Affected: vllm-project vllm

vLLM is an inference and serving engine for large language models. Prior to 0.32.0, vllm/tokenizers/hf.py computes max_token_id with an off-by-one upper bound, allowing a pre-tokenized prompt ID equal to the vocabulary size to pass validation. For a tensor-parallel size of one and an exact multiple-of-64 vocabulary, VocabParallelEmbedding.forward does not mask that value before F.embedding uses it, causing a CUDA device-side assertion and terminating the engine. This issue is fixed in version 0.32.0.

Affected: PyPI: vllm

For a model whose vocabulary size is an exact multiple of the embedding padding size (64) — e.g. any Llama-2-family model, vocab 32000 — a single request carrying a pre-tokenized prompt with token id equal to the vocab size (32000) passes vLLM's input-id validation but is one past the last embedding row. At tensor-parallel size 1 the embedding lookup does not mask indices, so the id reaches `F.embedding` out of range and raises a CUDA device-side assert, which poisons the engine's CUDA context and takes the engine down. Any client that can send pre-tokenized input (`/v1/completions` with `prompt` as token ids, `/tokenize`-fed flows, or the `kv_transfer_params["prompt_token_ids"]` reuse path) can trigger it.

## Root cause `vllm/v1/engine/input_processor.py` accepts a token id when `id <= max(tokenizer.max_token_id, model_config.get_vocab_size() - 1)`. `vllm/tokenizers/hf.py` computes `max_token_id = max(max(vocab.values()), tokenizer.vocab_size)`. For Llama-2 tokenizers `max(vocab.values()) = 31999` but `tokenizer.vocab_size = 32000`, so `max_token_id = 32000` — one greater than the largest valid id. The engine bound therefore becomes `max(32000, 31999) = 32000`, and id 32000 is accepted. The model's embedding has `pad_vocab_size(32000, 64) = 32000` rows (no padding), valid indices 0..31999. At `VocabParallelEmbedding.forward` with `tp_size == 1` there is no masking, so index 32000 hits `F.embedding` out of range.

## Affected - tp=1 (single GPU per model — common per-copy l…