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### Description These changes add rotary embedding and packed qkv input to gqa. As of now, the changes are only supported with Flash-Attention (SM >= 80) but should soon be supported with Memory Efficient Attention as well. ### Motivation and Context With the fusion of rotary embedding into this Attention op, we hope to observe some perf gain. The packed QKV should also provide some perf gain in the context of certain models, like Llama2, that would benefit from running ops on the fused QKV matrix, rather than the separate Q, K, and V. --------- Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> |
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| .. | ||
| github | ||
| __init__.py | ||
| amd_hipify.py | ||
| build.py | ||
| clean_docker_image_cache.py | ||
| compile_triton.py | ||
| coverage.py | ||
| gen_def.py | ||
| get_docker_image.py | ||
| logger.py | ||
| op_registration_utils.py | ||
| op_registration_validator.py | ||
| patch_manylinux.py | ||
| policheck_exclusions.xml | ||
| reduce_op_kernels.py | ||
| replace_urls_in_deps.py | ||
| requirements-transformers-test.txt | ||
| set-trigger-rules.py | ||
| update_tsaoptions.py | ||
| upload_python_package_to_azure_storage.py | ||