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Feature sparse linalg solvers - #2841

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abagusetty:feature-sparse-linalg-solvers
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Feature sparse linalg solvers#2841
abagusetty wants to merge 141 commits into
IntelPython:masterfrom
abagusetty:feature-sparse-linalg-solvers

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@abagusetty abagusetty commented Apr 9, 2026

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Adds support for from dpnp.scipy.sparse.linalg import LinearOperator, cg, gmres, minres
Fixes: #2831

  • Have you provided a meaningful PR description?
  • Have you added a test, reproducer or referred to an issue with a reproducer?
  • Have you tested your changes locally for CPU and GPU devices?
  • Have you made sure that new changes do not introduce compiler warnings?
  • Have you checked performance impact of proposed changes?
  • Have you added documentation for your changes, if necessary?
  • Have you added your changes to the changelog?

abagusetty and others added 30 commits April 2, 2026 12:52
…oneMKL hooks

- _interface.py: add full operator algebra (.H, .T, +, *, **, neg),
  _AdjointLinearOperator, _TransposedLinearOperator, _SumLinearOperator,
  _ProductLinearOperator, _ScaledLinearOperator, _PowerLinearOperator,
  IdentityOperator, MatrixLinearOperator, _AdjointMatrixOperator,
  _CustomLinearOperator factory dispatch; extend aslinearoperator
  to handle dpnp sparse and dense arrays

- _iterative.py: add _make_system (dtype validation, preconditioner
  wiring, working dtype selection); add _make_fast_matvec CSR/oneMKL
  SpMV hook; fix GMRES Arnoldi inner product to single oneMKL BLAS
  gemv (dpnp.dot) instead of slow Python vdot loop; offload
  Hessenberg lstsq to numpy.linalg.lstsq (CPU, matches CuPy);
  fix SciPy host-fallback tol->rtol deprecation via _scipy_tol_kwarg;
  add preconditioner support to CG; keep MINRES as SciPy-backed stub

Refs: CuPy v14.0.1 cupyx/scipy/sparse/linalg/_interface.py,
      cupyx/scipy/sparse/linalg/_iterative.py"
…gmres, minres

Modeled after CuPy's cupyx_tests/scipy_tests/sparse_tests/test_linalg.py.
Covers:
  - LinearOperator: shape, dtype inference, matvec/rmatvec/matmat,
    subclassing, __matmul__, __call__, edge cases
  - aslinearoperator: dense array, duck-type, identity passthrough,
    rmatvec from dense, invalid inputs
  - cg: SPD convergence, scipy reference match, x0 warm start, b_ndim=2,
    callback, atol, LinearOperator path, invalid inputs,
    non-convergence info check
  - gmres: diag-dominant convergence, scipy reference match, restart
    variants, x0, b_ndim=2, callbacks, complex systems, atol,
    non-convergence info check, Hilbert-matrix stress test
  - minres: SPD, symmetric-indefinite, scipy reference, shift parameter,
    non-square guard, LinearOperator path, callback
  - Integration: parametric (n, dtype) cross-solver tests via LinearOperator
  - Import smoke tests: __all__ completeness
- Use dpnp.tests.helper: assert_dtype_allclose, generate_random_numpy_array,
  get_all_dtypes, get_float_complex_dtypes, has_support_aspect64
- Use dpnp.tests.third_party.cupy testing harness (with_requires, etc.)
- Use numpy.testing assert_allclose / assert_array_equal / assert_raises
- Use dpnp.asnumpy() instead of numpy.asarray()
- Use pytest parametrize ids matching existing test conventions
- Use is_scipy_available() helper from tests/helper.py
- Strict class-per-solver organisation matching TestCholesky / TestDet etc.
…or dtype

Two bugs fixed:
1. _init_dtype() was calling dpnp.zeros(n) which defaults to float64,
   so a float32 matvec would upcast and return float64, making the
   inferred dtype wrong.  Fix: use dpnp.zeros(n, dtype=dpnp.int8) as
   SciPy/CuPy do — any numeric matvec will promote int8 to its own dtype.
2. _CustomLinearOperator.__init__ called _init_dtype() even when an
   explicit dtype was already supplied, overwriting the caller's value.
   Fix: _init_dtype() now short-circuits when self.dtype is already set.
…ption handling

Align gemv.cpp with the conventions established in blas/gemm.cpp:

Headers added:
- ext/common.hpp         (dpctl_td_ns, consistent with other extensions)
- utils/memory_overlap.hpp   (MemoryOverlap guard on x vs y)
- utils/output_validation.hpp (CheckWritable + AmpleMemory on y)
- utils/type_utils.hpp       (validate_type_for_device<T> in impl)
- <sstream>                  (needed for stringstream error_msg)

Exception handling added in sparse_gemv_impl():
- try/catch(oneapi::mkl::exception) around all oneMKL sparse calls
- try/catch(sycl::exception) around all oneMKL sparse calls
- release_matrix_handle cleanup in the exception error path
- throw std::runtime_error with descriptive message on catch

Input validation added in sparse_gemv():
- ndim checks: x and y must be 1-D
- queues_are_compatible() across all 5 USM arrays
- MemoryOverlap()(x, y) aliasing guard
- CheckWritable::throw_if_not_writable(y)
- AmpleMemory::throw_if_not_ample(y, num_rows)
- keep_args_alive() at function exit (was missing, returning empty event)
… table

Modeled after blas/gemm.cpp (2-D table: value type x index type) and
blas/gemv.cpp (dispatch vector pattern with ContigFactory + init_dispatch_table).

Changes:
- Add sparse/types_matrix.hpp with SparseGemvTypePairSupportFactory<Tv, Ti>
  encoding the 4 supported combinations: {float32,float64} x {int32,int64}
- Rewrite sparse_gemv_impl() to take typeless char* pointers (matching
  the blas gemv_impl signature style) — type info flows through template
  params only, no runtime branching inside the impl
- Replace the 60-line if/else val_typenum/idx_typenum chain in sparse_gemv()
  with a 2-D dispatch table lookup (gemv_dispatch_table[val_id][idx_id])
- Rename init_sparse_gemv_dispatch_vector -> init_sparse_gemv_dispatch_table
  and implement it via init_dispatch_table<> from ext/common.hpp
- All validation guards and exception handling from prior commit are preserved
…se_gemv_dispatch_table

Follows the rename made in gemv.cpp when the dispatch mechanism was
changed from a 1-D vector to a 2-D table (value type x index type).
All other declarations (sparse_gemv signature, parameters) are unchanged.
The oneMKL 2025-2 sparse BLAS API deprecated the old 8-argument
set_csr_data(queue, handle, nrows, ncols, index_base, row_ptr, col_ind,
values, deps) overload in favour of a new signature that takes the
sparse matrix handle as `spmat` and adds an explicit `nnz` argument:

  set_csr_data(queue, spmat, nrows, ncols, nnz, index_base,
               row_ptr, col_ind, values, deps)

Fixes:
- Replace old set_csr_data call with the new nnz-aware signature
- Silences the resulting -Wunused-parameter warning on `nnz` (now used)
- No functional change; all other logic is unchanged
…tring

Line 477: `hasattr(A, "rmatmat\")` had a Markdown-escaped backslash
leaked into the Python source, causing an unterminated string literal.
Fixed to `hasattr(A, "rmatmat")`.
dpnp.ndarray blocks implicit NumPy conversion via __array__ to prevent
silent dtype=object arrays. All test assertions must use .asnumpy()
to materialize device arrays onto the host explicitly.

Also replaces numpy.asarray(x_dp) in _rel_residual helper.
…dation order

- _iterative.py: raise NotImplementedError for M != None *before* the
  _HOST_N_THRESHOLD SciPy fast-path in cg() and gmres(), so the contract
  is enforced regardless of system size (fixes test_cg_preconditioner_unsupported_raises,
  test_gmres_preconditioner_unsupported_raises).
- _iterative.py: validate callback_type and raise NotImplementedError for
  'pr_norm' *before* the _HOST_N_THRESHOLD branch in gmres(), so small-n
  systems also see the error (fixes test_gmres_callback_type_pr_norm_raises).
- _iterative.py: pass callback_type='legacy' to scipy.sparse.linalg.gmres
  when delegating on the fast path to suppress SciPy DeprecationWarning.
- test_scipy_sparse_linalg.py: add dtype=numpy.float64 to expected arange()
  calls in test_identity_operator and test_gmres_happy_breakdown so strict
  NumPy 2.0 dtype-equality checks pass (float64 result vs int64 expected).
- Replace .asnumpy() method calls with dpnp.asnumpy() module fn
  (asnumpy is not an ndarray method in dpnp; it is a top-level fn)
- Fix dpnp.any(x) ambiguous truth value in x0 zero-check; replace
  with explicit `x0 is not None` guard for r0 initialisation
- Fix V_mat.T.conj() -> dpnp.conj(V_mat.T) in GMRES Arnoldi step
- Guard minres beta sqrt against tiny negative floats: sqrt(abs(...))
- Unify GMRES Hessenberg h_np assignment to avoid .real stripping
  producing wrong dtype for complex systems
- Fix float() cast on dpnp scalar norm inside GMRES inner h_j1 line
…failures)

The committed code used hypot(gbar, oldb) as delta_k which is the
gamma (norm) from the PREVIOUS rotation step, not the correct diagonal
entry from applying the previous Givens rotation to the current column.

The correct Paige-Saunders (1975) two-rotation recurrence is:

  oldeps = epsln
  delta  = cs * dbar + sn * alpha   # apply previous rotation
  gbar_k = sn * dbar - cs * alpha   # residual -> new rotation input
  epsln  = sn * beta
  dbar   = -cs * beta

  gamma = hypot(gbar_k, beta)       # NEW rotation eliminates beta
  cs    = gbar_k / gamma
  sn    = beta   / gamma

  w_new = (v - oldeps*w - delta*w2) / gamma  # three-term update

This matches scipy.sparse.linalg.minres and Choi (2006) eq. 6.11.

The buggy recurrence produced solutions ~1.08x away from the true
solution (rel_err ~1e0) instead of the expected ~1e-13.

Co-authored-by: fix-minres-recurrence
Comment thread dpnp/backend/extensions/sparse/CMakeLists.txt Outdated
Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com>
Comment thread dpnp/backend/extensions/blas/gemv.cpp Outdated
Comment thread dpnp/backend/extensions/blas/gemv.cpp Outdated
Comment thread dpnp/backend/extensions/blas/gemv.cpp Outdated
Comment thread dpnp/backend/extensions/blas/gemv.hpp Outdated
Comment thread dpnp/backend/extensions/sparse/gemv.cpp Outdated
Comment thread dpnp/backend/extensions/sparse/gemv.cpp Outdated
Comment thread dpnp/backend/extensions/sparse/gemv.cpp Outdated
Comment thread dpnp/scipy/sparse/linalg/__init__.py Outdated
and a small set of Krylov solvers (``cg``, ``gmres``, ``minres``).
"""

from __future__ import annotations

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Do we need that? Is it used somewhere?

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It is removed!

Comment thread dpnp/scipy/sparse/__init__.py Outdated

__all__ = [
"linalg",
"SparseABC",

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Why do we need to expose SparseABC as part of scipy.sparse submodule?
SciPy does not did that.

Comment thread dpnp/scipy/_lib/_sparse.py Outdated
@@ -0,0 +1,27 @@
"""Sparse base class and predicate, mirroring scipy/_lib/_sparse.py.

@antonwolfy antonwolfy Jul 7, 2026

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Should we mimic and move the file to dpnp/scipy/_lib/_sparse.py?

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Moved it to mimic scipy style. Good idea indeed


from abc import ABC


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Suggested change
__all__ = ["SparseABC", "issparse"]

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Done!

"""Abstract base for all dpnp.scipy.sparse format classes."""


def issparse(x):

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We need to add rendering of documentation for the new sparse submodule.
Use doc/reference/scipy_linalg.rst as a reference.

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Done!

Comment thread dpnp/scipy/sparse/_csr.py Outdated
# set_csr_data + optimize_gemv must complete before any compute
# call can dispatch against the handle. This is the only blocking
# sync; subsequent matvecs return without waiting.
ev.wait()

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Why do we need to have that call as a blocking? We can add the returned event under control of SequentialOrderManager.
Or alternatively we no need to return the event from _sparse_gemv_init and to implement the wait inside.

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Added a aadd_event_pair to be consistent with the rest of codebase. Remove the blocking sync

Comment thread dpnp/scipy/sparse/_csr.py Outdated
_manager.add_event_pair(ht_ev, comp_ev)
return y

# Dense fallback. Materialises ``self`` once -- this path is

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I wonder if it is a fare option to implement the fallback silently. It does not seem present in CuPy. CuPy raises the explicit exception for unsupported dtypes. While SciPy support any dtypes, since implements own sparse kernels.

In fallback case we have to allocate the full dense array, which may cause protentional OOM issue.
And so leaving the fallback implementation under the user control might be more friendly.

Also, as an option, we can implement upcasting of input arrays to support more wide range of dtypes.

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Dense fallback removed and dot raises TypeError for unsupported value/index dtype (CuPy-style, no O(M·N) densify/OOM). A few tests were added too

Comment thread dpnp/scipy/sparse/_csr.py Outdated
Comment thread dpnp/scipy/sparse/_csr.py Outdated
return

release_fn = getattr(si, "_sparse_gemv_release", None)
if release_fn is None:

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When there might be no _sparse_gemv_release available?

Comment thread dpnp/scipy/sparse/_csr.py Outdated

try:
# pylint: disable-next=not-callable
release_fn(self._spmv_exec_q, handle, [])

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Why no dependencies need to be passed?

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Also release_fn returns the SYCL event but seems no handling

abagusetty and others added 13 commits July 7, 2026 07:25
Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com>
- _base.py: add copyright header, __all__, scipy-style issparse docstring
- __init__.py: drop SparseABC from public __all__ (scipy does not expose it)
- _csr.py: add copyright header
- _csr.py: accept dpnp.ndarray and usm_ndarray via is_supported_array_type
- _csr.py: use issparse for the csr-copy construction branch
- _csr.py: validate components with check_supported_arrays_type
- _csr.py: check queues via get_execution_queue + ExecutionPlacementError
- _csr.py: support empty csr_matrix((M, N)) and CSR shape inference
- _csr.py: allocate output with empty_like(shape=)
- _csr.py: chain init event via SequentialOrderManager instead of blocking wait
- _csr.py: remove silent dense fallback; raise for unsupported dtype (CuPy-style)
- _csr.py: handle the returned release event in __del__ and clarify guards
- _csr.py: raise NotImplementedError for unsupported scipy/cupy CSR ops
- linalg/__init__.py: drop unused __future__ annotations import
- doc: add scipy_sparse reference page and wire into toctree
- tests: cover usm_ndarray construction/dot, empty/inferred shape, unsupported dtype
- _csr.py: pass submitted_events as depends to _sparse_gemv_init
- _csr.py: pass depends + register returned event on release in __del__
- _iterative.py: allocate matvec output via empty_like(shape=)
- _iterative.py: drop stale _make_fast_matvec fallback comments
Mirror SciPy's scipy/_lib/_sparse.py layout for the shared SparseABC /
issparse helpers.

- add dpnp/scipy/_lib package
- update imports in scipy.sparse __init__ and _csr
- register dpnp.scipy._lib in setup.py
- _csr.py: asarray-normalize dense/component inputs to dpnp.ndarray so
  internal ops (sort_indices, toarray, dot) work uniformly
- _csr.py, _iterative.py: pass dpnp.get_usm_ndarray(...) to the sparse
  pybind bindings, accepting both dpnp.ndarray and usm_ndarray
- _interface.py: LinearOperator.dot and aslinearoperator accept
  usm_ndarray via is_supported_array_type (keep numpy-reject message)
- _interface.py: numpydoc for LinearOperator, matvec, rmatvec, matmat,
  rmatmat, dot, aslinearoperator (Parameters/Returns, scipy refs)
- _iterative.py: scipy-style numpydoc for cg, gmres, minres
- tests: cover aslinearoperator/matvec with usm_ndarray dense input
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Request support for scipy.sparse.linalg LinearOperator, GMRES, and MINRES

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