See the previous published edition.
Welcome to the twenty-nineth issue of the MLIR (bi)Weekly, a newsletter covering developments in MLIR, and related projects in the ecosystem. MLIR (bi)Weekly is brought to you by a collective effort of contributors, we welcome your contributions!
- Two exciting papers in the “recent publications” section at the bottom of this newsletter, check them out!
- The Intel AMX dialect was added and discussed at the last open meeting (see “recent talks” section below).
- Multiple RFCs are in-flight with patches up for review:
- The initial revision for the DataLayout infrastructure has landed.
OpInterfaceRewritePatternclasses are being added to make matching operations with
interfaceseasier (and much more efficient).
OwningRewritePatternList::insertare undergoing a rename to
RewritePatternSet::addrespectively. (Likely more minor/major refactorings related to canonicalization patterns still to come)
OwningRewritePatternListnow supports a simpler
insertthat accepts a function pointer that implements the
matchAndRewriteof the pattern.
- The greedy pattern rewrite driver now populates its initial worklist top-down (which may result in some test case churn for users).
- The destructors of Attribute/Type storage objects are now called when a context is destroyed, i.e. complex destructors are now supported (e.g. for parameters that dynamically allocate memory).
- Operation Asm Format: Support for “else” groups is being added to optional elements. This allows for specifying a group of elements to parse/print when an optional group is not present.
- A new AMX vector dialect for Intel Advanced Matrix Extensions has been added
- unleashes the power of AMX using MLIR concepts (2-d vectors, memrefs, etc.) with just a few new operations
- includes fully functional integration tests (running on a Sapphire Rapids emulator) which test correctness but also document usage
- Sparse compiler: stress testing continues, but ran clean for millions of test without finding new issues; some more discussion of adding sparse tensor type, work will start shortly.
- The SPIR-V dialect sees more ops for Vulkan graphics: spv.Image.
- A few more patches landed into the SPIR-V dialect to improve op naming consistency.
- OpenMP: Pretty printer and parser for the work-sharing loop landed.
- The Memref type now accepts an arbitrary attribute to model the memory space:
The memory space of a memref is specified by a target-specific attribute. It might be an integer value, string, dictionary or custom dialect attribute. The empty memory space (attribute is None) is target specific.
In the Ecosystem
IREE : An Experimental MLIR Execution Environment
- CUDA backend
- Enabled CUDA E2E tests in IREE CI for several HLO ops
- Wrote documentation about design choices for the CUDA backend and how to run CUDA E2E
- Starting codegen improvements by adding tiling and distribution to blocks for element-wise ops
mlir-npcomp: Prototype for compiling Numpy programs
- First iteration of roadmap
- Rewrite of GlobalizeObjectGraph to allow importing ResNet and a speech-to-text model from TorchHub PR (IR for speech-to-text model)
XLA GPU backend
- Migrated While and Conditional ops emitter to use LMHLO operations.
- We can now instantiated a full LMHLO graph from an XLA computation and use it to emit LLVM.
- We fixed the handling of return values for c-wrappers to also support returning memrefs (or anything else that lowers to a struct type in LLVM). Returning structs is not well defined, so instead we now pass a pointer to the result struct as first argument.
- We improved broadcast elimination for partially static shapes, enabling more fusion as a result.
- Fixed a precision issue with the approximation for tanh.
- Ongoing work in the area of auto-vectorizing at the MLIR level and enabling fusion with broadcasts and dynamic shapes.
CIRCT : Circuit IR Compilers and Tools aka ‘MLIR for hardware’
- Initial Python bindings support is in progress
- 2021-03-18: MLIR AMX Vector Dialect ; slides - recording
- 2021-03-11: Sparse Tensor Type Discussion ; recording