| // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py |
| // RUN: mlir-opt %s -sparsification | FileCheck %s |
| |
| // Test to demonstrate the difference between non-annotated dense tensors |
| // and all-dense-annotated "sparse" tensors. The former class remains as |
| // two-dimensional tensors that are bufferized by subsequent passes. The |
| // latter class is linearized into one-dimensional buffers that are backed |
| // by the runtime support library. |
| |
| #DenseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }> |
| |
| #trait_2d = { |
| 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) + 1" |
| } |
| |
| #trait_3d = { |
| indexing_maps = [ |
| affine_map<(i,j,k) -> (i,j,k)>, // A |
| affine_map<(i,j,k) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel", "reduction"], |
| doc = "X(i,j) += A(i,j,k)" |
| } |
| |
| // |
| // Test with an all-dense-annotated "sparse" matrix as input and |
| // a non-annotated dense matrix as output that is not inplacable. |
| // This results in an explicit allocation to facilitate output. |
| // |
| // CHECK-LABEL: func @dense1( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32> {linalg.inplaceable = false}) -> tensor<32x16xf32> { |
| // CHECK: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 |
| // CHECK: %[[VAL_3:.*]] = arith.constant 32 : index |
| // CHECK: %[[VAL_4:.*]] = arith.constant 16 : index |
| // CHECK: %[[VAL_5:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_6:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> |
| // CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> |
| // CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32x16xf32> |
| // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32x16xf32> to memref<32x16xf32> |
| // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { |
| // CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index |
| // CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index |
| // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xf32> |
| // CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_14]], %[[VAL_2]] : f32 |
| // CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_10]], %[[VAL_11]]] : memref<32x16xf32> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<32x16xf32> |
| // CHECK: return %[[VAL_16]] : tensor<32x16xf32> |
| // CHECK: } |
| func @dense1(%arga: tensor<32x16xf32, #DenseMatrix>, |
| %argx: tensor<32x16xf32> {linalg.inplaceable = false}) |
| -> tensor<32x16xf32> { |
| %c = arith.constant 1.0 : f32 |
| %0 = linalg.generic #trait_2d |
| ins(%arga: tensor<32x16xf32, #DenseMatrix>) |
| outs(%argx: tensor<32x16xf32>) { |
| ^bb(%a: f32, %x: f32): |
| %1 = arith.addf %a, %c : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32x16xf32> |
| return %0 : tensor<32x16xf32> |
| } |
| |
| // |
| // Test with an all-dense-annotated "sparse" matrix as input and |
| // a non-annotated dense matrix as output that is inplacable. |
| // This allows updating the dense output in place. |
| // |
| // CHECK-LABEL: func @dense2( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32> {linalg.inplaceable = true}) -> tensor<32x16xf32> { |
| // CHECK: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 |
| // CHECK: %[[VAL_3:.*]] = arith.constant 32 : index |
| // CHECK: %[[VAL_4:.*]] = arith.constant 16 : index |
| // CHECK: %[[VAL_5:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_6:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> 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]] { |
| // CHECK: %[[VAL_11:.*]] = arith.muli %[[VAL_9]], %[[VAL_4]] : index |
| // CHECK: %[[VAL_12:.*]] = arith.addi %[[VAL_11]], %[[VAL_10]] : index |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_13]], %[[VAL_2]] : f32 |
| // CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_9]], %[[VAL_10]]] : memref<32x16xf32> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_15:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<32x16xf32> |
| // CHECK: return %[[VAL_15]] : tensor<32x16xf32> |
| // CHECK: } |
| func @dense2(%arga: tensor<32x16xf32, #DenseMatrix>, |
| %argx: tensor<32x16xf32> {linalg.inplaceable = true}) |
| -> tensor<32x16xf32> { |
| %c = arith.constant 1.0 : f32 |
| %0 = linalg.generic #trait_2d |
| ins(%arga: tensor<32x16xf32, #DenseMatrix>) |
| outs(%argx: tensor<32x16xf32>) { |
| ^bb(%a: f32, %x: f32): |
| %1 = arith.addf %a, %c : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32x16xf32> |
| return %0 : tensor<32x16xf32> |
| } |
| |
| // |
| // Test with a non-annotated dense matrix as input and |
| // an all-dense annotated "sparse" matrix as output. |
| // The rewriting would fail if argx was not in-placeable. |
| // |
| // CHECK-LABEL: func @dense3( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> {linalg.inplaceable = true}) -> tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { |
| // 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: 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 |
| // CHECK: %[[VAL_12:.*]] = arith.addi %[[VAL_11]], %[[VAL_10]] : index |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_9]], %[[VAL_10]]] : memref<32x16xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_13]], %[[VAL_2]] : f32 |
| // 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: } |
| func @dense3(%arga: tensor<32x16xf32>, |
| %argx: tensor<32x16xf32, #DenseMatrix> {linalg.inplaceable = true}) |
| -> tensor<32x16xf32, #DenseMatrix> { |
| %c = arith.constant 1.0 : f32 |
| %0 = linalg.generic #trait_2d |
| ins(%arga: tensor<32x16xf32>) |
| outs(%argx: tensor<32x16xf32, #DenseMatrix>) { |
| ^bb(%a: f32, %x: f32): |
| %1 = arith.addf %a, %c : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32x16xf32, #DenseMatrix> |
| return %0 : tensor<32x16xf32, #DenseMatrix> |
| } |
| |
| |
| // |
| // Test with a non-annotated dense matrix as input and |
| // an all-dense annotated "sparse" matrix as output. |
| // The rewriting would fail if argx was not in-placeable. |
| // The missing innermost "k" index (due to a reduction) is accounted |
| // for by scalarizing the reduction operation for the output tensor. |
| // |
| // CHECK-LABEL: func @dense4( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> {linalg.inplaceable = true}) -> tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { |
| // 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: 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 |
| // CHECK: %[[VAL_12:.*]] = arith.addi %[[VAL_11]], %[[VAL_10]] : index |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<?xf32> |
| // CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f32) { |
| // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_9]], %[[VAL_10]], %[[VAL_15]]] : memref<32x16x8xf32> |
| // CHECK: %[[VAL_18:.*]] = arith.addf %[[VAL_16]], %[[VAL_17]] : f32 |
| // CHECK: scf.yield %[[VAL_18]] : f32 |
| // CHECK: } |
| // 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: } |
| func @dense4(%arga: tensor<32x16x8xf32>, |
| %argx: tensor<32x16xf32, #DenseMatrix> {linalg.inplaceable = true}) |
| -> tensor<32x16xf32, #DenseMatrix> { |
| %0 = linalg.generic #trait_3d |
| ins(%arga: tensor<32x16x8xf32>) |
| outs(%argx: tensor<32x16xf32, #DenseMatrix>) { |
| ^bb(%a: f32, %x: f32): |
| %1 = arith.addf %x, %a : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32x16xf32, #DenseMatrix> |
| return %0 : tensor<32x16xf32, #DenseMatrix> |
| } |
