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rocm_jax/jaxlib/lapack.py
Leello Tadesse Dadi f9a246ac19 schur lapack wrapper
2021-09-29 14:29:52 +02:00

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# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Shims that allow the XLA CPU backend to call scipy-provided LAPACK kernels
# via CustomCallWithLayout.
import numpy as np
from jaxlib import xla_client
from . import _lapack
for _name, _value in _lapack.registrations().items():
xla_client.register_custom_call_target(_name, _value, platform="cpu")
_ops = xla_client.ops
Shape = xla_client.Shape
def _constant_s32_scalar(c, x):
return _ops.Constant(c, np.int32(x))
# TODO(phawkins): it would be nice to avoid duplicating code for each type.
# ?trsm(left_side, lower, trans_a, diag, m, n, alpha, a, b):
# triangular solve
def trsm(c, alpha, a, b, left_side=False, lower=False, trans_a=False,
conj_a=False, diag=False):
a_shape = c.get_shape(a)
b_shape = c.get_shape(b)
dtype = b_shape.element_type()
dims = b_shape.dimensions()
m, n = dims[-2:]
k = m if left_side else n
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
num_b = 1
for d in batch_dims:
num_b *= d
if batch_dims + (k, k) != a_shape.dimensions() or a_shape.element_type() != dtype:
raise ValueError("Argument mismatch for trsm, got {} and {}".format(
a_shape, b_shape))
if dtype == np.float32:
fn = b"blas_strsm"
elif dtype == np.float64:
fn = b"blas_dtrsm"
elif dtype == np.complex64:
fn = b"blas_ctrsm"
elif dtype == np.complex128:
fn = b"blas_ztrsm"
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
if conj_a and not trans_a:
raise NotImplementedError("Conjugation without transposition not supported")
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
return _ops.CustomCallWithLayout(
c, fn,
operands=(
_constant_s32_scalar(c, int(left_side)),
_constant_s32_scalar(c, int(lower)),
_constant_s32_scalar(c, (2 if conj_a else 1) if trans_a else 0),
_constant_s32_scalar(c, int(diag)),
_constant_s32_scalar(c, m),
_constant_s32_scalar(c, n),
_constant_s32_scalar(c, num_b),
alpha, a, b),
shape_with_layout=Shape.array_shape(dtype, b_shape.dimensions(), layout),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(dtype, (), ()),
Shape.array_shape(dtype, a_shape.dimensions(), layout),
Shape.array_shape(dtype, b_shape.dimensions(), layout),
))
jax_trsm = trsm
# # ?getrf: LU decomposition
def getrf(c, a):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
if dtype == np.float32:
fn = b"lapack_sgetrf"
elif dtype == np.float64:
fn = b"lapack_dgetrf"
elif dtype == np.complex64:
fn = b"lapack_cgetrf"
elif dtype == np.complex128:
fn = b"lapack_zgetrf"
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
out = _ops.CustomCallWithLayout(
c, fn,
operands=(
_constant_s32_scalar(c, b),
_constant_s32_scalar(c, m),
_constant_s32_scalar(c, n),
a),
shape_with_layout=Shape.tuple_shape((
Shape.array_shape(
dtype,
batch_dims + (m, n),
(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(
np.dtype(np.int32),
batch_dims + (min(m, n),),
tuple(range(num_bd, -1, -1))),
Shape.array_shape(np.dtype(np.int32), batch_dims,
tuple(range(num_bd - 1, -1, -1))),
)),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(
dtype,
batch_dims + (m, n),
(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
))
return tuple(_ops.GetTupleElement(out, i) for i in range(3))
# # ?geqrf: QR decomposition
def geqrf(c, a):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
if dtype == np.float32:
fn = b"lapack_sgeqrf"
lwork = _lapack.lapack_sgeqrf_workspace(m, n)
elif dtype == np.float64:
fn = b"lapack_dgeqrf"
lwork = _lapack.lapack_dgeqrf_workspace(m, n)
elif dtype == np.complex64:
fn = b"lapack_cgeqrf"
lwork = _lapack.lapack_cgeqrf_workspace(m, n)
elif dtype == np.complex128:
fn = b"lapack_zgeqrf"
lwork = _lapack.lapack_zgeqrf_workspace(m, n)
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
out = _ops.CustomCallWithLayout(
c, fn,
operands=(
_constant_s32_scalar(c, b),
_constant_s32_scalar(c, m),
_constant_s32_scalar(c, n),
_constant_s32_scalar(c, lwork),
a,
),
shape_with_layout=Shape.tuple_shape((
Shape.array_shape(
dtype,
batch_dims + (m, n),
(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(
np.dtype(dtype),
batch_dims + (min(m, n),),
tuple(range(num_bd, -1, -1))),
Shape.array_shape(np.dtype(np.int32), batch_dims,
tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(np.dtype(dtype), (lwork,), (0,)),
)),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(
dtype,
batch_dims + (m, n),
(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
))
return tuple(_ops.GetTupleElement(out, i) for i in range(3))
# # ?orgqr: product of elementary Householder reflectors:
def orgqr(c, a, tau):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
tau_dims = c.get_shape(tau).dimensions()
assert tau_dims[:-1] == dims[:-2]
k = tau_dims[-1]
if dtype == np.float32:
fn = b"lapack_sorgqr"
lwork = _lapack.lapack_sorgqr_workspace(m, n, k)
elif dtype == np.float64:
fn = b"lapack_dorgqr"
lwork = _lapack.lapack_dorgqr_workspace(m, n, k)
elif dtype == np.complex64:
fn = b"lapack_cungqr"
lwork = _lapack.lapack_cungqr_workspace(m, n, k)
elif dtype == np.complex128:
fn = b"lapack_zungqr"
lwork = _lapack.lapack_zungqr_workspace(m, n, k)
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
out = _ops.CustomCallWithLayout(
c, fn,
operands=(
_constant_s32_scalar(c, b),
_constant_s32_scalar(c, m),
_constant_s32_scalar(c, n),
_constant_s32_scalar(c, k),
_constant_s32_scalar(c, lwork),
a,
tau,
),
shape_with_layout=Shape.tuple_shape((
Shape.array_shape(
dtype,
batch_dims + (m, n),
(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(np.dtype(np.int32), batch_dims,
tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(dtype, (lwork,), (0,)),
)),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(
dtype,
batch_dims + (m, n),
(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(
dtype,
batch_dims + (k,),
tuple(range(num_bd, -1, -1))),
))
return tuple(_ops.GetTupleElement(out, i) for i in range(2))
# ?potrf: Cholesky decomposition
def potrf(c, a, lower=False):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
m, n = dims[-2:]
if m != n:
raise ValueError("potrf expects a square matrix, got {}".format(a_shape))
if dtype == np.float32:
fn = b"lapack_spotrf"
elif dtype == np.float64:
fn = b"lapack_dpotrf"
elif dtype == np.complex64:
fn = b"lapack_cpotrf"
elif dtype == np.complex128:
fn = b"lapack_zpotrf"
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
out = _ops.CustomCallWithLayout(
c, fn,
operands=(_constant_s32_scalar(c, int(lower)),
_constant_s32_scalar(c, b), _constant_s32_scalar(c, n), a),
shape_with_layout=Shape.tuple_shape((
Shape.array_shape(dtype, dims, layout),
Shape.array_shape(
np.dtype(np.int32), batch_dims, tuple(range(num_bd - 1, -1, -1))),
)),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(dtype, dims, layout),
))
return tuple(_ops.GetTupleElement(out, i) for i in range(2))
# # ?gesdd: Singular value decomposition
def gesdd(c, a, full_matrices=True, compute_uv=True):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
if dtype == np.float32:
fn = b"lapack_sgesdd"
singular_vals_dtype = np.float32
lwork = _lapack.sgesdd_work_size(m, n, compute_uv, full_matrices)
workspace = (
Shape.array_shape(np.dtype(np.int32),
(_lapack.gesdd_iwork_size(m, n),), (0,)),
Shape.array_shape(dtype, (lwork,), (0,)),
)
elif dtype == np.float64:
fn = b"lapack_dgesdd"
singular_vals_dtype = np.float64
lwork = _lapack.dgesdd_work_size(m, n, compute_uv, full_matrices)
workspace = (
Shape.array_shape(np.dtype(np.int32),
(_lapack.gesdd_iwork_size(m, n),), (0,)),
Shape.array_shape(dtype, (lwork,), (0,)),
)
elif dtype == np.complex64:
fn = b"lapack_cgesdd"
singular_vals_dtype = np.float32
lwork = _lapack.cgesdd_work_size(m, n, compute_uv, full_matrices)
workspace = (
Shape.array_shape(np.dtype(np.int32), (_lapack.gesdd_iwork_size(m, n),),
(0,)),
Shape.array_shape(np.dtype(np.float32),
(_lapack.cgesdd_rwork_size(m, n, int(compute_uv)),),
(0,)),
Shape.array_shape(dtype, (lwork,), (0,)),
)
elif dtype == np.complex128:
fn = b"lapack_zgesdd"
singular_vals_dtype = np.float64
lwork = _lapack.zgesdd_work_size(m, n, compute_uv, full_matrices)
workspace = (
Shape.array_shape(np.dtype(np.int32), (_lapack.gesdd_iwork_size(m, n),),
(0,)),
Shape.array_shape(np.dtype(np.float64),
(_lapack.cgesdd_rwork_size(m, n, int(compute_uv)),),
(0,)),
Shape.array_shape(dtype, (lwork,), (0,)),
)
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
scalar_layout = tuple(range(num_bd - 1, -1, -1))
vector_layout = (num_bd,) + scalar_layout
matrix_layout = (num_bd, num_bd + 1) + scalar_layout
out = _ops.CustomCallWithLayout(
c, fn,
operands=(_constant_s32_scalar(c, int(full_matrices)),
_constant_s32_scalar(c, int(compute_uv)),
_constant_s32_scalar(c, b),
_constant_s32_scalar(c, m), _constant_s32_scalar(c, n),
_constant_s32_scalar(c, lwork), a),
shape_with_layout=Shape.tuple_shape((
Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
Shape.array_shape(np.dtype(singular_vals_dtype),
batch_dims + (min(m, n),), vector_layout),
Shape.array_shape(dtype,
batch_dims + (m, m if full_matrices else min(m, n)),
matrix_layout),
Shape.array_shape(dtype,
batch_dims + (n if full_matrices else min(m, n), n),
matrix_layout),
Shape.array_shape(np.dtype(np.int32), batch_dims, scalar_layout),
) + workspace
),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
))
return (_ops.GetTupleElement(out, 1), _ops.GetTupleElement(out, 2),
_ops.GetTupleElement(out, 3), _ops.GetTupleElement(out, 4))
# # syevd: Symmetric eigendecomposition
def syevd(c, a, lower=False):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
assert m == n
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
if dtype == np.float32:
fn = b"lapack_ssyevd"
eigvals_type = np.float32
workspace = (Shape.array_shape(dtype, (_lapack.syevd_work_size(n),), (0,)),
Shape.array_shape(np.dtype(np.int32),
(_lapack.syevd_iwork_size(n),), (0,)))
elif dtype == np.float64:
fn = b"lapack_dsyevd"
eigvals_type = np.float64
workspace = (Shape.array_shape(dtype, (_lapack.syevd_work_size(n),), (0,)),
Shape.array_shape(np.dtype(np.int32),
(_lapack.syevd_iwork_size(n),), (0,)))
elif dtype == np.complex64:
fn = b"lapack_cheevd"
eigvals_type = np.float32
workspace = (Shape.array_shape(dtype, (_lapack.heevd_work_size(n),), (0,)),
Shape.array_shape(np.dtype(np.float32),
(_lapack.heevd_rwork_size(n),), (0,)),
Shape.array_shape(np.dtype(np.int32),
(_lapack.syevd_iwork_size(n),), (0,)))
elif dtype == np.complex128:
fn = b"lapack_zheevd"
eigvals_type = np.float64
workspace = (Shape.array_shape(dtype, (_lapack.heevd_work_size(n),), (0,)),
Shape.array_shape(np.dtype(np.float64),
(_lapack.heevd_rwork_size(n),), (0,)),
Shape.array_shape(np.dtype(np.int32),
(_lapack.syevd_iwork_size(n),), (0,)))
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
out = _ops.CustomCallWithLayout(
c, fn,
operands=(_constant_s32_scalar(c, 1 if lower else 0),
_constant_s32_scalar(c, b),
_constant_s32_scalar(c, n),
a),
shape_with_layout=Shape.tuple_shape((
Shape.array_shape(dtype, dims, layout),
Shape.array_shape(np.dtype(eigvals_type), batch_dims + (n,),
tuple(range(num_bd, -1, -1))),
Shape.array_shape(np.dtype(np.int32), batch_dims,
tuple(range(num_bd - 1, -1, -1))))
+ workspace
),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(dtype, dims, layout),
))
return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 1),
_ops.GetTupleElement(out, 2))
# # geev: Nonsymmetric eigendecomposition
def geev(c, a, jobvl=True, jobvr=True):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
assert m == n
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
jobvl_c = ord('V' if jobvl else 'N')
jobvr_c = ord('V' if jobvr else 'N')
if dtype == np.float32:
fn = b"lapack_sgeev"
real = True
eigvecs_type = np.complex64
workspaces = (Shape.array_shape(np.dtype(np.float32), (n, n), (0, 1)),
Shape.array_shape(np.dtype(np.float32), (n, n), (0, 1)),
Shape.array_shape(np.dtype(np.float32), (n, n), (0, 1)))
eigvals = (Shape.array_shape(np.dtype(np.float32), batch_dims + (n,),
tuple(range(num_bd, -1, -1))),
Shape.array_shape(np.dtype(np.float32), batch_dims + (n,),
tuple(range(num_bd, -1, -1))))
elif dtype == np.float64:
fn = b"lapack_dgeev"
real = True
eigvecs_type = np.complex128
workspaces = (Shape.array_shape(np.dtype(np.float64), (n, n), (0, 1)),
Shape.array_shape(np.dtype(np.float64), (n, n), (0, 1)),
Shape.array_shape(np.dtype(np.float64), (n, n), (0, 1)))
eigvals = (Shape.array_shape(np.dtype(np.float64), batch_dims + (n,),
tuple(range(num_bd, -1, -1))),
Shape.array_shape(np.dtype(np.float64), batch_dims + (n,),
tuple(range(num_bd, -1, -1))))
elif dtype == np.complex64:
fn = b"lapack_cgeev"
real = False
eigvecs_type = np.complex64
workspaces = (Shape.array_shape(np.dtype(np.complex64), (n, n), (0, 1)),
Shape.array_shape(np.dtype(np.float32), (2 * n,), (0,)))
eigvals = (Shape.array_shape(np.dtype(np.complex64), batch_dims + (n,),
tuple(range(num_bd, -1, -1))),)
elif dtype == np.complex128:
fn = b"lapack_zgeev"
real = False
eigvecs_type = np.complex128
workspaces = (Shape.array_shape(np.dtype(np.complex128), (n, n), (0, 1)),
Shape.array_shape(np.dtype(np.float64), (2 * n,), (0,)))
eigvals = (Shape.array_shape(np.dtype(np.complex128), batch_dims + (n,),
tuple(range(num_bd, -1, -1))),)
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
out = _ops.CustomCallWithLayout(
c, fn,
operands=(_constant_s32_scalar(c, b),
_constant_s32_scalar(c, n),
_ops.Constant(c, np.uint8(jobvl_c)),
_ops.Constant(c, np.uint8(jobvr_c)),
a),
shape_with_layout=Shape.tuple_shape(workspaces + eigvals + (
Shape.array_shape(np.dtype(eigvecs_type), dims, layout),
Shape.array_shape(np.dtype(eigvecs_type), dims, layout),
Shape.array_shape(np.dtype(np.int32), batch_dims,
tuple(range(num_bd - 1, -1, -1))))
),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.uint8), (), ()),
Shape.array_shape(np.dtype(np.uint8), (), ()),
Shape.array_shape(dtype, dims, layout),
))
if real:
return (_ops.Complex(_ops.GetTupleElement(out, 3),
_ops.GetTupleElement(out, 4)),
_ops.GetTupleElement(out, 5), _ops.GetTupleElement(out, 6),
_ops.GetTupleElement(out, 7))
else:
return (_ops.GetTupleElement(out, 2), _ops.GetTupleElement(out, 3),
_ops.GetTupleElement(out, 4), _ops.GetTupleElement(out, 5))
# # gees : Schur factorization
def gees(c, a, jobvs=True, sort=False, select=None):
a_shape = c.get_shape(a)
dtype = a_shape.element_type()
dims = a_shape.dimensions()
assert len(dims) >= 2
m, n = dims[-2:]
assert m == n
batch_dims = tuple(dims[:-2])
num_bd = len(batch_dims)
b = 1
for d in batch_dims:
b *= d
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
if sort:
raise NotImplementedError(
"The sort feature of LAPACK's gees routine is not implemented.")
jobvs = ord('V' if jobvs else 'N')
sort = ord('S' if sort else 'N')
if dtype == np.float32 or dtype == np.float64:
fn = b"lapack_sgees" if dtype == np.float32 else b"lapack_dgees"
schurvecs_type = dtype
workspaces = (Shape.array_shape(np.dtype(schurvecs_type), dims, layout),)
eigvals = (Shape.array_shape(
np.dtype(dtype), batch_dims + (n,), tuple(range(num_bd, -1, -1))),
Shape.array_shape(
np.dtype(dtype), batch_dims + (n,),
tuple(range(num_bd, -1, -1))))
elif dtype == np.complex64 or dtype == np.complex128:
fn = b"lapack_cgees" if dtype == np.complex64 else b"lapack_zgees"
schurvecs_type = dtype
workspaces = (
Shape.array_shape(np.dtype(schurvecs_type), dims, layout),
Shape.array_shape(
np.dtype(np.float32 if dtype == np.complex64 else np.float64),
(n,), (0,)))
eigvals = (Shape.array_shape(
np.dtype(dtype), batch_dims + (n,), tuple(range(num_bd, -1, -1))),)
else:
raise NotImplementedError("Unsupported dtype {}".format(dtype))
out = _ops.CustomCallWithLayout(
c,
fn,
operands=(
_constant_s32_scalar(c, b),
_constant_s32_scalar(c, n),
_ops.Constant(c, np.uint8(jobvs)),
_ops.Constant(c, np.uint8(sort)),
#figure out how to put the callable select function here
a),
shape_with_layout=Shape.tuple_shape(workspaces + eigvals + (
Shape.array_shape(np.dtype(schurvecs_type), dims, layout),
Shape.array_shape(
np.dtype(np.int32), batch_dims, tuple(range(num_bd - 1, -1, -1))),
Shape.array_shape(
np.dtype(np.int32), batch_dims, tuple(range(num_bd -
1, -1, -1))))),
operand_shapes_with_layout=(
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.int32), (), ()),
Shape.array_shape(np.dtype(np.uint8), (), ()),
Shape.array_shape(np.dtype(np.uint8), (), ()),
Shape.array_shape(dtype, dims, layout),
))
if sort == ord('S'):
return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 3),
_ops.GetTupleElement(out, 4), _ops.GetTupleElement(out, 5))
else:
return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 3),
_ops.GetTupleElement(out, 5))
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