| // RUN: mlir-opt %s -sparsification | FileCheck %s --check-prefix=CHECK-HIR |
| // |
| // RUN: mlir-opt %s -sparsification --sparse-tensor-conversion | \ |
| // RUN: FileCheck %s --check-prefix=CHECK-MIR |
| // |
| // RUN: mlir-opt %s -sparsification --sparse-tensor-conversion \ |
| // RUN: --func-bufferize --tensor-constant-bufferize \ |
| // RUN: --tensor-bufferize --finalizing-bufferize | \ |
| // RUN: FileCheck %s --check-prefix=CHECK-LIR |
| |
| #CSR = #sparse_tensor.encoding<{dimLevelType = [ "dense", "compressed" ]}> |
| |
| #trait_matvec = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (j)>, // b |
| affine_map<(i,j) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel","reduction"], |
| doc = "x(i) += A(i,j) * b(j)" |
| } |
| |
| // CHECK-HIR-LABEL: func @matvec( |
| // CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>, |
| // CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> { |
| // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index |
| // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-HIR: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> |
| // CHECK-HIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> |
| // CHECK-HIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> |
| // CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> |
| // CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { |
| // CHECK-HIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> |
| // CHECK-HIR-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index |
| // CHECK-HIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK-HIR-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<32xf64> |
| // CHECK-HIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) { |
| // CHECK-HIR: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> |
| // CHECK-HIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xf64> |
| // CHECK-HIR: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<64xf64> |
| // CHECK-HIR: %[[VAL_23:.*]] = arith.mulf %[[VAL_21]], %[[VAL_22]] : f64 |
| // CHECK-HIR: %[[VAL_24:.*]] = arith.addf %[[VAL_19]], %[[VAL_23]] : f64 |
| // CHECK-HIR: scf.yield %[[VAL_24]] : f64 |
| // CHECK-HIR: } |
| // CHECK-HIR: memref.store %[[VAL_17]], %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<32xf64> |
| // CHECK-HIR: } |
| // CHECK-HIR: %[[VAL_26:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xf64> |
| // CHECK-HIR: return %[[VAL_26]] : tensor<32xf64> |
| // CHECK-HIR: } |
| |
| // CHECK-MIR-LABEL: func @matvec( |
| // CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, |
| // CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>, |
| // CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> { |
| // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index |
| // CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-MIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> |
| // CHECK-MIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> |
| // CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64> |
| // CHECK-MIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> |
| // CHECK-MIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> |
| // CHECK-MIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> |
| // CHECK-MIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> |
| // CHECK-MIR: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { |
| // CHECK-MIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK-MIR-DAG: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_5]] : index |
| // CHECK-MIR-DAG: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex> |
| // CHECK-MIR-DAG: %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<32xf64> |
| // CHECK-MIR: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_21:.*]] = %[[VAL_18]]) -> (f64) { |
| // CHECK-MIR: %[[VAL_22:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex> |
| // CHECK-MIR: %[[VAL_23:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xf64> |
| // CHECK-MIR: %[[VAL_24:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_22]]] : memref<64xf64> |
| // CHECK-MIR: %[[VAL_25:.*]] = arith.mulf %[[VAL_23]], %[[VAL_24]] : f64 |
| // CHECK-MIR: %[[VAL_26:.*]] = arith.addf %[[VAL_21]], %[[VAL_25]] : f64 |
| // CHECK-MIR: scf.yield %[[VAL_26]] : f64 |
| // CHECK-MIR: } |
| // CHECK-MIR: memref.store %[[VAL_19]], %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<32xf64> |
| // CHECK-MIR: } |
| // CHECK-MIR: %[[VAL_28:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xf64> |
| // CHECK-MIR: return %[[VAL_28]] : tensor<32xf64> |
| // CHECK-MIR: } |
| |
| // CHECK-LIR-LABEL: func @matvec( |
| // CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, |
| // CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>, |
| // CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> { |
| // CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index |
| // CHECK-LIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-LIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> |
| // CHECK-LIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> |
| // CHECK-LIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64> |
| // CHECK-LIR: %[[VAL_9:.*]] = memref.alloc() : memref<32xf64> |
| // CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_9]] : memref<32xf64> to memref<32xf64> |
| // CHECK-LIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { |
| // CHECK-LIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> |
| // CHECK-LIR-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index |
| // CHECK-LIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK-LIR-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<32xf64> |
| // CHECK-LIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) { |
| // CHECK-LIR: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> |
| // CHECK-LIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xf64> |
| // CHECK-LIR: %[[VAL_22:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_20]]] : memref<64xf64> |
| // CHECK-LIR: %[[VAL_23:.*]] = arith.mulf %[[VAL_21]], %[[VAL_22]] : f64 |
| // CHECK-LIR: %[[VAL_24:.*]] = arith.addf %[[VAL_19]], %[[VAL_23]] : f64 |
| // CHECK-LIR: scf.yield %[[VAL_24]] : f64 |
| // CHECK-LIR: } |
| // CHECK-LIR: memref.store %[[VAL_17]], %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<32xf64> |
| // CHECK-LIR: } |
| // CHECK-LIR: return %[[VAL_9]] : memref<32xf64> |
| // CHECK-LIR: } |
| |
| func @matvec(%arga: tensor<32x64xf64, #CSR>, |
| %argb: tensor<64xf64>, |
| %argx: tensor<32xf64>) -> tensor<32xf64> { |
| %0 = linalg.generic #trait_matvec |
| ins(%arga, %argb : tensor<32x64xf64, #CSR>, tensor<64xf64>) |
| outs(%argx: tensor<32xf64>) { |
| ^bb(%A: f64, %b: f64, %x: f64): |
| %0 = arith.mulf %A, %b : f64 |
| %1 = arith.addf %x, %0 : f64 |
| linalg.yield %1 : f64 |
| } -> tensor<32xf64> |
| return %0 : tensor<32xf64> |
| } |
