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Implement dpnp.logaddexp2 function (#1955)
* Add implementation of dpnp.logaddexp2 * Update third party tests * Add more tests * Update description based on review comments * Add missing space in docstring
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‎dpnp/backend/extensions/ufunc/CMakeLists.txt

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@@ -30,6 +30,7 @@ set(_elementwise_sources
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/fmax.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/fmin.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/fmod.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/logaddexp2.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/radians.cpp
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)
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‎dpnp/backend/extensions/ufunc/elementwise_functions/common.cpp

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#include "fmax.hpp"
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#include "fmin.hpp"
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#include "fmod.hpp"
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#include "logaddexp2.hpp"
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#include "radians.hpp"
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namespace py = pybind11;
@@ -46,6 +47,7 @@ void init_elementwise_functions(py::module_ m)
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init_fmax(m);
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init_fmin(m);
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init_fmod(m);
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init_logaddexp2(m);
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init_radians(m);
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}
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} // namespace dpnp::extensions::ufunc

‎dpnp/backend/extensions/ufunc/elementwise_functions/logaddexp2.cpp

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//*****************************************************************************
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// Copyright (c) 2024, Intel Corporation
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without
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// maxification, are permitted provided that the following conditions are met:
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// - Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// - Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
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// THE POSSIBILITY OF SUCH DAMAGE.
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//*****************************************************************************
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#include <sycl/sycl.hpp>
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#include "dpctl4pybind11.hpp"
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#include "kernels/elementwise_functions/logaddexp2.hpp"
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#include "logaddexp2.hpp"
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#include "populate.hpp"
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// include a local copy of elementwise common header from dpctl tensor:
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// dpctl/tensor/libtensor/source/elementwise_functions/elementwise_functions.hpp
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// TODO: replace by including dpctl header once available
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#include "../../elementwise_functions/elementwise_functions.hpp"
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// dpctl tensor headers
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#include "kernels/elementwise_functions/common.hpp"
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#include "kernels/elementwise_functions/logaddexp.hpp"
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#include "utils/type_dispatch.hpp"
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namespace dpnp::extensions::ufunc
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{
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namespace py = pybind11;
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namespace py_int = dpnp::extensions::py_internal;
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namespace td_ns = dpctl::tensor::type_dispatch;
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namespace impl
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{
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namespace ew_cmn_ns = dpctl::tensor::kernels::elementwise_common;
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namespace logaddexp_ns = dpctl::tensor::kernels::logaddexp;
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// Supports the same types table as for logaddexp function in dpctl
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template <typename T1, typename T2>
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using OutputType = logaddexp_ns::LogAddExpOutputType<T1, T2>;
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using dpnp::kernels::logaddexp2::Logaddexp2Functor;
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template <typename argT1,
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typename argT2,
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typename resT,
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unsigned int vec_sz = 4,
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unsigned int n_vecs = 2,
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bool enable_sg_loadstore = true>
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using ContigFunctor =
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ew_cmn_ns::BinaryContigFunctor<argT1,
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argT2,
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resT,
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Logaddexp2Functor<argT1, argT2, resT>,
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vec_sz,
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n_vecs,
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enable_sg_loadstore>;
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template <typename argT1, typename argT2, typename resT, typename IndexerT>
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using StridedFunctor =
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ew_cmn_ns::BinaryStridedFunctor<argT1,
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argT2,
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resT,
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IndexerT,
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Logaddexp2Functor<argT1, argT2, resT>>;
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using ew_cmn_ns::binary_contig_impl_fn_ptr_t;
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using ew_cmn_ns::binary_contig_matrix_contig_row_broadcast_impl_fn_ptr_t;
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using ew_cmn_ns::binary_contig_row_contig_matrix_broadcast_impl_fn_ptr_t;
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using ew_cmn_ns::binary_strided_impl_fn_ptr_t;
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static binary_contig_impl_fn_ptr_t
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logaddexp2_contig_dispatch_table[td_ns::num_types][td_ns::num_types];
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static int logaddexp2_output_typeid_table[td_ns::num_types][td_ns::num_types];
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static binary_strided_impl_fn_ptr_t
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logaddexp2_strided_dispatch_table[td_ns::num_types][td_ns::num_types];
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MACRO_POPULATE_DISPATCH_TABLES(logaddexp2);
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} // namespace impl
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void init_logaddexp2(py::module_ m)
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{
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using arrayT = dpctl::tensor::usm_ndarray;
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using event_vecT = std::vector<sycl::event>;
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{
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impl::populate_logaddexp2_dispatch_tables();
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using impl::logaddexp2_contig_dispatch_table;
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using impl::logaddexp2_output_typeid_table;
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using impl::logaddexp2_strided_dispatch_table;
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auto logaddexp2_pyapi = [&](const arrayT &src1, const arrayT &src2,
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const arrayT &dst, sycl::queue &exec_q,
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const event_vecT &depends = {}) {
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return py_int::py_binary_ufunc(
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src1, src2, dst, exec_q, depends,
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logaddexp2_output_typeid_table,
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logaddexp2_contig_dispatch_table,
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logaddexp2_strided_dispatch_table,
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// no support of C-contig row with broadcasting in OneMKL
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td_ns::NullPtrTable<
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impl::
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binary_contig_matrix_contig_row_broadcast_impl_fn_ptr_t>{},
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td_ns::NullPtrTable<
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impl::
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binary_contig_row_contig_matrix_broadcast_impl_fn_ptr_t>{});
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};
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m.def("_logaddexp2", logaddexp2_pyapi, "", py::arg("src1"),
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py::arg("src2"), py::arg("dst"), py::arg("sycl_queue"),
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py::arg("depends") = py::list());
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auto logaddexp2_result_type_pyapi = [&](const py::dtype &dtype1,
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const py::dtype &dtype2) {
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return py_int::py_binary_ufunc_result_type(
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dtype1, dtype2, logaddexp2_output_typeid_table);
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};
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m.def("_logaddexp2_result_type", logaddexp2_result_type_pyapi);
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}
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}
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} // namespace dpnp::extensions::ufunc

‎dpnp/backend/extensions/ufunc/elementwise_functions/logaddexp2.hpp

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//*****************************************************************************
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// Copyright (c) 2024, Intel Corporation
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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// - Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// - Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
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// THE POSSIBILITY OF SUCH DAMAGE.
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//*****************************************************************************
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#pragma once
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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namespace dpnp::extensions::ufunc
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{
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void init_logaddexp2(py::module_ m);
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} // namespace dpnp::extensions::ufunc

‎dpnp/backend/kernels/elementwise_functions/logaddexp2.hpp

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//*****************************************************************************
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// Copyright (c) 2024, Intel Corporation
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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// - Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// - Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
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// THE POSSIBILITY OF SUCH DAMAGE.
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//*****************************************************************************
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#pragma once
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#include <cmath>
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#include <sycl/sycl.hpp>
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namespace dpnp::kernels::logaddexp2
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{
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constexpr double log2e = 1.442695040888963407359924681001892137;
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template <typename T>
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inline T log2_1p(T x)
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{
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return T(log2e) * sycl::log1p(x);
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}
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template <typename T>
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inline T logaddexp2(T x, T y)
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{
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if (x == y) {
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// handles infinities of the same sign
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return x + 1;
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}
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const T tmp = x - y;
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if (tmp > 0) {
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return x + log2_1p(sycl::exp2(-tmp));
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}
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else if (tmp <= 0) {
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return y + log2_1p(sycl::exp2(tmp));
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}
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return std::numeric_limits<T>::quiet_NaN();
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}
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template <typename argT1, typename argT2, typename resT>
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struct Logaddexp2Functor
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{
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using supports_sg_loadstore = std::true_type;
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using supports_vec = std::false_type;
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resT operator()(const argT1 &in1, const argT2 &in2) const
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{
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return logaddexp2<resT>(in1, in2);
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}
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};
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} // namespace dpnp::kernels::logaddexp2

‎dpnp/dpnp_iface_trigonometric.py

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"log1p",
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"log2",
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"logaddexp",
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"logaddexp2",
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"logsumexp",
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"rad2deg",
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"radians",
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See Also
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--------
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:obj:`dpnp.log` : Natural logarithm, element-wise.
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:obj:`dpnp.log2` : Return the base 2 logarithm of the input array, element-wise.
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:obj:`dpnp.log2` : Return the base-2 logarithm of the input array, element-wise.
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:obj:`dpnp.log1p` : Return the natural logarithm of one plus the input array, element-wise.
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Examples
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:obj:`dpnp.expm1` : ``exp(x) - 1``, the inverse of :obj:`dpnp.log1p`.
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:obj:`dpnp.log` : Natural logarithm, element-wise.
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:obj:`dpnp.log10` : Return the base 10 logarithm of the input array, element-wise.
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:obj:`dpnp.log2` : Return the base 2 logarithm of the input array, element-wise.
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:obj:`dpnp.log2` : Return the base-2 logarithm of the input array, element-wise.
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Examples
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--------
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--------
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:obj:`dpnp.log` : Natural logarithm, element-wise.
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:obj:`dpnp.exp` : Exponential, element-wise.
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:obj:`dpnp.logsumdexp` : Logarithm of the sum of exponents of elements in the input array.
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:obj:`dpnp.logaddexp2`: Logarithm of the sum of exponentiations of inputs in
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base-2, element-wise.
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:obj:`dpnp.logsumexp` : Logarithm of the sum of exponents of elements in the
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input array.
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Examples
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--------
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)
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_LOGADDEXP2_DOCSTRING = """
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Calculates the logarithm of the sum of exponents in base-2 for each element
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`x1_i` of the input array `x1` with the respective element `x2_i` of the input
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array `x2`.
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This function calculates `log2(2**x1 + 2**x2)`. It is useful in machine
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learning when the calculated probabilities of events may be so small as
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to exceed the range of normal floating point numbers. In such cases the base-2
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logarithm of the calculated probability can be used instead. This function
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allows adding probabilities stored in such a fashion.
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For full documentation refer to :obj:`numpy.logaddexp2`.
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Parameters
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----------
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x1 : {dpnp.ndarray, usm_ndarray, scalar}
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First input array, expected to have a real-valued floating-point
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data type.
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Both inputs `x1` and `x2` can not be scalars at the same time.
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x2 : {dpnp.ndarray, usm_ndarray, scalar}
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Second input array, also expected to have a real-valued
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floating-point data type.
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Both inputs `x1` and `x2` can not be scalars at the same time.
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out : {None, dpnp.ndarray, usm_ndarray}, optional
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Output array to populate.
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Array must have the correct shape and the expected data type.
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Default: ``None``.
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order : {"C", "F", "A", "K"}, optional
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Memory layout of the newly output array, if parameter `out` is ``None``.
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Default: ``"K"``.
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Returns
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-------
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out : dpnp.ndarray
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An array containing the element-wise results. The data type
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of the returned array is determined by the Type Promotion Rules.
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Limitations
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-----------
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Parameters `where` and `subok` are supported with their default values.
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Keyword arguments `kwargs` are currently unsupported.
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Otherwise ``NotImplementedError`` exception will be raised.
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See Also
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--------
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:obj:`dpnp.logaddexp`: Natural logarithm of the sum of exponentiations of
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inputs, element-wise.
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:obj:`dpnp.logsumexp` : Logarithm of the sum of exponentiations of the inputs.
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Examples
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--------
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>>> import dpnp as np
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>>> prob1 = np.log2(np.array(1e-50))
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>>> prob2 = np.log2(np.array(2.5e-50))
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>>> prob12 = np.logaddexp2(prob1, prob2)
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>>> prob1, prob2, prob12
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(array(-166.09640474), array(-164.77447665), array(-164.28904982))
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>>> 2**prob12
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array(3.5e-50)
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"""
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1480+
logaddexp2 = DPNPBinaryFunc(
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"logaddexp2",
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ufi._logaddexp2_result_type,
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ufi._logaddexp2,
1484+
_LOGADDEXP2_DOCSTRING,
1485+
)
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14151488
def logsumexp(x, /, *, axis=None, dtype=None, keepdims=False, out=None):
14161489
"""
14171490
Calculates the logarithm of the sum of exponents of elements in
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:obj:`dpnp.exp` : Exponential, element-wise.
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:obj:`dpnp.logaddexp` : Logarithm of the sum of exponents of
14741547
the inputs, element-wise.
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:obj:`dpnp.logaddexp2` : Logarithm of the sum of exponents of
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the inputs in base-2, element-wise.
14751550
14761551
Examples
14771552
--------

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