[Mlir-commits] [mlir] [mlir][sme] Add e2e test for lowering mmt4d to sme (PR #208226)

[Ege Beysel]

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+// DEFINE: %{compile} =  mlir-opt %s \
+// DEFINE:    -transform-interpreter -test-transform-dialect-erase-schedule \
+// DEFINE:    -canonicalize -test-lower-to-arm-sme -convert-vector-to-llvm="enable-arm-sve" \
+// DEFINE:    -test-lower-to-llvm
+// DEFINE: %{entry_point} = main
+// DEFINE: %{run} = %mcr_aarch64_cmd -e %{entry_point} -entry-point-result=void --march=aarch64 --mattr="+sve,+sme"\
+// DEFINE:    -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils,%native_arm_sme_abi_shlib
+
+// 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.
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egebeysel wrote:

While I was suggesting this, I assumed that we would be making both M and N dims scalable, but that's not the case. I think we should actually do this, i.e. use the inner tiles `8*vscale` for both M and N dims, that would be the actual use-case.

https://github.com/llvm/llvm-project/pull/208226