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[mlir][docs] Fix broken links of LIFT #150152

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Jul 24, 2025
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[mlir][docs] Fix broken links of LIFT #150152

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10 changes: 5 additions & 5 deletions mlir/docs/Rationale/RationaleLinalgDialect.md
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Expand Up @@ -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/),
Expand Down Expand Up @@ -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
Expand All @@ -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
Expand Down
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