| // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py |
| // RUN: mlir-opt %s -sparsification | FileCheck %s |
| |
| // Example with cyclic iteration graph with sparse and dense constraints, |
| // but an acyclic iteration graph using sparse constraints only. |
| |
| #SparseTensor = #sparse_tensor.encoding<{ |
| dimLevelType = [ "dense", "dense", "dense", "compressed", |
| "compressed", "dense", "dense", "dense" ] |
| }> |
| |
| #trait_mul = { |
| indexing_maps = [ |
| affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A |
| affine_map<(i,j,k,l,m,n,o,p) -> (p,o,n,m,l,k,j,i)>, // B |
| affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)> // X |
| ], |
| iterator_types = ["parallel", "parallel", "parallel", "parallel", |
| "parallel", "parallel", "parallel", "parallel"], |
| doc = "X(i,j,k,l,m,n,o,p) = A(i,j,k,l,m,n,o,p) * B(p,o,n,m,l,k,j,i)" |
| } |
| |
| // CHECK-LABEL: func @mul( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> { |
| // CHECK: %[[VAL_3:.*]] = arith.constant 3 : index |
| // CHECK: %[[VAL_4:.*]] = arith.constant 4 : index |
| // CHECK: %[[VAL_5:.*]] = arith.constant 10 : index |
| // CHECK: %[[VAL_6:.*]] = arith.constant 20 : index |
| // CHECK: %[[VAL_7:.*]] = arith.constant 30 : index |
| // CHECK: %[[VAL_8:.*]] = arith.constant 60 : index |
| // CHECK: %[[VAL_9:.*]] = arith.constant 70 : index |
| // CHECK: %[[VAL_10:.*]] = arith.constant 80 : index |
| // CHECK: %[[VAL_11:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_12:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> |
| // CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> |
| // CHECK: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> |
| // CHECK: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> |
| // CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> |
| // CHECK: %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: %[[VAL_20:.*]] = memref.alloc() : memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: memref.copy %[[VAL_19]], %[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32> to memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] { |
| // CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_9]] : index |
| // CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : index |
| // CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_26:.*]] = arith.muli %[[VAL_24]], %[[VAL_8]] : index |
| // CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_26]], %[[VAL_25]] : index |
| // CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_27]]] : memref<?xindex> |
| // CHECK: %[[VAL_29:.*]] = arith.addi %[[VAL_27]], %[[VAL_12]] : index |
| // CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_29]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_31:.*]] = %[[VAL_28]] to %[[VAL_30]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_31]]] : memref<?xindex> |
| // CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_31]]] : memref<?xindex> |
| // CHECK: %[[VAL_34:.*]] = arith.addi %[[VAL_31]], %[[VAL_12]] : index |
| // CHECK: %[[VAL_35:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_36:.*]] = %[[VAL_33]] to %[[VAL_35]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_36]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_38:.*]] = %[[VAL_11]] to %[[VAL_7]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_39:.*]] = arith.muli %[[VAL_36]], %[[VAL_7]] : index |
| // CHECK: %[[VAL_40:.*]] = arith.addi %[[VAL_39]], %[[VAL_38]] : index |
| // CHECK: scf.for %[[VAL_41:.*]] = %[[VAL_11]] to %[[VAL_6]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_42:.*]] = arith.muli %[[VAL_40]], %[[VAL_6]] : index |
| // CHECK: %[[VAL_43:.*]] = arith.addi %[[VAL_42]], %[[VAL_41]] : index |
| // CHECK: scf.for %[[VAL_44:.*]] = %[[VAL_11]] to %[[VAL_5]] step %[[VAL_12]] { |
| // CHECK: %[[VAL_45:.*]] = arith.muli %[[VAL_43]], %[[VAL_5]] : index |
| // CHECK: %[[VAL_46:.*]] = arith.addi %[[VAL_45]], %[[VAL_44]] : index |
| // CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_44]], %[[VAL_41]], %[[VAL_38]], %[[VAL_37]], %[[VAL_32]], %[[VAL_25]], %[[VAL_22]], %[[VAL_21]]] : memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: %[[VAL_48:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_46]]] : memref<?xf32> |
| // CHECK: %[[VAL_49:.*]] = arith.mulf %[[VAL_47]], %[[VAL_48]] : f32 |
| // CHECK: memref.store %[[VAL_49]], %[[VAL_20]]{{\[}}%[[VAL_44]], %[[VAL_41]], %[[VAL_38]], %[[VAL_37]], %[[VAL_32]], %[[VAL_25]], %[[VAL_22]], %[[VAL_21]]] : memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_50:.*]] = bufferization.to_tensor %[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32> |
| // CHECK: return %[[VAL_50]] : tensor<10x20x30x40x50x60x70x80xf32> |
| // CHECK: } |
| func @mul(%arga: tensor<10x20x30x40x50x60x70x80xf32>, |
| %argb: tensor<80x70x60x50x40x30x20x10xf32, #SparseTensor>, |
| %argx: tensor<10x20x30x40x50x60x70x80xf32>) |
| -> tensor<10x20x30x40x50x60x70x80xf32> { |
| %0 = linalg.generic #trait_mul |
| ins(%arga, %argb: tensor<10x20x30x40x50x60x70x80xf32>, |
| tensor<80x70x60x50x40x30x20x10xf32, #SparseTensor>) |
| outs(%argx: tensor<10x20x30x40x50x60x70x80xf32>) { |
| ^bb(%a: f32, %b: f32, %x: f32): |
| %0 = arith.mulf %a, %b : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<10x20x30x40x50x60x70x80xf32> |
| return %0 : tensor<10x20x30x40x50x60x70x80xf32> |
| } |
