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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.mtx" \
// 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" ],
pointerBitWidth = 32,
indexBitWidth = 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 @sampled_dense_dense(%args: tensor<?x?xf32, #SparseMatrix>,
%arga: tensor<?x?xf32>,
%argb: tensor<?x?xf32>,
%argx: tensor<?x?xf32> {linalg.inplaceable = true}) -> 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 = mulf %a, %b : f32
%1 = mulf %s, %0 : f32
%2 = addf %x, %1 : f32
linalg.yield %2 : f32
} -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func @entry() {
%d0 = constant 0.0 : f32
%c0 = constant 0 : index
%c1 = constant 1 : index
%c5 = constant 5 : index
%c10 = constant 10 : index
// Setup memory for the dense matrices and initialize.
%adata = memref.alloc(%c5, %c10) : memref<?x?xf32>
%bdata = memref.alloc(%c10, %c5) : memref<?x?xf32>
%xdata = memref.alloc(%c5, %c5) : memref<?x?xf32>
scf.for %i = %c0 to %c5 step %c1 {
scf.for %j = %c0 to %c5 step %c1 {
memref.store %d0, %xdata[%i, %j] : memref<?x?xf32>
}
%p = addi %i, %c1 : index
%q = index_cast %p : index to i32
%d = sitofp %q : i32 to f32
scf.for %j = %c0 to %c10 step %c1 {
memref.store %d, %adata[%i, %j] : memref<?x?xf32>
memref.store %d, %bdata[%j, %i] : memref<?x?xf32>
}
}
%a = memref.tensor_load %adata : memref<?x?xf32>
%b = memref.tensor_load %bdata : memref<?x?xf32>
%x = memref.tensor_load %xdata : memref<?x?xf32>
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%s = sparse_tensor.new %fileName : !llvm.ptr<i8> 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 )
//
%r = memref.buffer_cast %0 : memref<?x?xf32>
scf.for %i = %c0 to %c5 step %c1 {
%v = vector.transfer_read %r[%i, %c0], %d0: memref<?x?xf32>, vector<5xf32>
vector.print %v : vector<5xf32>
}
// Release the resources.
memref.dealloc %adata : memref<?x?xf32>
memref.dealloc %bdata : memref<?x?xf32>
memref.dealloc %xdata : memref<?x?xf32>
return
}
}
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