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tf_ranking_tfrecord.py
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tf_ranking_tfrecord.py
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# Copyright 2024 The TensorFlow Ranking Authors.
#
# 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.
r"""TF-Ranking example code for proto formats stored in TFRecord.
The supported proto formats are listed at ../python/data.py.
--------------------------------------------------------------------------------
Sample command lines:
MODEL_DIR=/tmp/output && \
TRAIN=tensorflow_ranking/examples/data/train_elwc.tfrecord && \
EVAL=tensorflow_ranking/examples/data/eval_elwc.tfrecord && \
VOCAB=tensorflow_ranking/examples/data/vocab.txt && \
WEIGHT_FEATURE_NAME="doc_weight" && \
rm -rf $MODEL_DIR && \
bazel build -c opt \
tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary && \
./bazel-bin/tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \
--train_path=$TRAIN \
--eval_path=$EVAL \
--vocab_path=$VOCAB \
--model_dir=$MODEL_DIR \
--data_format=example_list_with_context \
--weights_feature_name=$WEIGHT_FEATURE_NAME
You can use TensorBoard to display the training results stored in $MODEL_DIR.
Notes:
* Use --alsologtostderr if the output is not printed into screen.
"""
from absl import flags
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_ranking as tfr
flags.DEFINE_enum(
"data_format", "example_list_with_context",
["example_list_with_context", "example_in_example", "sequence_example"],
"Data format defined in data.py.")
flags.DEFINE_string("train_path", None, "Input file path used for training.")
flags.DEFINE_string("eval_path", None, "Input file path used for eval.")
flags.DEFINE_string("vocab_path", None,
"Vocabulary path for query and document tokens.")
flags.DEFINE_string("model_dir", None, "Output directory for models.")
flags.DEFINE_integer("batch_size", 32, "The batch size for train.")
flags.DEFINE_integer("num_train_steps", 15000, "Number of steps for train.")
flags.DEFINE_float("learning_rate", 0.05, "Learning rate for optimizer.")
flags.DEFINE_float("dropout_rate", 0.8, "The dropout rate before output layer.")
flags.DEFINE_list("hidden_layer_dims", ["64", "32", "16"],
"Sizes for hidden layers.")
flags.DEFINE_integer(
"list_size", None,
"List size used for training. Use None for dynamic list size.")
flags.DEFINE_integer("group_size", 1, "Group size used in score function.")
flags.DEFINE_string("loss", "approx_ndcg_loss",
"The RankingLossKey for the loss function.")
flags.DEFINE_string("weights_feature_name", "",
"The name of the feature where unbiased learning-to-rank "
"weights are stored.")
flags.DEFINE_bool("listwise_inference", False,
"If true, exports accept `data_format` while serving.")
flags.DEFINE_bool(
"use_document_interaction", False,
"If True, use Document Interaction Network to capture cross-document "
"interactions as additional features for scoring.")
flags.DEFINE_integer(
"num_attention_layers", 1, "number of attention layers. See "
"`tfr.keras.layers.DocumentInteractionAttention`.")
flags.DEFINE_integer(
"num_attention_heads", 1, "number of self attention heads. See "
"`tfr.keras.layers.DocumentInteractionAttention`.")
flags.DEFINE_integer(
"head_size", 128, "Size of attention head. See "
"`tfr.keras.layers.DocumentInteractionAttention`.")
FLAGS = flags.FLAGS
_LABEL_FEATURE = "relevance"
_PADDING_LABEL = -1
_EMBEDDING_DIMENSION = 20
_MASK = "mask"
def context_feature_columns():
"""Returns context feature names to column definitions."""
if FLAGS.vocab_path:
sparse_column = tf.feature_column.categorical_column_with_vocabulary_file(
key="query_tokens", vocabulary_file=FLAGS.vocab_path)
else:
sparse_column = tf.feature_column.categorical_column_with_hash_bucket(
key="query_tokens", hash_bucket_size=100)
query_embedding_column = tf.feature_column.embedding_column(
sparse_column, _EMBEDDING_DIMENSION)
return {"query_tokens": query_embedding_column}
def example_feature_columns(use_weight_feature=True):
"""Returns the example feature columns."""
if FLAGS.vocab_path:
sparse_column = tf.feature_column.categorical_column_with_vocabulary_file(
key="document_tokens", vocabulary_file=FLAGS.vocab_path)
else:
sparse_column = tf.feature_column.categorical_column_with_hash_bucket(
key="document_tokens", hash_bucket_size=100)
document_embedding_column = tf.feature_column.embedding_column(
sparse_column, _EMBEDDING_DIMENSION)
feature_columns = {"document_tokens": document_embedding_column}
if use_weight_feature and FLAGS.weights_feature_name:
feature_columns[FLAGS.weights_feature_name] = (
tf.feature_column.numeric_column(FLAGS.weights_feature_name,
default_value=1.))
return feature_columns
def make_input_fn(file_pattern,
batch_size,
randomize_input=True,
num_epochs=None):
"""Returns `Estimator` `input_fn` for TRAIN and EVAL.
Args:
file_pattern: (string) file pattern for the TFRecord input data.
batch_size: (int) number of input examples to process per batch.
randomize_input: (bool) if true, randomize input example order. It should
almost always be true except for unittest/debug purposes.
num_epochs: (int) Number of times the input dataset must be repeated. None
to repeat the data indefinitely.
Returns:
An `input_fn` for `Estimator`.
"""
tf.compat.v1.logging.info("FLAGS.data_format={}".format(FLAGS.data_format))
def _input_fn():
"""Defines the input_fn."""
context_feature_spec = tf.feature_column.make_parse_example_spec(
context_feature_columns().values())
label_column = tf.feature_column.numeric_column(
_LABEL_FEATURE, dtype=tf.int64, default_value=_PADDING_LABEL)
example_feature_spec = tf.feature_column.make_parse_example_spec(
list(example_feature_columns().values()) + [label_column])
dataset = tfr.data.build_ranking_dataset(
file_pattern=file_pattern,
data_format=FLAGS.data_format,
batch_size=batch_size,
list_size=FLAGS.list_size,
context_feature_spec=context_feature_spec,
example_feature_spec=example_feature_spec,
reader=tf.data.TFRecordDataset,
shuffle=randomize_input,
num_epochs=num_epochs,
mask_feature_name=_MASK)
features = tf.compat.v1.data.make_one_shot_iterator(dataset).get_next()
label = tf.squeeze(features.pop(_LABEL_FEATURE), axis=2)
label = tf.cast(label, tf.float32)
return features, label
return _input_fn
def make_serving_input_fn():
"""Returns serving input fn."""
context_feature_spec = tf.feature_column.make_parse_example_spec(
context_feature_columns().values())
example_feature_spec = tf.feature_column.make_parse_example_spec(
example_feature_columns().values())
if FLAGS.listwise_inference:
# Exports accept the specified FLAGS.data_format during serving.
return tfr.data.build_ranking_serving_input_receiver_fn(
data_format=FLAGS.data_format,
context_feature_spec=context_feature_spec,
example_feature_spec=example_feature_spec,
mask_feature_name=_MASK)
elif FLAGS.group_size == 1:
# Exports accept tf.Example when group_size = 1.
feature_spec = {}
feature_spec.update(example_feature_spec)
feature_spec.update(context_feature_spec)
return tf_estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec)
else:
raise ValueError("FLAGS.group_size should be 1, but is {} when "
"FLAGS.export_listwise_inference is False".format(
FLAGS.group_size))
def make_transform_fn():
"""Returns a transform_fn that converts features to dense Tensors."""
def _transform_fn(features, mode):
"""Defines transform_fn."""
if mode == tf_estimator.ModeKeys.PREDICT and not FLAGS.listwise_inference:
# We expect tf.Example as input during serving. In this case, group_size
# must be set to 1.
if FLAGS.group_size != 1:
raise ValueError(
"group_size should be 1 to be able to export model, but get %s" %
FLAGS.group_size)
context_features, example_features = (
tfr.feature.encode_pointwise_features(
features=features,
context_feature_columns=context_feature_columns(),
example_feature_columns=example_feature_columns(),
mode=mode,
scope="transform_layer"))
else:
mask = features.pop(_MASK)
context_features, example_features = tfr.feature.encode_listwise_features(
features=features,
context_feature_columns=context_feature_columns(),
example_feature_columns=example_feature_columns(),
mode=mode,
scope="transform_layer")
# Document interaction attention layer.
if FLAGS.use_document_interaction:
training = (mode == tf_estimator.ModeKeys.TRAIN)
concat_tensor = tfr.keras.layers.ConcatFeatures()(
inputs=(context_features, example_features, mask))
din_layer = tfr.keras.layers.DocumentInteractionAttention(
num_heads=FLAGS.num_attention_heads,
head_size=FLAGS.head_size,
num_layers=FLAGS.num_attention_layers,
dropout=FLAGS.dropout_rate)
example_features["document_interaction_embedding"] = din_layer(
inputs=(concat_tensor, mask), training=training)
return context_features, example_features
return _transform_fn
def make_score_fn():
"""Returns a scoring function to build `EstimatorSpec`."""
def _score_fn(context_features, group_features, mode, params, config):
"""Defines the network to score a group of documents."""
del [params, config]
with tf.compat.v1.name_scope("input_layer"):
context_input = [
tf.compat.v1.layers.flatten(context_features[name])
for name in sorted(context_feature_columns())
]
group_input = [
tf.compat.v1.layers.flatten(group_features[name])
for name in sorted(example_feature_columns(use_weight_feature=False))
]
if FLAGS.use_document_interaction:
group_input.append(
tf.compat.v1.layers.flatten(
group_features["document_interaction_embedding"]))
input_layer = tf.concat(context_input + group_input, 1)
tf.compat.v1.summary.scalar("input_sparsity",
tf.nn.zero_fraction(input_layer))
tf.compat.v1.summary.scalar("input_max",
tf.reduce_max(input_tensor=input_layer))
tf.compat.v1.summary.scalar("input_min",
tf.reduce_min(input_tensor=input_layer))
is_training = (mode == tf_estimator.ModeKeys.TRAIN)
cur_layer = input_layer
cur_layer = tf.compat.v1.layers.batch_normalization(
cur_layer, training=is_training, momentum=0.99)
for i, layer_width in enumerate(int(d) for d in FLAGS.hidden_layer_dims):
cur_layer = tf.compat.v1.layers.dense(cur_layer, units=layer_width)
cur_layer = tf.compat.v1.layers.batch_normalization(
cur_layer, training=is_training, momentum=0.99)
cur_layer = tf.nn.relu(cur_layer)
tf.compat.v1.summary.scalar("fully_connected_{}_sparsity".format(i),
tf.nn.zero_fraction(cur_layer))
cur_layer = tf.compat.v1.layers.dropout(
inputs=cur_layer, rate=FLAGS.dropout_rate, training=is_training)
logits = tf.compat.v1.layers.dense(cur_layer, units=FLAGS.group_size)
return logits
return _score_fn
def eval_metric_fns():
"""Returns a dict from name to metric functions."""
metric_fns = {}
metric_fns.update({
"metric/%s" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [
tfr.metrics.RankingMetricKey.ARP,
tfr.metrics.RankingMetricKey.ORDERED_PAIR_ACCURACY,
]
})
metric_fns.update({
"metric/ndcg@%d" % topn: tfr.metrics.make_ranking_metric_fn(
tfr.metrics.RankingMetricKey.NDCG, topn=topn)
for topn in [1, 3, 5, 10]
})
for topn in [1, 3, 5, 10]:
metric_fns["metric/weighted_ndcg@%d" % topn] = (
tfr.metrics.make_ranking_metric_fn(
tfr.metrics.RankingMetricKey.NDCG,
weights_feature_name=FLAGS.weights_feature_name, topn=topn))
return metric_fns
def train_and_eval():
"""Train and Evaluate."""
train_input_fn = make_input_fn(FLAGS.train_path, FLAGS.batch_size)
eval_input_fn = make_input_fn(
FLAGS.eval_path, FLAGS.batch_size, randomize_input=False, num_epochs=1)
optimizer = tf.compat.v1.train.AdagradOptimizer(
learning_rate=FLAGS.learning_rate)
def _train_op_fn(loss):
"""Defines train op used in ranking head."""
update_ops = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)
minimize_op = optimizer.minimize(
loss=loss, global_step=tf.compat.v1.train.get_global_step())
train_op = tf.group([minimize_op, update_ops])
return train_op
ranking_head = tfr.head.create_ranking_head(
loss_fn=tfr.losses.make_loss_fn(
FLAGS.loss,
weights_feature_name=FLAGS.weights_feature_name),
eval_metric_fns=eval_metric_fns(),
train_op_fn=_train_op_fn)
estimator = tf_estimator.Estimator(
model_fn=tfr.model.make_groupwise_ranking_fn(
group_score_fn=make_score_fn(),
group_size=FLAGS.group_size,
transform_fn=make_transform_fn(),
ranking_head=ranking_head),
model_dir=FLAGS.model_dir,
config=tf_estimator.RunConfig(save_checkpoints_steps=1000))
train_spec = tf_estimator.TrainSpec(
input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
exporters = tf_estimator.LatestExporter(
"saved_model_exporter", serving_input_receiver_fn=make_serving_input_fn())
eval_spec = tf_estimator.EvalSpec(
name="eval",
input_fn=eval_input_fn,
steps=1,
exporters=exporters,
start_delay_secs=0,
throttle_secs=15)
# Train and validate.
tf_estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def main(_):
tf.compat.v1.set_random_seed(1234)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
if FLAGS.use_document_interaction and not FLAGS.listwise_inference:
raise ValueError("Only listwise inference is compatible for models "
"using Document Interaction Network.")
train_and_eval()
if __name__ == "__main__":
flags.mark_flag_as_required("train_path")
flags.mark_flag_as_required("eval_path")
flags.mark_flag_as_required("model_dir")
tf.compat.v1.app.run()