[Mlir-commits] [mlir] [mlir][docs] Fix broken links of LIFT (PR #150152)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Tue Jul 22 18:56:05 PDT 2025
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir
Author: Longsheng Mou (CoTinker)
<details>
<summary>Changes</summary>
Fixes #<!-- -->150080.
---
Full diff: https://github.com/llvm/llvm-project/pull/150152.diff
1 Files Affected:
- (modified) mlir/docs/Rationale/RationaleLinalgDialect.md (+5-5)
``````````diff
diff --git a/mlir/docs/Rationale/RationaleLinalgDialect.md b/mlir/docs/Rationale/RationaleLinalgDialect.md
index 7b5137ede3ae7..8975b0a7d515e 100644
--- a/mlir/docs/Rationale/RationaleLinalgDialect.md
+++ b/mlir/docs/Rationale/RationaleLinalgDialect.md
@@ -118,7 +118,7 @@ pragmatic solution. The following non-exhaustive list refers to some of the
projects that influenced Linalg design:
- [ONNX](https://onnx.ai/),
-- [LIFT](https://www.lift-project.org/),
+- [LIFT](https://lift-project.github.io/),
- [XLA](https://www.tensorflow.org/xla/architecture),
- [Halide](https://halide-lang.org/) and [TVM](https://tvm.apache.org/),
- [TACO](http://tensor-compiler.org/),
@@ -171,12 +171,12 @@ Linalg hopes to additionally address the following:
other, thus simplifying the intermediate representation.
### Lessons from LIFT<a name="lessonslift"></a>
-[LIFT](https://www.lift-project.org/) is a system to write computational
+[LIFT](https://lift-project.github.io/) is a system to write computational
kernels based on functional abstractions. Transformations are
represented by additional nodes in the IR, whose semantics are at the
level of the algorithm (e.g. `partialReduce`).
LIFT applies and composes transformations by using [local rewrite
-rules](https://www.lift-project.org/presentations/2015/ICFP-2015.pdf) that
+rules](https://lift-project.github.io/publications/2015/steuwer15generating.pdf) that
embed these additional nodes directly in the functional abstraction.
Similarly to LIFT, Linalg uses local rewrite rules implemented with the MLIR
@@ -194,9 +194,9 @@ Linalg builds on, and helps separate concerns in the LIFT approach as follows:
LIFT is expected to further influence the design of Linalg as it evolves. In
particular, extending the data structure abstractions to support non-dense
tensors can use the experience of LIFT abstractions for
-[sparse](https://www.lift-project.org/publications/2016/harries16sparse.pdf)
+[sparse](https://lift-project.github.io/publications/2016/harries16sparse.pdf)
and [position-dependent
-arrays](https://www.lift-project.org/publications/2019/pizzuti19positiondependentarrays.pdf).
+arrays](https://lift-project.github.io/publications/2019/pizzuti19positiondependentarrays.pdf).
### Lessons from XLA<a name="lessonsxla"></a>
[XLA](https://www.tensorflow.org/xla/architecture) is one of the first
``````````
</details>
https://github.com/llvm/llvm-project/pull/150152
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