[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844

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[WIP] Add standalone batch norm support via depthwise conv conversion.#11844
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Summary

Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.

Fixes#11586

Key changes:

  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms

Uses mathematical equivalence:
conv_weight = bn_weight / sqrt(bn_var + eps)
conv_bias = bn_bias - bn_mean * conv_weight

Test plan

# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844

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

✅ No Failures

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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!

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[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
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[WIP] Add standalone batch norm support via depthwise conv conversion.#11844
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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
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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");
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[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
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[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Support Standalone Batch Norm


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@ayushrajdahal@keyprocedure@facebook-github-bot@nil-is-all

, '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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[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
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ayushrajdahal:standalone-batchnorm
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[WIP] Add standalone batch norm support via depthwise conv conversion.#11844
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@ayushrajdahalayushrajdahal
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Jun 22, 2025

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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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Jun 22, 2025•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
As of commit 3a62512 with merge base daebcde (image):

💚 Looks good so far! There are no failures yet. 💚
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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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Jul 14, 2025

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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
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Support Standalone Batch Norm


4 participants

@ayushrajdahal@keyprocedure@facebook-github-bot@nil-is-all

, '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('^' + ".*" + '

Skip to content
[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
Closed
ayushrajdahal wants to merge 1 commit into
pytorch:mainfrom 
ayushrajdahal:standalone-batchnorm
Closed
[WIP] Add standalone batch norm support via depthwise conv conversion.#11844
ayushrajdahal wants to merge 1 commit into
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@ayushrajdahalayushrajdahal
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Jun 22, 2025

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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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Jun 22, 2025•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
As of commit 3a62512 with merge base daebcde (image):

💚 Looks good so far! There are no failures yet. 💚
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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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
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@keyprocedure
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Jul 14, 2025

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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
Stale pull requests will automatically be closed after 30 days of inactivity.

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
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Support Standalone Batch Norm


4 participants

@ayushrajdahal@keyprocedure@facebook-github-bot@nil-is-all

, '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('^' + ".*" + '

Skip to content
[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
Closed
ayushrajdahal wants to merge 1 commit into
pytorch:mainfrom 
ayushrajdahal:standalone-batchnorm
Closed
[WIP] Add standalone batch norm support via depthwise conv conversion.#11844
ayushrajdahal wants to merge 1 commit into
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Jun 22, 2025

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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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Jun 22, 2025•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
As of commit 3a62512 with merge base daebcde (image):

💚 Looks good so far! There are no failures yet. 💚
This comment was automatically generated by Dr. CI and updates every 15 minutes.
@facebook-github-botfacebook-github-bot
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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.
@keyprocedure
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Jul 14, 2025

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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
Stale pull requests will automatically be closed after 30 days of inactivity.
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
Stale pull requests will automatically be closed after 30 days of inactivity.

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
Stale pull requests will automatically be closed after 30 days of inactivity.
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
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Support Standalone Batch Norm


4 participants

@ayushrajdahal@keyprocedure@facebook-github-bot@nil-is-all

, '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
[WIP] Add standalone batch norm support via depthwise conv conversion. - #11844
Closed
ayushrajdahal wants to merge 1 commit into
pytorch:mainfrom 
ayushrajdahal:standalone-batchnorm
Closed
[WIP] Add standalone batch norm support via depthwise conv conversion.#11844
ayushrajdahal wants to merge 1 commit into
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ayushrajdahal:standalone-batchnorm
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@ayushrajdahalayushrajdahal
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Jun 22, 2025

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Summary
Implement ConvertBatchNormToDepthwiseConvPass to handle standalone batch normalization operations that cannot be fused with preceding convolutions.
Fixes#11586
Key changes:
  • New pass converts standalone BatchNorm to equivalent 1x1 depthwise convolutions
  • Updated BatchNormConfig to support both fusable and standalone batch norms
  • Added pass to XNNPACKPassManager before existing fusion pass
  • Updated tests to expect successful partitioning of standalone batch norms
Uses mathematical equivalence:

conv_weight = bn_weight / sqrt(bn_var + eps)

conv_bias = bn_bias - bn_mean * conv_weight
Test plan
# test fused batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_fusion.py-v# for standalone batch normpython-mpytestbackends/xnnpack/test/passes/test_batch_norm_to_depthwise_conv.py-v
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Jun 22, 2025•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11844
Note: Links to docs will display an error until the docs builds have been completed.
✅ No Failures
As of commit 3a62512 with merge base daebcde (image):

💚 Looks good so far! There are no failures yet. 💚
This comment was automatically generated by Dr. CI and updates every 15 minutes.
@facebook-github-botfacebook-github-bot
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This PR needs a release notes: label
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@keyprocedure
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Jul 14, 2025

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Hi @penknife6153, I worked on the linear fused batchnorm PR and noticed some overlap between our work. Let me know if I can help with anything!
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Sep 13, 2025

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
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Nov 12, 2025

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
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Stale pull requests will automatically be closed after 30 days of inactivity.

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Jan 11, 2026

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
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If you want the bot to never mark this PR stale again, add the no-stale label.
Stale pull requests will automatically be closed after 30 days of inactivity.
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Mar 13, 2026

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale. 
Feel free to remove the Stale label if you feel this was a mistake. 
If you are unable to remove the Stale label please contact a maintainer in order to do so. 
If you want the bot to never mark this PR stale again, add the no-stale label.
Stale pull requests will automatically be closed after 30 days of inactivity.
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@digantdesaidigantdesaiAwaiting requested review from digantdesaidigantdesai will be requested when the pull request is marked ready for reviewdigantdesai is a code owner
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