[DTensor] implement dist_split as a sharding prop rule #93306
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XilunWu
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/93306
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ghstack-source-id: 17951d93ac33b0c1c58caf83c2767cb91c121494 Pull Request resolved: #93306
XilunWu
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ghstack-source-id: 737cb0f8c42183cc069c23d2bb219422fe0541e1 Pull Request resolved: #93306
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ghstack-source-id: 95deefbd55f41ae0d2985935c086b577eb7b8021 Pull Request resolved: #93306
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| # TODO: just like slice op, split replicates before splitting | ||
| # on a sharded dimension | ||
| # TODO: shall we consider partial??? |
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we should consider partial (maybe we can add this later), and because the dtensor_ops test does not generate partial inputs, we also need to add the partial inputs to op db
| placements=unshard_tensor_dim(input_spec.placements, dim=dim), | ||
| shape=input_spec.shape, | ||
| ndim=input_spec.ndim, | ||
| ) |
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nit: add a check to partial input_spec and raise NotImplementedError so we know to implement this later?
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ghstack-source-id: fb8f5360b3a521480329ed2fa2a817c2de018161 Pull Request resolved: #93306
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| need_reshard = False | ||
| if is_tensor_dim_sharded(input_spec, dim=dim): |
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This somehow broke TP's code logic. Because a common technique people are using is that the DTensor is sharded on the last dim and they call split on the last dim too. We still want the result to be sharded on dim=-1.
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Currently after split, we got replicate as a DTensor.
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