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|
// DEFINE: %{option} = enable-runtime-library=false
// 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 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}
#Dense = #sparse_tensor.encoding<{
lvlTypes = ["dense", "dense"]
}>
#SortedCOO = #sparse_tensor.encoding<{
lvlTypes = [ "compressed-nu", "singleton" ]
}>
#CSR = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ]
}>
#DCSR = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ]
}>
#Row = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "dense" ]
}>
module {
func.func @dump_dense(%arg0: tensor<4x3xf64, #Dense>) {
%c0 = arith.constant 0 : index
%fu = arith.constant 99.0 : f64
%v = sparse_tensor.values %arg0 : tensor<4x3xf64, #Dense> to memref<?xf64>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<12xf64>
vector.print %vv : vector<12xf64>
return
}
func.func @dump_coo(%arg0: tensor<4x3xf64, #SortedCOO>) {
%c0 = arith.constant 0 : index
%cu = arith.constant -1 : index
%fu = arith.constant 99.0 : f64
%p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex>
%i0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
%i1 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
%v = sparse_tensor.values %arg0 : tensor<4x3xf64, #SortedCOO> to memref<?xf64>
%vp0 = vector.transfer_read %p0[%c0], %cu: memref<?xindex>, vector<2xindex>
vector.print %vp0 : vector<2xindex>
%vi0 = vector.transfer_read %i0[%c0], %cu: memref<?xindex, strided<[?], offset: ?>>, vector<4xindex>
vector.print %vi0 : vector<4xindex>
%vi1 = vector.transfer_read %i1[%c0], %cu: memref<?xindex, strided<[?], offset: ?>>, vector<4xindex>
vector.print %vi1 : vector<4xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<4xf64>
vector.print %vv : vector<4xf64>
return
}
func.func @dump_csr(%arg0: tensor<4x3xf64, #CSR>) {
%c0 = arith.constant 0 : index
%cu = arith.constant -1 : index
%fu = arith.constant 99.0 : f64
%p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<4x3xf64, #CSR> to memref<?xindex>
%i1 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<4x3xf64, #CSR> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<4x3xf64, #CSR> to memref<?xf64>
%vp1 = vector.transfer_read %p1[%c0], %cu: memref<?xindex>, vector<5xindex>
vector.print %vp1 : vector<5xindex>
%vi1 = vector.transfer_read %i1[%c0], %cu: memref<?xindex>, vector<4xindex>
vector.print %vi1 : vector<4xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<4xf64>
vector.print %vv : vector<4xf64>
return
}
func.func @dump_dcsr(%arg0: tensor<4x3xf64, #DCSR>) {
%c0 = arith.constant 0 : index
%cu = arith.constant -1 : index
%fu = arith.constant 99.0 : f64
%p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
%i0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
%p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
%i1 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<4x3xf64, #DCSR> to memref<?xf64>
%vp0 = vector.transfer_read %p0[%c0], %cu: memref<?xindex>, vector<2xindex>
vector.print %vp0 : vector<2xindex>
%vi0 = vector.transfer_read %i0[%c0], %cu: memref<?xindex>, vector<3xindex>
vector.print %vi0 : vector<3xindex>
%vp1 = vector.transfer_read %p1[%c0], %cu: memref<?xindex>, vector<4xindex>
vector.print %vp1 : vector<4xindex>
%vi1 = vector.transfer_read %i1[%c0], %cu: memref<?xindex>, vector<4xindex>
vector.print %vi1 : vector<4xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<4xf64>
vector.print %vv : vector<4xf64>
return
}
func.func @dump_row(%arg0: tensor<4x3xf64, #Row>) {
%c0 = arith.constant 0 : index
%cu = arith.constant -1 : index
%fu = arith.constant 99.0 : f64
%p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<4x3xf64, #Row> to memref<?xindex>
%i0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<4x3xf64, #Row> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<4x3xf64, #Row> to memref<?xf64>
%vp0 = vector.transfer_read %p0[%c0], %cu: memref<?xindex>, vector<2xindex>
vector.print %vp0 : vector<2xindex>
%vi0 = vector.transfer_read %i0[%c0], %cu: memref<?xindex>, vector<3xindex>
vector.print %vi0 : vector<3xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<9xf64>
vector.print %vv : vector<9xf64>
return
}
//
// Main driver. We test the contents of various sparse tensor
// schemes when they are still empty and after a few insertions.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
%f1 = arith.constant 1.0 : f64
%f2 = arith.constant 2.0 : f64
%f3 = arith.constant 3.0 : f64
%f4 = arith.constant 4.0 : f64
//
// Dense case.
//
// CHECK: ( 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 4 )
//
%densea = bufferization.alloc_tensor() : tensor<4x3xf64, #Dense>
%dense1 = sparse_tensor.insert %f1 into %densea[%c0, %c0] : tensor<4x3xf64, #Dense>
%dense2 = sparse_tensor.insert %f2 into %dense1[%c2, %c2] : tensor<4x3xf64, #Dense>
%dense3 = sparse_tensor.insert %f3 into %dense2[%c3, %c0] : tensor<4x3xf64, #Dense>
%dense4 = sparse_tensor.insert %f4 into %dense3[%c3, %c2] : tensor<4x3xf64, #Dense>
%densem = sparse_tensor.load %dense4 hasInserts : tensor<4x3xf64, #Dense>
call @dump_dense(%densem) : (tensor<4x3xf64, #Dense>) -> ()
//
// COO case.
//
// CHECK-NEXT: ( 0, 4 )
// CHECK-NEXT: ( 0, 2, 3, 3 )
// CHECK-NEXT: ( 0, 2, 0, 2 )
// CHECK-NEXT: ( 1, 2, 3, 4 )
//
%cooa = bufferization.alloc_tensor() : tensor<4x3xf64, #SortedCOO>
%coo1 = sparse_tensor.insert %f1 into %cooa[%c0, %c0] : tensor<4x3xf64, #SortedCOO>
%coo2 = sparse_tensor.insert %f2 into %coo1[%c2, %c2] : tensor<4x3xf64, #SortedCOO>
%coo3 = sparse_tensor.insert %f3 into %coo2[%c3, %c0] : tensor<4x3xf64, #SortedCOO>
%coo4 = sparse_tensor.insert %f4 into %coo3[%c3, %c2] : tensor<4x3xf64, #SortedCOO>
%coom = sparse_tensor.load %coo4 hasInserts : tensor<4x3xf64, #SortedCOO>
call @dump_coo(%coom) : (tensor<4x3xf64, #SortedCOO>) -> ()
//
// CSR case.
//
// CHECK-NEXT: ( 0, 1, 1, 2, 4 )
// CHECK-NEXT: ( 0, 2, 0, 2 )
// CHECK-NEXT: ( 1, 2, 3, 4 )
//
%csra = bufferization.alloc_tensor() : tensor<4x3xf64, #CSR>
%csr1 = sparse_tensor.insert %f1 into %csra[%c0, %c0] : tensor<4x3xf64, #CSR>
%csr2 = sparse_tensor.insert %f2 into %csr1[%c2, %c2] : tensor<4x3xf64, #CSR>
%csr3 = sparse_tensor.insert %f3 into %csr2[%c3, %c0] : tensor<4x3xf64, #CSR>
%csr4 = sparse_tensor.insert %f4 into %csr3[%c3, %c2] : tensor<4x3xf64, #CSR>
%csrm = sparse_tensor.load %csr4 hasInserts : tensor<4x3xf64, #CSR>
call @dump_csr(%csrm) : (tensor<4x3xf64, #CSR>) -> ()
//
// DCSR case.
//
// CHECK-NEXT: ( 0, 3 )
// CHECK-NEXT: ( 0, 2, 3 )
// CHECK-NEXT: ( 0, 1, 2, 4 )
// CHECK-NEXT: ( 0, 2, 0, 2 )
// CHECK-NEXT: ( 1, 2, 3, 4 )
//
%dcsra = bufferization.alloc_tensor() : tensor<4x3xf64, #DCSR>
%dcsr1 = sparse_tensor.insert %f1 into %dcsra[%c0, %c0] : tensor<4x3xf64, #DCSR>
%dcsr2 = sparse_tensor.insert %f2 into %dcsr1[%c2, %c2] : tensor<4x3xf64, #DCSR>
%dcsr3 = sparse_tensor.insert %f3 into %dcsr2[%c3, %c0] : tensor<4x3xf64, #DCSR>
%dcsr4 = sparse_tensor.insert %f4 into %dcsr3[%c3, %c2] : tensor<4x3xf64, #DCSR>
%dcsrm = sparse_tensor.load %dcsr4 hasInserts : tensor<4x3xf64, #DCSR>
call @dump_dcsr(%dcsrm) : (tensor<4x3xf64, #DCSR>) -> ()
//
// Row case.
//
// CHECK-NEXT: ( 0, 3 )
// CHECK-NEXT: ( 0, 2, 3 )
// CHECK-NEXT: ( 1, 0, 0, 0, 0, 2, 3, 0, 4 )
//
%rowa = bufferization.alloc_tensor() : tensor<4x3xf64, #Row>
%row1 = sparse_tensor.insert %f1 into %rowa[%c0, %c0] : tensor<4x3xf64, #Row>
%row2 = sparse_tensor.insert %f2 into %row1[%c2, %c2] : tensor<4x3xf64, #Row>
%row3 = sparse_tensor.insert %f3 into %row2[%c3, %c0] : tensor<4x3xf64, #Row>
%row4 = sparse_tensor.insert %f4 into %row3[%c3, %c2] : tensor<4x3xf64, #Row>
%rowm = sparse_tensor.load %row4 hasInserts : tensor<4x3xf64, #Row>
call @dump_row(%rowm) : (tensor<4x3xf64, #Row>) -> ()
//
// NOE sanity check.
//
// CHECK-NEXT: 12
// CHECK-NEXT: 4
// CHECK-NEXT: 4
// CHECK-NEXT: 4
// CHECK-NEXT: 9
//
%noe1 = sparse_tensor.number_of_entries %densem : tensor<4x3xf64, #Dense>
%noe2 = sparse_tensor.number_of_entries %coom : tensor<4x3xf64, #SortedCOO>
%noe3 = sparse_tensor.number_of_entries %csrm : tensor<4x3xf64, #CSR>
%noe4 = sparse_tensor.number_of_entries %dcsrm : tensor<4x3xf64, #DCSR>
%noe5 = sparse_tensor.number_of_entries %rowm : tensor<4x3xf64, #Row>
vector.print %noe1 : index
vector.print %noe2 : index
vector.print %noe3 : index
vector.print %noe4 : index
vector.print %noe5 : index
// Release resources.
bufferization.dealloc_tensor %densem : tensor<4x3xf64, #Dense>
bufferization.dealloc_tensor %coom : tensor<4x3xf64, #SortedCOO>
bufferization.dealloc_tensor %csrm : tensor<4x3xf64, #CSR>
bufferization.dealloc_tensor %dcsrm : tensor<4x3xf64, #DCSR>
bufferization.dealloc_tensor %rowm : tensor<4x3xf64, #Row>
return
}
}
|
