eb177803bf
1. Remove the trailing comma for the last element of memref and add closing parenthesis. 2. Change integration tests to use the new format.
129 lines
5.6 KiB
MLIR
129 lines
5.6 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: %{sparsifier_opts} = enable-runtime-library=false
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// RUN: %{compile} | %{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} | %{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} | %{run_sve} | FileCheck %s %}
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#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
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#trait_op = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a (in)
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affine_map<(i) -> (i)>, // b (in)
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"],
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doc = "x(i) = a(i) OP b(i)"
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}
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module {
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func.func @cadd(%arga: tensor<?xcomplex<f64>, #SparseVector>,
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%argb: tensor<?xcomplex<f64>, #SparseVector>)
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-> tensor<?xcomplex<f64>, #SparseVector> {
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%c = arith.constant 0 : index
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%d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
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%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
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%0 = linalg.generic #trait_op
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ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
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tensor<?xcomplex<f64>, #SparseVector>)
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outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
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^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
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%1 = complex.add %a, %b : complex<f64>
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linalg.yield %1 : complex<f64>
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} -> tensor<?xcomplex<f64>, #SparseVector>
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return %0 : tensor<?xcomplex<f64>, #SparseVector>
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}
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func.func @cmul(%arga: tensor<?xcomplex<f64>, #SparseVector>,
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%argb: tensor<?xcomplex<f64>, #SparseVector>)
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-> tensor<?xcomplex<f64>, #SparseVector> {
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%c = arith.constant 0 : index
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%d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
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%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
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%0 = linalg.generic #trait_op
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ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
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tensor<?xcomplex<f64>, #SparseVector>)
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outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
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^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
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%1 = complex.mul %a, %b : complex<f64>
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linalg.yield %1 : complex<f64>
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} -> tensor<?xcomplex<f64>, #SparseVector>
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return %0 : tensor<?xcomplex<f64>, #SparseVector>
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}
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// Driver method to call and verify complex kernels.
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func.func @main() {
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// Setup sparse vectors.
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%v1 = arith.constant sparse<
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[ [0], [28], [31] ],
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[ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f64>>
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%v2 = arith.constant sparse<
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[ [1], [28], [31] ],
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[ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f64>>
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%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
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%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
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// Call sparse vector kernels.
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%0 = call @cadd(%sv1, %sv2)
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: (tensor<?xcomplex<f64>, #SparseVector>,
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tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
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%1 = call @cmul(%sv1, %sv2)
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: (tensor<?xcomplex<f64>, #SparseVector>,
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tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
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//
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// Verify the results.
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 4
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// CHECK-NEXT: dim = ( 32 )
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// CHECK-NEXT: lvl = ( 32 )
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// CHECK-NEXT: pos[0] : ( 0, 4 )
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// CHECK-NEXT: crd[0] : ( 0, 1, 28, 31 )
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// CHECK-NEXT: values : ( ( 511.13, 2 ), ( 1, 0 ), ( 5, 4 ), ( 8, 6 ) )
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// CHECK-NEXT: ----
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//
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// CHECK-NEXT: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 2
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// CHECK-NEXT: dim = ( 32 )
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// CHECK-NEXT: lvl = ( 32 )
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// CHECK-NEXT: pos[0] : ( 0, 2 )
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// CHECK-NEXT: crd[0] : ( 28, 31 )
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// CHECK-NEXT: values : ( ( 6, 8 ), ( 15, 18 ) )
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %0 : tensor<?xcomplex<f64>, #SparseVector>
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sparse_tensor.print %1 : tensor<?xcomplex<f64>, #SparseVector>
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// Release the resources.
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bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f64>, #SparseVector>
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bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f64>, #SparseVector>
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bufferization.dealloc_tensor %0 : tensor<?xcomplex<f64>, #SparseVector>
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bufferization.dealloc_tensor %1 : tensor<?xcomplex<f64>, #SparseVector>
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return
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}
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}
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