Why does the size of token_ids generated by the large model during vllm inference exceed the length of the vocabulary?

General
1

Why does the size of token_ids generated by the large model during vllm inference exceed the length of the vocabulary?

2

The size of token_ids generated by some large models (e.g., Qwen, LLaMA, OPT) during vLLM inference can exceed the tokenizer vocabulary length because these models often have a model config vocab_size that is larger than the actual number of tokens in the tokenizer. This is usually due to padding the embedding matrix to a multiple of 8 or 16 for efficient GPU computation. The extra token IDs (beyond the tokenizer’s range) are not mapped to any real token and, if sampled, can cause out-of-vocabulary errors or decoding issues. This is a known issue and not unique to vLLM; it can be mitigated by masking out-of-vocab logits or ensuring the model only samples valid token IDs, but not all vLLM versions handle this automatically. See detailed explanations in vllm issue #13175 and vllm issue #340.

Would you like more detail on how to prevent or handle this in vLLM?

Sources:

3

is there any other issues would cause this problem?

4

Besides the vocab_size/tokenizer length mismatch described in vllm issue #340, other issues that can cause token_ids to exceed the vocabulary length include: (1) improper masking of logits before sampling, especially in models where the lm_head output dimension is larger than the tokenizer vocab; (2) fine-tuned or quantized models where the tokenizer and model config are not kept in sync; and (3) speculative decoding or draft/target model vocab mismatches, as discussed in vllm issue #7252 and vllm issue #24211.

Would you like more detail on how these issues manifest or how to address them?

Sources:

5

Any method to find the real reason of this problem?

6

To find the reason for token_ids exceeding the vocabulary length, check if the model’s config vocab_size is larger than the tokenizer’s length, and inspect whether the logits masking is applied to prevent sampling out-of-vocab tokens. Also, review error logs for “out of vocabulary” or “index out of bounds” messages, and compare the model and tokenizer files for mismatches. See vllm issue #340 and vllm issue #24211 for diagnostic steps.

Would you like a step-by-step guide to debug this in your environment?

Sources:

7

yep. I need a step-by-step guide.

8

Certainly! Here’s a step-by-step guide to debug why token_ids exceed the vocabulary length in vLLM:

  1. Check Model and Tokenizer Vocab Sizes

    • Load both the model config and tokenizer, and print their vocab sizes:
      from transformers import AutoConfig, AutoTokenizer
config = AutoConfig.from_pretrained("your-model")
tokenizer = AutoTokenizer.from_pretrained("your-model")
print("Model vocab_size:", config.vocab_size)
print("Tokenizer length:", len(tokenizer))
    • If config.vocab_size > len(tokenizer), this is likely the root cause (vllm issue #340).

  2. Review Error Logs

    • Look for errors like Token id X is out of vocabulary or index out of bounds in your vLLM logs. These indicate that the model generated a token ID not present in the tokenizer (vllm issue #24211).

  3. Inspect Logits Masking

    • Ensure that your inference code or vLLM version masks logits for token IDs >= tokenizer length. If not, out-of-vocab tokens can be sampled. Some users patch this by setting logits for out-of-vocab tokens to -inf (vllm issue #13175).

  4. Check for Model/Tokenizer Mismatches

    • Confirm that the model and tokenizer are from the same source and version. Mismatches can cause vocab inconsistencies.

  5. Test with a Minimal Example

    • Run a short script to reproduce the error with a known prompt and model. This helps isolate the issue.

  6. Consult vLLM Issues and Docs

    • Search for your model name and error message in vLLM GitHub issues for model-specific advice.

Let me know if you need code snippets for any of these steps or further troubleshooting help.

Sources: