Description
Your current environment
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System Info
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 4.0.2
Libc version : glibc-2.35
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PyTorch Info
PyTorch version : 2.6.0+cu124
Is debug build : False
CUDA used to build PyTorch : 12.4
ROCM used to build PyTorch : N/A
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Python Environment
Python version : 3.11.13 | packaged by conda-forge | (main, Jun 4 2025, 14:48:23) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-5.15.0-125-generic-x86_64-with-glibc2.35
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CUDA / GPU Info
Is CUDA available : True
CUDA runtime version : 12.4.131
CUDA_MODULE_LOADING set to : LAZY
🐛 Describe the bug
I've been testing image quality with Gemma3-12B-instruct on vLLM and seeing lower accuracy with v1 engine compared to v0. The issue appears to be that v1 doesn't support bidirectional attention for image tokens. Can you please let us know when will bidirectional attention support be added to v1 engine? Since v0 is much slower, fixing v1 would be super useful.
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