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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
// An example of a quantized sparse matmul. With the zero offset for the
// sparse input, the sparse compiler 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 @entry() {
%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>
return
}
}
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