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[mlir][sparse] added sparse out element wise mult integration test 

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D114822
This commit is contained in:
Aart Bik 2021-11-30 14:28:58 -08:00
parent f15a854567
commit 61e353e0b6
1 changed files with 82 additions and 0 deletions
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_out_mult_elt.mlir Normal file
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// RUN: mlir-opt %s \
// RUN: --sparsification --sparse-tensor-conversion \
// RUN: --linalg-bufferize --convert-linalg-to-loops \
// RUN: --convert-vector-to-scf --convert-scf-to-std \
// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
// RUN: --std-bufferize --finalizing-bufferize --lower-affine \
// RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-math-to-llvm \
// RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ]
}>
#trait_mult_elt = {
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 {
// Sparse kernel.
func @sparse_mult_elt(
%arga: tensor<32x16xf32, #DCSR>, %argb: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> {
%c16 = arith.constant 16 : index
%c32 = arith.constant 32 : index
%argx = sparse_tensor.init [%c32, %c16] : tensor<32x16xf32, #DCSR>
%0 = linalg.generic #trait_mult_elt
ins(%arga, %argb: tensor<32x16xf32, #DCSR>, tensor<32x16xf32, #DCSR>)
outs(%argx: tensor<32x16xf32, #DCSR>) {
^bb(%a: f32, %b: f32, %x: f32):
%1 = arith.mulf %a, %b : f32
linalg.yield %1 : f32
} -> tensor<32x16xf32, #DCSR>
return %0 : tensor<32x16xf32, #DCSR>
}
// Driver method to call and verify kernel.
func @entry() {
%c0 = arith.constant 0 : index
%f1 = arith.constant -1.0 : f32
// Setup very sparse matrices.
%ta = arith.constant sparse<
[ [2,2], [15,15], [31,0], [31,14] ], [ 2.0, 3.0, -2.0, 4.0 ]
> : tensor<32x16xf32>
%tb = arith.constant sparse<
[ [1,1], [2,0], [2,2], [2,15], [31,0], [31,15] ], [ 5.0, 6.0, 7.0, 8.0, -10.0, 9.0 ]
> : tensor<32x16xf32>
%sta = sparse_tensor.convert %ta
: tensor<32x16xf32> to tensor<32x16xf32, #DCSR>
%stb = sparse_tensor.convert %tb
: tensor<32x16xf32> to tensor<32x16xf32, #DCSR>
// Call kernel.
%0 = call @sparse_mult_elt(%sta, %stb)
: (tensor<32x16xf32, #DCSR>,
tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR>
//
// Verify results. Only two entries stored in result!
//
// CHECK: ( 14, 20, -1, -1 )
//
%val = sparse_tensor.values %0 : tensor<32x16xf32, #DCSR> to memref<?xf32>
%vv = vector.transfer_read %val[%c0], %f1: memref<?xf32>, vector<4xf32>
vector.print %vv : vector<4xf32>
// Release the resources.
sparse_tensor.release %sta : tensor<32x16xf32, #DCSR>
sparse_tensor.release %stb : tensor<32x16xf32, #DCSR>
sparse_tensor.release %0 : tensor<32x16xf32, #DCSR>
return
}
}