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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.
"""Tests for nlp.serving.export_saved_model."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.nlp.configs import bert
from official.nlp.configs import encoders
from official.nlp.serving import export_savedmodel
from official.nlp.serving import export_savedmodel_util
from official.nlp.tasks import masked_lm
from official.nlp.tasks import sentence_prediction
from official.nlp.tasks import tagging
class ExportSavedModelTest(tf.test.TestCase, parameterized.TestCase):
def test_create_export_module(self):
export_module = export_savedmodel.create_export_module(
task_name="SentencePrediction",
config_file=None,
serving_params={
"inputs_only": False,
"parse_sequence_length": 10
})
self.assertEqual(export_module.name, "sentence_prediction")
self.assertFalse(export_module.params.inputs_only)
self.assertEqual(export_module.params.parse_sequence_length, 10)
def test_sentence_prediction(self):
config = sentence_prediction.SentencePredictionConfig(
model=sentence_prediction.ModelConfig(
encoder=encoders.EncoderConfig(
bert=encoders.BertEncoderConfig(vocab_size=30522,
num_layers=1)),
num_classes=2))
task = sentence_prediction.SentencePredictionTask(config)
model = task.build_model()
ckpt = tf.train.Checkpoint(model=model)
ckpt_path = ckpt.save(self.get_temp_dir())
export_module_cls = export_savedmodel.lookup_export_module(task)
serving_params = {"inputs_only": False}
params = export_module_cls.Params(**serving_params)
export_module = export_module_cls(params=params, model=model)
export_dir = export_savedmodel_util.export(
export_module,
function_keys=["serve"],
checkpoint_path=ckpt_path,
export_savedmodel_dir=self.get_temp_dir())
imported = tf.saved_model.load(export_dir)
serving_fn = imported.signatures["serving_default"]
dummy_ids = tf.ones((1, 5), dtype=tf.int32)
inputs = dict(
input_word_ids=dummy_ids,
input_mask=dummy_ids,
input_type_ids=dummy_ids)
ref_outputs = model(inputs)
outputs = serving_fn(**inputs)
self.assertAllClose(ref_outputs, outputs["outputs"])
self.assertEqual(outputs["outputs"].shape, (1, 2))
def test_masked_lm(self):
config = masked_lm.MaskedLMConfig(
model=bert.PretrainerConfig(
encoder=encoders.EncoderConfig(
bert=encoders.BertEncoderConfig(vocab_size=30522,
num_layers=1)),
cls_heads=[
bert.ClsHeadConfig(inner_dim=10, num_classes=2, name="foo")
]))
task = masked_lm.MaskedLMTask(config)
model = task.build_model()
ckpt = tf.train.Checkpoint(model=model)
ckpt_path = ckpt.save(self.get_temp_dir())
export_module_cls = export_savedmodel.lookup_export_module(task)
serving_params = {
"cls_head_name": "foo",
"parse_sequence_length": 10,
"max_predictions_per_seq": 5
}
params = export_module_cls.Params(**serving_params)
export_module = export_module_cls(params=params, model=model)
export_dir = export_savedmodel_util.export(
export_module,
function_keys={
"serve": "serving_default",
"serve_examples": "serving_examples"
},
checkpoint_path=ckpt_path,
export_savedmodel_dir=self.get_temp_dir())
imported = tf.saved_model.load(export_dir)
self.assertSameElements(imported.signatures.keys(),
["serving_default", "serving_examples"])
serving_fn = imported.signatures["serving_default"]
dummy_ids = tf.ones((1, 10), dtype=tf.int32)
dummy_pos = tf.ones((1, 5), dtype=tf.int32)
outputs = serving_fn(
input_word_ids=dummy_ids,
input_mask=dummy_ids,
input_type_ids=dummy_ids,
masked_lm_positions=dummy_pos)
self.assertEqual(outputs["classification"].shape, (1, 2))
@parameterized.parameters(True, False)
def test_tagging(self, output_encoder_outputs):
hidden_size = 768
num_classes = 3
config = tagging.TaggingConfig(
model=tagging.ModelConfig(
encoder=encoders.EncoderConfig(
bert=encoders.BertEncoderConfig(
hidden_size=hidden_size, num_layers=1))),
class_names=["class_0", "class_1", "class_2"])
task = tagging.TaggingTask(config)
model = task.build_model()
ckpt = tf.train.Checkpoint(model=model)
ckpt_path = ckpt.save(self.get_temp_dir())
export_module_cls = export_savedmodel.lookup_export_module(task)
serving_params = {
"parse_sequence_length": 10,
}
params = export_module_cls.Params(
**serving_params, output_encoder_outputs=output_encoder_outputs)
export_module = export_module_cls(params=params, model=model)
export_dir = export_savedmodel_util.export(
export_module,
function_keys={
"serve": "serving_default",
"serve_examples": "serving_examples"
},
checkpoint_path=ckpt_path,
export_savedmodel_dir=self.get_temp_dir())
imported = tf.saved_model.load(export_dir)
self.assertCountEqual(imported.signatures.keys(),
["serving_default", "serving_examples"])
serving_fn = imported.signatures["serving_default"]
dummy_ids = tf.ones((1, 5), dtype=tf.int32)
inputs = dict(
input_word_ids=dummy_ids,
input_mask=dummy_ids,
input_type_ids=dummy_ids)
outputs = serving_fn(**inputs)
self.assertEqual(outputs["logits"].shape, (1, 5, num_classes))
if output_encoder_outputs:
self.assertEqual(outputs["encoder_outputs"].shape, (1, 5, hidden_size))
if __name__ == "__main__":
tf.test.main()
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