commit | 7016b8c82b0f28c9688d27c0032349bbce2cd2cf | [log] [tgz] |
---|---|---|
author | Trevor Morris <tmorris@nvidia.com> | Wed May 17 10:47:44 2023 -0700 |
committer | GitHub <noreply@github.com> | Wed May 17 10:47:44 2023 -0700 |
tree | 652158ecdd8babef7457a0a38767f7833d632e66 | |
parent | 0e90e29fd0ba91100df0a704e9999fe82060c5d6 [diff] |
Support mhlo.collective_permute with NCCL (#13502) Adds support for `mhlo.collective_permute`. During lowering, the `source_target_pairs` are converted into a table of send/recv ids which can be indexed by the local rank. The send/recv id pair is stored in the `param` field of HAL `CollectiveKind::SendRecv`. The send and receive ids are packed as 16 bit integers into the 32 bit param. Fixes https://github.com/openxla/iree/issues/13100
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.