MLIR News, 3rd edition (3/20/2020)

MLIR Newsletter
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Welcome to the third issue of the MLIR (bi)Weekly, a newsletter (published on Friday) 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!

See the previous published edition.

Highlights

  • A new dialect to model AVX512 intrinsics has been added.
  • The initial MLIR Fortran IR (FIR) has landed!
  • TensorFlow has a new runtime: TFRT! We had a deep dive at the last Open Design meeting (slides - recording)
  • The hasNoSideEffect traits on operations has be removed in favor of a richer interface.

MLIR Core

Infrastructure

  • PatternMatchResult has been replaced with LogicalResult, as PatternMatchState has been deprecated and removed.
  • Patterns can now notify the rewriter why a match fails via rewriter.notifyMatchFailure.
    • The output is visible in DialectConversion via -debug-only=dialect-conversion
  • The inliner now detects, and erases, single-use callables.
  • The MemoryEffectOpInterface has replaced HasNoSideEffect making DCE/CSE more powerful. Documentation for this feature should come soon!
  • Attribute names are no longer required to be identifiers.

Table-driven Infrastructure

  • The declaration of dialect classes can be generated by ODS via gen-dialect-decls

Code Generation

  • “Named ops” on tensors and buffers: RFC and sketch of Linalg “named ops”
  • Vector dialects: vector.contract lowering to llvm.intr.matrix_multiply.
  • Vector dialects: work starts on llvm.intr.masked.load/store and vector.transpose
  • Vector dialects: AVX512 dialect RFC, dialect landed in core
  • ModelBuilder for E2E Runs on CPU: llvm.intr.matrix_multiply benchmarking and comparison with naive vanilla MLIR lowering (~8x benefit measured).
  • Collaborations: @fhahn started exposing row-major LLVM vector intrinsics.

SPIR-V

  • SPIR-V type hierarchy gains the ability to query required capabilities/extensions.
  • A new attribute, spv.vce, is introduced to contain the (version, capabilities, extensions) triple; spv.module op is revamped to take advantage of this new attribute.
  • Conversions towards SPIR-V are now generally target environment aware. This includes both ops and types: types not available on the target will be rewritten; ops not available will not be generated.
  • A pass is added to deduce a spv.module’s (version, capabilities, extensions).
  • The Vulkan runner now uses C wrapper for calling into the Vulkan runtime.

In the Ecosystem

Flang, the LLVM Fortran Compiler

The initial MLIR Fortran IR (FIR) has landed!

IREE : An Experimental MLIR Execution Environment

  • Codegen and ops (all correctness focused right now):
    • Enabled many misc op integration tests to use new LinAlg based codegen on CPU through HLO->LinAlg conversions
    • Switched from simple hand-coded conv shader to LinAlg based Conv for CPU/GPU (test case)
    • Enable gemm through LinAlg path on GPU (commit)
    • Handle multiple reduction dimensions on LinAlg lowering of HLO reduce (commit)
    • Simple lowering of HLO reshape to (linalg.reshape + linalg.copy)
  • Integrations:
    • Initial lowerings of TensorFlow string type and ops (commit)
    • Compiler entry points for compiling from HLO protos (for JAX demo)
    • Started work on JNI bindings
  • Build:
    • MSVC compatibility and vulkan integration demos working via CMake on Windows
    • Python bindings can set LLVM options and dump nice stack traces
  • Misc:
    • Updated workload calculations to be dynamic shape friendly and be more ready for codegen of CPU kernel concurrency
    • Work in progress work to plumb (ranked) dynamic shapes through from frontend to codegen backends (eta of ~1 more week for example dynamic shape test cases, including dynamic LSTM, batch MLP, etc)

Teckyl: An MLIR frontend for Tensor Operations

Reusing the TensorComprehension frontend, this project enables to take input like:

def mm(float(M,K) A, float(K,N) B) -> (float(M,N) C) {
  C(i,j) +=! A(i,k) * B(k,j) where i in 0:M, k in 0:K, j in 0:N
}

and produce MLIR Linalg from it!

TensorFlow

PlaidML

Recent Talks

  • The MLIR Shape Dialect work was presented at the MLIR Open Design meeting (slides - recording)
  • MLIR: Accelerating TF with compilers ; recording was presented at the TF Dev Summit 2020
  • Google announced a new runtime for TensorFlow! We had a deep dive on TFRT at the last Open Design meeting (slides - recording)
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