GPUTOK: GPU Accelerated Byte Level BPE Tokenization
This work addresses the problem of inefficient tokenization for developers and researchers working with large language models, providing an incremental improvement in performance.
The authors tackled the problem of slow CPU tokenizers for large language models by developing a GPU-based byte-level BPE tokenizer, achieving a 1.7x speedup over tiktoken and 7.6x speedup over the HuggingFace GPT-2 tokenizer for long inputs. The GPU tokenizer maintains output quality within 1 percentage point of existing tokenizers on generation tasks.
As large language models move toward million-token context windows, CPU tokenizers become a major slowdown because they process text one step at a time while powerful GPUs sit unused. We built a GPU-based byte-level BPE tokenizer that follows GPT-2's merge rules. It includes a basic BlockBPE-style kernel and a faster, optimized version that uses cuCollections static map, CUB reductions, and a pybind11 interface for Python. On WikiText103 sequences up to 131k tokens, the optimized GPU tokenizer produces the same tokens as a CPU version and, for the longest inputs, is about 1.7x faster than tiktoken and about 7.6x faster than the HuggingFace GPT-2 tokenizer. Nsight profiling shows that 70-80% of CUDA API time goes to memory allocation, so adding memory pooling should give the biggest speed boost next. Tests on generation tasks using WikiText103 prompts show that our GPU tokenizer's outputs stay within about one percentage point of tiktoken and HuggingFace GPT-2 on similarity and overlap metrics, meaning it keeps output quality while making long-context inference more practical.