From e0cd84ad454aa3a25e7cab22fdea42906af41792 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Fri, 1 Mar 2024 23:10:36 +0800 Subject: [PATCH 1/8] pt: Add support for dipole and polar training --- .../dpmodel/atomic_model/base_atomic_model.py | 7 +- deepmd/dpmodel/model/make_model.py | 3 + deepmd/dpmodel/model/transform_output.py | 4 + deepmd/pt/loss/__init__.py | 4 + deepmd/pt/loss/tensor.py | 162 ++++++++++++++++ .../model/atomic_model/base_atomic_model.py | 7 +- deepmd/pt/model/model/__init__.py | 2 +- deepmd/pt/model/model/dipole_model.py | 2 + deepmd/pt/model/model/dp_zbl_model.py | 2 + deepmd/pt/model/model/ener_model.py | 2 + deepmd/pt/model/model/make_model.py | 3 + deepmd/pt/model/model/polar_model.py | 2 + deepmd/pt/model/model/transform_output.py | 4 + deepmd/pt/model/task/dipole.py | 2 +- deepmd/pt/model/task/polarizability.py | 2 +- deepmd/pt/train/training.py | 63 +++++-- .../dipole/dipole_input_torch.json | 84 +++++++++ .../atomic_system/nopbc | 0 .../atomic_system/set.000/atomic_dipole.npy | Bin 0 -> 184448 bytes .../atomic_system/set.000/box.npy | Bin 0 -> 3008 bytes .../atomic_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../atomic_system/type.raw | 1 + .../atomic_system/type_map.raw | 2 + .../global_system/nopbc | 0 .../global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../global_system/set.000/dipole.npy | Bin 0 -> 1088 bytes .../global_system/type.raw | 1 + .../global_system/type_map.raw | 2 + .../atomic_system/nopbc | 0 .../atomic_system/set.000/atomic_dipole.npy | Bin 0 -> 184448 bytes .../atomic_system/set.000/box.npy | Bin 0 -> 3008 bytes .../atomic_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../atomic_system/type.raw | 1 + .../atomic_system/type_map.raw | 2 + .../global_system/nopbc | 0 .../global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../global_system/set.000/dipole.npy | Bin 0 -> 1088 bytes .../global_system/type.raw | 1 + .../global_system/type_map.raw | 2 + .../water_tensor/polar/polar_input_torch.json | 90 +++++++++ .../set.000/atomic_polarizability.npy | Bin 0 -> 829568 bytes .../atomic_system/set.000/box.npy | Bin 0 -> 2288 bytes .../atomic_system/set.000/coord.npy | Bin 0 -> 138368 bytes .../atomic_system/type.raw | 1 + .../atomic_system/type_map.raw | 2 + .../global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../global_system/set.000/polarizability.npy | Bin 0 -> 3008 bytes .../global_system/type.raw | 1 + .../global_system/type_map.raw | 2 + .../set.000/atomic_polarizability.npy | Bin 0 -> 829568 bytes .../atomic_system/set.000/box.npy | Bin 0 -> 2288 bytes .../atomic_system/set.000/coord.npy | Bin 0 -> 138368 bytes .../atomic_system/type.raw | 1 + .../atomic_system/type_map.raw | 2 + .../global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../global_system/set.000/polarizability.npy | Bin 0 -> 3008 bytes .../global_system/type.raw | 1 + .../global_system/type_map.raw | 2 + source/tests/pt/test_training.py | 177 ++++++++++++++++++ .../water_tensor/dipole/atomic_system/nopbc | 0 .../atomic_system/set.000/atomic_dipole.npy | Bin 0 -> 184448 bytes .../dipole/atomic_system/set.000/box.npy | Bin 0 -> 3008 bytes .../dipole/atomic_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../dipole/atomic_system/type.raw | 1 + .../dipole/atomic_system/type_map.raw | 2 + .../water_tensor/dipole/global_system/nopbc | 0 .../dipole/global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../dipole/global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../dipole/global_system/set.000/dipole.npy | Bin 0 -> 1088 bytes .../dipole/global_system/type.raw | 1 + .../dipole/global_system/type_map.raw | 2 + .../set.000/atomic_polarizability.npy | Bin 0 -> 829568 bytes .../polar/atomic_system/set.000/box.npy | Bin 0 -> 2288 bytes .../polar/atomic_system/set.000/coord.npy | Bin 0 -> 138368 bytes .../water_tensor/polar/atomic_system/type.raw | 1 + .../polar/atomic_system/type_map.raw | 2 + .../polar/global_system/set.000/box.npy | Bin 0 -> 3008 bytes .../polar/global_system/set.000/coord.npy | Bin 0 -> 184448 bytes .../global_system/set.000/polarizability.npy | Bin 0 -> 3008 bytes .../water_tensor/polar/global_system/type.raw | 1 + .../polar/global_system/type_map.raw | 2 + source/tests/pt/water_tensor/se_e2_a.json | 84 +++++++++ 86 files changed, 723 insertions(+), 19 deletions(-) create mode 100644 deepmd/pt/loss/tensor.py create mode 100644 examples/water_tensor/dipole/dipole_input_torch.json create mode 100644 examples/water_tensor/dipole/training_data_reformat/atomic_system/nopbc create mode 100644 examples/water_tensor/dipole/training_data_reformat/atomic_system/set.000/atomic_dipole.npy create mode 100644 examples/water_tensor/dipole/training_data_reformat/atomic_system/set.000/box.npy create mode 100644 examples/water_tensor/dipole/training_data_reformat/atomic_system/set.000/coord.npy create mode 100644 examples/water_tensor/dipole/training_data_reformat/atomic_system/type.raw create mode 100644 examples/water_tensor/dipole/training_data_reformat/atomic_system/type_map.raw create mode 100644 examples/water_tensor/dipole/training_data_reformat/global_system/nopbc create mode 100644 examples/water_tensor/dipole/training_data_reformat/global_system/set.000/box.npy create mode 100644 examples/water_tensor/dipole/training_data_reformat/global_system/set.000/coord.npy create mode 100644 examples/water_tensor/dipole/training_data_reformat/global_system/set.000/dipole.npy create mode 100644 examples/water_tensor/dipole/training_data_reformat/global_system/type.raw create mode 100644 examples/water_tensor/dipole/training_data_reformat/global_system/type_map.raw create mode 100644 examples/water_tensor/dipole/validation_data_reformat/atomic_system/nopbc create mode 100644 examples/water_tensor/dipole/validation_data_reformat/atomic_system/set.000/atomic_dipole.npy create mode 100644 examples/water_tensor/dipole/validation_data_reformat/atomic_system/set.000/box.npy create mode 100644 examples/water_tensor/dipole/validation_data_reformat/atomic_system/set.000/coord.npy create mode 100644 examples/water_tensor/dipole/validation_data_reformat/atomic_system/type.raw create mode 100644 examples/water_tensor/dipole/validation_data_reformat/atomic_system/type_map.raw create mode 100644 examples/water_tensor/dipole/validation_data_reformat/global_system/nopbc create mode 100644 examples/water_tensor/dipole/validation_data_reformat/global_system/set.000/box.npy create mode 100644 examples/water_tensor/dipole/validation_data_reformat/global_system/set.000/coord.npy create mode 100644 examples/water_tensor/dipole/validation_data_reformat/global_system/set.000/dipole.npy create mode 100644 examples/water_tensor/dipole/validation_data_reformat/global_system/type.raw create mode 100644 examples/water_tensor/dipole/validation_data_reformat/global_system/type_map.raw create mode 100644 examples/water_tensor/polar/polar_input_torch.json create mode 100644 examples/water_tensor/polar/training_data_reformat/atomic_system/set.000/atomic_polarizability.npy create mode 100644 examples/water_tensor/polar/training_data_reformat/atomic_system/set.000/box.npy create mode 100644 examples/water_tensor/polar/training_data_reformat/atomic_system/set.000/coord.npy create mode 100644 examples/water_tensor/polar/training_data_reformat/atomic_system/type.raw create mode 100644 examples/water_tensor/polar/training_data_reformat/atomic_system/type_map.raw create mode 100644 examples/water_tensor/polar/training_data_reformat/global_system/set.000/box.npy create mode 100644 examples/water_tensor/polar/training_data_reformat/global_system/set.000/coord.npy create mode 100644 examples/water_tensor/polar/training_data_reformat/global_system/set.000/polarizability.npy create mode 100644 examples/water_tensor/polar/training_data_reformat/global_system/type.raw create mode 100644 examples/water_tensor/polar/training_data_reformat/global_system/type_map.raw create mode 100644 examples/water_tensor/polar/validation_data_reformat/atomic_system/set.000/atomic_polarizability.npy create mode 100644 examples/water_tensor/polar/validation_data_reformat/atomic_system/set.000/box.npy create mode 100644 examples/water_tensor/polar/validation_data_reformat/atomic_system/set.000/coord.npy create mode 100644 examples/water_tensor/polar/validation_data_reformat/atomic_system/type.raw create mode 100644 examples/water_tensor/polar/validation_data_reformat/atomic_system/type_map.raw create mode 100644 examples/water_tensor/polar/validation_data_reformat/global_system/set.000/box.npy create mode 100644 examples/water_tensor/polar/validation_data_reformat/global_system/set.000/coord.npy create mode 100644 examples/water_tensor/polar/validation_data_reformat/global_system/set.000/polarizability.npy create mode 100644 examples/water_tensor/polar/validation_data_reformat/global_system/type.raw create mode 100644 examples/water_tensor/polar/validation_data_reformat/global_system/type_map.raw create mode 100644 source/tests/pt/water_tensor/dipole/atomic_system/nopbc create mode 100644 source/tests/pt/water_tensor/dipole/atomic_system/set.000/atomic_dipole.npy create mode 100644 source/tests/pt/water_tensor/dipole/atomic_system/set.000/box.npy create mode 100644 source/tests/pt/water_tensor/dipole/atomic_system/set.000/coord.npy create mode 100644 source/tests/pt/water_tensor/dipole/atomic_system/type.raw create mode 100644 source/tests/pt/water_tensor/dipole/atomic_system/type_map.raw create mode 100644 source/tests/pt/water_tensor/dipole/global_system/nopbc create mode 100644 source/tests/pt/water_tensor/dipole/global_system/set.000/box.npy create mode 100644 source/tests/pt/water_tensor/dipole/global_system/set.000/coord.npy create mode 100644 source/tests/pt/water_tensor/dipole/global_system/set.000/dipole.npy create mode 100644 source/tests/pt/water_tensor/dipole/global_system/type.raw create mode 100644 source/tests/pt/water_tensor/dipole/global_system/type_map.raw create mode 100644 source/tests/pt/water_tensor/polar/atomic_system/set.000/atomic_polarizability.npy create mode 100644 source/tests/pt/water_tensor/polar/atomic_system/set.000/box.npy create mode 100644 source/tests/pt/water_tensor/polar/atomic_system/set.000/coord.npy create mode 100644 source/tests/pt/water_tensor/polar/atomic_system/type.raw create mode 100644 source/tests/pt/water_tensor/polar/atomic_system/type_map.raw create mode 100644 source/tests/pt/water_tensor/polar/global_system/set.000/box.npy create mode 100644 source/tests/pt/water_tensor/polar/global_system/set.000/coord.npy create mode 100644 source/tests/pt/water_tensor/polar/global_system/set.000/polarizability.npy create mode 100644 source/tests/pt/water_tensor/polar/global_system/type.raw create mode 100644 source/tests/pt/water_tensor/polar/global_system/type_map.raw create mode 100644 source/tests/pt/water_tensor/se_e2_a.json diff --git a/deepmd/dpmodel/atomic_model/base_atomic_model.py b/deepmd/dpmodel/atomic_model/base_atomic_model.py index 09d33203a1..92aaeb3f84 100644 --- a/deepmd/dpmodel/atomic_model/base_atomic_model.py +++ b/deepmd/dpmodel/atomic_model/base_atomic_model.py @@ -78,7 +78,12 @@ def forward_common_atomic( if self.atom_excl is not None: atom_mask = self.atom_excl.build_type_exclude_mask(atype) for kk in ret_dict.keys(): - ret_dict[kk] = ret_dict[kk] * atom_mask[:, :, None] + out_shape = ret_dict[kk].shape + ret_dict[kk] = ( + ret_dict[kk].reshape([out_shape[0], out_shape[1], -1]) + * atom_mask[:, :, None] + ).reshape(out_shape) + ret_dict["mask"] = atom_mask return ret_dict diff --git a/deepmd/dpmodel/model/make_model.py b/deepmd/dpmodel/model/make_model.py index e8b1ecc390..33e929ac8d 100644 --- a/deepmd/dpmodel/model/make_model.py +++ b/deepmd/dpmodel/model/make_model.py @@ -159,6 +159,9 @@ def call( aparam=ap, do_atomic_virial=do_atomic_virial, ) + nf, nloc = nlist.shape[:2] + if "mask" in model_predict_lower: + model_predict_lower["mask"] = model_predict_lower["mask"][:, :nloc] model_predict = communicate_extended_output( model_predict_lower, self.model_output_def(), diff --git a/deepmd/dpmodel/model/transform_output.py b/deepmd/dpmodel/model/transform_output.py index c87c79f7d4..8cb3ae6730 100644 --- a/deepmd/dpmodel/model/transform_output.py +++ b/deepmd/dpmodel/model/transform_output.py @@ -28,6 +28,8 @@ def fit_output_to_model_output( """ model_ret = dict(fit_ret.items()) for kk, vv in fit_ret.items(): + if kk in ["mask"]: + continue vdef = fit_output_def[kk] shap = vdef.shape atom_axis = -(len(shap) + 1) @@ -59,6 +61,8 @@ def communicate_extended_output( """ new_ret = {} + if "mask" in model_ret: + new_ret["mask"] = model_ret["mask"] for kk in model_output_def.keys_outp(): vv = model_ret[kk] vdef = model_output_def[kk] diff --git a/deepmd/pt/loss/__init__.py b/deepmd/pt/loss/__init__.py index d3a095ce13..d2f6ab9e52 100644 --- a/deepmd/pt/loss/__init__.py +++ b/deepmd/pt/loss/__init__.py @@ -8,9 +8,13 @@ from .loss import ( TaskLoss, ) +from .tensor import ( + TensorLoss, +) __all__ = [ "DenoiseLoss", "EnergyStdLoss", + "TensorLoss", "TaskLoss", ] diff --git a/deepmd/pt/loss/tensor.py b/deepmd/pt/loss/tensor.py new file mode 100644 index 0000000000..ee42536557 --- /dev/null +++ b/deepmd/pt/loss/tensor.py @@ -0,0 +1,162 @@ +# 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 TensorLoss(TaskLoss): + def __init__( + self, + tensor_name: str, + tensor_size: int, + label_name: str, + pref_atomic: float = 0.0, + pref: 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.tensor_name = tensor_name + self.tensor_size = tensor_size + self.label_name = label_name + self.local_weight = pref_atomic + self.global_weight = pref + self.inference = inference + + assert ( + self.local_weight >= 0.0 and self.global_weight >= 0.0 + ), "Can not assign negative weight to `pref` and `pref_atomic`" + self.has_local_weight = self.local_weight > 0.0 or inference + self.has_global_weight = self.global_weight > 0.0 or inference + assert self.has_local_weight or self.has_global_weight, AssertionError( + "Can not assian zero weight both to `pref` and `pref_atomic`" + ) + + def forward(self, model_pred, label, natoms, learning_rate=0.0, mae=False): + """Return loss on local and global tensors. + + Parameters + ---------- + model_pred : dict[str, torch.Tensor] + Model predictions. + label : dict[str, torch.Tensor] + Labels. + natoms : int + The local atom number. + + Returns + ------- + loss: torch.Tensor + Loss for model to minimize. + more_loss: dict[str, torch.Tensor] + Other losses for display. + """ + del learning_rate, mae + loss = torch.tensor(0.0, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + more_loss = {} + if ( + self.has_local_weight + and self.tensor_name in model_pred + and "atomic_" + self.label_name in label + ): + local_tensor_pred = model_pred[self.tensor_name].reshape( + [-1, natoms, self.tensor_size] + ) + local_tensor_label = label["atomic_" + self.label_name].reshape( + [-1, natoms, self.tensor_size] + ) + diff = (local_tensor_pred - local_tensor_label).reshape( + [-1, self.tensor_size] + ) + if "mask" in model_pred: + diff = diff[model_pred["mask"].reshape([-1]).bool()] + l2_local_loss = torch.mean(torch.square(diff)) + if not self.inference: + more_loss[f"l2_local_{self.tensor_name}_loss"] = l2_local_loss.detach() + loss += self.local_weight * l2_local_loss + rmse_local = l2_local_loss.sqrt() + more_loss[f"rmse_local_{self.tensor_name}"] = rmse_local.detach() + if ( + self.has_global_weight + and "global_" + self.tensor_name in model_pred + and self.label_name in label + ): + global_tensor_pred = model_pred["global_" + self.tensor_name].reshape( + [-1, self.tensor_size] + ) + global_tensor_label = label[self.label_name].reshape([-1, self.tensor_size]) + diff = global_tensor_pred - global_tensor_label + if "mask" in model_pred: + atom_num = model_pred["mask"].sum(-1, keepdim=True) + l2_global_loss = 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 = torch.mean(torch.square(diff)) + if not self.inference: + more_loss[ + f"l2_global_{self.tensor_name}_loss" + ] = l2_global_loss.detach() + loss += self.global_weight * l2_global_loss + rmse_global = l2_global_loss.sqrt() / atom_num + more_loss[f"rmse_global_{self.tensor_name}"] = rmse_global.detach() + return loss, more_loss + + @property + def label_requirement(self) -> List[DataRequirementItem]: + """Return data label requirements needed for this loss calculation.""" + label_requirement = [] + if self.has_local_weight: + label_requirement.append( + DataRequirementItem( + "atomic_" + self.label_name, + ndof=self.tensor_size, + atomic=True, + must=False, + high_prec=False, + ) + ) + if self.has_global_weight: + label_requirement.append( + DataRequirementItem( + self.label_name, + ndof=self.tensor_size, + atomic=False, + must=False, + high_prec=False, + ) + ) + return label_requirement diff --git a/deepmd/pt/model/atomic_model/base_atomic_model.py b/deepmd/pt/model/atomic_model/base_atomic_model.py index d6de3dfc88..a1ca81bc1b 100644 --- a/deepmd/pt/model/atomic_model/base_atomic_model.py +++ b/deepmd/pt/model/atomic_model/base_atomic_model.py @@ -88,7 +88,12 @@ def forward_common_atomic( if self.atom_excl is not None: atom_mask = self.atom_excl(atype) for kk in ret_dict.keys(): - ret_dict[kk] = ret_dict[kk] * atom_mask[:, :, None] + out_shape = ret_dict[kk].shape + ret_dict[kk] = ( + ret_dict[kk].reshape([out_shape[0], out_shape[1], -1]) + * atom_mask[:, :, None] + ).reshape(out_shape) + ret_dict["mask"] = atom_mask return ret_dict diff --git a/deepmd/pt/model/model/__init__.py b/deepmd/pt/model/model/__init__.py index 87eb391a7e..bd354af8d8 100644 --- a/deepmd/pt/model/model/__init__.py +++ b/deepmd/pt/model/model/__init__.py @@ -94,7 +94,7 @@ def get_model(model_params): fitting_net["type"] = fitting_net.get("type", "ener") fitting_net["ntypes"] = descriptor.get_ntypes() fitting_net["mixed_types"] = descriptor.mixed_types() - fitting_net["embedding_width"] = descriptor.get_dim_out() + fitting_net["embedding_width"] = descriptor.get_dim_emb() fitting_net["dim_descrpt"] = descriptor.get_dim_out() grad_force = "direct" not in fitting_net["type"] if not grad_force: diff --git a/deepmd/pt/model/model/dipole_model.py b/deepmd/pt/model/model/dipole_model.py index 6629541459..8b6f2c47c1 100644 --- a/deepmd/pt/model/model/dipole_model.py +++ b/deepmd/pt/model/model/dipole_model.py @@ -50,6 +50,8 @@ def forward( model_predict["atom_virial"] = model_ret["dipole_derv_c"].squeeze( -3 ) + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] else: model_predict = model_ret model_predict["updated_coord"] += coord diff --git a/deepmd/pt/model/model/dp_zbl_model.py b/deepmd/pt/model/model/dp_zbl_model.py index f2af0fff52..dcf1c36e83 100644 --- a/deepmd/pt/model/model/dp_zbl_model.py +++ b/deepmd/pt/model/model/dp_zbl_model.py @@ -63,6 +63,8 @@ def forward( model_predict["atom_virial"] = model_ret["energy_derv_c"].squeeze(-3) else: model_predict["force"] = model_ret["dforce"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] return model_predict @torch.jit.export diff --git a/deepmd/pt/model/model/ener_model.py b/deepmd/pt/model/model/ener_model.py index 1a5706dbbf..cd4f78a2e2 100644 --- a/deepmd/pt/model/model/ener_model.py +++ b/deepmd/pt/model/model/ener_model.py @@ -52,6 +52,8 @@ def forward( ) else: model_predict["force"] = model_ret["dforce"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] else: model_predict = model_ret model_predict["updated_coord"] += coord diff --git a/deepmd/pt/model/model/make_model.py b/deepmd/pt/model/model/make_model.py index 98f0a18241..196e1497b1 100644 --- a/deepmd/pt/model/model/make_model.py +++ b/deepmd/pt/model/model/make_model.py @@ -154,6 +154,9 @@ def forward_common( fparam=fp, aparam=ap, ) + nf, nloc = nlist.shape[:2] + if "mask" in model_predict_lower: + model_predict_lower["mask"] = model_predict_lower["mask"][:, :nloc] model_predict = communicate_extended_output( model_predict_lower, self.model_output_def(), diff --git a/deepmd/pt/model/model/polar_model.py b/deepmd/pt/model/model/polar_model.py index d956a0344c..bf430c6706 100644 --- a/deepmd/pt/model/model/polar_model.py +++ b/deepmd/pt/model/model/polar_model.py @@ -42,6 +42,8 @@ def forward( model_predict = {} model_predict["polar"] = model_ret["polar"] model_predict["global_polar"] = model_ret["polar_redu"] + if "mask" in model_ret: + model_predict["mask"] = model_ret["mask"] else: model_predict = model_ret model_predict["updated_coord"] += coord diff --git a/deepmd/pt/model/model/transform_output.py b/deepmd/pt/model/model/transform_output.py index 730e6b29d0..cc9f026ab2 100644 --- a/deepmd/pt/model/model/transform_output.py +++ b/deepmd/pt/model/model/transform_output.py @@ -154,6 +154,8 @@ def fit_output_to_model_output( redu_prec = env.GLOBAL_PT_ENER_FLOAT_PRECISION model_ret = dict(fit_ret.items()) for kk, vv in fit_ret.items(): + if kk in ["mask"]: + continue vdef = fit_output_def[kk] shap = vdef.shape atom_axis = -(len(shap) + 1) @@ -192,6 +194,8 @@ def communicate_extended_output( """ redu_prec = env.GLOBAL_PT_ENER_FLOAT_PRECISION new_ret = {} + if "mask" in model_ret: + new_ret["mask"] = model_ret["mask"] for kk in model_output_def.keys_outp(): vv = model_ret[kk] vdef = model_output_def[kk] diff --git a/deepmd/pt/model/task/dipole.py b/deepmd/pt/model/task/dipole.py index 7d2dd221db..21372888d6 100644 --- a/deepmd/pt/model/task/dipole.py +++ b/deepmd/pt/model/task/dipole.py @@ -157,7 +157,7 @@ def compute_output_stats( The path to the stat file. """ - raise NotImplementedError + pass def forward( self, diff --git a/deepmd/pt/model/task/polarizability.py b/deepmd/pt/model/task/polarizability.py index 9483d1eb4a..fa4f6d7f37 100644 --- a/deepmd/pt/model/task/polarizability.py +++ b/deepmd/pt/model/task/polarizability.py @@ -184,7 +184,7 @@ def compute_output_stats( The path to the stat file. """ - raise NotImplementedError + pass def forward( self, diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py index ef8a53e656..cb24c82c0c 100644 --- a/deepmd/pt/train/training.py +++ b/deepmd/pt/train/training.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import functools import logging +import math import time from copy import ( deepcopy, @@ -22,6 +23,7 @@ from deepmd.pt.loss import ( DenoiseLoss, EnergyStdLoss, + TensorLoss, ) from deepmd.pt.model.model import ( get_model, @@ -186,14 +188,14 @@ def get_dataloader_and_buffer(_data, _params): valid_numb_batch, ) - def get_single_model( - _model_params, + def single_model_stat( + _model, + _data_stat_nbatch, _training_data, _validation_data, _stat_file_path, _data_requirement, ): - model = get_model(deepcopy(_model_params)).to(DEVICE) _training_data.add_data_requirement(_data_requirement) if _validation_data is not None: _validation_data.add_data_requirement(_data_requirement) @@ -204,16 +206,21 @@ def get_sample(): sampled = make_stat_input( _training_data.systems, _training_data.dataloaders, - _model_params.get("data_stat_nbatch", 10), + _data_stat_nbatch, ) return sampled - model.compute_or_load_stat( + _model.compute_or_load_stat( sampled_func=get_sample, stat_file_path=_stat_file_path, ) if isinstance(_stat_file_path, DPH5Path): _stat_file_path.root.close() + + def get_single_model( + _model_params, + ): + model = get_model(deepcopy(_model_params)).to(DEVICE) return model def get_lr(lr_params): @@ -224,7 +231,7 @@ def get_lr(lr_params): lr_exp = LearningRateExp(**lr_params) return lr_exp - def get_loss(loss_params, start_lr, _ntypes): + def get_loss(loss_params, start_lr, _ntypes, _model): loss_type = loss_params.get("type", "ener") if loss_type == "ener": loss_params["starter_learning_rate"] = start_lr @@ -232,6 +239,17 @@ def get_loss(loss_params, start_lr, _ntypes): elif loss_type == "denoise": loss_params["ntypes"] = _ntypes return DenoiseLoss(**loss_params) + elif loss_type == "tensor": + tensor_name = _model.model_output_type() + loss_params["tensor_name"] = tensor_name + loss_params["tensor_size"] = math.prod( + _model.model_output_def()[tensor_name].shape + ) + label_name = tensor_name + if label_name == "polar": + label_name = "polarizability" + loss_params["label_name"] = label_name + return TensorLoss(**loss_params) else: raise NotImplementedError @@ -253,12 +271,26 @@ def get_loss(loss_params, start_lr, _ntypes): else: self.opt_type, self.opt_param = get_opt_param(training_params) + # Model + dp_random.seed(training_params["seed"]) + if not self.multi_task: + self.model = get_single_model( + model_params, + ) + else: + self.model = {} + for model_key in self.model_keys: + self.model[model_key] = get_single_model( + model_params["model_dict"][model_key], + ) + # Loss if not self.multi_task: self.loss = get_loss( config["loss"], config["learning_rate"]["start_lr"], len(model_params["type_map"]), + self.model, ) else: self.loss = {} @@ -269,13 +301,16 @@ def get_loss(loss_params, start_lr, _ntypes): else: lr_param = config["learning_rate"]["start_lr"] ntypes = len(model_params["model_dict"][model_key]["type_map"]) - self.loss[model_key] = get_loss(loss_param, lr_param, ntypes) + self.loss[model_key] = get_loss( + loss_param, lr_param, ntypes, self.model[model_key] + ) - # Data + Model + # Data dp_random.seed(training_params["seed"]) if not self.multi_task: - self.model = get_single_model( - model_params, + single_model_stat( + self.model, + model_params.get("data_stat_nbatch", 10), training_data, validation_data, stat_file_path, @@ -295,11 +330,11 @@ def get_loss(loss_params, start_lr, _ntypes): self.validation_dataloader, self.validation_data, self.valid_numb_batch, - self.model, - ) = {}, {}, {}, {}, {}, {} + ) = {}, {}, {}, {}, {} for model_key in self.model_keys: - self.model[model_key] = get_single_model( - model_params["model_dict"][model_key], + single_model_stat( + self.model[model_key], + model_params["model_dict"][model_key].get("data_stat_nbatch", 10), training_data[model_key], validation_data[model_key], stat_file_path[model_key], diff --git a/examples/water_tensor/dipole/dipole_input_torch.json b/examples/water_tensor/dipole/dipole_input_torch.json new file mode 100644 index 0000000000..f6903d3334 --- /dev/null +++ b/examples/water_tensor/dipole/dipole_input_torch.json @@ -0,0 +1,84 @@ +{ + "_comment1": " model parameters", + "model": { + "type_map": [ + "O", + "H" + ], + "atom_exclude_types": [ + 1 + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 3.80, + "rcut": 4.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 6, + "type_one_side": true, + "precision": "float64", + "seed": 1, + "_comment2": " that's all" + }, + "fitting_net": { + "type": "dipole", + "neuron": [ + 100, + 100, + 100 + ], + "resnet_dt": true, + "precision": "float64", + "seed": 1, + "_comment3": " that's all" + }, + "_comment4": " that's all" + }, + "learning_rate": { + "type": "exp", + "start_lr": 0.01, + "decay_steps": 5000, + "_comment5": "that's all" + }, + "loss": { + "type": "tensor", + "pref": 1.0, + "pref_atomic": 1.0, + "_comment6": " that's all" + }, + "_comment7": " traing controls", + "training": { + "training_data": { + "systems": [ + "./training_data_reformat/atomic_system", + "./training_data_reformat/global_system" + ], + "batch_size": "auto", + "_comment8": "that's all" + }, + "validation_data": { + "systems": [ + "./validation_data_reformat/atomic_system", + "./validation_data_reformat/global_system" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment9": "that's all" + }, + "numb_steps": 2000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment10": "that's all" + }, + "_comment11": "that's all" +} diff --git a/examples/water_tensor/dipole/training_data_reformat/atomic_system/nopbc 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+ 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "type_one_side": true, + "precision": "float64", + "seed": 1, + "_comment2": " that's all" + }, + "fitting_net": { + "type": "polar", + "fit_diag": false, + "neuron": [ + 100, + 100, + 100 + ], + "resnet_dt": true, + "precision": "float64", + "seed": 1, + "_comment3": " that's all" + }, + "_comment4": " that's all" + }, + + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.01, + "stop_lr": 3.51e-7, + "_comment5": "that's all" + }, + "loss": { + "type": "tensor", + "pref_atomic": 1.0, + "pref": 1.0, + "_comment6": "that's all" + }, + + "_comment7": " traing controls", + "training": { + "training_data": { + "systems": [ + "./training_data_reformat/atomic_system", + "./training_data_reformat/global_system" + ], + "batch_size": "auto", + "_comment8": "that's all" + }, + "validation_data": { + "systems": [ + "./validation_data_reformat/atomic_system", + "./validation_data_reformat/global_system" + ], + 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1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 diff --git a/examples/water_tensor/polar/validation_data_reformat/global_system/type_map.raw b/examples/water_tensor/polar/validation_data_reformat/global_system/type_map.raw new file mode 100644 index 0000000000..e900768b1d --- /dev/null +++ b/examples/water_tensor/polar/validation_data_reformat/global_system/type_map.raw @@ -0,0 +1,2 @@ +O +H diff --git a/source/tests/pt/test_training.py b/source/tests/pt/test_training.py index 4e73fc4f8a..1613c9e1c2 100644 --- a/source/tests/pt/test_training.py +++ b/source/tests/pt/test_training.py @@ -103,5 +103,182 @@ def tearDown(self) -> None: DPTrainTest.tearDown(self) +class TestDipoleModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/dipole/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/dipole/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_se_e2_a) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "dipole" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestDipoleModelDPA1(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/dipole/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/dipole/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa1) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "dipole" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestDipoleModelDPA2(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/dipole/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/dipole/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa2) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "dipole" + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestPolarModelSeA(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/polar/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/polar/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_se_e2_a) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "polar" + self.config["model"]["fitting_net"]["fit_diag"] = False + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestPolarModelDPA1(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/polar/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/polar/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa1) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "polar" + self.config["model"]["fitting_net"]["fit_diag"] = False + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + +class TestPolarModelDPA2(unittest.TestCase, DPTrainTest): + def setUp(self): + input_json = str(Path(__file__).parent / "water_tensor/se_e2_a.json") + with open(input_json) as f: + self.config = json.load(f) + data_file_atomic = str( + Path(__file__).parent / "water_tensor/polar/atomic_system" + ) + data_file_global = str( + Path(__file__).parent / "water_tensor/polar/global_system" + ) + self.config["training"]["training_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["training"]["validation_data"]["systems"] = [ + data_file_atomic, + data_file_global, + ] + self.config["model"] = deepcopy(model_dpa2) + self.config["model"]["atom_exclude_types"] = [1] + self.config["model"]["fitting_net"]["type"] = "polar" + self.config["model"]["fitting_net"]["fit_diag"] = False + self.config["training"]["numb_steps"] = 1 + self.config["training"]["save_freq"] = 1 + + def tearDown(self) -> None: + DPTrainTest.tearDown(self) + + if __name__ == "__main__": unittest.main() diff --git a/source/tests/pt/water_tensor/dipole/atomic_system/nopbc b/source/tests/pt/water_tensor/dipole/atomic_system/nopbc new file mode 100644 index 0000000000..e69de29bb2 diff --git a/source/tests/pt/water_tensor/dipole/atomic_system/set.000/atomic_dipole.npy b/source/tests/pt/water_tensor/dipole/atomic_system/set.000/atomic_dipole.npy new file mode 100644 index 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embedding_width, + dim_descrpt, neuron, bias_atom_e=None, out_dim=1, @@ -315,7 +315,7 @@ def __init__( """ super().__init__() self.ntypes = ntypes - self.dim_descrpt = embedding_width + self.dim_descrpt = dim_descrpt self.use_tebd = use_tebd self.out_dim = out_dim if bias_atom_e is None: @@ -329,7 +329,7 @@ def __init__( for type_i in range(self.ntypes): one = ResidualDeep( type_i, - embedding_width, + dim_descrpt, neuron, 0.0, out_dim=out_dim, @@ -344,7 +344,7 @@ def __init__( for type_i in range(self.ntypes): bias_type = 0.0 if self.use_tebd else bias_atom_e[type_i] one = ResidualDeep( - type_i, embedding_width, neuron, bias_type, resnet_dt=resnet_dt + type_i, dim_descrpt, neuron, bias_type, resnet_dt=resnet_dt ) filter_layers.append(one) self.filter_layers = torch.nn.ModuleList(filter_layers) diff --git a/source/tests/common/test_examples.py b/source/tests/common/test_examples.py index 49abcf2f90..647bee2bbb 100644 --- a/source/tests/common/test_examples.py +++ b/source/tests/common/test_examples.py @@ -33,6 +33,8 @@ p_examples / "nopbc" / "train" / "input.json", p_examples / "water_tensor" / "dipole" / "dipole_input.json", p_examples / "water_tensor" / "polar" / "polar_input.json", + p_examples / "water_tensor" / "dipole" / "dipole_input_torch.json", + p_examples / "water_tensor" / "polar" / "polar_input_torch.json", p_examples / "water_multi_task" / "ener_dipole" / "input.json", p_examples / "fparam" / "train" / "input.json", p_examples / "fparam" / "train" / "input_aparam.json", diff --git a/source/tests/pt/water_tensor/se_e2_a.json b/source/tests/pt/water_tensor/se_e2_a.json index 6d9231406a..e53caafc96 100644 --- a/source/tests/pt/water_tensor/se_e2_a.json +++ b/source/tests/pt/water_tensor/se_e2_a.json @@ -44,7 +44,8 @@ }, "learning_rate": { "type": "exp", - "start_lr": 0.01, + "start_lr": 0.001, + "stop_lr": 3.51e-8, "decay_steps": 5000, "_comment5": "that's all" }, From bf2d04376d444819944e00af8058c1ce66423c68 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sun, 3 Mar 2024 01:55:25 +0800 Subject: [PATCH 3/8] delete mask hack --- deepmd/dpmodel/model/transform_output.py | 4 ---- deepmd/pt/model/model/transform_output.py | 4 ---- 2 files changed, 8 deletions(-) diff --git a/deepmd/dpmodel/model/transform_output.py b/deepmd/dpmodel/model/transform_output.py index 8cb3ae6730..c87c79f7d4 100644 --- a/deepmd/dpmodel/model/transform_output.py +++ b/deepmd/dpmodel/model/transform_output.py @@ -28,8 +28,6 @@ def fit_output_to_model_output( """ model_ret = dict(fit_ret.items()) for kk, vv in fit_ret.items(): - if kk in ["mask"]: - continue vdef = fit_output_def[kk] shap = vdef.shape atom_axis = -(len(shap) + 1) @@ -61,8 +59,6 @@ def communicate_extended_output( """ new_ret = {} - if "mask" in model_ret: - new_ret["mask"] = model_ret["mask"] for kk in model_output_def.keys_outp(): vv = model_ret[kk] vdef = model_output_def[kk] diff --git a/deepmd/pt/model/model/transform_output.py b/deepmd/pt/model/model/transform_output.py index cc9f026ab2..730e6b29d0 100644 --- a/deepmd/pt/model/model/transform_output.py +++ b/deepmd/pt/model/model/transform_output.py @@ -154,8 +154,6 @@ def fit_output_to_model_output( redu_prec = env.GLOBAL_PT_ENER_FLOAT_PRECISION model_ret = dict(fit_ret.items()) for kk, vv in fit_ret.items(): - if kk in ["mask"]: - continue vdef = fit_output_def[kk] shap = vdef.shape atom_axis = -(len(shap) + 1) @@ -194,8 +192,6 @@ def communicate_extended_output( """ redu_prec = env.GLOBAL_PT_ENER_FLOAT_PRECISION new_ret = {} - if "mask" in model_ret: - new_ret["mask"] = model_ret["mask"] for kk in model_output_def.keys_outp(): vv = model_ret[kk] vdef = model_output_def[kk] From 7ed81c1523e9f11a9b48362e01fca827c3439cc5 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sun, 3 Mar 2024 02:00:20 +0800 Subject: [PATCH 4/8] delete mask hack --- deepmd/dpmodel/model/make_model.py | 3 --- deepmd/pt/model/model/make_model.py | 3 --- 2 files changed, 6 deletions(-) diff --git a/deepmd/dpmodel/model/make_model.py b/deepmd/dpmodel/model/make_model.py index 0e4dbd0cd9..f30f6a4021 100644 --- a/deepmd/dpmodel/model/make_model.py +++ b/deepmd/dpmodel/model/make_model.py @@ -159,9 +159,6 @@ def call( aparam=ap, do_atomic_virial=do_atomic_virial, ) - nf, nloc = nlist.shape[:2] - if "mask" in model_predict_lower: - model_predict_lower["mask"] = model_predict_lower["mask"][:, :nloc] model_predict = communicate_extended_output( model_predict_lower, self.model_output_def(), diff --git a/deepmd/pt/model/model/make_model.py b/deepmd/pt/model/model/make_model.py index 10b015d541..60b71400fb 100644 --- a/deepmd/pt/model/model/make_model.py +++ b/deepmd/pt/model/model/make_model.py @@ -154,9 +154,6 @@ def forward_common( fparam=fp, aparam=ap, ) - nf, nloc = nlist.shape[:2] - if "mask" in model_predict_lower: - model_predict_lower["mask"] = model_predict_lower["mask"][:, :nloc] model_predict = communicate_extended_output( model_predict_lower, self.model_output_def(), From cbafe00261c079b13fe76c664522a088e87d598b Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sun, 3 Mar 2024 02:03:06 +0800 Subject: [PATCH 5/8] Update training.py --- deepmd/pt/train/training.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py index 039f1c4394..b6a154bec4 100644 --- a/deepmd/pt/train/training.py +++ b/deepmd/pt/train/training.py @@ -205,19 +205,19 @@ def single_model_stat( _training_data.add_data_requirement(_data_requirement) if _validation_data is not None: _validation_data.add_data_requirement(_data_requirement) - if model.get_dim_fparam() > 0: + if _model.get_dim_fparam() > 0: fparam_requirement_items = [ DataRequirementItem( - "fparam", model.get_dim_fparam(), atomic=False, must=True + "fparam", _model.get_dim_fparam(), atomic=False, must=True ) ] _training_data.add_data_requirement(fparam_requirement_items) if _validation_data is not None: _validation_data.add_data_requirement(fparam_requirement_items) - if model.get_dim_aparam() > 0: + if _model.get_dim_aparam() > 0: aparam_requirement_items = [ DataRequirementItem( - "aparam", model.get_dim_aparam(), atomic=True, must=True + "aparam", _model.get_dim_aparam(), atomic=True, must=True ) ] _training_data.add_data_requirement(aparam_requirement_items) From f3c7bf2e95128411e1bea40cbb910651a594b612 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sun, 3 Mar 2024 02:16:34 +0800 Subject: [PATCH 6/8] Fix output defs --- deepmd/dpmodel/infer/deep_eval.py | 12 ++++++------ deepmd/dpmodel/model/base_model.py | 2 +- deepmd/dpmodel/model/make_model.py | 9 ++------- deepmd/dpmodel/output_def.py | 2 ++ deepmd/pt/infer/deep_eval.py | 14 +++++++------- deepmd/pt/model/model/make_model.py | 9 ++------- deepmd/pt/train/training.py | 12 +++++++----- 7 files changed, 27 insertions(+), 33 deletions(-) diff --git a/deepmd/dpmodel/infer/deep_eval.py b/deepmd/dpmodel/infer/deep_eval.py index 1fd36bd7e8..22267c895a 100644 --- a/deepmd/dpmodel/infer/deep_eval.py +++ b/deepmd/dpmodel/infer/deep_eval.py @@ -123,16 +123,16 @@ def get_dim_aparam(self) -> int: @property def model_type(self) -> Type["DeepEvalWrapper"]: """The the evaluator of the model type.""" - model_type = self.dp.model_output_type() - if model_type == "energy": + model_output_type = self.dp.model_output_type() + if "energy" in model_output_type: return DeepPot - elif model_type == "dos": + elif "dos" in model_output_type: return DeepDOS - elif model_type == "dipole": + elif "dipole" in model_output_type: return DeepDipole - elif model_type == "polar": + elif "polar" in model_output_type: return DeepPolar - elif model_type == "wfc": + elif "wfc" in model_output_type: return DeepWFC else: raise RuntimeError("Unknown model type") diff --git a/deepmd/dpmodel/model/base_model.py b/deepmd/dpmodel/model/base_model.py index e7cc8d9272..95c448442e 100644 --- a/deepmd/dpmodel/model/base_model.py +++ b/deepmd/dpmodel/model/base_model.py @@ -89,7 +89,7 @@ def is_aparam_nall(self) -> bool: """ @abstractmethod - def model_output_type(self) -> str: + def model_output_type(self) -> List[str]: """Get the output type for the model.""" @abstractmethod diff --git a/deepmd/dpmodel/model/make_model.py b/deepmd/dpmodel/model/make_model.py index f30f6a4021..d1f671c8de 100644 --- a/deepmd/dpmodel/model/make_model.py +++ b/deepmd/dpmodel/model/make_model.py @@ -76,7 +76,7 @@ def model_output_def(self): """Get the output def for the model.""" return ModelOutputDef(self.atomic_output_def()) - def model_output_type(self) -> str: + def model_output_type(self) -> List[str]: """Get the output type for the model.""" output_def = self.model_output_def() var_defs = output_def.var_defs @@ -85,12 +85,7 @@ def model_output_type(self) -> str: for kk, vv in var_defs.items() if vv.category == OutputVariableCategory.OUT ] - if len(vars) == 1: - return vars[0] - elif len(vars) == 0: - raise ValueError("No valid output type found") - else: - raise ValueError(f"Multiple valid output types found: {vars}") + return vars def call( self, diff --git a/deepmd/dpmodel/output_def.py b/deepmd/dpmodel/output_def.py index d816ed4e84..0bc248495a 100644 --- a/deepmd/dpmodel/output_def.py +++ b/deepmd/dpmodel/output_def.py @@ -1,5 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import functools +import math from enum import ( IntEnum, ) @@ -193,6 +194,7 @@ def __init__( ): self.name = name self.shape = list(shape) + self.output_size = math.prod(self.shape) self.atomic = atomic self.reduciable = reduciable self.r_differentiable = r_differentiable diff --git a/deepmd/pt/infer/deep_eval.py b/deepmd/pt/infer/deep_eval.py index f75052166b..bf6a5b0306 100644 --- a/deepmd/pt/infer/deep_eval.py +++ b/deepmd/pt/infer/deep_eval.py @@ -148,18 +148,18 @@ def get_dim_aparam(self) -> int: @property def model_type(self) -> "DeepEvalWrapper": """The the evaluator of the model type.""" - model_type = self.dp.model["Default"].model_output_type() - if model_type == "energy": + model_output_type = self.dp.model["Default"].model_output_type() + if "energy" in model_output_type: return DeepPot - elif model_type == "dos": + elif "dos" in model_output_type: return DeepDOS - elif model_type == "dipole": + elif "dipole" in model_output_type: return DeepDipole - elif model_type == "polar": + elif "polar" in model_output_type: return DeepPolar - elif model_type == "global_polar": + elif "global_polar" in model_output_type: return DeepGlobalPolar - elif model_type == "wfc": + elif "wfc" in model_output_type: return DeepWFC else: raise RuntimeError("Unknown model type") diff --git a/deepmd/pt/model/model/make_model.py b/deepmd/pt/model/model/make_model.py index 60b71400fb..f9daa916a8 100644 --- a/deepmd/pt/model/model/make_model.py +++ b/deepmd/pt/model/model/make_model.py @@ -75,7 +75,7 @@ def model_output_def(self): return ModelOutputDef(self.atomic_output_def()) @torch.jit.export - def model_output_type(self) -> str: + def model_output_type(self) -> List[str]: """Get the output type for the model.""" output_def = self.model_output_def() var_defs = output_def.var_defs @@ -86,12 +86,7 @@ def model_output_type(self) -> str: # .value is critical for JIT if vv.category == OutputVariableCategory.OUT.value: vars.append(kk) - if len(vars) == 1: - return vars[0] - elif len(vars) == 0: - raise ValueError("No valid output type found") - else: - raise ValueError(f"Multiple valid output types found: {vars}") + return vars # cannot use the name forward. torch script does not work def forward_common( diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py index b6a154bec4..77e6b1c709 100644 --- a/deepmd/pt/train/training.py +++ b/deepmd/pt/train/training.py @@ -1,7 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import functools import logging -import math import time from copy import ( deepcopy, @@ -264,11 +263,14 @@ def get_loss(loss_params, start_lr, _ntypes, _model): loss_params["ntypes"] = _ntypes return DenoiseLoss(**loss_params) elif loss_type == "tensor": - tensor_name = _model.model_output_type() + model_output_type = _model.model_output_type() + if "mask" in model_output_type: + model_output_type.pop(model_output_type.index("mask")) + tensor_name = model_output_type[0] loss_params["tensor_name"] = tensor_name - loss_params["tensor_size"] = math.prod( - _model.model_output_def()[tensor_name].shape - ) + loss_params["tensor_size"] = _model.model_output_def()[ + tensor_name + ].output_size label_name = tensor_name if label_name == "polar": label_name = "polarizability" From 967736a526eb48e44e6159dd4afeeae02c8d3bf1 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sun, 3 Mar 2024 02:43:06 +0800 Subject: [PATCH 7/8] Update output_def.py --- deepmd/dpmodel/output_def.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/deepmd/dpmodel/output_def.py b/deepmd/dpmodel/output_def.py index 0bc248495a..ac41513246 100644 --- a/deepmd/dpmodel/output_def.py +++ b/deepmd/dpmodel/output_def.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import functools -import math from enum import ( IntEnum, ) @@ -194,7 +193,11 @@ def __init__( ): self.name = name self.shape = list(shape) - self.output_size = math.prod(self.shape) + # jit doesn't support math.prod(self.shape) + self.output_size = 1 + len_shape = len(self.shape) + for i in range(len_shape): + self.output_size *= self.shape[i] self.atomic = atomic self.reduciable = reduciable self.r_differentiable = r_differentiable From 7c1c9780d82f57ff704f78166768838fb8081012 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sun, 3 Mar 2024 03:42:10 +0800 Subject: [PATCH 8/8] Update test_training.py --- source/tests/pt/test_training.py | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/source/tests/pt/test_training.py b/source/tests/pt/test_training.py index 51d5eb9dde..c3d1e36492 100644 --- a/source/tests/pt/test_training.py +++ b/source/tests/pt/test_training.py @@ -34,10 +34,25 @@ def test_trainable(self): fix_params = deepcopy(self.config) fix_params["model"]["descriptor"]["trainable"] = False fix_params["model"]["fitting_net"]["trainable"] = False - trainer_fix = get_trainer(fix_params) - model_dict_before_training = deepcopy(trainer_fix.model.state_dict()) - trainer_fix.run() - model_dict_after_training = deepcopy(trainer_fix.model.state_dict()) + free_descriptor = hasattr(self, "not_all_grad") and self.not_all_grad + if free_descriptor: + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + # we only check trainable for fitting net + fix_params["model"]["descriptor"]["trainable"] = True + trainer_fix = get_trainer(fix_params) + model_dict_before_training = deepcopy( + trainer_fix.model.fitting_net.state_dict() + ) + trainer_fix.run() + model_dict_after_training = deepcopy( + trainer_fix.model.fitting_net.state_dict() + ) + else: + trainer_fix = get_trainer(fix_params) + model_dict_before_training = deepcopy(trainer_fix.model.state_dict()) + trainer_fix.run() + model_dict_after_training = deepcopy(trainer_fix.model.state_dict()) for key in model_dict_before_training: torch.testing.assert_allclose( model_dict_before_training[key], model_dict_after_training[key] @@ -253,6 +268,9 @@ def setUp(self): self.config["model"]["fitting_net"]["fit_diag"] = False self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + self.not_all_grad = True def tearDown(self) -> None: DPTrainTest.tearDown(self) @@ -283,6 +301,9 @@ def setUp(self): self.config["model"]["fitting_net"]["fit_diag"] = False self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + self.not_all_grad = True def tearDown(self) -> None: DPTrainTest.tearDown(self) @@ -313,6 +334,9 @@ def setUp(self): self.config["model"]["fitting_net"]["fit_diag"] = False self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 + # can not set requires_grad false for all parameters, + # because the input coord has no grad, thus the loss if all set to false + self.not_all_grad = True def tearDown(self) -> None: DPTrainTest.tearDown(self)

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