[Usage]: How to use vLLM with Tensor input (customized tokenizer).

[keli-wen]

Your current environment

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PyTorch version: 2.1.2+cu121
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CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1036-azure-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe
Nvidia driver version: 545.23.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
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/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

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Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] flake8-bugbear==24.1.17
[pip3] mypy==1.8.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.25.2
[pip3] nvidia-pytriton==0.5.3
[pip3] pytorch-lightning==2.1.4
[pip3] torch==2.1.2
[pip3] torchaudio==2.2.0
[pip3] torchmetrics==1.3.1
[pip3] torchvision==0.17.0
[pip3] triton==2.1.0
[pip3] tritonclient==2.43.0
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[conda] nvidia-pytriton           0.5.3                    pypi_0    pypi
[conda] pytorch-lightning         2.1.4                    pypi_0    pypi
[conda] torch                     2.1.2                    pypi_0    pypi
[conda] torchaudio                2.2.0                    pypi_0    pypi
[conda] torchmetrics              1.3.1                    pypi_0    pypi
[conda] torchvision               0.17.0                   pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi
[conda] tritonclient              2.43.0                   pypi_0    pypiROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.3.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
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GPU0     X      0-23    0               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
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  PIX  = Connection traversing at most a single PCIe bridge
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How would you like to use vllm

Hello,

I am currently working on a finance-related Large Language Model (LLM) project. In this project, I'm using a customized tokenizer which is inherited from nn.Module instead of transformers.PreTrainedTokenizer.

class BaseOrderTokenizer(nn.Module):
    """Tokenizer for order info."""

    def __init__(
        self,
        *args,
        **kwargs,
    ) -> None:
        super().__init__()
        ...

Our model employs the Llama2 architecture for the decoder. However, I am uncertain about how to effectively integrate vLLM with our model that utilizes our customized tokenizer.

I would like to know if the following pipeline is feasible with the current version of vLLM: executing our tokenize method first, followed by using LLM.generate for generation tasks.

# pseudocode
tokens = tokenize(input)
output = llm.generate(tokens)

More specifically:

• Does vLLM currently support Tensor input?
• Is it possible to bypass providing a tokenizer, or to only provide a dummy tokenizer without actually employing it in the process?

Thank you for your patience and assistance. I eagerly await a response from the vLLM team.

[rkooo567]

I believe if you implement tokenizer class that works with

vllm/vllm/transformers_utils/tokenizer_group/tokenizer_group.py

Line 42 in 3492859

return tokenizer.encode(prompt)

this API, you can use

vllm/vllm/entrypoints/llm.py

Line 118 in 3492859

def set_tokenizer(

to set tokenizer.

[keli-wen]

✨Thanks for your reply!

It appears that my issue aligns closely with the following discussions:

• [Feature]: make _init_tokenizer optional and support initiating LLMEngine without tokenizer
• [Usage]: Is it possible to generate without detokenizing?

Our tokenizer is actually a simple nn.Module and is quite different from PreTrainedTokenizer.

class BaseOrderTokenizer(nn.Module):
    """Tokenizer for order info."""

    def __init__(
        self,
        max_order_index: int,
        emb_dim: int,
        num_max_orders: int,
    ) -> None:
        super().__init__()
        self.max_order_index = max_order_index
        self.num_max_orders = num_max_orders
        self.emb_dim = emb_dim

    def forward(self, features: Tensor) -> Tensor:
        raise NotImplementedError()

Essentially, it's an embedding layer. While I can implement a tokenizer.encode by modifying the forward function, implementing some functions in detokenize (e.g., code) is not feasible.

Moreover, the initialization of LLM requires the provision of a tokenizer. However, from an engineering perspective, decoupling generate from tokenizer might make the usage of vLLM more flexible. Currently, it seems there are limitations in using vLLM for non-NLP tasks - I can't directly use Tensor and custom tokenizer as input, even though I'm working with the Llama2 architecture, which is supported by vLLM.

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