xref: /aosp_15_r20/external/pytorch/torch/csrc/api/src/optim/lbfgs.cpp (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

#include <torch/optim/lbfgs.h>

#include <torch/csrc/autograd/generated/variable_factories.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/serialize/archive.h>
#include <torch/utils.h>

#include <ATen/ATen.h>
#include <c10/util/irange.h>

#include <algorithm>
#include <cmath>
#include <functional>
#include <vector>

namespace torch {
namespace optim {

LBFGSOptions::LBFGSOptions(double lr) : lr_(lr) {}

bool operator==(const LBFGSOptions& lhs, const LBFGSOptions& rhs) {
  return (lhs.lr() == rhs.lr()) && (lhs.max_iter() == rhs.max_iter()) &&
      (lhs.max_eval() == rhs.max_eval()) &&
      (lhs.tolerance_grad() == rhs.tolerance_grad()) &&
      (lhs.tolerance_change() == rhs.tolerance_change() &&
       (lhs.history_size() == rhs.history_size())) &&
      (lhs.line_search_fn() == rhs.line_search_fn());
}

void LBFGSOptions::serialize(torch::serialize::OutputArchive& archive) const {
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(lr);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(max_iter);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(max_eval);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(tolerance_grad);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(tolerance_change);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(history_size);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(line_search_fn);
}

void LBFGSOptions::serialize(torch::serialize::InputArchive& archive) {
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(double, lr);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(int64_t, max_iter);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_OPTIONAL(int64_t, max_eval);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(double, tolerance_grad);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(double, tolerance_change);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(int64_t, history_size);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_OPTIONAL(std::string, line_search_fn);
}

double LBFGSOptions::get_lr() const {
  return lr();
}

void LBFGSOptions::set_lr(const double lr) {
  this->lr(lr);
}

template <typename T>
bool if_container_equal(T lhs, T rhs) {
  if (!(lhs.size() == rhs.size()))
    return false;
  for (const auto i : c10::irange(lhs.size())) {
    if (!torch::equal(lhs.at(i), rhs.at(i)))
      return false;
  }
  return true;
}

bool operator==(const LBFGSParamState& lhs, const LBFGSParamState& rhs) {
  auto isNull = [](const std::optional<std::vector<Tensor>>& val) {
    return val == std::nullopt;
  };
  return (lhs.func_evals() == rhs.func_evals()) &&
      (lhs.n_iter() == rhs.n_iter()) && (lhs.t() == rhs.t()) &&
      (lhs.prev_loss() == rhs.prev_loss()) &&
      torch::equal_if_defined(lhs.d(), rhs.d()) &&
      torch::equal_if_defined(lhs.H_diag(), rhs.H_diag()) &&
      torch::equal_if_defined(lhs.prev_flat_grad(), rhs.prev_flat_grad()) &&
      if_container_equal(lhs.old_dirs(), rhs.old_dirs()) &&
      if_container_equal(lhs.old_stps(), rhs.old_stps()) &&
      if_container_equal(lhs.ro(), rhs.ro()) &&
      ((isNull(lhs.al()) && isNull(rhs.al())) ||
       (!isNull(lhs.al()) && !isNull(rhs.al()) &&
        if_container_equal(*lhs.al(), *rhs.al())));
}

void LBFGSParamState::serialize(
    torch::serialize::OutputArchive& archive) const {
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(func_evals);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(n_iter);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(t);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(prev_loss);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(d);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(H_diag);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG(prev_flat_grad);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG_DEQUE(old_dirs);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG_DEQUE(old_stps);
  _TORCH_OPTIM_SERIALIZE_TORCH_ARG_DEQUE(ro);
  // Python version only serializes state vars if explicitly defined
  if (al() != std::nullopt) {
    _TORCH_OPTIM_SERIALIZE_TORCH_ARG(al);
  }
}

void LBFGSParamState::serialize(torch::serialize::InputArchive& archive) {
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(int64_t, func_evals);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(int64_t, n_iter);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(double, t);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(double, prev_loss);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(Tensor, d);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(Tensor, H_diag);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(Tensor, prev_flat_grad);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_DEQUE(std::deque<Tensor>, old_dirs);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_DEQUE(std::deque<Tensor>, old_stps);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_DEQUE(std::deque<Tensor>, ro);
  _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_OPTIONAL(std::vector<Tensor>, al);
}

Tensor LBFGS::_gather_flat_grad() {
  std::vector<Tensor> views;
  for (const auto& p : param_groups_.at(0).params()) {
    if (!p.grad().defined()) {
      views.emplace_back(p.new_empty({p.numel()}).zero_());
    } else if (p.grad().is_sparse()) {
      views.emplace_back(p.grad().to_dense().view(-1));
    } else {
      views.emplace_back(p.grad().view(-1));
    }
  }
  return torch::cat(views, 0);
}

int64_t LBFGS::_numel() {
  if (_numel_cache == std::nullopt) {
    auto res = 0;
    for (const auto& p : param_groups_.at(0).params()) {
      res += p.numel();
    }
    _numel_cache = res;
  }
  return *_numel_cache;
}

void LBFGS::_add_grad(const double step_size, const Tensor& update) {
  auto offset = 0;
  for (auto& p : param_groups_.at(0).params()) {
    auto numel = p.numel();
    // view as to avoid deprecated pointwise semantics
    p.add_(
        update.index({at::indexing::Slice(offset, offset + numel)}).view_as(p),
        step_size);
    offset += numel;
  }
  TORCH_INTERNAL_ASSERT(offset == _numel());
}

void LBFGS::_set_param(const std::vector<Tensor>& params_data) {
  auto& _params = param_groups_.at(0).params();
  TORCH_INTERNAL_ASSERT(params_data.size() == _params.size());
  for (const auto i : c10::irange(_params.size())) {
    _params.at(i).copy_(params_data.at(i));
  }
}

std::vector<Tensor> LBFGS::_clone_param() {
  std::vector<Tensor> result;
  for (const auto& p : param_groups_.at(0).params()) {
    result.emplace_back(p.clone(at::MemoryFormat::Contiguous));
  }
  return result;
}

std::tuple<double, Tensor> LBFGS::_directional_evaluate(
    const LossClosure& closure,
    const std::vector<Tensor>& x,
    double t,
    const Tensor& d) {
  _add_grad(t, d);
  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
  double loss;
  {
    torch::AutoGradMode enable_grad(true);
    loss = closure().item<double>();
  }
  auto flat_grad = _gather_flat_grad();
  _set_param(x);
  return std::make_tuple(loss, flat_grad);
}

static double _cubic_interpolate(
    double x1,
    double f1,
    double g1,
    double x2,
    double f2,
    double g2,
    std::optional<std::tuple<double, double>> bounds = std::nullopt) {
  // ported from https://github.com/torch/optim/blob/master/polyinterp.lua
  // Compute bounds of interpolation area
  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
  double xmin_bound, xmax_bound;
  if (bounds != std::nullopt) {
    std::tie(xmin_bound, xmax_bound) = *bounds;
  } else {
    std::tie(xmin_bound, xmax_bound) =
        (x1 <= x2) ? std::make_tuple(x1, x2) : std::make_tuple(x2, x1);
  }
  // Code for most common case: cubic interpolation of 2 points
  //   w/ function and derivative values for both
  // Solution in this case (where x2 is the farthest point):
  //   d1 = g1 + g2 - 3*(f1-f2)/(x1-x2);
  //   d2 = sqrt(d1^2 - g1*g2);
  //   min_pos = x2 - (x2 - x1)*((g2 + d2 - d1)/(g2 - g1 + 2*d2));
  //   t_new = min(max(min_pos,xmin_bound),xmax_bound);

  auto d1 = (g1 + g2) - (3 * (f1 - f2) / (x1 - x2));
  auto d2_square = std::pow(d1, 2) - g1 * g2;
  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
  double d2;
  if (d2_square >= 0) {
    d2 = std::sqrt(d2_square);
    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
    double min_pos;
    if (x1 <= x2) {
      min_pos = x2 - ((x2 - x1) * ((g2 + d2 - d1) / (g2 - g1 + 2 * d2)));
    } else {
      min_pos = x1 - ((x1 - x2) * ((g1 + d2 - d1) / (g1 - g2 + 2 * d2)));
    }
    return std::min(std::max(min_pos, xmin_bound), xmax_bound);
  } else {
    return (xmin_bound + xmax_bound) / 2;
  }
}

using Function = std::function<std::tuple<double, Tensor>(
    const std::vector<Tensor>& x,
    double t,
    const Tensor& d)>;
static std::tuple<double, Tensor, double, int64_t> _strong_wolfe(
    const Function& obj_func,
    const std::vector<Tensor>& x,
    double t,
    const Tensor& d,
    double f,
    Tensor g,
    const Tensor& gtd,
    double c1 = 1e-4,
    double c2 = 0.9, // // NOLINT(cppcoreguidelines-avoid-magic-numbers)
    double tolerance_change = 1e-9,
    double max_ls = 25) { // NOLINT(cppcoreguidelines-avoid-magic-numbers)

  auto val = [](const Tensor& t) { return t.item<double>(); };

  auto d_norm = val(d.abs().max());
  g = g.clone(at::MemoryFormat::Contiguous);
  // evaluate objective and gradient using initial step
  auto [f_new, g_new] = obj_func(x, t, d);
  int64_t ls_func_evals = 1;
  auto gtd_new = g_new.dot(d);

  // bracket an interval containing a point satisfying the Wolfe criteria
  double t_prev = 0;
  auto f_prev = f;
  auto g_prev = g;
  auto gtd_prev = gtd;
  bool done = false;
  auto ls_iter = 0;
  std::vector<double> bracket, bracket_f;
  std::vector<Tensor> bracket_g, bracket_gtd;

  while (ls_iter < max_ls) {
    // check conditions
    if ((f_new > (f + c1 * t * val(gtd))) ||
        (ls_iter > 1 && (f_new >= f_prev))) {
      bracket = {t_prev, t};
      bracket_f = {f_prev, f_new};
      bracket_g = {g_prev, g_new.clone(at::MemoryFormat::Contiguous)};
      bracket_gtd = {gtd_prev, gtd_new};
      break;
    }
    if (std::abs(val(gtd_new)) <= (-c2 * val(gtd))) {
      bracket = {t, t};
      bracket_f = {f_new, f_new};
      bracket_g = {g_new, g_new};
      done = true;
      break;
    }
    if (val(gtd_new) >= 0) {
      bracket = {t_prev, t};
      bracket_f = {f_prev, f_new};
      bracket_g = {g_prev, g_new.clone(at::MemoryFormat::Contiguous)};
      bracket_gtd = {gtd_prev, gtd_new};
      break;
    }
    // interpolate
    auto min_step = t +
        0.01 * (t - t_prev); // NOLINT(cppcoreguidelines-avoid-magic-numbers)
    auto max_step = t * 10; // NOLINT(cppcoreguidelines-avoid-magic-numbers)
    auto tmp = t;
    t = _cubic_interpolate(
        t_prev,
        f_prev,
        val(gtd_prev),
        t,
        f_new,
        val(gtd_new),
        std::make_tuple(min_step, max_step));
    // next step
    t_prev = tmp;
    f_prev = f_new;
    g_prev = g_new.clone(at::MemoryFormat::Contiguous);
    gtd_prev = gtd_new;
    std::tie(f_new, g_new) = obj_func(x, t, d);
    ls_func_evals += 1;
    gtd_new = g_new.dot(d);
    ls_iter += 1;
  }
  // reached max number of iterations?
  if (ls_iter == max_ls) {
    bracket = {0, t};
    bracket_f = {f, f_new};
    bracket_g = {g, g_new};
  }

  // zoom phase: we now have a point satisfying the criteria, or
  // a bracket around it. We refine the bracket until we find the
  // exact point satisfying the criteria
  bool insuf_progress = false;
  // find high and low points in bracket
  auto [low_pos, high_pos] = bracket_f[0] <= bracket_f[1]
      ? std::make_tuple(0, 1)
      : std::make_tuple(1, 0);
  while (!done && (ls_iter < max_ls)) {
    // compute new trial value
    t = _cubic_interpolate(
        bracket[0],
        bracket_f[0],
        val(bracket_gtd[0]),
        bracket[1],
        bracket_f[1],
        val(bracket_gtd[1]));

    // test that we are making sufficient progress:
    // in case `t` is so close to boundary, we mark that we are making
    // insufficient progress, and if
    //   + we have made insufficient progress in the last step, or
    //   + `t` is at one of the boundary,
    // we will move `t` to a position which is `0.1 * len(bracket)`
    // away from the nearest boundary point.
    double bracket_max = std::max(bracket[0], bracket[1]);
    auto bracket_min = std::min(bracket[0], bracket[1]);
    auto eps = 0.1 *
        (bracket_max -
         bracket_min); // // NOLINT(cppcoreguidelines-avoid-magic-numbers)
    if (std::min(bracket_max - t, t - bracket_min) < eps) {
      // interpolation close to boundary
      if (insuf_progress || (t >= bracket_max) || (t <= bracket_min)) {
        // evaluate at 0.1 away from boundary
        t = (std::abs(t - bracket_max) < std::abs(t - bracket_min))
            ? bracket_max - eps
            : bracket_min + eps;
        insuf_progress = false;
      } else {
        insuf_progress = true;
      }
    } else {
      insuf_progress = false;
    }

    // Evaluate new point
    std::tie(f_new, g_new) = obj_func(x, t, d);
    ls_func_evals += 1;
    gtd_new = g_new.dot(d);
    ls_iter += 1;

    if ((f_new > (f + c1 * t * val(gtd))) || (f_new >= bracket_f[low_pos])) {
      // Armijo condition not satisfied or not lower than lowest point
      // # Armijo condition not satisfied or not lower than lowest point
      bracket[high_pos] = t;
      bracket_f[high_pos] = f_new;
      bracket_g[high_pos] = g_new.clone(at::MemoryFormat::Contiguous);
      bracket_gtd[high_pos] = gtd_new;
      std::tie(low_pos, high_pos) = bracket_f[0] <= bracket_f[1]
          ? std::make_tuple(0, 1)
          : std::make_tuple(1, 0);
    } else {
      if (val(at::abs(gtd_new)) <= (-c2 * val(gtd))) {
        // Wolfe conditions satisfied
        done = true;
      } else if ((val(gtd_new) * (bracket[high_pos] - bracket[low_pos])) >= 0) {
        // old high becomes new low
        bracket[high_pos] = bracket[low_pos];
        bracket_f[high_pos] = bracket_f[low_pos];
        bracket_g[high_pos] = bracket_g[low_pos];
        bracket_gtd[high_pos] = bracket_gtd[low_pos];
      }

      // new point becomes new low
      bracket[low_pos] = t;
      bracket_f[low_pos] = f_new;
      bracket_g[low_pos] = g_new.clone(at::MemoryFormat::Contiguous);
      bracket_gtd[low_pos] = gtd_new;
    }

    // line-search bracket is so small
    if ((std::abs(bracket[1] - bracket[0]) * d_norm) < tolerance_change)
      break;
  }

  // return stuff
  t = bracket[low_pos];
  f_new = bracket_f[low_pos];
  g_new = bracket_g[low_pos];
  return std::make_tuple(f_new, g_new, t, ls_func_evals);
}

Tensor LBFGS::step(LossClosure closure) {
  NoGradGuard no_grad;
  TORCH_CHECK(closure != nullptr, "LBFGS requires a closure function");
  TORCH_INTERNAL_ASSERT(param_groups_.size() == 1);
  auto val = [](const Tensor& t) { return t.item<double>(); };

  auto& group = param_groups_.at(0);
  auto& _params = group.params();
  const auto& options = static_cast<const LBFGSOptions&>(group.options());
  auto lr = options.lr();
  auto max_iter = options.max_iter();
  auto max_eval = options.max_eval();
  auto tolerance_grad = options.tolerance_grad();
  auto tolerance_change = options.tolerance_change();
  auto line_search_fn = options.line_search_fn();
  auto history_size = options.history_size();

  // NOTE: LBFGS has only global state, but we register it as state for
  // the first param, because this helps with casting in load_state_dict
  auto param_state = state_.find(_params.at(0).unsafeGetTensorImpl());
  if (param_state == state_.end()) {
    state_[_params.at(0).unsafeGetTensorImpl()] =
        std::make_unique<LBFGSParamState>();
  }
  auto& state = static_cast<LBFGSParamState&>(
      *state_[_params.at(0).unsafeGetTensorImpl()]);
  // evaluate initial f(x) and df/dx
  Tensor orig_loss;
  {
    torch::AutoGradMode enable_grad(true);
    orig_loss = closure();
  }

  auto loss = val(orig_loss);
  auto current_evals = 1;
  state.func_evals(state.func_evals() + 1);
  auto flat_grad = _gather_flat_grad();
  auto opt_cond = (val(flat_grad.abs().max()) <= tolerance_grad);

  // optimal condition
  if (opt_cond) {
    return orig_loss;
  }

  // tensors cached in state (for tracing)
  auto& d = state.d();
  auto& t = state.t();
  auto& old_dirs = state.old_dirs();
  auto& old_stps = state.old_stps();
  auto& ro = state.ro();
  auto& H_diag = state.H_diag();
  auto& prev_flat_grad = state.prev_flat_grad();
  auto& prev_loss = state.prev_loss();

  int n_iter = 0;

  // optimize for a max of max_iter iterations
  while (n_iter < max_iter) {
    // keep track of nb of iterations
    n_iter += 1;
    state.n_iter(state.n_iter() + 1);

    // compute gradient descent direction
    if (state.n_iter() == 1) {
      d = flat_grad.neg();
      H_diag = torch::tensor(1);
      old_dirs = {};
      old_stps = {};
      ro = {};
    } else {
      // do lbfgs update (update memory)
      auto y = flat_grad.sub(prev_flat_grad);
      auto s = d.mul(t);
      auto ys = y.dot(s); // y*s
      if (val(ys) > 1e-10) { // NOLINT(cppcoreguidelines-avoid-magic-numbers)
        // updating memory
        if (static_cast<int64_t>(old_dirs.size()) == history_size) {
          // shift history by one (limited-memory)
          old_dirs.pop_front();
          old_stps.pop_front();
          ro.pop_front();
        }
        // store new direction/step
        old_dirs.emplace_back(y);
        old_stps.emplace_back(s);
        ro.emplace_back(1. / ys);

        // update scale of initial Hessian approximation
        H_diag = ys / y.dot(y); // (y*y)
      }

      // compute the approximate (L-BFGS) inverse Hessian
      // multiplied by the gradient
      int64_t num_old = static_cast<int64_t>(old_dirs.size());

      if (state.al() == std::nullopt) {
        state.al(std::vector<Tensor>(history_size));
      }
      auto& al = state.al();

      // iteration in L-BFGS loop collapsed to use just one buffer
      auto q = flat_grad.neg();
      for (int64_t i = num_old - 1; i > -1; i--) {
        (*al).at(i) = old_stps.at(i).dot(q) * ro.at(i);
        q.add_(old_dirs.at(i), -val((*al).at(i)));
      }

      // multiply by initial Hessian
      // r/d is the final direction
      auto r = torch::mul(q, H_diag);
      d = r;
      for (const auto i : c10::irange(num_old)) {
        auto be_i = old_dirs.at(i).dot(r) * ro.at(i);
        r.add_(old_stps.at(i), val((*al).at(i) - be_i));
      }
    }

    if (!prev_flat_grad.defined()) {
      prev_flat_grad = flat_grad.clone(at::MemoryFormat::Contiguous);
    } else {
      prev_flat_grad.copy_(flat_grad);
    }
    prev_loss = loss;

    // ############################################################
    // # compute step length
    // ############################################################
    // reset initial guess for step size
    if (state.n_iter() == 1) {
      t = std::min(1., 1. / val(flat_grad.abs().sum())) * lr;
    } else {
      t = lr;
    }

    // directional derivative
    auto gtd = flat_grad.dot(d); // g * d

    // directional derivative is below tolerance
    if (val(gtd) > -tolerance_change)
      break;

    // optional line search: user function
    auto ls_func_evals = 0;
    if (line_search_fn != std::nullopt) {
      TORCH_CHECK(
          *line_search_fn == "strong_wolfe",
          "only 'strong_wolfe' is supported");
      auto x_init = _clone_param();
      auto obj_func =
          [&](const std::vector<Tensor>& x, double t, const Tensor& d) {
            return _directional_evaluate(closure, x, t, d);
          };
      std::tie(loss, flat_grad, t, ls_func_evals) =
          _strong_wolfe(obj_func, x_init, t, d, loss, flat_grad, gtd);
      _add_grad(t, d);
      opt_cond = (val(flat_grad.abs().max()) <= tolerance_grad);
    } else {
      // no line search, simply move with fixed-step
      _add_grad(t, d);
      if (n_iter != max_iter) {
        // re-evaluate function only if not in last iteration
        // the reason we do this: in a stochastic setting,
        // no use to re-evaluate that function here
        {
          torch::AutoGradMode enable_grad(true);
          loss = val(closure());
        }
        flat_grad = _gather_flat_grad();
        opt_cond = val(torch::max(flat_grad.abs())) <= tolerance_grad;
        ls_func_evals = 1;
      }
    }
    // update func eval
    current_evals += ls_func_evals;
    state.func_evals(state.func_evals() + ls_func_evals);

    // ############################################################
    // # check conditions
    // ############################################################
    if (n_iter == max_iter)
      break;

    if (current_evals >= *max_eval)
      break;

    // optimal condition
    if (opt_cond)
      break;

    // lack of progress
    if (val(d.mul(t).abs().max()) <= tolerance_change)
      break;

    if (std::abs(loss - prev_loss) < tolerance_change)
      break;
  }

  return orig_loss;
}

void LBFGS::save(serialize::OutputArchive& archive) const {
  serialize(*this, archive);
}

void LBFGS::load(serialize::InputArchive& archive) {
  IValue pytorch_version;
  if (archive.try_read("pytorch_version", pytorch_version)) {
    serialize(*this, archive);
  } else { // deserializing archives saved in old format (prior to
           // version 1.5.0)
    TORCH_WARN(
        "Your serialized LBFGS optimizer is still using the old serialization format. "
        "The func_evals and n_iter value in state will be set to 0, ro will be set to an empty deque "
        "and al will be set to std::nullopt because the old LBFGS optimizer didn't save these values."
        "You should re-save your LBFGS optimizer to use the new serialization format.");
    Tensor d, t, H_diag, prev_flat_grad, prev_loss;
    std::deque<Tensor> old_dirs, old_stps;
    archive("d", d, /*is_buffer=*/true);
    archive("t", t, /*is_buffer=*/true);
    archive("H_diag", H_diag, /*is_buffer=*/true);
    archive("prev_flat_grad", prev_flat_grad, /*is_buffer=*/true);
    archive("prev_loss", prev_loss, /*is_buffer=*/true);
    torch::optim::serialize(archive, "old_dirs", old_dirs);
    torch::optim::serialize(archive, "old_stps", old_stps);

    // NOTE: LBFGS has only global state, but we register it as state for
    // the first param, because this helps with casting in load_state_dict
    auto state = std::make_unique<LBFGSParamState>();
    state->d(d);
    state->t(t.item<double>());
    state->H_diag(H_diag);
    state->prev_flat_grad(prev_flat_grad);
    state->prev_loss(prev_loss.item<double>());
    state->old_dirs(old_dirs);
    state->old_stps(old_stps);
    state_[param_groups_.at(0).params().at(0).unsafeGetTensorImpl()] =
        std::move(state);
  }
}
} // namespace optim
} // namespace torch