mindspore.scipy.optimize.line_search 源代码

# Copyright 2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""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 ..utils import _to_scalar, _to_tensor, grad


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.
    """
    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 - fpa * db
    d2[1] = fc - fa - fpa * dc

    a2, b2 = mnp.dot(d1, d2) / denom
    radical = b2 * b2 - 3. * a2 * fpa
    xmin = a + (-b2 + mnp.sqrt(radical)) / (3. * a2)
    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.
    """
    db = b - a
    b2 = (fb - fa - fpa * db) / (db ** 2)
    xmin = a - fpa / (2. * b2)
    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.
    """
    # Constant tensors which avoid loop unrolling
    const_float_one = _to_tensor(1., dtype=a_low.dtype)
    const_bool_false = _to_tensor(False)
    const_int_zero = _to_tensor(0)
    state = {
        "done": const_bool_false,
        "failed": const_bool_false,
        "j": const_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": const_float_one,
        "phi_star": phi_low,
        "dphi_star": dphi_low,
        "g_star": g_0,
        "nfev": const_int_zero,
        "ngev": const_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"])

        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, jac):
        """Initialize LineSearch."""
        super(LineSearch, self).__init__()
        self.func = func
        self.jac = jac

    def construct(self, xk, pk, old_fval=None, old_old_fval=None, gfk=None, c1=1e-4, c2=0.9, maxiter=20):
        def fval_and_grad(alpha):
            xkk = xk + alpha * pk
            fkk = self.func(xkk)
            gkk = self.jac(xkk)
            return fkk, gkk, mnp.dot(gkk, pk)

        # Constant tensors which avoid loop unrolling
        const_float_zero = _to_tensor(0., dtype=xk.dtype)
        const_float_one = _to_tensor(1., dtype=xk.dtype)
        const_bool_false = _to_tensor(False)
        const_int_zero = _to_tensor(0)
        const_int_one = _to_tensor(1)

        if old_fval is None or gfk is None:
            nfev, ngev = const_int_one, const_int_one
            phi_0, g_0, dphi_0 = fval_and_grad(const_float_zero)
        else:
            nfev, ngev = const_int_zero, const_int_zero
            phi_0, g_0 = old_fval, gfk
            dphi_0 = mnp.dot(g_0, pk)

        if old_old_fval is None:
            start_value = const_float_one
        else:
            old_phi0 = old_old_fval
            candidate_start_value = 1.01 * 2 * (phi_0 - old_phi0) / dphi_0
            start_value = mnp.where(
                mnp.isfinite(candidate_start_value),
                mnp.minimum(candidate_start_value, const_float_one),
                const_float_one
            )

        state = {
            "done": const_bool_false,
            "failed": const_bool_false,
            "i": const_int_one,
            "a_i": const_float_zero,
            "phi_i": phi_0,
            "dphi_i": dphi_0,
            "nfev": nfev,
            "ngev": ngev,
            "a_star": const_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"])

            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