commit | 0d9c5a80e2cda8c31e9d381029bb5d848de9326b | [log] [tgz] |
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author | Han-Chung Wang <hanhan0912@gmail.com> | Mon Sep 23 09:59:25 2024 -0700 |
committer | GitHub <noreply@github.com> | Mon Sep 23 16:59:25 2024 +0000 |
tree | e17c0601d5a43dd01ce04963bd4a1b64558bb2a8 | |
parent | 9d7eb9f77d4adcffc2c64a01100ea5f0f6ceafee [diff] |
[GPU][DT] Add support for materializing tensor.empty and linalg.fill ops (#18563) The revisions moves the materialization patterns of tensor.empty and linalg.fill to "populateShapeIndependentMaterializeEncodingPatterns" set; updates the comments. This set of patterns lower the ops with encodings to the same op with materialized types. It adds the tile swizzle shape inference to the tensor.empty pattern and moves the utility to the "Utility methods" section without changes. This is a step towards https://github.com/iree-org/iree/issues/18554 --------- Signed-off-by: hanhanW <hanhan0912@gmail.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
Package | Release status |
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GitHub release (stable) | |
GitHub release (nightly) | |
Python iree-compiler | |
Python iree-runtime |
Host platform | Build status |
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Linux | |
macOS | |
Windows |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
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.