WIP - MLIR News 72th Edition (26th Nov 2024)

MLIR Newsletter
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Welcome to the 72th issue of the MLIR Newsletter covering developments in MLIR, and related projects in the ecosystem. We welcome your contributions (contact: javed.absar@gmail.com). Click here to see previous editions.

Highlights, Discussions & RFCs

MLIR Commits Recently:

  • Matthias added "support for bufferizing external functions that have no
    body. Such functions were previously rejected by One-Shot Bufferize if
    *they returned a tensor value. [click here].

  • Jakub added a fast walk-based pattern rewrite driver: walkAndApplyPatterns. It does not iterate until a fixpoint and does not perform folding or DCE. It should have much lower overhead than the greed pattern rewrite driver for simple pattern sets. [PR]

  • Lialan added VectorEmulateNarrowType to “support loading of unaligned vectors” [click here].

  • Andrzej split GenericPadOpVectorizationPattern into two patterns – "With this change, we gain the following: 1 a clear separation between pre-processing and vectorization transformations/stages; 2. a path to support masked vectorisation for tensor.insert_slice (with a dedicated pattern for vectorization, it is much easier to specify the input vector sizes used in masking); 3. more opportunities to vectorize tensor.insert_slice." [[click here]](Split GenericPadOpVectorizationPattern into two patterns).

  • This [commit] adds extra checks/assertions to the
    ConversionPatternRewriterImpl::notifyOpReplaced to improve its
    robustness. This change is in preparation of merging the 1:1 and 1:N dialect
    conversion drivers.

  • The 1:N type converter derived from the 1:1 type converter and extends it with 1:N target materializations. This [commit] merges the two type converters and stores 1:N target materializations in the 1:1 type converter. This is in preparation of merging the 1:1 and 1:N dialect conversion infrastructures.

  • [Commit] adds a canonicalization pattern for scf.forall that replaces constant induction variables with a constant index. There is a similar
    canonicalization that completely removes constant induction variables from the loop, but that pattern does not apply on foralls with mappings, so this one is necessary for those cases.

  • Shahid introduced transpose semantic to ‘linalg.matmul’ ops. [click here]. The main goal of this patch is to extend the semantic of ‘linalg.matmul’ named op to include per operand transpose semantic while also laying out a way to move ops definition from OpDSL to tablegen. Hence, it is implemented in tablegen.

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