Delete tf2_bert_encoder_checkpoint_converter.py

tf2_bert_encoder_checkpoint_converter.py

@@ -1,160 +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.
-
-"""A converter from a V1 BERT encoder checkpoint to a V2 encoder checkpoint.
-
-The conversion will yield an object-oriented checkpoint that can be used
-to restore a BertEncoder or BertPretrainerV2 object (see the `converted_model`
-FLAG below).
-"""
-
-import os
-
-from absl import app
-from absl import flags
-
-import tensorflow as tf, tf_keras
-from official.legacy.bert import configs
-from official.modeling import tf_utils
-from official.nlp.modeling import models
-from official.nlp.modeling import networks
-from official.nlp.tools import tf1_bert_checkpoint_converter_lib
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_string("bert_config_file", None,
-                    "Bert configuration file to define core bert layers.")
-flags.DEFINE_string(
-    "checkpoint_to_convert", None,
-    "Initial checkpoint from a pretrained BERT model core (that is, only the "
-    "BertModel, with no task heads.)")
-flags.DEFINE_string("converted_checkpoint_path", None,
-                    "Name for the created object-based V2 checkpoint.")
-flags.DEFINE_string("checkpoint_model_name", "encoder",
-                    "The name of the model when saving the checkpoint, i.e., "
-                    "the checkpoint will be saved using: "
-                    "tf.train.Checkpoint(FLAGS.checkpoint_model_name=model).")
-flags.DEFINE_enum(
-    "converted_model", "encoder", ["encoder", "pretrainer"],
-    "Whether to convert the checkpoint to a `BertEncoder` model or a "
-    "`BertPretrainerV2` model (with mlm but without classification heads).")
-
-
-def _create_bert_model(cfg):
-  """Creates a BERT keras core model from BERT configuration.
-
-  Args:
-    cfg: A `BertConfig` to create the core model.
-
-  Returns:
-    A BertEncoder network.
-  """
-  bert_encoder = networks.BertEncoder(
-      vocab_size=cfg.vocab_size,
-      hidden_size=cfg.hidden_size,
-      num_layers=cfg.num_hidden_layers,
-      num_attention_heads=cfg.num_attention_heads,
-      intermediate_size=cfg.intermediate_size,
-      activation=tf_utils.get_activation(cfg.hidden_act),
-      dropout_rate=cfg.hidden_dropout_prob,
-      attention_dropout_rate=cfg.attention_probs_dropout_prob,
-      max_sequence_length=cfg.max_position_embeddings,
-      type_vocab_size=cfg.type_vocab_size,
-      initializer=tf_keras.initializers.TruncatedNormal(
-          stddev=cfg.initializer_range),
-      embedding_width=cfg.embedding_size)
-
-  return bert_encoder
-
-
-def _create_bert_pretrainer_model(cfg):
-  """Creates a BERT keras core model from BERT configuration.
-
-  Args:
-    cfg: A `BertConfig` to create the core model.
-
-  Returns:
-    A BertPretrainerV2 model.
-  """
-  bert_encoder = _create_bert_model(cfg)
-  pretrainer = models.BertPretrainerV2(
-      encoder_network=bert_encoder,
-      mlm_activation=tf_utils.get_activation(cfg.hidden_act),
-      mlm_initializer=tf_keras.initializers.TruncatedNormal(
-          stddev=cfg.initializer_range))
-  # Makes sure the pretrainer variables are created.
-  _ = pretrainer(pretrainer.inputs)
-  return pretrainer
-
-
-def convert_checkpoint(bert_config,
-                       output_path,
-                       v1_checkpoint,
-                       checkpoint_model_name="model",
-                       converted_model="encoder"):
-  """Converts a V1 checkpoint into an OO V2 checkpoint."""
-  output_dir, _ = os.path.split(output_path)
-  tf.io.gfile.makedirs(output_dir)
-
-  # Create a temporary V1 name-converted checkpoint in the output directory.
-  temporary_checkpoint_dir = os.path.join(output_dir, "temp_v1")
-  temporary_checkpoint = os.path.join(temporary_checkpoint_dir, "ckpt")
-
-  tf1_bert_checkpoint_converter_lib.convert(
-      checkpoint_from_path=v1_checkpoint,
-      checkpoint_to_path=temporary_checkpoint,
-      num_heads=bert_config.num_attention_heads,
-      name_replacements=(
-          tf1_bert_checkpoint_converter_lib.BERT_V2_NAME_REPLACEMENTS),
-      permutations=tf1_bert_checkpoint_converter_lib.BERT_V2_PERMUTATIONS,
-      exclude_patterns=["adam", "Adam"])
-
-  if converted_model == "encoder":
-    model = _create_bert_model(bert_config)
-  elif converted_model == "pretrainer":
-    model = _create_bert_pretrainer_model(bert_config)
-  else:
-    raise ValueError("Unsupported converted_model: %s" % converted_model)
-
-  # Create a V2 checkpoint from the temporary checkpoint.
-  tf1_bert_checkpoint_converter_lib.create_v2_checkpoint(
-      model, temporary_checkpoint, output_path, checkpoint_model_name)
-
-  # Clean up the temporary checkpoint, if it exists.
-  try:
-    tf.io.gfile.rmtree(temporary_checkpoint_dir)
-  except tf.errors.OpError:
-    # If it doesn't exist, we don't need to clean it up; continue.
-    pass
-
-
-def main(argv):
-  if len(argv) > 1:
-    raise app.UsageError("Too many command-line arguments.")
-
-  output_path = FLAGS.converted_checkpoint_path
-  v1_checkpoint = FLAGS.checkpoint_to_convert
-  checkpoint_model_name = FLAGS.checkpoint_model_name
-  converted_model = FLAGS.converted_model
-  bert_config = configs.BertConfig.from_json_file(FLAGS.bert_config_file)
-  convert_checkpoint(
-      bert_config=bert_config,
-      output_path=output_path,
-      v1_checkpoint=v1_checkpoint,
-      checkpoint_model_name=checkpoint_model_name,
-      converted_model=converted_model)
-
-
-if __name__ == "__main__":
-  app.run(main)