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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Product reductions - kept in a seperate file as these are not supported by
// the AArch64 SVE backend (so the set-up is a bit different to
// sparse_reducitons.mlir)
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#CSC = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimToLvl = affine_map<(i,j) -> (j,i)>
}>
//
// Traits for tensor operations.
//
#trait_mat_reduce_rowwise = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A (in)
affine_map<(i,j) -> (i)> // X (out)
],
iterator_types = ["parallel", "reduction"],
doc = "X(i) = PROD_j A(i,j)"
}
module {
func.func @redProdLex(%arga: tensor<?x?xf64, #CSR>) -> tensor<?xf64, #SparseVector> {
%c0 = arith.constant 0 : index
%cf1 = arith.constant 1.0 : f64
%d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #CSR>
%xv = bufferization.alloc_tensor(%d0): tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_mat_reduce_rowwise
ins(%arga: tensor<?x?xf64, #CSR>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: f64, %b: f64):
%1 = sparse_tensor.reduce %a, %b, %cf1 : f64 {
^bb0(%x: f64, %y: f64):
%2 = arith.mulf %x, %y : f64
sparse_tensor.yield %2 : f64
}
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
func.func @redProdExpand(%arga: tensor<?x?xf64, #CSC>) -> tensor<?xf64, #SparseVector> {
%c0 = arith.constant 0 : index
%cf1 = arith.constant 1.0 : f64
%d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #CSC>
%xv = bufferization.alloc_tensor(%d0): tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_mat_reduce_rowwise
ins(%arga: tensor<?x?xf64, #CSC>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: f64, %b: f64):
%1 = sparse_tensor.reduce %a, %b, %cf1 : f64 {
^bb0(%x: f64, %y: f64):
%2 = arith.mulf %x, %y : f64
sparse_tensor.yield %2 : f64
}
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Dumps a sparse vector of type f64.
func.func @dump_vec(%arg0: tensor<?xf64, #SparseVector>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant 0.0 : f64
%0 = sparse_tensor.values %arg0 : tensor<?xf64, #SparseVector> to memref<?xf64>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<8xf64>
vector.print %1 : vector<8xf64>
// Dump the dense vector to verify structure is correct.
%dv = sparse_tensor.convert %arg0 : tensor<?xf64, #SparseVector> to tensor<?xf64>
%2 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<16xf64>
vector.print %2 : vector<16xf64>
return
}
// Dump a sparse matrix.
func.func @dump_mat(%arg0: tensor<?x?xf64, #CSR>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant 0.0 : f64
%0 = sparse_tensor.values %arg0 : tensor<?x?xf64, #CSR> to memref<?xf64>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<16xf64>
vector.print %1 : vector<16xf64>
%dm = sparse_tensor.convert %arg0 : tensor<?x?xf64, #CSR> to tensor<?x?xf64>
%2 = vector.transfer_read %dm[%c0, %c0], %d0: tensor<?x?xf64>, vector<5x5xf64>
vector.print %2 : vector<5x5xf64>
return
}
// Driver method to call and verify vector kernels.
func.func @entry() {
%c0 = arith.constant 0 : index
// Setup sparse matrices.
%m1 = arith.constant sparse<
[ [0,0], [0,1], [1,0], [2,2], [2,3], [2,4], [3,0], [3,2], [3,3] ],
[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
> : tensor<4x5xf64>
%m2 = arith.constant sparse<
[ [0,0], [1,3], [2,0], [2,3], [3,1], [4,1] ],
[6.0, 5.0, 4.0, 3.0, 2.0, 11.0 ]
> : tensor<5x4xf64>
%sm1 = sparse_tensor.convert %m1 : tensor<4x5xf64> to tensor<?x?xf64, #CSR>
%sm2r = sparse_tensor.convert %m2 : tensor<5x4xf64> to tensor<?x?xf64, #CSR>
%sm2c = sparse_tensor.convert %m2 : tensor<5x4xf64> to tensor<?x?xf64, #CSC>
// Call sparse matrix kernels.
%1 = call @redProdLex(%sm1) : (tensor<?x?xf64, #CSR>) -> tensor<?xf64, #SparseVector>
%2 = call @redProdExpand(%sm2c) : (tensor<?x?xf64, #CSC>) -> tensor<?xf64, #SparseVector>
//
// Verify the results.
//
// CHECK: ( 2, 3, 120, 504, 0, 0, 0, 0 )
// CHECK-NEXT: ( 2, 3, 120, 504, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( 6, 5, 12, 2, 11, 0, 0, 0 )
// CHECK-NEXT: ( 6, 5, 12, 2, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
//
call @dump_mat(%sm1) : (tensor<?x?xf64, #CSR>) -> ()
call @dump_mat(%sm2r) : (tensor<?x?xf64, #CSR>) -> ()
call @dump_vec(%1) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_vec(%2) : (tensor<?xf64, #SparseVector>) -> ()
// Release the resources.
bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #CSR>
bufferization.dealloc_tensor %sm2r : tensor<?x?xf64, #CSR>
bufferization.dealloc_tensor %sm2c : tensor<?x?xf64, #CSC>
bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector>
return
}
}
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