seq2seq test case

official/nlp/modeling/models/seq2seq_transformer.py

@@ -29,7 +29,6 @@ from official.nlp.modeling import layers
 from official.nlp.modeling.layers import position_embedding
 from official.nlp.modeling.layers import transformer
 from official.nlp.modeling.ops import beam_search
-from official.nlp.transformer import metrics
 from official.nlp.transformer import model_utils
 from official.nlp.transformer.utils.tokenizer import EOS_ID
 

official/nlp/modeling/models/seq2seq_transformer_test.py

+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test Transformer model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from official.nlp.modeling.models import seq2seq_transformer
+from official.nlp.transformer import model_params
+
+class TransformerV2Test(tf.test.TestCase):
+
+  def setUp(self):
+    self.params = params = model_params.TINY_PARAMS
+    params["batch_size"] = params["default_batch_size"] = 16
+    params["use_synthetic_data"] = True
+    params["hidden_size"] = 12
+    params["num_hidden_layers"] = 2
+    params["filter_size"] = 14
+    params["num_heads"] = 2
+    params["vocab_size"] = 41
+    params["extra_decode_length"] = 2
+    params["beam_size"] = 3
+    params["dtype"] = tf.float32
+
+  def test_create_model_train(self):
+    inputs = tf.keras.layers.Input((None,), dtype="int64", name="inputs")
+    targets = tf.keras.layers.Input((None,), dtype="int64", name="targets")
+    internal_model = seq2seq_transformer.Seq2SeqTransformer(self.params)
+    logits = internal_model([inputs, targets], training=True)
+    model = tf.keras.Model([inputs, targets], logits)
+    inputs, outputs = model.inputs, model.outputs
+    self.assertEqual(len(inputs), 2)
+    self.assertEqual(len(outputs), 1)
+    self.assertEqual(inputs[0].shape.as_list(), [None, None])
+    self.assertEqual(inputs[0].dtype, tf.int64)
+    self.assertEqual(inputs[1].shape.as_list(), [None, None])
+    self.assertEqual(inputs[1].dtype, tf.int64)
+    self.assertEqual(outputs[0].shape.as_list(), [None, None, 41])
+    self.assertEqual(outputs[0].dtype, tf.float32)
+
+  def test_create_model_not_train(self):
+    inputs = tf.keras.layers.Input((None,), dtype="int64", name="inputs")
+    internal_model = seq2seq_transformer.Seq2SeqTransformer(self.params)
+    ret = internal_model([inputs], training=False)
+    outputs, scores = ret["outputs"], ret["scores"]
+    model = tf.keras.Model(inputs, [outputs, scores])
+    inputs, outputs = model.inputs, model.outputs
+    self.assertEqual(len(inputs), 1)
+    self.assertEqual(len(outputs), 2)
+    self.assertEqual(inputs[0].shape.as_list(), [None, None])
+    self.assertEqual(inputs[0].dtype, tf.int64)
+    self.assertEqual(outputs[0].shape.as_list(), [None, None])
+    self.assertEqual(outputs[0].dtype, tf.int32)
+    self.assertEqual(outputs[1].shape.as_list(), [None])
+    self.assertEqual(outputs[1].dtype, tf.float32)
+
+
+if __name__ == "__main__":
+  tf.test.main()