[Bugfix] Fix crash with llama 3.2 vision models and guided decoding

[tjohnson31415]

Sending a request that includes guided decoding to a Llama 3.2 Vision model crashes the engine with a CUDA device-side error:

ERROR 10-23 20:28:40 engine.py:165]   File "/home/vllm/vllm/lib64/python3.12/site-packages/vllm/model_executor/layers/logits_processor.py", line 144, in _apply_logits_processors
ERROR 10-23 20:28:40 engine.py:165]     logits_row = logits_processor(past_tokens_ids,
ERROR 10-23 20:28:40 engine.py:165]                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 10-23 20:28:40 engine.py:165]   File "/home/vllm/vllm/lib64/python3.12/site-packages/lmformatenforcer/integrations/vllm.py", line 29, in __call__
ERROR 10-23 20:28:40 engine.py:165]     self.mask[allowed_tokens] = 0
ERROR 10-23 20:28:40 engine.py:165]     ~~~~~~~~~^^^^^^^^^^^^^^^^
ERROR 10-23 20:28:40 engine.py:165] RuntimeError: CUDA error: device-side assert triggered
ERROR 10-23 20:28:40 engine.py:165] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
ERROR 10-23 20:28:40 engine.py:165] 

All credit to @pavlo-ruban who described the root cause problem and identified this solution; I'm just creating a PR out of it.

FIX #8952

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[tjohnson31415]

The fix in this PR is just for outlines. I looked into lm-format-enforcer as well and it would need a different approach: there was a changed made to it to support passing in a vocab_size separate from the tokenizer's vocab_size
noamgat/lm-format-enforcer@c61f00c

So we'd need to detect that the model's vocab size differs from the tokenizer and pass the vocab_size through. I think that could be a separate PR though so we can get the default of using outlines working.

[njhill]

@tjohnson31415 when you tried with pytorch did you copy to gpu first?

allowed_tokens = torch.tensor(allowed_tokens, device=scores.device)
allowed_tokens = allowed_tokens.masked_select(allowed_tokens < scores.shape[-1])
mask.index_fill_(allowed_tokens, 0)

[tjohnson31415]

Yep. Creating a torch.Tensor from a large list is oddly slow, whether on CPU side or GPU side it was about the same in my testing: 15ms for ~120k element list. Creating an np.array is less than 5ms. The rest of the operations are fast in either case 🤔

[njhill]

Thanks @tjohnson31415! I was intrigued and tried this myself. I found the same thing as you, that creating a tensor from a python list was the most expensive part. But actually it's about 30% faster overall if you combine the two!

allowed_tokens = np.array(allowed_tokens, dtype=np.int64)
allowed_tokens = torch.tensor(allowed_tokens, device=scores.device)
allowed_tokens = allowed_tokens.masked_select(allowed_tokens < scores.shape[-1])
mask.index_fill_(0, allowed_tokens, 0)

[njhill]

I pushed an update with this change because @tjohnson31415 is OOO for a few days.