[ET-VK] Implement SDPA with fused ops #14130
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## Context As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps: 1. Compute attention weights, multiplying QT x K_cache, and applying scale and mask 2. Compute softmax normalization of computed attention weights 3. Compute final output by multiplying attention weights with V cache This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped. ## Impact Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s Differential Revision: [D82053493](https://our.internmc.facebook.com/intern/diff/D82053493/) [ghstack-poisoned]
SS-JIA
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## Context As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps: 1. Compute attention weights, multiplying QT x K_cache, and applying scale and mask 2. Compute softmax normalization of computed attention weights 3. Compute final output by multiplying attention weights with V cache This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped. ## Impact Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s Differential Revision: [D82053493](https://our.internmc.facebook.com/intern/diff/D82053493/) ghstack-source-id: 308592117 Pull Request resolved: #14130
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## Context As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps: 1. Compute attention weights, multiplying QT x K_cache, and applying scale and mask 2. Compute softmax normalization of computed attention weights 3. Compute final output by multiplying attention weights with V cache This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped. ## Impact Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s Differential Revision: [D82053493](https://our.internmc.facebook.com/intern/diff/D82053493/) [ghstack-poisoned]
SS-JIA
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Sep 9, 2025
Pull Request resolved: #14130 ## Context As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps: 1. Compute attention weights, multiplying QT x K_cache, and applying scale and mask 2. Compute softmax normalization of computed attention weights 3. Compute final output by multiplying attention weights with V cache This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped. ## Impact Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s ghstack-source-id: 308621243 @exported-using-ghexport Differential Revision: [D82053493](https://our.internmc.facebook.com/intern/diff/D82053493/)
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This pull request was exported from Phabricator. Differential Revision: D82053493 |
## Context As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps: 1. Compute attention weights, multiplying QT x K_cache, and applying scale and mask 2. Compute softmax normalization of computed attention weights 3. Compute final output by multiplying attention weights with V cache This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped. ## Impact Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s Differential Revision: [D82053493](https://our.internmc.facebook.com/intern/diff/D82053493/) [ghstack-poisoned]
SS-JIA
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that referenced
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Sep 10, 2025
Pull Request resolved: #14130 ## Context As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps: 1. Compute attention weights, multiplying QT x K_cache, and applying scale and mask 2. Compute softmax normalization of computed attention weights 3. Compute final output by multiplying attention weights with V cache This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped. ## Impact Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s ghstack-source-id: 308660072 @exported-using-ghexport Differential Revision: [D82053493](https://our.internmc.facebook.com/intern/diff/D82053493/)
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This pull request was exported from Phabricator. Differential Revision: D82053493 |
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Stack from ghstack (oldest at bottom):
Context
As title; optimize the SDPA operator by introducing shaders to perform the operation in 3 steps:
This new implementation is much more efficient than the existing one, which performed slicing, repeat_interleave, and transposition of projected and cache tensors as separate steps. The fusion of scale and mask with the computation of attention weights also allows for the computation of elements within the mask region to be skipped.
Impact
Decode latency for LLMs is much improved. For llama 3.2 3B generating ~250 tokens, decode latency increases from ~15 tok/s to ~21.5 tok/s
Differential Revision: D82053493