// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s
#SparseMatrix = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>
// A contrived example that demonstrates the many different ways
// in which scalar values can be involved in a sparse kernel
// through the linalg generic op.
#trait = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A (sparse tensor)
affine_map<(i,j) -> ()>, // p (scalar tensor)
affine_map<(i,j) -> ()>, // q (true scalar)
affine_map<(i,j) -> (i,j)> // X (dense tensor out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) += A(i,j) * p * q * r * s * 2.2"
}
// CHECK-LABEL: func @mul(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse{{[0-9]*}}>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<f32>,
// CHECK-SAME: %[[VAL_2:.*2]]: f32,
// CHECK-SAME: %[[VAL_3:.*3]]: f32,
// CHECK-SAME: %[[VAL_4:.*4]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2.200000e+00 : f32
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.addf %[[VAL_2]], %[[VAL_3]] : f32
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xf32>
// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_4]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_14]][] : memref<f32>
// CHECK-DAG: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_7]] {
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_19]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_19]], %[[VAL_7]] : index
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_21]] to %[[VAL_23]] step %[[VAL_7]] {
// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_24]]] : memref<?xindex>
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK: %[[VAL_27:.*]] = arith.mulf %[[VAL_26]], %[[VAL_16]] : f32
// CHECK: %[[VAL_28:.*]] = arith.mulf %[[VAL_27]], %[[VAL_2]] : f32
// CHECK: %[[VAL_29:.*]] = arith.mulf %[[VAL_28]], %[[VAL_3]] : f32
// CHECK: %[[VAL_30:.*]] = arith.mulf %[[VAL_29]], %[[VAL_8]] : f32
// CHECK: %[[VAL_31:.*]] = arith.mulf %[[VAL_30]], %[[VAL_5]] : f32
// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_20]], %[[VAL_25]]] : memref<32x16xf32>
// CHECK: %[[VAL_33:.*]] = arith.addf %[[VAL_31]], %[[VAL_32]] : f32
// CHECK: memref.store %[[VAL_33]], %[[VAL_15]]{{\[}}%[[VAL_20]], %[[VAL_25]]] : memref<32x16xf32>
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_34:.*]] = bufferization.to_tensor %[[VAL_15]] : memref<32x16xf32>
// CHECK: return %[[VAL_34]] : tensor<32x16xf32>
// CHECK: }
func.func @mul(%arga: tensor<32x16xf32, #SparseMatrix>,
%argp: tensor<f32>,
%argq: f32,
%argr: f32,
%argx: tensor<32x16xf32>) -> tensor<32x16xf32> {
%s = arith.addf %argq, %argr : f32
%c = arith.constant 2.2 : f32
%0 = linalg.generic #trait
ins(%arga, %argp, %argq: tensor<32x16xf32, #SparseMatrix>, tensor<f32>, f32)
outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %p: f32, %q: f32, %x: f32):
%0 = arith.mulf %a, %p : f32 // scalar tensor argument
%1 = arith.mulf %0, %q : f32 // scalar argument
%2 = arith.mulf %1, %argr : f32 // scalar argument from outside block
%3 = arith.mulf %2, %s : f32 // scalar value from outside block
%4 = arith.mulf %3, %c : f32 // direct constant from outside block
%5 = arith.addf %4, %x : f32
linalg.yield %5 : f32
} -> tensor<32x16xf32>
return %0 : tensor<32x16xf32>
}
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