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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/test.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>
#SparseTensor = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed", "compressed",
"compressed", "compressed", "compressed", "compressed" ],
// Note that any dimOrdering permutation should give the same results
// since, even though it impacts the sparse storage scheme layout,
// it should not change the semantics.
dimOrdering = 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 @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 = addf %x, %a : f64
linalg.yield %0 : f64
} -> tensor<7x3xf64>
return %0 : tensor<7x3xf64>
}
func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads tensor from file and calls the sparse kernel.
//
func @entry() {
%d0 = constant 0.0 : f64
%c0 = constant 0 : index
%c1 = constant 1 : index
%c3 = constant 3 : index
%c7 = constant 7 : index
// Setup matrix memory that is initialized to zero.
%xdata = memref.alloc() : memref<7x3xf64>
scf.for %i = %c0 to %c7 step %c1 {
scf.for %j = %c0 to %c3 step %c1 {
memref.store %d0, %xdata[%i, %j] : memref<7x3xf64>
}
}
%x = memref.tensor_load %xdata : memref<7x3xf64>
// Read the sparse tensor from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%a = sparse_tensor.new %fileName : !llvm.ptr<i8> 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: ( 4.224, 6.21, 0 )
// CHECK: ( 0, 0, 15.455 )
// CHECK: ( 0, 0, 0 )
// CHECK: ( 0, 0, 0 )
// CHECK: ( 0, 0, 0 )
// CHECK: ( 7, 0, 0 )
//
%r = memref.buffer_cast %0 : memref<7x3xf64>
scf.for %i = %c0 to %c7 step %c1 {
%v = vector.transfer_read %r[%i, %c0], %d0: memref<7x3xf64>, vector<3xf64>
vector.print %v : vector<3xf64>
}
// Release the resources.
memref.dealloc %xdata : memref<7x3xf64>
return
}
}
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