//--------------------------------------------------------------------------------------------------
// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
//
// Set-up that's shared across all tests in this directory. In principle, this
// config could be moved to lit.local.cfg. However, there are downstream users that
// do not use these LIT config files. Hence why this is kept inline.
//
// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true
// RUN: %{compile} | %{run} | FileCheck %s
//
#AllDense = #sparse_tensor.encoding<{
map = (i, j) -> (
i : dense,
j : dense
)
}>
#AllDenseT = #sparse_tensor.encoding<{
map = (i, j) -> (
j : dense,
i : dense
)
}>
#CSR = #sparse_tensor.encoding<{
map = (i, j) -> (
i : dense,
j : compressed
)
}>
#DCSR = #sparse_tensor.encoding<{
map = (i, j) -> (
i : compressed,
j : compressed
)
}>
#CSC = #sparse_tensor.encoding<{
map = (i, j) -> (
j : dense,
i : compressed
)
}>
#DCSC = #sparse_tensor.encoding<{
map = (i, j) -> (
j : compressed,
i : compressed
)
}>
#BSR = #sparse_tensor.encoding<{
map = (i, j) -> (
i floordiv 2 : compressed,
j floordiv 4 : compressed,
i mod 2 : dense,
j mod 4 : dense
)
}>
#BSRC = #sparse_tensor.encoding<{
map = (i, j) -> (
i floordiv 2 : compressed,
j floordiv 4 : compressed,
j mod 4 : dense,
i mod 2 : dense
)
}>
#BSC = #sparse_tensor.encoding<{
map = (i, j) -> (
j floordiv 4 : compressed,
i floordiv 2 : compressed,
i mod 2 : dense,
j mod 4 : dense
)
}>
#BSCC = #sparse_tensor.encoding<{
map = (i, j) -> (
j floordiv 4 : compressed,
i floordiv 2 : compressed,
j mod 4 : dense,
i mod 2 : dense
)
}>
#BSR0 = #sparse_tensor.encoding<{
map = (i, j) -> (
i floordiv 2 : dense,
j floordiv 4 : compressed,
i mod 2 : dense,
j mod 4 : dense
)
}>
#BSC0 = #sparse_tensor.encoding<{
map = (i, j) -> (
j floordiv 4 : dense,
i floordiv 2 : compressed,
i mod 2 : dense,
j mod 4 : dense
)
}>
#COOAoS = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#COOSoA = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))
}>
module {
//
// Main driver that tests sparse tensor storage.
//
func.func @main() {
%x = arith.constant dense <[
[ 1, 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, 3, 4, 0, 5, 0, 0 ] ]> : tensor<4x8xi32>
%XO = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #AllDense>
%XT = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #AllDenseT>
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 32
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 4, 8 )
// CHECK-NEXT: values : ( 1, 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, 3, 4, 0, 5, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %XO : tensor<4x8xi32, #AllDense>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 32
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 8, 4 )
// CHECK-NEXT: values : ( 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %XT : tensor<4x8xi32, #AllDenseT>
%a = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #CSR>
%b = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #DCSR>
%c = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #CSC>
%d = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #DCSC>
%e = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #BSR>
%f = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #BSRC>
%g = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #BSC>
%h = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #BSCC>
%i = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #BSR0>
%j = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #BSC0>
%AoS = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #COOAoS>
%SoA = sparse_tensor.convert %x : tensor<4x8xi32> to tensor<4x8xi32, #COOSoA>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 4, 8 )
// CHECK-NEXT: pos[1] : ( 0, 2, 2, 2, 5 )
// CHECK-NEXT: crd[1] : ( 0, 2, 2, 3, 5 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: ----
sparse_tensor.print %a : tensor<4x8xi32, #CSR>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 4, 8 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 3 )
// CHECK-NEXT: pos[1] : ( 0, 2, 5 )
// CHECK-NEXT: crd[1] : ( 0, 2, 2, 3, 5 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: ----
sparse_tensor.print %b : tensor<4x8xi32, #DCSR>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 8, 4 )
// CHECK-NEXT: pos[1] : ( 0, 1, 1, 3, 4, 4, 5, 5, 5 )
// CHECK-NEXT: crd[1] : ( 0, 0, 3, 3, 3 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: ----
sparse_tensor.print %c : tensor<4x8xi32, #CSC>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 8, 4 )
// CHECK-NEXT: pos[0] : ( 0, 4 )
// CHECK-NEXT: crd[0] : ( 0, 2, 3, 5 )
// CHECK-NEXT: pos[1] : ( 0, 1, 3, 4, 5 )
// CHECK-NEXT: crd[1] : ( 0, 0, 3, 3, 3 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: ----
sparse_tensor.print %d : tensor<4x8xi32, #DCSC>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 2, 2, 2, 4 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 1 )
// CHECK-NEXT: pos[1] : ( 0, 1, 3 )
// CHECK-NEXT: crd[1] : ( 0, 0, 1 )
// CHECK-NEXT: values : ( 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %e : tensor<4x8xi32, #BSR>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 2, 2, 4, 2 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 1 )
// CHECK-NEXT: pos[1] : ( 0, 1, 3 )
// CHECK-NEXT: crd[1] : ( 0, 0, 1 )
// CHECK-NEXT: values : ( 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0, 0, 5, 0, 0, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %f : tensor<4x8xi32, #BSRC>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 2, 2, 2, 4 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 1 )
// CHECK-NEXT: pos[1] : ( 0, 2, 3 )
// CHECK-NEXT: crd[1] : ( 0, 1, 1 )
// CHECK-NEXT: values : ( 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %g : tensor<4x8xi32, #BSC>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 2, 2, 4, 2 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 1 )
// CHECK-NEXT: pos[1] : ( 0, 2, 3 )
// CHECK-NEXT: crd[1] : ( 0, 1, 1 )
// CHECK-NEXT: values : ( 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0, 0, 5, 0, 0, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %h : tensor<4x8xi32, #BSCC>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 2, 2, 2, 4 )
// CHECK-NEXT: pos[1] : ( 0, 1, 3 )
// CHECK-NEXT: crd[1] : ( 0, 0, 1 )
// CHECK-NEXT: values : ( 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %i : tensor<4x8xi32, #BSR0>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 2, 2, 2, 4 )
// CHECK-NEXT: pos[1] : ( 0, 2, 3 )
// CHECK-NEXT: crd[1] : ( 0, 1, 1 )
// CHECK-NEXT: values : ( 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 0 )
// CHECK-NEXT: ----
sparse_tensor.print %j : tensor<4x8xi32, #BSC0>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 4, 8 )
// CHECK-NEXT: pos[0] : ( 0, 5 )
// CHECK-NEXT: crd[0] : ( 0, 0, 0, 2, 3, 2, 3, 3, 3, 5 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: ----
sparse_tensor.print %AoS : tensor<4x8xi32, #COOAoS>
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: dim = ( 4, 8 )
// CHECK-NEXT: lvl = ( 4, 8 )
// CHECK-NEXT: pos[0] : ( 0, 5 )
// CHECK-NEXT: crd[0] : ( 0, 0, 3, 3, 3 )
// CHECK-NEXT: crd[1] : ( 0, 2, 2, 3, 5 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
// CHECK-NEXT: ----
sparse_tensor.print %SoA : tensor<4x8xi32, #COOSoA>
// Release the resources.
bufferization.dealloc_tensor %XO : tensor<4x8xi32, #AllDense>
bufferization.dealloc_tensor %XT : tensor<4x8xi32, #AllDenseT>
bufferization.dealloc_tensor %a : tensor<4x8xi32, #CSR>
bufferization.dealloc_tensor %b : tensor<4x8xi32, #DCSR>
bufferization.dealloc_tensor %c : tensor<4x8xi32, #CSC>
bufferization.dealloc_tensor %d : tensor<4x8xi32, #DCSC>
bufferization.dealloc_tensor %e : tensor<4x8xi32, #BSR>
bufferization.dealloc_tensor %f : tensor<4x8xi32, #BSRC>
bufferization.dealloc_tensor %g : tensor<4x8xi32, #BSC>
bufferization.dealloc_tensor %h : tensor<4x8xi32, #BSCC>
bufferization.dealloc_tensor %i : tensor<4x8xi32, #BSR0>
bufferization.dealloc_tensor %j : tensor<4x8xi32, #BSC0>
bufferization.dealloc_tensor %AoS : tensor<4x8xi32, #COOAoS>
bufferization.dealloc_tensor %SoA : tensor<4x8xi32, #COOSoA>
return
}
}
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