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# Copyright 2023 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."""
import tensorflow as tf, tf_keras
from official.legacy.transformer import model_params
from official.legacy.transformer import transformer
class TransformerV2Test(tf.test.TestCase):
def setUp(self):
super().setUp()
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):
model = transformer.create_model(self.params, True)
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):
model = transformer.create_model(self.params, False)
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)
def test_export(self):
model = transformer.Transformer(self.params, name="transformer_v2")
export_dir = self.get_temp_dir()
batch_size = 5
max_length = 6
class SaveModule(tf.Module):
def __init__(self, model):
super(SaveModule, self).__init__()
self.model = model
@tf.function
def serve(self, x):
return self.model.call([x], training=False)
save_module = SaveModule(model)
tensor_shape = (None, None)
sample_input = tf.zeros((batch_size, max_length), dtype=tf.int64)
_ = save_module.serve(sample_input)
signatures = dict(
serving_default=save_module.serve.get_concrete_function(
tf.TensorSpec(shape=tensor_shape, dtype=tf.int64, name="x")))
tf.saved_model.save(save_module, export_dir, signatures=signatures)
imported = tf.saved_model.load(export_dir)
serving_fn = imported.signatures["serving_default"]
all_outputs = serving_fn(sample_input)
output = all_outputs["outputs"]
output_shapes = output.shape.as_list()
self.assertEqual(output_shapes[0], batch_size)
self.assertEqual(output_shapes[1],
max_length + model.params["extra_decode_length"])
if __name__ == "__main__":
tf.test.main()
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