123 lines
4.7 KiB
MLIR
123 lines
4.7 KiB
MLIR
//--------------------------------------------------------------------------------------------------
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// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
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//
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// Set-up that's shared across all tests in this directory. In principle, this
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// config could be moved to lit.local.cfg. However, there are downstream users that
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// do not use these LIT config files. Hence why this is kept inline.
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//
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// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
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// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
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// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
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// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
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// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
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// DEFINE: %{run_opts} = -e main -entry-point-result=void
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// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
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// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs}
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//
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// DEFINE: %{env} =
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//--------------------------------------------------------------------------------------------------
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// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx"
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// RUN: %{compile} | env %{env} %{run} | FileCheck %s
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
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// RUN: %{compile} | env %{env} %{run} | FileCheck %s
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//
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// Do the same run, but now with direct IR generation and vectorization.
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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
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// RUN: %{compile} | env %{env} %{run} | FileCheck %s
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//
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// Do the same run, but now with direct IR generation and VLA vectorization.
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// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | env %{env} %{run_sve} | FileCheck %s %}
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!Filename = !llvm.ptr
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#DenseMatrix = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : dense, d1 : dense)
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}>
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#SparseMatrix = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : dense, d1 : compressed),
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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) * 2"
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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 multiplied 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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%c2 = arith.constant 2.0 : f64
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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 = tensor.empty(%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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%0 = arith.mulf %a, %c2 : f64
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linalg.yield %0 : 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 @main() {
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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: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 25
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// CHECK-NEXT: dim = ( 5, 5 )
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// CHECK-NEXT: lvl = ( 5, 5 )
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// CHECK-NEXT: values : ( 2, 0, 0, 2.8, 0, 0, 4, 0, 0, 5, 0, 0, 6, 0, 0, 8.2, 0, 0, 8, 0, 0, 10.4, 0, 0, 10,
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %0 : tensor<?x?xf64, #DenseMatrix>
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// Release the resources.
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bufferization.dealloc_tensor %a : tensor<?x?xf64, #SparseMatrix>
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bufferization.dealloc_tensor %0 : tensor<?x?xf64, #DenseMatrix>
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return
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}
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}
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