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[Bugfix] Don't use FULL_AND_PIECEWISE cudagraph for Llama4 #26033

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[Bugfix] Don't use FULL_AND_PIECEWISE cudagraph for Llama4 #26033

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7 changes: 4 additions & 3 deletions vllm/config/vllm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -329,11 +329,12 @@ def __post_init__(self):
self.compilation_config.cudagraph_mode = \
CUDAGraphMode.FULL_AND_PIECEWISE

# pooling models and encoder-decoder models
# do not support full cudagraphs
# pooling models, encoder-decoder models, and models with
# chunked attention do not support full cudagraphs
if 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.is_encoder_decoder
or self.model_config.attention_chunk_size is not None):
self.compilation_config.cudagraph_mode = \
CUDAGraphMode.PIECEWISE
Comment on lines 334 to 339
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@gemini-code-assist gemini-code-assist bot Oct 1, 2025

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high

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

else:
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