| //-------------------------------------------------------------------------------------------------- |
| // 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 direct IR generation and 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 direct IR generation and VLA vectorization. |
| // RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} |
| |
| |
| #SparseVector = #sparse_tensor.encoding<{ |
| map = (d0) -> (d0 : compressed) |
| }> |
| |
| #SparseMatrix = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : compressed, d1 : compressed) |
| }> |
| |
| #trait_1d = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a |
| affine_map<(i) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel"], |
| doc = "X(i) = a(i) op i" |
| } |
| |
| #trait_2d = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = A(i,j) op i op j" |
| } |
| |
| // |
| // Test with indices. Note that a lot of results are actually |
| // dense, but this is done to stress test all the operations. |
| // |
| module { |
| |
| // |
| // Kernel that uses index in the index notation (conjunction). |
| // |
| func.func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>) |
| -> tensor<8xi64, #SparseVector> { |
| %init = tensor.empty() : tensor<8xi64, #SparseVector> |
| %r = linalg.generic #trait_1d |
| ins(%arga: tensor<8xi64, #SparseVector>) |
| outs(%init: tensor<8xi64, #SparseVector>) { |
| ^bb(%a: i64, %x: i64): |
| %i = linalg.index 0 : index |
| %ii = arith.index_cast %i : index to i64 |
| %m1 = arith.muli %a, %ii : i64 |
| linalg.yield %m1 : i64 |
| } -> tensor<8xi64, #SparseVector> |
| return %r : tensor<8xi64, #SparseVector> |
| } |
| |
| // |
| // Kernel that uses index in the index notation (disjunction). |
| // |
| func.func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>) |
| -> tensor<8xi64, #SparseVector> { |
| %init = tensor.empty() : tensor<8xi64, #SparseVector> |
| %r = linalg.generic #trait_1d |
| ins(%arga: tensor<8xi64, #SparseVector>) |
| outs(%init: tensor<8xi64, #SparseVector>) { |
| ^bb(%a: i64, %x: i64): |
| %i = linalg.index 0 : index |
| %ii = arith.index_cast %i : index to i64 |
| %m1 = arith.addi %a, %ii : i64 |
| linalg.yield %m1 : i64 |
| } -> tensor<8xi64, #SparseVector> |
| return %r : tensor<8xi64, #SparseVector> |
| } |
| |
| // |
| // Kernel that uses indices in the index notation (conjunction). |
| // |
| func.func @sparse_index_2d_conj(%arga: tensor<3x4xi64, #SparseMatrix>) |
| -> tensor<3x4xi64, #SparseMatrix> { |
| %init = tensor.empty() : tensor<3x4xi64, #SparseMatrix> |
| %r = linalg.generic #trait_2d |
| ins(%arga: tensor<3x4xi64, #SparseMatrix>) |
| outs(%init: tensor<3x4xi64, #SparseMatrix>) { |
| ^bb(%a: i64, %x: i64): |
| %i = linalg.index 0 : index |
| %j = linalg.index 1 : index |
| %ii = arith.index_cast %i : index to i64 |
| %jj = arith.index_cast %j : index to i64 |
| %m1 = arith.muli %ii, %a : i64 |
| %m2 = arith.muli %jj, %m1 : i64 |
| linalg.yield %m2 : i64 |
| } -> tensor<3x4xi64, #SparseMatrix> |
| return %r : tensor<3x4xi64, #SparseMatrix> |
| } |
| |
| // |
| // Kernel that uses indices in the index notation (disjunction). |
| // |
| func.func @sparse_index_2d_disj(%arga: tensor<3x4xi64, #SparseMatrix>) |
| -> tensor<3x4xi64, #SparseMatrix> { |
| %init = tensor.empty() : tensor<3x4xi64, #SparseMatrix> |
| %r = linalg.generic #trait_2d |
| ins(%arga: tensor<3x4xi64, #SparseMatrix>) |
| outs(%init: tensor<3x4xi64, #SparseMatrix>) { |
| ^bb(%a: i64, %x: i64): |
| %i = linalg.index 0 : index |
| %j = linalg.index 1 : index |
| %ii = arith.index_cast %i : index to i64 |
| %jj = arith.index_cast %j : index to i64 |
| %m1 = arith.addi %ii, %a : i64 |
| %m2 = arith.addi %jj, %m1 : i64 |
| linalg.yield %m2 : i64 |
| } -> tensor<3x4xi64, #SparseMatrix> |
| return %r : tensor<3x4xi64, #SparseMatrix> |
| } |
| |
| func.func @add_outer_2d(%arg0: tensor<2x3xf32, #SparseMatrix>) |
| -> tensor<2x3xf32, #SparseMatrix> { |
| %0 = tensor.empty() : tensor<2x3xf32, #SparseMatrix> |
| %1 = linalg.generic #trait_2d |
| ins(%arg0 : tensor<2x3xf32, #SparseMatrix>) |
| outs(%0 : tensor<2x3xf32, #SparseMatrix>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %2 = linalg.index 0 : index |
| %3 = arith.index_cast %2 : index to i64 |
| %4 = arith.uitofp %3 : i64 to f32 |
| %5 = arith.addf %arg1, %4 : f32 |
| linalg.yield %5 : f32 |
| } -> tensor<2x3xf32, #SparseMatrix> |
| return %1 : tensor<2x3xf32, #SparseMatrix> |
| } |
| |
| // |
| // Main driver. |
| // |
| func.func @main() { |
| %c0 = arith.constant 0 : index |
| %du = arith.constant -1 : i64 |
| %df = arith.constant -1.0 : f32 |
| |
| // Setup input sparse vector. |
| %v1 = arith.constant sparse<[[2], [4]], [ 10, 20]> : tensor<8xi64> |
| %sv = sparse_tensor.convert %v1 : tensor<8xi64> to tensor<8xi64, #SparseVector> |
| |
| // Setup input "sparse" vector. |
| %v2 = arith.constant dense<[ 1, 2, 4, 8, 16, 32, 64, 128 ]> : tensor<8xi64> |
| %dv = sparse_tensor.convert %v2 : tensor<8xi64> to tensor<8xi64, #SparseVector> |
| |
| // Setup input sparse matrix. |
| %m1 = arith.constant sparse<[[1,1], [2,3]], [10, 20]> : tensor<3x4xi64> |
| %sm = sparse_tensor.convert %m1 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix> |
| |
| // Setup input "sparse" matrix. |
| %m2 = arith.constant dense <[ [ 1, 1, 1, 1 ], |
| [ 1, 2, 1, 1 ], |
| [ 1, 1, 3, 4 ] ]> : tensor<3x4xi64> |
| %dm = sparse_tensor.convert %m2 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix> |
| |
| // Setup input sparse f32 matrix. |
| %mf32 = arith.constant sparse<[[0,1], [1,2]], [10.0, 41.0]> : tensor<2x3xf32> |
| %sf32 = sparse_tensor.convert %mf32 : tensor<2x3xf32> to tensor<2x3xf32, #SparseMatrix> |
| |
| // Call the kernels. |
| %0 = call @sparse_index_1d_conj(%sv) : (tensor<8xi64, #SparseVector>) |
| -> tensor<8xi64, #SparseVector> |
| %1 = call @sparse_index_1d_disj(%sv) : (tensor<8xi64, #SparseVector>) |
| -> tensor<8xi64, #SparseVector> |
| %2 = call @sparse_index_1d_conj(%dv) : (tensor<8xi64, #SparseVector>) |
| -> tensor<8xi64, #SparseVector> |
| %3 = call @sparse_index_1d_disj(%dv) : (tensor<8xi64, #SparseVector>) |
| -> tensor<8xi64, #SparseVector> |
| %4 = call @sparse_index_2d_conj(%sm) : (tensor<3x4xi64, #SparseMatrix>) |
| -> tensor<3x4xi64, #SparseMatrix> |
| %5 = call @sparse_index_2d_disj(%sm) : (tensor<3x4xi64, #SparseMatrix>) |
| -> tensor<3x4xi64, #SparseMatrix> |
| %6 = call @sparse_index_2d_conj(%dm) : (tensor<3x4xi64, #SparseMatrix>) |
| -> tensor<3x4xi64, #SparseMatrix> |
| %7 = call @sparse_index_2d_disj(%dm) : (tensor<3x4xi64, #SparseMatrix>) |
| -> tensor<3x4xi64, #SparseMatrix> |
| |
| // |
| // Verify result. |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 2 |
| // CHECK-NEXT: dim = ( 8 ) |
| // CHECK-NEXT: lvl = ( 8 ) |
| // CHECK-NEXT: pos[0] : ( 0, 2 |
| // CHECK-NEXT: crd[0] : ( 2, 4 |
| // CHECK-NEXT: values : ( 20, 80 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 8 |
| // CHECK-NEXT: dim = ( 8 ) |
| // CHECK-NEXT: lvl = ( 8 ) |
| // CHECK-NEXT: pos[0] : ( 0, 8 |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7 |
| // CHECK-NEXT: values : ( 0, 1, 12, 3, 24, 5, 6, 7 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 8 |
| // CHECK-NEXT: dim = ( 8 ) |
| // CHECK-NEXT: lvl = ( 8 ) |
| // CHECK-NEXT: pos[0] : ( 0, 8 |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7 |
| // CHECK-NEXT: values : ( 0, 2, 8, 24, 64, 160, 384, 896 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 8 |
| // CHECK-NEXT: dim = ( 8 ) |
| // CHECK-NEXT: lvl = ( 8 ) |
| // CHECK-NEXT: pos[0] : ( 0, 8 |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7 |
| // CHECK-NEXT: values : ( 1, 3, 6, 11, 20, 37, 70, 135 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 2 |
| // CHECK-NEXT: dim = ( 3, 4 ) |
| // CHECK-NEXT: lvl = ( 3, 4 ) |
| // CHECK-NEXT: pos[0] : ( 0, 2 |
| // CHECK-NEXT: crd[0] : ( 1, 2 |
| // CHECK-NEXT: pos[1] : ( 0, 1, 2 |
| // CHECK-NEXT: crd[1] : ( 1, 3 |
| // CHECK-NEXT: values : ( 10, 120 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 12 |
| // CHECK-NEXT: dim = ( 3, 4 ) |
| // CHECK-NEXT: lvl = ( 3, 4 ) |
| // CHECK-NEXT: pos[0] : ( 0, 3 |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2 |
| // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12 |
| // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 |
| // CHECK-NEXT: values : ( 0, 1, 2, 3, 1, 12, 3, 4, 2, 3, 4, 25 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 12 |
| // CHECK-NEXT: dim = ( 3, 4 ) |
| // CHECK-NEXT: lvl = ( 3, 4 ) |
| // CHECK-NEXT: pos[0] : ( 0, 3 |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2 |
| // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12 |
| // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 |
| // CHECK-NEXT: values : ( 0, 0, 0, 0, 0, 2, 2, 3, 0, 2, 12, 24 |
| // CHECK-NEXT: ---- |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 12 |
| // CHECK-NEXT: dim = ( 3, 4 ) |
| // CHECK-NEXT: lvl = ( 3, 4 ) |
| // CHECK-NEXT: pos[0] : ( 0, 3 |
| // CHECK-NEXT: crd[0] : ( 0, 1, 2 |
| // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12 |
| // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 |
| // CHECK-NEXT: values : ( 1, 2, 3, 4, 2, 4, 4, 5, 3, 4, 7, 9 |
| // CHECK-NEXT: ---- |
| // |
| sparse_tensor.print %0 : tensor<8xi64, #SparseVector> |
| sparse_tensor.print %1 : tensor<8xi64, #SparseVector> |
| sparse_tensor.print %2 : tensor<8xi64, #SparseVector> |
| sparse_tensor.print %3 : tensor<8xi64, #SparseVector> |
| sparse_tensor.print %4 : tensor<3x4xi64, #SparseMatrix> |
| sparse_tensor.print %5 : tensor<3x4xi64, #SparseMatrix> |
| sparse_tensor.print %6 : tensor<3x4xi64, #SparseMatrix> |
| sparse_tensor.print %7 : tensor<3x4xi64, #SparseMatrix> |
| |
| // |
| // Call the f32 kernel, verify the result. |
| // |
| // CHECK: ---- Sparse Tensor ---- |
| // CHECK-NEXT: nse = 6 |
| // CHECK-NEXT: dim = ( 2, 3 ) |
| // CHECK-NEXT: lvl = ( 2, 3 ) |
| // CHECK-NEXT: pos[0] : ( 0, 2 |
| // CHECK-NEXT: crd[0] : ( 0, 1 |
| // CHECK-NEXT: pos[1] : ( 0, 3, 6 |
| // CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 1, 2 |
| // CHECK-NEXT: values : ( 0, 10, 0, 1, 1, 42 |
| // CHECK-NEXT: ---- |
| // |
| %100 = call @add_outer_2d(%sf32) : (tensor<2x3xf32, #SparseMatrix>) |
| -> tensor<2x3xf32, #SparseMatrix> |
| sparse_tensor.print %100 : tensor<2x3xf32, #SparseMatrix> |
| |
| // Release resources. |
| bufferization.dealloc_tensor %sv : tensor<8xi64, #SparseVector> |
| bufferization.dealloc_tensor %dv : tensor<8xi64, #SparseVector> |
| bufferization.dealloc_tensor %0 : tensor<8xi64, #SparseVector> |
| bufferization.dealloc_tensor %1 : tensor<8xi64, #SparseVector> |
| bufferization.dealloc_tensor %2 : tensor<8xi64, #SparseVector> |
| bufferization.dealloc_tensor %3 : tensor<8xi64, #SparseVector> |
| bufferization.dealloc_tensor %sm : tensor<3x4xi64, #SparseMatrix> |
| bufferization.dealloc_tensor %dm : tensor<3x4xi64, #SparseMatrix> |
| bufferization.dealloc_tensor %4 : tensor<3x4xi64, #SparseMatrix> |
| bufferization.dealloc_tensor %5 : tensor<3x4xi64, #SparseMatrix> |
| bufferization.dealloc_tensor %6 : tensor<3x4xi64, #SparseMatrix> |
| bufferization.dealloc_tensor %7 : tensor<3x4xi64, #SparseMatrix> |
| bufferization.dealloc_tensor %sf32 : tensor<2x3xf32, #SparseMatrix> |
| bufferization.dealloc_tensor %100 : tensor<2x3xf32, #SparseMatrix> |
| |
| return |
| } |
| } |
