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.
108 lines
4.0 KiB
MLIR
108 lines
4.0 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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// 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
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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
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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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#CSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>
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#trait_scale = {
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indexing_maps = [
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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) = X(i,j) * 2"
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}
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//
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// Integration test that lowers a kernel annotated as sparse to actual sparse
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// code, initializes a matching sparse storage scheme from a dense tensor,
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// and runs the resulting code with the JIT compiler.
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//
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module {
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//
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// A kernel that scales a sparse matrix A by a factor of 2.0.
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//
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func.func @sparse_scale(%argx: tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR> {
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%c = arith.constant 2.0 : f32
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%0 = linalg.generic #trait_scale
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outs(%argx: tensor<8x8xf32, #CSR>) {
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^bb(%x: f32):
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%1 = arith.mulf %x, %c : f32
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linalg.yield %1 : f32
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} -> tensor<8x8xf32, #CSR>
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return %0 : tensor<8x8xf32, #CSR>
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}
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//
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// Main driver that converts a dense tensor into a sparse tensor
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// and then calls the sparse scaling kernel with the sparse tensor
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// as input argument.
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//
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func.func @main() {
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%c0 = arith.constant 0 : index
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%f0 = arith.constant 0.0 : f32
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// Initialize a dense tensor.
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%0 = arith.constant dense<[
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[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
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[0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 1.0, 0.0, 0.0, 5.0, 0.0, 0.0, 0.0],
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[0.0, 1.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 7.0, 1.0],
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[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 8.0]
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]> : tensor<8x8xf32>
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// Convert dense tensor to sparse tensor and call sparse kernel.
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%1 = sparse_tensor.convert %0 : tensor<8x8xf32> to tensor<8x8xf32, #CSR>
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%2 = call @sparse_scale(%1)
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: (tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR>
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// Print the resulting compacted values for verification.
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 16
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// CHECK-NEXT: dim = ( 8, 8 )
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// CHECK-NEXT: lvl = ( 8, 8 )
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// CHECK-NEXT: pos[1] : ( 0, 3, 4, 5, 6, 8, 11, 14, 16 )
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// CHECK-NEXT: crd[1] : ( 0, 2, 7, 1, 2, 3, 1, 4, 1, 2, 5, 2, 6, 7, 2, 7 )
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// CHECK-NEXT: values : ( 2, 2, 2, 4, 6, 8, 2, 10, 2, 2, 12, 2, 14, 2, 2, 16 )
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %2 : tensor<8x8xf32, #CSR>
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// Release the resources.
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bufferization.dealloc_tensor %1 : tensor<8x8xf32, #CSR>
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return
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}
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}
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