//--------------------------------------------------------------------------------------------------
// 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 enable-buffer-initialization=true
// 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 %}
#SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : 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 = tensor.empty(%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 = tensor.empty(%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 @main() {
%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: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 12
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 12 )
// CHECK-NEXT: crd[0] : ( 0, 3, 5, 11, 13, 17, 18, 20, 21, 28, 29, 31 )
// CHECK-NEXT: values : ( 1.5, 1.5, 10.2, 11.3, 1, 1, nan, nan, inf, inf, 0, 0 )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 9
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 9 )
// CHECK-NEXT: crd[0] : ( 0, 3, 5, 11, 13, 17, 18, 21, 31 )
// CHECK-NEXT: values : ( -2147483648, 2147483647, 1000, 1, 0, 1, 1000, 2147483646, 2147483647 )
// CHECK-NEXT: ----
//
sparse_tensor.print %0 : tensor<?xf64, #SparseVector>
sparse_tensor.print %1 : tensor<?xi32, #SparseVector>
// 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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