//--------------------------------------------------------------------------------------------------
// 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} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
// RUN: %{compile} | %{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} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>
// An example of a quantized sparse matmul. With the zero offset for the
// sparse input, the sparsifier generates very efficient code for the
// x(i,j) += (ext(a(i,k)) - 2) * ext(b(k,j))
// operation.
module {
func.func @quantized_matmul(%input1: tensor<5x3xi8>,
%input2: tensor<3x6xi8, #DCSR>,
%output: tensor<5x6xi32>) -> tensor<5x6xi32> {
%c0 = arith.constant 0 : i32
%c2 = arith.constant 2 : i32
%0 = linalg.quantized_matmul
ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32)
outs(%output : tensor<5x6xi32>) -> tensor<5x6xi32>
return %0: tensor<5x6xi32>
}
func.func @main() {
%c0 = arith.constant 0 : index
%i0 = arith.constant 0 : i32
%input1 = arith.constant dense<[
[ -128, 3, 127 ],
[ 0, 0, 0 ],
[ 11, 1, 0 ],
[ 0, 5, -1 ],
[ 13, 0, 3 ]
]> : tensor<5x3xi8>
%input2 = arith.constant dense<[
[ 127, 0, -128, 0, 0, 3 ],
[ 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 100, 10, 0 ]
]> : tensor<3x6xi8>
%sparse_input2 = sparse_tensor.convert %input2 : tensor<3x6xi8> to tensor<3x6xi8, #DCSR>
// Call the kernel.
%output = arith.constant dense<0> : tensor<5x6xi32>
%0 = call @quantized_matmul(%input1, %sparse_input2, %output)
: (tensor<5x3xi8>,
tensor<3x6xi8, #DCSR>,
tensor<5x6xi32>) -> tensor<5x6xi32>
//
// Verify the output.
//
// CHECK: ( ( -16510, 0, 16640, 12500, 1250, -390 ),
// CHECK-SAME: ( -254, 0, 256, -200, -20, -6 ),
// CHECK-SAME: ( 1143, 0, -1152, -200, -20, 27 ),
// CHECK-SAME: ( -254, 0, 256, -300, -30, -6 ),
// CHECK-SAME: ( 1397, 0, -1408, 100, 10, 33 ) )
//
%v = vector.transfer_read %0[%c0, %c0], %i0
: tensor<5x6xi32>, vector<5x6xi32>
vector.print %v : vector<5x6xi32>
// Release the resources.
bufferization.dealloc_tensor %sparse_input2 : tensor<3x6xi8, #DCSR>
bufferization.dealloc_tensor %0 : tensor<5x6xi32>
return
}
}
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