Add dpnp.linalg.eigvalsh() implementation

dpnp/backend/extensions/lapack/heevd.hpp

@@ -44,7 +44,7 @@ extern std::pair<sycl::event, sycl::event>
           const std::int8_t upper_lower,
           dpctl::tensor::usm_ndarray eig_vecs,
           dpctl::tensor::usm_ndarray eig_vals,
-          const std::vector<sycl::event> &depends);
+          const std::vector<sycl::event> &depends = {});
 
 extern void init_heevd_dispatch_table(void);
 } // namespace lapack

dpnp/linalg/dpnp_iface_linalg.py

@@ -69,6 +69,7 @@
     "eig",
     "eigh",
     "eigvals",
+    "eigvalsh",
     "inv",
     "matrix_power",
     "matrix_rank",
@@ -247,6 +248,19 @@ def eigh(a, UPLO="L"):
 
     For full documentation refer to :obj:`numpy.linalg.eigh`.
 
+    Parameters
+    ----------
+    a : (..., M, M) {dpnp.ndarray, usm_ndarray}
+        A complex- or real-valued array whose eigenvalues and eigenvectors are to be computed.
+    UPLO : {"L", "U"}, optional
+        Specifies the calculation uses either the lower ("L") or upper ("U")
+        triangular part of the matrix.
+        Regardless of this choice, only the real parts of the diagonal are
+        considered to preserve the Hermite matrix property.
+        It therefore follows that the imaginary part of the diagonal
+        will always be treated as zero.
+        Default: "L".
+
     Returns
     -------
     w : (..., M) dpnp.ndarray
@@ -256,15 +270,13 @@ def eigh(a, UPLO="L"):
         The column ``v[:, i]`` is the normalized eigenvector corresponding
         to the eigenvalue ``w[i]``.
 
-    Limitations
-    -----------
-    Parameter `a` is supported as :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
-    Input array data types are limited by supported DPNP :ref:`Data types`.
-
     See Also
     --------
-    :obj:`dpnp.eig` : eigenvalues and right eigenvectors for non-symmetric arrays.
-    :obj:`dpnp.eigvals` : eigenvalues of non-symmetric arrays.
+    :obj:`dpnp.linalg.eigvalsh` : Compute the eigenvalues of a complex Hermitian or
+                                  real symmetric matrix.
+    :obj:`dpnp.linalg.eig` : Compute the eigenvalues and right eigenvectors of
+                             a square array.
+    :obj:`dpnp.linalg.eigvals` : Compute the eigenvalues of a general matrix.
 
     Examples
     --------
@@ -282,20 +294,13 @@ def eigh(a, UPLO="L"):
     """
 
     dpnp.check_supported_arrays_type(a)
+    check_stacked_2d(a)
+    check_stacked_square(a)
 
+    UPLO = UPLO.upper()
     if UPLO not in ("L", "U"):
         raise ValueError("UPLO argument must be 'L' or 'U'")
 
-    if a.ndim < 2:
-        raise ValueError(
-            "%d-dimensional array given. Array must be "
-            "at least two-dimensional" % a.ndim
-        )
-
-    m, n = a.shape[-2:]
-    if m != n:
-        raise ValueError("Last 2 dimensions of the array must be square")
-
     return dpnp_eigh(a, UPLO=UPLO)
 
 
@@ -327,6 +332,64 @@ def eigvals(input):
     return call_origin(numpy.linalg.eigvals, input)
 
 
+def eigvalsh(a, UPLO="L"):
+    """
+    eigvalsh(a, UPLO="L")
+
+    Compute the eigenvalues of a complex Hermitian or real symmetric matrix.
+
+    Main difference from :obj:`dpnp.linalg.eigh`: the eigenvectors are not computed.
+
+    For full documentation refer to :obj:`numpy.linalg.eigvalsh`.
+
+    Parameters
+    ----------
+    a : (..., M, M) {dpnp.ndarray, usm_ndarray}
+        A complex- or real-valued array whose eigenvalues are to be computed.
+    UPLO : {"L", "U"}, optional
+        Specifies the calculation uses either the lower ("L") or upper ("U")
+        triangular part of the matrix.
+        Regardless of this choice, only the real parts of the diagonal are
+        considered to preserve the Hermite matrix property.
+        It therefore follows that the imaginary part of the diagonal
+        will always be treated as zero.
+        Default: "L".
+
+    Returns
+    -------
+    w : (..., M) dpnp.ndarray
+        The eigenvalues in ascending order, each repeated according to
+        its multiplicity.
+
+    See Also
+    --------
+    :obj:`dpnp.linalg.eigh` : Return the eigenvalues and eigenvectors of a complex Hermitian
+                              (conjugate symmetric) or a real symmetric matrix.
+    :obj:`dpnp.linalg.eigvals` : Compute the eigenvalues of a general matrix.
+    :obj:`dpnp.linalg.eig` : Compute the eigenvalues and right eigenvectors of
+                             a general matrix.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> from dpnp import linalg as LA
+    >>> a = np.array([[1, -2j], [2j, 5]])
+    >>> LA.eigvalsh(a)
+    array([0.17157288, 5.82842712])
+
+    """
+
+    dpnp.check_supported_arrays_type(a)
+    check_stacked_2d(a)
+    check_stacked_square(a)
+
+    UPLO = UPLO.upper()
+    if UPLO not in ("L", "U"):
+        raise ValueError("UPLO argument must be 'L' or 'U'")
+
+    return dpnp_eigh(a, UPLO=UPLO, eigen_mode="N")
+
+
 def inv(a):
     """
     Compute the (multiplicative) inverse of a matrix.

dpnp/linalg/dpnp_utils_linalg.py

@@ -888,72 +888,91 @@ def dpnp_det(a):
     return det.reshape(shape)
 
 
-def dpnp_eigh(a, UPLO):
+def dpnp_eigh(a, UPLO, eigen_mode="V"):
     """
-    dpnp_eigh(a, UPLO)
+    dpnp_eigh(a, UPLO, eigen_mode="V")
 
     Return the eigenvalues and eigenvectors of a complex Hermitian
     (conjugate symmetric) or a real symmetric matrix.
+    Can return both eigenvalues and eigenvectors (`eigen_mode="V"`) or
+    only eigenvalues (`eigen_mode="N"`).
 
     The main calculation is done by calling an extension function
     for LAPACK library of OneMKL. Depending on input type of `a` array,
     it will be either ``heevd`` (for complex types) or ``syevd`` (for others).
 
     """
 
-    a_usm_type = a.usm_type
-    a_sycl_queue = a.sycl_queue
-    a_order = "C" if a.flags.c_contiguous else "F"
-    a_usm_arr = dpnp.get_usm_ndarray(a)
+    # get resulting type of arrays with eigenvalues and eigenvectors
+    v_type = _common_type(a)
+    w_type = _real_type(v_type)
 
-    # 'V' means both eigenvectors and eigenvalues will be calculated
-    jobz = _jobz["V"]
+    if a.size == 0:
+        w = dpnp.empty_like(a, shape=a.shape[:-1], dtype=w_type)
+        if eigen_mode == "V":
+            v = dpnp.empty_like(a, dtype=v_type)
+            return w, v
+        return w
+
+    # `eigen_mode` can be either "N" or "V", specifying the computation mode
+    # for OneMKL LAPACK `syevd` and `heevd` routines.
+    # "V" (default) means both eigenvectors and eigenvalues will be calculated
+    # "N" means only eigenvalues will be calculated
+    jobz = _jobz[eigen_mode]
     uplo = _upper_lower[UPLO]
 
-    # get resulting type of arrays with eigenvalues and eigenvectors
-    a_dtype = a.dtype
-    lapack_func = "_syevd"
-    if dpnp.issubdtype(a_dtype, dpnp.complexfloating):
-        lapack_func = "_heevd"
-        v_type = a_dtype
-        w_type = dpnp.float64 if a_dtype == dpnp.complex128 else dpnp.float32
-    elif dpnp.issubdtype(a_dtype, dpnp.floating):
-        v_type = w_type = a_dtype
-    elif a_sycl_queue.sycl_device.has_aspect_fp64:
-        v_type = w_type = dpnp.float64
-    else:
-        v_type = w_type = dpnp.float32
+    # Get LAPACK function (_syevd for real or _heevd for complex data types)
+    # to compute all eigenvalues and, optionally, all eigenvectors
+    lapack_func = (
+        "_heevd" if dpnp.issubdtype(v_type, dpnp.complexfloating) else "_syevd"
+    )
+
+    a_sycl_queue = a.sycl_queue
+    a_order = "C" if a.flags.c_contiguous else "F"
 
     if a.ndim > 2:
-        w = dpnp.empty(
-            a.shape[:-1],
+        is_cpu_device = a.sycl_device.has_aspect_cpu
+        orig_shape = a.shape
+        # get 3d input array by reshape
+        a = a.reshape(-1, orig_shape[-2], orig_shape[-1])
+        a_usm_arr = dpnp.get_usm_ndarray(a)
+
+        # allocate a memory for dpnp array of eigenvalues
+        w = dpnp.empty_like(
+            a,
+            shape=orig_shape[:-1],
             dtype=w_type,
-            usm_type=a_usm_type,
-            sycl_queue=a_sycl_queue,
         )
+        w_orig_shape = w.shape
+        # get 2d dpnp array with eigenvalues by reshape
+        w = w.reshape(-1, w_orig_shape[-1])
 
         # need to loop over the 1st dimension to get eigenvalues and eigenvectors of 3d matrix A
-        op_count = a.shape[0]
-        if op_count == 0:
-            return w, dpnp.empty_like(a, dtype=v_type)
-
-        eig_vecs = [None] * op_count
-        ht_copy_ev = [None] * op_count
-        ht_lapack_ev = [None] * op_count
-        for i in range(op_count):
+        batch_size = a.shape[0]
+        eig_vecs = [None] * batch_size
+        ht_list_ev = [None] * batch_size * 2
+        for i in range(batch_size):
             # oneMKL LAPACK assumes fortran-like array as input, so
             # allocate a memory with 'F' order for dpnp array of eigenvectors
             eig_vecs[i] = dpnp.empty_like(a[i], order="F", dtype=v_type)
 
             # use DPCTL tensor function to fill the array of eigenvectors with content of input array
-            ht_copy_ev[i], copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
+            ht_list_ev[2 * i], copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
                 src=a_usm_arr[i],
                 dst=eig_vecs[i].get_array(),
                 sycl_queue=a_sycl_queue,
             )
 
+            # TODO: Remove this w/a when MKLD-17201 is solved.
+            # Waiting for a host task executing an OneMKL LAPACK syevd call
+            # on CPU causes deadlock due to serialization of all host tasks
+            # in the queue.
+            # We need to wait for each host tasks before calling _seyvd to avoid deadlock.
+            if lapack_func == "_syevd" and is_cpu_device:
+                ht_list_ev[2 * i].wait()
+
             # call LAPACK extension function to get eigenvalues and eigenvectors of a portion of matrix A
-            ht_lapack_ev[i], _ = getattr(li, lapack_func)(
+            ht_list_ev[2 * i + 1], _ = getattr(li, lapack_func)(
                 a_sycl_queue,
                 jobz,
                 uplo,
@@ -962,29 +981,43 @@ def dpnp_eigh(a, UPLO):
                 depends=[copy_ev],
             )
 
-        for i in range(op_count):
-            ht_lapack_ev[i].wait()
-            ht_copy_ev[i].wait()
+        dpctl.SyclEvent.wait_for(ht_list_ev)
+
+        w = w.reshape(w_orig_shape)
+
+        if eigen_mode == "V":
+            # combine the list of eigenvectors into a single array
+            v = dpnp.array(eig_vecs, order=a_order).reshape(orig_shape)
+            return w, v
+        return w
 
-        # combine the list of eigenvectors into a single array
-        v = dpnp.array(eig_vecs, order=a_order)
-        return w, v
     else:
-        # oneMKL LAPACK assumes fortran-like array as input, so
-        # allocate a memory with 'F' order for dpnp array of eigenvectors
-        v = dpnp.empty_like(a, order="F", dtype=v_type)
+        a_usm_arr = dpnp.get_usm_ndarray(a)
+        ht_list_ev = []
+        copy_ev = dpctl.SyclEvent()
 
-        # use DPCTL tensor function to fill the array of eigenvectors with content of input array
-        ht_copy_ev, copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
-            src=a_usm_arr, dst=v.get_array(), sycl_queue=a_sycl_queue
-        )
+        # When `eigen_mode == "N"` (jobz == 0), OneMKL LAPACK does not overwrite the input array.
+        # If the input array 'a' is already F-contiguous and matches the target data type,
+        # we can avoid unnecessary memory allocation and data copying.
+        if eigen_mode == "N" and a_order == "F" and a.dtype == v_type:
+            v = a
+
+        else:
+            # oneMKL LAPACK assumes fortran-like array as input, so
+            # allocate a memory with 'F' order for dpnp array of eigenvectors
+            v = dpnp.empty_like(a, order="F", dtype=v_type)
+
+            # use DPCTL tensor function to fill the array of eigenvectors with content of input array
+            ht_copy_ev, copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
+                src=a_usm_arr, dst=v.get_array(), sycl_queue=a_sycl_queue
+            )
+            ht_list_ev.append(ht_copy_ev)
 
         # allocate a memory for dpnp array of eigenvalues
-        w = dpnp.empty(
-            a.shape[:-1],
+        w = dpnp.empty_like(
+            a,
+            shape=a.shape[:-1],
             dtype=w_type,
-            usm_type=a_usm_type,
-            sycl_queue=a_sycl_queue,
         )
 
         # call LAPACK extension function to get eigenvalues and eigenvectors of matrix A
@@ -996,8 +1029,9 @@ def dpnp_eigh(a, UPLO):
             w.get_array(),
             depends=[copy_ev],
         )
+        ht_list_ev.append(ht_lapack_ev)
 
-        if a_order != "F":
+        if eigen_mode == "V" and a_order != "F":
             # need to align order of eigenvectors with one of input matrix A
             out_v = dpnp.empty_like(v, order=a_order)
             ht_copy_out_ev, _ = ti._copy_usm_ndarray_into_usm_ndarray(
@@ -1006,14 +1040,13 @@ def dpnp_eigh(a, UPLO):
                 sycl_queue=a_sycl_queue,
                 depends=[lapack_ev],
             )
-            ht_copy_out_ev.wait()
+            ht_list_ev.append(ht_copy_out_ev)
         else:
             out_v = v
 
-        ht_lapack_ev.wait()
-        ht_copy_ev.wait()
+        dpctl.SyclEvent.wait_for(ht_list_ev)
 
-        return w, out_v
+        return (w, out_v) if eigen_mode == "V" else w
 
 
 def dpnp_inv_batched(a, res_type):