[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into named data map - #9295

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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into named data map - #9295
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into named data map - #9295
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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: #9153 by @mcr229

^ 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/mcr229/9/base

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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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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");
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into named data map - #9295
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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: #9153 by @mcr229

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ghstack PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/9/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/mcr229/9/head

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@diff-train-skip-merge
@mcr229
[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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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");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into named data map - #9295
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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: #9153 by @mcr229

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ghstack PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/9/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/mcr229/9/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/8/orig

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@diff-train-skip-merge
@mcr229
[ExecuTorch][Weight Sharing][XNNPACK] Serialize constant tensors into…
c646522
… named data map
Pull Request resolved: #9153
We serialize tensors into the named data map, and return the output in preprocess result. Allowing for XNNPACK to share tensors with the same name (instead of duplicating).
A key change here is with fused tensors. For BN and Convolution Fusion, we fuse the conv weights and bias with the BN parameters creating new tensors. We then create get_attr nodes for these new parameters. Due to the graph.fx interpreter in export pass base, the new names we create for these new tensors are lost each time. As a result, at the end we introduce a new pass to preserve the names we created. This seems a little hacky for now, but is the only way to preserve the new fused names.
Differential Revision: [D70315207](https://our.internmc.facebook.com/intern/diff/D70315207/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D70315207/)!
ghstack-source-id: 271732046
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9295
Note: Links to docs will display an error until the docs builds have been completed.
❌ 1 New Failure
As of commit c646522 with merge base 630d0cc (image):
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