24355365981

Committed 13 Apr 2026 04:45PM UTC coverage: 81.073% (-1.0%) from 82.034%

Build # 24355365981

Build Type

Pull #2841

github

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web-flow

Commit Message

Merge eecf9d6cb into 71d3c2316

Pull Request Pull Request #2841: Feature sparse linalg solvers

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76.92

/dpnp/backend/extensions/sparse/sparse_py.cpp

//*****************************************************************************
// Copyright (c) 2025, 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.
// - Neither the name of the copyright holder nor the names of its contributors
//   may be used to endorse or promote products derived from this software
//   without specific prior written permission.
//
// 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 <cstdint>
#include <tuple>
#include <vector>

#include <pybind11/pybind11.h>
#include <pybind11/stl.h>

#include <sycl/sycl.hpp>

#include <dpctl4pybind11.hpp>

#include "gemv.hpp"

namespace py = pybind11;

using dpnp::extensions::sparse::init_sparse_gemv_dispatch_tables;
using dpnp::extensions::sparse::sparse_gemv_compute;
using dpnp::extensions::sparse::sparse_gemv_init;
using dpnp::extensions::sparse::sparse_gemv_release;

PYBIND11_MODULE(_sparse_impl, m)
{
    init_sparse_gemv_dispatch_tables();

    // ------------------------------------------------------------------
    // _using_onemath()
    //
    // Reports whether the module was compiled against the portable
    // OneMath interface (USE_ONEMATH) rather than direct oneMKL.
    // ------------------------------------------------------------------
    m.def("_using_onemath", []() -> bool {
#ifdef USE_ONEMATH
        return true;
#else
        return false;
#endif
    });

    // ------------------------------------------------------------------
    // _sparse_gemv_init(exec_q, trans, row_ptr, col_ind, values,
    //                   num_rows, num_cols, nnz, depends)
    //     -> (handle: int, val_type_id: int, event)
    //
    // Calls init_matrix_handle + set_csr_data + optimize_gemv ONCE.
    //
    // The returned handle is an opaque uintptr_t; val_type_id is the
    // dpctl typenum lookup id of the matrix value dtype and MUST be
    // passed back to _sparse_gemv_compute so the C++ layer can verify
    // that x and y dtype match the handle.
    //
    // LIFETIME CONTRACT: the caller must keep row_ptr / col_ind / values
    // USM allocations alive until _sparse_gemv_release has been called
    // AND its returned event has completed. The handle does not copy
    // the CSR arrays.
    // ------------------------------------------------------------------
    m.def(
        "_sparse_gemv_init",
        [](sycl::queue &exec_q, const int trans,
           const dpctl::tensor::usm_ndarray &row_ptr,
           const dpctl::tensor::usm_ndarray &col_ind,
           const dpctl::tensor::usm_ndarray &values,
           const std::int64_t num_rows, const std::int64_t num_cols,
           const std::int64_t nnz, const std::vector<sycl::event> &depends)
            -> std::tuple<std::uintptr_t, int, sycl::event> {
            return sparse_gemv_init(exec_q, trans, row_ptr, col_ind, values,
                                    num_rows, num_cols, nnz, depends);
        },
        py::arg("exec_q"), py::arg("trans"), py::arg("row_ptr"),
        py::arg("col_ind"), py::arg("values"), py::arg("num_rows"),
        py::arg("num_cols"), py::arg("nnz"), py::arg("depends"),
        "Initialise oneMKL sparse matrix handle "
        "(set_csr_data + optimize_gemv). "
        "Returns (handle_ptr: int, val_type_id: int, event). "
        "Call once per operator.");

    // ------------------------------------------------------------------
    // _sparse_gemv_compute(exec_q, handle, val_type_id, trans, alpha,
    //                     x, beta, y, num_rows, num_cols, depends)
    //     -> gemv_event
    //
    // Fires sparse::gemv using a pre-built handle. Verifies x and y
    // dtype match val_type_id from init, and that shapes agree with
    // op(A) dimensions (swapped for trans != N).
    //
    // Only the cheap MKL kernel is dispatched; no analysis overhead.
    // No host_task keep-alive is submitted -- pybind11 refcounts the
    // usm_ndarrays across the call, and sequencing of subsequent work
    // on the same queue happens automatically.
    // ------------------------------------------------------------------
    m.def(
        "_sparse_gemv_compute",
        [](sycl::queue &exec_q, const std::uintptr_t handle_ptr,
           const int val_type_id, const int trans, const double alpha,
           const dpctl::tensor::usm_ndarray &x, const double beta,
           const dpctl::tensor::usm_ndarray &y, const std::int64_t num_rows,
           const std::int64_t num_cols,
           const std::vector<sycl::event> &depends) -> sycl::event {
            return sparse_gemv_compute(exec_q, handle_ptr, val_type_id, trans,
                                       alpha, x, beta, y, num_rows, num_cols,
                                       depends);
        },
        py::arg("exec_q"), py::arg("handle"), py::arg("val_type_id"),
        py::arg("trans"), py::arg("alpha"), py::arg("x"), py::arg("beta"),
        py::arg("y"), py::arg("num_rows"), py::arg("num_cols"),
        py::arg("depends"),
        "Execute sparse::gemv using a pre-built handle. "
        "Returns the gemv event.");

    // ------------------------------------------------------------------
    // _sparse_gemv_release(exec_q, handle, depends) -> event
    //
    // Releases the matrix_handle allocated by _sparse_gemv_init.
    // Must be called exactly once per handle after all compute calls
    // referencing it have completed. The returned event depends on the
    // release, so callers can chain CSR buffer deallocation on it.
    // ------------------------------------------------------------------
    m.def(
        "_sparse_gemv_release",
        [](sycl::queue &exec_q, const std::uintptr_t handle_ptr,
           const std::vector<sycl::event> &depends) -> sycl::event {
            return sparse_gemv_release(exec_q, handle_ptr, depends);
        },
        py::arg("exec_q"), py::arg("handle"), py::arg("depends"),
        "Release the oneMKL matrix_handle created by _sparse_gemv_init.");
}

1	//*****************************************************************************
2	// Copyright (c) 2025, 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	// - Neither the name of the copyright holder nor the names of its contributors
13	// may be used to endorse or promote products derived from this software
14	// without specific prior written permission.
15	//
16	// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
17	// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
18	// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
19	// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
20	// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
21	// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
22	// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
23	// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
24	// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
25	// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
26	// POSSIBILITY OF SUCH DAMAGE.
27	//*****************************************************************************
28
29	#include <cstdint>
30	#include <tuple>
31	#include <vector>
32
33	#include <pybind11/pybind11.h>
34	#include <pybind11/stl.h>
35
36	#include <sycl/sycl.hpp>
37
38	#include <dpctl4pybind11.hpp>
39
40	#include "gemv.hpp"
41
42	namespace py = pybind11;
43
44	using dpnp::extensions::sparse::init_sparse_gemv_dispatch_tables;
45	using dpnp::extensions::sparse::sparse_gemv_compute;
46	using dpnp::extensions::sparse::sparse_gemv_init;
47	using dpnp::extensions::sparse::sparse_gemv_release;
48
49	PYBIND11_MODULE(_sparse_impl, m)
50	{	2✔
51	init_sparse_gemv_dispatch_tables();	2✔
52
53	// ------------------------------------------------------------------
54	// _using_onemath()
55	//
56	// Reports whether the module was compiled against the portable
57	// OneMath interface (USE_ONEMATH) rather than direct oneMKL.
58	// ------------------------------------------------------------------
59	m.def("_using_onemath", []() -> bool {	2✔
60	#ifdef USE_ONEMATH
61	return true;
62	#else
NEW 63	return false;	×
NEW 64	#endif	×
NEW 65	});	×
66
67	// ------------------------------------------------------------------
68	// _sparse_gemv_init(exec_q, trans, row_ptr, col_ind, values,
69	// num_rows, num_cols, nnz, depends)
70	// -> (handle: int, val_type_id: int, event)
71	//
72	// Calls init_matrix_handle + set_csr_data + optimize_gemv ONCE.
73	//
74	// The returned handle is an opaque uintptr_t; val_type_id is the
75	// dpctl typenum lookup id of the matrix value dtype and MUST be
76	// passed back to _sparse_gemv_compute so the C++ layer can verify
77	// that x and y dtype match the handle.
78	//
79	// LIFETIME CONTRACT: the caller must keep row_ptr / col_ind / values
80	// USM allocations alive until _sparse_gemv_release has been called
81	// AND its returned event has completed. The handle does not copy
82	// the CSR arrays.
83	// ------------------------------------------------------------------
84	m.def(	2✔
85	"_sparse_gemv_init",	2✔
86	[](sycl::queue &exec_q, const int trans,	2✔
87	const dpctl::tensor::usm_ndarray &row_ptr,	2✔
88	const dpctl::tensor::usm_ndarray &col_ind,	2✔
89	const dpctl::tensor::usm_ndarray &values,	2✔
90	const std::int64_t num_rows, const std::int64_t num_cols,	2✔
91	const std::int64_t nnz, const std::vector<sycl::event> &depends)	2✔
92	-> std::tuple<std::uintptr_t, int, sycl::event> {	2✔
NEW 93	return sparse_gemv_init(exec_q, trans, row_ptr, col_ind, values,	×
NEW 94	num_rows, num_cols, nnz, depends);	×
NEW 95	},	×
96	py::arg("exec_q"), py::arg("trans"), py::arg("row_ptr"),	2✔
97	py::arg("col_ind"), py::arg("values"), py::arg("num_rows"),	2✔
98	py::arg("num_cols"), py::arg("nnz"), py::arg("depends"),	2✔
99	"Initialise oneMKL sparse matrix handle "	2✔
100	"(set_csr_data + optimize_gemv). "	2✔
101	"Returns (handle_ptr: int, val_type_id: int, event). "	2✔
102	"Call once per operator.");	2✔
103
104	// ------------------------------------------------------------------
105	// _sparse_gemv_compute(exec_q, handle, val_type_id, trans, alpha,
106	// x, beta, y, num_rows, num_cols, depends)
107	// -> gemv_event
108	//
109	// Fires sparse::gemv using a pre-built handle. Verifies x and y
110	// dtype match val_type_id from init, and that shapes agree with
111	// op(A) dimensions (swapped for trans != N).
112	//
113	// Only the cheap MKL kernel is dispatched; no analysis overhead.
114	// No host_task keep-alive is submitted -- pybind11 refcounts the
115	// usm_ndarrays across the call, and sequencing of subsequent work
116	// on the same queue happens automatically.
117	// ------------------------------------------------------------------
118	m.def(	2✔
119	"_sparse_gemv_compute",	2✔
120	[](sycl::queue &exec_q, const std::uintptr_t handle_ptr,	2✔
121	const int val_type_id, const int trans, const double alpha,	2✔
122	const dpctl::tensor::usm_ndarray &x, const double beta,	2✔
123	const dpctl::tensor::usm_ndarray &y, const std::int64_t num_rows,	2✔
124	const std::int64_t num_cols,	2✔
125	const std::vector<sycl::event> &depends) -> sycl::event {	2✔
NEW 126	return sparse_gemv_compute(exec_q, handle_ptr, val_type_id, trans,	×
NEW 127	alpha, x, beta, y, num_rows, num_cols,	×
NEW 128	depends);	×
NEW 129	},	×
130	py::arg("exec_q"), py::arg("handle"), py::arg("val_type_id"),	2✔
131	py::arg("trans"), py::arg("alpha"), py::arg("x"), py::arg("beta"),	2✔
132	py::arg("y"), py::arg("num_rows"), py::arg("num_cols"),	2✔
133	py::arg("depends"),	2✔
134	"Execute sparse::gemv using a pre-built handle. "	2✔
135	"Returns the gemv event.");	2✔
136
137	// ------------------------------------------------------------------
138	// _sparse_gemv_release(exec_q, handle, depends) -> event
139	//
140	// Releases the matrix_handle allocated by _sparse_gemv_init.
141	// Must be called exactly once per handle after all compute calls
142	// referencing it have completed. The returned event depends on the
143	// release, so callers can chain CSR buffer deallocation on it.
144	// ------------------------------------------------------------------
145	m.def(	2✔
146	"_sparse_gemv_release",	2✔
147	[](sycl::queue &exec_q, const std::uintptr_t handle_ptr,	2✔
148	const std::vector<sycl::event> &depends) -> sycl::event {	2✔
NEW 149	return sparse_gemv_release(exec_q, handle_ptr, depends);	×
NEW 150	},	×
151	py::arg("exec_q"), py::arg("handle"), py::arg("depends"),	2✔
152	"Release the oneMKL matrix_handle created by _sparse_gemv_init.");	2✔
153	}	2✔

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