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// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#X = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "dense", "dense" ],
dimToLvl = affine_map<(i,j,k) -> (k,i,j)>
}>
#trait = {
indexing_maps = [
affine_map<(i,j,k) -> (k,i,j)>, // A (in)
affine_map<(i,j,k) -> (i,j,k)> // X (out)
],
iterator_types = ["parallel", "parallel", "parallel"]
}
// CHECK-LABEL: func @sparse_static_dims(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 20 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_9]] : memref<20x30x10xf32>)
// 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: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] {
// CHECK: %[[VAL_15:.*]] = arith.muli %[[VAL_13]], %[[VAL_2]] : index
// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_15]], %[[VAL_14]] : index
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf32>
// CHECK: memref.store %[[VAL_17]], %[[VAL_9]]{{\[}}%[[VAL_14]], %[[VAL_10]], %[[VAL_11]]] : memref<20x30x10xf32>
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_18:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<20x30x10xf32>
// CHECK: return %[[VAL_18]] : tensor<20x30x10xf32>
// CHECK: }
func.func @sparse_static_dims(%arga: tensor<10x20x30xf32, #X>,
%argx: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
%0 = linalg.generic #trait
ins(%arga: tensor<10x20x30xf32, #X>)
outs(%argx: tensor<20x30x10xf32>) {
^bb(%a : f32, %x: f32):
linalg.yield %a : f32
} -> tensor<20x30x10xf32>
return %0 : tensor<20x30x10xf32>
}
// CHECK-LABEL: func @sparse_dynamic_dims(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?x?xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_10]] : memref<?x?x?xf32>)
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] {
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] {
// CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_7]], %[[VAL_11]] : index
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {
// CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_8]], %[[VAL_14]] : index
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_17]]] : memref<?xf32>
// CHECK: memref.store %[[VAL_18]], %[[VAL_10]]{{\[}}%[[VAL_15]], %[[VAL_11]], %[[VAL_12]]] : memref<?x?x?xf32>
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_19:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<?x?x?xf32>
// CHECK: return %[[VAL_19]] : tensor<?x?x?xf32>
// CHECK: }
func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
%argx: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?x?x?xf32, #X>)
outs(%argx: tensor<?x?x?xf32>) {
^bb(%a : f32, %x: f32):
linalg.yield %a : f32
} -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
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