6232a8f3d6
Now that we have an AllocTensorOp (previously InitTensorOp) in the bufferization dialect, the InitOp in the sparse dialect is no longer needed. Differential Revision: https://reviews.llvm.org/D126180
95 lines
3.2 KiB
MLIR
95 lines
3.2 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
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// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \
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// RUN: mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
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// RUN: FileCheck %s
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!Filename = !llvm.ptr<i8>
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#DenseMatrix = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "dense" ],
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dimOrdering = affine_map<(i,j) -> (i,j)>
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}>
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#SparseMatrix = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (i,j)>
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}>
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#trait_assign = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = A(i,j)"
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}
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//
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// Integration test that demonstrates assigning a sparse tensor
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// to an all-dense annotated "sparse" tensor, which effectively
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// result in inserting the nonzero elements into a linearized array.
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//
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// Note that there is a subtle difference between a non-annotated
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// tensor and an all-dense annotated tensor. Both tensors are assumed
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// dense, but the former remains an n-dimensional memref whereas the
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// latter is linearized into a one-dimensional memref that is further
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// lowered into a storage scheme that is backed by the runtime support
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// library.
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module {
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//
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// A kernel that assigns elements from A to X.
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//
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func.func @dense_output(%arga: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix> {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #SparseMatrix>
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%d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #SparseMatrix>
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%init = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DenseMatrix>
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%0 = linalg.generic #trait_assign
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ins(%arga: tensor<?x?xf64, #SparseMatrix>)
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outs(%init: tensor<?x?xf64, #DenseMatrix>) {
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^bb(%a: f64, %x: f64):
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linalg.yield %a : f64
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} -> tensor<?x?xf64, #DenseMatrix>
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return %0 : tensor<?x?xf64, #DenseMatrix>
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}
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func.func private @getTensorFilename(index) -> (!Filename)
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//
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// Main driver that reads matrix from file and calls the kernel.
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//
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func.func @entry() {
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%d0 = arith.constant 0.0 : f64
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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// Read the sparse matrix from file, construct sparse storage.
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%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
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%a = sparse_tensor.new %fileName
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: !Filename to tensor<?x?xf64, #SparseMatrix>
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// Call the kernel.
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%0 = call @dense_output(%a)
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: (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix>
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//
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// Print the linearized 5x5 result for verification.
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//
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// CHECK: ( 1, 0, 0, 1.4, 0, 0, 2, 0, 0, 2.5, 0, 0, 3, 0, 0, 4.1, 0, 0, 4, 0, 0, 5.2, 0, 0, 5 )
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//
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%m = sparse_tensor.values %0
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: tensor<?x?xf64, #DenseMatrix> to memref<?xf64>
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%v = vector.load %m[%c0] : memref<?xf64>, vector<25xf64>
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vector.print %v : vector<25xf64>
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// Release the resources.
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sparse_tensor.release %a : tensor<?x?xf64, #SparseMatrix>
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sparse_tensor.release %0 : tensor<?x?xf64, #DenseMatrix>
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return
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}
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}
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