| //-------------------------------------------------------------------------------------------------- |
| // WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS. |
| // |
| // Set-up that's shared across all tests in this directory. In principle, this |
| // config could be moved to lit.local.cfg. However, there are downstream users that |
| // do not use these LIT config files. Hence why this is kept inline. |
| // |
| // DEFINE: %{sparsifier_opts} = enable-runtime-library=true |
| // DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts} |
| // DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}" |
| // DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}" |
| // DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils |
| // DEFINE: %{run_opts} = -e main -entry-point-result=void |
| // DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs} |
| // DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs} |
| // |
| // DEFINE: %{env} = |
| //-------------------------------------------------------------------------------------------------- |
| |
| // RUN: %{compile} | %{run} | FileCheck %s |
| // |
| // Do the same run, but now with direct IR generation. |
| // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false |
| // RUN: %{compile} | %{run} | FileCheck %s |
| // |
| // Do the same run, but now with vectorization. |
| // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true |
| // RUN: %{compile} | %{run} | FileCheck %s |
| // |
| // Do the same run, but now with VLA vectorization. |
| // RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} |
| |
| // Current fails for SVE, see https://github.com/llvm/llvm-project/issues/60626 |
| // UNSUPPORTED: target=aarch64{{.*}} |
| |
| #SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }> |
| |
| #trait_op = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)> // X (out) |
| ], |
| iterator_types = ["parallel"], |
| doc = "X(i) = OP X(i)" |
| } |
| |
| module { |
| // Performs zero-preserving math to sparse vector. |
| func.func @sparse_tanh(%vec: tensor<?xf64, #SparseVector>) |
| -> tensor<?xf64, #SparseVector> { |
| %0 = linalg.generic #trait_op |
| outs(%vec: tensor<?xf64, #SparseVector>) { |
| ^bb(%x: f64): |
| %1 = math.tanh %x : f64 |
| linalg.yield %1 : f64 |
| } -> tensor<?xf64, #SparseVector> |
| return %0 : tensor<?xf64, #SparseVector> |
| } |
| |
| // Driver method to call and verify vector kernels. |
| func.func @main() { |
| // Setup sparse vector. |
| %v1 = arith.constant sparse< |
| [ [0], [3], [11], [17], [20], [21], [28], [29], [31] ], |
| [ -1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0 ] |
| > : tensor<32xf64> |
| %sv1 = sparse_tensor.convert %v1 |
| : tensor<32xf64> to tensor<?xf64, #SparseVector> |
| |
| // Call sparse vector kernel. |
| %0 = call @sparse_tanh(%sv1) : (tensor<?xf64, #SparseVector>) |
| -> tensor<?xf64, #SparseVector> |
| |
| // |
| // Verify the results (within some precision). |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 9 |
| // CHECK-NEXT: dim = ( 32 ) |
| // CHECK-NEXT: lvl = ( 32 ) |
| // CHECK-NEXT: pos[0] : ( 0, 9 |
| // CHECK-NEXT: crd[0] : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 |
| // CHECK-NEXT: values : ({{ -0.761[0-9]*, 0.761[0-9]*, 0.96[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 1}} |
| // CHECK-NEXT: ---- |
| // |
| sparse_tensor.print %0 : tensor<?xf64, #SparseVector> |
| |
| // Release the resources. |
| bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector> |
| return |
| } |
| } |