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[ET Device Support] Device-aware memory planning: separate buffers per device type#18375

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[ET Device Support] Device-aware memory planning: separate buffers per device type#18375
Gasoonjia wants to merge 1 commit intogh/gasoonjia/145/basefrom
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…r device type

Extends memory planning to separate device tensors from CPU tensors into distinct
memory buffers. Non-CPU TensorSpecs (e.g., CUDA) are pre-assigned device-specific
mem_ids before the greedy/naive algorithm runs, ensuring they get planned into
independent memory buffers that never share space with CPU tensors.

Differential Revision: [D97447105](https://our.internmc.facebook.com/intern/diff/D97447105/)

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pytorch-bot bot commented Mar 20, 2026

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/18375

Note: Links to docs will display an error until the docs builds have been completed.

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@meta-cla meta-cla bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Mar 20, 2026
Gasoonjia added a commit that referenced this pull request Mar 20, 2026
…r device type

Extends memory planning to separate device tensors from CPU tensors into distinct
memory buffers. Non-CPU TensorSpecs (e.g., CUDA) are pre-assigned device-specific
mem_ids before the greedy/naive algorithm runs, ensuring they get planned into
independent memory buffers that never share space with CPU tensors.

Differential Revision: [D97447105](https://our.internmc.facebook.com/intern/diff/D97447105/)

ghstack-source-id: 355133801
Pull Request resolved: #18375
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