| //-------------------------------------------------------------------------------------------------- |
| // 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} | %{env} %{run} | FileCheck %s |
| // |
| // Do the same run, but now with direct IR generation. |
| // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false |
| // RUN: %{compile} | %{env} %{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} | %{env} %{run} | FileCheck %s |
| // |
| // Do the same run, but now with direct IR generation and VLA vectorization. |
| // RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{env} %{run_sve} | FileCheck %s %} |
| |
| |
| #COO_2D = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton), posWidth = 32, crdWidth = 32 }> |
| #COO_3D = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(nonunique), d2 : singleton), posWidth = 32, crdWidth = 32 }> |
| |
| module { |
| func.func private @printMemref3dF32(%ptr : tensor<?x?x?xf32> {bufferization.access = "read"}) attributes { llvm.emit_c_interface } |
| func.func private @printMemref2dF32(%ptr : tensor<?x?xf32> {bufferization.access = "read"}) attributes { llvm.emit_c_interface } |
| |
| func.func @test_sparse_rhs(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> { |
| %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D> |
| %0 = tensor.empty() : tensor<5x6xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32> |
| %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32> |
| |
| // Note: tensor.collapse_shape is a metadata-only operation on dense tensors |
| // but requires reallocation on sparse tensors. |
| bufferization.dealloc_tensor %collapsed : tensor<6x6xf32, #COO_2D> |
| |
| return %ret1 : tensor<?x?x?xf32> |
| } |
| |
| func.func @test_sparse_all(%arg0: tensor<5x6xf32, #COO_2D>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> { |
| %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D> |
| %0 = tensor.empty() : tensor<5x6xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32> |
| %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32> |
| |
| // Note: tensor.collapse_shape is a metadata-only operation on dense tensors |
| // but requires reallocation on sparse tensors. |
| bufferization.dealloc_tensor %collapsed : tensor<6x6xf32, #COO_2D> |
| |
| return %ret1 : tensor<?x?x?xf32> |
| } |
| |
| func.func @test_dense(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32>) -> tensor<?x?x?xf32> { |
| %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32> into tensor<6x6xf32> |
| %0 = tensor.empty() : tensor<5x6xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32> |
| %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32> |
| return %ret1 : tensor<?x?x?xf32> |
| } |
| |
| func.func @test_sparse_all_2(%arg0: tensor<5x6xf32, #COO_2D>, %arg1: tensor<2x3x6xf32, #COO_3D>) -> tensor<?x?x?xf32> { |
| // collapse the first two level this time, as this is the level requires coiterations. |
| %collapsed = tensor.collapse_shape %arg1 [[0, 1], [2]] : tensor<2x3x6xf32, #COO_3D> into tensor<6x6xf32, #COO_2D> |
| %0 = tensor.empty() : tensor<5x6xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32> |
| %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32> |
| |
| // Note: tensor.collapse_shape is a metadata-only operation on dense tensors |
| // but requires reallocation on sparse tensors. |
| bufferization.dealloc_tensor %collapsed : tensor<6x6xf32, #COO_2D> |
| |
| return %ret1 : tensor<?x?x?xf32> |
| } |
| |
| |
| func.func @main() { |
| // Setup two sparse vectors. |
| %d1 = arith.constant sparse< |
| [ [0, 0], [1, 1], [2, 2], [2, 3], [4, 5] ], |
| [1.0, 2.0, 3.0, 4.0, 5.0] |
| > : tensor<5x6xf32> |
| |
| %d2 = arith.constant sparse< |
| [ [0, 0, 0], [1, 1, 1], [2, 1, 1] ], |
| [ 6.0, 7.0, 8.0] |
| > : tensor<6x2x3xf32> |
| %shape = arith.constant dense<[2, 3, 6]> : tensor<3xi32> |
| |
| %d3 = tensor.reshape %d2(%shape): (tensor<6x2x3xf32>, tensor<3xi32>) -> tensor<2x3x6xf32> |
| %s1 = sparse_tensor.convert %d1 : tensor<5x6xf32> to tensor<5x6xf32, #COO_2D> |
| %s2 = sparse_tensor.convert %d2 : tensor<6x2x3xf32> to tensor<6x2x3xf32, #COO_3D> |
| %s3 = sparse_tensor.convert %d3 : tensor<2x3x6xf32> to tensor<2x3x6xf32, #COO_3D> |
| |
| // CHECK: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data = |
| // CHECK-NEXT:[ |
| // CHECK-SAME: [ |
| // CHECK-SAME: [6, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 14, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 24, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]]] |
| %do1 = call @test_dense(%d1, %d2) : (tensor<5x6xf32>, tensor<6x2x3xf32>) -> tensor<?x?x?xf32> |
| call @printMemref3dF32(%do1) : (tensor<?x?x?xf32>) -> () |
| |
| // Same results. |
| // CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data = |
| // CHECK-NEXT:[ |
| // CHECK-SAME: [ |
| // CHECK-SAME: [6, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 14, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 24, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]]] |
| %so1 = call @test_sparse_rhs(%d1, %s2): (tensor<5x6xf32>, tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> |
| call @printMemref3dF32(%so1) : (tensor<?x?x?xf32>) -> () |
| |
| // Same results. |
| // CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data = |
| // CHECK-NEXT:[ |
| // CHECK-SAME: [ |
| // CHECK-SAME: [6, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 14, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 24, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]]] |
| %so2 = call @test_sparse_all(%s1, %s2): (tensor<5x6xf32, #COO_2D>, tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> |
| call @printMemref3dF32(%so2) : (tensor<?x?x?xf32>) -> () |
| |
| // Same results. |
| // CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data = |
| // CHECK-NEXT:[ |
| // CHECK-SAME: [ |
| // CHECK-SAME: [6, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 14, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 24, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]], |
| // CHECK-NEXT: [ |
| // CHECK-SAME: [0, 0, 0], |
| // CHECK-NEXT: [0, 0, 0]]] |
| %so3 = call @test_sparse_all_2(%s1, %s3): (tensor<5x6xf32, #COO_2D>, tensor<2x3x6xf32, #COO_3D>) -> tensor<?x?x?xf32> |
| call @printMemref3dF32(%so2) : (tensor<?x?x?xf32>) -> () |
| |
| bufferization.dealloc_tensor %s1 : tensor<5x6xf32, #COO_2D> |
| bufferization.dealloc_tensor %s2 : tensor<6x2x3xf32, #COO_3D> |
| bufferization.dealloc_tensor %s3 : tensor<2x3x6xf32, #COO_3D> |
| bufferization.dealloc_tensor %do1 : tensor<?x?x?xf32> |
| bufferization.dealloc_tensor %so1 : tensor<?x?x?xf32> |
| bufferization.dealloc_tensor %so2 : tensor<?x?x?xf32> |
| bufferization.dealloc_tensor %so3 : tensor<?x?x?xf32> |
| |
| return |
| } |
| } |
