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# Motivation According to [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842) and [[RFC] Intel GPU Runtime Upstreaming for Allocator](https://github.com/pytorch/pytorch/issues/116322), we will upstream the key functionality of device `Allocator` dedicated for XPU to PyTorch. And following our design prepare to generalize `Allocator` in parallel. # Design In the current design, XPU uses an `XPUAllocator` class, inherited from `c10::Allocator`. `XPUAllocator` is a manager to handle `DeviceCachingAllocator`, which is a per-device implementation of the caching mechanism to manage the already cached or newly allocated memory. The caching mechanism is similar to other backends, like CUDA. We can visualize the design as below. <p align="center"> <img width="162" alt="image" src="https://github.com/pytorch/pytorch/assets/106960996/6b17b8cf-e7d1-48b4-b684-f830c409d218"> </p> # Additional Context We're going to implement our design gradually. This PR covers the device `Allocator` dedicated to XPU. The second PR covers the host `Allocator`. Besides these PRs, we plan to generalize the device `Allocator` device-agnostic through another PR. In this PR, our device `Allocator` has the same memory management mechanism as CUDA, but lacks features such as expendable segments and statistics. We will add these features back in the subsequent PR which intend to generalize `Allocator`. The differences with CUDA: only key functionality, and lack of AsyncAllocator, gpu_trace, history_record, graph functionality, memory snapshot, memory statistics, expandable segment... Pull Request resolved: https://github.com/pytorch/pytorch/pull/118091 Approved by: https://github.com/EikanWang, https://github.com/gujinghui, https://github.com/jgong5, https://github.com/albanD ghstack dependencies: #117611, #117619, #117734 |
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Please see the Writing documentation section of CONTRIBUTING.md for details on both writing and building the docs.