| // 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: 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: 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 |
| |
| #CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }> |
| |
| #trait_scale = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = X(i,j) * 2" |
| } |
| |
| // |
| // Integration test that lowers a kernel annotated as sparse to actual sparse |
| // code, initializes a matching sparse storage scheme from a dense tensor, |
| // and runs the resulting code with the JIT compiler. |
| // |
| module { |
| // |
| // A kernel that scales a sparse matrix A by a factor of 2.0. |
| // |
| func @sparse_scale(%argx: tensor<8x8xf32, #CSR> |
| {linalg.inplaceable = true}) -> tensor<8x8xf32, #CSR> { |
| %c = arith.constant 2.0 : f32 |
| %0 = linalg.generic #trait_scale |
| outs(%argx: tensor<8x8xf32, #CSR>) { |
| ^bb(%x: f32): |
| %1 = arith.mulf %x, %c : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<8x8xf32, #CSR> |
| return %0 : tensor<8x8xf32, #CSR> |
| } |
| |
| // |
| // Main driver that converts a dense tensor into a sparse tensor |
| // and then calls the sparse scaling kernel with the sparse tensor |
| // as input argument. |
| // |
| func @entry() { |
| %c0 = arith.constant 0 : index |
| %f0 = arith.constant 0.0 : f32 |
| |
| // Initialize a dense tensor. |
| %0 = arith.constant dense<[ |
| [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0], |
| [0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], |
| [0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], |
| [0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0], |
| [0.0, 1.0, 0.0, 0.0, 5.0, 0.0, 0.0, 0.0], |
| [0.0, 1.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0], |
| [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 7.0, 1.0], |
| [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 8.0] |
| ]> : tensor<8x8xf32> |
| |
| // Convert dense tensor to sparse tensor and call sparse kernel. |
| %1 = sparse_tensor.convert %0 : tensor<8x8xf32> to tensor<8x8xf32, #CSR> |
| %2 = call @sparse_scale(%1) |
| : (tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR> |
| |
| // Print the resulting compacted values for verification. |
| // |
| // CHECK: ( 2, 2, 2, 4, 6, 8, 2, 10, 2, 2, 12, 2, 14, 2, 2, 16 ) |
| // |
| %m = sparse_tensor.values %2 : tensor<8x8xf32, #CSR> to memref<?xf32> |
| %v = vector.transfer_read %m[%c0], %f0: memref<?xf32>, vector<16xf32> |
| vector.print %v : vector<16xf32> |
| |
| // Release the resources. |
| sparse_tensor.release %1 : tensor<8x8xf32, #CSR> |
| |
| return |
| } |
| } |