| //-------------------------------------------------------------------------------------------------- |
| // 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} = |
| //-------------------------------------------------------------------------------------------------- |
| |
| // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false |
| |
| // RUN: %{compile} | %{run} | FileCheck %s |
| |
| // TODO: support slices on lib path |
| |
| #CSR = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : dense, d1 : compressed) |
| }> |
| |
| #CSR_SLICE = #sparse_tensor.encoding<{ |
| map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed) |
| }> |
| |
| #CSR_SLICE_DYN = #sparse_tensor.encoding<{ |
| map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : dense, d1 : compressed) |
| }> |
| |
| #COO = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton) |
| }> |
| |
| #COO_SLICE = #sparse_tensor.encoding<{ |
| map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : compressed(nonunique), d1 : singleton) |
| }> |
| |
| #COO_SLICE_DYN = #sparse_tensor.encoding<{ |
| map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed(nonunique), d1 : singleton) |
| }> |
| |
| |
| |
| module { |
| func.func @foreach_print_non_slice(%A: tensor<4x4xf64, #CSR>) { |
| sparse_tensor.foreach in %A : tensor<4x4xf64, #CSR> do { |
| ^bb0(%1: index, %2: index, %v: f64) : |
| vector.print %1: index |
| vector.print %2: index |
| vector.print %v: f64 |
| } |
| return |
| } |
| |
| func.func @foreach_print_slice(%A: tensor<4x4xf64, #CSR_SLICE>) { |
| sparse_tensor.foreach in %A : tensor<4x4xf64, #CSR_SLICE> do { |
| ^bb0(%1: index, %2: index, %v: f64) : |
| vector.print %1: index |
| vector.print %2: index |
| vector.print %v: f64 |
| } |
| return |
| } |
| |
| func.func @foreach_print_slice_dyn(%A: tensor<?x?xf64, #CSR_SLICE_DYN>) { |
| sparse_tensor.foreach in %A : tensor<?x?xf64, #CSR_SLICE_DYN> do { |
| ^bb0(%1: index, %2: index, %v: f64) : |
| vector.print %1: index |
| vector.print %2: index |
| vector.print %v: f64 |
| } |
| return |
| } |
| |
| func.func @foreach_print_slice_coo(%A: tensor<4x4xf64, #COO_SLICE>) { |
| sparse_tensor.foreach in %A : tensor<4x4xf64, #COO_SLICE> do { |
| ^bb0(%1: index, %2: index, %v: f64) : |
| vector.print %1: index |
| vector.print %2: index |
| vector.print %v: f64 |
| } |
| return |
| } |
| |
| func.func @foreach_print_slice_coo_dyn(%A: tensor<?x?xf64, #COO_SLICE_DYN>) { |
| sparse_tensor.foreach in %A : tensor<?x?xf64, #COO_SLICE_DYN> do { |
| ^bb0(%1: index, %2: index, %v: f64) : |
| vector.print %1: index |
| vector.print %2: index |
| vector.print %v: f64 |
| } |
| return |
| } |
| |
| func.func @main() { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %c2 = arith.constant 2 : index |
| %c4 = arith.constant 4 : index |
| |
| %sa = arith.constant dense<[ |
| [ 0.0, 2.1, 0.0, 0.0, 0.0, 6.1, 0.0, 0.0 ], |
| [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [ 0.0, 2.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ], |
| [ 0.0, 0.0, 0.1, 0.0, 0.0, 2.1, 0.0, 0.0 ], |
| [ 0.0, 0.0, 0.0, 0.0, 3.1, 0.0, 0.0, 0.0 ], |
| [ 0.0, 2.3, 0.0, 0.0, 0.0, 0.0, 3.3, 0.0 ], |
| [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ] |
| ]> : tensor<8x8xf64> |
| |
| |
| %tmp = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #CSR> |
| %a = tensor.extract_slice %tmp[1, 1][4, 4][1, 2] : tensor<8x8xf64, #CSR> to |
| tensor<4x4xf64, #CSR_SLICE> |
| |
| %tmp_coo = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #COO> |
| %a_coo = tensor.extract_slice %tmp_coo[1, 1][4, 4][1, 2] : tensor<8x8xf64, #COO> to |
| tensor<4x4xf64, #COO_SLICE> |
| // Foreach on sparse tensor slices directly |
| // |
| // CHECK: 1 |
| // CHECK-NEXT: 0 |
| // CHECK-NEXT: 2.3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 2.1 |
| // |
| call @foreach_print_slice(%a) : (tensor<4x4xf64, #CSR_SLICE>) -> () |
| // Same results for COO |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 0 |
| // CHECK-NEXT: 2.3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 2.1 |
| // |
| call @foreach_print_slice_coo(%a_coo) : (tensor<4x4xf64, #COO_SLICE>) -> () |
| |
| %dense = tensor.extract_slice %sa[1, 1][4, 4][1, 2] : tensor<8x8xf64> to |
| tensor<4x4xf64> |
| %b = sparse_tensor.convert %dense : tensor<4x4xf64> to tensor<4x4xf64, #CSR> |
| // Foreach on sparse tensor instead of slice they should yield the same result. |
| // |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 0 |
| // CHECK-NEXT: 2.3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 2.1 |
| // |
| call @foreach_print_non_slice(%b) : (tensor<4x4xf64, #CSR>) -> () |
| |
| // The same slice, but with dynamic encoding. |
| // TODO: Investigates why reusing the same %tmp above would cause bufferization |
| // errors. |
| %tmp1 = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #CSR> |
| %a_dyn = tensor.extract_slice %tmp1[%c1, %c1][%c4, %c4][%c1, %c2] : tensor<8x8xf64, #CSR> to |
| tensor<?x?xf64, #CSR_SLICE_DYN> |
| |
| %tmp1_coo = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #COO> |
| %a_dyn_coo = tensor.extract_slice %tmp1_coo[%c1, %c1][%c4, %c4][%c1, %c2] : tensor<8x8xf64, #COO> to |
| tensor<?x?xf64, #COO_SLICE_DYN> |
| // |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 0 |
| // CHECK-NEXT: 2.3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 2.1 |
| // |
| call @foreach_print_slice_dyn(%a_dyn) : (tensor<?x?xf64, #CSR_SLICE_DYN>) -> () |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 0 |
| // CHECK-NEXT: 2.3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 1 |
| // CHECK-NEXT: 3 |
| // CHECK-NEXT: 2 |
| // CHECK-NEXT: 2.1 |
| // |
| call @foreach_print_slice_coo_dyn(%a_dyn_coo) : (tensor<?x?xf64, #COO_SLICE_DYN>) -> () |
| |
| bufferization.dealloc_tensor %tmp : tensor<8x8xf64, #CSR> |
| bufferization.dealloc_tensor %tmp1 : tensor<8x8xf64, #CSR> |
| bufferization.dealloc_tensor %tmp_coo : tensor<8x8xf64, #COO> |
| bufferization.dealloc_tensor %tmp1_coo : tensor<8x8xf64, #COO> |
| bufferization.dealloc_tensor %b : tensor<4x4xf64, #CSR> |
| return |
| } |
| } |
