[ET][Memory planning] Improve greedy memory planning. #7995
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Pull Request resolved: #7925 ATT ghstack-source-id: 263342054 @exported-using-ghexport Differential Revision: [D68448333](https://our.internmc.facebook.com/intern/diff/D68448333/)
Pull Request resolved: #7926 This diff replaces the old greedy algorithm. Older algorithm resulted in 35% worse compared to theoretical optimum. THis matter for long context even more since additional overhead can be few hundred MB. For example the theorical optimial for llama3_2 8B, 4-bit quantized modelw ith context length of 2k needs about 1G of memory. This theoretcial max can be observed by looking at the peaks in memory profile. Current agorithm resulted in about 1.6GB of planned memory. New algorithm reduce that to about 1.1G. ghstack-source-id: 263342052 @exported-using-ghexport Differential Revision: [D68448332](https://our.internmc.facebook.com/intern/diff/D68448332/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/7995
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* Fix memory profiling for memory.view ops Pull Request resolved: #7925 ATT ghstack-source-id: 263342054 @exported-using-ghexport Differential Revision: [D68448333](https://our.internmc.facebook.com/intern/diff/D68448333/) * [ET][Memory planning] Improve greedy memory planning. Pull Request resolved: #7926 This diff replaces the old greedy algorithm. Older algorithm resulted in 35% worse compared to theoretical optimum. THis matter for long context even more since additional overhead can be few hundred MB. For example the theorical optimial for llama3_2 8B, 4-bit quantized modelw ith context length of 2k needs about 1G of memory. This theoretcial max can be observed by looking at the peaks in memory profile. Current agorithm resulted in about 1.6GB of planned memory. New algorithm reduce that to about 1.1G. ghstack-source-id: 263342052 @exported-using-ghexport Differential Revision: [D68448332](https://our.internmc.facebook.com/intern/diff/D68448332/) --------- Co-authored-by: Kimish Patel <[email protected]>
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* Fix memory profiling for memory.view ops Pull Request resolved: pytorch#7925 ATT ghstack-source-id: 263342054 @exported-using-ghexport Differential Revision: [D68448333](https://our.internmc.facebook.com/intern/diff/D68448333/) * [ET][Memory planning] Improve greedy memory planning. Pull Request resolved: pytorch#7926 This diff replaces the old greedy algorithm. Older algorithm resulted in 35% worse compared to theoretical optimum. THis matter for long context even more since additional overhead can be few hundred MB. For example the theorical optimial for llama3_2 8B, 4-bit quantized modelw ith context length of 2k needs about 1G of memory. This theoretcial max can be observed by looking at the peaks in memory profile. Current agorithm resulted in about 1.6GB of planned memory. New algorithm reduce that to about 1.1G. ghstack-source-id: 263342052 @exported-using-ghexport Differential Revision: [D68448332](https://our.internmc.facebook.com/intern/diff/D68448332/) --------- Co-authored-by: Kimish Patel <[email protected]>
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #7926
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/kimishpatel/151/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/kimishpatel/151/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/kimishpatel/150/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/kimishpatel/151/orig
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