commit | 6f33cd4ab31bbf19c01b91536bd9ab085d0bcee3 | [log] [tgz] |
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author | Han-Chung Wang <hanhan0912@gmail.com> | Thu Jan 16 10:04:35 2025 -0800 |
committer | GitHub <noreply@github.com> | Thu Jan 16 18:04:35 2025 +0000 |
tree | 57a93ed6f3b075b67ff64461fe080f164b59b0aa | |
parent | 3e8c81c83cab4e5b15d6ac2194f0b8afe6b1d5de [diff] |
[Stream] Specialize encoding for TensorPhaseOp that have result_encoding (#19707) There are three Stream ops that only have the `result_encoding` operand: TensorEmptyOp, TensorSplatOp, TensorConstantOp. Only empty ops and splat ops can support the specialization at this moment because they are pure shape-like operation. For TensorConstantOp, we return a failure if the encoding is present. Because we do not know how to update the constant with the layout at this moment. It could be done by adding interface methods to `EncodingAttrInterface`. --------- 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.
Releases notes are published on GitHub releases.
Package | Release status |
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GitHub release (stable) | |
GitHub release (nightly) | |
Python iree-base-compiler | |
Python iree-base-runtime |
Operating system | Build status |
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Linux | |
macOS | |
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
Date | Title | Recording | Slides |
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2021-06-09 | IREE Runtime Design Tech Talk | recording | slides |
2020-08-20 | IREE CodeGen (MLIR Open Design Meeting) | recording | slides |
2020-03-18 | Interactive HAL IR Walkthrough | recording | |
2020-01-31 | End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting) | recording | slides |
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.