| // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py |
| // RUN: mlir-opt %s -sparsification | FileCheck %s |
| |
| #CSR = #sparse_tensor.encoding<{ |
| dimLevelType = [ "dense", "compressed" ], |
| dimOrdering = affine_map<(i,j) -> (i,j)> |
| }> |
| |
| #DCSR = #sparse_tensor.encoding<{ |
| dimLevelType = [ "compressed", "compressed" ], |
| dimOrdering = affine_map<(i,j) -> (i,j)> |
| }> |
| |
| #trait_scale_inpl = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = X(i,j) * 2" |
| } |
| |
| // CHECK-LABEL: func @sparse_simply_dynamic1( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> |
| // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex> |
| // CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_3]] : index |
| // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32> |
| // CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_16]], %[[VAL_1]] : f32 |
| // CHECK: memref.store %[[VAL_17]], %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_18:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: return %[[VAL_18]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: } |
| func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable = true}) -> tensor<32x16xf32, #DCSR> { |
| %c = arith.constant 2.0 : f32 |
| %0 = linalg.generic #trait_scale_inpl |
| outs(%argx: tensor<32x16xf32, #DCSR>) { |
| ^bb(%x: f32): |
| %1 = arith.mulf %x, %c : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32x16xf32, #DCSR> |
| return %0 : tensor<32x16xf32, #DCSR> |
| } |
| |
| #trait_elt_wise_mult = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = A(i,j) * X(i,j)" |
| } |
| |
| // CHECK-LABEL: func @sparse_simply_dynamic2( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex> |
| // CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_15]]] : memref<?xindex> |
| // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_3]] : index |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_17]]] : memref<?xindex> |
| // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_14]], %[[VAL_3]] : index |
| // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex> |
| // CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_16]], %[[VAL_24:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) { |
| // CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_18]] : index |
| // CHECK: %[[VAL_26:.*]] = arith.cmpi ult, %[[VAL_24]], %[[VAL_21]] : index |
| // CHECK: %[[VAL_27:.*]] = arith.andi %[[VAL_25]], %[[VAL_26]] : i1 |
| // CHECK: scf.condition(%[[VAL_27]]) %[[VAL_23]], %[[VAL_24]] : index, index |
| // CHECK: } do { |
| // CHECK: ^bb0(%[[VAL_28:.*]]: index, %[[VAL_29:.*]]: index): |
| // CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_28]]] : memref<?xindex> |
| // CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_29]]] : memref<?xindex> |
| // CHECK: %[[VAL_32:.*]] = arith.cmpi ult, %[[VAL_31]], %[[VAL_30]] : index |
| // CHECK: %[[VAL_33:.*]] = select %[[VAL_32]], %[[VAL_31]], %[[VAL_30]] : index |
| // CHECK: %[[VAL_34:.*]] = arith.cmpi eq, %[[VAL_30]], %[[VAL_33]] : index |
| // CHECK: %[[VAL_35:.*]] = arith.cmpi eq, %[[VAL_31]], %[[VAL_33]] : index |
| // CHECK: %[[VAL_36:.*]] = arith.andi %[[VAL_34]], %[[VAL_35]] : i1 |
| // CHECK: scf.if %[[VAL_36]] { |
| // CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xf32> |
| // CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_28]]] : memref<?xf32> |
| // CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_37]], %[[VAL_38]] : f32 |
| // CHECK: memref.store %[[VAL_39]], %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xf32> |
| // CHECK: } else { |
| // CHECK: } |
| // CHECK: %[[VAL_40:.*]] = arith.cmpi eq, %[[VAL_30]], %[[VAL_33]] : index |
| // CHECK: %[[VAL_41:.*]] = arith.addi %[[VAL_28]], %[[VAL_3]] : index |
| // CHECK: %[[VAL_42:.*]] = select %[[VAL_40]], %[[VAL_41]], %[[VAL_28]] : index |
| // CHECK: %[[VAL_43:.*]] = arith.cmpi eq, %[[VAL_31]], %[[VAL_33]] : index |
| // CHECK: %[[VAL_44:.*]] = arith.addi %[[VAL_29]], %[[VAL_3]] : index |
| // CHECK: %[[VAL_45:.*]] = select %[[VAL_43]], %[[VAL_44]], %[[VAL_29]] : index |
| // CHECK: scf.yield %[[VAL_42]], %[[VAL_45]] : index, index |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_46:.*]] = sparse_tensor.load %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: return %[[VAL_46]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: } |
| func @sparse_simply_dynamic2(%arga: tensor<32x16xf32, #CSR>, |
| %argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable = true}) -> tensor<32x16xf32, #DCSR> { |
| %0 = linalg.generic #trait_elt_wise_mult |
| ins(%arga: tensor<32x16xf32, #CSR>) |
| outs(%argx: tensor<32x16xf32, #DCSR>) { |
| ^bb(%a: f32, %x: f32): |
| %1 = arith.mulf %x, %a : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32x16xf32, #DCSR> |
| return %0 : tensor<32x16xf32, #DCSR> |
| } |
| |
| #trait_scale = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = A(i,j) * 2.0" |
| } |
| |
| // CHECK-LABEL: func @sparse_truly_dynamic( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 10 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 20 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.init{{\[}}%[[VAL_2]], %[[VAL_3]]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: %[[VAL_11:.*]] = memref.alloca(%[[VAL_5]]) : memref<?xindex> |
| // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_2]] step %[[VAL_4]] { |
| // CHECK: memref.store %[[VAL_12]], %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<?xindex> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<?xindex> |
| // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index |
| // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_4]] { |
| // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex> |
| // CHECK: memref.store %[[VAL_17]], %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<?xf32> |
| // CHECK: %[[VAL_19:.*]] = arith.mulf %[[VAL_18]], %[[VAL_1]] : f32 |
| // CHECK: sparse_tensor.lex_insert %[[VAL_7]], %[[VAL_11]], %[[VAL_19]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_7]] hasInserts : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>> |
| // CHECK: return %[[VAL_20]] : tensor<10x20xf32, #sparse_tensor.encoding<{ |
| // CHECK: } |
| func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20xf32, #DCSR> { |
| %s = arith.constant 2.0 : f32 |
| %d10 = arith.constant 10 : index |
| %d20 = arith.constant 20 : index |
| %xm = sparse_tensor.init [%d10, %d20] : tensor<10x20xf32, #DCSR> |
| %0 = linalg.generic #trait_scale |
| ins(%arga: tensor<10x20xf32, #CSR>) |
| outs(%xm: tensor<10x20xf32, #DCSR>) { |
| ^bb(%a: f32, %x: f32): |
| %1 = arith.mulf %a, %s : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<10x20xf32, #DCSR> |
| return %0 : tensor<10x20xf32, #DCSR> |
| } |
