how to add text preprocessing tokenization step into Tensorflow model - tensorflow

Question

I have a TensorFlow model SavedModel which includes saved_model.pb and variables folder. The preprocessing step has not been incorporated into this model that's why I need to do preprocessing(Tokenization etc) before feeding the data to the model for the prediction aspect.
I am looking for an approach that I can incorporate the preprocessing step into the model. I have seen examples here and here however they are image data.
Just to get an idea how the training part has been done, this is a portion of the code that we did training (if you need the implementation of the function I have used here, please let me know(I did not include it to make my question more understandable ))
Training:
processor = IntentProcessor(FLAGS.data_path, FLAGS.test_data_path,
FLAGS.test_proportion, FLAGS.seed, FLAGS.do_early_stopping)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps)
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples()
num_iter_per_epoch = int(len(train_examples) / FLAGS.train_batch_size)
num_train_steps = num_iter_per_epoch * FLAGS.num_train_epochs
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.output_dir,
save_checkpoints_steps=num_iter_per_epoch)
best_temperature = 1.0 # Initiate the best T value as 1.0 and will
# update this during the training
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(processor.le.classes_),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
best_temperature=best_temperature,
seed=FLAGS.seed)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
# add parameters by passing a prams variable
if FLAGS.do_train:
train_features = convert_examples_to_features(
train_examples, FLAGS.max_seq_length, tokenizer)
train_labels = processor.get_train_labels()
train_input_fn = input_fn_builder(
features=train_features,
is_training=True,
batch_size=FLAGS.train_batch_size,
seed=FLAGS.seed,
labels=train_labels
)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
And this is the preprocessing that I use for the training:
LABEL_LIST = ['negative', 'neutral', 'positive']
INTENT_MAP = {i: LABEL_LIST[i] for i in range(len(LABEL_LIST))}
BATCH_SIZE = 1
MAX_SEQ_LEN = 70
def convert_examples_to_features(texts, max_seq_length, tokenizer):
"""Loads a data file into a list of InputBatchs.
texts is the list of input text
"""
features = {}
input_ids_list = []
input_mask_list = []
segment_ids_list = []
for (ex_index, text) in enumerate(texts):
tokens_a = tokenizer.tokenize(str(text))
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# print(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
input_ids_list.append(input_ids)
input_mask_list.append(input_mask)
segment_ids_list.append(segment_ids)
features['input_ids'] = np.asanyarray(input_ids_list)
features['input_mask'] = np.asanyarray(input_mask_list)
features['segment_ids'] = np.asanyarray(segment_ids_list)
# tf.data.Dataset.from_tensor_slices needs to pass numpy array not
# tensor, or the tensor graph (shape) should match
return features
and inferencing would be like this:
def inference(texts,MODEL_DIR, VOCAB_FILE):
if not isinstance(texts, list):
texts = [texts]
tokenizer = FullTokenizer(vocab_file=VOCAB_FILE, do_lower_case=False)
features = convert_examples_to_features(texts, MAX_SEQ_LEN, tokenizer)
predict_fn = predictor.from_saved_model(MODEL_DIR)
response = predict_fn(features)
#print(response)
return get_sentiment(response)
def preprocess(texts):
if not isinstance(texts, list):
texts = [texts]
tokenizer = FullTokenizer(vocab_file=VOCAB_FILE, do_lower_case=False)
features = convert_examples_to_features(texts, MAX_SEQ_LEN, tokenizer)
return features
def get_sentiment(response):
idx = response['intent'].tolist()
print(idx)
print(INTENT_MAP.get(idx[0]))
outputs = []
for i in range(0, len(idx)):
outputs.append({
"sentiment": INTENT_MAP.get(idx[i]),
"confidence": response['prob'][i][idx[i]]
})
return outputs
sentence = 'The movie is ok'
inference(sentence, args.model_path, args.vocab_path)
And this is the implementation of model_fn_builder:
def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, best_temperature, seed):
"""Returns multi-intents `model_fn` closure for Estimator"""
def model_fn(features, labels, mode,
params): # pylint: disable=unused-argument
"""The `model_fn` for Estimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(
" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss, per_example_loss, logits) = create_intent_model(
bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, mode, seed)
tvars = tf.trainable_variables()
initialized_variable_names = None
if init_checkpoint:
(assignment_map,
initialized_variable_names) = \
modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, labels, logits):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
accuracy = tf.metrics.accuracy(labels, predictions)
loss = tf.metrics.mean(per_example_loss)
return {
"eval_accuracy": accuracy,
"eval_loss": loss
}
eval_metrics = metric_fn(per_example_loss, labels, logits)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=eval_metrics)
elif mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'intent': tf.argmax(logits, axis=-1, output_type=tf.int32),
'prob': tf.nn.softmax(logits / tf.constant(best_temperature)),
'logits': logits
}
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions)
return output_spec
return model_fn
And this is the implementation of create_intent_model
def create_intent_model(bert_config, is_training, input_ids, input_mask,
segment_ids,
labels, num_labels, mode, seed):
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=False,
seed=seed
)
output_layer = model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
with tf.variable_scope("loss"):
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02, seed=seed))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9, seed=seed)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
loss = None
per_example_loss = None
if mode == tf.estimator.ModeKeys.TRAIN or mode == \
tf.estimator.ModeKeys.EVAL:
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
loss = tf.reduce_mean(per_example_loss)
return loss, per_example_loss, logits
This is the list tensorflow related libraries:
tensorboard==1.15.0
tensorflow-estimator==1.15.1
tensorflow-gpu==1.15.0
There is good documentation here, however, it uses Keras API. Plus, I don't know how can I incorporate preprocessing layer here even with the Keras API.
Again, my final goal is to incorporate the preprocessing step into the model building phase so that when I later load the model I directly pass the The movie is ok to the model?
I just need the idea on how to incorporate a preprocessing layer into this code which is function based.
Thanks in advance~

Answer 1

You can use the TextVectorization layer as follows. But to answer your question fully, I'd need to know what's in model_fn_builder() function. I'll show how you can do this with Keras model building API.
class BertTextProcessor(tf.keras.layers.Layer):
def __init__(self, max_length):
super().__init__()
self.max_length = max_length
# Here I'm setting any preprocessing to none
# by default this layer lowers case and remove punctuation
# i.e. tokens like [CLS] would become cls
self.vectorizer = tf.keras.layers.TextVectorization(output_sequence_length=max_length, standardize=None)
def call(self, inputs):
inputs = "[CLS] " + inputs + " [SEP]"
tok_inputs = self.vectorizer(inputs)
return {
"input_ids": tok_inputs,
"input_mask": tf.cast(tok_inputs != 0, 'int32'),
"segment_ids": tf.zeros_like(tok_inputs)
}
def adapt(self, data):
data = "[CLS] " + data + " [SEP]"
self.vectorizer.adapt(data)
def get_config(self):
return {
"max_length": self.max_length
}
Usage,
input_str = tf.constant(["movie is okay good plot very nice", "terrible movie bad actors not good"])
proc = BertTextProcessor(8)
# You need to call this so that the vectorizer layer learns the vocabulary
proc.adapt(input_str)
print(proc(input_str))
which outputs,
{'input_ids': <tf.Tensor: shape=(2, 10), dtype=int64, numpy=
array([[ 5, 2, 12, 9, 3, 8, 6, 11, 4, 0],
[ 5, 7, 2, 13, 14, 10, 3, 4, 0, 0]])>, 'input_mask': <tf.Tensor: shape=(2, 10), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=int32)>, 'segment_ids': <tf.Tensor: shape=(2, 10), dtype=int64, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])>}
You can use this layer as an input for a Keras model as you would use any layer.
You can also get the vocabulary using, proc.vectorizer.get_vocabulary() which returns,
['',
'[UNK]',
'movie',
'good',
'[SEP]',
'[CLS]',
'very',
'terrible',
'plot',
'okay',
'not',
'nice',
'is',
'bad',
'actors']
Alternative with tf-models-official
To get data in a format accepted by BERT, you can also use the tf-models-official library. Specifically, you can use the BertPackInputs object.
I recently updated code for one of my books and in Chapter 13/13.1_Spam_Classification you can see how it is used. The section Generating the correct input format for BERT shows how this could be done.
Edit: How to do this in tensorflow==1.15.0
In order to do this in TensorFlow 1.x you will need some reworking as lot of functionality in the original answer is missing. Here's an example of how you can do this, you will need to adapt this code accordingly to your specific usecase/method.
lookup_layer = tf.lookup.StaticHashTable(
tf.lookup.TextFileInitializer(
"vocab.txt", tf.string, tf.lookup.TextFileIndex.WHOLE_LINE,
tf.int64, tf.lookup.TextFileIndex.LINE_NUMBER, delimiter=" "),
100
)
text = tf.constant(["bad film", "movie is okay good plot very nice", "terrible movie bad actors not good"])
text = "[CLS]" + text + "[SEP]"
text = tf.strings.split(text, result_type="RaggedTensor")
text_dense = text.to_tensor("[PAD]")
out = lookup_layer.lookup(text_dense)
with tf.Session() as sess:
sess.run(tf.tables_initializer())
print(sess.run(out))