[ET-VK] custom memory pools #10831
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[ET-VK] custom memory pools #10831
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## Context Currently, in ET-VK all memory allocations are done through the default memory pool of VMA (Vulkan Memory Allocator). This has some consequences: * Memory Pool Behaviour (e.g. block size, allocation algorithm, etc.) cannot be configured on a per-model basis * When a model is unloaded, GPU memory is not guaranteed to be freed since VMA may elect to hold on to the allocated memory for future use This PR introduces the ability for models to use a custom VMA memory pool to allocate memory for tensors belonging to that model. This addresses the two points listed above, and makes it possible for VMA to release all memory used for a model once the model is unloaded. Additionally, using a custom VMA memory pool is optional. Differential Revision: [D74617843](https://our.internmc.facebook.com/intern/diff/D74617843/) [ghstack-poisoned]
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## Context Currently, in ET-VK all memory allocations are done through the default memory pool of VMA (Vulkan Memory Allocator). This has some consequences: * Memory Pool Behaviour (e.g. block size, allocation algorithm, etc.) cannot be configured on a per-model basis * When a model is unloaded, GPU memory is not guaranteed to be freed since VMA may elect to hold on to the allocated memory for future use This PR introduces the ability for models to use a custom VMA memory pool to allocate memory for tensors belonging to that model. This addresses the two points listed above, and makes it possible for VMA to release all memory used for a model once the model is unloaded. Additionally, using a custom VMA memory pool is optional. Differential Revision: [D74617843](https://our.internmc.facebook.com/intern/diff/D74617843/) ghstack-source-id: 283566254 Pull Request resolved: #10831
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/10831
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ❌ 4 New FailuresAs of commit 1a6a05b with merge base a868166 ( NEW FAILURES - The following jobs have failed:
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Stack from ghstack (oldest at bottom):
Context
Currently, in ET-VK all memory allocations are done through the default memory pool of VMA (Vulkan Memory Allocator). This has some consequences:
This PR introduces the ability for models to use a custom VMA memory pool to allocate memory for tensors belonging to that model. This addresses the two points listed above, and makes it possible for VMA to release all memory used for a model once the model is unloaded. Additionally, using a custom VMA memory pool is optional.
Differential Revision: D74617843