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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.mtx" \
// DEFINE: 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} = TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// 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 | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
!Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
posWidth = 32,
crdWidth = 32
}>
#trait_sampled_dense_dense = {
indexing_maps = [
affine_map<(i,j,k) -> (i,j)>, // S
affine_map<(i,j,k) -> (i,k)>, // A
affine_map<(i,j,k) -> (k,j)>, // B
affine_map<(i,j,k) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel", "reduction"],
doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)"
}
//
// 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 computes a sampled matrix matrix multiplication.
//
func.func @sampled_dense_dense(%args: tensor<?x?xf32, #SparseMatrix>,
%arga: tensor<?x?xf32>,
%argb: tensor<?x?xf32>,
%argx: tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = linalg.generic #trait_sampled_dense_dense
ins(%args, %arga, %argb: tensor<?x?xf32, #SparseMatrix>, tensor<?x?xf32>, tensor<?x?xf32>)
outs(%argx: tensor<?x?xf32>) {
^bb(%s: f32, %a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32
%1 = arith.mulf %s, %0 : f32
%2 = arith.addf %x, %1 : f32
linalg.yield %2 : f32
} -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
func.func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func.func @entry() {
%d0 = arith.constant 0.0 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c5 = arith.constant 5 : index
%c10 = arith.constant 10 : index
// Initialize dense matrices.
%x = tensor.generate %c5, %c5 {
^bb0(%i : index, %j : index):
tensor.yield %d0 : f32
} : tensor<?x?xf32>
%a = tensor.generate %c5, %c10 {
^bb0(%i: index, %j: index):
%p = arith.addi %i, %c1 : index
%q = arith.index_cast %p : index to i32
%d = arith.sitofp %q : i32 to f32
tensor.yield %d : f32
} : tensor<?x?xf32>
%b = tensor.generate %c10, %c5 {
^bb0(%i: index, %j: index):
%p = arith.addi %j, %c1 : index
%q = arith.index_cast %p : index to i32
%d = arith.sitofp %q : i32 to f32
tensor.yield %d : f32
} : tensor<?x?xf32>
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%s = sparse_tensor.new %fileName : !Filename to tensor<?x?xf32, #SparseMatrix>
// Call the kernel.
%0 = call @sampled_dense_dense(%s, %a, %b, %x)
: (tensor<?x?xf32, #SparseMatrix>,
tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// Print the result for verification.
//
// CHECK: ( 10, 0, 0, 56, 0 )
// CHECK: ( 0, 80, 0, 0, 250 )
// CHECK: ( 0, 0, 270, 0, 0 )
// CHECK: ( 164, 0, 0, 640, 0 )
// CHECK: ( 0, 520, 0, 0, 1250 )
//
scf.for %i = %c0 to %c5 step %c1 {
%v = vector.transfer_read %0[%i, %c0], %d0: tensor<?x?xf32>, vector<5xf32>
vector.print %v : vector<5xf32>
}
// Release the resources.
bufferization.dealloc_tensor %s : tensor<?x?xf32, #SparseMatrix>
return
}
}
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