-
Notifications
You must be signed in to change notification settings - Fork 504
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'devel' into feat/linear-jit
- Loading branch information
Showing
33 changed files
with
546 additions
and
12 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
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 +1,256 @@ | ||
| # SPDX-License-Identifier: LGPL-3.0-or-later | ||
| from typing import ( | ||
| List, | ||
| ) | ||
|
|
||
| import torch | ||
|
|
||
| from deepmd.pt.loss.loss import ( | ||
| TaskLoss, | ||
| ) | ||
| from deepmd.pt.utils import ( | ||
| env, | ||
| ) | ||
| from deepmd.utils.data import ( | ||
| DataRequirementItem, | ||
| ) | ||
|
|
||
|
|
||
| class DOSLoss(TaskLoss): | ||
| def __init__( | ||
| self, | ||
| starter_learning_rate: float, | ||
| numb_dos: int, | ||
| start_pref_dos: float = 1.00, | ||
| limit_pref_dos: float = 1.00, | ||
| start_pref_cdf: float = 1000, | ||
| limit_pref_cdf: float = 1.00, | ||
| start_pref_ados: float = 0.0, | ||
| limit_pref_ados: float = 0.0, | ||
| start_pref_acdf: float = 0.0, | ||
| limit_pref_acdf: float = 0.0, | ||
| inference=False, | ||
| **kwargs, | ||
| ): | ||
| r"""Construct a loss for local and global tensors. | ||
| Parameters | ||
| ---------- | ||
| tensor_name : str | ||
| The name of the tensor in the model predictions to compute the loss. | ||
| tensor_size : int | ||
| The size (dimension) of the tensor. | ||
| label_name : str | ||
| The name of the tensor in the labels to compute the loss. | ||
| pref_atomic : float | ||
| The prefactor of the weight of atomic loss. It should be larger than or equal to 0. | ||
| pref : float | ||
| The prefactor of the weight of global loss. It should be larger than or equal to 0. | ||
| inference : bool | ||
| If true, it will output all losses found in output, ignoring the pre-factors. | ||
| **kwargs | ||
| Other keyword arguments. | ||
| """ | ||
| super().__init__() | ||
| self.starter_learning_rate = starter_learning_rate | ||
| self.numb_dos = numb_dos | ||
| self.inference = inference | ||
|
|
||
| self.start_pref_dos = start_pref_dos | ||
| self.limit_pref_dos = limit_pref_dos | ||
| self.start_pref_cdf = start_pref_cdf | ||
| self.limit_pref_cdf = limit_pref_cdf | ||
|
|
||
| self.start_pref_ados = start_pref_ados | ||
| self.limit_pref_ados = limit_pref_ados | ||
| self.start_pref_acdf = start_pref_acdf | ||
| self.limit_pref_acdf = limit_pref_acdf | ||
|
|
||
| assert ( | ||
| self.start_pref_dos >= 0.0 | ||
| and self.limit_pref_dos >= 0.0 | ||
| and self.start_pref_cdf >= 0.0 | ||
| and self.limit_pref_cdf >= 0.0 | ||
| and self.start_pref_ados >= 0.0 | ||
| and self.limit_pref_ados >= 0.0 | ||
| and self.start_pref_acdf >= 0.0 | ||
| and self.limit_pref_acdf >= 0.0 | ||
| ), "Can not assign negative weight to `pref` and `pref_atomic`" | ||
|
|
||
| self.has_dos = (start_pref_dos != 0.0 and limit_pref_dos != 0.0) or inference | ||
| self.has_cdf = (start_pref_cdf != 0.0 and limit_pref_cdf != 0.0) or inference | ||
| self.has_ados = (start_pref_ados != 0.0 and limit_pref_ados != 0.0) or inference | ||
| self.has_acdf = (start_pref_acdf != 0.0 and limit_pref_acdf != 0.0) or inference | ||
|
|
||
| assert ( | ||
| self.has_dos or self.has_cdf or self.has_ados or self.has_acdf | ||
| ), AssertionError("Can not assian zero weight both to `pref` and `pref_atomic`") | ||
|
|
||
| def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False): | ||
| """Return loss on local and global tensors. | ||
| Parameters | ||
| ---------- | ||
| input_dict : dict[str, torch.Tensor] | ||
| Model inputs. | ||
| model : torch.nn.Module | ||
| Model to be used to output the predictions. | ||
| label : dict[str, torch.Tensor] | ||
| Labels. | ||
| natoms : int | ||
| The local atom number. | ||
| Returns | ||
| ------- | ||
| model_pred: dict[str, torch.Tensor] | ||
| Model predictions. | ||
| loss: torch.Tensor | ||
| Loss for model to minimize. | ||
| more_loss: dict[str, torch.Tensor] | ||
| Other losses for display. | ||
| """ | ||
| model_pred = model(**input_dict) | ||
|
|
||
| coef = learning_rate / self.starter_learning_rate | ||
| pref_dos = ( | ||
| self.limit_pref_dos + (self.start_pref_dos - self.limit_pref_dos) * coef | ||
| ) | ||
| pref_cdf = ( | ||
| self.limit_pref_cdf + (self.start_pref_cdf - self.limit_pref_cdf) * coef | ||
| ) | ||
| pref_ados = ( | ||
| self.limit_pref_ados + (self.start_pref_ados - self.limit_pref_ados) * coef | ||
| ) | ||
| pref_acdf = ( | ||
| self.limit_pref_acdf + (self.start_pref_acdf - self.limit_pref_acdf) * coef | ||
| ) | ||
|
|
||
| loss = torch.zeros(1, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)[0] | ||
| more_loss = {} | ||
| if self.has_ados and "atom_dos" in model_pred and "atom_dos" in label: | ||
| find_local = label.get("find_atom_dos", 0.0) | ||
| pref_ados = pref_ados * find_local | ||
| local_tensor_pred_dos = model_pred["atom_dos"].reshape( | ||
| [-1, natoms, self.numb_dos] | ||
| ) | ||
| local_tensor_label_dos = label["atom_dos"].reshape( | ||
| [-1, natoms, self.numb_dos] | ||
| ) | ||
| diff = (local_tensor_pred_dos - local_tensor_label_dos).reshape( | ||
| [-1, self.numb_dos] | ||
| ) | ||
| if "mask" in model_pred: | ||
| diff = diff[model_pred["mask"].reshape([-1]).bool()] | ||
| l2_local_loss_dos = torch.mean(torch.square(diff)) | ||
| if not self.inference: | ||
| more_loss["l2_local_dos_loss"] = self.display_if_exist( | ||
| l2_local_loss_dos.detach(), find_local | ||
| ) | ||
| loss += pref_ados * l2_local_loss_dos | ||
| rmse_local_dos = l2_local_loss_dos.sqrt() | ||
| more_loss["rmse_local_dos"] = self.display_if_exist( | ||
| rmse_local_dos.detach(), find_local | ||
| ) | ||
| if self.has_acdf and "atom_dos" in model_pred and "atom_dos" in label: | ||
| find_local = label.get("find_atom_dos", 0.0) | ||
| pref_acdf = pref_acdf * find_local | ||
| local_tensor_pred_cdf = torch.cusum( | ||
| model_pred["atom_dos"].reshape([-1, natoms, self.numb_dos]), dim=-1 | ||
| ) | ||
| local_tensor_label_cdf = torch.cusum( | ||
| label["atom_dos"].reshape([-1, natoms, self.numb_dos]), dim=-1 | ||
| ) | ||
| diff = (local_tensor_pred_cdf - local_tensor_label_cdf).reshape( | ||
| [-1, self.numb_dos] | ||
| ) | ||
| if "mask" in model_pred: | ||
| diff = diff[model_pred["mask"].reshape([-1]).bool()] | ||
| l2_local_loss_cdf = torch.mean(torch.square(diff)) | ||
| if not self.inference: | ||
| more_loss["l2_local_cdf_loss"] = self.display_if_exist( | ||
| l2_local_loss_cdf.detach(), find_local | ||
| ) | ||
| loss += pref_acdf * l2_local_loss_cdf | ||
| rmse_local_cdf = l2_local_loss_cdf.sqrt() | ||
| more_loss["rmse_local_cdf"] = self.display_if_exist( | ||
| rmse_local_cdf.detach(), find_local | ||
| ) | ||
| if self.has_dos and "dos" in model_pred and "dos" in label: | ||
| find_global = label.get("find_dos", 0.0) | ||
| pref_dos = pref_dos * find_global | ||
| global_tensor_pred_dos = model_pred["dos"].reshape([-1, self.numb_dos]) | ||
| global_tensor_label_dos = label["dos"].reshape([-1, self.numb_dos]) | ||
| diff = global_tensor_pred_dos - global_tensor_label_dos | ||
| if "mask" in model_pred: | ||
| atom_num = model_pred["mask"].sum(-1, keepdim=True) | ||
| l2_global_loss_dos = torch.mean( | ||
| torch.sum(torch.square(diff) * atom_num, dim=0) / atom_num.sum() | ||
| ) | ||
| atom_num = torch.mean(atom_num.float()) | ||
| else: | ||
| atom_num = natoms | ||
| l2_global_loss_dos = torch.mean(torch.square(diff)) | ||
| if not self.inference: | ||
| more_loss["l2_global_dos_loss"] = self.display_if_exist( | ||
| l2_global_loss_dos.detach(), find_global | ||
| ) | ||
| loss += pref_dos * l2_global_loss_dos | ||
| rmse_global_dos = l2_global_loss_dos.sqrt() / atom_num | ||
| more_loss["rmse_global_dos"] = self.display_if_exist( | ||
| rmse_global_dos.detach(), find_global | ||
| ) | ||
| if self.has_cdf and "dos" in model_pred and "dos" in label: | ||
| find_global = label.get("find_dos", 0.0) | ||
| pref_cdf = pref_cdf * find_global | ||
| global_tensor_pred_cdf = torch.cusum( | ||
| model_pred["dos"].reshape([-1, self.numb_dos]), dim=-1 | ||
| ) | ||
| global_tensor_label_cdf = torch.cusum( | ||
| label["dos"].reshape([-1, self.numb_dos]), dim=-1 | ||
| ) | ||
| diff = global_tensor_pred_cdf - global_tensor_label_cdf | ||
| if "mask" in model_pred: | ||
| atom_num = model_pred["mask"].sum(-1, keepdim=True) | ||
| l2_global_loss_cdf = torch.mean( | ||
| torch.sum(torch.square(diff) * atom_num, dim=0) / atom_num.sum() | ||
| ) | ||
| atom_num = torch.mean(atom_num.float()) | ||
| else: | ||
| atom_num = natoms | ||
| l2_global_loss_cdf = torch.mean(torch.square(diff)) | ||
| if not self.inference: | ||
| more_loss["l2_global_cdf_loss"] = self.display_if_exist( | ||
| l2_global_loss_cdf.detach(), find_global | ||
| ) | ||
| loss += pref_cdf * l2_global_loss_cdf | ||
| rmse_global_dos = l2_global_loss_cdf.sqrt() / atom_num | ||
| more_loss["rmse_global_cdf"] = self.display_if_exist( | ||
| rmse_global_dos.detach(), find_global | ||
| ) | ||
| return model_pred, loss, more_loss | ||
|
|
||
| @property | ||
| def label_requirement(self) -> List[DataRequirementItem]: | ||
| """Return data label requirements needed for this loss calculation.""" | ||
| label_requirement = [] | ||
| if self.has_ados or self.has_acdf: | ||
| label_requirement.append( | ||
| DataRequirementItem( | ||
| "atom_dos", | ||
| ndof=self.numb_dos, | ||
| atomic=True, | ||
| must=False, | ||
| high_prec=False, | ||
| ) | ||
| ) | ||
| if self.has_dos or self.has_cdf: | ||
| label_requirement.append( | ||
| DataRequirementItem( | ||
| "dos", | ||
| ndof=self.numb_dos, | ||
| atomic=False, | ||
| must=False, | ||
| high_prec=False, | ||
| ) | ||
| ) | ||
| return label_requirement |
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
Oops, something went wrong.