92 lines
3.6 KiB
MLIR
92 lines
3.6 KiB
MLIR
//--------------------------------------------------------------------------------------------------
|
|
// 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_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}
|
|
//
|
|
// 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 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 VLA vectorization.
|
|
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
|
|
|
|
// Current fails for SVE, see https://github.com/llvm/llvm-project/issues/60626
|
|
// UNSUPPORTED: target=aarch64{{.*}}
|
|
|
|
#SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
|
|
|
|
#trait_op = {
|
|
indexing_maps = [
|
|
affine_map<(i) -> (i)> // X (out)
|
|
],
|
|
iterator_types = ["parallel"],
|
|
doc = "X(i) = OP X(i)"
|
|
}
|
|
|
|
module {
|
|
// Performs zero-preserving math to sparse vector.
|
|
func.func @sparse_tanh(%vec: tensor<?xf64, #SparseVector>)
|
|
-> tensor<?xf64, #SparseVector> {
|
|
%0 = linalg.generic #trait_op
|
|
outs(%vec: tensor<?xf64, #SparseVector>) {
|
|
^bb(%x: f64):
|
|
%1 = math.tanh %x : f64
|
|
linalg.yield %1 : f64
|
|
} -> tensor<?xf64, #SparseVector>
|
|
return %0 : tensor<?xf64, #SparseVector>
|
|
}
|
|
|
|
// Driver method to call and verify vector kernels.
|
|
func.func @main() {
|
|
// Setup sparse vector.
|
|
%v1 = arith.constant sparse<
|
|
[ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],
|
|
[ -1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0 ]
|
|
> : tensor<32xf64>
|
|
%sv1 = sparse_tensor.convert %v1
|
|
: tensor<32xf64> to tensor<?xf64, #SparseVector>
|
|
|
|
// Call sparse vector kernel.
|
|
%0 = call @sparse_tanh(%sv1) : (tensor<?xf64, #SparseVector>)
|
|
-> tensor<?xf64, #SparseVector>
|
|
|
|
//
|
|
// Verify the results (within some precision).
|
|
//
|
|
// CHECK: ---- 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, 11, 17, 20, 21, 28, 29, 31
|
|
// CHECK-NEXT: values : ({{ -0.761[0-9]*, 0.761[0-9]*, 0.96[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 1}}
|
|
// CHECK-NEXT: ----
|
|
//
|
|
sparse_tensor.print %0 : tensor<?xf64, #SparseVector>
|
|
|
|
// Release the resources.
|
|
bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
|
|
return
|
|
}
|
|
}
|