diff options
Diffstat (limited to 'mlir/test/Dialect/SparseTensor/dense.mlir')
| -rw-r--r-- | mlir/test/Dialect/SparseTensor/dense.mlir | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/mlir/test/Dialect/SparseTensor/dense.mlir b/mlir/test/Dialect/SparseTensor/dense.mlir index 52db814..2d8dcfe 100644 --- a/mlir/test/Dialect/SparseTensor/dense.mlir +++ b/mlir/test/Dialect/SparseTensor/dense.mlir @@ -32,14 +32,14 @@ // a non-annotated dense matrix as output. // // CHECK-LABEL: func @dense1( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>, +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse{{[0-9]*}}>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> +// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xf32> // CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { @@ -73,14 +73,14 @@ func.func @dense1(%arga: tensor<32x16xf32, #DenseMatrix>, // // CHECK-LABEL: func @dense2( // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse{{[0-9]*}}>) -> tensor<32x16xf32, #sparse{{[0-9]*}}> { // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32x16xf32> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> +// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xf32> // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: %[[VAL_11:.*]] = arith.muli %[[VAL_9]], %[[VAL_4]] : index @@ -90,8 +90,8 @@ func.func @dense1(%arga: tensor<32x16xf32, #DenseMatrix>, // CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<?xf32> // CHECK: } // CHECK: } -// CHECK: %[[VAL_15:.*]] = sparse_tensor.load %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> -// CHECK: return %[[VAL_15]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> +// CHECK: %[[VAL_15:.*]] = sparse_tensor.load %[[VAL_1]] : tensor<32x16xf32, #sparse{{[0-9]*}}> +// CHECK: return %[[VAL_15]] : tensor<32x16xf32, #sparse{{[0-9]*}}> // CHECK: } func.func @dense2(%arga: tensor<32x16xf32>, %argx: tensor<32x16xf32, #DenseMatrix>) @@ -116,14 +116,14 @@ func.func @dense2(%arga: tensor<32x16xf32>, // // CHECK-LABEL: func @dense3( // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse{{[0-9]*}}>) -> tensor<32x16xf32, #sparse{{[0-9]*}}> { // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32x16x8xf32> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}}>> to memref<?xf32> +// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xf32> // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: %[[VAL_11:.*]] = arith.muli %[[VAL_9]], %[[VAL_4]] : index @@ -137,8 +137,8 @@ func.func @dense2(%arga: tensor<32x16xf32>, // CHECK: memref.store %[[VAL_19:.*]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<?xf32> // CHECK: } // CHECK: } -// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> -// CHECK: return %[[VAL_20]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> +// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_1]] : tensor<32x16xf32, #sparse{{[0-9]*}}> +// CHECK: return %[[VAL_20]] : tensor<32x16xf32, #sparse{{[0-9]*}}> // CHECK: } func.func @dense3(%arga: tensor<32x16x8xf32>, %argx: tensor<32x16xf32, #DenseMatrix>) |