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|
// DEFINE: %{option_vec} =
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{run_option} =
// DEFINE: %{cpu_runner} = mlir-cpu-runner
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = %{cpu_runner} \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils %{run_option} | \
// 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_vec} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// REDEFINE: %{option} = "%{option_vec}"
// RUN: %{compile} | %{run}
// Do the same run, but with VLA vectorization.
// REDEFINE: %{option} = "enable-arm-sve=true %{option_vec}"
// REDEFINE: %{cpu_runner} = %mcr_aarch64_cmd
// REDEFINE: %{run_option} = %VLA_ARCH_ATTR_OPTIONS
// RUN: %if mlir_arm_sve_tests %{ %{compile} | %{run} %}
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
//
// Traits for tensor operations.
//
#trait_vec = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"]
}
#trait_mat = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A (in)
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"]
}
module {
// Invert the structure of a sparse vector. Present values become missing.
// Missing values are filled with 1 (i32). Output is sparse.
func.func @vector_complement_sparse(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {
%c = arith.constant 0 : index
%ci1 = arith.constant 1 : i32
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
%0 = linalg.generic #trait_vec
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xi32, #SparseVector>) {
^bb(%a: f64, %x: i32):
%1 = sparse_tensor.unary %a : f64 to i32
present={}
absent={
sparse_tensor.yield %ci1 : i32
}
linalg.yield %1 : i32
} -> tensor<?xi32, #SparseVector>
return %0 : tensor<?xi32, #SparseVector>
}
// Invert the structure of a sparse vector, where missing values are
// filled with 1. For a dense output, the sparse compiler initializes
// the buffer to all zero at all other places.
func.func @vector_complement_dense(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xi32>
%0 = linalg.generic #trait_vec
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xi32>) {
^bb(%a: f64, %x: i32):
%1 = sparse_tensor.unary %a : f64 to i32
present={}
absent={
%ci1 = arith.constant 1 : i32
sparse_tensor.yield %ci1 : i32
}
linalg.yield %1 : i32
} -> tensor<?xi32>
return %0 : tensor<?xi32>
}
// Negate existing values. Fill missing ones with +1.
func.func @vector_negation(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
%c = arith.constant 0 : index
%cf1 = arith.constant 1.0 : f64
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_vec
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: f64, %x: f64):
%1 = sparse_tensor.unary %a : f64 to f64
present={
^bb0(%x0: f64):
%ret = arith.negf %x0 : f64
sparse_tensor.yield %ret : f64
}
absent={
sparse_tensor.yield %cf1 : f64
}
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Performs B[i] = i * A[i].
func.func @vector_magnify(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_vec
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: f64, %x: f64):
%idx = linalg.index 0 : index
%1 = sparse_tensor.unary %a : f64 to f64
present={
^bb0(%x0: f64):
%tmp = arith.index_cast %idx : index to i64
%idxf = arith.uitofp %tmp : i64 to f64
%ret = arith.mulf %x0, %idxf : f64
sparse_tensor.yield %ret : f64
}
absent={}
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Clips values to the range [3, 7].
func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cfmin = arith.constant 3.0 : f64
%cfmax = arith.constant 7.0 : f64
%d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
%d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
%xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
%0 = linalg.generic #trait_mat
ins(%argx: tensor<?x?xf64, #DCSR>)
outs(%xv: tensor<?x?xf64, #DCSR>) {
^bb(%a: f64, %x: f64):
%1 = sparse_tensor.unary %a: f64 to f64
present={
^bb0(%x0: f64):
%mincmp = arith.cmpf "ogt", %x0, %cfmin : f64
%x1 = arith.select %mincmp, %x0, %cfmin : f64
%maxcmp = arith.cmpf "olt", %x1, %cfmax : f64
%x2 = arith.select %maxcmp, %x1, %cfmax : f64
sparse_tensor.yield %x2 : f64
}
absent={}
linalg.yield %1 : f64
} -> tensor<?x?xf64, #DCSR>
return %0 : tensor<?x?xf64, #DCSR>
}
// Slices matrix and only keep the value of the lower-right corner of the original
// matrix (i.e., A[2/d0 ..][2/d1 ..]), and set other values to 99.
func.func @matrix_slice(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
%d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
%xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
%0 = linalg.generic #trait_mat
ins(%argx: tensor<?x?xf64, #DCSR>)
outs(%xv: tensor<?x?xf64, #DCSR>) {
^bb(%a: f64, %x: f64):
%row = linalg.index 0 : index
%col = linalg.index 1 : index
%1 = sparse_tensor.unary %a: f64 to f64
present={
^bb0(%x0: f64):
%v = arith.constant 99.0 : f64
%two = arith.constant 2 : index
%r = arith.muli %two, %row : index
%c = arith.muli %two, %col : index
%cmp1 = arith.cmpi "ult", %r, %d0 : index
%tmp = arith.select %cmp1, %v, %x0 : f64
%cmp2 = arith.cmpi "ult", %c, %d1 : index
%result = arith.select %cmp2, %v, %tmp : f64
sparse_tensor.yield %result : f64
}
absent={}
linalg.yield %1 : f64
} -> tensor<?x?xf64, #DCSR>
return %0 : tensor<?x?xf64, #DCSR>
}
// Dumps a sparse vector of type f64.
func.func @dump_vec_f64(%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<32xf64>
vector.print %1 : vector<32xf64>
// Dump the dense vector to verify structure is correct.
%dv = sparse_tensor.convert %arg0 : tensor<?xf64, #SparseVector> to tensor<?xf64>
%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<32xf64>
vector.print %3 : vector<32xf64>
return
}
// Dumps a sparse vector of type i32.
func.func @dump_vec_i32(%arg0: tensor<?xi32, #SparseVector>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant 0 : i32
%0 = sparse_tensor.values %arg0 : tensor<?xi32, #SparseVector> to memref<?xi32>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xi32>, vector<24xi32>
vector.print %1 : vector<24xi32>
// Dump the dense vector to verify structure is correct.
%dv = sparse_tensor.convert %arg0 : tensor<?xi32, #SparseVector> to tensor<?xi32>
%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xi32>, vector<32xi32>
vector.print %3 : vector<32xi32>
return
}
// Dump a sparse matrix.
func.func @dump_mat(%arg0: tensor<?x?xf64, #DCSR>) {
%c0 = arith.constant 0 : index
%d0 = arith.constant 0.0 : f64
%0 = sparse_tensor.values %arg0 : tensor<?x?xf64, #DCSR> 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, #DCSR> to tensor<?x?xf64>
%3 = vector.transfer_read %dm[%c0, %c0], %d0: tensor<?x?xf64>, vector<4x8xf64>
vector.print %3 : vector<4x8xf64>
return
}
// Driver method to call and verify vector kernels.
func.func @entry() {
%cmu = arith.constant -99 : i32
%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<32xf64>
%sv1 = sparse_tensor.convert %v1 : tensor<32xf64> to tensor<?xf64, #SparseVector>
// Setup sparse matrices.
%m1 = arith.constant sparse<
[ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [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<4x8xf64>
%sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>
// Call sparse vector kernels.
%0 = call @vector_complement_sparse(%sv1)
: (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector>
%1 = call @vector_negation(%sv1)
: (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
%2 = call @vector_magnify(%sv1)
: (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
// Call sparse matrix kernels.
%3 = call @matrix_clip(%sm1)
: (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
%4 = call @matrix_slice(%sm1)
: (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
// Call kernel with dense output.
%5 = call @vector_complement_dense(%sv1) : (tensor<?xf64, #SparseVector>) -> tensor<?xi32>
//
// Verify the results.
//
// CHECK: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 7, 8, 0, 9 )
// CHECK-NEXT: ( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 )
// CHECK-NEXT: ( 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 )
// CHECK-NEXT: ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )
// CHECK-NEXT: ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )
// CHECK-NEXT: ( 0, 6, 33, 68, 100, 126, 196, 232, 279, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0, 68, 0, 0, 100, 126, 0, 0, 0, 0, 0, 0, 196, 232, 0, 279 )
// CHECK-NEXT: ( 3, 3, 3, 4, 5, 6, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 3, 3, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 3 ), ( 0, 0, 4, 0, 5, 0, 0, 6 ), ( 7, 0, 7, 7, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 99, 99, 99, 99, 5, 6, 99, 99, 99, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 99, 99, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 99 ), ( 0, 0, 99, 0, 5, 0, 0, 6 ), ( 99, 0, 99, 99, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 )
//
call @dump_vec_f64(%sv1) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_vec_i32(%0) : (tensor<?xi32, #SparseVector>) -> ()
call @dump_vec_f64(%1) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_vec_f64(%2) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_mat(%3) : (tensor<?x?xf64, #DCSR>) -> ()
call @dump_mat(%4) : (tensor<?x?xf64, #DCSR>) -> ()
%v = vector.transfer_read %5[%c0], %cmu: tensor<?xi32>, vector<32xi32>
vector.print %v : vector<32xi32>
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %0 : tensor<?xi32, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %5 : tensor<?xi32>
return
}
}
|
