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Row gather linop #901
Row gather linop #901
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Codecov Report
@@ Coverage Diff @@
## develop #901 +/- ##
===========================================
+ Coverage 92.27% 93.38% +1.11%
===========================================
Files 476 470 -6
Lines 39410 38214 -1196
===========================================
- Hits 36367 35688 -679
+ Misses 3043 2526 -517
Continue to review full report at Codecov.
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Any feedback is welcome, especially for the name. |
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My feedback so far.
core/CMakeLists.txt
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@@ -32,17 +32,18 @@ target_sources(ginkgo | |||
matrix/permutation.cpp | |||
matrix/sellp.cpp | |||
matrix/sparsity_csr.cpp | |||
#matrix/row_gatherer.cpp |
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why is this commented? Because this is not finalized (you marked it as need-feedback
)?
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Thanks, I forget to delete it.
all row gather call dense function, so we do not really need the core.
still need that to instantiate all class
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Mostly formatting and documentation comments.
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Not a full review, but some minor comments ...
core/test/utils/assertions.hpp
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@@ -624,6 +625,55 @@ ::testing::AssertionResult array_equal(const std::string& first_expression, | |||
second); | |||
} | |||
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/** | |||
* array_equal overload: first is ConstArrayView. |
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Could one use doxygens \overload command here, see https://www.doxygen.nl/manual/commands.html#cmdoverload?
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it requires us to set SORT_MEMBER_DOCS = NO, to avoid the reordering issue
@@ -1010,7 +1029,6 @@ std::unique_ptr<Dense<ValueType>> Dense<ValueType>::row_gather( | |||
return result; | |||
} | |||
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I believe this empty line should stay
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It is overloaded, so one empty is enough
@@ -1022,20 +1040,125 @@ std::unique_ptr<Dense<ValueType>> Dense<ValueType>::row_gather( | |||
return result; | |||
} | |||
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And here is one empty line missing
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It is overloaded, so one empty is enough
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Some comments
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LGTM!
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Only minor nits. Mostly LGTM!
core/test/matrix/row_gatherer.cpp
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using TestType = ::testing::Types< | ||
std::tuple<float, float, gko::int32>, std::tuple<float, double, gko::int32>, | ||
std::tuple<double, double, gko::int32>, | ||
std::tuple<double, float, gko::int32>, | ||
std::tuple<std::complex<float>, std::complex<float>, gko::int32>, | ||
std::tuple<std::complex<float>, std::complex<double>, gko::int32>, | ||
std::tuple<std::complex<double>, std::complex<double>, gko::int32>, | ||
std::tuple<std::complex<double>, std::complex<float>, gko::int32>>; |
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Can probably be moved into core/test/utils.hpp
?
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TYPED_TEST(RowGatherer, KnowsItsSizeAndValues) | ||
{ | ||
this->assert_equal_to_original_mtx(this->mtx.get()); |
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Maybe to reduce the amount of code, GKO_ASSERT_MTX_NEAR
may be sufficient ?
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no, because the row gatherer does not have the write_to(matrix_data), can not use GKO_ASSERT_MTX_NEAR
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Wouldn't it be useful to add that feature, then?
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LGTM for the most part, just a few points on the interface I would like to discuss
include/ginkgo/core/base/lin_op.hpp
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/** | ||
* The RowGatherable is an interface that allows to get the row gather result of | ||
* a LinOp. | ||
* | ||
* @tparam IndexType the type of the indices used to extract/gather the rows | ||
*/ | ||
template <typename IndexType> | ||
class RowGatherable { |
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Do we need a new Mixin here? row gather on CSR and Dense have two quite separate functions, and don't need to be made generic? Also the fact that std::unique_ptr is not covariant as a return type makes for a weird interface for the _linop overload.
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Or do you need this for some mixed-precision application?
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Yes, I think so? because the RowGatherer does not have value type information, I need this interface to call row_gather.
I thought CSR and Dense have the same interface for row_gather, doesn't it?
I do not understand the last sentence. could you describe it more detail?
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you can't override a std::unique_ptr<LinOp>
function() with a std::unique_ptr<Dense>
function(). Covariance means that if your class is more specialized, your return type can also be more specialized. This is true for pointers, but not for smart pointers.
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Good to know this! does the compiler show the override only partially message for this kind on pointers?
I think the return type is not considered in the function overload, so I still need to use two version for them?
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I forgot that I was blocking this, sorry. A few small nits, most of my comments have been adressed.
Still, I don't think we need a separate interface for behavior that is only implemented in Dense.
The advanced row_gather might also use a separate name like row_gather_add or similar, though I'm not 100% sure about that.
How about you call the dense kernels inside RowGatherer, which means you can use mixed_precision_dispatch to figure out their dimensions? That eliminates the need for an interface.
Also, a RowGatherer::write function might simplify things in the tests.
core/test/utils.hpp
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struct PairTypenameNameGenerator { | ||
template <typename T> | ||
template <typename T, | ||
std::enable_if_t<std::tuple_size<T>::value == 2, bool> = true> |
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Nit: That would be a TupleTypenameNameGenerator, not Pair. No need to provide two overloads
/** | ||
* Returns a pointer to the array of row_gatherer_indices. | ||
* | ||
* @return the pointer to the row_gatherer_indices array. | ||
*/ | ||
index_type* get_row_gatherer_indices() noexcept | ||
{ | ||
return row_gatherer_indices_.get_data(); | ||
} |
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/** | |
* Returns a pointer to the array of row_gatherer_indices. | |
* | |
* @return the pointer to the row_gatherer_indices array. | |
*/ | |
index_type* get_row_gatherer_indices() noexcept | |
{ | |
return row_gatherer_indices_.get_data(); | |
} | |
/** | |
* Returns a pointer to the row index array. | |
* | |
* @return the pointer to the row index array. | |
*/ | |
index_type* get_row_indices() noexcept | |
{ | |
return row_gatherer_indices_.get_data(); | |
} |
/** | ||
* @copydoc get_row_gatherer_indices() | ||
* | ||
* @note This is the constant version of the function, which can be | ||
* significantly more memory efficient than the non-constant version, | ||
* so always prefer this version. | ||
*/ | ||
const index_type* get_const_row_gatherer_indices() const noexcept | ||
{ | ||
return row_gatherer_indices_.get_const_data(); | ||
} |
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same here
template <typename IndicesArray> | ||
RowGatherer(std::shared_ptr<const Executor> exec, const dim<2>& size, | ||
IndicesArray&& row_gatherer_indices) | ||
: EnableLinOp<RowGatherer>(exec, size), | ||
row_gatherer_indices_{ | ||
exec, std::forward<IndicesArray>(row_gatherer_indices)} | ||
{ | ||
GKO_ASSERT_EQ(size[0], row_gatherer_indices_.get_num_elems()); | ||
} |
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I know we do it like this in other places, but this makes the interface unnecessarily generic and forces it to be in the header.
template <typename IndicesArray> | |
RowGatherer(std::shared_ptr<const Executor> exec, const dim<2>& size, | |
IndicesArray&& row_gatherer_indices) | |
: EnableLinOp<RowGatherer>(exec, size), | |
row_gatherer_indices_{ | |
exec, std::forward<IndicesArray>(row_gatherer_indices)} | |
{ | |
GKO_ASSERT_EQ(size[0], row_gatherer_indices_.get_num_elems()); | |
} | |
RowGatherer(std::shared_ptr<const Executor> exec, const dim<2>& size, | |
Array<IndexType> row_gatherer_indices) | |
: EnableLinOp<RowGatherer>(exec, size), | |
row_gatherer_indices_{ | |
exec, std::move(row_gatherer_indices)} | |
{ | |
GKO_ASSERT_EQ(size[0], row_gatherer_indices_.get_num_elems()); | |
} |
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Ah nevermind, using this interface allows us to avoid a single copy if the array is on another executor than the RowGatherer
template <typename IndexType = int32> | ||
class RowGatherer : public EnableLinOp<RowGatherer<IndexType>>, | ||
public EnableCreateMethod<RowGatherer<IndexType>> { | ||
friend class EnableCreateMethod<RowGatherer>; |
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Can also use a create_const method
* | ||
* @param exec Executor associated to the LinOp | ||
*/ | ||
RowGatherer(std::shared_ptr<const Executor> exec) |
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All these member functions can be moved to the .cpp file
Do you mean call the dense kernels in the row gatherer apply function? I do not understand what dimensions to figure out. Could you give more detail? The interface can be used by Csr, too. Csr->row_gather(index) = Csr
Yes, but I am a little scared about setting the type which is not used by the class-self. |
@yhmtsai Since this PR only involves Dense, I don't see the advantage of this approach. Adding Csr would introduce another feature hidden behind an apply(...) function like #942, which I would like to avoid as much as possible. |
About write: Fft also declares a value_type, even though it doesn't have values strictly speaking. |
the precision is also dispatched in the row_gather of dense. |
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@upsj row gather only consider dense now, so apply will go over all possible dense type to call row_gather |
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Co-authored-by: Thomas Grützmacher <thomas.gruetzmacher@kit.edu>
add overload of array_equal for ConstArrayView
Co-authored-by: Gregor Olenik <gregor.olenik@web.de> Co-authored-by: Thomas Grützmacher <thomas.gruetzmacher@kit.edu>
Co-authored-by: Thomas Grützmacher <thomas.gruetzmacher@kit.edu>
Co-authored-by: Pratik Nayak <pratikvn@protonmail.com> Co-authored-by: Tobias Ribizel <ribizel@kit.edu>
Co-authored-by: Pratik Nayak <pratikvn@protonmail.com> Co-authored-by: Tobias Ribizel <ribizel@kit.edu>
Co-authored-by: Tobias Ribizel <ribizel@kit.edu>
… create_const Co-authored-by: Tobias Ribizel <ribizel@kit.edu>
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Note: This PR changes the Ginkgo ABI:
For details check the full ABI diff under Artifacts here |
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Advertise release 1.5.0 and last changes + Add changelog, + Update third party libraries + A small fix to a CMake file See PR: #1195 The Ginkgo team is proud to announce the new Ginkgo minor release 1.5.0. This release brings many important new features such as: - MPI-based multi-node support for all matrix formats and most solvers; - full DPC++/SYCL support, - functionality and interface for GPU-resident sparse direct solvers, - an interface for wrapping solvers with scaling and reordering applied, - a new algebraic Multigrid solver/preconditioner, - improved mixed-precision support, - support for device matrix assembly, and much more. If you face an issue, please first check our [known issues page](/~https://github.com/ginkgo-project/ginkgo/wiki/Known-Issues) and the [open issues list](/~https://github.com/ginkgo-project/ginkgo/issues) and if you do not find a solution, feel free to [open a new issue](/~https://github.com/ginkgo-project/ginkgo/issues/new/choose) or ask a question using the [github discussions](/~https://github.com/ginkgo-project/ginkgo/discussions). Supported systems and requirements: + For all platforms, CMake 3.13+ + C++14 compliant compiler + Linux and macOS + GCC: 5.5+ + clang: 3.9+ + Intel compiler: 2018+ + Apple LLVM: 8.0+ + NVHPC: 22.7+ + Cray Compiler: 14.0.1+ + CUDA module: CUDA 9.2+ or NVHPC 22.7+ + HIP module: ROCm 4.0+ + DPC++ module: Intel OneAPI 2021.3 with oneMKL and oneDPL. Set the CXX compiler to `dpcpp`. + Windows + MinGW and Cygwin: GCC 5.5+ + Microsoft Visual Studio: VS 2019 + CUDA module: CUDA 9.2+, Microsoft Visual Studio + OpenMP module: MinGW or Cygwin. Algorithm and important feature additions: + Add MPI-based multi-node for all matrix formats and solvers (except GMRES and IDR). ([#676](#676), [#908](#908), [#909](#909), [#932](#932), [#951](#951), [#961](#961), [#971](#971), [#976](#976), [#985](#985), [#1007](#1007), [#1030](#1030), [#1054](#1054), [#1100](#1100), [#1148](#1148)) + Porting the remaining algorithms (preconditioners like ISAI, Jacobi, Multigrid, ParILU(T) and ParIC(T)) to DPC++/SYCL, update to SYCL 2020, and improve support and performance ([#896](#896), [#924](#924), [#928](#928), [#929](#929), [#933](#933), [#943](#943), [#960](#960), [#1057](#1057), [#1110](#1110), [#1142](#1142)) + Add a Sparse Direct interface supporting GPU-resident numerical LU factorization, symbolic Cholesky factorization, improved triangular solvers, and more ([#957](#957), [#1058](#1058), [#1072](#1072), [#1082](#1082)) + Add a ScaleReordered interface that can wrap solvers and automatically apply reorderings and scalings ([#1059](#1059)) + Add a Multigrid solver and improve the aggregation based PGM coarsening scheme ([#542](#542), [#913](#913), [#980](#980), [#982](#982), [#986](#986)) + Add infrastructure for unified, lambda-based, backend agnostic, kernels and utilize it for some simple kernels ([#833](#833), [#910](#910), [#926](#926)) + Merge different CUDA, HIP, DPC++ and OpenMP tests under a common interface ([#904](#904), [#973](#973), [#1044](#1044), [#1117](#1117)) + Add a device_matrix_data type for device-side matrix assembly ([#886](#886), [#963](#963), [#965](#965)) + Add support for mixed real/complex BLAS operations ([#864](#864)) + Add a FFT LinOp for all but DPC++/SYCL ([#701](#701)) + Add FBCSR support for NVIDIA and AMD GPUs and CPUs with OpenMP ([#775](#775)) + Add CSR scaling ([#848](#848)) + Add array::const_view and equivalent to create constant matrices from non-const data ([#890](#890)) + Add a RowGatherer LinOp supporting mixed precision to gather dense matrix rows ([#901](#901)) + Add mixed precision SparsityCsr SpMV support ([#970](#970)) + Allow creating CSR submatrix including from (possibly discontinuous) index sets ([#885](#885), [#964](#964)) + Add a scaled identity addition (M <- aI + bM) feature interface and impls for Csr and Dense ([#942](#942)) Deprecations and important changes: + Deprecate AmgxPgm in favor of the new Pgm name. ([#1149](#1149)). + Deprecate specialized residual norm classes in favor of a common `ResidualNorm` class ([#1101](#1101)) + Deprecate CamelCase non-polymorphic types in favor of snake_case versions (like array, machine_topology, uninitialized_array, index_set) ([#1031](#1031), [#1052](#1052)) + Bug fix: restrict gko::share to rvalue references (*possible interface break*) ([#1020](#1020)) + Bug fix: when using cuSPARSE's triangular solvers, specifying the factory parameter `num_rhs` is now required when solving for more than one right-hand side, otherwise an exception is thrown ([#1184](#1184)). + Drop official support for old CUDA < 9.2 ([#887](#887)) Improved performance additions: + Reuse tmp storage in reductions in solvers and add a mutable workspace to all solvers ([#1013](#1013), [#1028](#1028)) + Add HIP unsafe atomic option for AMD ([#1091](#1091)) + Prefer vendor implementations for Dense dot, conj_dot and norm2 when available ([#967](#967)). + Tuned OpenMP SellP, COO, and ELL SpMV kernels for a small number of RHS ([#809](#809)) Fixes: + Fix various compilation warnings ([#1076](#1076), [#1183](#1183), [#1189](#1189)) + Fix issues with hwloc-related tests ([#1074](#1074)) + Fix include headers for GCC 12 ([#1071](#1071)) + Fix for simple-solver-logging example ([#1066](#1066)) + Fix for potential memory leak in Logger ([#1056](#1056)) + Fix logging of mixin classes ([#1037](#1037)) + Improve value semantics for LinOp types, like moved-from state in cross-executor copy/clones ([#753](#753)) + Fix some matrix SpMV and conversion corner cases ([#905](#905), [#978](#978)) + Fix uninitialized data ([#958](#958)) + Fix CUDA version requirement for cusparseSpSM ([#953](#953)) + Fix several issues within bash-script ([#1016](#1016)) + Fixes for `NVHPC` compiler support ([#1194](#1194)) Other additions: + Simplify and properly name GMRES kernels ([#861](#861)) + Improve pkg-config support for non-CMake libraries ([#923](#923), [#1109](#1109)) + Improve gdb pretty printer ([#987](#987), [#1114](#1114)) + Add a logger highlighting inefficient allocation and copy patterns ([#1035](#1035)) + Improved and optimized test random matrix generation ([#954](#954), [#1032](#1032)) + Better CSR strategy defaults ([#969](#969)) + Add `move_from` to `PolymorphicObject` ([#997](#997)) + Remove unnecessary device_guard usage ([#956](#956)) + Improvements to the generic accessor for mixed-precision ([#727](#727)) + Add a naive lower triangular solver implementation for CUDA ([#764](#764)) + Add support for int64 indices from CUDA 11 onward with SpMV and SpGEMM ([#897](#897)) + Add a L1 norm implementation ([#900](#900)) + Add reduce_add for arrays ([#831](#831)) + Add utility to simplify Dense View creation from an existing Dense vector ([#1136](#1136)). + Add a custom transpose implementation for Fbcsr and Csr transpose for unsupported vendor types ([#1123](#1123)) + Make IDR random initilization deterministic ([#1116](#1116)) + Move the algorithm choice for triangular solvers from Csr::strategy_type to a factory parameter ([#1088](#1088)) + Update CUDA archCoresPerSM ([#1175](#1116)) + Add kernels for Csr sparsity pattern lookup ([#994](#994)) + Differentiate between structural and numerical zeros in Ell/Sellp ([#1027](#1027)) + Add a binary IO format for matrix data ([#984](#984)) + Add a tuple zip_iterator implementation ([#966](#966)) + Simplify kernel stubs and declarations ([#888](#888)) + Simplify GKO_REGISTER_OPERATION with lambdas ([#859](#859)) + Simplify copy to device in tests and examples ([#863](#863)) + More verbose output to array assertions ([#858](#858)) + Allow parallel compilation for Jacobi kernels ([#871](#871)) + Change clang-format pointer alignment to left ([#872](#872)) + Various improvements and fixes to the benchmarking framework ([#750](#750), [#759](#759), [#870](#870), [#911](#911), [#1033](#1033), [#1137](#1137)) + Various documentation improvements ([#892](#892), [#921](#921), [#950](#950), [#977](#977), [#1021](#1021), [#1068](#1068), [#1069](#1069), [#1080](#1080), [#1081](#1081), [#1108](#1108), [#1153](#1153), [#1154](#1154)) + Various CI improvements ([#868](#868), [#874](#874), [#884](#884), [#889](#889), [#899](#899), [#903](#903), [#922](#922), [#925](#925), [#930](#930), [#936](#936), [#937](#937), [#958](#958), [#882](#882), [#1011](#1011), [#1015](#1015), [#989](#989), [#1039](#1039), [#1042](#1042), [#1067](#1067), [#1073](#1073), [#1075](#1075), [#1083](#1083), [#1084](#1084), [#1085](#1085), [#1139](#1139), [#1178](#1178), [#1187](#1187))
This PR adds the row gather linop (Rowgatherer) interface to use dense row gather operation.
Also add mixed operation and the advanced row gather operation