Create a MemoryPlanningAlgo class. #11824
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11824
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New Failures, 4 PendingAs of commit c610a30 with merge base da36d8a ( NEW FAILURES - The following jobs have failed:
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Summary: Refactor our memory planning algos into a `MemoryPlanningAlgo` base class + algo-specific implementation in derived class. This refactor separates common utility functions + constraint handling to the base class. This way, we can add support for hierarchical graphs (using maps), and add more types of constraints (like pinning a tensor to specific dtcm bank) without changing the algo itself. Reviewed By: zonglinpeng Differential Revision: D76954785
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Summary: Pull Request resolved: pytorch#11824 Refactor our memory planning algos into a `MemoryPlanningAlgo` base class + algo-specific implementation in derived class. This refactor separates common utility functions + constraint handling to the base class. This way, we can add support for hierarchical graphs (using maps), and add more types of constraints (like pinning a tensor to specific dtcm bank) without changing the algo itself. Reviewed By: zonglinpeng Differential Revision: D76954785
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Summary: Refactor our memory planning algos into a `MemoryPlanningAlgo` base class + algo-specific implementation in derived class. This refactor separates common utility functions + constraint handling to the base class. This way, we can add support for hierarchical graphs (using maps), and add more types of constraints (like pinning a tensor to specific dtcm bank) without changing the algo itself. Reviewed By: zonglinpeng Differential Revision: D76954785
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Summary: Refactor our memory planning algos into a `MemoryPlanningAlgo` base class + algo-specific implementation in derived class. This refactor separates common utility functions + constraint handling to the base class. This way, we can add support for hierarchical graphs (using maps), and add more types of constraints (like pinning a tensor to specific dtcm bank) without changing the algo itself. Reviewed By: zonglinpeng Differential Revision: D76954785
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Differential Revision: D76954785 Pull Request resolved: pytorch#11824
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Summary:
Refactor our memory planning algos into a
MemoryPlanningAlgobase class + algo-specific implementation in derived class. This refactor separates common utility functions + constraint handling to the base class.This way, we can add support for hierarchical graphs (using maps), and add more types of constraints (like pinning a tensor to specific dtcm bank) without changing the algo itself.
Differential Revision: D76954785