[ET-VK] Implement SDPA with fused ops - #14139

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[ET-VK] Implement SDPA with fused ops#14139
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

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@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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[ET-VK] Implement SDPA with fused ops - #14139

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SS-JIA merged 1 commit into
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gh/SS-JIA/324/orig
Sep 10, 2025
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[ET-VK] Implement SDPA with fused ops#14139
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

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❌ 2 New Failures, 141 Pending

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@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[ET-VK] Implement SDPA with fused ops - #14139

Merged
SS-JIA merged 1 commit into
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gh/SS-JIA/324/orig
Sep 10, 2025
Merged

[ET-VK] Implement SDPA with fused ops#14139
SS-JIA merged 1 commit into
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gh/SS-JIA/324/orig

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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 141 Pending

As of commit 5252297 with merge base 245630a (image):

NEW FAILURES - The following jobs have failed:

This comment was automatically generated by Dr. CI and updates every 15 minutes.

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@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
264 of 288 checks passed
@SS-JIA
SS-JIA deleted the gh/SS-JIA/324/orig branch September 10, 2025 05:32
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[ET-VK] Implement SDPA with fused ops - #14139

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SS-JIA merged 1 commit into
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gh/SS-JIA/324/orig
Sep 10, 2025
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[ET-VK] Implement SDPA with fused ops#14139
SS-JIA merged 1 commit into
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 141 Pending

As of commit 5252297 with merge base 245630a (image):

NEW FAILURES - The following jobs have failed:

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@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
264 of 288 checks passed
@SS-JIA
SS-JIA deleted the gh/SS-JIA/324/orig branch September 10, 2025 05:32
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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[ET-VK] Implement SDPA with fused ops - #14139

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SS-JIA merged 1 commit into
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Sep 10, 2025
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[ET-VK] Implement SDPA with fused ops#14139
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 141 Pending

As of commit 5252297 with merge base 245630a (image):

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@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
264 of 288 checks passed
@SS-JIA
SS-JIA deleted the gh/SS-JIA/324/orig branch September 10, 2025 05:32
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[ET-VK] Implement SDPA with fused ops - #14139

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[ET-VK] Implement SDPA with fused ops#14139
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

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❌ 2 New Failures, 141 Pending

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@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
264 of 288 checks passed
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SS-JIA deleted the gh/SS-JIA/324/orig branch September 10, 2025 05:32
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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[ET-VK] Implement SDPA with fused ops - #14139

Merged
SS-JIA merged 1 commit into
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gh/SS-JIA/324/orig
Sep 10, 2025
Merged

[ET-VK] Implement SDPA with fused ops#14139
SS-JIA merged 1 commit into
mainfrom
gh/SS-JIA/324/orig

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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 141 Pending

As of commit 5252297 with merge base 245630a (image):

NEW FAILURES - The following jobs have failed:

This comment was automatically generated by Dr. CI and updates every 15 minutes.

@meta-clameta-claBot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 10, 2025
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This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

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For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.

@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
264 of 288 checks passed
@SS-JIA
SS-JIA deleted the gh/SS-JIA/324/orig branch September 10, 2025 05:32
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content

[ET-VK] Implement SDPA with fused ops - #14139

Merged
SS-JIA merged 1 commit into
mainfrom
gh/SS-JIA/324/orig
Sep 10, 2025
Merged

[ET-VK] Implement SDPA with fused ops#14139
SS-JIA merged 1 commit into
mainfrom
gh/SS-JIA/324/orig

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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #14130 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/324/orig
@diff-train-skip-merge

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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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14139

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 141 Pending

As of commit 5252297 with merge base 245630a (image):

NEW FAILURES - The following jobs have failed:

This comment was automatically generated by Dr. CI and updates every 15 minutes.

@meta-clameta-claBot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 10, 2025
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This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.

@SS-JIA
SS-JIA merged commit 1487792 into mainSep 10, 2025
264 of 288 checks passed
@SS-JIA
SS-JIA deleted the gh/SS-JIA/324/orig branch September 10, 2025 05:32
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