Add MultiBERTs conversion script (#13077)

* Init multibert checkpoint conversion script

* Rename conversion script

* Fix MultiBerts Conversion Script

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

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)