Added add/mul for nested dense [B, *, D], [B, 1, D] case (CUDA-only) #88289
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/88289
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 FailuresAs of commit ddd6f12: The following jobs have failed:
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mikaylagawarecki
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ghstack-source-id: 7ca8b3c3c0218e3f7c0ca3903e6510cacb8e0f9c Pull Request resolved: #88289
mikaylagawarecki
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release notes: nested tensor
…-only)" [ghstack-poisoned]
mikaylagawarecki
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ghstack-source-id: fa0d9593327442bcbc983096916bc41a46ec9019 Pull Request resolved: #88289
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…CUDA-only)" [ghstack-poisoned]
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ghstack-source-id: 0acc750a59bac110b2d8086d9b428b3812e75115 Pull Request resolved: #88289
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Current test failures seem unrelated |
cpuhrsch
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…CUDA-only)" [ghstack-poisoned]
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ghstack-source-id: e40878da8d04c71b787a5e19b83e3311a681dd9c Pull Request resolved: #88289
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Merge failedReason: The following mandatory check(s) failed (Rule Dig deeper by viewing the failures on hud Details for Dev Infra teamRaised by workflow job |
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@pytorchbot rebase -s |
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@pytorchbot successfully started a rebase job. Check the current status here |
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Tried to rebase and push PR #88289, but it was already up to date |
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@pytorchbot merge -f "failure is unrelated" |
Merge startedYour change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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…ytorch#88289) Pull Request resolved: pytorch#88289 Approved by: https://github.com/cpuhrsch
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…ytorch#88289) Pull Request resolved: pytorch#88289 Approved by: https://github.com/cpuhrsch
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…nse (CUDA only)" Small hack to reuse the ESUHM kernel from #88289 for [B, *] nested, [B, 1] dense elementwise add / mul. Simply treat the inputs as [B, *, 1], [B, 1, 1]. This is added to satisfy an ask from the Ads team. Future work: full general broadcasting support between mixed nested / dense. cc cpuhrsch bhosmer drisspg mikaylagawarecki [ghstack-poisoned]
pytorchmergebot
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…nly) (#95620) Small hack to reuse the 3D custom kernel from #88289 for [B, *] nested, [B, 1] dense elementwise add / mul. Simply treat the inputs as [B, *, 1], [B, 1, 1]. This is added to satisfy an internal ask. Future work: full general broadcasting support between mixed nested / dense. Pull Request resolved: #95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
jhavukainen
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Mar 15, 2024
…nly) (pytorch#95620) Small hack to reuse the 3D custom kernel from pytorch#88289 for [B, *] nested, [B, 1] dense elementwise add / mul. Simply treat the inputs as [B, *, 1], [B, 1, 1]. This is added to satisfy an internal ask. Future work: full general broadcasting support between mixed nested / dense. Pull Request resolved: pytorch#95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
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