Tokenizers tokenizer

[gabe-l-hart]

Dependencies

This PR is part of a sequence in support of adding Granite Code. It depends on merging the following PRs:

• Safetensors: Safetensors #1255
• Bias tensors: Bias tensors #1259
• Tied word embeddings: Tied word embeddings #1260

Issues

Closes #1251

Description

This PR adds partial support for models that use the tokenizers (as opposed to tiktoken or sentencepiece) for tokenization. This PR only addresses support in the python runner, and it does so by creating a new class in the tokenizer module that simply wraps tokenizers.

Discussion

I'm not sure this is the correct direction to go for solving this since the tokenizers library is not (to the best of my knowledge) portable to the various export formats (yet). There are two main challenges to extending more tokenizer support outside of simply wrapping tokenizers:

Pre-tokenizers

For may tokenizers, multiple regexes are used in sequence to split the raw string. Not being a regex expert myself, it's not immediately clear to me if it's possible to merge this kind of multi-pass splitting into a single regex. For other tokenizers, a single regex is used, but it is a different expression than any of those currently implemented in tiktoken.

From my investigation, I think there are a few candidate paths forward:

1. Provide a c++ implementation of the various tokenization routines from tokenizers in a separate implementation of the Tokenizer class.
2. Extend the existing c++ TikToken class to support multiple regexes in the pre-tokenizer
   • This would also correspond with needing to make the set of patterns configurable and therefore serialized into the tokenizer.model artifact, or somehow making these tokenizer arguments an argument at instantiation time.

NOTE: The corresponding tokenization in llama.cpp lives here. This code is a full implementation of a unified tokenizer with configuration to dispatch between known patterns and optimized implementations. The config for the model that indicates which tokenizer to use is stored in the model's GGUF file directly, so at load time, the correct tokenizer is found based on that value.

Special Tokens

Even for models that use a single regex (and even the llama regex), models may use different special tokens for special functionality (chat template, FIM, tool calling, other custom prompting). Since the tokenizer.model, only the vocab is stored, so there is not currently any way to note the special tokens in serialization (similar to the need for configuration of pre-tokenizers).

[pytorch-bot]

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