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
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mikaylagawarecki
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release notes: nested tensor
…-only)" [ghstack-poisoned]
mikaylagawarecki
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…CUDA-only)" [ghstack-poisoned]
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Current test failures seem unrelated |
cpuhrsch
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Generally looks good (see last nit). Also we'll need to mark this CUDA kernel for iterative improvements once we have end to end benchmarks.
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@pytorchbot merge |
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@pytorchbot rebase -s |
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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" |
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…ytorch#88289) Pull Request resolved: pytorch#88289 Approved by: https://github.com/cpuhrsch
…ytorch#88289) Pull Request resolved: pytorch#88289 Approved by: https://github.com/cpuhrsch
…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]
…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
…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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