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
121 lines
3.2 KiB
MLIR
121 lines
3.2 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=false
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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 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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// TODO: Pack only support CodeGen Path
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#SortedCOO = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed-nu", "singleton" ]
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}>
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#SortedCOOI32 = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed-nu", "singleton" ],
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pointerBitWidth = 32,
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indexBitWidth = 32
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}>
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module {
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//
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// Main driver.
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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 : f64
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%i0 = arith.constant 0 : i32
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//
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// Initialize a 3-dim dense tensor.
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//
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%data = arith.constant dense<
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[ 1.0, 2.0, 3.0]
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> : tensor<3xf64>
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%index = arith.constant dense<
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[[ 1, 2],
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[ 5, 6],
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[ 7, 8]]
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> : tensor<3x2xindex>
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%index32 = arith.constant dense<
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[[ 1, 2],
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[ 5, 6],
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[ 7, 8]]
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> : tensor<3x2xi32>
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%s4 = sparse_tensor.pack %data, %index : tensor<3xf64>, tensor<3x2xindex>
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to tensor<10x10xf64, #SortedCOO>
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// CHECK:1
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// CHECK-NEXT:2
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// CHECK-NEXT:1
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//
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// CHECK-NEXT:5
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// CHECK-NEXT:6
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// CHECK-NEXT:2
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//
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// CHECK-NEXT:7
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// CHECK-NEXT:8
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// CHECK-NEXT:3
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sparse_tensor.foreach in %s4 : tensor<10x10xf64, #SortedCOO> do {
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^bb0(%1: index, %2: index, %v: f64) :
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vector.print %1: index
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vector.print %2: index
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vector.print %v: f64
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}
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%s5= sparse_tensor.pack %data, %index32 : tensor<3xf64>, tensor<3x2xi32>
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to tensor<10x10xf64, #SortedCOOI32>
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// CHECK-NEXT:1
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// CHECK-NEXT:2
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// CHECK-NEXT:1
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//
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// CHECK-NEXT:5
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// CHECK-NEXT:6
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// CHECK-NEXT:2
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//
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// CHECK-NEXT:7
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// CHECK-NEXT:8
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// CHECK-NEXT:3
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sparse_tensor.foreach in %s5 : tensor<10x10xf64, #SortedCOOI32> do {
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^bb0(%1: index, %2: index, %v: f64) :
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vector.print %1: index
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vector.print %2: index
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vector.print %v: f64
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}
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%d, %i, %n = sparse_tensor.unpack %s5 : tensor<10x10xf64, #SortedCOOI32>
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to tensor<3xf64>, tensor<3x2xi32>, i32
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// CHECK-NEXT: ( 1, 2, 3 )
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%vd = vector.transfer_read %d[%c0], %f0 : tensor<3xf64>, vector<3xf64>
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vector.print %vd : vector<3xf64>
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// CHECK-NEXT: ( ( 1, 2 ), ( 5, 6 ), ( 7, 8 ) )
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%vi = vector.transfer_read %i[%c0, %c0], %i0 : tensor<3x2xi32>, vector<3x2xi32>
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vector.print %vi : vector<3x2xi32>
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// CHECK-NEXT: 3
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vector.print %n : i32
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return
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}
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}
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