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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
// DEFINE: mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{command}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{command}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{command}
// UNSUPPORTED: target=aarch64{{.*}}
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{lvlTypes = ["dense"]}>
#trait_vec_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"]
}
module {
// Creates a dense vector using the minimum values from two input sparse vectors.
// When there is no overlap, include the present value in the output.
func.func @vector_min(%arga: tensor<?xbf16, #SparseVector>,
%argb: tensor<?xbf16, #SparseVector>) -> tensor<?xbf16, #DenseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xbf16, #SparseVector>
%xv = bufferization.alloc_tensor (%d) : tensor<?xbf16, #DenseVector>
%0 = linalg.generic #trait_vec_op
ins(%arga, %argb: tensor<?xbf16, #SparseVector>, tensor<?xbf16, #SparseVector>)
outs(%xv: tensor<?xbf16, #DenseVector>) {
^bb(%a: bf16, %b: bf16, %x: bf16):
%1 = sparse_tensor.binary %a, %b : bf16, bf16 to bf16
overlap={
^bb0(%a0: bf16, %b0: bf16):
%cmp = arith.cmpf "olt", %a0, %b0 : bf16
%2 = arith.select %cmp, %a0, %b0: bf16
sparse_tensor.yield %2 : bf16
}
left=identity
right=identity
linalg.yield %1 : bf16
} -> tensor<?xbf16, #DenseVector>
return %0 : tensor<?xbf16, #DenseVector>
}
// Dumps a dense vector of type bf16.
func.func @dump_vec(%arg0: tensor<?xbf16, #DenseVector>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant -1.0 : bf16
%0 = sparse_tensor.values %arg0 : tensor<?xbf16, #DenseVector> to memref<?xbf16>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xbf16>, vector<32xbf16>
%f1 = arith.extf %1: vector<32xbf16> to vector<32xf32>
vector.print %f1 : vector<32xf32>
return
}
// Driver method to call and verify the kernel.
func.func @entry() {
%c0 = arith.constant 0 : index
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],
[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
> : tensor<32xbf16>
%v2 = arith.constant sparse<
[ [1], [3], [4], [10], [16], [18], [21], [28], [29], [31] ],
[11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0 ]
> : tensor<32xbf16>
%sv1 = sparse_tensor.convert %v1 : tensor<32xbf16> to tensor<?xbf16, #SparseVector>
%sv2 = sparse_tensor.convert %v2 : tensor<32xbf16> to tensor<?xbf16, #SparseVector>
// Call the sparse vector kernel.
%0 = call @vector_min(%sv1, %sv2)
: (tensor<?xbf16, #SparseVector>,
tensor<?xbf16, #SparseVector>) -> tensor<?xbf16, #DenseVector>
//
// Verify the result.
//
// CHECK: ( 1, 11, 0, 2, 13, 0, 0, 0, 0, 0, 14, 3, 0, 0, 0, 0, 15, 4, 16, 0, 5, 6, 0, 0, 0, 0, 0, 0, 7, 8, 0, 9 )
call @dump_vec(%0) : (tensor<?xbf16, #DenseVector>) -> ()
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xbf16, #SparseVector>
bufferization.dealloc_tensor %sv2 : tensor<?xbf16, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xbf16, #DenseVector>
return
}
}
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