dce7a7cf69
The changes in this p.r. mostly center around the tests that use the flag sparse_compiler (also: sparse-compiler).
174 lines
7.1 KiB
MLIR
174 lines
7.1 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 entry -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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// RUN: %{compile} | %{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 enable-buffer-initialization=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 vectorization.
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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=4 enable-buffer-initialization=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 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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#CSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense, d1 : compressed)}>
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#CSC = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d1 : dense, d0 : compressed)
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}>
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//
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// Traits for tensor operations.
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//
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#trait_vec_select = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // A
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affine_map<(i) -> (i)> // C (out)
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],
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iterator_types = ["parallel"]
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}
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#trait_mat_select = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A (in)
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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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}
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module {
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func.func @vecSelect(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
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%c0 = arith.constant 0 : index
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%cf1 = arith.constant 1.0 : f64
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%d0 = tensor.dim %arga, %c0 : tensor<?xf64, #SparseVector>
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%xv = tensor.empty(%d0): tensor<?xf64, #SparseVector>
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%0 = linalg.generic #trait_vec_select
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ins(%arga: tensor<?xf64, #SparseVector>)
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outs(%xv: tensor<?xf64, #SparseVector>) {
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^bb(%a: f64, %b: f64):
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%1 = sparse_tensor.select %a : f64 {
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^bb0(%x: f64):
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%keep = arith.cmpf "oge", %x, %cf1 : f64
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sparse_tensor.yield %keep : i1
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}
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linalg.yield %1 : f64
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} -> tensor<?xf64, #SparseVector>
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return %0 : tensor<?xf64, #SparseVector>
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}
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func.func @matUpperTriangle(%arga: tensor<?x?xf64, #CSR>) -> tensor<?x?xf64, #CSR> {
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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, #CSR>
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%d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #CSR>
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%xv = tensor.empty(%d0, %d1): tensor<?x?xf64, #CSR>
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%0 = linalg.generic #trait_mat_select
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ins(%arga: tensor<?x?xf64, #CSR>)
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outs(%xv: tensor<?x?xf64, #CSR>) {
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^bb(%a: f64, %b: f64):
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%row = linalg.index 0 : index
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%col = linalg.index 1 : index
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%1 = sparse_tensor.select %a : f64 {
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^bb0(%x: f64):
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%keep = arith.cmpi "ugt", %col, %row : index
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sparse_tensor.yield %keep : i1
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}
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linalg.yield %1 : f64
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} -> tensor<?x?xf64, #CSR>
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return %0 : tensor<?x?xf64, #CSR>
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}
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// Dumps a sparse vector of type f64.
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func.func @dump_vec(%arg0: tensor<?xf64, #SparseVector>) {
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// Dump the values array to verify only sparse contents are stored.
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%c0 = arith.constant 0 : index
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%d0 = arith.constant 0.0 : f64
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%0 = sparse_tensor.values %arg0 : tensor<?xf64, #SparseVector> to memref<?xf64>
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%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<8xf64>
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vector.print %1 : vector<8xf64>
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// Dump the dense vector to verify structure is correct.
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%dv = sparse_tensor.convert %arg0 : tensor<?xf64, #SparseVector> to tensor<?xf64>
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%2 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<16xf64>
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vector.print %2 : vector<16xf64>
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return
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}
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// Dump a sparse matrix.
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func.func @dump_mat(%arg0: tensor<?x?xf64, #CSR>) {
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// Dump the values array to verify only sparse contents are stored.
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%c0 = arith.constant 0 : index
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%d0 = arith.constant 0.0 : f64
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%0 = sparse_tensor.values %arg0 : tensor<?x?xf64, #CSR> to memref<?xf64>
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%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<16xf64>
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vector.print %1 : vector<16xf64>
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%dm = sparse_tensor.convert %arg0 : tensor<?x?xf64, #CSR> to tensor<?x?xf64>
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%2 = vector.transfer_read %dm[%c0, %c0], %d0: tensor<?x?xf64>, vector<5x5xf64>
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vector.print %2 : vector<5x5xf64>
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return
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}
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// Driver method to call and verify vector kernels.
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func.func @entry() {
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%c0 = arith.constant 0 : index
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// Setup sparse matrices.
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%v1 = arith.constant sparse<
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[ [1], [3], [5], [7], [9] ],
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[ 1.0, 2.0, -4.0, 0.0, 5.0 ]
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> : tensor<10xf64>
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%m1 = arith.constant sparse<
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[ [0, 3], [1, 4], [2, 1], [2, 3], [3, 3], [3, 4], [4, 2] ],
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[ 1., 2., 3., 4., 5., 6., 7.]
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> : tensor<5x5xf64>
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%sv1 = sparse_tensor.convert %v1 : tensor<10xf64> to tensor<?xf64, #SparseVector>
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%sm1 = sparse_tensor.convert %m1 : tensor<5x5xf64> to tensor<?x?xf64, #CSR>
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// Call sparse matrix kernels.
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%1 = call @vecSelect(%sv1) : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
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%2 = call @matUpperTriangle(%sm1) : (tensor<?x?xf64, #CSR>) -> tensor<?x?xf64, #CSR>
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//
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// Verify the results.
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//
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// CHECK: ( 1, 2, -4, 0, 5, 0, 0, 0 )
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// CHECK-NEXT: ( 0, 1, 0, 2, 0, -4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0 )
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// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
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// CHECK-NEXT: ( ( 0, 0, 0, 1, 0 ), ( 0, 0, 0, 0, 2 ), ( 0, 3, 0, 4, 0 ), ( 0, 0, 0, 5, 6 ), ( 0, 0, 7, 0, 0 ) )
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// CHECK-NEXT: ( 1, 2, 5, 0, 0, 0, 0, 0 )
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// CHECK-NEXT: ( 0, 1, 0, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0 )
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// CHECK-NEXT: ( 1, 2, 4, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
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// CHECK-NEXT: ( ( 0, 0, 0, 1, 0 ), ( 0, 0, 0, 0, 2 ), ( 0, 0, 0, 4, 0 ), ( 0, 0, 0, 0, 6 ), ( 0, 0, 0, 0, 0 ) )
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//
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call @dump_vec(%sv1) : (tensor<?xf64, #SparseVector>) -> ()
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call @dump_mat(%sm1) : (tensor<?x?xf64, #CSR>) -> ()
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call @dump_vec(%1) : (tensor<?xf64, #SparseVector>) -> ()
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call @dump_mat(%2) : (tensor<?x?xf64, #CSR>) -> ()
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// Release the resources.
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bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #CSR>
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bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %2 : tensor<?x?xf64, #CSR>
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return
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}
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}
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