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attention_implementation_mismatch
performance-attention-implementa-when-using-qwen2vl-with-flash-attention2-for-visio-db464d68
IFWhen using Qwen2VL with flash-attention2 for vision and eager attention for the language model, generating text results in repeated words and near-zero evaluation scores.
THENEnsure that both the vision module and the language model use the same attention implementation. If flash-attention2 is used, apply it to both components. Alternatively, force eager attention for both. Currently, setting attention for the text model separately is not straightforward; a fix is pending. As a workaround, avoid mixing flash attention in vision with eager attention in text.
Tier 170%
attention_implementation_mismatch
performance-attention-implementa-mixing-flash-attention-for-the-vision-encoder-with-19776f89
IFMixing flash attention for the vision encoder with eager attention for the LLM in Qwen2VL causes the model to produce repetitive, nonsensical output and a near-zero evaluation score.
THENEnsure both the vision and text (LLM) parts of Qwen2VL use the same attention implementation. For consistent behavior and best accuracy, set both to 'flash_attention_2' via the `attn_implementation` dict parameter (e.g., `attn_implementation={'vision_config': 'flash_attention_2', '': 'flash_attention_2'}`) or both to 'eager'. Avoid the combination where vision uses flash and text uses eager, as it breaks generation.
Tier 170%
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