128 lines
5.0 KiB
MLIR
128 lines
5.0 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
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// DEFINE: mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{command}
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
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// RUN: %{command}
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//
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// Do the same run, but now with direct IR generation and vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
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// RUN: %{command}
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#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
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#trait_op = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a (in)
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affine_map<(i) -> (i)>, // b (in)
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"],
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doc = "x(i) = a(i) OP b(i)"
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}
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module {
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func.func @cadd(%arga: tensor<?xcomplex<f32>, #SparseVector>,
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%argb: tensor<?xcomplex<f32>, #SparseVector>)
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-> tensor<?xcomplex<f32>, #SparseVector> {
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%c = arith.constant 0 : index
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%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
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%xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f32>, #SparseVector>
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%0 = linalg.generic #trait_op
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ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
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tensor<?xcomplex<f32>, #SparseVector>)
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outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
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^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
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%1 = complex.add %a, %b : complex<f32>
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linalg.yield %1 : complex<f32>
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} -> tensor<?xcomplex<f32>, #SparseVector>
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return %0 : tensor<?xcomplex<f32>, #SparseVector>
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}
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func.func @cmul(%arga: tensor<?xcomplex<f32>, #SparseVector>,
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%argb: tensor<?xcomplex<f32>, #SparseVector>)
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-> tensor<?xcomplex<f32>, #SparseVector> {
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%c = arith.constant 0 : index
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%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
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%xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f32>, #SparseVector>
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%0 = linalg.generic #trait_op
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ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
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tensor<?xcomplex<f32>, #SparseVector>)
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outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
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^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
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%1 = complex.mul %a, %b : complex<f32>
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linalg.yield %1 : complex<f32>
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} -> tensor<?xcomplex<f32>, #SparseVector>
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return %0 : tensor<?xcomplex<f32>, #SparseVector>
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}
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func.func @dump(%arg0: tensor<?xcomplex<f32>, #SparseVector>, %d: index) {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%mem = sparse_tensor.values %arg0 : tensor<?xcomplex<f32>, #SparseVector> to memref<?xcomplex<f32>>
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scf.for %i = %c0 to %d step %c1 {
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%v = memref.load %mem[%i] : memref<?xcomplex<f32>>
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%real = complex.re %v : complex<f32>
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%imag = complex.im %v : complex<f32>
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vector.print %real : f32
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vector.print %imag : f32
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}
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return
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}
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// Driver method to call and verify complex kernels.
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func.func @entry() {
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// Setup sparse vectors.
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%v1 = arith.constant sparse<
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[ [0], [28], [31] ],
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[ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f32>>
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%v2 = arith.constant sparse<
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[ [1], [28], [31] ],
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[ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f32>>
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%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
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%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
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// Call sparse vector kernels.
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%0 = call @cadd(%sv1, %sv2)
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: (tensor<?xcomplex<f32>, #SparseVector>,
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tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>
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%1 = call @cmul(%sv1, %sv2)
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: (tensor<?xcomplex<f32>, #SparseVector>,
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tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>
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//
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// Verify the results.
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//
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// CHECK: 511.13
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// CHECK-NEXT: 2
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// CHECK-NEXT: 1
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// CHECK-NEXT: 0
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// CHECK-NEXT: 5
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// CHECK-NEXT: 4
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// CHECK-NEXT: 8
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// CHECK-NEXT: 6
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// CHECK-NEXT: 6
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// CHECK-NEXT: 8
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// CHECK-NEXT: 15
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// CHECK-NEXT: 18
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//
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%d1 = arith.constant 4 : index
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%d2 = arith.constant 2 : index
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call @dump(%0, %d1) : (tensor<?xcomplex<f32>, #SparseVector>, index) -> ()
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call @dump(%1, %d2) : (tensor<?xcomplex<f32>, #SparseVector>, index) -> ()
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// Release the resources.
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bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f32>, #SparseVector>
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bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f32>, #SparseVector>
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bufferization.dealloc_tensor %0 : tensor<?xcomplex<f32>, #SparseVector>
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bufferization.dealloc_tensor %1 : tensor<?xcomplex<f32>, #SparseVector>
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return
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}
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}
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