Delete convert_multiberts_checkpoint_to_pytorch.py (#13852)

src/transformers/models/bert/convert_multiberts_checkpoint_to_pytorch.py

-# Copyright 2021 The HuggingFace Team. 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.
-
-"""
-This script can be used to convert a head-less TF 2.x MultiBERTs model to PyTorch, as published on the official GitHub:
-https://github.com/tensorflow/models/tree/master/official/nlp/bert
-"""
-
-import argparse
-import os
-
-import tensorflow as tf
-import torch
-
-from transformers import BertConfig, BertForPreTraining
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def convert_multibert_checkpoint_to_pytorch(tf_checkpoint_path, config_path, save_path):
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
-
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    arrays = []
-    config = BertConfig.from_pretrained(config_path)
-    model = BertForPreTraining(config)
-
-    layer_nums = []
-    for full_name, shape in init_vars:
-        array = tf.train.load_variable(tf_path, full_name)
-        names.append(full_name)
-        split_names = full_name.split("/")
-        for name in split_names:
-            if name.startswith("layer_"):
-                layer_nums.append(int(name.split("_")[-1]))
-
-        arrays.append(array)
-    logger.info(f"Read a total of {len(arrays):,} layers")
-
-    name_to_array = dict(zip(names, arrays))
-
-    # Check that number of layers match
-    assert config.num_hidden_layers == len(list(set(layer_nums)))
-
-    state_dict = model.state_dict()
-
-    # Need to do this explicitly as it is a buffer
-    position_ids = state_dict["bert.embeddings.position_ids"]
-    new_state_dict = {"bert.embeddings.position_ids": position_ids}
-
-    # Encoder Layers
-    for weight_name in names:
-        pt_weight_name = weight_name.replace("kernel", "weight").replace("gamma", "weight").replace("beta", "bias")
-        name_split = pt_weight_name.split("/")
-        for name_idx, name in enumerate(name_split):
-            if name.startswith("layer_"):
-                name_split[name_idx] = name.replace("_", ".")
-
-        if name_split[-1].endswith("embeddings"):
-            name_split.append("weight")
-
-        if name_split[0] == "cls":
-            if name_split[-1] == "output_bias":
-                name_split[-1] = "bias"
-            if name_split[-1] == "output_weights":
-                name_split[-1] = "weight"
-
-        if name_split[-1] == "weight" and name_split[-2] == "dense":
-            name_to_array[weight_name] = name_to_array[weight_name].T
-
-        pt_weight_name = ".".join(name_split)
-
-        new_state_dict[pt_weight_name] = torch.from_numpy(name_to_array[weight_name])
-
-    new_state_dict["cls.predictions.decoder.weight"] = new_state_dict["bert.embeddings.word_embeddings.weight"].clone()
-    new_state_dict["cls.predictions.decoder.bias"] = new_state_dict["cls.predictions.bias"].clone().T
-    # Load State Dict
-    model.load_state_dict(new_state_dict)
-
-    # Save PreTrained
-    logger.info(f"Saving pretrained model to {save_path}")
-    model.save_pretrained(save_path)
-
-    return model
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--tf_checkpoint_path",
-        type=str,
-        default="./seed_0/bert.ckpt",
-        required=False,
-        help="Path to the TensorFlow 2.x checkpoint path.",
-    )
-    parser.add_argument(
-        "--bert_config_file",
-        type=str,
-        default="./bert_config.json",
-        required=False,
-        help="The config json file corresponding to the BERT model. This specifies the model architecture.",
-    )
-    parser.add_argument(
-        "--save_path",
-        type=str,
-        required=True,
-        help="Path to the output PyTorch model (must include filename).",
-    )
-    args = parser.parse_args()
-
-    convert_multibert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.save_path)