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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/test.tns" \
// DEFINE: mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils,%mlir_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} = TENSOR0="%mlir_src_dir/test/Integration/data/test.tns" \
// REDEFINE: %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 --dlopen=%mlir_runner_utils | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
!Filename = !llvm.ptr<i8>
#SparseTensor = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed", "compressed", "compressed",
"compressed", "compressed", "compressed", "compressed" ],
// Note that any dimToLvl permutation should give the same results
// since, even though it impacts the sparse storage scheme layout,
// it should not change the semantics.
dimToLvl = affine_map<(i,j,k,l,m,n,o,p) -> (p,o,j,k,i,l,m,n)>
}>
#trait_flatten = {
indexing_maps = [
affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A
affine_map<(i,j,k,l,m,n,o,p) -> (i,j)> // X (out)
],
iterator_types = [ "parallel", "parallel", "reduction", "reduction",
"reduction", "reduction", "reduction", "reduction" ],
doc = "X(i,j) += A(i,j,k,l,m,n,o,p)"
}
//
// Integration test that lowers a kernel annotated as sparse to
// actual sparse code, initializes a matching sparse storage scheme
// from file, and runs the resulting code with the JIT compiler.
//
module {
//
// A kernel that flattens a rank 8 tensor into a dense matrix.
//
func.func @kernel_flatten(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>,
%argx: tensor<7x3xf64>)
-> tensor<7x3xf64> {
%0 = linalg.generic #trait_flatten
ins(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>)
outs(%argx: tensor<7x3xf64>) {
^bb(%a: f64, %x: f64):
%0 = arith.addf %x, %a : f64
linalg.yield %0 : f64
} -> tensor<7x3xf64>
return %0 : tensor<7x3xf64>
}
func.func private @getTensorFilename(index) -> (!Filename)
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
//
// Main driver that reads tensor from file and calls the sparse kernel.
//
func.func @entry() {
%d0 = arith.constant 0.0 : f64
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c7 = arith.constant 7 : index
// Setup matrix memory that is initialized to zero.
%x = arith.constant dense<0.000000e+00> : tensor<7x3xf64>
// Read the sparse tensor from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%a = sparse_tensor.new %fileName : !Filename to tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>
// Call the kernel.
%0 = call @kernel_flatten(%a, %x)
: (tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>, tensor<7x3xf64>) -> tensor<7x3xf64>
// Print the result for verification.
//
// CHECK: {{\[}}[6.25, 0, 0],
// CHECK-NEXT: [4.224, 6.21, 0],
// CHECK-NEXT: [0, 0, 15.455],
// CHECK-NEXT: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0],
// CHECK-NEXT: [7, 0, 0]]
//
%1 = tensor.cast %0 : tensor<7x3xf64> to tensor<*xf64>
call @printMemrefF64(%1) : (tensor<*xf64>) -> ()
// Release the resources.
bufferization.dealloc_tensor %a : tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>
return
}
}
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