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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}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=4 reassociate-fp-reductions=true enable-index-optimizations=true enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
// Reduction in this file _are_ supported by the AArch64 SVE backend
#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_matmul = {
indexing_maps = [
affine_map<(i,j,k) -> (i,k)>, // A
affine_map<(i,j,k) -> (k,j)>, // B
affine_map<(i,j,k) -> (i,j)> // C (out)
],
iterator_types = ["parallel", "parallel", "reduction"],
doc = "C(i,j) = SUM_k A(i,k) * B(k,j)"
}
module {
func.func @min_plus_csrcsr(%arga: tensor<?x?xf64, #CSR>,
%argb: tensor<?x?xf64, #CSR>) -> tensor<?x?xf64, #CSR> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%maxf = arith.constant 1.0e999 : f64
%d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #CSR>
%d1 = tensor.dim %argb, %c1 : tensor<?x?xf64, #CSR>
%xm = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #CSR>
%0 = linalg.generic #trait_matmul
ins(%arga, %argb: tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSR>)
outs(%xm: tensor<?x?xf64, #CSR>) {
^bb(%a: f64, %b: f64, %output: f64):
%1 = sparse_tensor.binary %a, %b : f64, f64 to f64
overlap = {
^bb0(%x: f64, %y: f64):
%3 = arith.addf %x, %y : f64
sparse_tensor.yield %3 : f64
}
left={}
right={}
%2 = sparse_tensor.reduce %1, %output, %maxf : f64 {
^bb0(%x: f64, %y: f64):
%cmp = arith.cmpf "olt", %x, %y : f64
%3 = arith.select %cmp, %x, %y : f64
sparse_tensor.yield %3 : f64
}
linalg.yield %2 : f64
} -> tensor<?x?xf64, #CSR>
return %0 : tensor<?x?xf64, #CSR>
}
func.func @min_plus_csrcsc(%arga: tensor<?x?xf64, #CSR>,
%argb: tensor<?x?xf64, #CSC>) -> tensor<?x?xf64, #CSR> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%maxf = arith.constant 1.0e999 : f64
%d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #CSR>
%d1 = tensor.dim %argb, %c1 : tensor<?x?xf64, #CSC>
%xm = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #CSR>
%0 = linalg.generic #trait_matmul
ins(%arga, %argb: tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSC>)
outs(%xm: tensor<?x?xf64, #CSR>) {
^bb(%a: f64, %b: f64, %output: f64):
%1 = sparse_tensor.binary %a, %b : f64, f64 to f64
overlap = {
^bb0(%x: f64, %y: f64):
%3 = arith.addf %x, %y : f64
sparse_tensor.yield %3 : f64
}
left={}
right={}
%2 = sparse_tensor.reduce %1, %output, %maxf : f64 {
^bb0(%x: f64, %y: f64):
%cmp = arith.cmpf "olt", %x, %y : f64
%3 = arith.select %cmp, %x, %y : f64
sparse_tensor.yield %3 : f64
}
linalg.yield %2 : f64
} -> tensor<?x?xf64, #CSR>
return %0 : tensor<?x?xf64, #CSR>
}
// 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.
%5 = call @min_plus_csrcsr(%sm1, %sm2r)
: (tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSR>) -> tensor<?x?xf64, #CSR>
%6 = call @min_plus_csrcsc(%sm1, %sm2c)
: (tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSC>) -> tensor<?x?xf64, #CSR>
//
// Verify the results.
//
// CHECK: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 1, 2, 0, 0, 0 ), ( 3, 0, 0, 0, 0 ), ( 0, 0, 4, 5, 6 ), ( 7, 0, 8, 9, 0 ), ( 0, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 6, 5, 4, 3, 2, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 6, 0, 0, 0, 0 ), ( 0, 0, 0, 5, 0 ), ( 4, 0, 0, 3, 0 ), ( 0, 2, 0, 0, 0 ), ( 0, 11, 0, 0, 0 ) )
// CHECK-NEXT: ( 7, 7, 9, 8, 7, 7, 12, 11, 11, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 7, 0, 0, 7, 0 ), ( 9, 0, 0, 0, 0 ), ( 8, 7, 0, 7, 0 ), ( 12, 11, 0, 11, 0 ), ( 0, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 7, 7, 9, 8, 7, 7, 12, 11, 11, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 7, 0, 0, 7, 0 ), ( 9, 0, 0, 0, 0 ), ( 8, 7, 0, 7, 0 ), ( 12, 11, 0, 11, 0 ), ( 0, 0, 0, 0, 0 ) )
//
call @dump_mat(%sm1) : (tensor<?x?xf64, #CSR>) -> ()
call @dump_mat(%sm2r) : (tensor<?x?xf64, #CSR>) -> ()
call @dump_mat(%5) : (tensor<?x?xf64, #CSR>) -> ()
call @dump_mat(%6) : (tensor<?x?xf64, #CSR>) -> ()
// 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 %5 : tensor<?x?xf64, #CSR>
bufferization.dealloc_tensor %6 : tensor<?x?xf64, #CSR>
return
}
}
|
