Token Generators#

Overview#

Setting up token generators is a crucial step in the preprocessing pipeline for machine learning tasks on Cerebras Systems. 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 and Dataset Parameters).

Supported token generators#

For Pretraining mode#

PretrainingTokenGenerator: General-purpose pretraining on large text corpora. When training_objective is set to mlm, it does MLM task processing.
MultiModalPretrainingTokenGenerator: For multimodal data, integrating text, and images. Initialized when is_multimodal is set to True in the config file.
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.

For Fine-tuning mode#

FinetuningTokenGenerator: General-purpose fine-tuning.
MultiModalFinetuningTokenGenerator: Fine-tuning with multimodal data. Initialized when is_multimodal is set to True in the config file.
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.

Flags Supported by Pre-built Token Generators#

This section lists all the flags that are supported by the various token generators, along with their default values.

Common Parameters#

This section lists down parameters that are common and can be used across all the token generators.

Table 8 Configuration Flags For Common Parameters#
Flag	Default Value	Description
use_ftfy	False	Fix text with ftfy.
ftfy_normalizer	NFC	Choose what kind of unicode normalization is applied. Usually, we apply NFC normalization, so that letters followed by combining characters become single combined characters. Using None applies no normalization while fixing text.
wikitext_detokenize	False	Use wikitext detokenizer to fix text.
min_sequence_len	10	Minimum token length to skip the sample.
input_ids_dtype	int32	dtype of processed input_ids.
input_mask_dtype	int32	dtype of processed input loss masks.
max_seq_length	2048	Maximum sequence length.

Pretraining Parameters#

This section lists down parameters that can be used for PretrainingTokenGenerator.

Table 9 Configuration Flags For Pretraining#
Flag	Default Value	Description
pack_sequences	True	Concatenate a document smaller than maximum sequence length with other documents, instead of filling it with Padding token.
inverted_mask	False	If False, 0 represents masked positions. If True 1 represents masked positions.
seed	0	Random seed used for generating short sequences
short_seq_prob	0.0	Probability of creating sequences which are shorter than the maximum sequence length.
split_text_to_tokenize	False	Whether to split the text into smaller chunks before tokenization. This is helpful for very long documents with tokenizers such as Llama tokenizer which performs quadratically in the text length.
chunk_len_to_split	2000	Length of the text chunks to split the text into before tokenization for slower tokenizers. Could be optionally used with the above flag split_text_to_tokenize. Without the previous flag, this argument will be ignored.
remove_bos_in_chunks	False	Whether to remove the BOS token from the beginning of the chunks. Set this to True when using split_test_to_tokenize and chunk_len_to_split to avoid having multiple BOS tokens in the middle of the text. Not applicable to all tokenizers.

Finetuning Parameters#

This section lists down parameters that can be used for FinetuningTokenGenerator.

Table 10 Configuration Flags For Finetuning#
Flag	Default Value	Description
chat_template	None	Custom chat template which overrides the tokenizer’s in built chat template. Useful when preprocessing chat format data.
semantic_drop_mask	{}	Dictionary which indicates which semantic region to drop from input data before tokenization
semantic_loss_weight	{}	Dictionary which indicates the loss mask of the different semantic regions post tokenization
end_of_turn_token	None	Required when the tokenizer inserts an end-of-turn token in the chat template after each turn, which is different from the EOS token.
sep_token	tokenizer’s `sep_token` if available else “<\|sep\|>”	Separator token to be used for separating prompt and completion for instruction style datasets

FIM Token Generator Parameters#

This section lists down parameters that can be used for FIMTokenGenerator.

Note

FIMTokenGenerator also uses config paramaters that are used by PretrainingTokenGenerator, in addition to the ones specified below.

Table 11 Configuration Flags For FIM#
Flag	Default Value	Description
fim_rate	0.90	Float specifying percentage of data to apply FIM transformation, instead of leaving as auto-regressive.
spm_rate	0.50	Float specifying percentage of FIM transformation to convert to prefix-suffix-middle (PSM) vs suffix-prefix-middle (SPM) formats.
fim_suffix_tok	None	Special token denoting suffix section in a FIM’ed context.
fim_prefix_tok	None	Special token denoting prefix section in a FIM’ed context.
fim_middle_tok	None	Special token denoting middle section in a FIM’ed context.

MLM Token Generator Parameters#

This section lists down parameters that can be used for PretrainingTokenGenerator when training_objective is set to mlm.

Note

In this case, it also uses all the config paramaters that are used by PretrainingTokenGenerator, in addition to the ones specified below.

Table 12 Configuration Flags For MLM#
Flag	Default Value	Description
mlm_fraction	0.15	Fraction of tokens to be masked in MLM tasks.
mlm_with_gather	False	MLM processing mode. When set to True the length of the returned labels is equal to mlm_fraction * msl, else it is equal to msl
ignore_index	-100	Required when mlm_with_gather is set to False. Presence of ignore_index value at a position in the labels indicates that this position will not be used for loss calculation.
excluded_tokens	[‘<cls>’, ‘<pad>’, ‘<eos>’, ‘<unk>’, ‘<null_1>’, ‘<mask>’]	Tokens to be excluded when masking. Provided only through YAML config.

VSL Finetuning Token Generator Parameters#

This section lists down parameters that can be used for VSLFineTuningTokenGenerator.

Note

VSLFineTuningTokenGenerator also uses the config paramaters that are used by FineTuningTokenGenerator, in addition to the ones specified below.

Table 13 Configuration Flags For VSL Finetuning#
Flag	Default Value	Description
use_vsl	True	Generate examples with multiple sequences packed together
position_ids_dtype	int32	dtype of token position ids.

Note

Increasing the read chunk size will increase the packing factor of VSL. So, the user needs to figure out the tradeoff between higher packing and processing time depending on the dataset’s packing factor.

VSL Pretraining Token Generator Parameters#

This section lists down parameters that can be used for VSLPretrainingTokenGenerator. use_vsl needs to be set to True in the train_input or eval_input section of the model config.

Note

VSLPretrainingTokenGenerator also uses the config paramaters that are used by PretrainingTokenGenerator, in addition to the ones specified below.

Table 14 Configuration Flags For VSL Pretraining#
Flag	Default Value	Description
use_vsl	True	Generate examples with multiple sequences packed together
fold_long_doc	True	Fold documents larger than max_seq_length into multiple sequences, instead of dropping them.

DPO Token Generator Parameters#

This section lists down parameters that can be used for DPOTokenGenerator.

Table 15 Configuration Flags For DPO#
Flag	Default Value	Description
max_prompt_length	512	If the sequence exceeds the `max_seq_length`, this parameters caps the prompt length to the specified limit.
response_delimiter	`<response>`	This is used to set the separator between `prompt` and `response`. The user need not set this value for general use-case.

Multimodal Pretraining Token Generator Parameters#

This section lists down parameters that can be used for MultiModalPretrainingTokenGenerator.

Table 16 Configuration Flags For Multimodal Pretraining#
Flag	Default Value	Description
image_dir	None	Absolute path of image directory. Used along with the relative path under the image_key field in read_hook_kwargs to check that images exist, and throw out examples with no image.
max_num_img	1	Maximum number of images allowed in one preprocessed sequence. Sequences with more than max_num_img images will be discarded
num_patches	None	Number of patches to represent an image. This is determined by the patch-size (in pixels) of the image-encoder, and the pixel count of the input images.
semantic_attention_mask	{}	Dictionary which indicates the attention mask of the different semantic regions.
semantic_drop_mask	{}	Dictionary which indicates which semantic region to drop from input data before tokenization
semantic_loss_weight	{}	Dictionary which indicates the loss mask of the different semantic regions post tokenization

Multimodal Finetuning Token Generator Parameters#

This section lists down parameters that can be used for MultiModalFinetuningTokenGenerator.

Note

MultiModalFinetuningTokenGenerator also uses config paramaters that are used by FineTuningTokenGenerator, in addition to the ones specifed below.

Table 17 Configuration Flags For Multimodal Finetuning#
Flag	Default Value	Description
image_dir	None	Absolute path of image directory. Used along with the relative path under the image_key field in read_hook_kwargs to check that images exist, and throw out examples with no image.
max_num_img	1	Maximum number of images allowed in one preprocessed sequence. Sequences with more than max_num_img images will be discarded
num_patches	None	Number of patches to represent an image. This is determined by the patch-size (in pixels) of the image-encoder, and the pixel count of the input images.
semantic_attention_mask	{}	Dictionary which indicates the attention mask of the different semantic regions.

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.

Procedure#

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>"

Note

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

Read Hooks

Custom Tokenizer