Delete export_tfhub.py

export_tfhub.py

@@ -1,219 +0,0 @@
-# Copyright 2024 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.
-
-r"""Exports a BERT-like encoder and its preprocessing as SavedModels for TF Hub.
-
-This tool creates preprocessor and encoder SavedModels suitable for uploading
-to https://tfhub.dev that implement the preprocessor and encoder APIs defined
-at https://www.tensorflow.org/hub/common_saved_model_apis/text.
-
-For a full usage guide, see
-https://github.com/tensorflow/models/blob/master/official/nlp/docs/tfhub.md
-
-Minimal usage examples:
-
-1) Exporting an Encoder from checkpoint and config.
-
-```
-export_tfhub \
-  --encoder_config_file=${BERT_DIR:?}/bert_encoder.yaml \
-  --model_checkpoint_path=${BERT_DIR:?}/bert_model.ckpt \
-  --vocab_file=${BERT_DIR:?}/vocab.txt \
-  --export_type=model \
-  --export_path=/tmp/bert_model
-```
-
-An --encoder_config_file can specify encoder types other than BERT.
-For BERT, a --bert_config_file in the legacy JSON format can be passed instead.
-
-Flag --vocab_file (and flag --do_lower_case, whose default value is guessed
-from the vocab_file path) capture how BertTokenizer was used in pre-training.
-Use flag --sp_model_file instead if SentencepieceTokenizer was used.
-
-Changing --export_type to model_with_mlm additionally creates an `.mlm`
-subobject on the exported SavedModel that can be called to produce
-the logits of the Masked Language Model task from pretraining.
-The help string for flag --model_checkpoint_path explains the checkpoint
-formats required for each --export_type.
-
-
-2) Exporting a preprocessor SavedModel
-
-```
-export_tfhub \
-  --vocab_file ${BERT_DIR:?}/vocab.txt \
-  --export_type preprocessing --export_path /tmp/bert_preprocessing
-```
-
-Be sure to use flag values that match the encoder and how it has been
-pre-trained (see above for --vocab_file vs --sp_model_file).
-
-If your encoder has been trained with text preprocessing for which tfhub.dev
-already has SavedModel, you could guide your users to reuse that one instead
-of exporting and publishing your own.
-
-TODO(b/175369555): When exporting to users of TensorFlow 2.4, add flag
-`--experimental_disable_assert_in_preprocessing`.
-"""
-
-from absl import app
-from absl import flags
-import gin
-
-from official.legacy.bert import configs
-from official.modeling import hyperparams
-from official.nlp.configs import encoders
-from official.nlp.tools import export_tfhub_lib
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_enum(
-    "export_type", "model",
-    ["model", "model_with_mlm", "preprocessing"],
-    "The overall type of SavedModel to export. Flags "
-    "--bert_config_file/--encoder_config_file and --vocab_file/--sp_model_file "
-    "control which particular encoder model and preprocessing are exported.")
-flags.DEFINE_string(
-    "export_path", None,
-    "Directory to which the SavedModel is written.")
-flags.DEFINE_string(
-    "encoder_config_file", None,
-    "A yaml file representing `encoders.EncoderConfig` to define the encoder "
-    "(BERT or other). "
-    "Exactly one of --bert_config_file and --encoder_config_file can be set. "
-    "Needed for --export_type model and model_with_mlm.")
-flags.DEFINE_string(
-    "bert_config_file", None,
-    "A JSON file with a legacy BERT configuration to define the BERT encoder. "
-    "Exactly one of --bert_config_file and --encoder_config_file can be set. "
-    "Needed for --export_type model and model_with_mlm.")
-flags.DEFINE_bool(
-    "copy_pooler_dense_to_encoder", False,
-    "When the model is trained using `BertPretrainerV2`, the pool layer "
-    "of next sentence prediction task exists in `ClassificationHead` passed "
-    "to `BertPretrainerV2`. If True, we will copy this pooler's dense layer "
-    "to the encoder that is exported by this tool (as in classic BERT). "
-    "Using `BertPretrainerV2` and leaving this False exports an untrained "
-    "(randomly initialized) pooling layer, which some authors recommend for "
-    "subsequent fine-tuning,")
-flags.DEFINE_string(
-    "model_checkpoint_path", None,
-    "File path to a pre-trained model checkpoint. "
-    "For --export_type model, this has to be an object-based (TF2) checkpoint "
-    "that can be restored to `tf.train.Checkpoint(encoder=encoder)` "
-    "for the `encoder` defined by the config file."
-    "(Legacy checkpoints with `model=` instead of `encoder=` are also "
-    "supported for now.) "
-    "For --export_type model_with_mlm, it must be restorable to "
-    "`tf.train.Checkpoint(**BertPretrainerV2(...).checkpoint_items)`. "
-    "(For now, `tf.train.Checkpoint(pretrainer=BertPretrainerV2(...))` is also "
-    "accepted.)")
-flags.DEFINE_string(
-    "vocab_file", None,
-    "For encoders trained on BertTokenzier input: "
-    "the vocabulary file that the encoder model was trained with. "
-    "Exactly one of --vocab_file and --sp_model_file can be set. "
-    "Needed for --export_type model, model_with_mlm and preprocessing.")
-flags.DEFINE_string(
-    "sp_model_file", None,
-    "For encoders trained on SentencepieceTokenzier input: "
-    "the SentencePiece .model file that the encoder model was trained with. "
-    "Exactly one of --vocab_file and --sp_model_file can be set. "
-    "Needed for --export_type model, model_with_mlm and preprocessing.")
-flags.DEFINE_bool(
-    "do_lower_case", None,
-    "Whether to lowercase before tokenization. "
-    "If left as None, and --vocab_file is set, do_lower_case will be enabled "
-    "if 'uncased' appears in the name of --vocab_file. "
-    "If left as None, and --sp_model_file set, do_lower_case defaults to true. "
-    "Needed for --export_type model, model_with_mlm and preprocessing.")
-flags.DEFINE_integer(
-    "default_seq_length", 128,
-    "The sequence length of preprocessing results from "
-    "top-level preprocess method. This is also the default "
-    "sequence length for the bert_pack_inputs subobject."
-    "Needed for --export_type preprocessing.")
-flags.DEFINE_bool(
-    "tokenize_with_offsets", False,  # TODO(b/181866850)
-    "Whether to export a .tokenize_with_offsets subobject for "
-    "--export_type preprocessing.")
-flags.DEFINE_multi_string(
-    "gin_file", default=None,
-    help="List of paths to the config files.")
-flags.DEFINE_multi_string(
-    "gin_params", default=None,
-    help="List of Gin bindings.")
-flags.DEFINE_bool(  # TODO(b/175369555): Remove this flag and its use.
-    "experimental_disable_assert_in_preprocessing", False,
-    "Export a preprocessing model without tf.Assert ops. "
-    "Usually, that would be a bad idea, except TF2.4 has an issue with "
-    "Assert ops in tf.functions used in Dataset.map() on a TPU worker, "
-    "and omitting the Assert ops lets SavedModels avoid the issue.")
-
-
-def main(argv):
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-  gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
-
-  if bool(FLAGS.vocab_file) == bool(FLAGS.sp_model_file):
-    raise ValueError("Exactly one of `vocab_file` and `sp_model_file` "
-                     "can be specified, but got %s and %s." %
-                     (FLAGS.vocab_file, FLAGS.sp_model_file))
-  do_lower_case = export_tfhub_lib.get_do_lower_case(
-      FLAGS.do_lower_case, FLAGS.vocab_file, FLAGS.sp_model_file)
-
-  if FLAGS.export_type in ("model", "model_with_mlm"):
-    if bool(FLAGS.bert_config_file) == bool(FLAGS.encoder_config_file):
-      raise ValueError("Exactly one of `bert_config_file` and "
-                       "`encoder_config_file` can be specified, but got "
-                       "%s and %s." %
-                       (FLAGS.bert_config_file, FLAGS.encoder_config_file))
-    if FLAGS.bert_config_file:
-      bert_config = configs.BertConfig.from_json_file(FLAGS.bert_config_file)
-      encoder_config = None
-    else:
-      bert_config = None
-      encoder_config = encoders.EncoderConfig()
-      encoder_config = hyperparams.override_params_dict(
-          encoder_config, FLAGS.encoder_config_file, is_strict=True)
-    export_tfhub_lib.export_model(
-        FLAGS.export_path,
-        bert_config=bert_config,
-        encoder_config=encoder_config,
-        model_checkpoint_path=FLAGS.model_checkpoint_path,
-        vocab_file=FLAGS.vocab_file,
-        sp_model_file=FLAGS.sp_model_file,
-        do_lower_case=do_lower_case,
-        with_mlm=FLAGS.export_type == "model_with_mlm",
-        copy_pooler_dense_to_encoder=FLAGS.copy_pooler_dense_to_encoder)
-
-  elif FLAGS.export_type == "preprocessing":
-    export_tfhub_lib.export_preprocessing(
-        FLAGS.export_path,
-        vocab_file=FLAGS.vocab_file,
-        sp_model_file=FLAGS.sp_model_file,
-        do_lower_case=do_lower_case,
-        default_seq_length=FLAGS.default_seq_length,
-        tokenize_with_offsets=FLAGS.tokenize_with_offsets,
-        experimental_disable_assert=
-        FLAGS.experimental_disable_assert_in_preprocessing)
-
-  else:
-    raise app.UsageError(
-        "Unknown value '%s' for flag --export_type" % FLAGS.export_type)
-
-
-if __name__ == "__main__":
-  app.run(main)