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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}
#TensorCSR = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "dense", "compressed" ]
}>
#TensorRow = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed", "dense" ]
}>
#CCoo = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed-nu", "singleton" ]
}>
#DCoo = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed-nu", "singleton" ]
}>
module {
func.func @dump(%arg0: tensor<5x4x3xf64, #TensorCSR>) {
%c0 = arith.constant 0 : index
%fu = arith.constant 99.0 : f64
%p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
%i0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
%p2 = sparse_tensor.positions %arg0 { level = 2 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
%i2 = sparse_tensor.coordinates %arg0 { level = 2 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #TensorCSR> to memref<?xf64>
%vp0 = vector.transfer_read %p0[%c0], %c0: memref<?xindex>, vector<2xindex>
vector.print %vp0 : vector<2xindex>
%vi0 = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<2xindex>
vector.print %vi0 : vector<2xindex>
%vp2 = vector.transfer_read %p2[%c0], %c0: memref<?xindex>, vector<9xindex>
vector.print %vp2 : vector<9xindex>
%vi2 = vector.transfer_read %i2[%c0], %c0: memref<?xindex>, vector<5xindex>
vector.print %vi2 : vector<5xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<5xf64>
vector.print %vv : vector<5xf64>
return
}
func.func @dump_row(%arg0: tensor<5x4x3xf64, #TensorRow>) {
%c0 = arith.constant 0 : index
%fu = arith.constant 99.0 : f64
%p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
%i0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
%p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
%i1 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #TensorRow> to memref<?xf64>
%vp0 = vector.transfer_read %p0[%c0], %c0: memref<?xindex>, vector<2xindex>
vector.print %vp0 : vector<2xindex>
%vi0 = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<2xindex>
vector.print %vi0 : vector<2xindex>
%vp1 = vector.transfer_read %p1[%c0], %c0: memref<?xindex>, vector<3xindex>
vector.print %vp1 : vector<3xindex>
%vi1 = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<4xindex>
vector.print %vi1 : vector<4xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<12xf64>
vector.print %vv : vector<12xf64>
return
}
func.func @dump_ccoo(%arg0: tensor<5x4x3xf64, #CCoo>) {
%c0 = arith.constant 0 : index
%fu = arith.constant 99.0 : f64
%p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
%i0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
%p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
%i1 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
%i2 = sparse_tensor.coordinates %arg0 { level = 2 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #CCoo> to memref<?xf64>
%vp0 = vector.transfer_read %p0[%c0], %c0: memref<?xindex>, vector<2xindex>
vector.print %vp0 : vector<2xindex>
%vi0 = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<2xindex>
vector.print %vi0 : vector<2xindex>
%vp1 = vector.transfer_read %p1[%c0], %c0: memref<?xindex>, vector<3xindex>
vector.print %vp1 : vector<3xindex>
%vi1 = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<5xindex>
vector.print %vi1 : vector<5xindex>
%vi2 = vector.transfer_read %i2[%c0], %c0: memref<?xindex>, vector<5xindex>
vector.print %vi2 : vector<5xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<5xf64>
vector.print %vv : vector<5xf64>
return
}
func.func @dump_dcoo(%arg0: tensor<5x4x3xf64, #DCoo>) {
%c0 = arith.constant 0 : index
%fu = arith.constant 99.0 : f64
%p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
%i1 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
%i2 = sparse_tensor.coordinates %arg0 { level = 2 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
%v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #DCoo> to memref<?xf64>
%vp1 = vector.transfer_read %p1[%c0], %c0: memref<?xindex>, vector<6xindex>
vector.print %vp1 : vector<6xindex>
%vi1 = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<5xindex>
vector.print %vi1 : vector<5xindex>
%vi2 = vector.transfer_read %i2[%c0], %c0: memref<?xindex>, vector<5xindex>
vector.print %vi2 : vector<5xindex>
%vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<5xf64>
vector.print %vv : vector<5xf64>
return
}
//
// Main driver.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
%c4 = arith.constant 4 : index
%f1 = arith.constant 1.1 : f64
%f2 = arith.constant 2.2 : f64
%f3 = arith.constant 3.3 : f64
%f4 = arith.constant 4.4 : f64
%f5 = arith.constant 5.5 : f64
//
// CHECK: ( 0, 2 )
// CHECK-NEXT: ( 3, 4 )
// CHECK-NEXT: ( 0, 2, 2, 2, 3, 3, 3, 4, 5 )
// CHECK-NEXT: ( 1, 2, 1, 2, 2 )
// CHECK-NEXT: ( 1.1, 2.2, 3.3, 4.4, 5.5 )
//
%tensora = bufferization.alloc_tensor() : tensor<5x4x3xf64, #TensorCSR>
%tensor1 = sparse_tensor.insert %f1 into %tensora[%c3, %c0, %c1] : tensor<5x4x3xf64, #TensorCSR>
%tensor2 = sparse_tensor.insert %f2 into %tensor1[%c3, %c0, %c2] : tensor<5x4x3xf64, #TensorCSR>
%tensor3 = sparse_tensor.insert %f3 into %tensor2[%c3, %c3, %c1] : tensor<5x4x3xf64, #TensorCSR>
%tensor4 = sparse_tensor.insert %f4 into %tensor3[%c4, %c2, %c2] : tensor<5x4x3xf64, #TensorCSR>
%tensor5 = sparse_tensor.insert %f5 into %tensor4[%c4, %c3, %c2] : tensor<5x4x3xf64, #TensorCSR>
%tensorm = sparse_tensor.load %tensor5 hasInserts : tensor<5x4x3xf64, #TensorCSR>
call @dump(%tensorm) : (tensor<5x4x3xf64, #TensorCSR>) -> ()
//
// CHECK-NEXT: ( 0, 2 )
// CHECK-NEXT: ( 3, 4 )
// CHECK-NEXT: ( 0, 2, 4 )
// CHECK-NEXT: ( 0, 3, 2, 3 )
// CHECK-NEXT: ( 0, 1.1, 2.2, 0, 3.3, 0, 0, 0, 4.4, 0, 0, 5.5 )
//
%rowa = bufferization.alloc_tensor() : tensor<5x4x3xf64, #TensorRow>
%row1 = sparse_tensor.insert %f1 into %rowa[%c3, %c0, %c1] : tensor<5x4x3xf64, #TensorRow>
%row2 = sparse_tensor.insert %f2 into %row1[%c3, %c0, %c2] : tensor<5x4x3xf64, #TensorRow>
%row3 = sparse_tensor.insert %f3 into %row2[%c3, %c3, %c1] : tensor<5x4x3xf64, #TensorRow>
%row4 = sparse_tensor.insert %f4 into %row3[%c4, %c2, %c2] : tensor<5x4x3xf64, #TensorRow>
%row5 = sparse_tensor.insert %f5 into %row4[%c4, %c3, %c2] : tensor<5x4x3xf64, #TensorRow>
%rowm = sparse_tensor.load %row5 hasInserts : tensor<5x4x3xf64, #TensorRow>
call @dump_row(%rowm) : (tensor<5x4x3xf64, #TensorRow>) -> ()
//
// CHECK: ( 0, 2 )
// CHECK-NEXT: ( 3, 4 )
// CHECK-NEXT: ( 0, 3, 5 )
// CHECK-NEXT: ( 0, 0, 3, 2, 3 )
// CHECK-NEXT: ( 1, 2, 1, 2, 2 )
// CHECK-NEXT: ( 1.1, 2.2, 3.3, 4.4, 5.5 )
%ccoo = bufferization.alloc_tensor() : tensor<5x4x3xf64, #CCoo>
%ccoo1 = sparse_tensor.insert %f1 into %ccoo[%c3, %c0, %c1] : tensor<5x4x3xf64, #CCoo>
%ccoo2 = sparse_tensor.insert %f2 into %ccoo1[%c3, %c0, %c2] : tensor<5x4x3xf64, #CCoo>
%ccoo3 = sparse_tensor.insert %f3 into %ccoo2[%c3, %c3, %c1] : tensor<5x4x3xf64, #CCoo>
%ccoo4 = sparse_tensor.insert %f4 into %ccoo3[%c4, %c2, %c2] : tensor<5x4x3xf64, #CCoo>
%ccoo5 = sparse_tensor.insert %f5 into %ccoo4[%c4, %c3, %c2] : tensor<5x4x3xf64, #CCoo>
%ccoom = sparse_tensor.load %ccoo5 hasInserts : tensor<5x4x3xf64, #CCoo>
call @dump_ccoo(%ccoom) : (tensor<5x4x3xf64, #CCoo>) -> ()
//
// CHECK-NEXT: ( 0, 0, 0, 0, 3, 5 )
// CHECK-NEXT: ( 0, 0, 3, 2, 3 )
// CHECK-NEXT: ( 1, 2, 1, 2, 2 )
// CHECK-NEXT: ( 1.1, 2.2, 3.3, 4.4, 5.5 )
%dcoo = bufferization.alloc_tensor() : tensor<5x4x3xf64, #DCoo>
%dcoo1 = sparse_tensor.insert %f1 into %dcoo[%c3, %c0, %c1] : tensor<5x4x3xf64, #DCoo>
%dcoo2 = sparse_tensor.insert %f2 into %dcoo1[%c3, %c0, %c2] : tensor<5x4x3xf64, #DCoo>
%dcoo3 = sparse_tensor.insert %f3 into %dcoo2[%c3, %c3, %c1] : tensor<5x4x3xf64, #DCoo>
%dcoo4 = sparse_tensor.insert %f4 into %dcoo3[%c4, %c2, %c2] : tensor<5x4x3xf64, #DCoo>
%dcoo5 = sparse_tensor.insert %f5 into %dcoo4[%c4, %c3, %c2] : tensor<5x4x3xf64, #DCoo>
%dcoom = sparse_tensor.load %dcoo5 hasInserts : tensor<5x4x3xf64, #DCoo>
call @dump_dcoo(%dcoom) : (tensor<5x4x3xf64, #DCoo>) -> ()
// NOE sanity check.
//
// CHECK-NEXT: 5
// CHECK-NEXT: 12
// CHECK-NEXT: 5
// CHECK-NEXT: 5
//
%noe1 = sparse_tensor.number_of_entries %tensorm : tensor<5x4x3xf64, #TensorCSR>
vector.print %noe1 : index
%noe2 = sparse_tensor.number_of_entries %rowm : tensor<5x4x3xf64, #TensorRow>
vector.print %noe2 : index
%noe3 = sparse_tensor.number_of_entries %ccoom : tensor<5x4x3xf64, #CCoo>
vector.print %noe3 : index
%noe4 = sparse_tensor.number_of_entries %dcoom : tensor<5x4x3xf64, #DCoo>
vector.print %noe4 : index
// Release resources.
bufferization.dealloc_tensor %tensorm : tensor<5x4x3xf64, #TensorCSR>
bufferization.dealloc_tensor %rowm : tensor<5x4x3xf64, #TensorRow>
bufferization.dealloc_tensor %ccoom : tensor<5x4x3xf64, #CCoo>
bufferization.dealloc_tensor %dcoom : tensor<5x4x3xf64, #DCoo>
return
}
}
|
