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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#ST1 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}>
#ST2 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "dense"]}>
//
// Trait for 3-d tensor operation.
//
#trait_scale = {
indexing_maps = [
affine_map<(i,j,k) -> (i,j,k)>, // A (in)
affine_map<(i,j,k) -> (i,j,k)> // X (out)
],
iterator_types = ["parallel", "parallel", "parallel"],
doc = "X(i,j,k) = A(i,j,k) * 2.0"
}
module {
// Scales a sparse tensor into a new sparse tensor.
func.func @tensor_scale(%arga: tensor<?x?x?xf64, #ST1>) -> tensor<?x?x?xf64, #ST2> {
%s = arith.constant 2.0 : f64
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%d0 = tensor.dim %arga, %c0 : tensor<?x?x?xf64, #ST1>
%d1 = tensor.dim %arga, %c1 : tensor<?x?x?xf64, #ST1>
%d2 = tensor.dim %arga, %c2 : tensor<?x?x?xf64, #ST1>
%xm = bufferization.alloc_tensor(%d0, %d1, %d2) : tensor<?x?x?xf64, #ST2>
%0 = linalg.generic #trait_scale
ins(%arga: tensor<?x?x?xf64, #ST1>)
outs(%xm: tensor<?x?x?xf64, #ST2>) {
^bb(%a: f64, %x: f64):
%1 = arith.mulf %a, %s : f64
linalg.yield %1 : f64
} -> tensor<?x?x?xf64, #ST2>
return %0 : tensor<?x?x?xf64, #ST2>
}
// Driver method to call and verify tensor kernel.
func.func @entry() {
%c0 = arith.constant 0 : index
%d1 = arith.constant -1.0 : f64
// Setup sparse tensor.
%t = arith.constant dense<
[ [ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0 ] ],
[ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] ],
[ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 3.0, 4.0, 0.0, 0.0, 0.0, 0.0, 5.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] ] ]> : tensor<3x4x8xf64>
%st = sparse_tensor.convert %t : tensor<3x4x8xf64> to tensor<?x?x?xf64, #ST1>
// Call sparse vector kernels.
%0 = call @tensor_scale(%st) : (tensor<?x?x?xf64, #ST1>) -> tensor<?x?x?xf64, #ST2>
// Sanity check on stored values.
//
// CHECK: 5
// CHECK-NEXT: ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: 24
// CHECK-NEXT: ( 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 8, 0, 0, 0, 0, 10 )
%m1 = sparse_tensor.values %st : tensor<?x?x?xf64, #ST1> to memref<?xf64>
%m2 = sparse_tensor.values %0 : tensor<?x?x?xf64, #ST2> to memref<?xf64>
%n1 = sparse_tensor.number_of_entries %st : tensor<?x?x?xf64, #ST1>
%n2 = sparse_tensor.number_of_entries %0 : tensor<?x?x?xf64, #ST2>
%v1 = vector.transfer_read %m1[%c0], %d1: memref<?xf64>, vector<5xf64>
%v2 = vector.transfer_read %m2[%c0], %d1: memref<?xf64>, vector<24xf64>
vector.print %n1 : index
vector.print %v1 : vector<5xf64>
vector.print %n2 : index
vector.print %v2 : vector<24xf64>
// Release the resources.
bufferization.dealloc_tensor %st : tensor<?x?x?xf64, #ST1>
bufferization.dealloc_tensor %0 : tensor<?x?x?xf64, #ST2>
return
}
}
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