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// RUN: mlir-opt %s \
// RUN: --sparsification --sparse-tensor-conversion \
// RUN: --convert-vector-to-scf --convert-scf-to-std \
// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
// RUN: --std-bufferize --finalizing-bufferize \
// RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/mttkrp_b.tns" \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
!Filename = type !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "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 {
//
// Computes Matricized Tensor Times Khatri-Rao Product (MTTKRP) kernel. See
// http://tensor-compiler.org/docs/data_analytics/index.html.
//
func @kernel_mttkrp(%argb: tensor<?x?x?xf64, #SparseMatrix>,
%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, #SparseMatrix>, tensor<?x?xf64>, tensor<?x?xf64>)
outs(%arga: tensor<?x?xf64>) {
^bb(%b: f64, %c: f64, %d: f64, %a: f64):
%0 = mulf %b, %c : f64
%1 = mulf %d, %0 : f64
%2 = addf %a, %1 : f64
linalg.yield %2 : f64
} -> tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func @entry() {
%i0 = constant 0. : f64
%c0 = constant 0 : index
%c1 = constant 1 : index
%c2 = constant 2 : index
%c3 = constant 3 : index
%c4 = constant 4 : index
%c5 = constant 5 : index
%c256 = constant 256 : index
// Read the sparse B input from a file.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%b = sparse_tensor.new %fileName
: !llvm.ptr<i8> to tensor<?x?x?xf64, #SparseMatrix>
// Initialize dense C and D inputs and dense output A.
%cdata = memref.alloc(%c3, %c5) : memref<?x?xf64>
scf.for %i = %c0 to %c3 step %c1 {
scf.for %j = %c0 to %c5 step %c1 {
%k0 = muli %i, %c5 : index
%k1 = addi %k0, %j : index
%k2 = index_cast %k1 : index to i32
%k = sitofp %k2 : i32 to f64
memref.store %k, %cdata[%i, %j] : memref<?x?xf64>
}
}
%c = memref.tensor_load %cdata : memref<?x?xf64>
%ddata = memref.alloc(%c4, %c5) : memref<?x?xf64>
scf.for %i = %c0 to %c4 step %c1 {
scf.for %j = %c0 to %c5 step %c1 {
%k0 = muli %i, %c5 : index
%k1 = addi %k0, %j : index
%k2 = index_cast %k1 : index to i32
%k = sitofp %k2 : i32 to f64
memref.store %k, %ddata[%i, %j] : memref<?x?xf64>
}
}
%d = memref.tensor_load %ddata : memref<?x?xf64>
%adata = memref.alloc(%c2, %c5) : memref<?x?xf64>
scf.for %i = %c0 to %c2 step %c1 {
scf.for %j = %c0 to %c5 step %c1 {
memref.store %i0, %adata[%i, %j] : memref<?x?xf64>
}
}
%a = memref.tensor_load %adata : memref<?x?xf64>
// Call kernel.
%0 = call @kernel_mttkrp(%b, %c, %d, %a)
: (tensor<?x?x?xf64, #SparseMatrix>,
tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?xf64>) -> tensor<?x?xf64>
// Print the result for verification.
//
// CHECK: ( ( 16075, 21930, 28505, 35800, 43815 ),
// CHECK: ( 10000, 14225, 19180, 24865, 31280 ) )
//
%m = memref.buffer_cast %0 : memref<?x?xf64>
%v = vector.transfer_read %m[%c0, %c0], %i0
: memref<?x?xf64>, vector<2x5xf64>
vector.print %v : vector<2x5xf64>
// Release the resources.
memref.dealloc %adata : memref<?x?xf64>
memref.dealloc %cdata : memref<?x?xf64>
memref.dealloc %ddata : memref<?x?xf64>
return
}
}
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