[ET-VK] Enable automatic dtype conversion when copying to/from staging buffer #14244
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…g buffer Pull Request resolved: #14222 ## Context During export, Vulkan sometimes converts certain tensor dtypes. The most common case of this is that int64 and float64 are internally represented as int32 and float32 tensors. The primary reason for this is to reduce the number of dtype variants that need to be generated for each shader, and also due to the fact that 64-bit types are not guaranteed to be supported. However, this raises an issue if an int64 or float64 tensor is marked as an input/output tensor of the model. The source/destination ETensor will have a different dtype than the internal representation, meaning that the input/output bytes will be interpreted incorrectly. ## Changes This diff fixes this behaviour by introducing the concept of a "staging dtype". This allows the staging buffer of a tensor to have a different dtype than the underlying GPU buffer or texture. When copying to/from the GPU resource, the dtype can then be converted to the correct dtype expected by the client code. As a bonus, also add an optional setting to force fp16 to be used internally for fp32 tensors. This allows models to access half precision inference without needing to incur the cost of dtype conversion ops being inserted into the graph, or needing to manually convert inputs/outputs to half type. ghstack-source-id: 309155136 Differential Revision: [D82234180](https://our.internmc.facebook.com/intern/diff/D82234180/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14244
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…g buffer (pytorch#14244) ## Context During export, Vulkan sometimes converts certain tensor dtypes. The most common case of this is that int64 and float64 are internally represented as int32 and float32 tensors. The primary reason for this is to reduce the number of dtype variants that need to be generated for each shader, and also due to the fact that 64-bit types are not guaranteed to be supported. However, this raises an issue if an int64 or float64 tensor is marked as an input/output tensor of the model. The source/destination ETensor will have a different dtype than the internal representation, meaning that the input/output bytes will be interpreted incorrectly. ## Changes This diff fixes this behaviour by introducing the concept of a "staging dtype". This allows the staging buffer of a tensor to have a different dtype than the underlying GPU buffer or texture. When copying to/from the GPU resource, the dtype can then be converted to the correct dtype expected by the client code. As a bonus, also add an optional setting to force fp16 to be used internally for fp32 tensors. This allows models to access half precision inference without needing to incur the cost of dtype conversion ops being inserted into the graph, or needing to manually convert inputs/outputs to half type. Differential Revision: [D82234180](https://our.internmc.facebook.com/intern/diff/D82234180/)
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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: #14222 by @SS-JIA
^ 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/327/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/327/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/327/orig
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