adding fused uint4x2_mixed_mm to inductor #106516
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Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/106516
Note: Links to docs will display an error until the docs builds have been completed. ✅ 2 Unrelated FailuresAs of commit a49ff9d: BROKEN TRUNK - The following job failed but were present on the merge base 858b465:👉 Rebase onto the `viable/strict` branch to avoid these failures
UNSTABLE - The following job failed but was likely due to flakiness present on trunk and has been marked as unstable:
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HDCharles
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Aug 3, 2023
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 567155d998962ce1ea80b357ce9da52ddff890d6 Pull Request resolved: #106516
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues. Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
HDCharles
added a commit
that referenced
this pull request
Aug 3, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues. Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 488967f85db3c0d6e365968f7e12360276d6d24c Pull Request resolved: #106516
jansel
approved these changes
Aug 4, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
HDCharles
added a commit
that referenced
this pull request
Aug 10, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 6a808e2113bfef238cce02d478cdcf7307ca9cbb Pull Request resolved: #106516
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
HDCharles
added a commit
that referenced
this pull request
Aug 10, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 34f17979f9626f8137c35b7ad42e456af0dbb69c Pull Request resolved: #106516
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
HDCharles
added a commit
that referenced
this pull request
Aug 11, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: dfbd0acfab6b844e9749009b4f28186ea0c9f11c Pull Request resolved: #106516
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
HDCharles
added a commit
that referenced
this pull request
Aug 14, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: f4c469aadf1806aef29a40c90f83b32d159de65f Pull Request resolved: #106516
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
HDCharles
added a commit
that referenced
this pull request
Aug 14, 2023
Summary: this is needed for int4 weight-only quantization, we're matching on the specific unpack operation that unpacks the uint4x2 into int4's so we can have a fused kernel for it. note, even if the user isn't specifically doing this, the two operations are mathematically equilvanet so it won't cause issues (for some reason int8 bitwise logic in triton and pytorch doesn't match so that's the only exception). Ideally at some point full prologue fusion for the mm arguments would be able to handle this chain but until then, this type of kernel is needed. Test Plan: python test/inductor/test_pattern_matcher.py -k "uint4x2" print test/inductor/test_torchinductor.py -k "uint4x2" Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 282ab424beb0e86ffeffb2931d994a3c0aa5dee2 Pull Request resolved: #106516
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Stack from ghstack (oldest at bottom):
Summary: this is needed for int4 weight-only quantization, we're
matching on the specific unpack operation that unpacks the uint4x2 into
int4's so we can have a fused kernel for it. note, even if the user
isn't specifically doing this, the two operations are mathematically
equilvanet so it won't cause issues (for some reason int8 bitwise logic
in triton and pytorch doesn't match so that's the only exception). Ideally
at some point full prologue fusion for the mm arguments would be able to
handle this chain but until then, this type of kernel is needed.
Test Plan:
python test/inductor/test_pattern_matcher.py -k "uint4x2"
print test/inductor/test_torchinductor.py -k "uint4x2"
Reviewers:
Subscribers:
Tasks:
Tags:
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov