[ET-VK] Used hashed layout instead of axis map UBO #6574
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Pull Request resolved: #6534 ## Context #6358 showed that passing in the axis map of a tensor via a specialization constant allows shaders to utilize the axis map in indexing calculations with minimal impact to latency. This diff extends that idea, and introduces the concept of a hashed layout. The hashed layout is a 32 bit integer where: 1. Bits 28-31: `axis_map[0]` 2. Bits 24-27: `axis_map[1]` 3. Bits 20-23: `axis_map[2]` 4. Bits 16-19: `axis_map[3]` 5. Bits 12-15: `packed_dim` 6. Bits 0-11: unused Essentially, the integer is divided into chunks of 4 bits, and each chunk is used to represent a value from the `axis_map` + `packed_dim`. This way, the entire description of how the tensor is represented as a texture can be passed into a compute shader with a single specialization constant. Within the compute shader, the axis map and packed dim can be extracted like so: ``` ${layout_declare_spec_const(C, "int", "in_layout", "DEFAULT_LAYOUT")} const lowp ivec4 in_axis_map = unhash_axis_map(in_layout); const lowp int in_packed_dim = unhash_packed_dim(in_layout); ``` Note that `lowp` can be used because the expected values are limited by the dimensionality of the tensor, therefore we expect only small values. ## Changes 1. Introduce `hashed_layout` 2. Replace all uses of `axis_map_ubo` with `hashed_layout` 3. Remove `axis_map_ubo` from `vTensor. This also reduces the size of the class. ghstack-source-id: 250928240 @exported-using-ghexport Differential Revision: [D65085141](https://our.internmc.facebook.com/intern/diff/D65085141/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/6574
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 60669cb with merge base db38bcc ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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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: #6534
^ 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/SS-JIA/130/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/130/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/130/orig
@diff-train-skip-merge