RFC: Compute only in int32/long/float/double for portable ops to save size - #9635

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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635

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

❌ 5 New Failures, 11 Pending

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swolchok added a commit that referenced this pull request Mar 25, 2025
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RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
@facebook-github-botfacebook-github-bot 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 Mar 25, 2025
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some sort of 16-bit microcontroller

wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day

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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%

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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.

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swolchok added a commit that referenced this pull request Mar 26, 2025
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RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:

torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.

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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.

digantdesai
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Comment threadkernels/portable/cpu/util/elementwise_util.h
// Gate above optimization off if we appear to be on some kind of 8-bit or
// 16-bit CPU, which would invalidate our assumption about 32-bit
// math being just as fast.
constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;

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RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
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RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
@swolchok
Update
ac64f9e
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@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
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View reviewed changes
Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
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RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
As of commit ac64f9e with merge base 811352d (image):
NEW FAILURES - The following jobs have failed:
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RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
@swolchok
Update
ac64f9e
[ghstack-poisoned]
swolchok added a commit
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Mar 26, 2025
@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
@swolchok
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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
digantdesai
reviewed
Mar 27, 2025
View reviewed changes
Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
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RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
As of commit ac64f9e with merge base 811352d (image):
NEW FAILURES - The following jobs have failed:
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RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
@swolchok
Update
ac64f9e
[ghstack-poisoned]
swolchok added a commit
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@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
@swolchok
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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
digantdesai
reviewed
Mar 27, 2025
View reviewed changes
Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
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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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RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
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RFC: Compute only in int32/long/float/double for portable ops to save size#9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
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RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
@swolchok
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
@swolchok
Update
ac64f9e
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@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
digantdesai
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View reviewed changes
Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
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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('^' + ".*" + '

Skip to content
RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
Closed
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Closed
RFC: Compute only in int32/long/float/double for portable ops to save size#9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
As of commit ac64f9e with merge base 811352d (image):
NEW FAILURES - The following jobs have failed:
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@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
@swolchok
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
@swolchok
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Mar 26, 2025•

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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
@swolchok
Update
ac64f9e
[ghstack-poisoned]
swolchok added a commit
that referenced
this pull request
Mar 26, 2025
@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
@swolchok
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
@swolchok
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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
digantdesai
reviewed
Mar 27, 2025
View reviewed changes
Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
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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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RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
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Closed
RFC: Compute only in int32/long/float/double for portable ops to save size#9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
@swolchok
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
As of commit ac64f9e with merge base 811352d (image):
NEW FAILURES - The following jobs have failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes.
swolchok added a commit
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Mar 25, 2025
@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
@swolchok
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
@swolchok
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commented
Mar 26, 2025•

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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
@swolchok
Update
ac64f9e
[ghstack-poisoned]
swolchok added a commit
that referenced
this pull request
Mar 26, 2025
@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
@swolchok
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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
@swolchok
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Mar 26, 2025

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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
digantdesai
reviewed
Mar 27, 2025
View reviewed changes
Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
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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); }
})();
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RFC: Compute only in int32/long/float/double for portable ops to save size - #9635
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Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9635
Note: Links to docs will display an error until the docs builds have been completed.
❌ 5 New Failures, 11 Pending
As of commit ac64f9e with merge base 811352d (image):
NEW FAILURES - The following jobs have failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes.
swolchok added a commit
that referenced
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@swolchok
RFC: Compute only in int32/long/float/double for portable ops to save…
dea567f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: 66b89b1
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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some sort of 16-bit microcontroller
wasn't sure if I was making things up, so: https://developer.arm.com/Processors/Ethos-U55 is a real example from the present day
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size impact: on my mac, test/build_size_test.sh reports that size_test_all_ops has size 1205136 before this PR and 1105856 after, a decrease of around 8%
@swolchok
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Mar 26, 2025•

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This is known to break tests, I think because it breaks SupportedTensorDtypes::SAME_AS_COMMON for reasons outlined in #9613, hence it is RFC status. The problem is fixable, but if we have directional concerns with this then I don't want to invest in fixing it.
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[ghstack-poisoned]
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RFC: Compute only in int32/long/float/double for portable ops to save…
da4308f
… size
Concern: what if we're running on some sort of 16-bit microcontroller where this is a pessimization?
ghstack-source-id: a91380c
ghstack-comment-id: 2752794343
Pull-Request-resolved: #9635
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Mar 26, 2025•

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per discussion with @manuelcandales, if we do this then we we need to cast through the "actual" compute type before casting to the output type so that we match ATen. example:
torch.ops.aten.mul(torch.tensor([100], dtype=torch.int8), torch.tensor([100], dtype=torch.int8), out=torch.zeros([1], dtype=torch.long))
tensor([16])

computing in int32 or int16 would cause this to yield 10000, not 16; casting through int8 would correct this.
@swolchok
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Mar 26, 2025

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This is a bad idea because smaller compute dtypes benefit from additional SIMD lanes.
digantdesai
digantdesai
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Mar 27, 2025
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Comment threadkernels/portable/cpu/util/elementwise_util.h
 // Gate above optimization off if we appear to be on some kind of 8-bit or
 // 16-bit CPU, which would invalidate our assumption about 32-bit
 // math being just as fast.
 constexpr bool cpu_appears_to_be_at_least_32_bit = sizeof(void*) >= 4 && sizeof(int) >= 4;
@digantdesaidigantdesaiMar 27, 2025

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