9797394893

Committed 04 Jul 2024 04:14PM UTC coverage: 54.375% (+0.4%) from 54.011%

Build # 9797394893

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Commit Message

Rework implementation of `dpnp.fmax` and `dpnp.fmin` functions (#1905)

* Implement dpnp.fmax and dpnp.fmin functions

* Updated existing tests and added new ones

* Removed unused code from cython backend

* Removed a reference to original descriptor

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88.24

/dpnp/backend/extensions/vm/fmax.cpp

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

#include <oneapi/mkl.hpp>
#include <sycl/sycl.hpp>

#include "dpctl4pybind11.hpp"

#include "common.hpp"
#include "fmax.hpp"

// include a local copy of elementwise common header from dpctl tensor:
// dpctl/tensor/libtensor/source/elementwise_functions/elementwise_functions.hpp
// TODO: replace by including dpctl header once available
#include "../elementwise_functions/elementwise_functions.hpp"

// dpctl tensor headers
#include "kernels/elementwise_functions/common.hpp"
#include "utils/type_dispatch.hpp"
#include "utils/type_utils.hpp"

namespace dpnp::extensions::vm
{
namespace ew_cmn_ns = dpctl::tensor::kernels::elementwise_common;
namespace py = pybind11;
namespace py_int = dpnp::extensions::py_internal;
namespace td_ns = dpctl::tensor::type_dispatch;
namespace tu_ns = dpctl::tensor::type_utils;

namespace impl
{
// OneMKL namespace with VM functions
namespace mkl_vm = oneapi::mkl::vm;

/**
 * @brief A factory to define pairs of supported types for which
 * MKL VM library provides support in oneapi::mkl::vm::fmax<T> function.
 *
 * @tparam T Type of input vectors `a` and `b` and of result vector `y`.
 */
template <typename T1, typename T2>
struct OutputType
{
    using value_type = typename std::disjunction<
        td_ns::BinaryTypeMapResultEntry<T1, double, T2, double, double>,
        td_ns::BinaryTypeMapResultEntry<T1, float, T2, float, float>,
        td_ns::DefaultResultEntry<void>>::result_type;
};

template <typename T1, typename T2>
static sycl::event fmax_contig_impl(sycl::queue &exec_q,
                                    std::size_t in_n,
                                    const char *in_a,
                                    py::ssize_t a_offset,
                                    const char *in_b,
                                    py::ssize_t b_offset,
                                    char *out_y,
                                    py::ssize_t out_offset,
                                    const std::vector<sycl::event> &depends)
{
    tu_ns::validate_type_for_device<T1>(exec_q);
    tu_ns::validate_type_for_device<T2>(exec_q);

    if ((a_offset != 0) || (b_offset != 0) || (out_offset != 0)) {
        throw std::runtime_error("Arrays offsets have to be equals to 0");
    }

    std::int64_t n = static_cast<std::int64_t>(in_n);
    const T1 *a = reinterpret_cast<const T1 *>(in_a);
    const T2 *b = reinterpret_cast<const T2 *>(in_b);

    using resTy = typename OutputType<T1, T2>::value_type;
    resTy *y = reinterpret_cast<resTy *>(out_y);

    return mkl_vm::fmax(exec_q,
                        n, // number of elements to be calculated
                        a, // pointer `a` containing 1st input vector of size n
                        b, // pointer `b` containing 2nd input vector of size n
                        y, // pointer `y` to the output vector of size n
                        depends);
}

using ew_cmn_ns::binary_contig_impl_fn_ptr_t;
using ew_cmn_ns::binary_contig_matrix_contig_row_broadcast_impl_fn_ptr_t;
using ew_cmn_ns::binary_contig_row_contig_matrix_broadcast_impl_fn_ptr_t;
using ew_cmn_ns::binary_strided_impl_fn_ptr_t;

static int output_typeid_vector[td_ns::num_types][td_ns::num_types];
static binary_contig_impl_fn_ptr_t contig_dispatch_vector[td_ns::num_types]
                                                         [td_ns::num_types];

MACRO_POPULATE_DISPATCH_TABLES(fmax);
} // namespace impl

void init_fmax(py::module_ m)
{
    using arrayT = dpctl::tensor::usm_ndarray;
    using event_vecT = std::vector<sycl::event>;

    impl::populate_dispatch_tables();
    using impl::contig_dispatch_vector;
    using impl::output_typeid_vector;

    auto fmax_pyapi = [&](sycl::queue &exec_q, const arrayT &src1,
                          const arrayT &src2, const arrayT &dst,
                          const event_vecT &depends = {}) {
        return py_int::py_binary_ufunc(
            src1, src2, dst, exec_q, depends, output_typeid_vector,
            contig_dispatch_vector,
            // no support of strided implementation in OneMKL
            td_ns::NullPtrTable<impl::binary_strided_impl_fn_ptr_t>{},
            // no support of C-contig row with broadcasting in OneMKL
            td_ns::NullPtrTable<
                impl::
                    binary_contig_matrix_contig_row_broadcast_impl_fn_ptr_t>{},
            td_ns::NullPtrTable<
                impl::
                    binary_contig_row_contig_matrix_broadcast_impl_fn_ptr_t>{});
    };
    m.def("_fmax", fmax_pyapi,
          "Call `fmax` function from OneMKL VM library to performs element "
          "by element computation of the modulus function of vector `src1` "
          "with respect to vector `src2` to resulting vector `dst`",
          py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"),
          py::arg("dst"), py::arg("depends") = py::list());

    auto fmax_need_to_call_pyapi = [&](sycl::queue &exec_q, const arrayT &src1,
                                       const arrayT &src2, const arrayT &dst) {
        return py_internal::need_to_call_binary_ufunc(exec_q, src1, src2, dst,
                                                      output_typeid_vector,
                                                      contig_dispatch_vector);
    };
    m.def("_mkl_fmax_to_call", fmax_need_to_call_pyapi,
          "Check input arguments to answer if `fmax` function from "
          "OneMKL VM library can be used",
          py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"),
          py::arg("dst"));
}
} // namespace dpnp::extensions::vm

1	//*****************************************************************************
2	// Copyright (c) 2024, Intel Corporation
3	// All rights reserved.
4	//
5	// Redistribution and use in source and binary forms, with or without
6	// modification, are permitted provided that the following conditions are met:
7	// - Redistributions of source code must retain the above copyright notice,
8	// this list of conditions and the following disclaimer.
9	// - Redistributions in binary form must reproduce the above copyright notice,
10	// this list of conditions and the following disclaimer in the documentation
11	// and/or other materials provided with the distribution.
12	//
13	// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
14	// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
15	// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
16	// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
17	// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
18	// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
19	// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
20	// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
21	// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
22	// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
23	// THE POSSIBILITY OF SUCH DAMAGE.
24	//*****************************************************************************
25
26	#include <oneapi/mkl.hpp>
27	#include <sycl/sycl.hpp>
28
29	#include "dpctl4pybind11.hpp"
30
31	#include "common.hpp"
32	#include "fmax.hpp"
33
34	// include a local copy of elementwise common header from dpctl tensor:
35	// dpctl/tensor/libtensor/source/elementwise_functions/elementwise_functions.hpp
36	// TODO: replace by including dpctl header once available
37	#include "../elementwise_functions/elementwise_functions.hpp"
38
39	// dpctl tensor headers
40	#include "kernels/elementwise_functions/common.hpp"
41	#include "utils/type_dispatch.hpp"
42	#include "utils/type_utils.hpp"
43
44	namespace dpnp::extensions::vm
45	{
46	namespace ew_cmn_ns = dpctl::tensor::kernels::elementwise_common;
47	namespace py = pybind11;
48	namespace py_int = dpnp::extensions::py_internal;
49	namespace td_ns = dpctl::tensor::type_dispatch;
50	namespace tu_ns = dpctl::tensor::type_utils;
51
52	namespace impl
53	{
54	// OneMKL namespace with VM functions
55	namespace mkl_vm = oneapi::mkl::vm;
56
57	/**
58	* @brief A factory to define pairs of supported types for which
59	* MKL VM library provides support in oneapi::mkl::vm::fmax<T> function.
60	*
61	* @tparam T Type of input vectors `a` and `b` and of result vector `y`.
62	*/
63	template <typename T1, typename T2>
64	struct OutputType
65	{
66	using value_type = typename std::disjunction<
67	td_ns::BinaryTypeMapResultEntry<T1, double, T2, double, double>,
68	td_ns::BinaryTypeMapResultEntry<T1, float, T2, float, float>,
69	td_ns::DefaultResultEntry<void>>::result_type;
70	};
71
72	template <typename T1, typename T2>
73	static sycl::event fmax_contig_impl(sycl::queue &exec_q,
74	std::size_t in_n,
75	const char *in_a,
76	py::ssize_t a_offset,
77	const char *in_b,
78	py::ssize_t b_offset,
79	char *out_y,
80	py::ssize_t out_offset,
81	const std::vector<sycl::event> &depends)
82	{	39✔
83	tu_ns::validate_type_for_device<T1>(exec_q);	39✔
84	tu_ns::validate_type_for_device<T2>(exec_q);	39✔
85
86	if ((a_offset != 0) \|\| (b_offset != 0) \|\| (out_offset != 0)) {	39!
NEW 87	throw std::runtime_error("Arrays offsets have to be equals to 0");	×
NEW 88	}	×
89
90	std::int64_t n = static_cast<std::int64_t>(in_n);	39✔
91	const T1 a = reinterpret_cast<const T1 >(in_a);	39✔
92	const T2 b = reinterpret_cast<const T2 >(in_b);	39✔
93
94	using resTy = typename OutputType<T1, T2>::value_type;	39✔
95	resTy y = reinterpret_cast<resTy >(out_y);	39✔
96
97	return mkl_vm::fmax(exec_q,	39✔
98	n, // number of elements to be calculated	39✔
99	a, // pointer `a` containing 1st input vector of size n	39✔
100	b, // pointer `b` containing 2nd input vector of size n	39✔
101	y, // pointer `y` to the output vector of size n	39✔
102	depends);	39✔
103	}	39✔
104
105	using ew_cmn_ns::binary_contig_impl_fn_ptr_t;
106	using ew_cmn_ns::binary_contig_matrix_contig_row_broadcast_impl_fn_ptr_t;
107	using ew_cmn_ns::binary_contig_row_contig_matrix_broadcast_impl_fn_ptr_t;
108	using ew_cmn_ns::binary_strided_impl_fn_ptr_t;
109
110	static int output_typeid_vector[td_ns::num_types][td_ns::num_types];
111	static binary_contig_impl_fn_ptr_t contig_dispatch_vector[td_ns::num_types]
112	[td_ns::num_types];
113
114	MACRO_POPULATE_DISPATCH_TABLES(fmax);
115	} // namespace impl
116
117	void init_fmax(py::module_ m)
118	{	1✔
119	using arrayT = dpctl::tensor::usm_ndarray;	1✔
120	using event_vecT = std::vector<sycl::event>;	1✔
121
122	impl::populate_dispatch_tables();	1✔
123	using impl::contig_dispatch_vector;	1✔
124	using impl::output_typeid_vector;	1✔
125
126	auto fmax_pyapi = [&](sycl::queue &exec_q, const arrayT &src1,	1✔
127	const arrayT &src2, const arrayT &dst,	1✔
128	const event_vecT &depends = {}) {	39✔
129	return py_int::py_binary_ufunc(	39✔
130	src1, src2, dst, exec_q, depends, output_typeid_vector,	39✔
131	contig_dispatch_vector,	39✔
132	// no support of strided implementation in OneMKL
133	td_ns::NullPtrTable<impl::binary_strided_impl_fn_ptr_t>{},	39✔
134	// no support of C-contig row with broadcasting in OneMKL
135	td_ns::NullPtrTable<	39✔
136	impl::	39✔
137	binary_contig_matrix_contig_row_broadcast_impl_fn_ptr_t>{},	39✔
138	td_ns::NullPtrTable<	39✔
139	impl::	39✔
140	binary_contig_row_contig_matrix_broadcast_impl_fn_ptr_t>{});	39✔
141	};	39✔
142	m.def("_fmax", fmax_pyapi,	1✔
143	"Call `fmax` function from OneMKL VM library to performs element "	1✔
144	"by element computation of the modulus function of vector `src1` "	1✔
145	"with respect to vector `src2` to resulting vector `dst`",	1✔
146	py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"),	1✔
147	py::arg("dst"), py::arg("depends") = py::list());	1✔
148
149	auto fmax_need_to_call_pyapi = [&](sycl::queue &exec_q, const arrayT &src1,	1✔
150	const arrayT &src2, const arrayT &dst) {	101✔
151	return py_internal::need_to_call_binary_ufunc(exec_q, src1, src2, dst,	101✔
152	output_typeid_vector,	101✔
153	contig_dispatch_vector);	101✔
154	};	101✔
155	m.def("_mkl_fmax_to_call", fmax_need_to_call_pyapi,	1✔
156	"Check input arguments to answer if `fmax` function from "	1✔
157	"OneMKL VM library can be used",	1✔
158	py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"),	1✔
159	py::arg("dst"));	1✔
160	}	1✔
161	} // namespace dpnp::extensions::vm

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