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[mlir][sparse] Add 3-dimensional sparse tensor multiplication integration test 

This diff adds an integration test which does element wise multiplication for two sparse 3-d tensors of size 3x3x5

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D129638
This commit is contained in:
Rajas Vanjape 2022-07-15 11:09:17 -07:00 committed by Aart Bik
parent 5b8337cf40
commit 1976ad70c5
1 changed files with 102 additions and 0 deletions
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tensor_mul.mlir Normal file
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// RUN: mlir-opt %s --sparse-compiler | \
// 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
#ST = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}>
//
// Trait for 3-d tensor element wise multiplication.
//
#trait_mul = {
indexing_maps = [
affine_map<(i,j,k) -> (i,j,k)>, // A (in)
affine_map<(i,j,k) -> (i,j,k)>, // B (in)
affine_map<(i,j,k) -> (i,j,k)> // X (out)
],
iterator_types = ["parallel", "parallel", "parallel"],
doc = "X(i,j,k) = A(i,j,k) * B(i,j,k)"
}
module {
// Multiplies two 3-d sparse tensors element-wise into a new sparse tensor.
func.func @tensor_mul(%arga: tensor<?x?x?xf64, #ST>,
%argb: tensor<?x?x?xf64, #ST>) -> tensor<?x?x?xf64, #ST> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%d0 = tensor.dim %arga, %c0 : tensor<?x?x?xf64, #ST>
%d1 = tensor.dim %arga, %c1 : tensor<?x?x?xf64, #ST>
%d2 = tensor.dim %arga, %c2 : tensor<?x?x?xf64, #ST>
%xt = bufferization.alloc_tensor(%d0, %d1, %d2) : tensor<?x?x?xf64, #ST>
%0 = linalg.generic #trait_mul
ins(%arga, %argb: tensor<?x?x?xf64, #ST>, tensor<?x?x?xf64, #ST>)
outs(%xt: tensor<?x?x?xf64, #ST>) {
^bb(%a: f64, %b: f64, %x: f64):
%1 = arith.mulf %a, %b : f64
linalg.yield %1 : f64
} -> tensor<?x?x?xf64, #ST>
return %0 : tensor<?x?x?xf64, #ST>
}
// Driver method to call and verify tensor multiplication kernel.
func.func @entry() {
%c0 = arith.constant 0 : index
%default_val = arith.constant -1.0 : f64
// Setup sparse tensor A
%ta = arith.constant dense<
[ [ [1.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ],
[1.2, 0.0, 3.5, 0.0, 0.0 ] ],
[ [0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ] ],
[ [2.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 4.0, 0.0, 0.0 ]] ]> : tensor<3x3x5xf64>
// Setup sparse tensor B
%tb = arith.constant dense<
[ [ [0.0, 0.0, 0.0, 0.0, 4.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ],
[2.0, 0.0, 1.0, 0.0, 0.0 ] ],
[ [0.0, 0.0, 0.0, 0.0, 9.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 7.0, 0.0, 0.0, 0.0 ] ],
[ [1.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 0.0, 0.0, 0.0 ],
[0.0, 0.0, 2.0, 0.0, 0.0 ]] ]> : tensor<3x3x5xf64>
%sta = sparse_tensor.convert %ta : tensor<3x3x5xf64> to tensor<?x?x?xf64, #ST>
%stb = sparse_tensor.convert %tb : tensor<3x3x5xf64> to tensor<?x?x?xf64, #ST>
// Call sparse tensor multiplication kernel.
%0 = call @tensor_mul(%sta, %stb)
: (tensor<?x?x?xf64, #ST>, tensor<?x?x?xf64, #ST>) -> tensor<?x?x?xf64, #ST>
// Verify results
//
// CHECK: ( 2.4, 3.5, 2, 8, -1, -1, -1, -1 )
// CHECK-NEXT: ( ( ( 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0 ), ( 2.4, 0, 3.5, 0, 0 ) ),
// CHECK-SAME: ( ( 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0 ) ),
// CHECK-SAME: ( ( 2, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0 ), ( 0, 0, 8, 0, 0 ) ) )
//
%m1 = sparse_tensor.values %0 : tensor<?x?x?xf64, #ST> to memref<?xf64>
%v1 = vector.transfer_read %m1[%c0], %default_val: memref<?xf64>, vector<8xf64>
vector.print %v1 : vector<8xf64>
// Print %0 in dense form.
%dt = sparse_tensor.convert %0 : tensor<?x?x?xf64, #ST> to tensor<?x?x?xf64>
%v2 = vector.transfer_read %dt[%c0, %c0, %c0], %default_val: tensor<?x?x?xf64>, vector<3x3x5xf64>
vector.print %v2 : vector<3x3x5xf64>
// Release the resources.
sparse_tensor.release %sta : tensor<?x?x?xf64, #ST>
sparse_tensor.release %stb : tensor<?x?x?xf64, #ST>
sparse_tensor.release %0 : tensor<?x?x?xf64, #ST>
return
}
}