e62f366b01
With the recent addition of "-mattr" and "-march" to the list of options supported by mlir-cpu-runner [1], the SVE integration tests can be updated to use mlir-cpu-runner instead of lli. This will allow better code re-use and more consistency This patch updates 2 tests to demonstrate the new logic. The remaining tests will be updated in the follow-up patches. [1] https://reviews.llvm.org/D146917 Depends on D155403 Differential Revision: https://reviews.llvm.org/D155405
320 lines
14 KiB
MLIR
320 lines
14 KiB
MLIR
// DEFINE: %{option_vec} =
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// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{run_option} =
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// DEFINE: %{cpu_runner} = mlir-cpu-runner
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
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// DEFINE: %{run} = %{cpu_runner} \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_c_runner_utils %{run_option} | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation and vectorization.
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// REDEFINE: %{option_vec} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
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// REDEFINE: %{option} = "%{option_vec}"
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// RUN: %{compile} | %{run}
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// Do the same run, but with VLA vectorization.
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// REDEFINE: %{option} = "enable-arm-sve=true %{option_vec}"
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// REDEFINE: %{cpu_runner} = %mcr_aarch64_cmd
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// REDEFINE: %{run_option} = %VLA_ARCH_ATTR_OPTIONS
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// RUN: %if mlir_arm_sve_tests %{ %{compile} | %{run} %}
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#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
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#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
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//
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// Traits for tensor operations.
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//
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#trait_vec = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a (in)
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"]
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}
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#trait_mat = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A (in)
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"]
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}
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module {
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// Invert the structure of a sparse vector. Present values become missing.
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// Missing values are filled with 1 (i32). Output is sparse.
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func.func @vector_complement_sparse(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {
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%c = arith.constant 0 : index
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%ci1 = arith.constant 1 : i32
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%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
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%xv = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
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%0 = linalg.generic #trait_vec
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ins(%arga: tensor<?xf64, #SparseVector>)
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outs(%xv: tensor<?xi32, #SparseVector>) {
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^bb(%a: f64, %x: i32):
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%1 = sparse_tensor.unary %a : f64 to i32
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present={}
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absent={
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sparse_tensor.yield %ci1 : i32
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}
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linalg.yield %1 : i32
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} -> tensor<?xi32, #SparseVector>
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return %0 : tensor<?xi32, #SparseVector>
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}
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// Invert the structure of a sparse vector, where missing values are
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// filled with 1. For a dense output, the sparse compiler initializes
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// the buffer to all zero at all other places.
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func.func @vector_complement_dense(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32> {
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%c = arith.constant 0 : index
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%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
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%xv = bufferization.alloc_tensor(%d) : tensor<?xi32>
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%0 = linalg.generic #trait_vec
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ins(%arga: tensor<?xf64, #SparseVector>)
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outs(%xv: tensor<?xi32>) {
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^bb(%a: f64, %x: i32):
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%1 = sparse_tensor.unary %a : f64 to i32
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present={}
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absent={
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%ci1 = arith.constant 1 : i32
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sparse_tensor.yield %ci1 : i32
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}
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linalg.yield %1 : i32
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} -> tensor<?xi32>
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return %0 : tensor<?xi32>
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}
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// Negate existing values. Fill missing ones with +1.
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func.func @vector_negation(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
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%c = arith.constant 0 : index
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%cf1 = arith.constant 1.0 : f64
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%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
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%xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
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%0 = linalg.generic #trait_vec
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ins(%arga: tensor<?xf64, #SparseVector>)
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outs(%xv: tensor<?xf64, #SparseVector>) {
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^bb(%a: f64, %x: f64):
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%1 = sparse_tensor.unary %a : f64 to f64
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present={
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^bb0(%x0: f64):
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%ret = arith.negf %x0 : f64
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sparse_tensor.yield %ret : f64
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}
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absent={
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sparse_tensor.yield %cf1 : f64
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}
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linalg.yield %1 : f64
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} -> tensor<?xf64, #SparseVector>
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return %0 : tensor<?xf64, #SparseVector>
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}
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// Performs B[i] = i * A[i].
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func.func @vector_magnify(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
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%c = arith.constant 0 : index
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%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
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%xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
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%0 = linalg.generic #trait_vec
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ins(%arga: tensor<?xf64, #SparseVector>)
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outs(%xv: tensor<?xf64, #SparseVector>) {
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^bb(%a: f64, %x: f64):
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%idx = linalg.index 0 : index
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%1 = sparse_tensor.unary %a : f64 to f64
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present={
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^bb0(%x0: f64):
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%tmp = arith.index_cast %idx : index to i64
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%idxf = arith.uitofp %tmp : i64 to f64
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%ret = arith.mulf %x0, %idxf : f64
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sparse_tensor.yield %ret : f64
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}
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absent={}
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linalg.yield %1 : f64
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} -> tensor<?xf64, #SparseVector>
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return %0 : tensor<?xf64, #SparseVector>
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}
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// Clips values to the range [3, 7].
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func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%cfmin = arith.constant 3.0 : f64
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%cfmax = arith.constant 7.0 : f64
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%d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
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%d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
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%xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
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%0 = linalg.generic #trait_mat
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ins(%argx: tensor<?x?xf64, #DCSR>)
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outs(%xv: tensor<?x?xf64, #DCSR>) {
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^bb(%a: f64, %x: f64):
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%1 = sparse_tensor.unary %a: f64 to f64
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present={
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^bb0(%x0: f64):
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%mincmp = arith.cmpf "ogt", %x0, %cfmin : f64
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%x1 = arith.select %mincmp, %x0, %cfmin : f64
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%maxcmp = arith.cmpf "olt", %x1, %cfmax : f64
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%x2 = arith.select %maxcmp, %x1, %cfmax : f64
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sparse_tensor.yield %x2 : f64
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}
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absent={}
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linalg.yield %1 : f64
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} -> tensor<?x?xf64, #DCSR>
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return %0 : tensor<?x?xf64, #DCSR>
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}
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// Slices matrix and only keep the value of the lower-right corner of the original
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// matrix (i.e., A[2/d0 ..][2/d1 ..]), and set other values to 99.
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func.func @matrix_slice(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
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%d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
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%xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
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%0 = linalg.generic #trait_mat
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ins(%argx: tensor<?x?xf64, #DCSR>)
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outs(%xv: tensor<?x?xf64, #DCSR>) {
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^bb(%a: f64, %x: f64):
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%row = linalg.index 0 : index
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%col = linalg.index 1 : index
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%1 = sparse_tensor.unary %a: f64 to f64
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present={
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^bb0(%x0: f64):
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%v = arith.constant 99.0 : f64
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%two = arith.constant 2 : index
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%r = arith.muli %two, %row : index
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%c = arith.muli %two, %col : index
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%cmp1 = arith.cmpi "ult", %r, %d0 : index
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%tmp = arith.select %cmp1, %v, %x0 : f64
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%cmp2 = arith.cmpi "ult", %c, %d1 : index
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%result = arith.select %cmp2, %v, %tmp : f64
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sparse_tensor.yield %result : f64
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}
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absent={}
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linalg.yield %1 : f64
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} -> tensor<?x?xf64, #DCSR>
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return %0 : tensor<?x?xf64, #DCSR>
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}
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// Dumps a sparse vector of type f64.
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func.func @dump_vec_f64(%arg0: tensor<?xf64, #SparseVector>) {
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// Dump the values array to verify only sparse contents are stored.
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%c0 = arith.constant 0 : index
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%d0 = arith.constant 0.0 : f64
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%0 = sparse_tensor.values %arg0 : tensor<?xf64, #SparseVector> to memref<?xf64>
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%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<32xf64>
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vector.print %1 : vector<32xf64>
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// Dump the dense vector to verify structure is correct.
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%dv = sparse_tensor.convert %arg0 : tensor<?xf64, #SparseVector> to tensor<?xf64>
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%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<32xf64>
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vector.print %3 : vector<32xf64>
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return
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}
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// Dumps a sparse vector of type i32.
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func.func @dump_vec_i32(%arg0: tensor<?xi32, #SparseVector>) {
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// Dump the values array to verify only sparse contents are stored.
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%c0 = arith.constant 0 : index
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%d0 = arith.constant 0 : i32
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%0 = sparse_tensor.values %arg0 : tensor<?xi32, #SparseVector> to memref<?xi32>
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%1 = vector.transfer_read %0[%c0], %d0: memref<?xi32>, vector<24xi32>
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vector.print %1 : vector<24xi32>
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// Dump the dense vector to verify structure is correct.
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%dv = sparse_tensor.convert %arg0 : tensor<?xi32, #SparseVector> to tensor<?xi32>
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%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xi32>, vector<32xi32>
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vector.print %3 : vector<32xi32>
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return
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}
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// Dump a sparse matrix.
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func.func @dump_mat(%arg0: tensor<?x?xf64, #DCSR>) {
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%c0 = arith.constant 0 : index
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%d0 = arith.constant 0.0 : f64
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%0 = sparse_tensor.values %arg0 : tensor<?x?xf64, #DCSR> to memref<?xf64>
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%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<16xf64>
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vector.print %1 : vector<16xf64>
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%dm = sparse_tensor.convert %arg0 : tensor<?x?xf64, #DCSR> to tensor<?x?xf64>
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%3 = vector.transfer_read %dm[%c0, %c0], %d0: tensor<?x?xf64>, vector<4x8xf64>
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vector.print %3 : vector<4x8xf64>
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return
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}
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// Driver method to call and verify vector kernels.
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func.func @entry() {
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%cmu = arith.constant -99 : i32
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%c0 = arith.constant 0 : index
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// Setup sparse vectors.
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%v1 = arith.constant sparse<
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[ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],
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[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
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> : tensor<32xf64>
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%sv1 = sparse_tensor.convert %v1 : tensor<32xf64> to tensor<?xf64, #SparseVector>
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// Setup sparse matrices.
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%m1 = arith.constant sparse<
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[ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [3,0], [3,2], [3,3] ],
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[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
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> : tensor<4x8xf64>
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%sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>
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// Call sparse vector kernels.
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%0 = call @vector_complement_sparse(%sv1)
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: (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector>
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%1 = call @vector_negation(%sv1)
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: (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
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%2 = call @vector_magnify(%sv1)
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: (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
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// Call sparse matrix kernels.
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%3 = call @matrix_clip(%sm1)
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: (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
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%4 = call @matrix_slice(%sm1)
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: (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
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// Call kernel with dense output.
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%5 = call @vector_complement_dense(%sv1) : (tensor<?xf64, #SparseVector>) -> tensor<?xi32>
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//
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// Verify the results.
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//
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// 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 )
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// 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 )
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// 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 )
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// 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 )
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// 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 )
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// 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 )
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// 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 )
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// 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 )
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// CHECK-NEXT: ( 3, 3, 3, 4, 5, 6, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0 )
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// 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 ) )
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// CHECK-NEXT: ( 99, 99, 99, 99, 5, 6, 99, 99, 99, 0, 0, 0, 0, 0, 0, 0 )
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// 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 ) )
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// 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 )
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//
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call @dump_vec_f64(%sv1) : (tensor<?xf64, #SparseVector>) -> ()
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call @dump_vec_i32(%0) : (tensor<?xi32, #SparseVector>) -> ()
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call @dump_vec_f64(%1) : (tensor<?xf64, #SparseVector>) -> ()
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call @dump_vec_f64(%2) : (tensor<?xf64, #SparseVector>) -> ()
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call @dump_mat(%3) : (tensor<?x?xf64, #DCSR>) -> ()
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call @dump_mat(%4) : (tensor<?x?xf64, #DCSR>) -> ()
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%v = vector.transfer_read %5[%c0], %cmu: tensor<?xi32>, vector<32xi32>
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vector.print %v : vector<32xi32>
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// Release the resources.
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bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR>
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bufferization.dealloc_tensor %0 : tensor<?xi32, #SparseVector>
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bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>
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bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSR>
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bufferization.dealloc_tensor %5 : tensor<?xi32>
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return
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}
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}
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