Beyond Language Models: Byte Models are Digital World Simulators

Model Description

1. weights-conversion.pth: bGPT pre-trained on IrishMAN for data conversion (between .abc and .mid).
2. weights-cpu.pth: bGPT pre-trained on CPU states for CPU state modelling (.bin).
3. weights-text.pth: bGPT pre-trained on Wikipedia for text generation/classification (.txt).
4. weights-image.pth: bGPT pre-trained on ImageNet for image generation/classification (.bmp).
5. weights-audio.pth: bGPT pre-trained on Librispeech for audio generation/classification (.wav).

Installation

1. Create and Activate Conda Environment
   bash

   1conda create --name bgpt python=3.7.9 2conda activate bgpt
2. Install Dependencies
   pip install -r requirements.txt
3. Install Pytorch
   pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
4. Download Pre-trained bGPT Weights (Optional)
   For those interested in starting with pre-trained models, bGPT weights are available on Hugging Face. This step is optional but recommended for users looking to leverage the model's capabilities without training from scratch.

Usage

• config.py: Configuration settings for training and inference.
• cpu-simulation.py: Simulate CPU states and operations.
• inference.py: Perform inference tasks (e.g., generation and conversion) using pre-trained models.
• train-cls.py: Training script for classification models.
• train-gen.py: Training script for generative models.
• utils.py: Utility functions supporting model operations and data processing.

Configuration

Training Configuration

• TRAIN_FOLDERS: Specify the dataset folders for training. Multiple folders can be included.
• EVAL_FOLDERS: Specify evaluation dataset folders.
• PRETRAINED_PATH: Path to pre-trained weights for transfer learning and fine-tuning.
• WEIGHTS_PATH & LOGS_PATH: Define locations to save trained weights and logs, respectively.
• NUM_EPOCHS, LEARNING_RATE, BATCH_SIZE: Control training duration, learning rate, and batch size for optimal learning.
• ACCUMULATION_STEPS: Set accumulation steps to emulate larger batch sizes, managing memory usage efficiently.
• PATCH_SAMPLING_BATCH_SIZE: Adjust batch size for patch sampling during training to reduce computational load, with 0 for full batch processing.

Inference Configuration

• INFERENCE_WEIGHTS_PATH: Path to weights for inference.
• MODE: Determines operation mode (convert or generate), guiding the model for specific outcomes.
• NUM_SAMPLES, TOP_K, TOP_P, TEMPERATURE: Set sampling strategy during inference to control the diversity of outputs.

Generative Modelling

1. Prepare Your Data: bGPT can handle any computer file type, including text, images, audio, executables, and encrypted or proprietary formats, without needing specific adjustments for each modality. This capability allows for straightforward and versatile training on a wide array of data. The only thing you need to do here is simply to split your data for training and evaluation.
2. Adjust Configuration Settings: Modify the config.py file to tailor the training process to your needs. At a minimum, you should update the TRAIN_FOLDERS and EVAL_FOLDERS to point to your actual data directories. Also, specify where to save the trained model weights and logs by setting WEIGHTS_PATH and LOGS_PATH. You may adjust other parameters based on your specific requirements. For instance, with the default PATCH_SIZE=16 and PATCH_LENGTH=512, bGPT can model byte sequences up to 8KB. If your training files are larger, and you have sufficient computational resources, consider increasing these parameters to accommodate the larger file sizes.
3. Leverage Pre-trained Weights (Optional): If you wish to fine-tune a pre-trained bGPT model, set PRETRAINED_PATH to the location of the pre-trained weights and ensure LOAD_FROM_PRETRAINED=True. To train a model from scratch, simply set LOAD_FROM_PRETRAINED=False.
4. Start Training: Run train-gen.py to begin the training process. The script will use the configurations set in config.py and apply the training data to learn generative models capable of producing new, unseen outputs in the format of your training data.

Data Conversion

1. Define Conversion Mode: In your config.py file, you'll define the CONVERSION_MODE setting, which governs how files are transformed. This setting offers two options: unidirectional and bidirectional conversion, denoted by "->" and "&" respectively.
   • Unidirectional Conversion: Denoted by "->", this mode signifies a one-way transformation from one format to another. For instance, if you want to convert text files to HTML, you'd set CONVERSION_MODE = "txt->html". This means the model will learn to convert text files specifically into HTML format, but not vice versa.
   • Bidirectional Conversion: Denoted by "&", this mode implies a two-way transformation between formats. For example, setting CONVERSION_MODE = "wav&mp3" instructs the model to learn bidirectional conversion between WAV and MP3 audio formats. In this mode, the model learns to convert files from WAV to MP3 and vice versa, allowing flexibility in both directions of conversion.
2. Prepare Your Data: Ensure your data pairs are stored within the same directory path in both TRAIN_FOLDERS and EVAL_FOLDERS. Each pair should share identical paths, including filenames, differing only in their file extensions. For instance, if converting between WAV and MP3 formats, ensure files like "path/audio.wav" and "path/audio.mp3" are paired accordingly. This strict alignment guarantees the script correctly associates files for conversion based on the specified mode.
3. Adjust Training Parameters: Although the conversion mode operates under the same training principles as generative modelling, you might want to adjust certain parameters in config.py to optimize the conversion process. This could include tuning the PATCH_SIZE and PATCH_LENGTH settings to better accommodate the file sizes commonly encountered in your conversion tasks.
4. Leverage Pre-trained Weights (Optional): Same as regular generative modelling, if you wish to fine-tune a pre-trained bGPT model, set PRETRAINED_PATH to the location of the pre-trained weights and ensure LOAD_FROM_PRETRAINED=True. To train a model from scratch, simply set LOAD_FROM_PRETRAINED=False.
5. Start Training for Conversion: When training bGPT in conversion mode, the model learns to map byte sequences from the source format to the target format (or vice versa in bidirectional mode). Execute train-gen.py to start the training process, ensuring that the CONVERSION_MODE is correctly set in your configuration file.

Classification

1. Prepare Labelled Data: Ensure your dataset consists of labelled data, which can be a mix of different formats. The model distinguishes between data types using the naming convention label.ext, where the label is derived from the filename, specifically filename.split('_')[0]. This means that the label for classification should be clearly reflected in the file name, such as "Business_1.txt". It is crucial to organize your files accordingly to facilitate accurate classification.
2. Generative Modelling Before Classification (Strongly Recommended): Before embarking on classification tasks, it is highly recommended to perform generative modelling on the same dataset. Starting with weights trained through generative modelling provides a solid foundation for further fine-tuning in classification tasks. To do this, set PRETRAINED_PATH to your generative model weights and ensure LOAD_FROM_PRETRAINED=True. Directly training a classification model from scratch without this pre-training step has been observed to result in significantly poorer performance. When fine-tuning for classification, ensure that WEIGHTS_PATH and LOGS_PATH are set to different locations to prevent overwriting previous models. Note that the classification model will inherit the bGPT's patch-level decoder and discard the byte-level decoder, so it's essential to keep the model parameters unchanged during this phase.
3. Start Training for Classification: Run train-cls.py to begin the classification training process. The script will utilize the previously set configurations and apply them to your labelled dataset. The model will learn to classify the input data into the defined categories based on the labels extracted from the filenames.

Inference

1. Set Up the Inference Configuration: First, ensure your config.py includes the configuration for inference as shown above. Adjust the parameters according to the task you want to perform:
   • INFERENCE_WEIGHTS_PATH: This should point to the location of your trained model weights that you intend to use for inference. For example, weights-conversion indicates the model trained for converting files from one format to another.
   • INPUT_EXT and TARGET_EXT: These parameters define the extensions of the input and target files, respectively. In the given configuration, the model expects input files with the .mid extension and will aim to convert them into files with the .abc extension.
   • MODE: Determines the mode of inference. convert mode is used for converting files from one format to another, while generate mode is used for generating new content.
   • NUM_SAMPLES, TOP_K, TOP_P, and TEMPERATURE: These parameters control the sampling strategy for generation tasks, influencing the diversity and creativity of the output.
2. Performing Conversion or Generation: Depending on the MODE you've set, the inference process will either convert input files to a new format or generate new content:
   • Conversion: In convert mode, ensure your input files (e.g., .mid) are placed in a designated directory. The model will read these files, apply the conversion process, and output files in the target format (e.g., .abc) in the specified output directory.
   • Generation: In generate mode, the model will generate samples from scratch. The number of samples to generate is specified by NUM_SAMPLES. The generated samples will be placed in the output directory.
3. Executing Inference: To start the inference process, run the script inference.py. Make sure the script is configured to use the settings from config.py for conducting the desired inference task.

CPU States Dataset

• Generate Mode: This mode is used to generate new instances of CPU states, simulating a wide range of program instructions to create a diverse dataset. It involves specifying the number of registers, memory size, and the range and quantity of instruction sequences to be generated. Example command:
  python cpu-simulation.py --mode generate --num_registers 10 --memory_size 1024 --dir_path cpu_states --num_min_program 1 --num_max_program 255 --num_train_instance 2100000 --num_test_instance 21000 --random_seed 0
  This generates training and testing instances with programs containing 1 to 255 instructions, tailored to the specified memory size and register count.
• Translate Mode: It is used to convert binary representations of CPU states into a human-readable format. This conversion is essential for manual inspection, debugging, and understanding the dataset's intricacies. Example command:
  python cpu-simulation.py --mode translate --states_path cpu_states/cpu.bin
• Evaluate Mode: To assess the accuracy of the model's predictions, the evaluate mode compares the predicted CPU states against the actual states within the dataset. Example command:
  python cpu-simulation.py --mode evaluate --dir_path cpu_states

BibTeX

@misc{wu2024language,
      title={Beyond Language Models: Byte Models are Digital World Simulators}, 
      author={Shangda Wu and Xu Tan and Zili Wang and Rui Wang and Xiaobing Li and Maosong Sun},
      year={2024},
      eprint={2402.19155},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}