Welcome to the world of AI pre-training with TencentPretrain! This powerful toolkit allows you to effectively pre-train and fine-tune models on data across various modalities, including text and vision. In this guide, we will break down the process, provide insights on features, and offer troubleshooting tips along the way.

Features of TencentPretrain

• Reproducibility: Consistently matches performances of renowned pre-trained models like BERT and GPT-2.
• Model Modularity: Easy combination of different components such as embedding, encoder, and decoder.
• Multimodal Support: Works with various data types including text, vision, and audio.
• Flexible Training Modes: Ranging from CPU, single GPU, to distributed training.
• Model Zoo: Access to an extensive collection of pre-trained models.
• SOTA Results: Capable of handling diverse downstream tasks with state-of-the-art performances.
• Rich Functionality: Provides many functions related to pre-training and model utilization.

Requirements

To get started with TencentPretrain, ensure you have the following installed:

• Python = 3.6
• torch = 1.1
• six = 1.12.0
• argparse, packaging, regex
• DeepSpeed for gigantic model training
• TensorFlow (if required for model conversion)
• SentencePiece for tokenization
• torchvision for vision model training

Quickstart Guide

To demonstrate how TencentPretrain works, let’s walk through a common usage scenario: pre-training and fine-tuning a BERT model.

Pre-Training with BERT

Consider pre-training a BERT model on a book review dataset. Here’s how it can be likened to building a house:

• Foundation (Pre-processing): Just as the foundation of a house gives stability, pre-processing your data ensures it is clean and formatted. Run the command:

python3 preprocess.py --corpus_path corpora/book_review_bert.txt --vocab_path models/google_zh_vocab.txt --dataset_path dataset.pt --processes_num 8 --data_processor bert

• Structure (Pre-training): The structured framework of your model comes next. Using multiple GPUs to construct your ‘house,’ run:

python3 pretrain.py --dataset_path dataset.pt --vocab_path models/google_zh_vocab.txt --pretrained_model_path models/google_zh_model.bin --config_path models/bertbase_config.json --output_model_path models/book_review_model.bin --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 --total_steps 5000 --save_checkpoint_steps 1000 --batch_size 32

• Finishing Touches (Fine-tuning): Every good house needs its finishing touches before occupancy. Fine-tune with the following:

python3 finetunerun_classifier.py --pretrained_model_path models/book_review_model.bin --vocab_path models/google_zh_vocab.txt --train_path datasets/book_review_train.tsv --dev_path datasets/book_review_dev.tsv --test_path datasets/book_review_test.tsv --epochs_num 3 --batch_size 32

Troubleshooting

If you encounter issues during any part of the process, consider the following solutions:

• Check if all required dependencies are properly installed.
• Make sure your data paths are correctly set and files are in the expected format.
• Use a single process for the initial runs to identify specific error messages.
• If encountering memory errors, reduce batch sizes or switch to a smaller model.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

More Resources

• For pre-training data, check out the Pre-training Data Wiki.
• Explore the Model Zoo for available models.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.