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1//--------------------------------------------------------------------------------------------------2// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.3//4// Set-up that's shared across all tests in this directory. In principle, this5// config could be moved to lit.local.cfg. However, there are downstream users that6// do not use these LIT config files. Hence why this is kept inline.7//8// DEFINE: %{sparsifier_opts} = enable-runtime-library=true9// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}10// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"11// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"12// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils13// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils14// DEFINE: %{run_opts} = -e main -entry-point-result=void15// DEFINE: %{run} = mlir-runner %{run_opts} %{run_libs}16// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve}17//18// DEFINE: %{env} =19//--------------------------------------------------------------------------------------------------20 21// REDEFINE: %{sparsifier_opts} = enable-runtime-library=true22// RUN: %{compile} | %{run} | FileCheck %s23//24// Do the same run, but now with direct IR generation.25// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true26// RUN: %{compile} | %{run} | FileCheck %s27 28#CCCC = #sparse_tensor.encoding<{ map = (d0, d1, d2, d3) -> (d0 : compressed, d1 : compressed, d2 : compressed, d3 : compressed), posWidth = 32, crdWidth = 32 }>29 30func.func @pooling_nhwc_sum_CCCC(%input: tensor<1x4x4x1xf32, #CCCC>, %filter: tensor<2x2xf32>) -> tensor<1x3x3x1xf32, #CCCC> {31 %init = tensor.empty() : tensor<1x3x3x1xf32, #CCCC>32 %0 = linalg.pooling_nhwc_sum {dilations = dense<1> : tensor<2xi64>,33 strides = dense<1> : tensor<2xi64>}34 ins (%input, %filter: tensor<1x4x4x1xf32, #CCCC>, tensor<2x2xf32>)35 outs (%init: tensor<1x3x3x1xf32, #CCCC>) -> tensor<1x3x3x1xf32, #CCCC>36 return %0 : tensor<1x3x3x1xf32, #CCCC>37}38 39func.func @pooling_nhwc_sum(%input: tensor<1x4x4x1xf32>, %filter: tensor<2x2xf32>) -> tensor<1x3x3x1xf32> {40 %init = arith.constant dense<[[ [[0.0], [0.0], [0.0]],41 [[0.0], [0.0], [0.0]],42 [[0.0], [0.0], [0.0]] ]]> : tensor<1x3x3x1xf32>43 %0 = linalg.pooling_nhwc_sum {dilations = dense<1> : tensor<2xi64>,44 strides = dense<1> : tensor<2xi64>}45 ins (%input, %filter: tensor<1x4x4x1xf32>, tensor<2x2xf32>)46 outs (%init: tensor<1x3x3x1xf32>) -> tensor<1x3x3x1xf32>47 return %0 : tensor<1x3x3x1xf32>48}49 50 51func.func @main() {52 %c0 = arith.constant 0 : index53 %zero = arith.constant 0.00000e+00 : f3254 55 %filter = arith.constant dense<56 [[ 1.0, 0.0],57 [ 0.0, 1.0]]58 > : tensor<2x2xf32>59 60 %in_dense = arith.constant dense<61 [[[[1.0], [2.0], [1.0], [2.0]],62 [[1.0], [2.0], [1.0], [2.0]],63 [[1.0], [2.0], [1.0], [2.0]],64 [[1.0], [2.0], [1.0], [2.0]]]]65 > : tensor<1x4x4x1xf32>66 67 %in_CCCC = sparse_tensor.convert %in_dense : tensor<1x4x4x1xf32> to tensor<1x4x4x1xf32, #CCCC>68 69 %dense_ret = call @pooling_nhwc_sum(%in_dense, %filter) : (tensor<1x4x4x1xf32>, tensor<2x2xf32>) -> tensor<1x3x3x1xf32>70 %CCCC_ret = call @pooling_nhwc_sum_CCCC(%in_CCCC, %filter) : (tensor<1x4x4x1xf32, #CCCC>, tensor<2x2xf32>) -> tensor<1x3x3x1xf32, #CCCC>71 72 // CHECK: ( ( ( ( 6 ), ( 6 ), ( 6 ) ), ( ( 6 ), ( 6 ), ( 6 ) ), ( ( 6 ), ( 6 ), ( 6 ) ) ) )73 %dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0, %c0], %zero74 : tensor<1x3x3x1xf32>, vector<1x3x3x1xf32>75 vector.print %dense_v : vector<1x3x3x1xf32>76 77 //78 // Sparse pooling should have the same output.79 //80 // CHECK: ---- Sparse Tensor ----81 // CHECK-NEXT: nse = 982 // CHECK-NEXT: dim = ( 1, 3, 3, 1 )83 // CHECK-NEXT: lvl = ( 1, 3, 3, 1 )84 // CHECK-NEXT: pos[0] : ( 0, 1 )85 // CHECK-NEXT: crd[0] : ( 0 )86 // CHECK-NEXT: pos[1] : ( 0, 3 )87 // CHECK-NEXT: crd[1] : ( 0, 1, 2 )88 // CHECK-NEXT: pos[2] : ( 0, 3, 6, 9 )89 // CHECK-NEXT: crd[2] : ( 0, 1, 2, 0, 1, 2, 0, 1, 2 )90 // CHECK-NEXT: pos[3] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 )91 // CHECK-NEXT: crd[3] : ( 0, 0, 0, 0, 0, 0, 0, 0, 0 )92 // CHECK-NEXT: values : ( 6, 6, 6, 6, 6, 6, 6, 6, 6 )93 // CHECK-NEXT: ----94 //95 sparse_tensor.print %CCCC_ret : tensor<1x3x3x1xf32, #CCCC>96 97 // Releases resources.98 bufferization.dealloc_tensor %in_CCCC : tensor<1x4x4x1xf32, #CCCC>99 bufferization.dealloc_tensor %CCCC_ret : tensor<1x3x3x1xf32, #CCCC>100 bufferization.dealloc_tensor %dense_ret : tensor<1x3x3x1xf32>101 return102}103