[mlir][sparse] deduplicate non-unique coordinates unconditionally

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D145621

diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
index f98efae76f8674558af8e5201ba330dca6a9623f..29900e55b2bb399e4d3692c7e7e6a4acb4a1707a 100644 (file)
--- a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
@@ -1287,6 +1287,7 @@ static bool translateBitsToTidDimPairs(
   const BitVector &simple = env.lat(li).simple;
 
   unsigned numloopCond = 0;
+  bool hasNonUnique = false;
   // Converts bits to array + dim pair
   env.merger().foreachTidDimPairInBits(
       all, [&, idx](unsigned b, unsigned tid, std::optional<unsigned> dim,
@@ -1305,6 +1306,7 @@ static bool translateBitsToTidDimPairs(
             if (!dim)
               return;
           }
+          hasNonUnique = !isUniqueDLT(dlt) || hasNonUnique;
           tids.push_back(tid);
           dims.push_back(*dim);
           numloopCond++;
@@ -1367,9 +1369,9 @@ static bool translateBitsToTidDimPairs(
   }
 
   assert(numloopCond > 0);
-  // If we just need to one loop conditions, the loop can be generated by a for
-  // loop.
-  return numloopCond == 1;
+  // If we just need to one loop conditions and the conditions is not imposed on
+  // non-unique level, the loop can be generated by a for loop.
+  return numloopCond == 1 && !hasNonUnique;
 }
 
 /// Starts a single loop in current sequence.

diff --git a/mlir/test/Dialect/SparseTensor/sorted_coo.mlir b/mlir/test/Dialect/SparseTensor/sorted_coo.mlir
index d63b2b7b86c84ce707364392bcfd9df08a56b4a6..7c6884f5588226e4498567ef8cf266d886f50e38 100644 (file)
--- a/mlir/test/Dialect/SparseTensor/sorted_coo.mlir
+++ b/mlir/test/Dialect/SparseTensor/sorted_coo.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt %s -sparsification | FileCheck %s
+// RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s
 
 #SortedCOO = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed-nu", "singleton" ]
@@ -36,23 +36,48 @@
 // Kernels that operate on SortedCOO format.
 //
 
-// CHECK-LABEL: func.func @sparse_scale(
-// CHECK-SAME:    %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> {
-// CHECK-DAG:     %[[VAL_1:.*]] = arith.constant 0 : index
-// CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 1 : index
-// CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32
-// CHECK-DAG:     %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf32>
-// CHECK:         %[[VAL_6:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex>
-// CHECK:         %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
-// CHECK:         scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] {
-// CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_8]]] : memref<?xf32>
-// CHECK:           %[[VAL_10:.*]] = arith.mulf %[[VAL_9]], %[[VAL_3]] : f32
-// CHECK:           memref.store %[[VAL_10]], %[[VAL_5]]{{\[}}%[[VAL_8]]] : memref<?xf32>
+// CHECK-LABEL:   func.func @sparse_scale(
+// CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> {
+// CHECK-DAG:       %[[VAL_1:.*]] = arith.constant false
+// CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
+// CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
+// CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f32
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf32>
+// CHECK-DAG:       %[[VAL_8:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
+// CHECK:           %[[VAL_10:.*]] = scf.while (%[[VAL_11:.*]] = %[[VAL_8]]) : (index) -> index {
+// CHECK:             %[[VAL_12:.*]] = arith.cmpi ult, %[[VAL_11]], %[[VAL_9]] : index
+// CHECK:             scf.condition(%[[VAL_12]]) %[[VAL_11]] : index
+// CHECK:           } do {
+// CHECK:           ^bb0(%[[VAL_13:.*]]: index):
+// CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_15:.*]] = scf.while (%[[VAL_16:.*]] = %[[VAL_13]]) : (index) -> index {
+// CHECK:               %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_9]] : index
+// CHECK:               %[[VAL_18:.*]] = scf.if %[[VAL_17]] -> (i1) {
+// CHECK:                 %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_20:.*]] = arith.cmpi eq, %[[VAL_19]], %[[VAL_14]] : index
+// CHECK:                 scf.yield %[[VAL_20]] : i1
+// CHECK:               } else {
+// CHECK:                 scf.yield %[[VAL_1]] : i1
+// CHECK:               }
+// CHECK:               scf.condition(%[[VAL_21:.*]]) %[[VAL_16]] : index
+// CHECK:             } do {
+// CHECK:             ^bb0(%[[VAL_22:.*]]: index):
+// CHECK:               %[[VAL_23:.*]] = arith.addi %[[VAL_22]], %[[VAL_3]] : index
+// CHECK:               scf.yield %[[VAL_23]] : index
+// CHECK:             }
+// CHECK:             scf.for %[[VAL_24:.*]] = %[[VAL_13]] to %[[VAL_25:.*]] step %[[VAL_3]] {
+// CHECK:               %[[VAL_26:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_24]]] : memref<?xf32>
+// CHECK:               %[[VAL_27:.*]] = arith.mulf %[[VAL_26]], %[[VAL_4]] : f32
+// CHECK:               memref.store %[[VAL_27]], %[[VAL_7]]{{\[}}%[[VAL_24]]] : memref<?xf32>
+// CHECK:             } {"Emitted from" = "linalg.generic"}
+// CHECK:             scf.yield %[[VAL_28:.*]] : index
+// CHECK:           } attributes {"Emitted from" = "linalg.generic"}
+// CHECK:           %[[VAL_29:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
+// CHECK:           return %[[VAL_29]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
 // CHECK:         }
-// CHECK:         %[[VAL_11:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
-// CHECK:         return %[[VAL_11]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
-// CHECK:       }
 func.func @sparse_scale(%argx: tensor<?x?xf32, #SortedCOO>) -> tensor<?x?xf32, #SortedCOO> {
   %c = arith.constant 2.0 : f32
   %0 = linalg.generic #trait_scale
@@ -64,33 +89,57 @@ func.func @sparse_scale(%argx: tensor<?x?xf32, #SortedCOO>) -> tensor<?x?xf32, #
   return %0 : tensor<?x?xf32, #SortedCOO>
 }
 
-// CHECK-LABEL: func.func @matvec(
-// CHECK-SAME:    %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
-// CHECK-SAME:    %[[VAL_1:.*]]: tensor<64xf64>,
-// CHECK-SAME:    %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
-// CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:     %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
-// CHECK-DAG:     %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
-// CHECK-DAG:     %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
-// CHECK-DAG:     %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
-// CHECK-DAG:     %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
-// CHECK:         scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] {
-// CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_14]]] : memref<32xf64>
-// CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xf64>
-// CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<64xf64>
-// CHECK:           %[[VAL_19:.*]] = arith.mulf %[[VAL_17]], %[[VAL_18]] : f64
-// CHECK:           %[[VAL_20:.*]] = arith.addf %[[VAL_15]], %[[VAL_19]] : f64
-// CHECK:           memref.store %[[VAL_20]], %[[VAL_10]]{{\[}}%[[VAL_14]]] : memref<32xf64>
+// CHECK-LABEL:   func.func @matvec(
+// CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME:      %[[VAL_1:.*]]: tensor<64xf64>,
+// CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
+// CHECK-DAG:       %[[VAL_3:.*]] = arith.constant false
+// CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
+// CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
+// CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK:           %[[VAL_13:.*]] = scf.while (%[[VAL_14:.*]] = %[[VAL_11]]) : (index) -> index {
+// CHECK:             %[[VAL_15:.*]] = arith.cmpi ult, %[[VAL_14]], %[[VAL_12]] : index
+// CHECK:             scf.condition(%[[VAL_15]]) %[[VAL_14]] : index
+// CHECK:           } do {
+// CHECK:           ^bb0(%[[VAL_16:.*]]: index):
+// CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_19:.*]] = scf.while (%[[VAL_20:.*]] = %[[VAL_16]]) : (index) -> index {
+// CHECK:               %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_12]] : index
+// CHECK:               %[[VAL_22:.*]] = scf.if %[[VAL_21]] -> (i1) {
+// CHECK:                 %[[VAL_23:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_24:.*]] = arith.cmpi eq, %[[VAL_23]], %[[VAL_18]] : index
+// CHECK:                 scf.yield %[[VAL_24]] : i1
+// CHECK:               } else {
+// CHECK:                 scf.yield %[[VAL_3]] : i1
+// CHECK:               }
+// CHECK:               scf.condition(%[[VAL_25:.*]]) %[[VAL_20]] : index
+// CHECK:             } do {
+// CHECK:             ^bb0(%[[VAL_26:.*]]: index):
+// CHECK:               %[[VAL_27:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
+// CHECK:               scf.yield %[[VAL_27]] : index
+// CHECK:             }
+// CHECK:             %[[VAL_28:.*]] = tensor.extract %[[VAL_2]]{{\[}}%[[VAL_17]]] : tensor<32xf64>
+// CHECK:             %[[VAL_29:.*]] = scf.for %[[VAL_30:.*]] = %[[VAL_16]] to %[[VAL_31:.*]] step %[[VAL_5]] iter_args(%[[VAL_32:.*]] = %[[VAL_28]]) -> (f64) {
+// CHECK:               %[[VAL_33:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_30]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:               %[[VAL_34:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_30]]] : memref<?xf64>
+// CHECK:               %[[VAL_35:.*]] = tensor.extract %[[VAL_1]]{{\[}}%[[VAL_33]]] : tensor<64xf64>
+// CHECK:               %[[VAL_36:.*]] = arith.mulf %[[VAL_34]], %[[VAL_35]] : f64
+// CHECK:               %[[VAL_37:.*]] = arith.addf %[[VAL_32]], %[[VAL_36]] : f64
+// CHECK:               scf.yield %[[VAL_37]] : f64
+// CHECK:             } {"Emitted from" = "linalg.generic"}
+// CHECK:             memref.store %[[VAL_38:.*]], %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref<32xf64>
+// CHECK:             scf.yield %[[VAL_39:.*]] : index
+// CHECK:           } attributes {"Emitted from" = "linalg.generic"}
+// CHECK:           %[[VAL_40:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<32xf64>
+// CHECK:           return %[[VAL_40]] : tensor<32xf64>
 // CHECK:         }
-// CHECK:         %[[VAL_21:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<32xf64>
-// CHECK:         return %[[VAL_21]] : tensor<32xf64>
-// CHECK:       }
 func.func @matvec(%arga: tensor<32x64xf64, #SortedCOO>,
                   %argb: tensor<64xf64>,
                   %argx: tensor<32xf64>) -> tensor<32xf64> {
@@ -193,7 +242,6 @@ func.func @matvec(%arga: tensor<32x64xf64, #SortedCOO>,
 // CHECK:                   %[[VAL_73:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_64]]] : memref<?xf64>
 // CHECK:                   %[[VAL_74:.*]] = arith.mulf %[[VAL_72]], %[[VAL_73]] : f64
 // CHECK:                   memref.store %[[VAL_74]], %[[VAL_15]]{{\[}}%[[VAL_31]], %[[VAL_68]]] : memref<32x64xf64>
-// CHECK:                 } else {
 // CHECK:                 }
 // CHECK:                 %[[VAL_75:.*]] = arith.cmpi eq, %[[VAL_65]], %[[VAL_68]] : index
 // CHECK:                 %[[VAL_76:.*]] = arith.addi %[[VAL_63]], %[[VAL_6]] : index
@@ -203,7 +251,6 @@ func.func @matvec(%arga: tensor<32x64xf64, #SortedCOO>,
 // CHECK:                 %[[VAL_80:.*]] = arith.select %[[VAL_78]], %[[VAL_79]], %[[VAL_64]] : index
 // CHECK:                 scf.yield %[[VAL_77]], %[[VAL_80]] : index, index
 // CHECK:               } attributes {"Emitted from" = "linalg.generic"}
-// CHECK:             } else {
 // CHECK:             }
 // CHECK:             %[[VAL_81:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_31]] : index
 // CHECK:             %[[VAL_82:.*]] = arith.select %[[VAL_81]], %[[VAL_83:.*]], %[[VAL_26]] : index

diff --git a/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir b/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
index f0ac713a245e96ffb0957b6b5ebbcb761268de1f..a8087a56906b067271bea6068b5eeedb118d087e 100644 (file)
--- a/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
@@ -3,51 +3,77 @@
 #COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
 #COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
 
+
 // CHECK-LABEL:   func.func @sparse_reshape_fused(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<5x6xf32>,
-// CHECK-SAME:      %[[VAL_1:.*]]: tensor<6x2x3xf32,
-// CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 5 : index
-// CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 3 : index
-// CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = tensor.empty() : tensor<5x6xf32>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index}
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index}
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index}
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index}
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]]
-// CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_6]] : memref<5x6xf32>
-// CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] {
-// CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xi32>
-// CHECK:             %[[VAL_15:.*]] = arith.extui %[[VAL_14]] : i32 to i64
-// CHECK:             %[[VAL_16:.*]] = arith.index_cast %[[VAL_15]] : i64 to index
-// CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xi32>
-// CHECK:             %[[VAL_18:.*]] = arith.extui %[[VAL_17]] : i32 to i64
-// CHECK:             %[[VAL_19:.*]] = arith.index_cast %[[VAL_18]] : i64 to index
-// CHECK:             scf.for %[[VAL_20:.*]] = %[[VAL_16]] to %[[VAL_19]] step %[[VAL_5]] {
-// CHECK:               %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xi32, strided<[?], offset: ?>>
-// CHECK:               %[[VAL_22:.*]] = arith.extui %[[VAL_21]] : i32 to i64
-// CHECK:               %[[VAL_23:.*]] = arith.index_cast %[[VAL_22]] : i64 to index
-// CHECK:               %[[VAL_24:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_13]], %[[VAL_23]]] : tensor<5x6xf32>
-// CHECK:               %[[VAL_25:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xi32, strided<[?], offset: ?>>
+// CHECK-SAME:      %[[VAL_1:.*]]: tensor<6x2x3xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>>) -> tensor<?x?x?xf32> {
+// CHECK-DAG:       %[[VAL_2:.*]] = arith.constant false
+// CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 5 : index
+// CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 3 : index
+// CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
+// CHECK-DAG:       %[[VAL_7:.*]] = tensor.empty() : tensor<5x6xf32>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index}
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index}
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index}
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index}
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]]
+// CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_7]] : memref<5x6xf32>
+// CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
+// CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xi32>
+// CHECK:             %[[VAL_16:.*]] = arith.extui %[[VAL_15]] : i32 to i64
+// CHECK:             %[[VAL_17:.*]] = arith.index_cast %[[VAL_16]] : i64 to index
+// CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xi32>
+// CHECK:             %[[VAL_19:.*]] = arith.extui %[[VAL_18]] : i32 to i64
+// CHECK:             %[[VAL_20:.*]] = arith.index_cast %[[VAL_19]] : i64 to index
+// CHECK:             %[[VAL_21:.*]] = scf.while (%[[VAL_22:.*]] = %[[VAL_17]]) : (index) -> index {
+// CHECK:               %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_20]] : index
+// CHECK:               scf.condition(%[[VAL_23]]) %[[VAL_22]] : index
+// CHECK:             } do {
+// CHECK:             ^bb0(%[[VAL_24:.*]]: index):
+// CHECK:               %[[VAL_25:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_24]]] : memref<?xi32, strided<[?], offset: ?>>
 // CHECK:               %[[VAL_26:.*]] = arith.extui %[[VAL_25]] : i32 to i64
 // CHECK:               %[[VAL_27:.*]] = arith.index_cast %[[VAL_26]] : i64 to index
-// CHECK:               %[[VAL_28:.*]] = arith.muli %[[VAL_27]], %[[VAL_3]] : index
-// CHECK:               %[[VAL_29:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xi32, strided<[?], offset: ?>>
-// CHECK:               %[[VAL_30:.*]] = arith.extui %[[VAL_29]] : i32 to i64
-// CHECK:               %[[VAL_31:.*]] = arith.index_cast %[[VAL_30]] : i64 to index
-// CHECK:               %[[VAL_32:.*]] = arith.addi %[[VAL_28]], %[[VAL_31]] : index
-// CHECK:               %[[VAL_33:.*]] = tensor.extract %[[VAL_6]]{{\[}}%[[VAL_13]], %[[VAL_32]]] : tensor<5x6xf32>
-// CHECK:               %[[VAL_34:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_20]]] : memref<?xf32>
-// CHECK:               %[[VAL_35:.*]] = arith.mulf %[[VAL_24]], %[[VAL_34]] : f32
-// CHECK:               %[[VAL_36:.*]] = arith.addf %[[VAL_33]], %[[VAL_35]] : f32
-// CHECK:               memref.store %[[VAL_36]], %[[VAL_12]]{{\[}}%[[VAL_13]], %[[VAL_32]]] : memref<5x6xf32>
-// CHECK:             } {"Emitted from" = "linalg.generic"}
+// CHECK:               %[[VAL_28:.*]] = scf.while (%[[VAL_29:.*]] = %[[VAL_24]]) : (index) -> index {
+// CHECK:                 %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_29]], %[[VAL_20]] : index
+// CHECK:                 %[[VAL_31:.*]] = scf.if %[[VAL_30]] -> (i1) {
+// CHECK:                   %[[VAL_32:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_29]]] : memref<?xi32, strided<[?], offset: ?>>
+// CHECK:                   %[[VAL_33:.*]] = arith.extui %[[VAL_32]] : i32 to i64
+// CHECK:                   %[[VAL_34:.*]] = arith.index_cast %[[VAL_33]] : i64 to index
+// CHECK:                   %[[VAL_35:.*]] = arith.cmpi eq, %[[VAL_34]], %[[VAL_27]] : index
+// CHECK:                   scf.yield %[[VAL_35]] : i1
+// CHECK:                 } else {
+// CHECK:                   scf.yield %[[VAL_2]] : i1
+// CHECK:                 }
+// CHECK:                 scf.condition(%[[VAL_36:.*]]) %[[VAL_29]] : index
+// CHECK:               } do {
+// CHECK:               ^bb0(%[[VAL_37:.*]]: index):
+// CHECK:                 %[[VAL_38:.*]] = arith.addi %[[VAL_37]], %[[VAL_6]] : index
+// CHECK:                 scf.yield %[[VAL_38]] : index
+// CHECK:               }
+// CHECK:               %[[VAL_39:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_14]], %[[VAL_27]]] : tensor<5x6xf32>
+// CHECK:               scf.for %[[VAL_40:.*]] = %[[VAL_24]] to %[[VAL_41:.*]] step %[[VAL_6]] {
+// CHECK:                 %[[VAL_42:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_40]]] : memref<?xi32, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_43:.*]] = arith.extui %[[VAL_42]] : i32 to i64
+// CHECK:                 %[[VAL_44:.*]] = arith.index_cast %[[VAL_43]] : i64 to index
+// CHECK:                 %[[VAL_45:.*]] = arith.muli %[[VAL_44]], %[[VAL_4]] : index
+// CHECK:                 %[[VAL_46:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_40]]] : memref<?xi32, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_47:.*]] = arith.extui %[[VAL_46]] : i32 to i64
+// CHECK:                 %[[VAL_48:.*]] = arith.index_cast %[[VAL_47]] : i64 to index
+// CHECK:                 %[[VAL_49:.*]] = arith.addi %[[VAL_45]], %[[VAL_48]] : index
+// CHECK:                 %[[VAL_50:.*]] = tensor.extract %[[VAL_7]]{{\[}}%[[VAL_14]], %[[VAL_49]]] : tensor<5x6xf32>
+// CHECK:                 %[[VAL_51:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_40]]] : memref<?xf32>
+// CHECK:                 %[[VAL_52:.*]] = arith.mulf %[[VAL_39]], %[[VAL_51]] : f32
+// CHECK:                 %[[VAL_53:.*]] = arith.addf %[[VAL_50]], %[[VAL_52]] : f32
+// CHECK:                 memref.store %[[VAL_53]], %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_49]]] : memref<5x6xf32>
+// CHECK:               } {"Emitted from" = "linalg.generic"}
+// CHECK:               scf.yield %[[VAL_54:.*]] : index
+// CHECK:             } attributes {"Emitted from" = "linalg.generic"}
 // CHECK:           } {"Emitted from" = "linalg.generic"}
-// CHECK:           %[[VAL_37:.*]] = bufferization.to_tensor %[[VAL_12]] : memref<5x6xf32>
-// CHECK:           %[[VAL_38:.*]] = tensor.expand_shape %[[VAL_37]] {{\[\[}}0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
-// CHECK:           %[[VAL_39:.*]] = tensor.cast %[[VAL_38]] : tensor<5x2x3xf32> to tensor<?x?x?xf32>
-// CHECK:           return %[[VAL_39]] : tensor<?x?x?xf32>
+// CHECK:           %[[VAL_55:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<5x6xf32>
+// CHECK:           %[[VAL_56:.*]] = tensor.expand_shape %[[VAL_55]] {{\[\[}}0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
+// CHECK:           %[[VAL_57:.*]] = tensor.cast %[[VAL_56]] : tensor<5x2x3xf32> to tensor<?x?x?xf32>
+// CHECK:           return %[[VAL_57]] : tensor<?x?x?xf32>
 // CHECK:         }
 func.func @sparse_reshape_fused(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {
   %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>