[mlir][sve][nfc] Merge the integration tests for linalg.matmul

mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/matmul.mlir

@@ -8,7 +8,10 @@
 
 // RUN: %{compile}
 
-// RUN: %{run} | FileCheck %s
+// RUN: %{run} | FileCheck %s --check-prefix=F32
+
+// REDEFINE: %{entry_point} = matmul_mixed_ty
+// RUN: %{run} | FileCheck %s --check-prefix=MIXED
 
 func.func @matmul_f32() {
   // Matrix dimensions
@@ -32,37 +35,75 @@ func.func @matmul_f32() {
   %C_out = linalg.matmul ins(%A, %B: tensor<?x?xf32>, tensor<?x?xf32>) outs(%C_in: tensor<?x?xf32>) -> tensor<?x?xf32>
 
   // Print and verify the output
-  // CHECK-LABEL: SVE: START OF TEST OUTPUT
+  // F32-LABEL: SVE: START OF TEST OUTPUT
   vector.print str "SVE: START OF TEST OUTPUT"
 
-  // CHECK-NEXT: Unranked Memref {{.*}} rank = 2 offset = 0 sizes = [5, 15] strides = [15, 1] data =
-  // CHECK-COUNT-5: [29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788]
+  // F32-NEXT: Unranked Memref {{.*}} rank = 2 offset = 0 sizes = [5, 15] strides = [15, 1] data =
+  // F32-COUNT-5: [29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788, 29.5788]
   %xf = tensor.cast %C_out : tensor<?x?xf32> to tensor<*xf32>
   call @printMemrefF32(%xf) : (tensor<*xf32>) -> ()
 
-  // CHECK-NEXT: SVE: END OF TEST OUTPUT
+  // F32-NEXT: SVE: END OF TEST OUTPUT
+  vector.print str "SVE: END OF TEST OUTPUT"
+
+  return
+}
+
+func.func @matmul_mixed_ty() {
+  // Matrix dimensions
+  %K = arith.constant 3 : index
+  %M = arith.constant 5 : index
+  %N = arith.constant 15 : index
+  %c0_i8 = arith.constant 0 : i8
+  %c0_i32 = arith.constant 0 : i32
+
+  // Allocate the matrices
+  %A_alloc = bufferization.alloc_tensor(%M, %K) : tensor<?x?xi8>
+  %B_alloc = bufferization.alloc_tensor(%K, %N) : tensor<?x?xi8>
+  %C_alloc = bufferization.alloc_tensor(%M, %N) : tensor<?x?xi32>
+
+  // Initialise the matrices
+  %pi = arith.constant  123 : i8
+  %A = linalg.fill ins(%pi : i8) outs(%A_alloc : tensor<?x?xi8>) -> tensor<?x?xi8>
+  %B = linalg.fill ins(%pi : i8) outs(%B_alloc : tensor<?x?xi8>) -> tensor<?x?xi8>
+  %C_in = linalg.fill ins(%c0_i32 : i32) outs(%C_alloc : tensor<?x?xi32>) -> tensor<?x?xi32>
+
+  // Matmul
+  %C_out = linalg.matmul ins(%A, %B: tensor<?x?xi8>, tensor<?x?xi8>) outs(%C_in: tensor<?x?xi32>) -> tensor<?x?xi32>
+
+  // Print and verify the output
+  // MIXED-LABEL: SVE: START OF TEST OUTPUT
+  vector.print str "SVE: START OF TEST OUTPUT"
+
+  // MIXED-NEXT: Unranked Memref {{.*}} rank = 2 offset = 0 sizes = [5, 15] strides = [15, 1] data =
+  // MIXED-COUNT-5: [45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387,   45387]
+  %xf = tensor.cast %C_out : tensor<?x?xi32> to tensor<*xi32>
+  call @printMemrefI32(%xf) : (tensor<*xi32>) -> ()
+
+  // MIXED-NEXT: SVE: END OF TEST OUTPUT
   vector.print str "SVE: END OF TEST OUTPUT"
 
   return
 }
 
 module attributes {transform.with_named_sequence} {
-transform.named_sequence @__transform_main(%module: !transform.any_op {transform.readonly}) {
-    %matmul = transform.structured.match ops{["linalg.matmul"]} in %module
-      : (!transform.any_op) -> !transform.any_op
+  // A sequence that will tile and vectorise a Matmul Op
+  transform.named_sequence @tile_and_vectorize_matmul(%func
+    : !transform.op<"func.func"> {transform.readonly}) {
+
+    // Step 0: Get a handle to the matmul Op
+    %matmul = transform.structured.match ops{["linalg.matmul"]} in %func
+      : (!transform.op<"func.func">) -> !transform.any_op
 
     // Step 1: Tile
-    %module_with_tiled_loops, %loops:3 = transform.structured.tile_using_for %matmul [2, [4], 1]
+    %tiled_matmul, %loops:3 = transform.structured.tile_using_for %matmul [2, [4], 1]
       : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    transform.print %tiled_matmul {name = "matmul lal"}: !transform.any_op
 
     // Step 2: Vectorize
-    %tiled_matmul = transform.structured.match ops{["linalg.matmul"]} in %module_with_tiled_loops
-      : (!transform.any_op) -> !transform.any_op
     transform.structured.vectorize %tiled_matmul vector_sizes [2, [4], 1] : !transform.any_op
 
     // Step 3: Lower vector.multi_reduction to vector.contract (+ some helpful patterns)
-    %func = transform.structured.match ops{["func.func"]} in %module
-      : (!transform.any_op) -> !transform.op<"func.func">
     transform.apply_patterns to %func {
       transform.apply_patterns.vector.reduction_to_contract
       transform.apply_patterns.vector.transfer_permutation_patterns
@@ -77,6 +118,21 @@ transform.named_sequence @__transform_main(%module: !transform.any_op {transform
 
     transform.yield
   }
+
+  // A sequence that goes over all functions in tis module and applies
+  // "tile_and_vectorize_matmul"
+  transform.named_sequence @__transform_main(%module: !transform.any_op {transform.readonly}) {
+    %funcs = transform.structured.match ops{["func.func"]} in %module
+        : (!transform.any_op) -> !transform.op<"func.func">
+
+    transform.foreach %funcs : !transform.op<"func.func"> {
+      ^bb2(%func : !transform.op<"func.func">):
+        transform.include @tile_and_vectorize_matmul failures(propagate)
+        (%func) : (!transform.op<"func.func">) -> ()
+    }
+    transform.yield
+  }
 }
 
 func.func private @printMemrefF32(%ptr : tensor<*xf32>)
+func.func private @printMemrefI32(%ptr : tensor<*xi32>)