[mlir][sparse] sorted coo co-iteration check test

Reviewed By: Peiming

Differential Revision: https://reviews.llvm.org/D134971

diff --git a/mlir/test/Dialect/SparseTensor/sorted_coo.mlir b/mlir/test/Dialect/SparseTensor/sorted_coo.mlir
index f77abfa..ecb200d 100644 (file)
--- a/mlir/test/Dialect/SparseTensor/sorted_coo.mlir
+++ b/mlir/test/Dialect/SparseTensor/sorted_coo.mlir
@@ -22,8 +22,18 @@
   doc = "x(i) += A(i,j) * b(j)"
 }
 
+#trait_mul = {
+  indexing_maps = [
+    affine_map<(i,j) -> (i,j)>, // X
+    affine_map<(i,j) -> (i,j)>, // Y
+    affine_map<(i,j) -> (i,j)>  // Z (out)
+  ],
+  iterator_types = ["parallel", "parallel"],
+  doc = "Z(i,j) = X(i,j) * Y(i,j)"
+}
+
 //
-// Two kernels that operate on SortedCOO format.
+// Kernels that operate on SortedCOO format.
 //
 
 // CHECK-LABEL: func.func @sparse_scale(
@@ -94,3 +104,96 @@ func.func @matvec(%arga: tensor<32x64xf64, #SortedCOO>,
   } -> tensor<32xf64>
   return %0 : tensor<32xf64>
 }
+
+// CHECK-LABEL: func.func @mateltmul(
+// CHECK-SAME:    %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME:    %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME:    %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
+// CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 0 : index
+// CHECK-DAG:     %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK-DAG:     %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK:         %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
+// CHECK:         linalg.fill ins(%[[VAL_3]] : f64) outs(%[[VAL_14]] : memref<32x64xf64>)
+// CHECK:         %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
+// CHECK:         %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK:         %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
+// CHECK:         %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK:         %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_15]], %[[VAL_21:.*]] = %[[VAL_17]]) : (index, index) -> (index, index) {
+// CHECK:           %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_16]] : index
+// CHECK:           %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_18]] : index
+// CHECK:           %[[VAL_24:.*]] = arith.andi %[[VAL_22]], %[[VAL_23]] : i1
+// CHECK:           scf.condition(%[[VAL_24]]) %[[VAL_20]], %[[VAL_21]] : index, index
+// CHECK:         } do {
+// CHECK:         ^bb0(%[[VAL_25:.*]]: index, %[[VAL_26:.*]]: index):
+// CHECK:           %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]]] : memref<?xindex>
+// CHECK:           %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_26]]] : memref<?xindex>
+// CHECK:           %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_27]] : index
+// CHECK:           %[[VAL_30:.*]] = arith.select %[[VAL_29]], %[[VAL_28]], %[[VAL_27]] : index
+// CHECK:           %[[VAL_31:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
+// CHECK:           %[[VAL_32:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
+// CHECK:           %[[VAL_33:.*]] = arith.andi %[[VAL_31]], %[[VAL_32]] : i1
+// CHECK:           scf.if %[[VAL_33]] {
+// CHECK:             %[[VAL_34:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
+// CHECK:             %[[VAL_35:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
+// CHECK:             %[[VAL_36:.*]]:2 = scf.while (%[[VAL_37:.*]] = %[[VAL_25]], %[[VAL_38:.*]] = %[[VAL_26]]) : (index, index) -> (index, index) {
+// CHECK:               %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_34]] : index
+// CHECK:               %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_38]], %[[VAL_35]] : index
+// CHECK:               %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1
+// CHECK:               scf.condition(%[[VAL_41]]) %[[VAL_37]], %[[VAL_38]] : index, index
+// CHECK:             } do {
+// CHECK:             ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index):
+// CHECK:               %[[VAL_44:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_42]]] : memref<?xindex>
+// CHECK:               %[[VAL_45:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_43]]] : memref<?xindex>
+// CHECK:               %[[VAL_46:.*]] = arith.cmpi ult, %[[VAL_45]], %[[VAL_44]] : index
+// CHECK:               %[[VAL_47:.*]] = arith.select %[[VAL_46]], %[[VAL_45]], %[[VAL_44]] : index
+// CHECK:               %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
+// CHECK:               %[[VAL_49:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
+// CHECK:               %[[VAL_50:.*]] = arith.andi %[[VAL_48]], %[[VAL_49]] : i1
+// CHECK:               scf.if %[[VAL_50]] {
+// CHECK:                 %[[VAL_51:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_42]]] : memref<?xf64>
+// CHECK:                 %[[VAL_52:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_43]]] : memref<?xf64>
+// CHECK:                 %[[VAL_53:.*]] = arith.mulf %[[VAL_51]], %[[VAL_52]] : f64
+// CHECK:                 memref.store %[[VAL_53]], %[[VAL_14]]{{\[}}%[[VAL_30]], %[[VAL_47]]] : memref<32x64xf64>
+// CHECK:               } else {
+// CHECK:               }
+// CHECK:               %[[VAL_54:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
+// CHECK:               %[[VAL_55:.*]] = arith.addi %[[VAL_42]], %[[VAL_5]] : index
+// CHECK:               %[[VAL_56:.*]] = arith.select %[[VAL_54]], %[[VAL_55]], %[[VAL_42]] : index
+// CHECK:               %[[VAL_57:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
+// CHECK:               %[[VAL_58:.*]] = arith.addi %[[VAL_43]], %[[VAL_5]] : index
+// CHECK:               %[[VAL_59:.*]] = arith.select %[[VAL_57]], %[[VAL_58]], %[[VAL_43]] : index
+// CHECK:               scf.yield %[[VAL_56]], %[[VAL_59]] : index, index
+// CHECK:             }
+// CHECK:           } else {
+// CHECK:           }
+// CHECK:           %[[VAL_60:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
+// CHECK:           %[[VAL_61:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
+// CHECK:           %[[VAL_62:.*]] = arith.select %[[VAL_60]], %[[VAL_61]], %[[VAL_25]] : index
+// CHECK:           %[[VAL_63:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
+// CHECK:           %[[VAL_64:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
+// CHECK:           %[[VAL_65:.*]] = arith.select %[[VAL_63]], %[[VAL_64]], %[[VAL_26]] : index
+// CHECK:           scf.yield %[[VAL_62]], %[[VAL_65]] : index, index
+// CHECK:         }
+// CHECK:         %[[VAL_66:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<32x64xf64>
+// CHECK:         return %[[VAL_66]] : tensor<32x64xf64>
+// CHECK:       }
+func.func @mateltmul(%argx: tensor<32x64xf64, #SortedCOO>,
+                     %argy: tensor<32x64xf64, #SortedCOO>,
+                     %argz: tensor<32x64xf64>) -> tensor<32x64xf64> {
+  %0 = linalg.generic #trait_mul
+      ins(%argx, %argy : tensor<32x64xf64, #SortedCOO>, tensor<32x64xf64, #SortedCOO>)
+      outs(%argz: tensor<32x64xf64>) {
+    ^bb(%x: f64, %y: f64, %z: f64):
+      %1 = arith.mulf %x, %y : f64
+      linalg.yield %1 : f64
+  } -> tensor<32x64xf64>
+  return %0 : tensor<32x64xf64>
+}