[mlir][ArmSME][nfc] Add integration test for i8 to i32 matmul

mlir/test/Integration/Dialect/Linalg/CPU/ArmSME/multi-tile-matmul-mixed-types.mlir

@@ -0,0 +1,123 @@
+// RUN: mlir-opt %s \
+// RUN:   -transform-interpreter -test-transform-dialect-erase-schedule  \
+// RUN:   -one-shot-bufferize="bufferize-function-boundaries" -canonicalize \
+// RUN:   -arm-sme-vector-legalization -canonicalize -cse \
+// RUN:   -convert-vector-to-arm-sme -arm-sme-outer-product-fusion \
+// RUN:   -allocate-arm-sme-tiles -convert-arm-sme-to-scf \
+// RUN:   -enable-arm-streaming="streaming-mode=streaming-locally za-mode=new-za only-if-required-by-ops" \
+// RUN:   -convert-vector-to-scf=full-unroll -convert-arm-sme-to-llvm \
+// RUN:   -test-lower-to-llvm | \
+// RUN: %mcr_aarch64_cmd \
+// RUN:   -e=main -entry-point-result=void \
+// RUN:   -march=aarch64 -mattr="+sve,+sme" \
+// RUN:   -shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%arm_sme_abi_shlib,%mlir_arm_runner_utils | \
+// RUN: FileCheck %s
+
+/// This is very similar to the SME multi-tile-matmul.mlir test, except that it
+/// tests a mixed i8 to i32 matmul and outer product fusion which fuses 16
+/// outer products (four per tile) into four 4-way outer products.
+
+/// NOTE: QEMU gives incorrect result for SME SMOPA 4-way outer product
+/// instruction (version <= 8.2.0, latest version at time of writing), see:
+/// https://gitlab.com/qemu-project/qemu/-/issues/2083
+/// This test is expected to fail until a fixed version of QEMU can be used.
+
+/// FIXME: Remove the 'XFAIL' below once a fixed QEMU version is available
+/// (and installed on CI buildbot).
+/// XFAIL: *
+
+func.func @matmul_i8_to_i32(%A : tensor<?x?xi8>, %B : tensor<?x?xi8>, %C : tensor<?x?xi32>) {
+  %res = linalg.matmul ins(%A, %B: tensor<?x?xi8>, tensor<?x?xi8>)
+                       outs(%C: tensor<?x?xi32>) -> tensor<?x?xi32>
+  %xf = tensor.cast %res : tensor<?x?xi32> to tensor<*xi32>
+  call @printMemrefI32(%xf) : (tensor<*xi32>) -> ()
+  return
+}
+
+func.func @main() {
+  /// Set SVL to 128-bit. This ensures this small matmul will use all four
+  /// 32-bit SME virtual tiles.
+  %c128 = arith.constant 128 : i32
+  func.call @setArmSVLBits(%c128) : (i32) -> ()
+
+  %c0 = arith.constant 0 : i32
+  %c7 = arith.constant 7 : index
+
+  %A = arith.constant dense<[
+    [1,  8, 15, 22, 29, 36, 43, 50, 57, 64, 71, 78, 85],
+    [2,  9, 16, 23, 30, 37, 44, 51, 58, 65, 72, 79, 86],
+    [3, 10, 17, 24, 31, 38, 45, 52, 59, 66, 73, 80, 87],
+    [4, 11, 18, 25, 32, 39, 46, 53, 60, 67, 74, 81, 88],
+    [5, 12, 19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89],
+    [6, 13, 20, 27, 34, 41, 48, 55, 62, 69, 76, 83, 90],
+    [7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 91]
+  ]> : tensor<7x13xi8>
+
+  %B_init = tensor.empty() : tensor<13x7xi8>
+  %B = linalg.transpose ins(%A: tensor<7x13xi8>)
+                        outs(%B_init: tensor<13x7xi8>) permutation = [1, 0]
+
+  %A_dyn = tensor.cast %A : tensor<7x13xi8> to tensor<?x?xi8>
+  %B_dyn = tensor.cast %B : tensor<13x7xi8> to tensor<?x?xi8>
+
+  %C_init = bufferization.alloc_tensor(%c7, %c7) : tensor<?x?xi32>
+  %C = linalg.fill ins(%c0 : i32) outs(%C_init : tensor<?x?xi32>) -> tensor<?x?xi32>
+
+  // CHECK: Unranked Memref {{.*}} rank = 2 offset = 0 sizes = [7, 7] strides = [7, 1] data =
+  // CHECK: [32955, 33514, 34073, 34632, 35191, 35750, 36309]
+  // CHECK: [33514, 34086, 34658, 35230, 35802, 36374, 36946]
+  // CHECK: [34073, 34658, 35243, 35828, 36413, 36998, 37583]
+  // CHECK: [34632, 35230, 35828, 36426, 37024, 37622, 38220]
+  // CHECK: [35191, 35802, 36413, 37024, 37635, 38246, 38857]
+  // CHECK: [35750, 36374, 36998, 37622, 38246, 38870, 39494]
+  // CHECK: [36309, 36946, 37583, 38220, 38857, 39494, 40131]
+  call @matmul_i8_to_i32(%A_dyn, %B_dyn, %C) : (tensor<?x?xi8>, tensor<?x?xi8>, tensor<?x?xi32>) -> ()
+
+  return
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%module : !transform.any_op {transform.consumed}) {
+    %matmul = transform.structured.match ops{["linalg.matmul"]} in %module
+      : (!transform.any_op) -> !transform.any_op
+
+    // Step 1: Tile for size [8] x [8] (unrolled by 4), which corresponds to
+    // (2 x SVLs) x (2 x SVLs), where SVLs is the number of 32-bit elements in a
+    // vector of SVL bits. This uses all four 32-bit SME virtual tiles.
+    %tiled_linalg_op, %loop_i, %loop_j, %loop_k = transform.structured.tile_using_for %matmul[[8], [8], 4]
+      : (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.op<"scf.for">, !transform.op<"scf.for">)
+
+    // Step 2: Vectorize.
+    transform.structured.vectorize %tiled_linalg_op vector_sizes [[8], [8], 4]
+      : !transform.any_op
+
+    // Step 3: Bufferize ahead of TransferReadDropUnitDimsPattern, which
+    // currently only supports memrefs.
+    %bufferize = transform.bufferization.one_shot_bufferize %module
+      {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op
+
+    %func = transform.structured.match ops{["func.func"]} in %bufferize
+      : (!transform.any_op) -> !transform.any_op
+
+    // Step 4: Lower vector.multi_reduction to vector.contract (+ some helpful patterns).
+    transform.apply_patterns to %func {
+      transform.apply_patterns.vector.lower_masked_transfers
+      transform.apply_patterns.vector.transfer_permutation_patterns
+      transform.apply_patterns.vector.reduction_to_contract
+    } : !transform.any_op
+
+    // Step 5: Lower vector.contract to vector.outerproduct. Also drop unit
+    // dims, specifically to prevent vector.transfer_read of vector<[8]x1xi32>,
+    // which can't be lowered in generic path.
+    transform.apply_patterns to %func {
+      transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct"
+      transform.apply_patterns.vector.lower_masks
+      transform.apply_patterns.vector.rank_reducing_subview_patterns
+    } : !transform.any_op
+
+    transform.yield
+  }
+}
+
+func.func private @printMemrefI32(%ptr : tensor<*xi32>)
+func.func private @setArmSVLBits(%bits : i32)