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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}
// Current fails for SVE, see https://github.com/llvm/llvm-project/issues/60626
// UNSUPPORTED: target=aarch64{{.*}}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 reassociate-fp-reductions=true enable-index-optimizations=true 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)> // 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>
}
// Dumps a sparse vector of type f64.
func.func @dump_vec_f64(%arg0: tensor<?xf64, #SparseVector>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant -1.0 : f64
%n = sparse_tensor.number_of_entries %arg0: tensor<?xf64, #SparseVector>
vector.print %n : index
%0 = sparse_tensor.values %arg0
: tensor<?xf64, #SparseVector> to memref<?xf64>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<9xf64>
vector.print %1 : vector<9xf64>
// Dump the dense vector to verify structure is correct.
%dv = sparse_tensor.convert %arg0
: tensor<?xf64, #SparseVector> to tensor<?xf64>
%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<32xf64>
vector.print %3 : vector<32xf64>
return
}
// Driver method to call and verify vector kernels.
func.func @entry() {
// 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: 9
// CHECK-NEXT: {{( -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: {{( -0.761[0-9]*, 0, 0, 0.761[0-9]*, 0, 0, 0, 0, 0, 0, 0, 0.96[0-9]*, 0, 0, 0, 0, 0, 0.99[0-9]*, 0, 0, 0.99[0-9]*, 0.99[0-9]*, 0, 0, 0, 0, 0, 0, 0.99[0-9]*, 0.99[0-9]*, 0, 1 )}}
//
call @dump_vec_f64(%0) : (tensor<?xf64, #SparseVector>) -> ()
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
return
}
}
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