Metal backend: Add AOTI shims for memory management #15021
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15021
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 1506e5f with merge base 6e0c9f6 ( FLAKY - The following job failed but was likely due to flakiness present on trunk:
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This was referenced Oct 10, 2025
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| ET_LOG( | ||
| Debug, | ||
| "aoti_torch_delete_tensor_object: successfull (Metal GPU memory)"); | ||
| return Error::Ok; |
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returning early causes memory leak, no?
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Maybe explicitly implement metal_free_buffer to properly clean up metal buffers and call the method whenever appropriate
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Implement the following shims: - aoti_torch_create_tensor_from_blob_v2 - aoti_torch_empty_strided - aoti_torch_delete_tensor_object - aoti_torch_copy_ - aoti_torch__reinterpret_tensor ghstack-source-id: e272dc3 ghstack-comment-id: 3392300212 Pull-Request: pytorch#15021
| int64_t opaque_metadata_size) { | ||
| ET_LOG(Debug, "aoti_torch_create_tensor_from_blob_v2: entered"); | ||
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| (void)device_type; |
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I think we should error out if device_type is unexpected.
larryliu0820
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Oct 16, 2025
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Implement the following shims: