1// RUN: mlir-opt %s --sparse-compiler | \ 2// RUN: mlir-cpu-runner \ 3// RUN: -e entry -entry-point-result=void \ 4// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ 5// RUN: FileCheck %s 6 7#DCSR = #sparse_tensor.encoding<{ 8 dimLevelType = [ "compressed", "compressed" ] 9}> 10 11#trait_mult_elt = { 12 indexing_maps = [ 13 affine_map<(i,j) -> (i,j)>, // A 14 affine_map<(i,j) -> (i,j)>, // B 15 affine_map<(i,j) -> (i,j)> // X (out) 16 ], 17 iterator_types = ["parallel", "parallel"], 18 doc = "X(i,j) = A(i,j) * B(i,j)" 19} 20 21module { 22 // Sparse kernel. 23 func.func @sparse_mult_elt( 24 %arga: tensor<32x16xf32, #DCSR>, %argb: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> { 25 %argx = bufferization.alloc_tensor() : tensor<32x16xf32, #DCSR> 26 %0 = linalg.generic #trait_mult_elt 27 ins(%arga, %argb: tensor<32x16xf32, #DCSR>, tensor<32x16xf32, #DCSR>) 28 outs(%argx: tensor<32x16xf32, #DCSR>) { 29 ^bb(%a: f32, %b: f32, %x: f32): 30 %1 = arith.mulf %a, %b : f32 31 linalg.yield %1 : f32 32 } -> tensor<32x16xf32, #DCSR> 33 return %0 : tensor<32x16xf32, #DCSR> 34 } 35 36 // Driver method to call and verify kernel. 37 func.func @entry() { 38 %c0 = arith.constant 0 : index 39 %f1 = arith.constant -1.0 : f32 40 41 // Setup very sparse matrices. 42 %ta = arith.constant sparse< 43 [ [2,2], [15,15], [31,0], [31,14] ], [ 2.0, 3.0, -2.0, 4.0 ] 44 > : tensor<32x16xf32> 45 %tb = arith.constant sparse< 46 [ [1,1], [2,0], [2,2], [2,15], [31,0], [31,15] ], [ 5.0, 6.0, 7.0, 8.0, -10.0, 9.0 ] 47 > : tensor<32x16xf32> 48 %sta = sparse_tensor.convert %ta 49 : tensor<32x16xf32> to tensor<32x16xf32, #DCSR> 50 %stb = sparse_tensor.convert %tb 51 : tensor<32x16xf32> to tensor<32x16xf32, #DCSR> 52 53 // Call kernel. 54 %0 = call @sparse_mult_elt(%sta, %stb) 55 : (tensor<32x16xf32, #DCSR>, 56 tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> 57 58 // 59 // Verify results. Only two entries stored in result! 60 // 61 // CHECK: ( 14, 20, -1, -1 ) 62 // 63 %val = sparse_tensor.values %0 : tensor<32x16xf32, #DCSR> to memref<?xf32> 64 %vv = vector.transfer_read %val[%c0], %f1: memref<?xf32>, vector<4xf32> 65 vector.print %vv : vector<4xf32> 66 67 // Release the resources. 68 bufferization.dealloc_tensor %sta : tensor<32x16xf32, #DCSR> 69 bufferization.dealloc_tensor %stb : tensor<32x16xf32, #DCSR> 70 bufferization.dealloc_tensor %0 : tensor<32x16xf32, #DCSR> 71 return 72 } 73} 74