tf2_encoder_checkpoint_converter.py

# Copyright 2019 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
from official.modeling import tf_utils
from official.nlp.bert import configs
from official.nlp.bert import tf1_checkpoint_converter_lib
from official.nlp.modeling import models
from official.nlp.modeling import networks

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_checkpoint_converter_lib.convert(
      checkpoint_from_path=v1_checkpoint,
      checkpoint_to_path=temporary_checkpoint,
      num_heads=bert_config.num_attention_heads,
      name_replacements=tf1_checkpoint_converter_lib.BERT_V2_NAME_REPLACEMENTS,
      permutations=tf1_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_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)