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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 enable-buffer-initialization=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 enable-buffer-initialization=true 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}
#SparseVector = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = OP a(i)"
}
module {
// Performs sign operation (using semi-ring unary op)
// with semantics that
// > 0 : +1.0
// < 0 : -1.0
// +Inf: +1.0
// -Inf: -1.0
// +NaN: +NaN
// -NaN: -NaN
// +0.0: +0.0
// -0.0: -0.0
func.func @sparse_sign(%arg0: tensor<?xf64, #SparseVector>)
-> tensor<?xf64, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arg0, %c0 : tensor<?xf64, #SparseVector>
%xin = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arg0: tensor<?xf64, #SparseVector>)
outs(%xin: tensor<?xf64, #SparseVector>) {
^bb0(%a: f64, %x: f64) :
%result = sparse_tensor.unary %a : f64 to f64
present={
^bb1(%s: f64):
%z = arith.constant 0.0 : f64
%1 = arith.cmpf one, %s, %z : f64
%2 = arith.uitofp %1 : i1 to f64
%3 = math.copysign %2, %s : f64
%4 = arith.cmpf uno, %s, %s : f64
%5 = arith.select %4, %s, %3 : f64
sparse_tensor.yield %5 : f64
}
absent={}
linalg.yield %result : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Driver method to call and verify sign kernel.
func.func @entry() {
%c0 = arith.constant 0 : index
%du = arith.constant 0.0 : f64
%pnan = arith.constant 0x7FF0000001000000 : f64
%nnan = arith.constant 0xFFF0000001000000 : f64
%pinf = arith.constant 0x7FF0000000000000 : f64
%ninf = arith.constant 0xFFF0000000000000 : f64
// Setup sparse vector.
%v1 = arith.constant sparse<
[ [0], [3], [5], [11], [13], [17], [18], [20], [21], [28], [29], [31] ],
[ -1.5, 1.5, -10.2, 11.3, 1.0, -1.0,
0x7FF0000001000000, // +NaN
0xFFF0000001000000, // -NaN
0x7FF0000000000000, // +Inf
0xFFF0000000000000, // -Inf
-0.0, // -Zero
0.0 // +Zero
]
> : tensor<32xf64>
%sv1 = sparse_tensor.convert %v1
: tensor<32xf64> to tensor<?xf64, #SparseVector>
// Call sign kernel.
%0 = call @sparse_sign(%sv1) : (tensor<?xf64, #SparseVector>)
-> tensor<?xf64, #SparseVector>
//
// Verify the results.
//
// CHECK: ( -1, 1, -1, 1, 1, -1, nan, -nan, 1, -1, -0, 0, 0 )
//
%1 = sparse_tensor.values %0 : tensor<?xf64, #SparseVector> to memref<?xf64>
%2 = vector.transfer_read %1[%c0], %du: memref<?xf64>, vector<13xf64>
vector.print %2 : vector<13xf64>
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xf64, #SparseVector>
return
}
}
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