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!25687 Add the BFGS, line_search and block_diag algorithms in scipy. 

Merge pull request !25687 from liqiliang/merge_to_master
This commit is contained in:
i-robot 2021-11-01 14:48:19 +00:00 committed by Gitee
parent eff2318bb6 dd829f53f9
commit 944733cd82
14 changed files with 1280 additions and 0 deletions
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1
cmake/package.cmake
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@ -278,6 +278,7 @@ install(
${CMAKE_SOURCE_DIR}/mindspore/parallel
${CMAKE_SOURCE_DIR}/mindspore/mindrecord
${CMAKE_SOURCE_DIR}/mindspore/numpy
${CMAKE_SOURCE_DIR}/mindspore/scipy
${CMAKE_SOURCE_DIR}/mindspore/train
${CMAKE_SOURCE_DIR}/mindspore/boost
${CMAKE_SOURCE_DIR}/mindspore/common

10
mindspore/scipy/__init__.py
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@ -13,3 +13,13 @@
# limitations under the License.
# ============================================================================
"""Scipy-like interfaces in mindspore."""
from . import optimize, linalg
from .optimize import *
from .linalg import *
__all__ = []
__all__.extend(optimize.__all__)
__all__.extend(linalg.__all__)
__all__.sort()

84
mindspore/scipy/linalg.py Executable file
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@ -0,0 +1,84 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""Linear algebra submodule"""
from .. import numpy as mnp
from .. import ops
__all__ = ['block_diag']
def block_diag(*arrs):
"""
Create a block diagonal matrix from provided arrays.
Given the inputs `A`, `B` and `C`, the output will have these
Tensor arranged on the diagonal::
[[A, 0, 0],
[0, B, 0],
[0, 0, C]]
Args:
A, B, C, ... (Tensor): up to 2-D
Input Tensors. A 1-D Tensor or a 2-D Tensor with shape ``(1,n)``.
Returns:
D (Tesnor): Tensor with `A`, `B`, `C`, ... on the diagonal. `D` has
the same dtype as `A`.
Raises:
ValueError: If there are tensors with dimensions higher than 2 in all arguments.
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> import numpy as onp
>>> from mindspore.common import Tensor
>>> from mindspore.scipy.linalg import block_diag
>>> A = Tensor(onp.array([[1, 0], [0, 1]]))
>>> B = Tensor(onp.array([[3, 4, 5], [6, 7, 8]]))
>>> C = Tensor(onp.array([[7]]))
>>> P = Tensor(onp.zeros((2, ), dtype='int32'))
>>> block_diag(A, B, C)
[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 3, 4, 5, 0],
[0, 0, 6, 7, 8, 0],
[0, 0, 0, 0, 0, 7]]
>>> block_diag(A, P, B, C)
[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 4, 5, 0],
[0, 0, 6, 7, 8, 0],
[0, 0, 0, 0, 0, 7]]
"""
if not arrs:
return mnp.zeros((1, 0))
bad_shapes = [i for i, a in enumerate(arrs) if a.ndim > 2]
if bad_shapes:
raise ValueError("Arguments to mindspore.scipy.linalg.block_diag must have at "
"most 2 dimensions, got {} at argument {}."
.format(arrs[bad_shapes[0]], bad_shapes[0]))
arrs = [mnp.atleast_2d(a) for a in arrs]
accum = arrs[0]
for arr in arrs[1:]:
_, c = arr.shape
arr = ops.Pad(((0, 0), (accum.shape[-1], 0)))(arr)
accum = ops.Pad(((0, 0), (0, c)))(accum)
accum = mnp.concatenate([accum, arr], axis=0)
return accum

19
mindspore/scipy/optimize/__init__.py Normal file
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@ -0,0 +1,19 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""Optimize submodule"""
from .minimize import minimize
from .line_search import line_search
__all__ = ["minimize", "line_search"]

228
mindspore/scipy/optimize/_bfgs.py Normal file
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@ -0,0 +1,228 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""BFGS"""
from typing import NamedTuple
from ... import nn
from ... import numpy as mnp
from ...common import Tensor
from ...ops import functional as F
from .line_search import LineSearch
from ..utils import _to_scalar
from ..utils import _INT_ZERO, _INT_ONE, _BOOL_FALSE
class _BFGSResults(NamedTuple):
"""Results from BFGS optimization.
Arg
converged (bool): `True`` if minimization converged.
failed (bool): `True`` if line search failed.
k (int): the number of iterations of the BFGS update.
nfev (int): total number of objective evaluations performed.
ngev (int): total number of jacobian evaluations
nhev (int): total number of hessian evaluations
x_k (Tensor): containing the last argument value found during the search. If
the search converged, then this value is the argmin of the objective
function.
f_k (float): containing the value of the objective function at `x_k`. If the
search converged, then this is the (local) minimum of the objective
function.
g_k (Tensor): containing the gradient of the objective function at `x_k`. If
the search converged the l2-norm of this tensor should be below the
tolerance.
H_k (Tensor): containing the inverse of the estimated Hessian.
old_old_fval (float): Function value for the point preceding x=x_k.
status (int): describing end state.
line_search_status (int): describing line search end state (only means
something if line search fails).
"""
converged: bool
failed: bool
k: int
nfev: int
ngev: int
nhev: int
x_k: Tensor
f_k: float
g_k: Tensor
H_k: Tensor
old_old_fval: float
status: int
line_search_status: int
class MinimizeBfgs(nn.Cell):
"""minimize bfgs"""
def __init__(self, func):
"""Initialize MinimizeBfgs."""
super(MinimizeBfgs, self).__init__()
self.func = func
self.line_search = LineSearch(func)
def construct(self, x0, maxiter=None, norm=mnp.inf, gtol=1e-5, line_search_maxiter=10):
def _my_norm(x, ord_=None):
if ord_ == mnp.inf:
res = mnp.max(mnp.abs(x))
else:
res = mnp.sqrt(mnp.sum(x ** 2))
return res
if maxiter is None:
maxiter = mnp.size(x0) * 200
d = x0.shape[0]
initial_H = mnp.eye(d, dtype=x0.dtype)
f_0 = self.func(x0)
g_0 = F.grad(self.func, grad_first_param=True)(x0)
state = {
"converged": _my_norm(g_0, ord_=mnp.inf) < gtol,
"failed": _BOOL_FALSE,
"k": _INT_ZERO,
"nfev": _INT_ONE,
"ngev": _INT_ONE,
"nhev": _INT_ZERO,
"x_k": x0,
"f_k": f_0,
"g_k": g_0,
"H_k": initial_H,
"old_old_fval": f_0 + _my_norm(g_0) / 2,
"status": _INT_ZERO,
"line_search_status": _INT_ZERO
}
while state["k"] < maxiter:
p_k = -1 * mnp.dot(state["H_k"], state["g_k"])
line_search_results = self.line_search(state["x_k"],
p_k,
old_fval=state["f_k"],
old_old_fval=state["old_old_fval"],
gfk=state["g_k"],
maxiter=line_search_maxiter)
state["nfev"] += line_search_results["nfev"]
state["ngev"] += line_search_results["ngev"]
state["failed"] = line_search_results["failed"] or mnp.logical_not(line_search_results["done"])
state["line_search_status"] = line_search_results["status"]
if state["failed"]:
break
s_k = line_search_results["a_star"] * p_k
x_kp1 = state["x_k"] + s_k
f_kp1 = line_search_results["phi_star"]
g_kp1 = line_search_results["g_star"]
y_k = g_kp1 - state["g_k"]
state["old_old_fval"] = state["f_k"]
state["converged"] = _my_norm(g_kp1, ord_=norm) < gtol
state["x_k"] = x_kp1
state["f_k"] = f_kp1
state["g_k"] = g_kp1
if state["converged"]:
break
rho_k = mnp.reciprocal(mnp.dot(y_k, s_k))
sy_k = mnp.expand_dims(s_k, axis=1) * mnp.expand_dims(y_k, axis=0)
term1 = rho_k * sy_k
sy_k_2 = mnp.expand_dims(y_k, axis=1) * mnp.expand_dims(s_k, axis=0)
term2 = rho_k * sy_k_2
I = mnp.eye(d)
term3 = mnp.matmul(I - term1, state["H_k"])
term4 = mnp.matmul(term3, I - term2)
term5 = rho_k * (mnp.expand_dims(s_k, axis=1) * mnp.expand_dims(s_k, axis=0))
H_kp1 = term4 + term5
state["H_k"] = H_kp1
# next iteration
state["k"] = state["k"] + 1
status = mnp.where(
state["converged"],
0, # converged
mnp.where(
state["k"] == maxiter,
1, # max iters reached
mnp.where(
state["failed"],
2 + state["line_search_status"], # ls failed (+ reason)
-1, # undefined
)
)
)
state["status"] = status
return state
def minimize_bfgs(func, x0, maxiter=None, norm=mnp.inf, gtol=1e-5, line_search_maxiter=10):
"""Minimize a function using BFGS.
Implements the BFGS algorithm from
Algorithm 6.1 from Wright and Nocedal, 'Numerical Optimization', 1999, pg.
136-143.
Args:
fun (Callable): function of the form f(x) where x is a flat Tensor and returns a real
scalar. The function should be composed of operations with vjp defined.
x0 (Tensor): initial guess.
maxiter (int, optional): maximum number of iterations.
norm (float): order of norm for convergence check. Default inf.
gtol (float): terminates minimization when |grad|_norm < g_tol.
line_search_maxiter (int): maximum number of linesearch iterations.
Returns:
Optimization result.
Supported Platforms:
``CPU`` ``GPU``
"""
if maxiter is None:
maxiter = mnp.size(x0) * 200
state = MinimizeBfgs(func)(x0, maxiter, norm, gtol)
# If running in graph mode, the state is a tuple.
if isinstance(state, tuple):
state = _BFGSResults(converged=_to_scalar(state[0]),
failed=_to_scalar(state[1]),
k=_to_scalar(state[2]),
nfev=_to_scalar(state[3]),
ngev=_to_scalar(state[4]),
nhev=_to_scalar(state[5]),
x_k=state[6],
f_k=_to_scalar(state[7]),
g_k=state[8],
H_k=state[9],
old_old_fval=_to_scalar(state[10]),
status=_to_scalar(state[11]),
line_search_status=_to_scalar(state[12]))
else:
state = _BFGSResults(converged=_to_scalar(state["converged"]),
failed=_to_scalar(state["failed"]),
k=_to_scalar(state["k"]),
nfev=_to_scalar(state["nfev"]),
ngev=_to_scalar(state["ngev"]),
nhev=_to_scalar(state["nhev"]),
x_k=state["x_k"],
f_k=_to_scalar(state["f_k"]),
g_k=state["g_k"],
H_k=state["H_k"],
old_old_fval=_to_scalar(state["old_old_fval"]),
status=_to_scalar(state["status"]),
line_search_status=_to_scalar(state["line_search_status"]))
return state

343
mindspore/scipy/optimize/line_search.py Normal file
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@ -0,0 +1,343 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""line search"""
from typing import NamedTuple
from ... import nn
from ... import numpy as mnp
from ...common import dtype as mstype
from ...common import Tensor
from ...ops import functional as F
from ..utils import _to_scalar
from ..utils import _to_tensor, _FLOAT_ZERO, _FLOAT_ONE, _INT_ZERO, _INT_ONE, _BOOL_FALSE
class _LineSearchResults(NamedTuple):
"""Results of line search results.
Args:
failed (bool): `True`` if the strong Wolfe criteria were satisfied
nit (int): number of iterations
nfev (int): number of functions evaluations
ngev (int): number of gradients evaluations
k (int): number of iterations
a_k (float): step size
f_k (float): final function value
g_k (Tensor): final gradient value
status (int): end status
"""
failed: bool
nit: int
nfev: int
ngev: int
k: int
a_k: float
f_k: float
g_k: Tensor
status: int
def _cubicmin(a, fa, fpa, b, fb, c, fc):
"""Finds the minimizer for a cubic polynomial that goes through the
points (a,fa), (b,fb), and (c,fc) with derivative at a of fpa.
"""
C = fpa
db = b - a
dc = c - a
denom = (db * dc) ** 2 * (db - dc)
d1 = mnp.zeros((2, 2))
d1[0, 0] = dc ** 2
d1[0, 1] = -db ** 2
d1[1, 0] = -dc ** 3
d1[1, 1] = db ** 3
d2 = mnp.zeros((2,))
d2[0] = fb - fa - C * db
d2[1] = fc - fa - C * dc
A, B = mnp.dot(d1, d2.flatten()) / denom
radical = B * B - 3. * A * C
xmin = a + (-B + mnp.sqrt(radical)) / (3. * A)
return xmin
def _quadmin(a, fa, fpa, b, fb):
"""Finds the minimizer for a quadratic polynomial that goes through
the points (a,fa), (b,fb) with derivative at a of fpa.
"""
D = fa
C = fpa
db = b - a
B = (fb - D - C * db) / (db ** 2)
xmin = a - C / (2. * B)
return xmin
def _zoom(fn, a_low, phi_low, dphi_low, a_high, phi_high, dphi_high, phi_0, g_0, dphi_0, c1, c2, is_run):
"""Implementation of zoom algorithm.
Algorithm 3.6 from Wright and Nocedal, 'Numerical Optimization', 1999, pg. 59-61.
Tries cubic, quadratic, and bisection methods of zooming.
"""
state = {
"done": _BOOL_FALSE,
"failed": _BOOL_FALSE,
"j": _INT_ZERO,
"a_low": a_low,
"phi_low": phi_low,
"dphi_low": dphi_low,
"a_high": a_high,
"phi_high": phi_high,
"dphi_high": dphi_high,
"a_rec": (a_low + a_high) / 2.,
"phi_rec": (phi_low + phi_high) / 2.,
"a_star": _FLOAT_ONE,
"phi_star": phi_low,
"dphi_star": dphi_low,
"g_star": g_0,
"nfev": _INT_ZERO,
"ngev": _INT_ZERO,
}
if mnp.logical_not(is_run):
return state
delta1 = 0.2
delta2 = 0.1
maxiter = 10 # scipy: 10 jax: 30
while mnp.logical_not(state["done"]) and state["j"] < maxiter:
dalpha = state["a_high"] - state["a_low"]
a = mnp.minimum(state["a_low"], state["a_high"])
b = mnp.maximum(state["a_low"], state["a_high"])
cchk = delta1 * dalpha
qchk = delta2 * dalpha
a_j_cubic = _cubicmin(state["a_low"], state["phi_low"], state["dphi_low"], state["a_high"],
state["phi_high"], state["a_rec"], state["phi_rec"])
use_cubic = state["j"] > 0 and mnp.isfinite(a_j_cubic) and \
mnp.logical_and(a_j_cubic > a + cchk, a_j_cubic < b - cchk)
a_j_quad = _quadmin(state["a_low"], state["phi_low"], state["dphi_low"], state["a_high"],
state["phi_high"])
use_quad = mnp.logical_not(use_cubic) and mnp.isfinite(a_j_quad) and \
mnp.logical_and(a_j_quad > a + qchk, a_j_quad < b - qchk)
a_j_bisection = (state["a_low"] + state["a_high"]) / 2.0
use_bisection = mnp.logical_not(use_cubic) and mnp.logical_not(use_quad)
a_j = mnp.where(use_cubic, a_j_cubic, state["a_rec"])
a_j = mnp.where(use_quad, a_j_quad, a_j)
a_j = mnp.where(use_bisection, a_j_bisection, a_j)
phi_j, g_j, dphi_j = fn(a_j)
state["nfev"] += 1
state["ngev"] += 1
j_to_high = (phi_j > phi_0 + c1 * a_j * dphi_0) or (phi_j >= state["phi_low"])
state["a_rec"] = mnp.where(j_to_high, state["a_high"], state["a_rec"])
state["phi_rec"] = mnp.where(j_to_high, state["phi_high"], state["phi_rec"])
state["a_high"] = mnp.where(j_to_high, a_j, state["a_high"])
state["phi_high"] = mnp.where(j_to_high, phi_j, state["phi_high"])
state["dphi_high"] = mnp.where(j_to_high, dphi_j, state["dphi_high"])
j_to_star = mnp.logical_not(j_to_high) and mnp.abs(dphi_j) <= -c2 * dphi_0
state["done"] = j_to_star
state["a_star"] = mnp.where(j_to_star, a_j, state["a_star"])
state["phi_star"] = mnp.where(j_to_star, phi_j, state["phi_star"])
state["g_star"] = mnp.where(j_to_star, g_j, state["g_star"])
state["dphi_star"] = mnp.where(j_to_star, dphi_j, state["dphi_star"])
low_to_high = mnp.logical_not(j_to_high) and mnp.logical_not(j_to_star) and \
dphi_j * (state["a_high"] - state["a_low"]) >= 0.
state["a_rec"] = mnp.where(low_to_high, state["a_high"], state["a_rec"])
state["phi_rec"] = mnp.where(low_to_high, state["phi_high"], state["phi_rec"])
state["a_high"] = mnp.where(low_to_high, a_low, state["a_high"])
state["phi_high"] = mnp.where(low_to_high, phi_low, state["phi_high"])
state["dphi_high"] = mnp.where(low_to_high, dphi_low, state["dphi_high"])
j_to_low = mnp.logical_not(j_to_high) and mnp.logical_not(j_to_star)
state["a_rec"] = mnp.where(j_to_low, state["a_low"], state["a_rec"])
state["phi_rec"] = mnp.where(j_to_low, state["phi_low"], state["phi_rec"])
state["a_low"] = mnp.where(j_to_low, a_j, state["a_low"])
state["phi_low"] = mnp.where(j_to_low, phi_j, state["phi_low"])
state["dphi_low"] = mnp.where(j_to_low, dphi_j, state["dphi_low"])
# next iteration
state["j"] = state["j"] + 1
state["failed"] = state["j"] == maxiter
return state
class LineSearch(nn.Cell):
"""Line Search that satisfies strong Wolfe conditions."""
def __init__(self, func):
"""Initialize LineSearch."""
super(LineSearch, self).__init__()
self.func = func
def construct(self, xk, pk, old_fval=None, old_old_fval=None, gfk=None,
c1=1e-4, c2=0.9, maxiter=3):
def fval_and_grad(alpha):
xkk = xk + alpha * pk
fkk = self.func(xkk)
gkk = F.grad(self.func, grad_first_param=True)(xkk)
return fkk, gkk, mnp.dot(gkk, pk)
if old_fval is None or gfk is None:
nfev, ngev = _INT_ONE, _INT_ONE
phi_0, g_0, dphi_0 = fval_and_grad(_FLOAT_ZERO)
else:
nfev, ngev = _INT_ZERO, _INT_ZERO
phi_0, g_0 = old_fval, gfk
dphi_0 = mnp.dot(g_0, pk)
if old_old_fval is None:
start_value = _FLOAT_ONE
else:
old_phi0 = old_old_fval
candidate_start_value = 1.01 * 2 * (phi_0 - old_phi0) / dphi_0
start_value = mnp.minimum(candidate_start_value, _FLOAT_ONE)
state = {
"done": _BOOL_FALSE,
"failed": _BOOL_FALSE,
"i": _INT_ONE,
"a_i": _FLOAT_ZERO,
"phi_i": phi_0,
"dphi_i": dphi_0,
"nfev": nfev,
"ngev": ngev,
"a_star": _FLOAT_ZERO,
"phi_star": phi_0,
"dphi_star": dphi_0,
"g_star": g_0,
}
while mnp.logical_not(state["done"]) and state["i"] <= maxiter:
a_i = mnp.where(state["i"] > 1, state["a_i"] * 2.0, start_value)
phi_i, g_i, dphi_i = fval_and_grad(a_i)
state["nfev"] += 1
state["ngev"] += 1
# Armijo condition
cond1 = (phi_i > phi_0 + c1 * a_i * dphi_0) or \
(phi_i >= state["phi_i"] and state["i"] > 1)
zoom1 = _zoom(fval_and_grad, state["a_i"], state["phi_i"], state["dphi_i"],
a_i, phi_i, dphi_i, phi_0, g_0, dphi_0, c1, c2, cond1)
state["nfev"] += zoom1["nfev"]
state["ngev"] += zoom1["ngev"]
state["done"] = cond1
state["failed"] = cond1 and zoom1["failed"]
state["a_star"] = mnp.where(cond1, zoom1["a_star"], state["a_star"])
state["phi_star"] = mnp.where(cond1, zoom1["phi_star"], state["phi_star"])
state["g_star"] = mnp.where(cond1, zoom1["g_star"], state["g_star"])
state["dphi_star"] = mnp.where(cond1, zoom1["dphi_star"], state["dphi_star"])
# curvature condition
cond2 = mnp.logical_not(cond1) and mnp.abs(dphi_i) <= -c2 * dphi_0
state["done"] = state["done"] or cond2
state["a_star"] = mnp.where(cond2, a_i, state["a_star"])
state["phi_star"] = mnp.where(cond2, phi_i, state["phi_star"])
state["g_star"] = mnp.where(cond2, g_i, state["g_star"])
state["dphi_star"] = mnp.where(cond2, dphi_i, state["dphi_star"])
# satisfying the strong wolf conditions
cond3 = mnp.logical_not(cond1) and mnp.logical_not(cond2) and dphi_i >= 0.
zoom2 = _zoom(fval_and_grad, a_i, phi_i, dphi_i, state["a_i"], state["phi_i"],
state["dphi_i"], phi_0, g_0, dphi_0, c1, c2, cond3)
state["nfev"] += zoom2["nfev"]
state["ngev"] += zoom2["ngev"]
state["done"] = state["done"] or cond3
state["failed"] = state["failed"] or (cond3 and zoom2["failed"])
state["a_star"] = mnp.where(cond3, zoom2["a_star"], state["a_star"])
state["phi_star"] = mnp.where(cond3, zoom2["phi_star"], state["phi_star"])
state["g_star"] = mnp.where(cond3, zoom2["g_star"], state["g_star"])
state["dphi_star"] = mnp.where(cond3, zoom2["dphi_star"], state["dphi_star"])
# next iteration
state["i"] += 1
state["a_i"] = a_i
state["phi_i"] = phi_i
state["dphi_i"] = dphi_i
state["status"] = mnp.where(
state["failed"],
1, # zoom failed
mnp.where(
state["i"] > maxiter,
3, # maxiter reached
0, # passed (should be)
),
)
state["a_star"] = mnp.where(
_to_tensor(state["a_star"].dtype != mstype.float64)
and (mnp.abs(state["a_star"]) < 1e-8),
mnp.sign(state["a_star"]) * 1e-8,
state["a_star"],
)
return state
def line_search(f, xk, pk, old_fval=None, old_old_fval=None, gfk=None, c1=1e-4,
c2=0.9, maxiter=20) -> _LineSearchResults:
"""Inexact line search that satisfies strong Wolfe conditions.
Algorithm 3.5 from Wright and Nocedal, 'Numerical Optimization', 1999, pg. 59-61
Args:
fun (function): function of the form f(x) where x is a flat Tensor and returns a real
scalar. The function should be composed of operations with vjp defined.
x0 (Tensor): initial guess.
pk (Tensor): direction to search in. Assumes the direction is a descent direction.
old_fval, gfk (Tensor): initial value of value_and_gradient as position.
old_old_fval (Tensor): unused argument, only for scipy API compliance.
maxiter (int): maximum number of iterations to search
c1, c2 (float): Wolfe criteria constant, see ref.
Returns:
LineSearchResults
Supported Platforms:
``CPU`` ``GPU``
"""
state = LineSearch(f)(xk, pk, old_fval, old_old_fval, gfk, c1, c2, maxiter)
# If running in graph mode, the state is a tuple.
if isinstance(state, tuple):
state = _LineSearchResults(failed=_to_scalar(state[0] or not state[1]),
nit=_to_scalar(state[2] - 1),
nfev=_to_scalar(state[6]),
ngev=_to_scalar(state[7]),
k=_to_scalar(state[2]),
a_k=_to_scalar(state[8]),
f_k=_to_scalar(state[9]),
g_k=state[11],
status=_to_scalar(state[12]))
else:
state = _LineSearchResults(failed=_to_scalar(state["failed"] or not state["done"]),
nit=_to_scalar(state["i"] - 1),
nfev=_to_scalar(state["nfev"]),
ngev=_to_scalar(state["ngev"]),
k=_to_scalar(state["i"]),
a_k=_to_scalar(state["a_star"]),
f_k=_to_scalar(state["phi_star"]),
g_k=state["g_star"],
status=_to_scalar(state["status"]))
return state

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""minimize"""
from typing import Optional
from typing import NamedTuple
from ...common import Tensor
from ._bfgs import minimize_bfgs
class OptimizeResults(NamedTuple):
"""Object holding optimization results.
Args:
x (Tensor): final solution.
success (bool): ``True`` if optimization succeeded.
status (int): solver specific return code. 0 means converged (nominal),
1=max BFGS iters reached, 3=zoom failed, 4=saddle point reached,
5=max line search iters reached, -1=undefined
fun (float): final function value.
jac (Tensor): final jacobian array.
hess_inv (Tensor, optional): final inverse Hessian estimate.
nfev (int): number of function calls used.
njev (int): number of gradient evaluations.
nit (int): number of iterations of the optimization algorithm.
"""
x: Tensor
success: bool
status: int
fun: float
jac: Tensor
hess_inv: Optional[Tensor]
nfev: int
njev: int
nit: int
def minimize(func, x0, args=(), *, method, tol=None, options=None) -> OptimizeResults:
"""Minimization of scalar function of one or more variables.
This API for this function matches SciPy with some minor deviations:
- Gradients of ``fun`` are calculated automatically using MindSpore's autodiff
support when required.
- The ``method`` argument is required. You must specify a solver.
- Various optional arguments in the SciPy interface have not yet been
implemented.
- Optimization results may differ from SciPy due to differences in the line
search implementation.
It does not yet support differentiation or arguments in the form of
multi-dimensional Tensor, but support for both is planned.
Note:
On CPU, the supported dtypes is float32.
On GPU, the supported dtypes is float32.
Args:
fun (Callable): the objective function to be minimized, ``fun(x, *args) -> float``,
where ``x`` is a 1-D array with shape ``(n,)`` and ``args`` is a tuple
of the fixed parameters needed to completely specify the function.
``fun`` must support differentiation.
x0 (Tensor): initial guess. Array of real elements of size ``(n,)``, where ``n`` is
the number of independent variables.
args (Tuple): extra arguments passed to the objective function.
method (str): solver type. Currently only ``"BFGS"`` is supported.
tol (float, optional): tolerance for termination. For detailed control, use solver-specific
options.
options (Mapping[str, Any], optional): a dictionary of solver options. All methods accept the following
generic options:
- maxiter (int): Maximum number of iterations to perform. Depending on the
method each iteration may use several function evaluations.
Returns:
An (OptimizeResults): class:`OptimizeResults` object.
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> import numpy as onp
>>> from mindspore.scipy.optimize import minimize
>>> from mindspore.common import Tensor
>>> x0 = Tensor(onp.zeros(2).astype(onp.float32))
>>> def func(p):
>>> x, y = p
>>> return (x ** 2 + y - 11.) ** 2 + (x + y ** 2 - 7.) ** 2
>>> res = minimize(func, x0, method='BFGS', options=dict(maxiter=None, gtol=1e-6))
>>> res.x
[3. 2.]
"""
if options is None:
options = {}
if not isinstance(args, tuple):
msg = "args argument to mindspore.scipy.optimize.minimize must be a tuple, got {}"
raise TypeError(msg.format(args))
def fun_with_args(args):
def inner_func(x):
return func(x, *args)
return inner_func
if method.lower() == 'bfgs':
results = minimize_bfgs(fun_with_args(args), x0, **options)
success = results.converged and results.failed
return OptimizeResults(x=results.x_k,
success=success,
status=results.status,
fun=results.f_k,
jac=results.g_k,
hess_inv=results.H_k,
nfev=results.nfev,
njev=results.ngev,
nit=results.k)
raise ValueError("Method {} not recognized".format(method))

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""internal utility functions"""
import numpy as onp
from ..common import Tensor
from ..common import dtype as mstype
from .utils_const import _type_convert, _raise_type_error
def _convert_64_to_32(tensor):
"""Convert tensor with float64/int64 types to float32/int32."""
if tensor.dtype == mstype.float64:
return tensor.astype("float32")
if tensor.dtype == mstype.int64:
return tensor.astype("int32")
return tensor
def _to_tensor(*args):
"""Returns each input as Tensor"""
res = ()
for arg in args:
if isinstance(arg, (int, float, bool, list, tuple)):
arg = _convert_64_to_32(_type_convert(Tensor, arg))
elif not isinstance(arg, Tensor):
_raise_type_error("Expect input to be array like.")
res += (arg,)
if len(res) == 1:
return res[0]
return res
def _to_scalar(arr):
"""Convert a scalar Tensor or ndarray to a scalar."""
if isinstance(arr, (int, float, bool)):
return arr
if isinstance(arr, Tensor):
if arr.shape:
return arr
arr = arr.asnumpy()
if isinstance(arr, onp.ndarray):
if arr.shape:
return arr
return arr.item()
raise ValueError("{} are not supported.".format(type(arr)))
_FLOAT_ONE = _to_tensor(1.0)
_FLOAT_ZERO = _to_tensor(0.0)
_INT_ZERO = _to_tensor(0)
_INT_ONE = _to_tensor(1)
_BOOL_TRUE = _to_tensor(True)
_BOOL_FALSE = _to_tensor(False)

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""internal graph-compatible utility functions"""
from ..ops.primitive import constexpr
@constexpr
def _type_convert(new_type, obj):
"""
Convert type of `obj` to `force`.
"""
return new_type(obj)
@constexpr
def _raise_type_error(info, param=None):
"""
Raise TypeError in both graph/pynative mode
Args:
info(str): info string to display
param(python obj): any object that can be recognized by graph mode. If is
not None, then param's type information will be extracted and displayed.
Default is None.
"""
if param is None:
raise TypeError(info)
raise TypeError(info + f"{type(param)}")

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""setup for pytest in mindspore.scipy"""
import mindspore.context as context
# pylint: disable=unused-argument
def setup_module(module):
context.set_context(mode=context.PYNATIVE_MODE)

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tests/st/scipy_st/test_linalg.py Normal file
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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""st for linalg."""
import pytest
import numpy as onp
import scipy as osp
from mindspore import context, Tensor
import mindspore.scipy as msp
from .utils import match_array
context.set_context(mode=context.PYNATIVE_MODE)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('args', [(), (1,), (7, -1), (3, 4, 5),
(onp.ones((3, 4), dtype=onp.float32), 5, onp.random.randn(5, 2).astype(onp.float32))])
def test_block_diag(args):
"""
Feature: ALL TO ALL
Description: test cases for block_diag
Expectation: the result match scipy
"""
tensor_args = tuple([Tensor(arg) for arg in args])
ms_res = msp.linalg.block_diag(*tensor_args)
scipy_res = osp.linalg.block_diag(*args)
match_array(ms_res.asnumpy(), scipy_res)

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""st for line_search."""
import pytest
import numpy as onp
from scipy.optimize.linesearch import line_search_wolfe2 as osp_line_search
import mindspore.numpy as mnp
from mindspore import context
from mindspore.scipy.optimize.line_search import line_search as msp_line_search
from .utils import match_array
context.set_context(mode=context.GRAPH_MODE)
def _scalar_func_1(np):
def f(x):
return -x - x ** 3 + x ** 4
def fprime(x):
return -1 - 3 * x ** 2 + 4 * x ** 3
return f, fprime
def _scalar_func_2(np):
def f(x):
return np.exp(-4 * x) + x ** 2
def fprime(x):
return -4 * np.exp(-4 * x) + 2 * x
return f, fprime
def _scalar_func_3(np):
def f(x):
return -np.sin(10 * x)
def fprime(x):
return -10 * np.cos(10 * x)
return f, fprime
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('maxiter, func, x, p', [(10, _scalar_func_1, 0., 1.),
(10, _scalar_func_2, 0., 1.),
(10, _scalar_func_3, 0., 1.)])
def test_scalar_search(maxiter, func, x, p):
"""
Feature: ALL TO ALL
Description: test cases for 1-d function
Expectation: the result match scipy
"""
osp_f, osp_fp = func(onp)
osp_x, osp_p = onp.array(x), onp.array(p)
osp_res = osp_line_search(osp_f, osp_fp, osp_x, osp_p, maxiter=maxiter)
msp_f, _ = func(mnp)
msp_x, msp_p = mnp.array(x), mnp.array(p)
msp_res = msp_line_search(msp_f, msp_x, msp_p, maxiter=maxiter)
match_array(msp_res.a_k, osp_res[0], error=5)
match_array(msp_res.f_k, osp_res[3], error=5)
def _line_func_1(np, *args):
def f(x):
return np.dot(x, x)
def fprime(x):
return 2 * x
return f, fprime
def _line_func_2(np, *args):
def f(x):
A = args[0]
return np.dot(x, np.dot(A, x)) + 1
def fprime(x):
A = args[0]
return np.dot(A + A.T, x)
return f, fprime
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('maxiter, func, x, p',
[(10, _line_func_1, [1.13689136, 0.09772497, 0.58295368, -0.39944903, 0.37005589],
[-1.30652685, 1.65813068, -0.11816405, -0.6801782, 0.66638308]),
(10, _line_func_1, [-0.52118931, -1.84306955, -0.477974, -0.47965581, 0.6203583],
[0.69845715, 0.00377089, 0.93184837, 0.33996498, -0.01568211]),
(10, _line_func_2, [0.15634897, 1.23029068, 1.20237985, -0.38732682, -0.30230275],
[-1.04855297, -1.42001794, -1.70627019, 1.9507754, -0.50965218]),
(10, _line_func_2, [0.42833187, 0.06651722, 0.3024719, -0.63432209, -0.36274117],
[-0.67246045, -0.35955316, -0.81314628, -1.7262826, 0.17742614])])
def test_line_search(maxiter, func, x, p):
"""
Feature: ALL TO ALL
Description: test cases for n-d function
Expectation: the result match scipy
"""
A = [[1.76405235, 0.40015721, 0.97873798, 2.2408932, 1.86755799],
[-0.97727788, 0.95008842, -0.15135721, -0.10321885, 0.4105985],
[0.14404357, 1.45427351, 0.76103773, 0.12167502, 0.44386323],
[0.33367433, 1.49407907, -0.20515826, 0.3130677, -0.85409574],
[-2.55298982, 0.6536186, 0.8644362, -0.74216502, 2.26975462]]
osp_x, osp_p, osp_A = onp.array(x), onp.array(p), onp.array(A)
osp_f, osp_fp = func(onp, osp_A)
osp_res = osp_line_search(osp_f, osp_fp, osp_x, osp_p, maxiter=maxiter)
msp_x, msp_p, msp_A = mnp.array(x), mnp.array(p), mnp.array(A)
msp_f, _ = func(mnp, msp_A)
msp_res = msp_line_search(msp_f, msp_x, msp_p, maxiter=maxiter)
match_array(msp_res.a_k, osp_res[0], error=5)
match_array(msp_res.f_k, osp_res[3], error=5)

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""st for optimize."""
import pytest
import numpy as onp
import scipy as osp
import mindspore.numpy as mnp
import mindspore.scipy as msp
from mindspore import context
from mindspore.common import Tensor
from .utils import match_array
context.set_context(mode=context.GRAPH_MODE)
def rosenbrock(np):
def func(x):
return np.sum(100. * np.diff(x) ** 2 + (1. - x[:-1]) ** 2)
return func
def himmelblau(np):
def func(p):
x, y = p
return (x ** 2 + y - 11.) ** 2 + (x + y ** 2 - 7.) ** 2
return func
def matyas(np):
def func(p):
x, y = p
return 0.26 * (x ** 2 + y ** 2) - 0.48 * x * y
return func
def eggholder(np):
def func(p):
x, y = p
return - (y + 47) * np.sin(np.sqrt(np.abs(x / 2. + y + 47.))) - x * np.sin(
np.sqrt(np.abs(x - (y + 47.))))
return func
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('dtype', [onp.float32])
@pytest.mark.parametrize('func_x0', [(rosenbrock, onp.zeros(2)),
(himmelblau, onp.zeros(2)),
(himmelblau, onp.array([92, 0.001])),
(matyas, onp.ones(2)),
(eggholder, onp.ones(2) * 100.)])
def test_bfgs(dtype, func_x0):
"""
Feature: ALL TO ALL
Description: test cases for bfgs
Expectation: the result match scipy
"""
func, x0 = func_x0
x0 = x0.astype(dtype)
x0_tensor = Tensor(x0)
ms_res = msp.optimize.minimize(func(mnp), x0_tensor, method='BFGS',
options=dict(maxiter=None, gtol=1e-6)).x
scipy_res = osp.optimize.minimize(func(onp), x0, method='BFGS').x
match_array(ms_res.asnumpy(), scipy_res, error=5)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('dtype', [onp.float32])
def test_bfgs_fixes4594(dtype):
"""
Feature: ALL TO ALL
Description: test cases for bfgs
Expectation: the result match scipy
"""
n = 2
A = Tensor(onp.eye(n, dtype=dtype)) * 1e4
def func(x):
return mnp.mean((mnp.dot(A, x)) ** 2)
results = msp.optimize.minimize(func, Tensor(onp.ones(n, dtype=dtype)), method='BFGS',
options=dict(maxiter=None, gtol=1e-6)).x
onp.testing.assert_allclose(results.asnumpy(), onp.zeros(n, dtype=dtype), rtol=1e-6, atol=1e-6)

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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
#
# http://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.
# ============================================================================
"""utility functions for mindspore.scipy st tests"""
import numpy as onp
from mindspore import Tensor
import mindspore.numpy as mnp
def to_tensor(obj, dtype=None):
if dtype is None:
res = Tensor(obj)
if res.dtype == mnp.float64:
res = res.astype(mnp.float32)
if res.dtype == mnp.int64:
res = res.astype(mnp.int32)
else:
res = Tensor(obj, dtype)
return res
def match_array(actual, expected, error=0):
if isinstance(actual, int):
actual = onp.asarray(actual)
if isinstance(expected, (int, tuple)):
expected = onp.asarray(expected)
if error > 0:
onp.testing.assert_almost_equal(actual, expected, decimal=error)
else:
onp.testing.assert_equal(actual, expected)