//--------------------------------------------------------------------------------------------------
// 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
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#SortedCOO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#SortedCOOSoA = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))
}>
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed)
}>
#trait = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)>, // B
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) = A(i,j) + B(i,j)"
}
module {
func.func @add_coo_csr(%arga: tensor<8x8xf32, #CSR>,
%argb: tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32> {
%empty = tensor.empty() : tensor<8x8xf32>
%zero = arith.constant 0.000000e+00 : f32
%init = linalg.fill
ins(%zero : f32)
outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
%0 = linalg.generic #trait
ins(%arga, %argb: tensor<8x8xf32, #CSR>,
tensor<8x8xf32, #SortedCOOSoA>)
outs(%init: tensor<8x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32
linalg.yield %0 : f32
} -> tensor<8x8xf32>
return %0 : tensor<8x8xf32>
}
func.func @add_coo_coo(%arga: tensor<8x8xf32, #SortedCOO>,
%argb: tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32> {
%empty = tensor.empty() : tensor<8x8xf32>
%zero = arith.constant 0.000000e+00 : f32
%init = linalg.fill
ins(%zero : f32)
outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
%0 = linalg.generic #trait
ins(%arga, %argb: tensor<8x8xf32, #SortedCOO>,
tensor<8x8xf32, #SortedCOOSoA>)
outs(%init: tensor<8x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32
linalg.yield %0 : f32
} -> tensor<8x8xf32>
return %0 : tensor<8x8xf32>
}
func.func @add_coo_coo_out_coo(%arga: tensor<8x8xf32, #SortedCOO>,
%argb: tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32, #SortedCOOSoA> {
%init = tensor.empty() : tensor<8x8xf32, #SortedCOOSoA>
%0 = linalg.generic #trait
ins(%arga, %argb: tensor<8x8xf32, #SortedCOO>,
tensor<8x8xf32, #SortedCOOSoA>)
outs(%init: tensor<8x8xf32, #SortedCOOSoA>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32
linalg.yield %0 : f32
} -> tensor<8x8xf32, #SortedCOOSoA>
return %0 : tensor<8x8xf32, #SortedCOOSoA>
}
func.func @add_coo_dense(%arga: tensor<8x8xf32>,
%argb: tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32> {
%empty = tensor.empty() : tensor<8x8xf32>
%zero = arith.constant 0.000000e+00 : f32
%init = linalg.fill
ins(%zero : f32)
outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
%0 = linalg.generic #trait
ins(%arga, %argb: tensor<8x8xf32>,
tensor<8x8xf32, #SortedCOOSoA>)
outs(%init: tensor<8x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32
linalg.yield %0 : f32
} -> tensor<8x8xf32>
return %0 : tensor<8x8xf32>
}
func.func @main() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c8 = arith.constant 8 : index
%A = arith.constant dense<
[ [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ],
[ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1 ],
[ 2.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2 ],
[ 3.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3 ],
[ 4.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 ],
[ 5.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5 ],
[ 6.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6 ],
[ 7.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7 ] ]
> : tensor<8x8xf32>
%B = arith.constant dense<
[ [ 7.8, 2.8, 3.8, 0.8, 3.8, 0.1, 7.8, 8.8 ],
[ 3.3, 2.3, 1.3, 4.3, 3.3, 6.3, 9.3, 8.3 ],
[ 6.6, 2.6, 3.6, 4.6, 3.6, 6.6, 7.6, 7.6 ],
[ 1.0, 3.0, 3.0, 4.0, 3.0, 6.0, 7.0, 8.0 ],
[ 0.1, 2.1, 3.1, 4.1, 3.1, 6.1, 7.1, 8.1 ],
[ 4.4, 2.4, 3.4, 4.4, 3.4, 6.4, 8.4, 8.4 ],
[ 5.5, 3.5, 1.5, 4.5, 3.5, 6.5, 7.5, 8.5 ],
[ 7.7, 2.7, 3.7, 0.7, 5.7, 3.7, 3.7, 0.7 ] ]
> : tensor<8x8xf32>
// Stress test with a "sparse" version of A and B.
%CSR_A = sparse_tensor.convert %A
: tensor<8x8xf32> to tensor<8x8xf32, #CSR>
%COO_A = sparse_tensor.convert %A
: tensor<8x8xf32> to tensor<8x8xf32, #SortedCOO>
%COO_B = sparse_tensor.convert %B
: tensor<8x8xf32> to tensor<8x8xf32, #SortedCOOSoA>
%C1 = call @add_coo_dense(%A, %COO_B) : (tensor<8x8xf32>,
tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32>
%C2 = call @add_coo_csr(%CSR_A, %COO_B) : (tensor<8x8xf32, #CSR>,
tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32>
%C3 = call @add_coo_coo(%COO_A, %COO_B) : (tensor<8x8xf32, #SortedCOO>,
tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32>
%COO_RET = call @add_coo_coo_out_coo(%COO_A, %COO_B) : (tensor<8x8xf32, #SortedCOO>,
tensor<8x8xf32, #SortedCOOSoA>)
-> tensor<8x8xf32, #SortedCOOSoA>
%C4 = sparse_tensor.convert %COO_RET : tensor<8x8xf32, #SortedCOOSoA> to tensor<8x8xf32>
//
// Verify computed matrix C.
//
// CHECK-COUNT-4: ( 8.8, 4.8, 6.8, 4.8, 8.8, 6.1, 14.8, 16.8 )
// CHECK-NEXT-COUNT-4: ( 4.4, 4.4, 4.4, 8.4, 8.4, 12.4, 16.4, 16.4 )
// CHECK-NEXT-COUNT-4: ( 8.8, 4.8, 6.8, 8.8, 8.8, 12.8, 14.8, 15.8 )
// CHECK-NEXT-COUNT-4: ( 4.3, 5.3, 6.3, 8.3, 8.3, 12.3, 14.3, 16.3 )
// CHECK-NEXT-COUNT-4: ( 4.5, 4.5, 6.5, 8.5, 8.5, 12.5, 14.5, 16.5 )
// CHECK-NEXT-COUNT-4: ( 9.9, 4.9, 6.9, 8.9, 8.9, 12.9, 15.9, 16.9 )
// CHECK-NEXT-COUNT-4: ( 12.1, 6.1, 5.1, 9.1, 9.1, 13.1, 15.1, 17.1 )
// CHECK-NEXT-COUNT-4: ( 15.4, 5.4, 7.4, 5.4, 11.4, 10.4, 11.4, 9.4 )
//
%f0 = arith.constant 0.0 : f32
scf.for %i = %c0 to %c8 step %c1 {
%v1 = vector.transfer_read %C1[%i, %c0], %f0
: tensor<8x8xf32>, vector<8xf32>
%v2 = vector.transfer_read %C2[%i, %c0], %f0
: tensor<8x8xf32>, vector<8xf32>
%v3 = vector.transfer_read %C3[%i, %c0], %f0
: tensor<8x8xf32>, vector<8xf32>
%v4 = vector.transfer_read %C4[%i, %c0], %f0
: tensor<8x8xf32>, vector<8xf32>
vector.print %v1 : vector<8xf32>
vector.print %v2 : vector<8xf32>
vector.print %v3 : vector<8xf32>
vector.print %v4 : vector<8xf32>
}
//
// Ensure that COO-SoA output has the same values.
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 64
// CHECK-NEXT: dim = ( 8, 8 )
// CHECK-NEXT: lvl = ( 8, 8 )
// CHECK-NEXT: pos[0] : ( 0, 64 )
// CHECK-NEXT: crd[0] : ( 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2,
// CHECK-SAME: 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4,
// CHECK-SAME: 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7,
// CHECK-SAME: 7, 7, 7, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3,
// CHECK-SAME: 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7,
// CHECK-SAME: 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3,
// CHECK-SAME: 4, 5, 6, 7 )
// CHECK-NEXT: values : ( 8.8, 4.8, 6.8, 4.8, 8.8, 6.1, 14.8, 16.8, 4.4, 4.4, 4.4, 8.4,
// CHECK-SAME: 8.4, 12.4, 16.4, 16.4, 8.8, 4.8, 6.8, 8.8, 8.8, 12.8, 14.8,
// CHECK-SAME: 15.8, 4.3, 5.3, 6.3, 8.3, 8.3, 12.3, 14.3, 16.3, 4.5, 4.5,
// CHECK-SAME: 6.5, 8.5, 8.5, 12.5, 14.5, 16.5, 9.9, 4.9, 6.9, 8.9, 8.9,
// CHECK-SAME: 12.9, 15.9, 16.9, 12.1, 6.1, 5.1, 9.1, 9.1, 13.1, 15.1, 17.1,
// CHECK-SAME: 15.4, 5.4, 7.4, 5.4, 11.4, 10.4, 11.4, 9.4 )
// CHECK-NEXT: ----
//
sparse_tensor.print %COO_RET : tensor<8x8xf32, #SortedCOOSoA>
// Release resources.
bufferization.dealloc_tensor %C1 : tensor<8x8xf32>
bufferization.dealloc_tensor %C2 : tensor<8x8xf32>
bufferization.dealloc_tensor %C3 : tensor<8x8xf32>
bufferization.dealloc_tensor %C4 : tensor<8x8xf32>
bufferization.dealloc_tensor %CSR_A : tensor<8x8xf32, #CSR>
bufferization.dealloc_tensor %COO_A : tensor<8x8xf32, #SortedCOO>
bufferization.dealloc_tensor %COO_B : tensor<8x8xf32, #SortedCOOSoA>
bufferization.dealloc_tensor %COO_RET : tensor<8x8xf32, #SortedCOOSoA>
return
}
}
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