[Mlir-commits] [mlir] [mlir][test] Add e2e test for linalg.mmt4d + SVE (PR #157815)

[Andrzej Warzyński]

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+// DEFINE: %{compile} =  mlir-opt %s \
+// DEFINE:    -transform-interpreter -test-transform-dialect-erase-schedule \
+// DEFINE:    -cse -canonicalize -test-lower-to-llvm
+// DEFINE: %{entry_point} = main
+// DEFINE: %{run} = mlir-runner -e %{entry_point} -entry-point-result=void \
+// DEFINE:    -shared-libs=%mlir_runner_utils,%mlir_c_runner_utils
+
+// RUN: %{compile} | %{run} | FileCheck %s
+
+//===----------------------------------------------------------------------===//
+/// HIGH-LEVEL OVERVIEW
+///
+/// End-to-end test for computing matrix-multiplication using linalg.mmt4d. In
+/// particular, demonstrates how the following MLIR sequence (implemented in
+/// @matmul_via_mmt4d):
+///
+///   A_pack = linalg.pack A
+///   B_pack = linalg.pack B
+///   C_pack = linalg.pack C
+///   out_pack = linalg.mmt4d(A_pack, B_pack, C_pack)
+///
+/// is equivalent to:
+///
+///  linalg.matmul(A, B, C)
+///
+/// (implemented in @matmul_via_matmul).
+///
+/// NOTES ON IMPLEMENTATION
+/// 1. The MMT4D example uses _scalable_ tile sizes for data tiling.
+///   * The matrix-multiplication dimension that's scalable: N.
+///
+/// 2. The lowering of linalg.mmt4d leverages scalable vectorisation.
+///   * The matrix-multiplication dimension that's scalable: N (to match data
+///     tiling configuration).
+///
+/// 3. Neither `linalg.pack` nor `linalg.unpack` are vectorised ATM.
+///
+/// 4. The MMT4D and Pack/Unpack Ops are kept in seperate functions to isolate
+///    the corresponding lowering and lowering configs.
+///   * TODO: Ideally, we should consider fusion opportunities by moving these
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banach-space wrote:

https://github.com/llvm/llvm-project/issues/159770

https://github.com/llvm/llvm-project/pull/157815