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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}
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "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 {
func.func @sparse_absf(%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 = math.absf %a : f64
linalg.yield %result : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
func.func @sparse_absi(%arg0: tensor<?xi32, #SparseVector>)
-> tensor<?xi32, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arg0, %c0 : tensor<?xi32, #SparseVector>
%xin = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arg0: tensor<?xi32, #SparseVector>)
outs(%xin: tensor<?xi32, #SparseVector>) {
^bb0(%a: i32, %x: i32) :
%result = math.absi %a : i32
linalg.yield %result : i32
} -> tensor<?xi32, #SparseVector>
return %0 : tensor<?xi32, #SparseVector>
}
// Driver method to call and verify sign kernel.
func.func @entry() {
%c0 = arith.constant 0 : index
%df = arith.constant 99.99 : f64
%di = arith.constant 9999 : i32
%pnan = arith.constant 0x7FF0000001000000 : f64
%nnan = arith.constant 0xFFF0000001000000 : f64
%pinf = arith.constant 0x7FF0000000000000 : f64
%ninf = arith.constant 0xFFF0000000000000 : f64
// Setup sparse vectors.
%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>
%v2 = arith.constant sparse<
[ [0], [3], [5], [11], [13], [17], [18], [21], [31] ],
[ -2147483648, -2147483647, -1000, -1, 0,
1, 1000, 2147483646, 2147483647
]
> : tensor<32xi32>
%sv1 = sparse_tensor.convert %v1
: tensor<32xf64> to tensor<?xf64, #SparseVector>
%sv2 = sparse_tensor.convert %v2
: tensor<32xi32> to tensor<?xi32, #SparseVector>
// Call abs kernels.
%0 = call @sparse_absf(%sv1) : (tensor<?xf64, #SparseVector>)
-> tensor<?xf64, #SparseVector>
%1 = call @sparse_absi(%sv2) : (tensor<?xi32, #SparseVector>)
-> tensor<?xi32, #SparseVector>
//
// Verify the results.
//
// CHECK: 12
// CHECK-NEXT: ( 1.5, 1.5, 10.2, 11.3, 1, 1, nan, nan, inf, inf, 0, 0 )
// CHECK-NEXT: 9
// CHECK-NEXT: ( -2147483648, 2147483647, 1000, 1, 0, 1, 1000, 2147483646, 2147483647 )
//
%x = sparse_tensor.values %0 : tensor<?xf64, #SparseVector> to memref<?xf64>
%y = sparse_tensor.values %1 : tensor<?xi32, #SparseVector> to memref<?xi32>
%a = vector.transfer_read %x[%c0], %df: memref<?xf64>, vector<12xf64>
%b = vector.transfer_read %y[%c0], %di: memref<?xi32>, vector<9xi32>
%na = sparse_tensor.number_of_entries %0 : tensor<?xf64, #SparseVector>
%nb = sparse_tensor.number_of_entries %1 : tensor<?xi32, #SparseVector>
vector.print %na : index
vector.print %a : vector<12xf64>
vector.print %nb : index
vector.print %b : vector<9xi32>
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %sv2 : tensor<?xi32, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xi32, #SparseVector>
return
}
}
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