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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,%mlir_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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 enable-buffer-initialization=true vl=4 reassociate-fp-reductions=true enable-index-optimizations=true 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 --dlopen=%mlir_runner_utils | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ]
}>
#DCSC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
//
// Integration test that tests conversions between sparse tensors,
// where the dynamic sizes of the shape of the enveloping tensor
// may change (the actual underlying sizes obviously never change).
//
module {
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Helper method to print values array. The transfer actually
// reads more than required to verify size of buffer as well.
//
func.func @dump(%arg0: memref<?xf64>) {
call @printMemref1dF64(%arg0) : (memref<?xf64>) -> ()
return
}
func.func @entry() {
%t1 = arith.constant sparse<
[ [0,0], [0,1], [0,63], [1,0], [1,1], [31,0], [31,63] ],
[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]> : tensor<32x64xf64>
%t2 = tensor.cast %t1 : tensor<32x64xf64> to tensor<?x?xf64>
// Four dense to sparse conversions.
%1 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSR>
%2 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSC>
%3 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSR>
%4 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSC>
// Two cross conversions.
%5 = sparse_tensor.convert %3 : tensor<?x?xf64, #DCSR> to tensor<?x?xf64, #DCSC>
%6 = sparse_tensor.convert %4 : tensor<?x?xf64, #DCSC> to tensor<?x?xf64, #DCSR>
//
// Check number_of_entries.
//
// CHECK-COUNT-6: 7
%n1 = sparse_tensor.number_of_entries %1 : tensor<?x?xf64, #DCSR>
%n2 = sparse_tensor.number_of_entries %2 : tensor<?x?xf64, #DCSC>
%n3 = sparse_tensor.number_of_entries %3 : tensor<?x?xf64, #DCSR>
%n4 = sparse_tensor.number_of_entries %4 : tensor<?x?xf64, #DCSC>
%n5 = sparse_tensor.number_of_entries %5 : tensor<?x?xf64, #DCSC>
%n6 = sparse_tensor.number_of_entries %6 : tensor<?x?xf64, #DCSR>
vector.print %n1 : index
vector.print %n2 : index
vector.print %n3 : index
vector.print %n4 : index
vector.print %n5 : index
vector.print %n6 : index
//
// All proper row-/column-wise?
//
// CHECK: [1, 2, 3, 4, 5, 6, 7
// CHECK: [1, 4, 6, 2, 5, 3, 7
// CHECK: [1, 2, 3, 4, 5, 6, 7
// CHECK: [1, 4, 6, 2, 5, 3, 7
// CHECK: [1, 4, 6, 2, 5, 3, 7
// CHECK: [1, 2, 3, 4, 5, 6, 7
//
%m1 = sparse_tensor.values %1 : tensor<?x?xf64, #DCSR> to memref<?xf64>
%m2 = sparse_tensor.values %2 : tensor<?x?xf64, #DCSC> to memref<?xf64>
%m3 = sparse_tensor.values %3 : tensor<?x?xf64, #DCSR> to memref<?xf64>
%m4 = sparse_tensor.values %4 : tensor<?x?xf64, #DCSC> to memref<?xf64>
%m5 = sparse_tensor.values %5 : tensor<?x?xf64, #DCSC> to memref<?xf64>
%m6 = sparse_tensor.values %6 : tensor<?x?xf64, #DCSR> to memref<?xf64>
call @dump(%m1) : (memref<?xf64>) -> ()
call @dump(%m2) : (memref<?xf64>) -> ()
call @dump(%m3) : (memref<?xf64>) -> ()
call @dump(%m4) : (memref<?xf64>) -> ()
call @dump(%m5) : (memref<?xf64>) -> ()
call @dump(%m6) : (memref<?xf64>) -> ()
// Release the resources.
bufferization.dealloc_tensor %1 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %2 : tensor<?x?xf64, #DCSC>
bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSC>
bufferization.dealloc_tensor %5 : tensor<?x?xf64, #DCSC>
bufferization.dealloc_tensor %6 : tensor<?x?xf64, #DCSR>
return
}
}
|
