106 lines
3.6 KiB
MLIR
106 lines
3.6 KiB
MLIR
// 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 : !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: ( 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
|
|
}
|
|
}
|