| // 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 --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/test.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=4" --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/test.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> |
| |
| #DCSR = #sparse_tensor.encoding<{ |
| dimLevelType = [ "compressed", "compressed" ], |
| dimOrdering = affine_map<(i,j) -> (i,j)> |
| }> |
| |
| #eltwise_mult = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) *= X(i,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 itself |
| // in an element-wise fashion. In this operation, we have |
| // a sparse tensor as output, but although the values of the |
| // sparse tensor change, its nonzero structure remains the same. |
| // |
| func @kernel_eltwise_mult(%argx: tensor<?x?xf64, #DCSR> {linalg.inplaceable = true}) |
| -> tensor<?x?xf64, #DCSR> { |
| %0 = linalg.generic #eltwise_mult |
| outs(%argx: tensor<?x?xf64, #DCSR>) { |
| ^bb(%x: f64): |
| %0 = arith.mulf %x, %x : f64 |
| linalg.yield %0 : f64 |
| } -> tensor<?x?xf64, #DCSR> |
| return %0 : tensor<?x?xf64, #DCSR> |
| } |
| |
| func private @getTensorFilename(index) -> (!Filename) |
| |
| // |
| // Main driver that reads matrix from file and calls the sparse kernel. |
| // |
| func @entry() { |
| %d0 = arith.constant 0.0 : f64 |
| %c0 = arith.constant 0 : index |
| |
| // Read the sparse matrix from file, construct sparse storage. |
| %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename) |
| %x = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #DCSR> |
| |
| // Call kernel. |
| %0 = call @kernel_eltwise_mult(%x) : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> |
| |
| // Print the result for verification. |
| // |
| // CHECK: ( 1, 1.96, 4, 6.25, 9, 16.81, 16, 27.04, 25 ) |
| // |
| %m = sparse_tensor.values %0 : tensor<?x?xf64, #DCSR> to memref<?xf64> |
| %v = vector.transfer_read %m[%c0], %d0: memref<?xf64>, vector<9xf64> |
| vector.print %v : vector<9xf64> |
| |
| // Release the resources. |
| sparse_tensor.release %x : tensor<?x?xf64, #DCSR> |
| |
| return |
| } |
| } |