bert-base-historic-multilingual-64k-td-cased
/
convert_token_dropping_bert_original_tf2_checkpoint_to_pytorch.py
| # Copyright 2022 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 converts a lm-head checkpoint from the "Token Dropping" implementation | |
| into a PyTorch-compatible BERT model. The official implementation of "Token Dropping" | |
| can be found in the TensorFlow Models repository: | |
| https://github.com/tensorflow/models/tree/master/official/projects/token_dropping | |
| """ | |
| import argparse | |
| import os | |
| import re | |
| import tensorflow as tf | |
| import torch | |
| from transformers import BertConfig, BertForMaskedLM | |
| from transformers.models.bert.modeling_bert import ( | |
| BertIntermediate, | |
| BertLayer, | |
| BertOutput, | |
| BertPooler, | |
| BertSelfAttention, | |
| BertSelfOutput, | |
| ) | |
| from transformers.utils import logging | |
| logging.set_verbosity_info() | |
| def convert_checkpoint_to_pytorch(tf_checkpoint_path: str, config_path: str, pytorch_dump_path: str): | |
| def get_masked_lm_array(name: str): | |
| full_name = f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE" | |
| array = tf.train.load_variable(tf_checkpoint_path, full_name) | |
| if "kernel" in name: | |
| array = array.transpose() | |
| return torch.from_numpy(array) | |
| def get_encoder_array(name: str): | |
| full_name = f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE" | |
| array = tf.train.load_variable(tf_checkpoint_path, full_name) | |
| if "kernel" in name: | |
| array = array.transpose() | |
| return torch.from_numpy(array) | |
| def get_encoder_layer_array(layer_index: int, name: str): | |
| full_name = f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE" | |
| array = tf.train.load_variable(tf_checkpoint_path, full_name) | |
| if "kernel" in name: | |
| array = array.transpose() | |
| return torch.from_numpy(array) | |
| def get_encoder_attention_layer_array(layer_index: int, name: str, orginal_shape): | |
| full_name = f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE" | |
| array = tf.train.load_variable(tf_checkpoint_path, full_name) | |
| array = array.reshape(orginal_shape) | |
| if "kernel" in name: | |
| array = array.transpose() | |
| return torch.from_numpy(array) | |
| print(f"Loading model based on config from {config_path}...") | |
| config = BertConfig.from_json_file(config_path) | |
| model = BertForMaskedLM(config) | |
| # Layers | |
| for layer_index in range(0, config.num_hidden_layers): | |
| layer: BertLayer = model.bert.encoder.layer[layer_index] | |
| # Self-attention | |
| self_attn: BertSelfAttention = layer.attention.self | |
| self_attn.query.weight.data = get_encoder_attention_layer_array(layer_index, "_query_dense/kernel", | |
| self_attn.query.weight.data.shape) | |
| self_attn.query.bias.data = get_encoder_attention_layer_array(layer_index, "_query_dense/bias", | |
| self_attn.query.bias.data.shape) | |
| self_attn.key.weight.data = get_encoder_attention_layer_array(layer_index, "_key_dense/kernel", | |
| self_attn.key.weight.data.shape) | |
| self_attn.key.bias.data = get_encoder_attention_layer_array(layer_index, "_key_dense/bias", | |
| self_attn.key.bias.data.shape) | |
| self_attn.value.weight.data = get_encoder_attention_layer_array(layer_index, "_value_dense/kernel", | |
| self_attn.value.weight.data.shape) | |
| self_attn.value.bias.data = get_encoder_attention_layer_array(layer_index, "_value_dense/bias", | |
| self_attn.value.bias.data.shape) | |
| # Self-attention Output | |
| self_output: BertSelfOutput = layer.attention.output | |
| self_output.dense.weight.data = get_encoder_attention_layer_array(layer_index, "_output_dense/kernel", | |
| self_output.dense.weight.data.shape) | |
| self_output.dense.bias.data = get_encoder_attention_layer_array(layer_index, "_output_dense/bias", | |
| self_output.dense.bias.data.shape) | |
| self_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/gamma") | |
| self_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/beta") | |
| # Intermediate | |
| intermediate: BertIntermediate = layer.intermediate | |
| intermediate.dense.weight.data = get_encoder_layer_array(layer_index, "_intermediate_dense/kernel") | |
| intermediate.dense.bias.data = get_encoder_layer_array(layer_index, "_intermediate_dense/bias") | |
| # Output | |
| bert_output: BertOutput = layer.output | |
| bert_output.dense.weight.data = get_encoder_layer_array(layer_index, "_output_dense/kernel") | |
| bert_output.dense.bias.data = get_encoder_layer_array(layer_index, "_output_dense/bias") | |
| bert_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_output_layer_norm/gamma") | |
| bert_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_output_layer_norm/beta") | |
| # Embeddings | |
| model.bert.embeddings.position_embeddings.weight.data = get_encoder_array("_position_embedding_layer/embeddings") | |
| model.bert.embeddings.token_type_embeddings.weight.data = get_encoder_array("_type_embedding_layer/embeddings") | |
| model.bert.embeddings.LayerNorm.weight.data = get_encoder_array("_embedding_norm_layer/gamma") | |
| model.bert.embeddings.LayerNorm.bias.data = get_encoder_array("_embedding_norm_layer/beta") | |
| # LM Head | |
| lm_head = model.cls.predictions.transform | |
| lm_head.dense.weight.data = get_masked_lm_array("dense/kernel") | |
| lm_head.dense.bias.data = get_masked_lm_array("dense/bias") | |
| lm_head.LayerNorm.weight.data = get_masked_lm_array("layer_norm/gamma") | |
| lm_head.LayerNorm.bias.data = get_masked_lm_array("layer_norm/beta") | |
| # It's in the masked-lm?! | |
| model.bert.embeddings.word_embeddings.weight.data = get_masked_lm_array("embedding_table") | |
| # Pooling | |
| model.bert.pooler = BertPooler(config=config) | |
| model.bert.pooler.dense.weight.data: BertPooler = get_encoder_array("_pooler_layer/kernel") | |
| model.bert.pooler.dense.bias.data: BertPooler = get_encoder_array("_pooler_layer/bias") | |
| # Export final model | |
| model.save_pretrained("./") | |
| # Integration test - should load without any errors ;) | |
| new_model = BertForMaskedLM.from_pretrained("./") | |
| print(new_model.eval()) | |
| print("Model conversion was done sucessfully!") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path." | |
| ) | |
| parser.add_argument( | |
| "--bert_config_file", | |
| type=str, | |
| required=True, | |
| help="The config json file corresponding to the BERT model. This specifies the model architecture.", | |
| ) | |
| parser.add_argument( | |
| "--pytorch_dump_path", | |
| type=str, | |
| required=True, | |
| help="Path to the output PyTorch model (must include filename).", | |
| ) | |
| args = parser.parse_args() | |
| convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path) | |