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Add implementation of dpnp.float_power (#1957)
* Add implementation of dpnp.float_power() * Updated third party tests * Added more tests to cover different scenarios * Mute back test for dpnp.floor_divide * Update docstring with another way to state NaN value * Address comment to update tests
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‎doc/reference/ufunc.rst‎

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@@ -20,21 +20,27 @@ Math operations
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dpnp.add
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dpnp.subtract
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dpnp.multiply
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dpnp.matmul
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dpnp.divide
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dpnp.logaddexp
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dpnp.logaddexp2
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dpnp.true_divide
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dpnp.floor_divide
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dpnp.negative
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dpnp.positive
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dpnp.power
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dpnp.float_power
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dpnp.remainder
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dpnp.mod
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dpnp.fmod
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dpnp.abs
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dpnp.divmod
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dpnp.absolute
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dpnp.fabs
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dpnp.rint
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dpnp.sign
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dpnp.heaviside
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dpnp.conj
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dpnp.conjugate
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dpnp.exp
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dpnp.exp2
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dpnp.log
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dpnp.log1p
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dpnp.proj
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dpnp.sqrt
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dpnp.cbrt
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dpnp.square
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dpnp.cbrt
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dpnp.reciprocal
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dpnp.rsqrt
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dpnp.gcd
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dpnp.lcm
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.. tip::
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The optional output arguments can be used to help you save memory
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for large calculations. If your arrays are large, complicated
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expressions can take longer than absolutely necessary due to the
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creation and (later) destruction of temporary calculation
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spaces. For example, the expression ``G = A * B + C`` is equivalent to
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``T1 = A * B; G = T1 + C; del T1``. It will be more quickly executed
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as ``G = A * B; add(G, C, G)`` which is the same as
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``G = A * B; G += C``.
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Trigonometric functions
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~~~~~~~~~~~~~~~~~~~~~~~

‎dpnp/backend/extensions/ufunc/CMakeLists.txt‎

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@@ -27,6 +27,7 @@ set(_elementwise_sources
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/common.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/degrees.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/fabs.cpp
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${CMAKE_CURRENT_SOURCE_DIR}/elementwise_functions/float_power.cpp
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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

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

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#include "degrees.hpp"
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#include "fabs.hpp"
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#include "float_power.hpp"
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#include "fmax.hpp"
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#include "fmin.hpp"
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#include "fmod.hpp"
@@ -44,6 +45,7 @@ void init_elementwise_functions(py::module_ m)
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{
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init_degrees(m);
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init_fabs(m);
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init_float_power(m);
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init_fmax(m);
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init_fmin(m);
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init_fmod(m);

‎dpnp/backend/extensions/ufunc/elementwise_functions/float_power.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 "float_power.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 "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 impl
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{
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namespace td_ns = dpctl::tensor::type_dispatch;
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// Supports only float and complex types
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template <typename T1, typename T2>
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struct OutputType
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{
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using value_type = typename std::disjunction<
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td_ns::BinaryTypeMapResultEntry<T1, float, T2, float, float>,
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td_ns::BinaryTypeMapResultEntry<T1, double, T2, double, double>,
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td_ns::BinaryTypeMapResultEntry<T1,
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std::complex<float>,
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T2,
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std::complex<float>,
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std::complex<float>>,
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td_ns::BinaryTypeMapResultEntry<T1,
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std::complex<double>,
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T2,
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std::complex<double>,
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std::complex<double>>,
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td_ns::DefaultResultEntry<void>>::result_type;
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};
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static int float_power_output_typeid_table[td_ns::num_types][td_ns::num_types];
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template <typename fnT, typename T1, typename T2>
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struct TypeMapFactory
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{
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std::enable_if_t<std::is_same<fnT, int>::value, int> get()
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{
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using rT = typename OutputType<T1, T2>::value_type;
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return td_ns::GetTypeid<rT>{}.get();
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}
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};
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void populate_float_power_dispatch_tables(void)
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{
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td_ns::DispatchTableBuilder<int, TypeMapFactory, td_ns::num_types> dvb;
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dvb.populate_dispatch_table(float_power_output_typeid_table);
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}
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} // namespace impl
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void init_float_power(py::module_ m)
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{
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impl::populate_float_power_dispatch_tables();
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using impl::float_power_output_typeid_table;
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auto float_power_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, float_power_output_typeid_table);
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};
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m.def("_float_power_result_type", float_power_result_type_pyapi);
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}
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} // namespace dpnp::extensions::ufunc

‎dpnp/backend/extensions/ufunc/elementwise_functions/float_power.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_float_power(py::module_ m);
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} // namespace dpnp::extensions::ufunc

‎dpnp/dpnp_iface_mathematical.py‎

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"divide",
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"ediff1d",
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"fabs",
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"float_power",
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"floor",
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"floor_divide",
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"fmax",
@@ -1364,6 +1365,105 @@ def ediff1d(x1, to_end=None, to_begin=None):
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)
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_FLOAT_POWER_DOCSTRING = """
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Calculates `x1_i` raised to `x2_i` for each element `x1_i` of the input array
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`x1` with the respective element `x2_i` of the input array `x2`.
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This differs from the power function in that boolean, integers, and float16 are
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promoted to floats with a minimum precision of float32 so that the result is
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always inexact. The intent is that the function will return a usable result for
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negative powers and seldom overflow for positive powers.
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Negative values raised to a non-integral value will return ``NaN``. To get
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complex results, cast the input to complex, or specify the ``dtype`` to be one
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of complex dtype.
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For full documentation refer to :obj:`numpy.float_power`.
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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 floating-point data types.
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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 floating-point data types.
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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. Array must have the correct shape and
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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 bases in `x1` raised to the exponents in `x2`
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element-wise.
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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 argument `kwargs` is 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.power` : Power function that preserves type.
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Examples
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--------
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>>> import dpnp as np
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Cube each element in an array:
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>>> x1 = np.arange(6)
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>>> x1
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array([0, 1, 2, 3, 4, 5])
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>>> np.float_power(x1, 3)
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array([ 0., 1., 8., 27., 64., 125.])
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Raise the bases to different exponents:
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>>> x2 = np.array([1.0, 2.0, 3.0, 3.0, 2.0, 1.0])
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>>> np.float_power(x1, x2)
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array([ 0., 1., 8., 27., 16., 5.])
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The effect of broadcasting:
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>>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
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>>> x2
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array([[1, 2, 3, 3, 2, 1],
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[1, 2, 3, 3, 2, 1]])
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>>> np.float_power(x1, x2)
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array([[ 0., 1., 8., 27., 16., 5.],
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[ 0., 1., 8., 27., 16., 5.]])
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Negative values raised to a non-integral value will result in ``NaN``:
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>>> x3 = np.array([-1, -4])
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>>> np.float_power(x3, 1.5)
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array([nan, nan])
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To get complex results, give the argument one of complex dtype, i.e.
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``dtype=np.complex64``:
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>>> np.float_power(x3, 1.5, dtype=np.complex64)
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array([1.1924881e-08-1.j, 9.5399045e-08-8.j], dtype=complex64)
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"""
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float_power = DPNPBinaryFunc(
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"float_power",
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ufi._float_power_result_type,
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ti._pow,
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_FLOAT_POWER_DOCSTRING,
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mkl_fn_to_call=vmi._mkl_pow_to_call,
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mkl_impl_fn=vmi._pow,
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binary_inplace_fn=ti._pow_inplace,
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)
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_FLOOR_DOCSTRING = """
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Returns the floor for each element `x_i` for input array `x`.
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@@ -2468,6 +2568,7 @@ def modf(x1, **kwargs):
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:obj:`dpnp.fmax` : Element-wise maximum of array elements.
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:obj:`dpnp.fmin` : Element-wise minimum of array elements.
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:obj:`dpnp.fmod` : Calculate the element-wise remainder of division.
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:obj:`dpnp.float_power` : Power function that promotes integers to floats.
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Examples
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--------

‎tests/skipped_tests.tbl‎

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tests/test_umath.py::test_umaths[('divmod', 'll')]
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tests/test_umath.py::test_umaths[('divmod', 'ff')]
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tests/test_umath.py::test_umaths[('divmod', 'dd')]
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tests/test_umath.py::test_umaths[('float_power', 'dd')]
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tests/test_umath.py::test_umaths[('frexp', 'f')]
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tests/test_umath.py::test_umaths[('frexp', 'd')]
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tests/test_umath.py::test_umaths[('gcd', 'ii')]

‎tests/skipped_tests_gpu.tbl‎

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tests/test_umath.py::test_umaths[('divmod', 'll')]
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tests/test_umath.py::test_umaths[('divmod', 'ff')]
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tests/test_umath.py::test_umaths[('divmod', 'dd')]
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tests/test_umath.py::test_umaths[('float_power', 'dd')]
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tests/test_umath.py::test_umaths[('floor_divide', 'ff')]
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tests/test_umath.py::test_umaths[('frexp', 'f')]
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tests/test_umath.py::test_umaths[('frexp', 'd')]

‎tests/test_sycl_queue.py‎

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@@ -655,6 +655,11 @@ def test_reduce_hypot(device):
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pytest.param("dot", [3, 4, 5], [1, 2, 3]),
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pytest.param("dot", [3 + 2j, 4 + 1j, 5], [1, 2 + 3j, 3]),
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pytest.param("extract", [False, True, True, False], [0, 1, 2, 3]),
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pytest.param(
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"float_power",
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[0, 1, 2, 3, 4, 5],
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[1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
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),
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pytest.param(
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"floor_divide", [1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]
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),

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