commit | c5d4b96a4aadbc86c6b63052e900adbc88037d38 | [log] [tgz] |
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author | Dave Liddell <dave.liddell@amd.com> | Fri Jun 14 10:53:31 2024 -0600 |
committer | GitHub <noreply@github.com> | Fri Jun 14 09:53:31 2024 -0700 |
tree | 24629e88c2699b82654f8c7edfa4ce6602091a8a | |
parent | 34282319af42dfbc5bf80c0514b5e8902ed7cb90 [diff] |
Allow flags to be set with greater flexibility (#17659) Changes to the python binding to allow iree.runtime.flags.parse_flags to take effect at times other than before the first time a driver is created. Also includes fixes for bugs exposed during the development of this feature. - Added "internal" API functions `create_hal_driver()` and `clear_hal_driver_cache()` to create a driver object independent of the cache, and to clear the cache, respectively - Added `HalDriver` class implementation functions for the above new API functions. Refactored class to share as much common code as possible. - Factored out driver URI processing into its own nested class for easier handling of URI components - Fixed dangling pointer bug. In the C layer flags are being kept by reference as string views, requiring the caller to keep the original flag strings (argc, argv) around for as long as the flags are being used. However, the python binding was using a local variable for those strings, letting them go out of scope and causing garbage values later on. The fix is to move the strings to a file scope variable. Flag handling does not appear to be getting used in a multi-threaded environment, as other aspects of flag handling use static variables with no mutex guarding that I could find. - Fixed runtime assert in Windows debug build for the improper use of std::vector<>::front() on an empty vector. The code never used the value of front(), as it was guarded by a check for the vector's size, but the assert prevents the debug build from running. --------- Signed-off-by: Dave Liddell <dave.liddell@amd.com> Signed-off-by: daveliddell <dave.liddell@amd.com>
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.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.