| commit | 63381a8309eb6b9f4e0cac25c98bb7e63647ac2c | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Thu Oct 19 11:09:49 2023 -0700 |
| committer | GitHub <noreply@github.com> | Thu Oct 19 18:09:49 2023 +0000 |
| tree | d41e2cb8fad56abad122239f01ec7b6308f2b496 | |
| parent | 87c968c70d0b587996c155053b9e84bf76f335af [diff] |
Switching external resources to be device-local only. (#14016) Previously all external resources (results returned by an invocation) were made host-visible and mappable and this prevented the use of queue-ordered allocations in CUDA as memory pools cannot service memory with associated host pointers. Depending on device the host-visible memory could also be much slower to access (or have more potential pitfalls with page management) vs pinned device-local memory and this got worse once we started doing more dispatches in-place on the results. Now all external buffers are by default allocated as device-local. Users will need to manually stage the buffers and otherwise they'll remain on-device. For externalized state this is a good thing as it means we'll keep state on device automatically. A temporary flag has been added to revert to the old mappable behavior with `--iree-stream-external-resources-mappable=true`. Note that some devices (like CPU) will always allow mapping even if not requested and users can avoid the copies by checking before performing the transfers. GPT2 CUDA post-change with alloca and no caching allocator enabled (~5us/invocation allocation overhead): 
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.