//--------------------------------------------------------------------------------------------------
// 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} =
//--------------------------------------------------------------------------------------------------
// REDEFINE: %{env} = TENSOR0=%mlir_src_dir/test/Integration/data/mttkrp_b.tns
// RUN: %{compile} | env %{env} %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
// RUN: %{compile} | env %{env} %{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} | env %{env} %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | env %{env} %{run_sve} | FileCheck %s %}
!Filename = !llvm.ptr
#SparseTensor = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed)
}>
#mttkrp = {
indexing_maps = [
affine_map<(i,j,k,l) -> (i,k,l)>, // B
affine_map<(i,j,k,l) -> (k,j)>, // C
affine_map<(i,j,k,l) -> (l,j)>, // D
affine_map<(i,j,k,l) -> (i,j)> // A (out)
],
iterator_types = ["parallel", "parallel", "reduction", "reduction"],
doc = "A(i,j) += B(i,k,l) * D(l,j) * C(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 {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
//
// Computes Matricized Tensor Times Khatri-Rao Product (MTTKRP) kernel. See
// http://tensor-compiler.org/docs/data_analytics/index.html.
//
func.func @kernel_mttkrp(%argb: tensor<?x?x?xf64, #SparseTensor>,
%argc: tensor<?x?xf64>,
%argd: tensor<?x?xf64>,
%arga: tensor<?x?xf64>)
-> tensor<?x?xf64> {
%0 = linalg.generic #mttkrp
ins(%argb, %argc, %argd:
tensor<?x?x?xf64, #SparseTensor>, tensor<?x?xf64>, tensor<?x?xf64>)
outs(%arga: tensor<?x?xf64>) {
^bb(%b: f64, %c: f64, %d: f64, %a: f64):
%0 = arith.mulf %b, %c : f64
%1 = arith.mulf %d, %0 : f64
%2 = arith.addf %a, %1 : f64
linalg.yield %2 : f64
} -> tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func.func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func.func @main() {
%f0 = arith.constant 0.0 : f64
%cst0 = arith.constant 0 : index
%cst1 = arith.constant 1 : index
%cst2 = arith.constant 2 : index
// Read the sparse input tensor B from a file.
%fileName = call @getTensorFilename(%cst0) : (index) -> (!Filename)
%b = sparse_tensor.new %fileName
: !Filename to tensor<?x?x?xf64, #SparseTensor>
// Get sizes from B, pick a fixed size for dim-2 of A.
%isz = tensor.dim %b, %cst0 : tensor<?x?x?xf64, #SparseTensor>
%jsz = arith.constant 5 : index
%ksz = tensor.dim %b, %cst1 : tensor<?x?x?xf64, #SparseTensor>
%lsz = tensor.dim %b, %cst2 : tensor<?x?x?xf64, #SparseTensor>
// Initialize dense input matrix C.
%c = tensor.generate %ksz, %jsz {
^bb0(%k : index, %j : index):
%k0 = arith.muli %k, %jsz : index
%k1 = arith.addi %k0, %j : index
%k2 = arith.index_cast %k1 : index to i32
%kf = arith.sitofp %k2 : i32 to f64
tensor.yield %kf : f64
} : tensor<?x?xf64>
// Initialize dense input matrix D.
%d = tensor.generate %lsz, %jsz {
^bb0(%l : index, %j : index):
%k0 = arith.muli %l, %jsz : index
%k1 = arith.addi %k0, %j : index
%k2 = arith.index_cast %k1 : index to i32
%kf = arith.sitofp %k2 : i32 to f64
tensor.yield %kf : f64
} : tensor<?x?xf64>
// Initialize dense output matrix A.
%a = tensor.generate %isz, %jsz {
^bb0(%i : index, %j: index):
tensor.yield %f0 : f64
} : tensor<?x?xf64>
// Call kernel.
%0 = call @kernel_mttkrp(%b, %c, %d, %a)
: (tensor<?x?x?xf64, #SparseTensor>,
tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?xf64>) -> tensor<?x?xf64>
// Print the result for verification.
//
// CHECK: {{\[}}[16075, 21930, 28505, 35800, 43815],
// CHECK-NEXT: [10000, 14225, 19180, 24865, 31280]]
//
%u = tensor.cast %0: tensor<?x?xf64> to tensor<*xf64>
call @printMemrefF64(%u) : (tensor<*xf64>) -> ()
// Release the resources.
bufferization.dealloc_tensor %b : tensor<?x?x?xf64, #SparseTensor>
bufferization.dealloc_tensor %c : tensor<?x?xf64>
bufferization.dealloc_tensor %d : tensor<?x?xf64>
bufferization.dealloc_tensor %a : tensor<?x?xf64>
return
}
}
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