-
Notifications
You must be signed in to change notification settings - Fork 170
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
d70664b
commit 04cf41a
Showing
4 changed files
with
159 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| import numpy as np | ||
|
|
||
| from LibMTL.weighting.abstract_weighting import AbsWeighting | ||
|
|
||
| try: | ||
| import cvxpy as cp | ||
| except ModuleNotFoundError: | ||
| from pip._internal import main as pip | ||
| pip(['install', '--user', 'cvxpy']) | ||
| import cvxpy as cp | ||
|
|
||
| class Nash_MTL(AbsWeighting): | ||
| r"""Nash-MTL. | ||
| This method is proposed in `Multi-Task Learning as a Bargaining Game (ICML 2022) <https://proceedings.mlr.press/v162/navon22a/navon22a.pdf>`_ \ | ||
| and implemented by modifying from the `official PyTorch implementation <https://github.com/AvivNavon/nash-mtl>`_. | ||
| Args: | ||
| update_weights_every (int, default=1): Period of weights update. | ||
| optim_niter (int, default=20): The max iteration of optimization solver. | ||
| max_norm (float, default=1.0): The max norm of the gradients. | ||
| .. warning:: | ||
| Nash_MTL is not supported by representation gradients, i.e., ``rep_grad`` must be ``False``. | ||
| """ | ||
| def __init__(self): | ||
| super(Nash_MTL, self).__init__() | ||
|
|
||
| def init_param(self): | ||
| self.step = 0 | ||
| self.prvs_alpha_param = None | ||
| self.init_gtg = np.eye(self.task_num) | ||
| self.prvs_alpha = np.ones(self.task_num, dtype=np.float32) | ||
| self.normalization_factor = np.ones((1,)) | ||
|
|
||
| def _stop_criteria(self, gtg, alpha_t): | ||
| return ( | ||
| (self.alpha_param.value is None) | ||
| or (np.linalg.norm(gtg @ alpha_t - 1 / (alpha_t + 1e-10)) < 1e-3) | ||
| or ( | ||
| np.linalg.norm(self.alpha_param.value - self.prvs_alpha_param.value) | ||
| < 1e-6 | ||
| ) | ||
| ) | ||
|
|
||
| def solve_optimization(self, gtg: np.array): | ||
| self.G_param.value = gtg | ||
| self.normalization_factor_param.value = self.normalization_factor | ||
|
|
||
| alpha_t = self.prvs_alpha | ||
| for _ in range(self.optim_niter): | ||
| self.alpha_param.value = alpha_t | ||
| self.prvs_alpha_param.value = alpha_t | ||
|
|
||
| try: | ||
| self.prob.solve(solver=cp.ECOS, warm_start=True, max_iters=100) | ||
| except: | ||
| self.alpha_param.value = self.prvs_alpha_param.value | ||
|
|
||
| if self._stop_criteria(gtg, alpha_t): | ||
| break | ||
|
|
||
| alpha_t = self.alpha_param.value | ||
|
|
||
| if alpha_t is not None: | ||
| self.prvs_alpha = alpha_t | ||
|
|
||
| return self.prvs_alpha | ||
|
|
||
| def _calc_phi_alpha_linearization(self): | ||
| G_prvs_alpha = self.G_param @ self.prvs_alpha_param | ||
| prvs_phi_tag = 1 / self.prvs_alpha_param + (1 / G_prvs_alpha) @ self.G_param | ||
| phi_alpha = prvs_phi_tag @ (self.alpha_param - self.prvs_alpha_param) | ||
| return phi_alpha | ||
|
|
||
| def _init_optim_problem(self): | ||
| self.alpha_param = cp.Variable(shape=(self.task_num,), nonneg=True) | ||
| self.prvs_alpha_param = cp.Parameter( | ||
| shape=(self.task_num,), value=self.prvs_alpha | ||
| ) | ||
| self.G_param = cp.Parameter( | ||
| shape=(self.task_num, self.task_num), value=self.init_gtg | ||
| ) | ||
| self.normalization_factor_param = cp.Parameter( | ||
| shape=(1,), value=np.array([1.0]) | ||
| ) | ||
|
|
||
| self.phi_alpha = self._calc_phi_alpha_linearization() | ||
|
|
||
| G_alpha = self.G_param @ self.alpha_param | ||
| constraint = [] | ||
| for i in range(self.task_num): | ||
| constraint.append( | ||
| -cp.log(self.alpha_param[i] * self.normalization_factor_param) | ||
| - cp.log(G_alpha[i]) | ||
| <= 0 | ||
| ) | ||
| obj = cp.Minimize( | ||
| cp.sum(G_alpha) + self.phi_alpha / self.normalization_factor_param | ||
| ) | ||
| self.prob = cp.Problem(obj, constraint) | ||
|
|
||
| def backward(self, losses, **kwargs): | ||
| self.update_weights_every = kwargs['update_weights_every'] | ||
| self.optim_niter = kwargs['optim_niter'] | ||
| self.max_norm = kwargs['max_norm'] | ||
|
|
||
| if self.step == 0: | ||
| self._init_optim_problem() | ||
| if (self.step % self.update_weights_every) == 0: | ||
| self.step += 1 | ||
|
|
||
| if self.rep_grad: | ||
| raise ValueError('No support method Nash_MTL with representation gradients (rep_grad=True)') | ||
| else: | ||
| self._compute_grad_dim() | ||
| grads = self._compute_grad(losses, mode='autograd') | ||
|
|
||
| GTG = torch.mm(grads, grads.t()) | ||
| self.normalization_factor = torch.norm(GTG).detach().cpu().numpy().reshape((1,)) | ||
| GTG = GTG / self.normalization_factor.item() | ||
| alpha = self.solve_optimization(GTG.cpu().detach().numpy()) | ||
| alpha = torch.from_numpy(alpha).to(torch.float32).to(self.device) | ||
| else: | ||
| self.step += 1 | ||
| alpha = self.prvs_alpha | ||
|
|
||
| torch.sum(alpha*losses).backward() | ||
|
|
||
| if self.max_norm > 0: | ||
| torch.nn.utils.clip_grad_norm_(self.get_share_params(), self.max_norm) | ||
|
|
||
| return alpha.detach().cpu().numpy() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters