| // RUN: mlir-opt %s \ |
| // RUN: --sparsification --sparse-tensor-conversion \ |
| // RUN: --convert-vector-to-scf --convert-scf-to-std \ |
| // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ |
| // RUN: --std-bufferize --finalizing-bufferize \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \ |
| // RUN: mlir-cpu-runner \ |
| // RUN: -e entry -entry-point-result=void \ |
| // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ |
| // RUN: FileCheck %s |
| // |
| // Do the same run, but now with SIMDization as well. This should not change the outcome. |
| // |
| // RUN: mlir-opt %s \ |
| // RUN: --sparsification="vectorization-strategy=2 vl=16 enable-simd-index32" --sparse-tensor-conversion \ |
| // RUN: --convert-vector-to-scf --convert-scf-to-std \ |
| // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ |
| // RUN: --std-bufferize --finalizing-bufferize --lower-affine \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \ |
| // RUN: mlir-cpu-runner \ |
| // RUN: -e entry -entry-point-result=void \ |
| // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ |
| // RUN: FileCheck %s |
| |
| !Filename = type !llvm.ptr<i8> |
| |
| #SparseMatrix = #sparse_tensor.encoding<{ |
| dimLevelType = [ "dense", "compressed" ], |
| pointerBitWidth = 8, |
| indexBitWidth = 8 |
| }> |
| |
| #matvec = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (j)>, // b |
| affine_map<(i,j) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel", "reduction"], |
| doc = "X(i) += A(i,j) * B(j)" |
| } |
| |
| // |
| // Integration test that lowers a kernel annotated as sparse to |
| // actual sparse code, initializes a matching sparse storage scheme |
| // from file, and runs the resulting code with the JIT compiler. |
| // |
| module { |
| // |
| // A kernel that multiplies a sparse matrix A with a dense vector b |
| // into a dense vector x. |
| // |
| func @kernel_matvec(%arga: tensor<?x?xi32, #SparseMatrix>, |
| %argb: tensor<?xi32>, |
| %argx: tensor<?xi32> {linalg.inplaceable = true}) |
| -> tensor<?xi32> { |
| %0 = linalg.generic #matvec |
| ins(%arga, %argb: tensor<?x?xi32, #SparseMatrix>, tensor<?xi32>) |
| outs(%argx: tensor<?xi32>) { |
| ^bb(%a: i32, %b: i32, %x: i32): |
| %0 = arith.muli %a, %b : i32 |
| %1 = arith.addi %x, %0 : i32 |
| linalg.yield %1 : i32 |
| } -> tensor<?xi32> |
| return %0 : tensor<?xi32> |
| } |
| |
| func private @getTensorFilename(index) -> (!Filename) |
| |
| // |
| // Main driver that reads matrix from file and calls the sparse kernel. |
| // |
| func @entry() { |
| %i0 = arith.constant 0 : i32 |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %c4 = arith.constant 4 : index |
| %c256 = arith.constant 256 : index |
| |
| // Read the sparse matrix from file, construct sparse storage. |
| %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename) |
| %a = sparse_tensor.new %fileName : !Filename to tensor<?x?xi32, #SparseMatrix> |
| |
| // Initialize dense vectors. |
| %bdata = memref.alloc(%c256) : memref<?xi32> |
| %xdata = memref.alloc(%c4) : memref<?xi32> |
| scf.for %i = %c0 to %c256 step %c1 { |
| %k = arith.addi %i, %c1 : index |
| %j = arith.index_cast %k : index to i32 |
| memref.store %j, %bdata[%i] : memref<?xi32> |
| } |
| scf.for %i = %c0 to %c4 step %c1 { |
| memref.store %i0, %xdata[%i] : memref<?xi32> |
| } |
| %b = bufferization.to_tensor %bdata : memref<?xi32> |
| %x = bufferization.to_tensor %xdata : memref<?xi32> |
| |
| // Call kernel. |
| %0 = call @kernel_matvec(%a, %b, %x) |
| : (tensor<?x?xi32, #SparseMatrix>, tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32> |
| |
| // Print the result for verification. |
| // |
| // CHECK: ( 889, 1514, -21, -3431 ) |
| // |
| %m = bufferization.to_memref %0 : memref<?xi32> |
| %v = vector.transfer_read %m[%c0], %i0: memref<?xi32>, vector<4xi32> |
| vector.print %v : vector<4xi32> |
| |
| // Release the resources. |
| memref.dealloc %bdata : memref<?xi32> |
| memref.dealloc %xdata : memref<?xi32> |
| sparse_tensor.release %a : tensor<?x?xi32, #SparseMatrix> |
| |
| return |
| } |
| } |