Support elementwise add / mul for [B, *] nested, [B, 1] dense (CUDA only) #95620
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…nly) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/95620
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…nly) ghstack-source-id: 0d3907b0ccb3f18c6f56c8d85d737f3964d97f83 Pull Request resolved: #95620
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there's no autograd support for this case, and yea the name should probably be removed from the code, good catch |
…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]
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…nly) ghstack-source-id: dbe6d7e23ebf514f6f1845ec56a04b365334e096 Pull Request resolved: #95620
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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: pytorch/pytorch#95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
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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: pytorch/pytorch#95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
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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: pytorch/pytorch#95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
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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: pytorch/pytorch#95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
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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: pytorch/pytorch#95620 Approved by: https://github.com/cpuhrsch, https://github.com/drisspg
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… (CUDA only) (pytorch#95620)" This reverts commit 68eec90.
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…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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Stack from ghstack (oldest at bottom):
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.
cc @cpuhrsch @bhosmer @drisspg @mikaylagawarecki