10, 6, 1], [5, 7, 9, 1]],
+            expected_output=[[5, 10, 6, 8], [5, 7, 9, 11]],
         )
 
     def test_errors_missing_special_tokens(self):

keras_nlp/models/roberta/roberta_tokenizer_test.py

@@ -37,7 +37,12 @@ def test_tokenizer_basics(self):
             cls=RobertaTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
+            # TODO: </s> should not get tokenized as <s>
             expected_output=[[0, 4, 5, 6, 4, 7, 0, 1], [4, 5, 4, 7]],
+            expected_detokenize_output=[
+                "<s> airplane at airport<s><pad>",
+                " airplane airport",
+            ],
         )
 
     def test_errors_missing_special_tokens(self):

keras_nlp/models/t5/t5_tokenizer_test.py

@@ -26,14 +26,14 @@ def setUp(self):
             # Generated using create_t5_test_proto.py
             "proto": os.path.join(self.get_test_data_dir(), "t5_test_vocab.spm")
         }
-        self.input_data = ["the quick brown fox.", "the earth is round."]
+        self.input_data = ["the quick brown fox", "the earth is round"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=T5Tokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[4, 9, 5, 2], [4, 6, 8, 2]],
+            expected_output=[[4, 9, 5, 7], [4, 6, 8, 10]],
         )
 
     def test_errors_missing_special_tokens(self):

keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer.py

@@ -157,9 +157,6 @@ def tokenize(self, inputs):
         return tf.add(tokens, 1)
 
     def detokenize(self, inputs):
-        if inputs.dtype == tf.string:
-            return super().detokenize(inputs)
-
         tokens = tf.ragged.boolean_mask(
             inputs, tf.not_equal(inputs, self.mask_token_id)
         )

keras_nlp/models/xlm_roberta/xlm_roberta_tokenizer_test.py

@@ -30,14 +30,14 @@ def setUp(self):
                 self.get_test_data_dir(), "xlm_roberta_test_vocab.spm"
             )
         }
-        self.input_data = ["the quick brown fox.", "the earth is round."]
+        self.input_data = ["the quick brown fox", "the earth is round"]
 
     def test_tokenizer_basics(self):
         self.run_preprocessing_layer_test(
             cls=XLMRobertaTokenizer,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
-            expected_output=[[6, 11, 7, 2], [6, 8, 10, 2]],
+            expected_output=[[6, 11, 7, 9], [6, 8, 10, 12]],
         )
 
     @pytest.mark.large

keras_nlp/tests/test_case.py

@@ -24,6 +24,7 @@
 from keras_nlp.backend import config
 from keras_nlp.backend import keras
 from keras_nlp.backend import ops
+from keras_nlp.tokenizers.tokenizer import Tokenizer
 from keras_nlp.utils.tensor_utils import is_float_dtype
 from keras_nlp.utils.tensor_utils import standardize_dtype
 
@@ -203,7 +204,7 @@ def run_preprocessing_layer_test(
         init_kwargs,
         input_data,
         expected_output=None,
-        batch_size=2,
+        expected_detokenize_output=None,
     ):
         """Run basic tests for a preprocessing layer."""
         layer = cls(**init_kwargs)
@@ -219,6 +220,13 @@ def run_preprocessing_layer_test(
         else:
             output = layer(input_data)
 
+        # For tokenizers only, also check detokenize.
+        if isinstance(layer, Tokenizer):
+            if not expected_detokenize_output:
+                expected_detokenize_output = input_data
+            detokenize_output = layer.detokenize(output)
+            self.assertAllEqual(detokenize_output, expected_detokenize_output)
+
         # Run with an unbatched dataset.
         output_ds = ds.map(layer).ragged_batch(1_000)
         self.assertAllClose(output, output_ds.get_single_element())