[device_mesh] Implement _unflatten on top of CuTe layout bookkeeping
#161224
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/161224
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_unflatten on top of CuTe layout bookkeeping
…ayout bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta [ghstack-poisoned]
…ayout bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta [ghstack-poisoned]
…ayout bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta [ghstack-poisoned]
…ayout bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta [ghstack-poisoned]
…ayout bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta [ghstack-poisoned]
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_unflatten on top of CuTe layout bookkeeping_unflatten on top of CuTe layout bookkeeping
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack.
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… bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta [ghstack-poisoned]
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… bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta ezyang msaroufim dcci [ghstack-poisoned]
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LGTM, some test suggestions.
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| backend_override: Optional[ | ||
| dict[ | ||
| Union[int, str], | ||
| Union[str, C10dBackend.Options, tuple[str, C10dBackend.Options]], | ||
| ] | ||
| ] = None, |
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Do we still want to support key being "int" option as this API already make mesh_dim_names mandatory?
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Good point, yeah let's make it str only.
… bookkeeping" cc H-Huang awgu wanchaol fegin wz337 wconstab d4l3k pragupta ezyang msaroufim dcci [ghstack-poisoned]
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pytorch#161224) Pull Request resolved: pytorch#161224 Approved by: https://github.com/lw, https://github.com/fegin ghstack dependencies: pytorch#164510
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack.
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pytorch#161224) Pull Request resolved: pytorch#161224 Approved by: https://github.com/lw, https://github.com/fegin ghstack dependencies: pytorch#164510
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
fegin
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
fegin
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
fegin
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
fegin
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
fegin
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
fegin
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
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This is a demonstration of how parallel_dims will be when using pytorch/pytorch#161224 stack. ghstack-source-id: d29d2e2 Pull-Request: #1885
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Stack from ghstack (oldest at bottom):
_unflattenon top of CuTe layout bookkeeping #161224cc @H-Huang @awgu @wanchaol @fegin @wz337 @wconstab @d4l3k @pragupta @ezyang @msaroufim @dcci