9048ea28da
This reverts commit 5561e174117ff395d65b6978d04b62c1a1275138 The logic was moved from cmake into lit fixing the issue that lead to the revert and potentially others with multi-config cmake generators Differential Revision: https://reviews.llvm.org/D143925
95 lines
3.2 KiB
MLIR
95 lines
3.2 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
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// DEFINE: %{run} = mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_c_runner_utils | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{option} = enable-runtime-library=false
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// RUN: %{compile} | %{run}
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// Do the same run, but now with direct IR generation and, if available, VLA
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// vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
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// REDEFINE: %{run} = %lli \
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// REDEFINE: --entry-function=entry_lli \
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// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
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// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
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// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
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// REDEFINE: FileCheck %s
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// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
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#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "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 @entry() {
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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: ( 2, 2, 2, 4, 6, 8, 2, 10, 2, 2, 12, 2, 14, 2, 2, 16 )
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//
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%m = sparse_tensor.values %2 : tensor<8x8xf32, #CSR> to memref<?xf32>
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%v = vector.transfer_read %m[%c0], %f0: memref<?xf32>, vector<16xf32>
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vector.print %v : vector<16xf32>
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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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