| //-------------------------------------------------------------------------------------------------- |
| // 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_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_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_sve} |
| // |
| // DEFINE: %{env} = |
| //-------------------------------------------------------------------------------------------------- |
| |
| // RUN: %{compile} | %{run} | FileCheck %s |
| // |
| // Do the same run, but now with direct IR generation. |
| // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true |
| // RUN: %{compile} | %{run} | FileCheck %s |
| // |
| // Do the same run, but now with vectorization. |
| // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true 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 %} |
| |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| #DCSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : compressed)}> |
| |
| // |
| // Traits for tensor operations. |
| // |
| #trait_vec = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel"] |
| } |
| #trait_mat = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A (in) |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"] |
| } |
| |
| module { |
| // Invert the structure of a sparse vector. Present values become missing. |
| // Missing values are filled with 1 (i32). Output is sparse. |
| func.func @vector_complement_sparse(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> { |
| %c = arith.constant 0 : index |
| %ci1 = arith.constant 1 : i32 |
| %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector> |
| %xv = tensor.empty(%d) : tensor<?xi32, #SparseVector> |
| %0 = linalg.generic #trait_vec |
| ins(%arga: tensor<?xf64, #SparseVector>) |
| outs(%xv: tensor<?xi32, #SparseVector>) { |
| ^bb(%a: f64, %x: i32): |
| %1 = sparse_tensor.unary %a : f64 to i32 |
| present={} |
| absent={ |
| sparse_tensor.yield %ci1 : i32 |
| } |
| linalg.yield %1 : i32 |
| } -> tensor<?xi32, #SparseVector> |
| return %0 : tensor<?xi32, #SparseVector> |
| } |
| |
| // Invert the structure of a sparse vector, where missing values are |
| // filled with 1. For a dense output, the sparsifier initializes |
| // the buffer to all zero at all other places. |
| func.func @vector_complement_dense(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32> { |
| %c = arith.constant 0 : index |
| %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector> |
| %xv = tensor.empty(%d) : tensor<?xi32> |
| %0 = linalg.generic #trait_vec |
| ins(%arga: tensor<?xf64, #SparseVector>) |
| outs(%xv: tensor<?xi32>) { |
| ^bb(%a: f64, %x: i32): |
| %1 = sparse_tensor.unary %a : f64 to i32 |
| present={} |
| absent={ |
| %ci1 = arith.constant 1 : i32 |
| sparse_tensor.yield %ci1 : i32 |
| } |
| linalg.yield %1 : i32 |
| } -> tensor<?xi32> |
| return %0 : tensor<?xi32> |
| } |
| |
| // Negate existing values. Fill missing ones with +1. |
| func.func @vector_negation(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> { |
| %c = arith.constant 0 : index |
| %cf1 = arith.constant 1.0 : f64 |
| %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector> |
| %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector> |
| %0 = linalg.generic #trait_vec |
| ins(%arga: tensor<?xf64, #SparseVector>) |
| outs(%xv: tensor<?xf64, #SparseVector>) { |
| ^bb(%a: f64, %x: f64): |
| %1 = sparse_tensor.unary %a : f64 to f64 |
| present={ |
| ^bb0(%x0: f64): |
| %ret = arith.negf %x0 : f64 |
| sparse_tensor.yield %ret : f64 |
| } |
| absent={ |
| sparse_tensor.yield %cf1 : f64 |
| } |
| linalg.yield %1 : f64 |
| } -> tensor<?xf64, #SparseVector> |
| return %0 : tensor<?xf64, #SparseVector> |
| } |
| |
| // Performs B[i] = i * A[i]. |
| func.func @vector_magnify(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> { |
| %c = arith.constant 0 : index |
| %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector> |
| %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector> |
| %0 = linalg.generic #trait_vec |
| ins(%arga: tensor<?xf64, #SparseVector>) |
| outs(%xv: tensor<?xf64, #SparseVector>) { |
| ^bb(%a: f64, %x: f64): |
| %idx = linalg.index 0 : index |
| %1 = sparse_tensor.unary %a : f64 to f64 |
| present={ |
| ^bb0(%x0: f64): |
| %tmp = arith.index_cast %idx : index to i64 |
| %idxf = arith.uitofp %tmp : i64 to f64 |
| %ret = arith.mulf %x0, %idxf : f64 |
| sparse_tensor.yield %ret : f64 |
| } |
| absent={} |
| linalg.yield %1 : f64 |
| } -> tensor<?xf64, #SparseVector> |
| return %0 : tensor<?xf64, #SparseVector> |
| } |
| |
| // Clips values to the range [3, 7]. |
| func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %cfmin = arith.constant 3.0 : f64 |
| %cfmax = arith.constant 7.0 : f64 |
| %d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR> |
| %d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR> |
| %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR> |
| %0 = linalg.generic #trait_mat |
| ins(%argx: tensor<?x?xf64, #DCSR>) |
| outs(%xv: tensor<?x?xf64, #DCSR>) { |
| ^bb(%a: f64, %x: f64): |
| %1 = sparse_tensor.unary %a: f64 to f64 |
| present={ |
| ^bb0(%x0: f64): |
| %mincmp = arith.cmpf "ogt", %x0, %cfmin : f64 |
| %x1 = arith.select %mincmp, %x0, %cfmin : f64 |
| %maxcmp = arith.cmpf "olt", %x1, %cfmax : f64 |
| %x2 = arith.select %maxcmp, %x1, %cfmax : f64 |
| sparse_tensor.yield %x2 : f64 |
| } |
| absent={} |
| linalg.yield %1 : f64 |
| } -> tensor<?x?xf64, #DCSR> |
| return %0 : tensor<?x?xf64, #DCSR> |
| } |
| |
| // Slices matrix and only keep the value of the lower-right corner of the original |
| // matrix (i.e., A[2/d0 ..][2/d1 ..]), and set other values to 99. |
| func.func @matrix_slice(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR> |
| %d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR> |
| %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR> |
| %0 = linalg.generic #trait_mat |
| ins(%argx: tensor<?x?xf64, #DCSR>) |
| outs(%xv: tensor<?x?xf64, #DCSR>) { |
| ^bb(%a: f64, %x: f64): |
| %row = linalg.index 0 : index |
| %col = linalg.index 1 : index |
| %1 = sparse_tensor.unary %a: f64 to f64 |
| present={ |
| ^bb0(%x0: f64): |
| %v = arith.constant 99.0 : f64 |
| %two = arith.constant 2 : index |
| %r = arith.muli %two, %row : index |
| %c = arith.muli %two, %col : index |
| %cmp1 = arith.cmpi "ult", %r, %d0 : index |
| %tmp = arith.select %cmp1, %v, %x0 : f64 |
| %cmp2 = arith.cmpi "ult", %c, %d1 : index |
| %result = arith.select %cmp2, %v, %tmp : f64 |
| sparse_tensor.yield %result : f64 |
| } |
| absent={} |
| linalg.yield %1 : f64 |
| } -> tensor<?x?xf64, #DCSR> |
| return %0 : tensor<?x?xf64, #DCSR> |
| } |
| |
| // Driver method to call and verify vector kernels. |
| func.func @main() { |
| %cmu = arith.constant -99 : i32 |
| %c0 = arith.constant 0 : index |
| |
| // Setup sparse vectors. |
| %v1 = arith.constant sparse< |
| [ [0], [3], [11], [17], [20], [21], [28], [29], [31] ], |
| [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ] |
| > : tensor<32xf64> |
| %sv1 = sparse_tensor.convert %v1 : tensor<32xf64> to tensor<?xf64, #SparseVector> |
| |
| // Setup sparse matrices. |
| %m1 = arith.constant sparse< |
| [ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [3,0], [3,2], [3,3] ], |
| [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ] |
| > : tensor<4x8xf64> |
| %sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR> |
| |
| // Call sparse vector kernels. |
| %0 = call @vector_complement_sparse(%sv1) |
| : (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> |
| %1 = call @vector_negation(%sv1) |
| : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> |
| %2 = call @vector_magnify(%sv1) |
| : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> |
| |
| // Call sparse matrix kernels. |
| %3 = call @matrix_clip(%sm1) |
| : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> |
| %4 = call @matrix_slice(%sm1) |
| : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> |
| |
| // Call kernel with dense output. |
| %5 = call @vector_complement_dense(%sv1) : (tensor<?xf64, #SparseVector>) -> tensor<?xi32> |
| |
| // |
| // Verify the results. |
| // |
| // 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 : ( 1, 2, 3, 4, 5, 6, 7, 8, 9 ) |
| // CHECK-NEXT: ---- |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 23 |
| // CHECK-NEXT: dim = ( 32 ) |
| // CHECK-NEXT: lvl = ( 32 ) |
| // CHECK-NEXT: pos[0] : ( 0, 23 ) |
| // CHECK-NEXT: crd[0] : ( 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 22, 23, 24, 25, 26, 27, 30 ) |
| // CHECK-NEXT: values : ( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ) |
| // CHECK-NEXT: ---- |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 32 |
| // CHECK-NEXT: dim = ( 32 ) |
| // CHECK-NEXT: lvl = ( 32 ) |
| // CHECK-NEXT: pos[0] : ( 0, 32 ) |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ) |
| // CHECK-NEXT: values : ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 ) |
| // CHECK-NEXT: ---- |
| // 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, 6, 33, 68, 100, 126, 196, 232, 279 ) |
| // CHECK-NEXT: ---- |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 9 |
| // CHECK-NEXT: dim = ( 4, 8 ) |
| // CHECK-NEXT: lvl = ( 4, 8 ) |
| // CHECK-NEXT: pos[0] : ( 0, 4 ) |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 ) |
| // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 ) |
| // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 ) |
| // CHECK-NEXT: values : ( 3, 3, 3, 4, 5, 6, 7, 7, 7 ) |
| // CHECK-NEXT: ---- |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 9 |
| // CHECK-NEXT: dim = ( 4, 8 ) |
| // CHECK-NEXT: lvl = ( 4, 8 ) |
| // CHECK-NEXT: pos[0] : ( 0, 4 ) |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 ) |
| // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 ) |
| // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 ) |
| // CHECK-NEXT: values : ( 99, 99, 99, 99, 5, 6, 99, 99, 99 ) |
| // CHECK-NEXT: ---- |
| // CHECK-NEXT: ( 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 ) |
| // |
| sparse_tensor.print %sv1 : tensor<?xf64, #SparseVector> |
| sparse_tensor.print %0 : tensor<?xi32, #SparseVector> |
| sparse_tensor.print %1 : tensor<?xf64, #SparseVector> |
| sparse_tensor.print %2 : tensor<?xf64, #SparseVector> |
| sparse_tensor.print %3 : tensor<?x?xf64, #DCSR> |
| sparse_tensor.print %4 : tensor<?x?xf64, #DCSR> |
| %v = vector.transfer_read %5[%c0], %cmu: tensor<?xi32>, vector<32xi32> |
| vector.print %v : vector<32xi32> |
| |
| // Release the resources. |
| bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector> |
| bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR> |
| bufferization.dealloc_tensor %0 : tensor<?xi32, #SparseVector> |
| bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector> |
| bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector> |
| bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR> |
| bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSR> |
| bufferization.dealloc_tensor %5 : tensor<?xi32> |
| return |
| } |
| } |
