36b66ab9ed
Slowly we are moving toward full support of sparse tensor *outputs*. First step was support for all-dense annotated "sparse" tensors. This step adds support for truly sparse tensors, but only for operations in which the values of a tensor change, but not the nonzero structure (this was refered to as "simply dynamic" in the [Bik96] thesis). Some background text was posted on discourse: https://llvm.discourse.group/t/sparse-tensors-in-mlir/3389/25 Reviewed By: gussmith23 Differential Revision: https://reviews.llvm.org/D104577
134 lines
9.0 KiB
MLIR
134 lines
9.0 KiB
MLIR
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
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// RUN: mlir-opt %s -sparsification | FileCheck %s
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#CSR = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (i,j)>
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}>
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#DCSR = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (i,j)>
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}>
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#trait_scale = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = X(i,j) * 2"
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}
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// CHECK-LABEL: func @sparse_simply_dynamic1(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> {
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// CHECK: %[[VAL_1:.*]] = constant 2.000000e+00 : f32
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// CHECK: %[[VAL_2:.*]] = constant 0 : index
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// CHECK: %[[VAL_3:.*]] = constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
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// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] {
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = addi %[[VAL_11]], %[[VAL_3]] : index
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] {
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32>
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// CHECK: %[[VAL_17:.*]] = mulf %[[VAL_16]], %[[VAL_1]] : f32
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// CHECK: memref.store %[[VAL_17]], %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_18:.*]] = sparse_tensor.tensor %[[VAL_4]], %[[VAL_5]], %[[VAL_6]], %[[VAL_7]], %[[VAL_8]] : memref<?xindex>, memref<?xindex>, memref<?xindex>, memref<?xindex>, memref<?xf32> to tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: return %[[VAL_18]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: }
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func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable = true}) -> tensor<32x16xf32, #DCSR> {
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%c = constant 2.0 : f32
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%0 = linalg.generic #trait_scale
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outs(%argx: tensor<32x16xf32, #DCSR>) {
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^bb(%x: f32):
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%1 = mulf %x, %c : f32
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linalg.yield %1 : f32
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} -> tensor<32x16xf32, #DCSR>
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return %0 : tensor<32x16xf32, #DCSR>
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}
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#trait_elt_wise_mult = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = A(i,j) * X(i,j)"
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}
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// CHECK-LABEL: func @sparse_simply_dynamic2(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> {
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// CHECK: %[[VAL_2:.*]] = constant 0 : index
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// CHECK: %[[VAL_3:.*]] = constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] {
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = addi %[[VAL_15]], %[[VAL_3]] : index
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_14]]] : memref<?xindex>
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// CHECK: %[[VAL_20:.*]] = addi %[[VAL_14]], %[[VAL_3]] : index
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_16]], %[[VAL_24:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) {
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// CHECK: %[[VAL_25:.*]] = cmpi ult, %[[VAL_23]], %[[VAL_18]] : index
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// CHECK: %[[VAL_26:.*]] = cmpi ult, %[[VAL_24]], %[[VAL_21]] : index
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// CHECK: %[[VAL_27:.*]] = and %[[VAL_25]], %[[VAL_26]] : i1
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// CHECK: scf.condition(%[[VAL_27]]) %[[VAL_23]], %[[VAL_24]] : index, index
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// CHECK: } do {
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// CHECK: ^bb0(%[[VAL_28:.*]]: index, %[[VAL_29:.*]]: index):
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// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_28]]] : memref<?xindex>
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// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_29]]] : memref<?xindex>
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// CHECK: %[[VAL_32:.*]] = cmpi ult, %[[VAL_31]], %[[VAL_30]] : index
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// CHECK: %[[VAL_33:.*]] = select %[[VAL_32]], %[[VAL_31]], %[[VAL_30]] : index
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// CHECK: %[[VAL_34:.*]] = cmpi eq, %[[VAL_30]], %[[VAL_33]] : index
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// CHECK: %[[VAL_35:.*]] = cmpi eq, %[[VAL_31]], %[[VAL_33]] : index
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// CHECK: %[[VAL_36:.*]] = and %[[VAL_34]], %[[VAL_35]] : i1
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// CHECK: scf.if %[[VAL_36]] {
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// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xf32>
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// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_28]]] : memref<?xf32>
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// CHECK: %[[VAL_39:.*]] = mulf %[[VAL_37]], %[[VAL_38]] : f32
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// CHECK: memref.store %[[VAL_39]], %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xf32>
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// CHECK: } else {
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// CHECK: }
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// CHECK: %[[VAL_40:.*]] = cmpi eq, %[[VAL_30]], %[[VAL_33]] : index
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// CHECK: %[[VAL_41:.*]] = addi %[[VAL_28]], %[[VAL_3]] : index
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// CHECK: %[[VAL_42:.*]] = select %[[VAL_40]], %[[VAL_41]], %[[VAL_28]] : index
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// CHECK: %[[VAL_43:.*]] = cmpi eq, %[[VAL_31]], %[[VAL_33]] : index
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// CHECK: %[[VAL_44:.*]] = addi %[[VAL_29]], %[[VAL_3]] : index
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// CHECK: %[[VAL_45:.*]] = select %[[VAL_43]], %[[VAL_44]], %[[VAL_29]] : index
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// CHECK: scf.yield %[[VAL_42]], %[[VAL_45]] : index, index
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_46:.*]] = sparse_tensor.tensor %[[VAL_7]], %[[VAL_8]], %[[VAL_9]], %[[VAL_10]], %[[VAL_11]] : memref<?xindex>, memref<?xindex>, memref<?xindex>, memref<?xindex>, memref<?xf32> to tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: return %[[VAL_46]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: }
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func @sparse_simply_dynamic2(%arga: tensor<32x16xf32, #CSR>,
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%argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable = true}) -> tensor<32x16xf32, #DCSR> {
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%0 = linalg.generic #trait_elt_wise_mult
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ins(%arga: tensor<32x16xf32, #CSR>)
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outs(%argx: tensor<32x16xf32, #DCSR>) {
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^bb(%a: f32, %x: f32):
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%1 = mulf %x, %a : f32
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linalg.yield %1 : f32
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} -> tensor<32x16xf32, #DCSR>
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return %0 : tensor<32x16xf32, #DCSR>
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}
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