Flash Attention Texture Compute Shader for Vulkan Backend Delegate #12982
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/12982
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New Failures, 1 Unrelated FailureAs of commit f3d7af7 with merge base afdbb85 ( NEW FAILURES - The following jobs have failed:
BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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…ytorch#12982) Summary: Pull Request resolved: pytorch#12982 Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader. Reviewed By: SS-JIA Differential Revision: D78836150
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…ytorch#12982) Summary: Pull Request resolved: pytorch#12982 Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader. Reviewed By: SS-JIA Differential Revision: D78836150
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…ytorch#12982) Summary: Pull Request resolved: pytorch#12982 Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader. Reviewed By: SS-JIA Differential Revision: D78836150
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…ytorch#12982) Summary: Pull Request resolved: pytorch#12982 Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader. Reviewed By: SS-JIA Differential Revision: D78836150
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…ytorch#12982) Summary: Pull Request resolved: pytorch#12982 Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader. Reviewed By: SS-JIA Differential Revision: D78836150
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…ytorch#12982) Summary: Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader. Reviewed By: SS-JIA Differential Revision: D78836150 cc @SS-JIA @manuelcandales @cbilgin
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Summary: Built flash attention compute shader for Vulkan backend delegate. The current implementation is not fully optimized, but is functional. This shader should speed up the SDPA process in the attention block of transformer inferencing as the previous implementation used many i/o operations. The implementation includes proper multi-query attention support for models like LLaMA, uses tiled block processing to reduce memory usage, and replaces multiple separate operations (matmul, softmax, masking) with a single efficient compute shader.
Reviewed By: SS-JIA
Differential Revision: D78836150
cc @SS-JIA @manuelcandales @cbilgin