[Bugfix] Don't use FULL_AND_PIECEWISE cudagraph for Llama4

[mgoin]

Purpose

FIX #25960

Test Plan

Test Result

vllm serve RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16 --load-format dummy --max-model-len 32K
...
... 'cudagraph_mode': <CUDAGraphMode.PIECEWISE: 1>, ...

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
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[LucasWilkinson]

I think it might be cleaner to do: #26034 ?

[gemini-code-assist]

Code Review

This pull request correctly adds a check to disable full CUDA graphs for models with chunked attention. This is a good bugfix. However, I've identified a potential issue with the placement of this check. It's located within a conditional block that only executes if cudagraph_mode is not explicitly set by the user. This could lead to runtime errors if a user provides an incompatible configuration. I've suggested a refactoring to make this check unconditional, which would improve the robustness of the configuration.

[gemini-code-assist]

While this change correctly identifies another condition for disabling full CUDA graphs, the check is located inside a block that only executes if cudagraph_mode is not explicitly set by the user (if self.compilation_config.cudagraph_mode is None:).

This means if a user explicitly sets cudagraph_mode to FULL or FULL_AND_PIECEWISE for a model with pooling, an encoder-decoder architecture, or chunked attention, the setting will not be overridden, which can lead to runtime errors.

To make the configuration more robust, I suggest moving this check, along with the existing ones for pooler_config and is_encoder_decoder, outside of the if self.compilation_config.cudagraph_mode is None: block. This would ensure that incompatible settings are always corrected, regardless of whether they are user-provided or default. A similar override is already performed for enforce_eager a few lines below.

Consider refactoring this logic to apply these correctness checks unconditionally. For example:

# In vllm/config/vllm.py, __post_init__

# ... after setting default cudagraph_mode

# pooling models, encoder-decoder models, and models with
# chunked attention do not support full cudagraphs.
# This check overrides user settings for correctness.
is_incompatible = (
    self.model_config is not None and (
        self.model_config.pooler_config is not None
        or self.model_config.is_encoder_decoder
        or self.model_config.attention_chunk_size is not None
    )
)
if is_incompatible and self.compilation_config.cudagraph_mode.has_full_cudagraphs():
    logger.warning(
        "The model has features that are not compatible with "
        "full CUDAGraphs. Disabling full CUDAGraphs."
    )
    if self.compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
        self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
    else: # FULL or FULL_DECODE_ONLY
        self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE