Token generators convert raw data into tokenized formats suitable for machine learning models, ensuring efficient and effective data processing. This guide covers the configuration of pre-built and custom token generators, along with examples and use cases.

​
Pre-Built Token Generators

Cerebras Model Zoo provides a comprehensive suite of pre-built token generators tailored to support various stages and tasks in the development of LLMs. The initialization of these token generators is dependent on the mode parameter that is specified in the config file (refer to Modes.

​
Flags Supported by Pre-Built Token Generators

​
Supported Token Generators - Pretraining Mode

  • PretrainingTokenGenerator: General-purpose pretraining on large text corpora. When training_objective is set to mlm, it does MLM task processing. For multimodal pretraining, is_multimodal is set to True.
  • FIMTokenGenerator: Designed for fill-in-the-middle tasks. Initialized when training_objective is set to fim in the config file.
  • VSLPretrainingTokenGenerator: For visual and language pretraining. Initialized when use_vsl is set to True in the config file.

​
Supported Token Generators - Finetuning Mode

  • FinetuningTokenGenerator: General-purpose fine-tuning. For multimodal finetuning, is_multimodal is set to True.
  • VSLFinetuningTokenGenerator: Fine-tuning for visual and language tasks. Initialized when use_vsl is set to True in the config file.

​
Other Supported Token Generators

  • DPOTokenGenerator: Focused on direct preference optimization (DPO) during token generation. Initialized when mode is set to dpo.
  • NLGTokenGenerator: Optimized for natural language generation tasks. Initialized when mode is set to nlg.

​
Custom Token Generators

In addition to pre-built token generators, the Model Zoo allows users to implement custom token generators. This enables arbitrary transformations of the input data before tokenization. To use custom token generators, ensure the configuration file is properly set up. Follow these steps: 1. Ensure that the mode param is set to custom, in order to be able to specify your own token generator. 2. Specify the path to the custom token generator class in the config file, in the token_generator param, within the setup section. This would look like: 
mode: "custom"
token_generator: "<path/to/custom-generator-class>"
The token_generator path should be specified with the class name being separated with a colon : from the module name, for the custom token generator be instantiated correctly.

​
Class Implementation Guidelines

The custom token generator must adhere to the following guidelines: 1. The constructor’s signature must be as follows: 
def __init__(
    self, params: Dict[str, Any], tokenizer: Any, eos_id: int, pad_id: int
):
    """
    Args:
        params (Dict[str, Any]): Parameters for the dataset and processing.
        tokenizer (Any): Tokenizer to use for tokenization.
        eos_id (int): End-of-sequence token ID.
        pad_id (int): Padding token ID.
    """
2. The custom token generator must implement an encode method, which tokenizes and encodes the data according to the user definition. For more examples on how the encode method looks like, refer to the code of pre-built token generators that are present in Model Zoo. 3. The signature of the encode method is given below, where it takes in a semantic_data_array: 
def encode(
    self, semantic_data_array: List[Dict[str, Any]]
) -> Tuple[Dict[str, Any], Dict[str, int]]:

​
Conclusion

Configuring token generators is an important step in the preprocessing pipeline for machine learning tasks on Cerebras Systems. By leveraging the comprehensive suite of pre-built token generators provided by Cerebras ModelZoo, you can efficiently handle various stages and tasks in the development of large language models. Additionally, the flexibility to implement custom token generators allows for tailored transformations of input data, meeting specific project requirements. The introduction of on-the-fly data processing further enhances the preprocessing workflow by reducing storage needs and increasing adaptability during training and evaluation. The examples provided for pretraining and fine-tuning configurations illustrate how to set up these processes seamlessly. Finally, the TokenFlow utility offers an invaluable tool for visualizing and debugging preprocessed data, ensuring data integrity and facilitating error detection. By following the guidelines and leveraging the tools outlined in this guide, you can optimize your preprocessing pipeline, leading to more efficient training and improved performance of your machine learning models on Cerebras Systems.