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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// RUN: %{compile} | %{run} | FileCheck %s22//23// Do the same run, but now with direct IR generation.24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false25// RUN: %{compile} | %{run} | FileCheck %s26//27// Do the same run, but now with direct IR generation and vectorization.28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true29// RUN: %{compile} | %{run} | FileCheck %s30//31// Do the same run, but now with direct IR generation and VLA vectorization.32// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}33 34#DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>35 36// An example of a quantized sparse matmul. With the zero offset for the37// sparse input, the sparsifier generates very efficient code for the38// x(i,j) += (ext(a(i,k)) - 2) * ext(b(k,j))39// operation.40module {41 42 func.func @quantized_matmul(%input1: tensor<5x3xi8>,43 %input2: tensor<3x6xi8, #DCSR>,44 %output: tensor<5x6xi32>) -> tensor<5x6xi32> {45 %c0 = arith.constant 0 : i3246 %c2 = arith.constant 2 : i3247 %0 = linalg.quantized_matmul48 ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32)49 outs(%output : tensor<5x6xi32>) -> tensor<5x6xi32>50 return %0: tensor<5x6xi32>51 }52 53 func.func @main() {54 %c0 = arith.constant 0 : index55 %i0 = arith.constant 0 : i3256 57 %input1 = arith.constant dense<[58 [ -128, 3, 127 ],59 [ 0, 0, 0 ],60 [ 11, 1, 0 ],61 [ 0, 5, -1 ],62 [ 13, 0, 3 ]63 ]> : tensor<5x3xi8>64 65 %input2 = arith.constant dense<[66 [ 127, 0, -128, 0, 0, 3 ],67 [ 0, 0, 0, 0, 0, 0 ],68 [ 0, 0, 0, 100, 10, 0 ]69 ]> : tensor<3x6xi8>70 71 %sparse_input2 = sparse_tensor.convert %input2 : tensor<3x6xi8> to tensor<3x6xi8, #DCSR>72 73 // Call the kernel.74 %output = arith.constant dense<0> : tensor<5x6xi32>75 %0 = call @quantized_matmul(%input1, %sparse_input2, %output)76 : (tensor<5x3xi8>,77 tensor<3x6xi8, #DCSR>,78 tensor<5x6xi32>) -> tensor<5x6xi32>79 80 //81 // Verify the output.82 //83 // CHECK: ( ( -16510, 0, 16640, 12500, 1250, -390 ),84 // CHECK-SAME: ( -254, 0, 256, -200, -20, -6 ),85 // CHECK-SAME: ( 1143, 0, -1152, -200, -20, 27 ),86 // CHECK-SAME: ( -254, 0, 256, -300, -30, -6 ),87 // CHECK-SAME: ( 1397, 0, -1408, 100, 10, 33 ) )88 //89 %v = vector.transfer_read %0[%c0, %c0], %i090 : tensor<5x6xi32>, vector<5x6xi32>91 vector.print %v : vector<5x6xi32>92 93 // Release the resources.94 bufferization.dealloc_tensor %sparse_input2 : tensor<3x6xi8, #DCSR>95 bufferization.dealloc_tensor %0 : tensor<5x6xi32>96 97 return98 }99}100