| commit | e9ae96370725a29afbc1032446b989504b59906f | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Tue Oct 04 08:13:30 2022 -0700 |
| committer | Ben Vanik <ben.vanik@gmail.com> | Wed Oct 12 10:15:36 2022 -0700 |
| tree | 6038f3317f1ec66927490372889e595715b8c4a6 | |
| parent | 7a27de5bdbf6a064af5f84c4fd82b7ef9a9476f7 [diff] |
Implementing basic `--iree-execution-model=async-external` support. Currently only coarse fences are supported: when the flag is specified exported functions will take a wait and signal fence pair. Upon return to a caller execution is not assumed to have completed and the caller can either wait on the signal fence or chain further invocations with it. Future invocation models will support specifying an arbitrary set of fences that allow for up to per-I/O granularity (attention layers could signal sooner than full decoders, etc) and specifying fences for in-place buffers (wait until buffer is available to write in before filling). This initial version is conservative and may include additional queue barriers in order to signal the user-provided fence but future improvements to timepoint elision and IPO will make that better. Nearly all models we work with today end up becoming async with the current heuristics. iree-run-module/mlir has been updated to support programs compiled with the async-external mode. iree-benchmark-module now supports pipelined and concurrent execution via the --batch_size= and --batch_concurrency= flags: batch_size defines how many invocations there are and batch_concurrency defines how many of those are able to run concurrently. Examples: --batch_size=1 --batch_concurrency=1: default single-shot invocation --batch_size=4 --batch_concurrency=1: 4 sequential invocations --batch_size=4 --batch_concurrency=4: 4 concurrent invocations --batch_size=4 --batch_concurrency=2: 2 concurrent sequences of 2 invocations
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.