From 05149b1a6b8e02848649d6742427235481befbfa Mon Sep 17 00:00:00 2001 From: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com> Date: Fri, 26 Jan 2024 11:37:23 +0800 Subject: [PATCH] fix code style (#177) * fix import * Update test_model.py * delete unused files * pre-commit * fix import * fix import * fix imports Signed-off-by: Jinzhe Zeng * fix jit error Signed-off-by: Jinzhe Zeng * disable --failfast so we can check all failure Signed-off-by: Jinzhe Zeng * ignore RUF005 Signed-off-by: Jinzhe Zeng * seed Signed-off-by: Jinzhe Zeng * fix seed Signed-off-by: Jinzhe Zeng --------- Signed-off-by: Jinzhe Zeng Co-authored-by: Duo <50307526+iProzd@users.noreply.github.com> Co-authored-by: Jinzhe Zeng --- .github/workflows/ci-test.yml | 2 +- .license-header.txt | 1 + .pre-commit-config.yaml | 113 + LICENSE | 2 +- README.md | 2 +- deepmd_pt/__init__.py | 5 +- deepmd_pt/entrypoints/__init__.py | 1 + deepmd_pt/entrypoints/main.py | 333 +- .../examples/water/dpa1-denoise/input.json | 174 +- .../water/dpa1-ener-model6/input.json | 188 +- .../dpa1-ener-warmup-grad-clip/input.json | 182 +- deepmd_pt/examples/water/dpa1-ener/input.json | 172 +- .../examples/water/dpa1-force-ener/input.json | 170 +- .../examples/water/dpa1-force/input.json | 170 +- .../examples/water/dpa2-denoise/input.json | 206 +- deepmd_pt/examples/water/dpa2-ener/input.json | 198 +- .../examples/water/dpa2-force-ener/input.json | 200 +- .../examples/water/dpa2-force/input.json | 201 +- .../examples/water/dpau-denoise/input.json | 130 +- deepmd_pt/examples/water/dpau-ener/input.json | 129 +- .../examples/water/dpau-force-ener/input.json | 127 +- .../examples/water/hybrid-denoise/input.json | 244 +- .../examples/water/hybrid-ener/input.json | 244 +- .../water/hybrid-force-ener/input.json | 230 +- deepmd_pt/examples/water/multitask/input.json | 370 +- deepmd_pt/examples/water/se_a/input.json | 144 +- deepmd_pt/infer/__init__.py | 1 + deepmd_pt/infer/deep_eval.py | 276 +- deepmd_pt/infer/inference.py | 251 +- deepmd_pt/loss/__init__.py | 19 +- deepmd_pt/loss/denoise.py | 58 +- deepmd_pt/loss/ener.py | 125 +- deepmd_pt/loss/loss.py | 8 +- deepmd_pt/model/__init__.py | 1 + deepmd_pt/model/backbone/__init__.py | 14 +- deepmd_pt/model/backbone/backbone.py | 18 +- deepmd_pt/model/backbone/evoformer2b.py | 90 +- deepmd_pt/model/descriptor/__init__.py | 56 +- deepmd_pt/model/descriptor/descriptor.py | 165 +- deepmd_pt/model/descriptor/dpa1.py | 288 +- deepmd_pt/model/descriptor/dpa2.py | 675 +- deepmd_pt/model/descriptor/env_mat.py | 21 +- deepmd_pt/model/descriptor/gaussian_lcc.py | 263 +- deepmd_pt/model/descriptor/hybrid.py | 206 +- deepmd_pt/model/descriptor/repformer_layer.py | 456 +- deepmd_pt/model/descriptor/repformers.py | 594 +- deepmd_pt/model/descriptor/se_a.py | 483 +- deepmd_pt/model/descriptor/se_atten.py | 251 +- deepmd_pt/model/model/__init__.py | 33 +- deepmd_pt/model/model/atomic_model.py | 123 +- deepmd_pt/model/model/dp_atomic_model.py | 381 +- deepmd_pt/model/model/ener.py | 100 +- deepmd_pt/model/model/make_model.py | 111 +- deepmd_pt/model/model/model.py | 143 +- deepmd_pt/model/model/transform_output.py | 304 +- deepmd_pt/model/network/__init__.py | 2 +- deepmd_pt/model/network/mlp.py | 333 +- deepmd_pt/model/network/network.py | 952 +- deepmd_pt/model/task/__init__.py | 41 +- deepmd_pt/model/task/atten_lcc.py | 63 +- deepmd_pt/model/task/denoise.py | 117 +- deepmd_pt/model/task/dipole.py | 42 +- deepmd_pt/model/task/ener.py | 183 +- deepmd_pt/model/task/fitting.py | 98 +- deepmd_pt/model/task/task.py | 10 +- deepmd_pt/model/task/type_predict.py | 46 +- deepmd_pt/optimizer/KFWrapper.py | 30 +- deepmd_pt/optimizer/LKF.py | 26 +- deepmd_pt/optimizer/__init__.py | 11 +- deepmd_pt/train/__init__.py | 1 + deepmd_pt/train/training.py | 534 +- deepmd_pt/train/wrapper.py | 149 +- deepmd_pt/utils/__init__.py | 1 + deepmd_pt/utils/ase_calc.py | 52 +- deepmd_pt/utils/auto_batch_size.py | 3 +- deepmd_pt/utils/cache.py | 5 +- deepmd_pt/utils/dataloader.py | 86 +- deepmd_pt/utils/dataset.py | 489 +- deepmd_pt/utils/dp_random.py | 5 +- deepmd_pt/utils/env.py | 21 +- deepmd_pt/utils/finetune.py | 87 +- deepmd_pt/utils/learning_rate.py | 16 +- deepmd_pt/utils/multi_task.py | 94 +- deepmd_pt/utils/nlist.py | 624 +- deepmd_pt/utils/plugin.py | 2 +- deepmd_pt/utils/preprocess.py | 142 +- deepmd_pt/utils/region.py | 145 +- deepmd_pt/utils/stat.py | 18 +- deepmd_pt/utils/utils.py | 14 +- deploy/CMakeLists.txt | 7 - deploy/test.cpp | 35 - deploy/test.py | 24 - deploy/test_model.cpp | 62 - pyproject.toml | 35 + rmse_over_step.png | Bin 42863 -> 0 bytes .../distributed_data_parallel_slurm_run.bash | 10 - .../distributed_data_parallel_slurm_setup.py | 44 - ...stributed_data_parallel_slurm_setup.sbatch | 24 - source/api_cc/build.sh | 4 +- source/api_cc/include/DeepPot.h | 112 +- source/api_cc/include/common.h | 634 +- source/api_cc/src/DeepPot.cc | 340 +- source/api_cc/src/common.cc | 95 +- source/api_cc/test/cppapi/CMakeLists.txt | 7 +- source/api_cc/test/cppapi/test.cpp | 49 +- source/lmp/build_lammps.sh | 2 +- source/lmp/pair_deepmd.cpp | 106 +- source/lmp/pair_deepmd.h | 16 +- tests/AlMgCu/input_AlCuMg.json | 15012 +++++++-------- tests/AlMgCu/job.json | 23 +- tests/Cu/job.json | 23 +- tests/Cu/se_e2_a.json | 128 +- tests/LiGePS/0310.json | 233 +- tests/LiGePS/job.json | 23 +- tests/__init__.py | 1 + tests/models/dpa1.json | 58 +- tests/models/dpa2.json | 76 +- tests/models/dpa2_hyb.json | 122 +- tests/test_LKF.py | 7 +- tests/test_autodiff.py | 245 +- tests/test_calculator.py | 40 +- tests/test_deeppot.py | 68 +- tests/test_descriptor.py | 138 +- tests/test_descriptor_dpa1.py | 488 +- tests/test_descriptor_dpa2.py | 400 +- tests/test_dp_test.py | 30 +- tests/test_embedding_net.py | 129 +- tests/test_env_mat.py | 78 +- tests/test_fitting_net.py | 86 +- tests/test_force_grad.py | 102 +- tests/test_jit.py | 58 +- tests/test_loss.py | 167 +- tests/test_lr.py | 35 +- tests/test_mlp.py | 523 +- tests/test_model.py | 339 +- tests/test_nlist.py | 339 +- tests/test_permutation.py | 455 +- tests/test_permutation_denoise.py | 131 +- tests/test_region.py | 119 +- tests/test_rot.py | 258 +- tests/test_rot_denoise.py | 195 +- tests/test_rotation.py | 120 +- tests/test_sampler.py | 119 +- tests/test_saveload_dpa1.py | 113 +- tests/test_saveload_se_e2_a.py | 108 +- tests/test_se_e2_a.py | 268 +- tests/test_smooth.py | 314 +- tests/test_smooth_denoise.py | 237 +- tests/test_stat.py | 162 +- tests/test_training.py | 41 +- tests/test_trans.py | 186 +- tests/test_trans_denoise.py | 130 +- tests/test_unused_params.py | 145 +- tests/water/input_AlCuMg.json | 15018 ++++++++-------- tests/water/job.json | 23 +- tests/water/lkf.json | 148 +- tests/water/se_atten.json | 162 +- tests/water/se_e2_a.json | 144 +- tests/water/water_large.json | 144 +- visualize.py | 71 - 160 files changed, 28112 insertions(+), 25170 deletions(-) create mode 100644 .license-header.txt create mode 100644 .pre-commit-config.yaml delete mode 100644 deploy/CMakeLists.txt delete mode 100644 deploy/test.cpp delete mode 100644 deploy/test.py delete mode 100644 deploy/test_model.cpp delete mode 100644 rmse_over_step.png delete mode 100644 slurm/distributed_data_parallel_slurm_run.bash delete mode 100644 slurm/distributed_data_parallel_slurm_setup.py delete mode 100644 slurm/distributed_data_parallel_slurm_setup.sbatch delete mode 100644 visualize.py diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml index aae5d92f..fc3ee39f 100644 --- a/.github/workflows/ci-test.yml +++ b/.github/workflows/ci-test.yml @@ -21,5 +21,5 @@ jobs: pip install -e . pip install -r tests/requirements.txt - name: Test - run: DEVICE=cpu PREPROCESS_DEVICE=cpu DISABLE_TQDM=True coverage run --source=./deepmd_pt -m unittest -v -f && coverage report + run: DEVICE=cpu PREPROCESS_DEVICE=cpu DISABLE_TQDM=True coverage run --source=./deepmd_pt -m unittest -v && coverage report - uses: codecov/codecov-action@v3 diff --git a/.license-header.txt b/.license-header.txt new file mode 100644 index 00000000..4352084d --- /dev/null +++ b/.license-header.txt @@ -0,0 +1 @@ +SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..73e1de74 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,113 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + exclude: "^.+\\.pth$" + - id: end-of-file-fixer + exclude: "^.+\\.pth$" + - id: check-yaml + - id: check-json + - id: check-added-large-files + args: ['--maxkb=1024', '--enforce-all'] + - id: check-merge-conflict + - id: check-symlinks + - id: check-toml +# Python +- repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + files: \.py$ + exclude: ^source/3rdparty +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.1.13 + hooks: + - id: ruff + args: ["--fix"] + exclude: ^source/3rdparty + types_or: [python, pyi, jupyter] + - id: ruff-format + exclude: ^source/3rdparty + types_or: [python, pyi, jupyter] +# numpydoc +- repo: https://github.com/Carreau/velin + rev: 0.0.12 + hooks: + - id: velin + args: ["--write"] + exclude: ^source/3rdparty +# Python inside docs +- repo: https://github.com/asottile/blacken-docs + rev: 1.16.0 + hooks: + - id: blacken-docs +# C++ +- repo: https://github.com/pre-commit/mirrors-clang-format + rev: v17.0.6 + hooks: + - id: clang-format + exclude: ^source/3rdparty|source/lib/src/gpu/cudart/.+\.inc +# CSS +- repo: https://github.com/pre-commit/mirrors-csslint + rev: v1.0.5 + hooks: + - id: csslint +# Shell +- repo: https://github.com/scop/pre-commit-shfmt + rev: v3.7.0-4 + hooks: + - id: shfmt +# CMake +- repo: https://github.com/cheshirekow/cmake-format-precommit + rev: v0.6.13 + hooks: + - id: cmake-format + #- id: cmake-lint +# license header +- repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.4 + hooks: + # C++, js + - id: insert-license + files: \.(c|cc|cpp|js|ts|h|hpp)$ + args: + - --license-filepath + - .license-header.txt + - --comment-style + - // + - --no-extra-eol + exclude: ^source/3rdparty|source/lib/src/gpu/cudart/.+\.inc + # CSS + - id: insert-license + files: \.(css|scss)$ + args: + - --license-filepath + - .license-header.txt + - --comment-style + - /*| *| */ + - --no-extra-eol + # Python + - id: insert-license + files: \.(py|pyx)$ + args: + - --license-filepath + - .license-header.txt + - --comment-style + - "#" + - --no-extra-eol + exclude: ^source/3rdparty + # HTML + - id: insert-license + files: \.(html|vue|xml)$ + args: + - --license-filepath + - .license-header.txt + - --comment-style + - + - --no-extra-eol +ci: + autoupdate_branch: devel diff --git a/LICENSE b/LICENSE index 153d416d..0a041280 100644 --- a/LICENSE +++ b/LICENSE @@ -162,4 +162,4 @@ General Public License ever published by the Free Software Foundation. whether future versions of the GNU Lesser General Public License shall apply, that proxy's public statement of acceptance of any version is permanent authorization for you to choose that version for the -Library. \ No newline at end of file +Library. diff --git a/README.md b/README.md index 82dee36b..c7eebcea 100644 --- a/README.md +++ b/README.md @@ -142,7 +142,7 @@ self.dataloaders: List[DataLoader] = [] for system in systems: dl = create_dataloader(system) self.dataloaders.append(dl) - for _ in range(len(dl)): # len(dl) == how many mini-batches in this system + for _ in range(len(dl)): # len(dl) == how many mini-batches in this system index.append(len(self.dataloaders) - 1) ``` diff --git a/deepmd_pt/__init__.py b/deepmd_pt/__init__.py index 4217e612..9dcc4ab4 100644 --- a/deepmd_pt/__init__.py +++ b/deepmd_pt/__init__.py @@ -1,6 +1,5 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later try: from ._version import version as __version__ except ImportError: - from .__about__ import ( - __version__, - ) + from .__about__ import __version__ as __version__ diff --git a/deepmd_pt/entrypoints/__init__.py b/deepmd_pt/entrypoints/__init__.py index e69de29b..6ceb116d 100644 --- a/deepmd_pt/entrypoints/__init__.py +++ b/deepmd_pt/entrypoints/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd_pt/entrypoints/main.py b/deepmd_pt/entrypoints/main.py index cc886365..85828042 100644 --- a/deepmd_pt/entrypoints/main.py +++ b/deepmd_pt/entrypoints/main.py @@ -1,62 +1,99 @@ -import os +# SPDX-License-Identifier: LGPL-3.0-or-later import argparse import json import logging +import os + import torch -from deepmd_pt.utils import env -from deepmd_pt.train import training -from deepmd_pt.infer import inference import torch.distributed as dist -from deepmd_pt.utils.dataset import DeepmdDataSet -from deepmd_pt.utils.dataloader import DpLoaderSet -from torch.distributed.elastic.multiprocessing.errors import record +from torch.distributed.elastic.multiprocessing.errors import ( + record, +) -from deepmd_pt.utils.finetune import change_finetune_model_params -from deepmd_pt.utils.stat import make_stat_input -from deepmd_pt.utils.multi_task import preprocess_shared_params -from deepmd_pt.model.descriptor import Descriptor +from deepmd_pt import ( + __version__, +) +from deepmd_pt.infer import ( + inference, +) +from deepmd_pt.model.descriptor import ( + Descriptor, +) +from deepmd_pt.train import ( + training, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.finetune import ( + change_finetune_model_params, +) +from deepmd_pt.utils.multi_task import ( + preprocess_shared_params, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) -from deepmd_pt import __version__ - -def get_trainer(config, init_model=None, restart_model=None, finetune_model=None, model_branch='', force_load=False): +def get_trainer( + config, + init_model=None, + restart_model=None, + finetune_model=None, + model_branch="", + force_load=False, +): # Initialize DDP - local_rank = os.environ.get('LOCAL_RANK') + local_rank = os.environ.get("LOCAL_RANK") if local_rank is not None: local_rank = int(local_rank) assert dist.is_nccl_available() - dist.init_process_group(backend='nccl') + dist.init_process_group(backend="nccl") multi_task = "model_dict" in config["model"] ckpt = init_model if init_model is not None else restart_model - config["model"] = change_finetune_model_params(ckpt, finetune_model, config["model"], multi_task=multi_task, model_branch=model_branch) + config["model"] = change_finetune_model_params( + ckpt, + finetune_model, + config["model"], + multi_task=multi_task, + model_branch=model_branch, + ) config["model"]["resuming"] = (finetune_model is not None) or (ckpt is not None) shared_links = None if multi_task: config["model"], shared_links = preprocess_shared_params(config["model"]) - def prepare_trainer_input_single(model_params_single, data_dict_single, loss_dict_single, suffix=''): - training_dataset_params = data_dict_single['training_data'] + def prepare_trainer_input_single( + model_params_single, data_dict_single, loss_dict_single, suffix="" + ): + training_dataset_params = data_dict_single["training_data"] type_split = False - if model_params_single['descriptor']['type'] in ['se_e2_a']: + if model_params_single["descriptor"]["type"] in ["se_e2_a"]: type_split = True - validation_dataset_params = data_dict_single['validation_data'] - training_systems = training_dataset_params['systems'] - validation_systems = validation_dataset_params['systems'] + validation_dataset_params = data_dict_single["validation_data"] + training_systems = training_dataset_params["systems"] + validation_systems = validation_dataset_params["systems"] # noise params noise_settings = None - if loss_dict_single.get('type', 'ener') == 'denoise': - noise_settings = {"noise_type": loss_dict_single.pop("noise_type", "uniform"), - "noise": loss_dict_single.pop("noise", 1.0), - "noise_mode": loss_dict_single.pop("noise_mode", "fix_num"), - "mask_num": loss_dict_single.pop("mask_num", 8), - "mask_prob": loss_dict_single.pop("mask_prob", 0.15), - "same_mask": loss_dict_single.pop("same_mask", False), - "mask_coord": loss_dict_single.pop("mask_coord", False), - "mask_type": loss_dict_single.pop("mask_type", False), - "max_fail_num": loss_dict_single.pop("max_fail_num", 10), - "mask_type_idx": len(model_params_single["type_map"]) - 1} + if loss_dict_single.get("type", "ener") == "denoise": + noise_settings = { + "noise_type": loss_dict_single.pop("noise_type", "uniform"), + "noise": loss_dict_single.pop("noise", 1.0), + "noise_mode": loss_dict_single.pop("noise_mode", "fix_num"), + "mask_num": loss_dict_single.pop("mask_num", 8), + "mask_prob": loss_dict_single.pop("mask_prob", 0.15), + "same_mask": loss_dict_single.pop("same_mask", False), + "mask_coord": loss_dict_single.pop("mask_coord", False), + "mask_type": loss_dict_single.pop("mask_type", False), + "max_fail_num": loss_dict_single.pop("max_fail_num", 10), + "mask_type_idx": len(model_params_single["type_map"]) - 1, + } # noise_settings = None # stat files @@ -66,114 +103,182 @@ def prepare_trainer_input_single(model_params_single, data_dict_single, loss_dic ### this design requires "rcut", "rcut_smth" and "sel" in the descriptor ### VERY BAD DESIGN!!!! ### not all descriptors provides these parameter in their constructor - default_stat_file_name = Descriptor.get_stat_name(model_params_single["descriptor"]) - model_params_single["stat_file_dir"] = data_dict_single.get("stat_file_dir", f"stat_files{suffix}") - model_params_single["stat_file"] = data_dict_single.get("stat_file", default_stat_file_name) - model_params_single["stat_file_path"] = os.path.join(model_params_single["stat_file_dir"], - model_params_single["stat_file"]) + default_stat_file_name = Descriptor.get_stat_name( + model_params_single["descriptor"] + ) + model_params_single["stat_file_dir"] = data_dict_single.get( + "stat_file_dir", f"stat_files{suffix}" + ) + model_params_single["stat_file"] = data_dict_single.get( + "stat_file", default_stat_file_name + ) + model_params_single["stat_file_path"] = os.path.join( + model_params_single["stat_file_dir"], model_params_single["stat_file"] + ) if not os.path.exists(model_params_single["stat_file_path"]): has_stat_file_path = False else: ### need to remove this default_stat_file_name = [] for descrpt in model_params_single["descriptor"]["list"]: - default_stat_file_name.append(f'stat_file_rcut{descrpt["rcut"]:.2f}_' - f'smth{descrpt["rcut_smth"]:.2f}_' - f'sel{descrpt["sel"]}_{descrpt["type"]}.npz') - model_params_single["stat_file_dir"] = data_dict_single.get("stat_file_dir", f"stat_files{suffix}") - model_params_single["stat_file"] = data_dict_single.get("stat_file", default_stat_file_name) - assert isinstance(model_params_single["stat_file"], list), "Stat file of hybrid descriptor must be a list!" + default_stat_file_name.append( + f'stat_file_rcut{descrpt["rcut"]:.2f}_' + f'smth{descrpt["rcut_smth"]:.2f}_' + f'sel{descrpt["sel"]}_{descrpt["type"]}.npz' + ) + model_params_single["stat_file_dir"] = data_dict_single.get( + "stat_file_dir", f"stat_files{suffix}" + ) + model_params_single["stat_file"] = data_dict_single.get( + "stat_file", default_stat_file_name + ) + assert isinstance( + model_params_single["stat_file"], list + ), "Stat file of hybrid descriptor must be a list!" stat_file_path = [] for stat_file_path_item in model_params_single["stat_file"]: - single_file_path = os.path.join(model_params_single["stat_file_dir"], stat_file_path_item) + single_file_path = os.path.join( + model_params_single["stat_file_dir"], stat_file_path_item + ) stat_file_path.append(single_file_path) if not os.path.exists(single_file_path): has_stat_file_path = False model_params_single["stat_file_path"] = stat_file_path # validation and training data - validation_data_single = DpLoaderSet(validation_systems, validation_dataset_params['batch_size'], - model_params_single, - type_split=type_split, noise_settings=noise_settings) + validation_data_single = DpLoaderSet( + validation_systems, + validation_dataset_params["batch_size"], + model_params_single, + type_split=type_split, + noise_settings=noise_settings, + ) if ckpt or finetune_model or has_stat_file_path: - train_data_single = DpLoaderSet(training_systems, training_dataset_params['batch_size'], - model_params_single, - type_split=type_split, noise_settings=noise_settings) + train_data_single = DpLoaderSet( + training_systems, + training_dataset_params["batch_size"], + model_params_single, + type_split=type_split, + noise_settings=noise_settings, + ) sampled_single = None else: - train_data_single = DpLoaderSet(training_systems, training_dataset_params['batch_size'], - model_params_single, - type_split=type_split) - data_stat_nbatch = model_params_single.get('data_stat_nbatch', 10) - sampled_single = make_stat_input(train_data_single.systems, train_data_single.dataloaders, data_stat_nbatch) + train_data_single = DpLoaderSet( + training_systems, + training_dataset_params["batch_size"], + model_params_single, + type_split=type_split, + ) + data_stat_nbatch = model_params_single.get("data_stat_nbatch", 10) + sampled_single = make_stat_input( + train_data_single.systems, + train_data_single.dataloaders, + data_stat_nbatch, + ) if noise_settings is not None: - train_data_single = DpLoaderSet(training_systems, training_dataset_params['batch_size'], - model_params_single, - type_split=type_split, noise_settings=noise_settings) + train_data_single = DpLoaderSet( + training_systems, + training_dataset_params["batch_size"], + model_params_single, + type_split=type_split, + noise_settings=noise_settings, + ) return train_data_single, validation_data_single, sampled_single if not multi_task: - train_data, validation_data, sampled = \ - prepare_trainer_input_single(config['model'], - config['training'], - config['loss']) + train_data, validation_data, sampled = prepare_trainer_input_single( + config["model"], config["training"], config["loss"] + ) else: train_data, validation_data, sampled = {}, {}, {} - for model_key in config['model']['model_dict']: - train_data[model_key], validation_data[model_key], sampled[model_key] = \ - prepare_trainer_input_single(config['model']['model_dict'][model_key], - config['training']['data_dict'][model_key], - config['loss_dict'][model_key], - suffix=f'_{model_key}') + for model_key in config["model"]["model_dict"]: + ( + train_data[model_key], + validation_data[model_key], + sampled[model_key], + ) = prepare_trainer_input_single( + config["model"]["model_dict"][model_key], + config["training"]["data_dict"][model_key], + config["loss_dict"][model_key], + suffix=f"_{model_key}", + ) - trainer = training.Trainer(config, train_data, sampled, validation_data=validation_data, init_model=init_model, - restart_model=restart_model, finetune_model=finetune_model, force_load=force_load, - shared_links=shared_links) + trainer = training.Trainer( + config, + train_data, + sampled, + validation_data=validation_data, + init_model=init_model, + restart_model=restart_model, + finetune_model=finetune_model, + force_load=force_load, + shared_links=shared_links, + ) return trainer def train(FLAGS): - logging.info('Configuration path: %s', FLAGS.INPUT) - with open(FLAGS.INPUT, 'r') as fin: + logging.info("Configuration path: %s", FLAGS.INPUT) + with open(FLAGS.INPUT) as fin: config = json.load(fin) - trainer = get_trainer(config, FLAGS.init_model, FLAGS.restart, FLAGS.finetune, FLAGS.model_branch, FLAGS.force_load) + trainer = get_trainer( + config, + FLAGS.init_model, + FLAGS.restart, + FLAGS.finetune, + FLAGS.model_branch, + FLAGS.force_load, + ) trainer.run() def test(FLAGS): - trainer = inference.Tester(FLAGS.model, - input_script=FLAGS.input_script, - system=FLAGS.system, - datafile=FLAGS.datafile, - numb_test=FLAGS.numb_test, - detail_file=FLAGS.detail_file, - shuffle_test=FLAGS.shuffle_test, - head=FLAGS.head) + trainer = inference.Tester( + FLAGS.model, + input_script=FLAGS.input_script, + system=FLAGS.system, + datafile=FLAGS.datafile, + numb_test=FLAGS.numb_test, + detail_file=FLAGS.detail_file, + shuffle_test=FLAGS.shuffle_test, + head=FLAGS.head, + ) trainer.run() def freeze(FLAGS): - model = torch.jit.script(inference.Tester(FLAGS.model, numb_test=1, head=FLAGS.head).model) - torch.jit.save(model, FLAGS.output, { - # TODO: _extra_files - }) + model = torch.jit.script( + inference.Tester(FLAGS.model, numb_test=1, head=FLAGS.head).model + ) + torch.jit.save( + model, + FLAGS.output, + { + # TODO: _extra_files + }, + ) + -#avoid logger conflicts of tf version +# avoid logger conflicts of tf version def clean_loggers(): logger = logging.getLogger() while logger.hasHandlers(): logger.removeHandler(logger.handlers[0]) + + @record def main(args=None): clean_loggers() logging.basicConfig( level=logging.WARNING if env.LOCAL_RANK else logging.INFO, - format=f"%(asctime)-15s {os.environ.get('RANK') or ''} [%(filename)s:%(lineno)d] %(levelname)s %(message)s" + format=f"%(asctime)-15s {os.environ.get('RANK') or ''} [%(filename)s:%(lineno)d] %(levelname)s %(message)s", + ) + logging.info("DeepMD version: %s", __version__) + parser = argparse.ArgumentParser( + description="A tool to manager deep models of potential energy surface." ) - logging.info('DeepMD version: %s', __version__) - parser = argparse.ArgumentParser(description='A tool to manager deep models of potential energy surface.') - subparsers = parser.add_subparsers(dest='command') - train_parser = subparsers.add_parser('train', help='Train a model.') - train_parser.add_argument('INPUT', help='A Json-format configuration file.') + subparsers = parser.add_subparsers(dest="command") + train_parser = subparsers.add_parser("train", help="Train a model.") + train_parser.add_argument("INPUT", help="A Json-format configuration file.") parser_train_subgroup = train_parser.add_mutually_exclusive_group() parser_train_subgroup.add_argument( "-i", @@ -200,13 +305,16 @@ def main(args=None): "-m", "--model-branch", type=str, - default='', + default="", help="Model branch chosen for fine-tuning if multi-task. If not specified, it will re-init the fitting net.", ) - train_parser.add_argument("--force-load", action="store_true", - help='Force load from ckpt, other missing tensors will init from scratch') + train_parser.add_argument( + "--force-load", + action="store_true", + help="Force load from ckpt, other missing tensors will init from scratch", + ) - test_parser = subparsers.add_parser('test', help='Test a model.') + test_parser = subparsers.add_parser("test", help="Test a model.") test_parser_subgroup = test_parser.add_mutually_exclusive_group() test_parser_subgroup.add_argument( "-s", @@ -242,7 +350,9 @@ def main(args=None): type=str, help="Task head to test if in multi-task mode.", ) - test_parser.add_argument("-n", "--numb-test", default=100, type=int, help="The number of data for test") + test_parser.add_argument( + "-n", "--numb-test", default=100, type=int, help="The number of data for test" + ) test_parser.add_argument( "-d", "--detail-file", @@ -254,10 +364,15 @@ def main(args=None): "--shuffle-test", action="store_true", default=False, help="Shuffle test data" ) - freeze_parser = subparsers.add_parser('freeze', help='Freeze a model.') - freeze_parser.add_argument('model', help='Resumes from checkpoint.') - freeze_parser.add_argument("-o", "--output", type=str, default='frozen_model.pth', - help="The frozen model path") + freeze_parser = subparsers.add_parser("freeze", help="Freeze a model.") + freeze_parser.add_argument("model", help="Resumes from checkpoint.") + freeze_parser.add_argument( + "-o", + "--output", + type=str, + default="frozen_model.pth", + help="The frozen model path", + ) freeze_parser.add_argument( "--head", default=None, @@ -266,16 +381,16 @@ def main(args=None): ) FLAGS = parser.parse_args(args) - if FLAGS.command == 'train': + if FLAGS.command == "train": train(FLAGS) - elif FLAGS.command == 'test': + elif FLAGS.command == "test": test(FLAGS) - elif FLAGS.command == 'freeze': + elif FLAGS.command == "freeze": freeze(FLAGS) else: - logging.error('Invalid command!') + logging.error("Invalid command!") parser.print_help() -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/deepmd_pt/examples/water/dpa1-denoise/input.json b/deepmd_pt/examples/water/dpa1-denoise/input.json index 4cb57f75..02337de8 100644 --- a/deepmd_pt/examples/water/dpa1-denoise/input.json +++ b/deepmd_pt/examples/water/dpa1-denoise/input.json @@ -1,92 +1,92 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H", - "MASKED_TOKEN" - ], - "descriptor": { - "type": "dpa1", - "sel": 120, - "rcut_smth": 0.5, - "rcut": 6.0, - "neuron": [ - 25, - 50, - 100 - ], - "axis_neuron": 16, - "attn": 128, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0 + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H", + "MASKED_TOKEN" + ], + "descriptor": { + "type": "dpa1", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "axis_neuron": 16, + "attn": 128, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0 + }, + "_comment": " that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "denoise", - "noise_type": "uniform", - "noise": 1.0, - "noise_mode": "fix_num", - "mask_num": 8, - "mask_prob": 0.15, - "mask_coord": true, - "mask_type": false, - "same_mask": false, - "max_fail_num": 10, - "masked_token_loss": 1.0, - "masked_coord_loss": 1.0, - "norm_loss": 0.0, - "use_l1": true, - "beta": 1.0, - "mask_loss_coord": true, - "mask_loss_token": false, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" + "loss": { + "type": "denoise", + "noise_type": "uniform", + "noise": 1.0, + "noise_mode": "fix_num", + "mask_num": 8, + "mask_prob": 0.15, + "mask_coord": true, + "mask_type": false, + "same_mask": false, + "max_fail_num": 10, + "masked_token_loss": 1.0, + "masked_coord_loss": 1.0, + "norm_loss": 0.0, + "use_l1": true, + "beta": 1.0, + "mask_loss_coord": true, + "mask_loss_token": false, + "_comment": " that's all" }, - "wandb_config":{ - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/dpa1-ener-model6/input.json b/deepmd_pt/examples/water/dpa1-ener-model6/input.json index ce097245..c2d19324 100644 --- a/deepmd_pt/examples/water/dpa1-ener-model6/input.json +++ b/deepmd_pt/examples/water/dpa1-ener-model6/input.json @@ -1,98 +1,100 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H" - ], - "type_embedding": { - "neuron": [8], - "tebd_input_mode": "dot_residual_t" + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "type_embedding": { + "neuron": [ + 8 + ], + "tebd_input_mode": "dot_residual_t" + }, + "descriptor": { + "type": "se_atten", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "attn": 128, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" }, - "descriptor": { - "type": "se_atten", - "sel": 120, - "rcut_smth": 0.5, - "rcut": 6.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "attn": 128, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0, - "_comment": " that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "wandb_config":{ - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/dpa1-ener-warmup-grad-clip/input.json b/deepmd_pt/examples/water/dpa1-ener-warmup-grad-clip/input.json index 1863988e..960e3cbb 100644 --- a/deepmd_pt/examples/water/dpa1-ener-warmup-grad-clip/input.json +++ b/deepmd_pt/examples/water/dpa1-ener-warmup-grad-clip/input.json @@ -1,96 +1,96 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_atten", - "sel": 120, - "rcut_smth": 0.5, - "rcut": 6.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "attn": 128, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0, - "_comment": " that's all" + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_atten", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "attn": 128, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "wandb_config":{ - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - }, - "numb_steps": 1000000, - "warmup_steps": 10000, - "gradient_max_norm": 5.0, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "warmup_steps": 10000, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/dpa1-ener/input.json b/deepmd_pt/examples/water/dpa1-ener/input.json index b466ee07..7da3d641 100644 --- a/deepmd_pt/examples/water/dpa1-ener/input.json +++ b/deepmd_pt/examples/water/dpa1-ener/input.json @@ -1,91 +1,91 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "dpa1", - "sel": 120, - "rcut_smth": 0.5, - "rcut": 6.0, - "neuron": [ - 25, - 50, - 100 - ], - "axis_neuron": 16, - "attn": 128, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0 + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "dpa1", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "axis_neuron": 16, + "attn": 128, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0 + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" }, - "validation_data": { - "systems": [ - 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"systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "numb_steps": 1000000, - "warmup_steps": 0, - "gradient_max_norm": 5.0, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 2000, - "_comment": "that's all", - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - } + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "numb_steps": 1000000, + "warmup_steps": 0, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 2000, + "_comment": "that's all", + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" } + } } diff --git a/deepmd_pt/examples/water/dpa2-force-ener/input.json b/deepmd_pt/examples/water/dpa2-force-ener/input.json index 30959bff..5804deb7 100644 --- a/deepmd_pt/examples/water/dpa2-force-ener/input.json +++ b/deepmd_pt/examples/water/dpa2-force-ener/input.json @@ -1,104 +1,108 @@ { - "_comment": "that's all", - "model": { - "type_embedding": { - "neuron": [ - 8 - ], - "tebd_input_mode": "concat" - }, - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "dpa2", - "repinit_rcut": 9.0, - "repinit_rcut_smth": 8.0, - "repinit_nsel": 120, - "repformer_rcut": 4.0, - "repformer_rcut_smth": 3.5, - "repformer_nsel": 40, - "repinit_neuron": [25, 50, 100], - "repinit_axis_neuron": 12, - "repinit_activation": "tanh", - "repformer_nlayers": 12, - "repformer_g1_dim": 128, - "repformer_g2_dim": 32, - "repformer_attn2_hidden": 32, - "repformer_attn2_nhead": 4, - "repformer_attn1_hidden": 128, - "repformer_attn1_nhead": 4, - "repformer_axis_dim": 4, - "repformer_update_h2": false, - "repformer_update_g1_has_conv": true, - "repformer_update_g1_has_grrg": true, - "repformer_update_g1_has_drrd": true, - "repformer_update_g1_has_attn": true, - "repformer_update_g2_has_g1g1": true, - "repformer_update_g2_has_attn": true, - "repformer_attn2_has_gate": true, - "repformer_add_type_ebd_to_seq": false - }, - "fitting_net": { - "type": "direct_force_ener", - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" + "_comment": "that's all", + "model": { + "type_embedding": { + "neuron": [ + 8 + ], + "tebd_input_mode": "concat" + }, + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "dpa2", + "repinit_rcut": 9.0, + "repinit_rcut_smth": 8.0, + "repinit_nsel": 120, + "repformer_rcut": 4.0, + "repformer_rcut_smth": 3.5, + "repformer_nsel": 40, + "repinit_neuron": [ + 25, + 50, + 100 + ], + "repinit_axis_neuron": 12, + "repinit_activation": "tanh", + "repformer_nlayers": 12, + "repformer_g1_dim": 128, + "repformer_g2_dim": 32, + "repformer_attn2_hidden": 32, + "repformer_attn2_nhead": 4, + "repformer_attn1_hidden": 128, + "repformer_attn1_nhead": 4, + "repformer_axis_dim": 4, + "repformer_update_h2": false, + "repformer_update_g1_has_conv": true, + "repformer_update_g1_has_grrg": true, + "repformer_update_g1_has_drrd": true, + "repformer_update_g1_has_attn": true, + "repformer_update_g2_has_g1g1": true, + "repformer_update_g2_has_attn": true, + "repformer_attn2_has_gate": true, + "repformer_add_type_ebd_to_seq": false }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.0002, - "stop_lr": 3.51e-08, - "_comment": "that's all" + "fitting_net": { + "type": "direct_force_ener", + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "numb_steps": 1000000, - "warmup_steps": 0, - "gradient_max_norm": 5.0, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 2000, - "_comment": "that's all", - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - } + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "numb_steps": 1000000, + "warmup_steps": 0, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 2000, + "_comment": "that's all", + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" } + } } diff --git a/deepmd_pt/examples/water/dpa2-force/input.json b/deepmd_pt/examples/water/dpa2-force/input.json index 80667dd7..7d61cf15 100644 --- a/deepmd_pt/examples/water/dpa2-force/input.json +++ b/deepmd_pt/examples/water/dpa2-force/input.json @@ -1,105 +1,108 @@ { - "_comment": "that's all", - "model": { - "type_embedding": { - "neuron": [ - 8 - ], - "tebd_input_mode": "concat" - }, - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "dpa2", - "repinit_rcut": 9.0, - "repinit_rcut_smth": 8.0, - "repinit_nsel": 120, - "repformer_rcut": 4.0, - "repformer_rcut_smth": 3.5, - "repformer_nsel": 40, - "repinit_neuron": [25, 50, 100], - "repinit_axis_neuron": 12, - "repinit_activation": "tanh", - "repformer_nlayers": 12, - "repformer_g1_dim": 128, - "repformer_g2_dim": 32, - "repformer_attn2_hidden": 32, - "repformer_attn2_nhead": 4, - "repformer_attn1_hidden": 128, - "repformer_attn1_nhead": 4, - "repformer_axis_dim": 4, - "repformer_update_h2": false, - "repformer_update_g1_has_conv": true, - "repformer_update_g1_has_grrg": true, - "repformer_update_g1_has_drrd": true, - "repformer_update_g1_has_attn": true, - "repformer_update_g2_has_g1g1": true, - "repformer_update_g2_has_attn": true, - "repformer_attn2_has_gate": true, - "repformer_add_type_ebd_to_seq": false - }, - "fitting_net": { - "type": "direct_force", - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" + "_comment": "that's all", + "model": { + "type_embedding": { + "neuron": [ + 8 + ], + "tebd_input_mode": "concat" + }, + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "dpa2", + "repinit_rcut": 9.0, + "repinit_rcut_smth": 8.0, + "repinit_nsel": 120, + "repformer_rcut": 4.0, + "repformer_rcut_smth": 3.5, + "repformer_nsel": 40, + "repinit_neuron": [ + 25, + 50, + 100 + ], + "repinit_axis_neuron": 12, + "repinit_activation": "tanh", + "repformer_nlayers": 12, + "repformer_g1_dim": 128, + "repformer_g2_dim": 32, + "repformer_attn2_hidden": 32, + "repformer_attn2_nhead": 4, + "repformer_attn1_hidden": 128, + "repformer_attn1_nhead": 4, + "repformer_axis_dim": 4, + "repformer_update_h2": false, + "repformer_update_g1_has_conv": true, + "repformer_update_g1_has_grrg": true, + "repformer_update_g1_has_drrd": true, + "repformer_update_g1_has_attn": true, + "repformer_update_g2_has_g1g1": true, + "repformer_update_g2_has_attn": true, + "repformer_attn2_has_gate": true, + "repformer_add_type_ebd_to_seq": false }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.0002, - "stop_lr": 3.51e-08, - "_comment": "that's all" + "fitting_net": { + "type": "direct_force", + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0, - "limit_pref_e": 0, - "start_pref_f": 1, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0, + "limit_pref_e": 0, + "start_pref_f": 1, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "numb_steps": 1000000, - "warmup_steps": 0, - "gradient_max_norm": 5.0, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 2000, - "_comment": "that's all", - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - } + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "numb_steps": 1000000, + "warmup_steps": 0, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 2000, + "_comment": "that's all", + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" } + } } - diff --git a/deepmd_pt/examples/water/dpau-denoise/input.json b/deepmd_pt/examples/water/dpau-denoise/input.json index c4ccffd1..56bc0dd3 100644 --- a/deepmd_pt/examples/water/dpau-denoise/input.json +++ b/deepmd_pt/examples/water/dpau-denoise/input.json @@ -1,10 +1,16 @@ { - "_comment": "that's all", - "model": { - "type_map": ["O","H","MASKED_TOKEN"], - "descriptor": { + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H", + "MASKED_TOKEN" + ], + "descriptor": { "type": "se_uni", - "sel": [40], + "sel": [ + 40 + ], "rcut_smth": 0.5, "rcut": 4.0, "nlayers": 3, @@ -22,65 +28,65 @@ "update_g1_has_attn": true, "update_g2_has_g1g1": true, "update_g2_has_attn": true, + "_comment": " that's all" + }, "_comment": " that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "denoise", - "noise_type": "uniform", - "noise": 1.0, - "noise_mode": "fix_num", - "mask_num": 8, - "mask_prob": 0.15, - "mask_coord": true, - "mask_type": false, - "same_mask": false, - "max_fail_num": 10, - "masked_token_loss": 1.0, - "masked_coord_loss": 1.0, - "norm_loss": 0.0, - "use_l1": true, - "beta": 1.0, - "mask_loss_coord": true, - "mask_loss_token": false, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "wandb_config":{ - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" + "loss": { + "type": "denoise", + "noise_type": "uniform", + "noise": 1.0, + "noise_mode": "fix_num", + "mask_num": 8, + "mask_prob": 0.15, + "mask_coord": true, + "mask_type": false, + "same_mask": false, + "max_fail_num": 10, + "masked_token_loss": 1.0, + "masked_coord_loss": 1.0, + "norm_loss": 0.0, + "use_l1": true, + "beta": 1.0, + "mask_loss_coord": true, + "mask_loss_token": false, + "_comment": " that's all" }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/dpau-ener/input.json b/deepmd_pt/examples/water/dpau-ener/input.json index a97c9044..c9b33929 100644 --- a/deepmd_pt/examples/water/dpau-ener/input.json +++ b/deepmd_pt/examples/water/dpau-ener/input.json @@ -1,10 +1,15 @@ { - "_comment": "that's all", - "model": { - "type_map": ["O","H"], - "descriptor": { + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { "type": "se_uni", - "sel": [40], + "sel": [ + 40 + ], "rcut_smth": 0.5, "rcut": 4.0, "nlayers": 3, @@ -22,65 +27,65 @@ "update_g1_has_attn": true, "update_g2_has_g1g1": true, "update_g2_has_attn": true, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, "_comment": " that's all" }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "wandb_config":{ - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/dpau-force-ener/input.json b/deepmd_pt/examples/water/dpau-force-ener/input.json index e07bf667..48193d4f 100644 --- a/deepmd_pt/examples/water/dpau-force-ener/input.json +++ b/deepmd_pt/examples/water/dpau-force-ener/input.json @@ -1,10 +1,15 @@ { - "_comment": "that's all", - "model": { - "type_map": ["O","H"], - "descriptor": { + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { "type": "se_uni", - "sel": [40], + "sel": [ + 40 + ], "rcut_smth": 0.5, "rcut": 4.0, "nlayers": 3, @@ -22,64 +27,64 @@ "update_g1_has_attn": true, "update_g2_has_g1g1": true, "update_g2_has_attn": true, + "_comment": " that's all" + }, + "fitting_net": { + "type": "direct_force_ener", + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, "_comment": " that's all" }, - "fitting_net": { - "type": "direct_force_ener", - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "wandb_config":{ - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/hybrid-denoise/input.json b/deepmd_pt/examples/water/hybrid-denoise/input.json index 063bcd41..bee0cb5c 100644 --- a/deepmd_pt/examples/water/hybrid-denoise/input.json +++ b/deepmd_pt/examples/water/hybrid-denoise/input.json @@ -1,128 +1,128 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H", - "MASKED_TOKEN" - ], - "descriptor": { - "type": "hybrid", - "hybrid_mode": "sequential", - "list": [ - { - "type": "se_atten", - "sel": 120, - "rcut_smth": 2.0, - "rcut": 9.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 12, - "seed": 1, - "attn": 128, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0, - "add": "concat", - "pre_add": true, - "_comment": " that's all" + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H", + "MASKED_TOKEN" + ], + "descriptor": { + "type": "hybrid", + "hybrid_mode": "sequential", + "list": [ + { + "type": "se_atten", + "sel": 120, + "rcut_smth": 2.0, + "rcut": 9.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 12, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0, + "add": "concat", + "pre_add": true, + "_comment": " that's all" + }, + { + "type": "se_uni", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "nlayers": 12, + "g1_dim": 128, + "g2_dim": 32, + "attn2_hidden": 32, + "attn2_nhead": 4, + "attn1_hidden": 128, + "attn1_nhead": 4, + "axis_dim": 4, + "update_h2": false, + "update_g1_has_conv": true, + "update_g1_has_grrg": true, + "update_g1_has_drrd": true, + "update_g1_has_attn": true, + "update_g2_has_g1g1": true, + "update_g2_has_attn": true, + "attn2_has_gate": true, + "add_type_ebd_to_seq": false, + "smooth": true, + "_comment": " that's all" + } + ] }, - { - "type": "se_uni", - "sel": 40, - "rcut_smth": 0.5, - "rcut": 4.0, - "nlayers": 12, - "g1_dim": 128, - "g2_dim": 32, - "attn2_hidden": 32, - "attn2_nhead": 4, - "attn1_hidden": 128, - "attn1_nhead": 4, - "axis_dim": 4, - "update_h2": false, - "update_g1_has_conv": true, - "update_g1_has_grrg": true, - "update_g1_has_drrd": true, - "update_g1_has_attn": true, - "update_g2_has_g1g1": true, - "update_g2_has_attn": true, - "attn2_has_gate": true, - "add_type_ebd_to_seq": false, - "smooth": true, - "_comment": " that's all" - } - ] + "_comment": " that's all" }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.0002, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "denoise", - "noise_type": "uniform", - "noise": 1.0, - "noise_mode": "fix_num", - "mask_num": 8, - "mask_prob": 0.15, - "mask_coord": true, - "mask_type": false, - "same_mask": false, - "max_fail_num": 10, - "masked_token_loss": 1.0, - "masked_coord_loss": 1.0, - "norm_loss": 0.0, - "use_l1": true, - "beta": 1.0, - "mask_loss_coord": true, - "mask_loss_token": false, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" + "loss": { + "type": "denoise", + "noise_type": "uniform", + "noise": 1.0, + "noise_mode": "fix_num", + "mask_num": 8, + "mask_prob": 0.15, + "mask_coord": true, + "mask_type": false, + "same_mask": false, + "max_fail_num": 10, + "masked_token_loss": 1.0, + "masked_coord_loss": 1.0, + "norm_loss": 0.0, + "use_l1": true, + "beta": 1.0, + "mask_loss_coord": true, + "mask_loss_token": false, + "_comment": " that's all" }, - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" - }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/deepmd_pt/examples/water/hybrid-ener/input.json b/deepmd_pt/examples/water/hybrid-ener/input.json index a9926e90..41c31450 100644 --- a/deepmd_pt/examples/water/hybrid-ener/input.json +++ b/deepmd_pt/examples/water/hybrid-ener/input.json @@ -1,127 +1,127 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "hybrid", - "hybrid_mode": "sequential", - "list": [ - { - "type": "se_atten", - "sel": 120, - "rcut_smth": 2.0, - "rcut": 9.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 12, - "seed": 1, - "attn": 128, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0, - "add": "concat", - "pre_add": true, - "_comment": " that's all" + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "hybrid", + "hybrid_mode": "sequential", + "list": [ + { + "type": "se_atten", + "sel": 120, + "rcut_smth": 2.0, + "rcut": 9.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 12, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0, + "add": "concat", + "pre_add": true, + "_comment": " that's all" + }, + { + "type": "se_uni", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "nlayers": 12, + "g1_dim": 128, + "g2_dim": 32, + "attn2_hidden": 32, + "attn2_nhead": 4, + "attn1_hidden": 128, + "attn1_nhead": 4, + "axis_dim": 4, + "update_h2": false, + "update_g1_has_conv": true, + "update_g1_has_grrg": true, + "update_g1_has_drrd": true, + "update_g1_has_attn": true, + "update_g2_has_g1g1": true, + "update_g2_has_attn": true, + "attn2_has_gate": true, + "add_type_ebd_to_seq": false, + "smooth": true, + "_comment": " that's all" + } + ] }, - { - "type": "se_uni", - "sel": 40, - "rcut_smth": 0.5, - "rcut": 4.0, - "nlayers": 12, - "g1_dim": 128, - "g2_dim": 32, - "attn2_hidden": 32, - "attn2_nhead": 4, - "attn1_hidden": 128, - "attn1_nhead": 4, - "axis_dim": 4, - "update_h2": false, - "update_g1_has_conv": true, - "update_g1_has_grrg": true, - "update_g1_has_drrd": true, - "update_g1_has_attn": true, - "update_g2_has_g1g1": true, - "update_g2_has_attn": true, - "attn2_has_gate": true, - "add_type_ebd_to_seq": false, - "smooth": true, - "_comment": " that's all" - } - ] - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.0002, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } -} \ No newline at end of file + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } +} diff --git a/deepmd_pt/examples/water/hybrid-force-ener/input.json b/deepmd_pt/examples/water/hybrid-force-ener/input.json index 27f98252..7e3988e8 100644 --- a/deepmd_pt/examples/water/hybrid-force-ener/input.json +++ b/deepmd_pt/examples/water/hybrid-force-ener/input.json @@ -1,120 +1,120 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "hybrid", - "list": [ - { - "type": "se_uni", - "sel": 40, - "rcut_smth": 0.5, - "rcut": 4.0, - "nlayers": 3, - "g1_dim": 128, - "g2_dim": 32, - "attn2_hidden": 32, - "attn2_nhead": 4, - "attn1_hidden": 128, - "attn1_nhead": 4, - "axis_dim": 4, - "update_h2": false, - "update_g1_has_conv": true, - "update_g1_has_grrg": true, - "update_g1_has_drrd": true, - "update_g1_has_attn": true, - "update_g2_has_g1g1": true, - "update_g2_has_attn": true, - "_comment": " that's all" + "_comment": "that's all", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "hybrid", + "list": [ + { + "type": "se_uni", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "nlayers": 3, + "g1_dim": 128, + "g2_dim": 32, + "attn2_hidden": 32, + "attn2_nhead": 4, + "attn1_hidden": 128, + "attn1_nhead": 4, + "axis_dim": 4, + "update_h2": false, + "update_g1_has_conv": true, + "update_g1_has_grrg": true, + "update_g1_has_drrd": true, + "update_g1_has_attn": true, + "update_g2_has_g1g1": true, + "update_g2_has_attn": true, + "_comment": " that's all" + }, + { + "type": "se_atten", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0, + "_comment": " that's all" + } + ] }, - { - "type": "se_atten", - "sel": 120, - "rcut_smth": 0.5, - "rcut": 6.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "attn": 128, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0, - "_comment": " that's all" - } - ] - }, - "fitting_net": { - "type": "direct_force_ener", - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-08, - "_comment": "that's all" - }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "_comment": " that's all" - }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" + "fitting_net": { + "type": "direct_force_ener", + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" }, - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } -} \ No newline at end of file + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } +} diff --git a/deepmd_pt/examples/water/multitask/input.json b/deepmd_pt/examples/water/multitask/input.json index b66ae24e..43b38cb0 100644 --- a/deepmd_pt/examples/water/multitask/input.json +++ b/deepmd_pt/examples/water/multitask/input.json @@ -1,196 +1,196 @@ { - "_comment": "that's all", - "model": { - "shared_dict": { - "type_map_all": [ - "O", - "H" + "_comment": "that's all", + "model": { + "shared_dict": { + "type_map_all": [ + "O", + "H" + ], + "tebd_1": { + "neuron": [ + 8 + ], + "tebd_input_mode": "concat" + }, + "dpa1_dpau_descriptor_1": { + "type": "hybrid", + "hybrid_mode": "sequential", + "list": [ + { + "type": "se_atten", + "sel": 120, + "rcut_smth": 8.0, + "rcut": 9.0, + "neuron": [ + 25, + 50, + 100 ], - "tebd_1": { - "neuron": [ - 8 - ], - "tebd_input_mode": "concat" - }, - "dpa1_dpau_descriptor_1": { - "type": "hybrid", - "hybrid_mode": "sequential", - "list": [ - { - "type": "se_atten", - "sel": 120, - "rcut_smth": 8.0, - "rcut": 9.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 12, - "seed": 1, - "attn": 128, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0, - "add": "concat", - "pre_add": true, - "_comment": " that's all" - }, - { - "type": "se_uni", - "sel": 40, - "rcut_smth": 3.5, - "rcut": 4.0, - "nlayers": 12, - "g1_dim": 128, - "g2_dim": 32, - "attn2_hidden": 32, - "attn2_nhead": 4, - "attn1_hidden": 128, - "attn1_nhead": 4, - "axis_dim": 4, - "update_h2": false, - "update_g1_has_conv": true, - "update_g1_has_grrg": true, - "update_g1_has_drrd": true, - "update_g1_has_attn": true, - "update_g2_has_g1g1": true, - "update_g2_has_attn": true, - "attn2_has_gate": true, - "smooth": true, - "add_type_ebd_to_seq": false, - "_comment": " that's all" - } - ] - }, - "_comment": "that's all" - }, - "model_dict": { - "water_1": { - "type_map": "type_map_all", - "type_embedding": "tebd_1", - "descriptor": "dpa1_dpau_descriptor_1", - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - } - }, - "water_2": { - "type_map": "type_map_all", - "type_embedding": "tebd_1", - "descriptor": "dpa1_dpau_descriptor_1", - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - } - } + "resnet_dt": false, + "axis_neuron": 12, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0, + "add": "concat", + "pre_add": true, + "_comment": " that's all" + }, + { + "type": "se_uni", + "sel": 40, + "rcut_smth": 3.5, + "rcut": 4.0, + "nlayers": 12, + "g1_dim": 128, + "g2_dim": 32, + "attn2_hidden": 32, + "attn2_nhead": 4, + "attn1_hidden": 128, + "attn1_nhead": 4, + "axis_dim": 4, + "update_h2": false, + "update_g1_has_conv": true, + "update_g1_has_grrg": true, + "update_g1_has_drrd": true, + "update_g1_has_attn": true, + "update_g2_has_g1g1": true, + "update_g2_has_attn": true, + "attn2_has_gate": true, + "smooth": true, + "add_type_ebd_to_seq": false, + "_comment": " that's all" + } + ] + }, + "_comment": "that's all" + }, + "model_dict": { + "water_1": { + "type_map": "type_map_all", + "type_embedding": "tebd_1", + "descriptor": "dpa1_dpau_descriptor_1", + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" } + }, + "water_2": { + "type_map": "type_map_all", + "type_embedding": "tebd_1", + "descriptor": "dpa1_dpau_descriptor_1", + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + } + } + } + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.0002, + "decay_rate": 0.98, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss_dict": { + "_comment": " that's all", + "water_1": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0 }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.0002, - "decay_rate": 0.98, - "stop_lr": 3.51e-08, - "_comment": "that's all" + "water_2": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0 + } + }, + "training": { + "model_prob": { + "water_1": 0.5, + "water_2": 0.5 }, - "loss_dict": { - "_comment": " that's all", - "water_1": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0 + "data_dict": { + "water_1": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "water_2": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0 + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "_comment": "that's all" } - }, - "training": { - "model_prob": { - "water_1": 0.5, - "water_2": 0.5 - }, - "data_dict": { - "water_1": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "_comment": "that's all" - } - }, - "water_2": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "_comment": "that's all" - } - } + }, + "water_2": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "numb_steps": 100000, - "warmup_steps": 0, - "gradient_max_norm": 5.0, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 2000, - "_comment": "that's all", - "wandb_config": { - "wandb_enabled": false, - "entity": "dp_model_engineering", - "project": "DPA" + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "_comment": "that's all" } + } + }, + "numb_steps": 100000, + "warmup_steps": 0, + "gradient_max_norm": 5.0, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 2000, + "_comment": "that's all", + "wandb_config": { + "wandb_enabled": false, + "entity": "dp_model_engineering", + "project": "DPA" } + } } diff --git a/deepmd_pt/examples/water/se_a/input.json b/deepmd_pt/examples/water/se_a/input.json index dce9eae9..053a721a 100644 --- a/deepmd_pt/examples/water/se_a/input.json +++ b/deepmd_pt/examples/water/se_a/input.json @@ -1,79 +1,79 @@ { - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_e2_a", - "sel": [ - 46, - 92 - ], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "data_stat_nbatch": 20, - "_comment": " that's all" + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "_comment": " that's all" + "data_stat_nbatch": 20, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "../data/data_0", + "../data/data_1", + "../data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": [ - "../data/data_0", - "../data/data_1", - "../data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "../data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "numb_steps": 100000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 10000, - "_comment": "that's all" + "validation_data": { + "systems": [ + "../data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" }, + "numb_steps": 100000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 10000, "_comment": "that's all" + }, + "_comment": "that's all" } diff --git a/deepmd_pt/infer/__init__.py b/deepmd_pt/infer/__init__.py index e69de29b..6ceb116d 100644 --- a/deepmd_pt/infer/__init__.py +++ b/deepmd_pt/infer/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd_pt/infer/deep_eval.py b/deepmd_pt/infer/deep_eval.py index 878f8a6b..f9a88464 100644 --- a/deepmd_pt/infer/deep_eval.py +++ b/deepmd_pt/infer/deep_eval.py @@ -1,37 +1,57 @@ -from pathlib import Path -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later +from pathlib import ( + Path, +) +from typing import ( + Callable, + List, + Optional, + Tuple, + Union, +) + import numpy as np -from deepmd_pt.utils.env import DEVICE, PREPROCESS_DEVICE, GLOBAL_PT_FLOAT_PRECISION -from deepmd_pt.model.model import get_model -from deepmd_pt.train.wrapper import ModelWrapper -from deepmd_pt.utils.preprocess import Region3D, normalize_coord, make_env_mat -from deepmd_pt.utils.dataloader import collate_batch -from typing import Callable, Optional, Tuple, Union, List -from deepmd_pt.utils import env -from deepmd_pt.utils.auto_batch_size import AutoBatchSize -from deepmd_utils.infer.deep_pot import DeepPot as DeepPotBase +import torch +from deepmd.infer.deep_pot import DeepPot as DeepPotBase + +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.train.wrapper import ( + ModelWrapper, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.auto_batch_size import ( + AutoBatchSize, +) +from deepmd_pt.utils.env import ( + DEVICE, + GLOBAL_PT_FLOAT_PRECISION, +) class DeepEval: def __init__( - self, - model_file: "Path", - auto_batch_size: Union[bool, int, AutoBatchSize] = True, + self, + model_file: "Path", + auto_batch_size: Union[bool, int, AutoBatchSize] = True, ): self.model_path = model_file state_dict = torch.load(model_file, map_location=env.DEVICE) if "model" in state_dict: state_dict = state_dict["model"] - self.input_param = state_dict['_extra_state']['model_params'] - self.input_param['resuming'] = True + self.input_param = state_dict["_extra_state"]["model_params"] + self.input_param["resuming"] = True self.multi_task = "model_dict" in self.input_param assert not self.multi_task, "multitask mode currently not supported!" - self.type_split = self.input_param['descriptor']['type'] in ['se_e2_a'] - self.type_map = self.input_param['type_map'] + self.type_split = self.input_param["descriptor"]["type"] in ["se_e2_a"] + self.type_map = self.input_param["type_map"] self.dp = ModelWrapper(get_model(self.input_param, None).to(DEVICE)) self.dp.load_state_dict(state_dict) - self.rcut = self.dp.model['Default'].descriptor.get_rcut() - self.sec = np.cumsum(self.dp.model['Default'].descriptor.get_sel()) + self.rcut = self.dp.model["Default"].descriptor.get_rcut() + self.sec = np.cumsum(self.dp.model["Default"].descriptor.get_sel()) if isinstance(auto_batch_size, bool): if auto_batch_size: self.auto_batch_size = AutoBatchSize() @@ -45,11 +65,11 @@ def __init__( raise TypeError("auto_batch_size should be bool, int, or AutoBatchSize") def eval( - self, - coords: Union[np.ndarray, torch.Tensor], - cells: Optional[Union[np.ndarray, torch.Tensor]], - atom_types: Union[np.ndarray, torch.Tensor, List[int]], - atomic: bool = False, + self, + coords: Union[np.ndarray, torch.Tensor], + cells: Optional[Union[np.ndarray, torch.Tensor]], + atom_types: Union[np.ndarray, torch.Tensor, List[int]], + atomic: bool = False, ): raise NotImplementedError @@ -63,7 +83,7 @@ def __init__( ): if neighbor_list is not None: raise NotImplementedError - super(DeepPot, self).__init__( + super().__init__( model_file, auto_batch_size=auto_batch_size, ) @@ -86,8 +106,12 @@ def eval( coords = np.array(coords) if cells is not None: cells = np.array(cells) - natoms, numb_test = self._get_natoms_and_nframes(coords, atom_types, len(atom_types.shape) > 1) - return self._eval_func(self._eval_model, numb_test, natoms)(coords, cells, atom_types, atomic) + natoms, numb_test = self._get_natoms_and_nframes( + coords, atom_types, len(atom_types.shape) > 1 + ) + return self._eval_func(self._eval_model, numb_test, natoms)( + coords, cells, atom_types, atomic + ) def _eval_func(self, inner_func: Callable, numb_test: int, natoms: int) -> Callable: """Wrapper method with auto batch size. @@ -155,28 +179,40 @@ def _eval_model( else: natoms = len(atom_types[0]) - coord_input = torch.tensor(coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) + coord_input = torch.tensor( + coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION + ).to(DEVICE) type_input = torch.tensor(atom_types, dtype=torch.long).to(DEVICE) if cells is not None: - box_input = torch.tensor(cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) + box_input = torch.tensor( + cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION + ).to(DEVICE) else: box_input = None - batch_output = model(coord_input, type_input, box=box_input, do_atomic_virial=atomic) + batch_output = model( + coord_input, type_input, box=box_input, do_atomic_virial=atomic + ) if isinstance(batch_output, tuple): batch_output = batch_output[0] - energy_out = batch_output['energy'].detach().cpu().numpy() - if 'atom_energy' in batch_output: - atomic_energy_out = batch_output['atom_energy'].detach().cpu().numpy() - force_out = batch_output['force'].detach().cpu().numpy() - virial_out = batch_output['virial'].detach().cpu().numpy() - if 'atomic_virial' in batch_output: - atomic_virial_out = batch_output['atomic_virial'].detach().cpu().numpy() + energy_out = batch_output["energy"].detach().cpu().numpy() + if "atom_energy" in batch_output: + atomic_energy_out = batch_output["atom_energy"].detach().cpu().numpy() + force_out = batch_output["force"].detach().cpu().numpy() + virial_out = batch_output["virial"].detach().cpu().numpy() + if "atomic_virial" in batch_output: + atomic_virial_out = batch_output["atomic_virial"].detach().cpu().numpy() if not atomic: return energy_out, force_out, virial_out else: - return energy_out, force_out, virial_out, atomic_energy_out, atomic_virial_out + return ( + energy_out, + force_out, + virial_out, + atomic_energy_out, + atomic_virial_out, + ) def get_ntypes(self) -> int: """Get the number of atom types of this model.""" @@ -213,8 +249,10 @@ def eval_model( atomic_virial_out = [] updated_coord_out = [] logits_out = [] - err_msg = f"All inputs should be the same format, " \ - f"but found {type(coords)}, {type(cells)}, {type(atom_types)} instead! " + err_msg = ( + f"All inputs should be the same format, " + f"but found {type(coords)}, {type(cells)}, {type(atom_types)} instead! " + ) return_tensor = True if isinstance(coords, torch.Tensor): if cells is not None: @@ -232,81 +270,143 @@ def eval_model( if len(atom_types.shape) == 1: natoms = len(atom_types) if isinstance(atom_types, torch.Tensor): - atom_types = torch.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape(nframes, -1) + atom_types = torch.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape( + nframes, -1 + ) else: atom_types = np.tile(atom_types, nframes).reshape(nframes, -1) else: natoms = len(atom_types[0]) - coord_input = torch.tensor(coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) + coord_input = torch.tensor( + coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION + ).to(DEVICE) type_input = torch.tensor(atom_types, dtype=torch.long).to(DEVICE) box_input = None if cells is None: pbc = False else: pbc = True - box_input = torch.tensor(cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) + box_input = torch.tensor( + cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION + ).to(DEVICE) num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size) for ii in range(num_iter): - batch_coord = coord_input[ii * infer_batch_size:(ii + 1) * infer_batch_size] - batch_atype = type_input[ii * infer_batch_size:(ii + 1) * infer_batch_size] + batch_coord = coord_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] + batch_atype = type_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] batch_box = None if pbc: - batch_box = box_input[ii * infer_batch_size:(ii + 1) * infer_batch_size] + batch_box = box_input[ii * infer_batch_size : (ii + 1) * infer_batch_size] batch_output = model(batch_coord, batch_atype, box=batch_box) if isinstance(batch_output, tuple): batch_output = batch_output[0] if not return_tensor: - if 'energy' in batch_output: - energy_out.append(batch_output['energy'].detach().cpu().numpy()) - if 'atom_energy' in batch_output: - atomic_energy_out.append(batch_output['atom_energy'].detach().cpu().numpy()) - if 'force' in batch_output: - force_out.append(batch_output['force'].detach().cpu().numpy()) - if 'virial' in batch_output: - virial_out.append(batch_output['virial'].detach().cpu().numpy()) - if 'atomic_virial' in batch_output: - atomic_virial_out.append(batch_output['atomic_virial'].detach().cpu().numpy()) - if 'updated_coord' in batch_output: - updated_coord_out.append(batch_output['updated_coord'].detach().cpu().numpy()) - if 'logits' in batch_output: - logits_out.append(batch_output['logits'].detach().cpu().numpy()) + if "energy" in batch_output: + energy_out.append(batch_output["energy"].detach().cpu().numpy()) + if "atom_energy" in batch_output: + atomic_energy_out.append( + batch_output["atom_energy"].detach().cpu().numpy() + ) + if "force" in batch_output: + force_out.append(batch_output["force"].detach().cpu().numpy()) + if "virial" in batch_output: + virial_out.append(batch_output["virial"].detach().cpu().numpy()) + if "atomic_virial" in batch_output: + atomic_virial_out.append( + batch_output["atomic_virial"].detach().cpu().numpy() + ) + if "updated_coord" in batch_output: + updated_coord_out.append( + batch_output["updated_coord"].detach().cpu().numpy() + ) + if "logits" in batch_output: + logits_out.append(batch_output["logits"].detach().cpu().numpy()) else: - if 'energy' in batch_output: - energy_out.append(batch_output['energy']) - if 'atom_energy' in batch_output: - atomic_energy_out.append(batch_output['atom_energy']) - if 'force' in batch_output: - force_out.append(batch_output['force']) - if 'virial' in batch_output: - virial_out.append(batch_output['virial']) - if 'atomic_virial' in batch_output: - atomic_virial_out.append(batch_output['atomic_virial']) - if 'updated_coord' in batch_output: - updated_coord_out.append(batch_output['updated_coord']) - if 'logits' in batch_output: - logits_out.append(batch_output['logits']) + if "energy" in batch_output: + energy_out.append(batch_output["energy"]) + if "atom_energy" in batch_output: + atomic_energy_out.append(batch_output["atom_energy"]) + if "force" in batch_output: + force_out.append(batch_output["force"]) + if "virial" in batch_output: + virial_out.append(batch_output["virial"]) + if "atomic_virial" in batch_output: + atomic_virial_out.append(batch_output["atomic_virial"]) + if "updated_coord" in batch_output: + updated_coord_out.append(batch_output["updated_coord"]) + if "logits" in batch_output: + logits_out.append(batch_output["logits"]) if not return_tensor: - energy_out = np.concatenate(energy_out) if energy_out else np.zeros([nframes, 1]) - atomic_energy_out = np.concatenate(atomic_energy_out) if atomic_energy_out else np.zeros([nframes, natoms, 1]) - force_out = np.concatenate(force_out) if force_out else np.zeros([nframes, natoms, 3]) - virial_out = np.concatenate(virial_out) if virial_out else np.zeros([nframes, 3, 3]) - atomic_virial_out = np.concatenate(atomic_virial_out) if atomic_virial_out else np.zeros([nframes, natoms, 3, 3]) - updated_coord_out = np.concatenate(updated_coord_out) if updated_coord_out else None + energy_out = ( + np.concatenate(energy_out) if energy_out else np.zeros([nframes, 1]) + ) + atomic_energy_out = ( + np.concatenate(atomic_energy_out) + if atomic_energy_out + else np.zeros([nframes, natoms, 1]) + ) + force_out = ( + np.concatenate(force_out) if force_out else np.zeros([nframes, natoms, 3]) + ) + virial_out = ( + np.concatenate(virial_out) if virial_out else np.zeros([nframes, 3, 3]) + ) + atomic_virial_out = ( + np.concatenate(atomic_virial_out) + if atomic_virial_out + else np.zeros([nframes, natoms, 3, 3]) + ) + updated_coord_out = ( + np.concatenate(updated_coord_out) if updated_coord_out else None + ) logits_out = np.concatenate(logits_out) if logits_out else None else: - energy_out = torch.cat(energy_out) if energy_out else torch.zeros([nframes, 1], dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) - atomic_energy_out = torch.cat(atomic_energy_out) if atomic_energy_out else torch.zeros([nframes, natoms, 1], dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) - force_out = torch.cat(force_out) if force_out else torch.zeros([nframes, natoms, 3], dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) - virial_out = torch.cat(virial_out) if virial_out else torch.zeros([nframes, 3, 3], dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) - atomic_virial_out = torch.cat(atomic_virial_out) if atomic_virial_out else torch.zeros([nframes, natoms, 3, 3], dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) + energy_out = ( + torch.cat(energy_out) + if energy_out + else torch.zeros([nframes, 1], dtype=GLOBAL_PT_FLOAT_PRECISION).to(DEVICE) + ) + atomic_energy_out = ( + torch.cat(atomic_energy_out) + if atomic_energy_out + else torch.zeros([nframes, natoms, 1], dtype=GLOBAL_PT_FLOAT_PRECISION).to( + DEVICE + ) + ) + force_out = ( + torch.cat(force_out) + if force_out + else torch.zeros([nframes, natoms, 3], dtype=GLOBAL_PT_FLOAT_PRECISION).to( + DEVICE + ) + ) + virial_out = ( + torch.cat(virial_out) + if virial_out + else torch.zeros([nframes, 3, 3], dtype=GLOBAL_PT_FLOAT_PRECISION).to( + DEVICE + ) + ) + atomic_virial_out = ( + torch.cat(atomic_virial_out) + if atomic_virial_out + else torch.zeros( + [nframes, natoms, 3, 3], dtype=GLOBAL_PT_FLOAT_PRECISION + ).to(DEVICE) + ) updated_coord_out = torch.cat(updated_coord_out) if updated_coord_out else None logits_out = torch.cat(logits_out) if logits_out else None if denoise: return updated_coord_out, logits_out - else: + else: if not atomic: return energy_out, force_out, virial_out else: - return energy_out, force_out, virial_out, atomic_energy_out, atomic_virial_out + return ( + energy_out, + force_out, + virial_out, + atomic_energy_out, + atomic_virial_out, + ) diff --git a/deepmd_pt/infer/inference.py b/deepmd_pt/infer/inference.py index 060a20bb..992d08eb 100644 --- a/deepmd_pt/infer/inference.py +++ b/deepmd_pt/infer/inference.py @@ -1,32 +1,58 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import logging import math -from copy import deepcopy -from typing import Any, Dict -from pathlib import Path -import numpy as np -import json +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) +import numpy as np import torch -from deepmd_pt.loss import EnergyStdLoss, DenoiseLoss -from deepmd_pt.model.model import get_model -from deepmd_pt.train.wrapper import ModelWrapper -from deepmd_pt.utils import dp_random -from deepmd_pt.utils.dataloader import BufferedIterator, DpLoaderSet -from deepmd_pt.utils.env import DEVICE, JIT -from deepmd_pt.utils.stat import make_stat_input -from torch.utils.data import DataLoader, RandomSampler from deepmd.common import ( expand_sys_str, ) +from torch.utils.data import ( + DataLoader, + RandomSampler, +) + +from deepmd_pt.loss import ( + DenoiseLoss, + EnergyStdLoss, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.train.wrapper import ( + ModelWrapper, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.env import ( + DEVICE, + JIT, +) if torch.__version__.startswith("2"): import torch._dynamo -class Tester(object): - - def __init__(self, model_ckpt, input_script=None, system=None, datafile=None, - numb_test=100, detail_file=None, shuffle_test=False, head=None): +class Tester: + def __init__( + self, + model_ckpt, + input_script=None, + system=None, + datafile=None, + numb_test=100, + detail_file=None, + shuffle_test=False, + head=None, + ): """Construct a DeePMD tester. Args: @@ -39,46 +65,64 @@ def __init__(self, model_ckpt, input_script=None, system=None, datafile=None, state_dict = torch.load(model_ckpt, map_location=DEVICE) if "model" in state_dict: state_dict = state_dict["model"] - model_params = state_dict['_extra_state']['model_params'] + model_params = state_dict["_extra_state"]["model_params"] self.multi_task = "model_dict" in model_params if self.multi_task: assert head is not None, "Head must be specified in multitask mode!" self.head = head - assert head in model_params['model_dict'], f"Specified head {head} not found in model {model_ckpt}! " \ - f"Available ones are {list(model_params['model_dict'].keys())}." - model_params = model_params['model_dict'][head] + assert head in model_params["model_dict"], ( + f"Specified head {head} not found in model {model_ckpt}! " + f"Available ones are {list(model_params['model_dict'].keys())}." + ) + model_params = model_params["model_dict"][head] state_dict_head = {"_extra_state": state_dict["_extra_state"]} for item in state_dict: if f"model.{head}." in item: - state_dict_head[item.replace(f"model.{head}.", "model.Default.")] = state_dict[item].clone() + state_dict_head[ + item.replace(f"model.{head}.", "model.Default.") + ] = state_dict[item].clone() state_dict = state_dict_head # Data if input_script is not None: - with open(input_script, 'r') as fin: + with open(input_script) as fin: self.input_script = json.load(fin) training_params = self.input_script["training"] if not self.multi_task: - assert "validation_data" in training_params, f"Validation systems not found in {input_script}!" + assert ( + "validation_data" in training_params + ), f"Validation systems not found in {input_script}!" self.systems = training_params["validation_data"]["systems"] self.batchsize = training_params["validation_data"]["batch_size"] - logging.info(f"Testing validation systems in input script: {input_script}") + logging.info( + f"Testing validation systems in input script: {input_script}" + ) else: - assert "data_dict" in training_params, f"Input script {input_script} is not in multi-task mode!" - assert head in training_params["data_dict"], \ - f"Specified head {head} not found in input script {input_script}! " \ + assert ( + "data_dict" in training_params + ), f"Input script {input_script} is not in multi-task mode!" + assert head in training_params["data_dict"], ( + f"Specified head {head} not found in input script {input_script}! " f"Available ones are {list(training_params['data_dict'].keys())}." - assert "validation_data" in training_params["data_dict"][head], \ - f"Validation systems not found in head {head} of {input_script}!" - self.systems = training_params["data_dict"][head]["validation_data"]["systems"] - self.batchsize = training_params["data_dict"][head]["validation_data"]["batch_size"] - logging.info(f"Testing validation systems in head {head} of input script: {input_script}") + ) + assert ( + "validation_data" in training_params["data_dict"][head] + ), f"Validation systems not found in head {head} of {input_script}!" + self.systems = training_params["data_dict"][head]["validation_data"][ + "systems" + ] + self.batchsize = training_params["data_dict"][head]["validation_data"][ + "batch_size" + ] + logging.info( + f"Testing validation systems in head {head} of input script: {input_script}" + ) elif system is not None: self.systems = expand_sys_str(system) self.batchsize = "auto" logging.info("Testing systems in path: %s", system) elif datafile is not None: - with open(datafile, 'r') as fin: + with open(datafile) as fin: self.systems = fin.read().splitlines() self.batchsize = "auto" logging.info("Testing systems in file: %s", datafile) @@ -101,19 +145,25 @@ def __init__(self, model_ckpt, input_script=None, system=None, datafile=None, # Loss if "fitting_net" not in model_params: - assert input_script is not None, "Denoise model must use --input-script mode!" + assert ( + input_script is not None + ), "Denoise model must use --input-script mode!" loss_params = self.input_script["loss"] loss_type = loss_params.pop("type", "ener") - assert loss_type == 'denoise', "Models without fitting_net only support denoise test!" - self.noise_settings = {"noise_type": loss_params.pop("noise_type", "uniform"), - "noise": loss_params.pop("noise", 1.0), - "noise_mode": loss_params.pop("noise_mode", "fix_num"), - "mask_num": loss_params.pop("mask_num", 8), - "same_mask": loss_params.pop("same_mask", False), - "mask_coord": loss_params.pop("mask_coord", False), - "mask_type": loss_params.pop("mask_type", False), - "mask_type_idx": len(model_params["type_map"]) - 1} - loss_params['ntypes'] = len(model_params['type_map']) + assert ( + loss_type == "denoise" + ), "Models without fitting_net only support denoise test!" + self.noise_settings = { + "noise_type": loss_params.pop("noise_type", "uniform"), + "noise": loss_params.pop("noise", 1.0), + "noise_mode": loss_params.pop("noise_mode", "fix_num"), + "mask_num": loss_params.pop("mask_num", 8), + "same_mask": loss_params.pop("same_mask", False), + "mask_coord": loss_params.pop("mask_coord", False), + "mask_type": loss_params.pop("mask_type", False), + "mask_type_idx": len(model_params["type_map"]) - 1, + } + loss_params["ntypes"] = len(model_params["type_map"]) self.loss = DenoiseLoss(**loss_params) else: self.noise_settings = None @@ -123,7 +173,7 @@ def __init__(self, model_ckpt, input_script=None, system=None, datafile=None, def get_data(data): batch_data = next(iter(data)) for key in batch_data.keys(): - if key == 'sid' or key == 'fid': + if key == "sid" or key == "fid": continue elif not isinstance(batch_data[key], list): if batch_data[key] is not None: @@ -141,7 +191,15 @@ def get_data(data): else: input_dict[item] = None label_dict = {} - for item in ["energy", "force", "virial", "clean_coord", "clean_type", "coord_mask", "type_mask"]: + for item in [ + "energy", + "force", + "virial", + "clean_coord", + "clean_type", + "coord_mask", + "type_mask", + ]: if item in batch_data: label_dict[item] = batch_data[item] return input_dict, label_dict @@ -155,11 +213,21 @@ def run(self): logging.info(f"# testing system : {system}") system_pred = [] system_label = [] - dataset = DpLoaderSet([system], self.batchsize, self.model_params, - type_split=self.type_split, noise_settings=self.noise_settings, shuffle=self.shuffle_test) - sampler = RandomSampler(dataset,replacement=True,num_samples=dataset.total_batch) - if sampler == None: - logging.warning("Sampler not specified!")#None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration. + dataset = DpLoaderSet( + [system], + self.batchsize, + self.model_params, + type_split=self.type_split, + noise_settings=self.noise_settings, + shuffle=self.shuffle_test, + ) + sampler = RandomSampler( + dataset, replacement=True, num_samples=dataset.total_batch + ) + if sampler is None: + logging.warning( + "Sampler not specified!" + ) # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration. dataloader = DataLoader( dataset, sampler=sampler, @@ -176,31 +244,55 @@ def run(self): try: input_dict, label_dict = self.get_data(data) except StopIteration: - if ii < dataset.total_batch:#Unexpected stop iteration.(test step < total batch) + if ( + ii < dataset.total_batch + ): # Unexpected stop iteration.(test step < total batch) raise StopIteration else: break model_pred, _, _ = self.wrapper(**input_dict) - system_pred.append({item: model_pred[item].detach().cpu().numpy() for item in model_pred}) - system_label.append({item: label_dict[item].detach().cpu().numpy() for item in label_dict}) + system_pred.append( + { + item: model_pred[item].detach().cpu().numpy() + for item in model_pred + } + ) + system_label.append( + { + item: label_dict[item].detach().cpu().numpy() + for item in label_dict + } + ) natoms = int(input_dict["atype"].shape[-1]) - _, more_loss = self.loss(model_pred, label_dict, natoms, 1.0, mae=True) # TODO: lr here is useless + _, more_loss = self.loss( + model_pred, label_dict, natoms, 1.0, mae=True + ) # TODO: lr here is useless if sys_natoms is None: sys_natoms = natoms else: - assert sys_natoms == natoms, "Frames in one system must be the same!" + assert ( + sys_natoms == natoms + ), "Frames in one system must be the same!" sum_natoms += natoms for k, v in more_loss.items(): if "mae" in k: single_results[k] = single_results.get(k, 0.0) + v * natoms else: - single_results[k] = single_results.get(k, 0.0) + v ** 2 * natoms + single_results[k] = single_results.get(k, 0.0) + v**2 * natoms if self.detail_file is not None: - save_detail_file(Path(self.detail_file), system_pred, system_label, sys_natoms, system_name=system, append=(cc != 0)) + save_detail_file( + Path(self.detail_file), + system_pred, + system_label, + sys_natoms, + system_name=system, + append=(cc != 0), + ) results = { - k: v / sum_natoms if "mae" in k else math.sqrt(v / sum_natoms) for k, v in single_results.items() + k: v / sum_natoms if "mae" in k else math.sqrt(v / sum_natoms) + for k, v in single_results.items() } - for item in sorted(list(results.keys())): + for item in sorted(results.keys()): logging.info(f"{item}: {results[item]:.4f}") logging.info("# ----------------------------------------------- ") for k, v in single_results.items(): @@ -208,14 +300,15 @@ def run(self): global_sum_natoms += sum_natoms global_results = { - k: v / global_sum_natoms if "mae" in k else math.sqrt(v / global_sum_natoms) for k, v in system_results.items() + k: v / global_sum_natoms if "mae" in k else math.sqrt(v / global_sum_natoms) + for k, v in system_results.items() } logging.info("# ----------weighted average of errors----------- ") if not self.multi_task: logging.info(f"# number of systems : {len(systems)}") else: logging.info(f"# number of systems for {self.head}: {len(systems)}") - for item in sorted(list(global_results.keys())): + for item in sorted(global_results.keys()): logging.info(f"{item}: {global_results[item]:.4f}") logging.info("# ----------------------------------------------- ") return global_results @@ -242,10 +335,12 @@ def save_txt_file( np.savetxt(fp, data, header=header) -def save_detail_file(detail_path, system_pred, system_label, natoms, system_name, append=False): +def save_detail_file( + detail_path, system_pred, system_label, natoms, system_name, append=False +): ntest = len(system_pred) - data_e = np.concatenate([item['energy'] for item in system_label]).reshape([-1, 1]) - pred_e = np.concatenate([item['energy'] for item in system_pred]).reshape([-1, 1]) + data_e = np.concatenate([item["energy"] for item in system_label]).reshape([-1, 1]) + pred_e = np.concatenate([item["energy"] for item in system_pred]).reshape([-1, 1]) pe = np.concatenate( ( data_e, @@ -267,8 +362,12 @@ def save_detail_file(detail_path, system_pred, system_label, natoms, system_name append=append, ) if "force" in system_pred[0]: - data_f = np.concatenate([item['force'] for item in system_label]).reshape([-1, 3]) - pred_f = np.concatenate([item['force'] for item in system_pred]).reshape([-1, 3]) + data_f = np.concatenate([item["force"] for item in system_label]).reshape( + [-1, 3] + ) + pred_f = np.concatenate([item["force"] for item in system_pred]).reshape( + [-1, 3] + ) pf = np.concatenate( ( data_f, @@ -283,8 +382,12 @@ def save_detail_file(detail_path, system_pred, system_label, natoms, system_name append=append, ) if "virial" in system_pred[0]: - data_v = np.concatenate([item['virial'] for item in system_label]).reshape([-1, 9]) - pred_v = np.concatenate([item['virial'] for item in system_pred]).reshape([-1, 9]) + data_v = np.concatenate([item["virial"] for item in system_label]).reshape( + [-1, 9] + ) + pred_v = np.concatenate([item["virial"] for item in system_pred]).reshape( + [-1, 9] + ) pv = np.concatenate( ( data_v, @@ -296,8 +399,8 @@ def save_detail_file(detail_path, system_pred, system_label, natoms, system_name detail_path.with_suffix(".v.out"), pv, header=f"{system_name}: data_vxx data_vxy data_vxz data_vyx data_vyy " - "data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx " - "pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz", + "data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx " + "pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz", append=append, ) pv_atom = pv / natoms @@ -305,7 +408,7 @@ def save_detail_file(detail_path, system_pred, system_label, natoms, system_name detail_path.with_suffix(".v_peratom.out"), pv_atom, header=f"{system_name}: data_vxx data_vxy data_vxz data_vyx data_vyy " - "data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx " - "pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz", + "data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx " + "pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz", append=append, ) diff --git a/deepmd_pt/loss/__init__.py b/deepmd_pt/loss/__init__.py index 10e74e31..d3a095ce 100644 --- a/deepmd_pt/loss/__init__.py +++ b/deepmd_pt/loss/__init__.py @@ -1,3 +1,16 @@ -from .loss import TaskLoss -from .ener import EnergyStdLoss -from .denoise import DenoiseLoss +# SPDX-License-Identifier: LGPL-3.0-or-later +from .denoise import ( + DenoiseLoss, +) +from .ener import ( + EnergyStdLoss, +) +from .loss import ( + TaskLoss, +) + +__all__ = [ + "DenoiseLoss", + "EnergyStdLoss", + "TaskLoss", +] diff --git a/deepmd_pt/loss/denoise.py b/deepmd_pt/loss/denoise.py index abdd89cc..0e3c6b5a 100644 --- a/deepmd_pt/loss/denoise.py +++ b/deepmd_pt/loss/denoise.py @@ -1,25 +1,30 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import torch - -from deepmd_pt.utils.env import GLOBAL_PT_FLOAT_PRECISION -from deepmd_pt.loss import TaskLoss import torch.nn.functional as F -from deepmd_pt.utils import env +from deepmd_pt.loss.loss import ( + TaskLoss, +) +from deepmd_pt.utils import ( + env, +) -class DenoiseLoss(TaskLoss): - def __init__(self, - ntypes, - masked_token_loss=1.0, - masked_coord_loss=1.0, - norm_loss=0.01, - use_l1=True, - beta=1.00, - mask_loss_coord=True, - mask_loss_token=True, - **kwargs): +class DenoiseLoss(TaskLoss): + def __init__( + self, + ntypes, + masked_token_loss=1.0, + masked_coord_loss=1.0, + norm_loss=0.01, + use_l1=True, + beta=1.00, + mask_loss_coord=True, + mask_loss_token=True, + **kwargs, + ): """Construct a layer to compute loss on coord, and type reconstruction.""" - super(DenoiseLoss, self).__init__() + super().__init__() self.ntypes = ntypes self.masked_token_loss = masked_token_loss self.masked_coord_loss = masked_coord_loss @@ -36,15 +41,16 @@ def __init__(self, def forward(self, model_pred, label, natoms, learning_rate, mae=False): """Return loss on coord and type denoise. - Returns: + Returns + ------- - loss: Loss to minimize. """ updated_coord = model_pred["updated_coord"] logits = model_pred["logits"] clean_coord = label["clean_coord"] clean_type = label["clean_type"] - coord_mask = label['coord_mask'] - type_mask = label['type_mask'] + coord_mask = label["coord_mask"] + type_mask = label["type_mask"] loss = torch.tensor(0.0, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) more_loss = {} @@ -60,7 +66,9 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): beta=self.beta, ) else: - coord_loss = torch.tensor(0.0, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + coord_loss = torch.tensor( + 0.0, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) else: coord_loss = F.smooth_l1_loss( updated_coord.view(-1, 3), @@ -69,7 +77,7 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): beta=self.beta, ) loss += self.masked_coord_loss * coord_loss - more_loss['coord_l1_error'] = coord_loss.detach() + more_loss["coord_l1_error"] = coord_loss.detach() if self.has_token: if self.mask_loss_token: masked_logits = logits[type_mask] @@ -81,7 +89,9 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): reduction="mean", ) else: - token_loss = torch.tensor(0.0, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + token_loss = torch.tensor( + 0.0, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) else: token_loss = F.nll_loss( F.log_softmax(logits.view(-1, self.ntypes - 1), dim=-1), @@ -89,11 +99,11 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): reduction="mean", ) loss += self.masked_token_loss * token_loss - more_loss['token_error'] = token_loss.detach() + more_loss["token_error"] = token_loss.detach() if self.has_norm: norm_x = model_pred["norm_x"] norm_delta_pair_rep = model_pred["norm_delta_pair_rep"] loss += self.norm_loss * (norm_x + norm_delta_pair_rep) - more_loss['norm_loss'] = norm_x.detach() + norm_delta_pair_rep.detach() + more_loss["norm_loss"] = norm_x.detach() + norm_delta_pair_rep.detach() return loss, more_loss diff --git a/deepmd_pt/loss/ener.py b/deepmd_pt/loss/ener.py index 3ab99059..ad5611fe 100644 --- a/deepmd_pt/loss/ener.py +++ b/deepmd_pt/loss/ener.py @@ -1,25 +1,34 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import torch -from deepmd_pt.utils.env import GLOBAL_PT_FLOAT_PRECISION -from deepmd_pt.loss import TaskLoss -from deepmd_pt.utils import env import torch.nn.functional as F +from deepmd_pt.loss.loss import ( + TaskLoss, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.env import ( + GLOBAL_PT_FLOAT_PRECISION, +) -class EnergyStdLoss(TaskLoss): - def __init__(self, - starter_learning_rate=1.0, - start_pref_e=0.0, - limit_pref_e=0.0, - start_pref_f=0.0, - limit_pref_f=0.0, - start_pref_v=0.0, - limit_pref_v=0.0, - use_l1_all: bool = False, - inference=False, - **kwargs): +class EnergyStdLoss(TaskLoss): + def __init__( + self, + starter_learning_rate=1.0, + start_pref_e=0.0, + limit_pref_e=0.0, + start_pref_f=0.0, + limit_pref_f=0.0, + start_pref_v=0.0, + limit_pref_v=0.0, + use_l1_all: bool = False, + inference=False, + **kwargs, + ): """Construct a layer to compute loss on energy, force and virial.""" - super(EnergyStdLoss, self).__init__() + super().__init__() self.starter_learning_rate = starter_learning_rate self.has_e = (start_pref_e != 0.0 and limit_pref_e != 0.0) or inference self.has_f = (start_pref_f != 0.0 and limit_pref_f != 0.0) or inference @@ -43,7 +52,8 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): - l_energy: Actual energy of all atoms. - l_force: Actual force per atom. - Returns: + Returns + ------- - loss: Loss to minimize. """ coef = learning_rate / self.starter_learning_rate @@ -54,71 +64,92 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False): more_loss = {} # more_loss['log_keys'] = [] # showed when validation on the fly # more_loss['test_keys'] = [] # showed when doing dp test - atom_norm = 1. / natoms - if self.has_e and 'energy' in model_pred and 'energy' in label: + atom_norm = 1.0 / natoms + if self.has_e and "energy" in model_pred and "energy" in label: if not self.use_l1_all: - l2_ener_loss = torch.mean(torch.square(model_pred['energy'] - label['energy'])) + l2_ener_loss = torch.mean( + torch.square(model_pred["energy"] - label["energy"]) + ) if not self.inference: - more_loss['l2_ener_loss'] = l2_ener_loss.detach() + more_loss["l2_ener_loss"] = l2_ener_loss.detach() loss += atom_norm * (pref_e * l2_ener_loss) rmse_e = l2_ener_loss.sqrt() * atom_norm - more_loss['rmse_e'] = rmse_e.detach() + more_loss["rmse_e"] = rmse_e.detach() # more_loss['log_keys'].append('rmse_e') else: # use l1 and for all atoms - l1_ener_loss = F.l1_loss(model_pred['energy'].reshape(-1), label['energy'].reshape(-1), reduction="sum") - loss += (pref_e * l1_ener_loss) - more_loss['mae_e'] = F.l1_loss(model_pred['energy'].reshape(-1), label['energy'].reshape(-1), reduction="mean").detach() + l1_ener_loss = F.l1_loss( + model_pred["energy"].reshape(-1), + label["energy"].reshape(-1), + reduction="sum", + ) + loss += pref_e * l1_ener_loss + more_loss["mae_e"] = F.l1_loss( + model_pred["energy"].reshape(-1), + label["energy"].reshape(-1), + reduction="mean", + ).detach() # more_loss['log_keys'].append('rmse_e') if mae: - mae_e = torch.mean(torch.abs(model_pred['energy'] - label['energy'])) * atom_norm - more_loss['mae_e'] = mae_e.detach() - mae_e_all = torch.mean(torch.abs(model_pred['energy'] - label['energy'])) - more_loss['mae_e_all'] = mae_e_all.detach() + mae_e = ( + torch.mean(torch.abs(model_pred["energy"] - label["energy"])) + * atom_norm + ) + more_loss["mae_e"] = mae_e.detach() + mae_e_all = torch.mean( + torch.abs(model_pred["energy"] - label["energy"]) + ) + more_loss["mae_e_all"] = mae_e_all.detach() - if self.has_f and 'force' in model_pred and 'force' in label: - if 'force_target_mask' in model_pred: - force_target_mask = model_pred['force_target_mask'] + if self.has_f and "force" in model_pred and "force" in label: + if "force_target_mask" in model_pred: + force_target_mask = model_pred["force_target_mask"] else: force_target_mask = None if not self.use_l1_all: if force_target_mask is not None: - diff_f = (label['force'] - model_pred['force']) * force_target_mask + diff_f = (label["force"] - model_pred["force"]) * force_target_mask force_cnt = force_target_mask.squeeze(-1).sum(-1) - l2_force_loss = torch.mean(torch.square(diff_f).mean(-1).sum(-1)/force_cnt) + l2_force_loss = torch.mean( + torch.square(diff_f).mean(-1).sum(-1) / force_cnt + ) else: - diff_f = label['force'] - model_pred['force'] + diff_f = label["force"] - model_pred["force"] l2_force_loss = torch.mean(torch.square(diff_f)) if not self.inference: - more_loss['l2_force_loss'] = l2_force_loss.detach() + more_loss["l2_force_loss"] = l2_force_loss.detach() loss += (pref_f * l2_force_loss).to(GLOBAL_PT_FLOAT_PRECISION) rmse_f = l2_force_loss.sqrt() - more_loss['rmse_f'] = rmse_f.detach() + more_loss["rmse_f"] = rmse_f.detach() else: - l1_force_loss = F.l1_loss(label['force'], model_pred['force'], reduction="none") + l1_force_loss = F.l1_loss( + label["force"], model_pred["force"], reduction="none" + ) if force_target_mask is not None: l1_force_loss *= force_target_mask force_cnt = force_target_mask.squeeze(-1).sum(-1) - more_loss['mae_f'] = (l1_force_loss.mean(-1).sum(-1) / force_cnt).mean() + more_loss["mae_f"] = ( + l1_force_loss.mean(-1).sum(-1) / force_cnt + ).mean() l1_force_loss = (l1_force_loss.sum(-1).sum(-1) / force_cnt).sum() else: - more_loss['mae_f'] = l1_force_loss.mean().detach() + more_loss["mae_f"] = l1_force_loss.mean().detach() l1_force_loss = l1_force_loss.sum(-1).mean(-1).sum() loss += (pref_f * l1_force_loss).to(GLOBAL_PT_FLOAT_PRECISION) if mae: mae_f = torch.mean(torch.abs(diff_f)) - more_loss['mae_f'] = mae_f.detach() + more_loss["mae_f"] = mae_f.detach() - if self.has_v and 'virial' in model_pred and 'virial' in label: - diff_v = label['virial'] - model_pred['virial'].reshape(-1, 9) + if self.has_v and "virial" in model_pred and "virial" in label: + diff_v = label["virial"] - model_pred["virial"].reshape(-1, 9) l2_virial_loss = torch.mean(torch.square(diff_v)) if not self.inference: - more_loss['l2_virial_loss'] = l2_virial_loss.detach() + more_loss["l2_virial_loss"] = l2_virial_loss.detach() loss += atom_norm * (pref_v * l2_virial_loss) rmse_v = l2_virial_loss.sqrt() * atom_norm - more_loss['rmse_v'] = rmse_v.detach() + more_loss["rmse_v"] = rmse_v.detach() if mae: mae_v = torch.mean(torch.abs(diff_v)) * atom_norm - more_loss['mae_v'] = mae_v.detach() + more_loss["mae_v"] = mae_v.detach() if not self.inference: - more_loss['rmse'] = torch.sqrt(loss.detach()) + more_loss["rmse"] = torch.sqrt(loss.detach()) return loss, more_loss diff --git a/deepmd_pt/loss/loss.py b/deepmd_pt/loss/loss.py index 4c8220b3..9f2c3a7e 100644 --- a/deepmd_pt/loss/loss.py +++ b/deepmd_pt/loss/loss.py @@ -1,14 +1,12 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import torch class TaskLoss(torch.nn.Module): - def __init__(self, **kwargs): """Construct loss.""" - super(TaskLoss, self).__init__() + super().__init__() def forward(self, model_pred, label, natoms, learning_rate): - """Return loss . - """ + """Return loss .""" raise NotImplementedError - diff --git a/deepmd_pt/model/__init__.py b/deepmd_pt/model/__init__.py index e69de29b..6ceb116d 100644 --- a/deepmd_pt/model/__init__.py +++ b/deepmd_pt/model/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd_pt/model/backbone/__init__.py b/deepmd_pt/model/backbone/__init__.py index 8b1d792a..a76bdb2a 100644 --- a/deepmd_pt/model/backbone/__init__.py +++ b/deepmd_pt/model/backbone/__init__.py @@ -1,2 +1,12 @@ -from .backbone import BackBone -from .evoformer2b import Evoformer2bBackBone +# SPDX-License-Identifier: LGPL-3.0-or-later +from .backbone import ( + BackBone, +) +from .evoformer2b import ( + Evoformer2bBackBone, +) + +__all__ = [ + "BackBone", + "Evoformer2bBackBone", +] diff --git a/deepmd_pt/model/backbone/backbone.py b/deepmd_pt/model/backbone/backbone.py index a16a98f9..ddeedfef 100644 --- a/deepmd_pt/model/backbone/backbone.py +++ b/deepmd_pt/model/backbone/backbone.py @@ -1,22 +1,12 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import torch -from deepmd_pt.utils import env - -try: - from typing import Final -except: - from torch.jit import Final - class BackBone(torch.nn.Module): def __init__(self, **kwargs): - """ - BackBone base method. - """ - super(BackBone, self).__init__() + """BackBone base method.""" + super().__init__() def forward(self, **kwargs): - """Calculate backBone. - """ + """Calculate backBone.""" raise NotImplementedError diff --git a/deepmd_pt/model/backbone/evoformer2b.py b/deepmd_pt/model/backbone/evoformer2b.py index 50c703f7..82a257f0 100644 --- a/deepmd_pt/model/backbone/evoformer2b.py +++ b/deepmd_pt/model/backbone/evoformer2b.py @@ -1,39 +1,34 @@ -import numpy as np -import torch - -from deepmd_pt.utils import env - -try: - from typing import Final -except: - from torch.jit import Final - -from deepmd_pt.model.backbone import BackBone -from deepmd_pt.model.network import Evoformer2bEncoder +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd_pt.model.backbone import ( + BackBone, +) +from deepmd_pt.model.network.network import ( + Evoformer2bEncoder, +) class Evoformer2bBackBone(BackBone): - def __init__(self, - nnei, - layer_num=6, - attn_head=8, - atomic_dim=1024, - pair_dim=100, - feature_dim=1024, - ffn_dim=2048, - post_ln=False, - final_layer_norm=True, - final_head_layer_norm=False, - emb_layer_norm=False, - atomic_residual=False, - evo_residual=False, - residual_factor=1.0, - activation_function="gelu", - **kwargs): - """Construct an evoformer backBone. - - """ - super(Evoformer2bBackBone, self).__init__() + def __init__( + self, + nnei, + layer_num=6, + attn_head=8, + atomic_dim=1024, + pair_dim=100, + feature_dim=1024, + ffn_dim=2048, + post_ln=False, + final_layer_norm=True, + final_head_layer_norm=False, + emb_layer_norm=False, + atomic_residual=False, + evo_residual=False, + residual_factor=1.0, + activation_function="gelu", + **kwargs, + ): + """Construct an evoformer backBone.""" + super().__init__() self.nnei = nnei self.layer_num = layer_num self.attn_head = attn_head @@ -41,7 +36,9 @@ def __init__(self, self.pair_dim = pair_dim self.feature_dim = feature_dim self.head_dim = feature_dim // attn_head - assert feature_dim % attn_head == 0, f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" + assert ( + feature_dim % attn_head == 0 + ), f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" self.ffn_dim = ffn_dim self.post_ln = post_ln self.final_layer_norm = final_layer_norm @@ -66,7 +63,8 @@ def __init__(self, atomic_residual=self.atomic_residual, evo_residual=self.evo_residual, residual_factor=self.residual_factor, - activation_function=self.activation_function) + activation_function=self.activation_function, + ) def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): """Encoder the atomic and pair representations. @@ -78,7 +76,8 @@ def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): - nlist_type: Neighbor types with shape [nframes, nloc, nnei]. - nlist_mask: Neighbor mask with shape [nframes, nloc, nnei], `False` if blank. - Returns: + Returns + ------- - atomic_rep: Atomic representation after encoder with shape [nframes, nloc, feature_dim]. - transformed_atomic_rep: Transformed atomic representation after encoder with shape [nframes, nloc, atomic_dim]. - pair_rep: Pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. @@ -86,6 +85,19 @@ def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): - norm_x: Normalization loss of atomic_rep. - norm_delta_pair_rep: Normalization loss of delta_pair_rep. """ - atomic_rep, transformed_atomic_rep, pair_rep, delta_pair_rep, norm_x, norm_delta_pair_rep = \ - self.encoder(atomic_rep, pair_rep, nlist, nlist_type, nlist_mask) - return atomic_rep, transformed_atomic_rep, pair_rep, delta_pair_rep, norm_x, norm_delta_pair_rep + ( + atomic_rep, + transformed_atomic_rep, + pair_rep, + delta_pair_rep, + norm_x, + norm_delta_pair_rep, + ) = self.encoder(atomic_rep, pair_rep, nlist, nlist_type, nlist_mask) + return ( + atomic_rep, + transformed_atomic_rep, + pair_rep, + delta_pair_rep, + norm_x, + norm_delta_pair_rep, + ) diff --git a/deepmd_pt/model/descriptor/__init__.py b/deepmd_pt/model/descriptor/__init__.py index ed0154af..4252e349 100644 --- a/deepmd_pt/model/descriptor/__init__.py +++ b/deepmd_pt/model/descriptor/__init__.py @@ -1,10 +1,46 @@ -from .env_mat import prod_env_mat_se_a -from .descriptor import Descriptor, DescriptorBlock, compute_std, make_default_type_embedding -from .se_a import DescrptSeA -from .dpa1 import DescrptDPA1 -from .dpa2 import DescrptDPA2 -from .hybrid import DescrptBlockHybrid -from .se_a import DescrptBlockSeA -from .se_atten import DescrptBlockSeAtten -from .repformers import DescrptBlockRepformers -from .gaussian_lcc import DescrptGaussianLcc +# SPDX-License-Identifier: LGPL-3.0-or-later +from .descriptor import ( + Descriptor, + DescriptorBlock, + compute_std, + make_default_type_embedding, +) +from .dpa1 import ( + DescrptBlockSeAtten, + DescrptDPA1, +) +from .dpa2 import ( + DescrptDPA2, +) +from .env_mat import ( + prod_env_mat_se_a, +) +from .gaussian_lcc import ( + DescrptGaussianLcc, +) +from .hybrid import ( + DescrptBlockHybrid, +) +from .repformers import ( + DescrptBlockRepformers, +) +from .se_a import ( + DescrptBlockSeA, + DescrptSeA, +) + +__all__ = [ + "Descriptor", + "DescriptorBlock", + "compute_std", + "make_default_type_embedding", + "DescrptBlockSeA", + "DescrptBlockSeAtten", + "DescrptSeA", + "DescrptDPA1", + "DescrptDPA2", + "prod_env_mat_se_a", + "DescrptGaussianLcc", + "DescrptBlockHybrid", + "DescrptBlockRepformers", +] diff --git a/deepmd_pt/model/descriptor/descriptor.py b/deepmd_pt/model/descriptor/descriptor.py index 2d207331..d1eb8860 100644 --- a/deepmd_pt/model/descriptor/descriptor.py +++ b/deepmd_pt/model/descriptor/descriptor.py @@ -1,21 +1,28 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from abc import ( + ABC, + abstractmethod, +) +from typing import ( + Callable, + List, + Optional, +) + import numpy as np import torch -from abc import ABC, abstractmethod -from typing import Callable, Optional, List - -from deepmd_pt.utils import env -from deepmd_pt.utils.plugin import Plugin, PluginVariant -from deepmd_pt.model.network import TypeEmbedNet -try: - from typing import Final -except: - from torch.jit import Final +from deepmd_pt.model.network.network import ( + TypeEmbedNet, +) +from deepmd_pt.utils.plugin import ( + Plugin, +) class Descriptor(torch.nn.Module, ABC): """The descriptor. - Given the atomic coordinates, atomic types and neighbor list, + Given the atomic coordinates, atomic types and neighbor list, calculate the descriptor. """ @@ -23,62 +30,38 @@ class Descriptor(torch.nn.Module, ABC): local_cluster = False @abstractmethod - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ + def get_rcut(self) -> float: + """Returns the cut-off radius.""" raise NotImplementedError @abstractmethod - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" raise NotImplementedError @abstractmethod - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" raise NotImplementedError @abstractmethod - def get_ntype(self)->int: - """ - Returns the number of element types - """ + def get_ntype(self) -> int: + """Returns the number of element types.""" raise NotImplementedError @abstractmethod - def get_dim_out(self)->int: - """ - Returns the output dimension - """ + def get_dim_out(self) -> int: + """Returns the output dimension.""" raise NotImplementedError @abstractmethod def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ + """Update mean and stddev for descriptor elements.""" raise NotImplementedError @abstractmethod def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - """Initialize the model bias by the statistics - """ - raise NotImplementedError - - @abstractmethod - def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ - raise NotImplementedError - - @abstractmethod - def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - """Initialize the model bias by the statistics - """ + """Initialize the model bias by the statistics.""" raise NotImplementedError @abstractmethod @@ -89,8 +72,7 @@ def forward( nlist, mapping: Optional[torch.Tensor] = None, ): - """Calculate descriptor. - """ + """Calculate descriptor.""" raise NotImplementedError @staticmethod @@ -140,7 +122,7 @@ def __new__(cls, *args, **kwargs): class DescriptorBlock(torch.nn.Module, ABC): """The building block of descriptor. - Given the input descriptor, provide with the atomic coordinates, + Given the input descriptor, provide with the atomic coordinates, atomic types and neighbor list, calculate the new descriptor. """ @@ -182,69 +164,74 @@ def __new__(cls, *args, **kwargs): return super().__new__(cls) @abstractmethod - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ + def get_rcut(self) -> float: + """Returns the cut-off radius.""" raise NotImplementedError @abstractmethod - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" raise NotImplementedError @abstractmethod - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type - """ + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" raise NotImplementedError @abstractmethod - def get_ntype(self)->int: - """ - Returns the number of element types - """ + def get_ntype(self) -> int: + """Returns the number of element types.""" raise NotImplementedError @abstractmethod - def get_dim_out(self)->int: - """ - Returns the output dimension - """ + def get_dim_out(self) -> int: + """Returns the output dimension.""" raise NotImplementedError @abstractmethod - def get_dim_in(self)->int: - """ - Returns the output dimension - """ + def get_dim_in(self) -> int: + """Returns the output dimension.""" raise NotImplementedError @abstractmethod def compute_input_stats(self, merged): - """Update mean and stddev for DescriptorBlock elements. - """ + """Update mean and stddev for DescriptorBlock elements.""" raise NotImplementedError @abstractmethod def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - """Initialize the model bias by the statistics - """ + """Initialize the model bias by the statistics.""" raise NotImplementedError def share_params(self, base_class, shared_level, resume=False): - assert self.__class__ == base_class.__class__, "Only descriptors of the same type can share params!" + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" if shared_level == 0: # link buffers - if hasattr(self, 'mean') and not resume: + if hasattr(self, "mean") and not resume: # in case of change params during resume - sumr_base, suma_base, sumn_base, sumr2_base, suma2_base = \ - base_class.sumr, base_class.suma, base_class.sumn, base_class.sumr2, base_class.suma2 - sumr, suma, sumn, sumr2, suma2 = self.sumr, self.suma, self.sumn, self.sumr2, self.suma2 - base_class.init_desc_stat(sumr_base + sumr, suma_base + suma, sumn_base + sumn, sumr2_base + sumr2, suma2_base + suma2) + sumr_base, suma_base, sumn_base, sumr2_base, suma2_base = ( + base_class.sumr, + base_class.suma, + base_class.sumn, + base_class.sumr2, + base_class.suma2, + ) + sumr, suma, sumn, sumr2, suma2 = ( + self.sumr, + self.suma, + self.sumn, + self.sumr2, + self.suma2, + ) + base_class.init_desc_stat( + sumr_base + sumr, + suma_base + suma, + sumn_base + sumn, + sumr2_base + sumr2, + suma2_base + suma2, + ) self.mean = base_class.mean self.stddev = base_class.stddev # self.load_state_dict(base_class.state_dict()) # this does not work, because it only inits the model @@ -263,8 +250,7 @@ def forward( extended_atype_embd: Optional[torch.Tensor] = None, mapping: Optional[torch.Tensor] = None, ): - """Calculate DescriptorBlock. - """ + """Calculate DescriptorBlock.""" raise NotImplementedError @@ -281,7 +267,6 @@ def compute_std(sumv2, sumv, sumn, rcut_r): def make_default_type_embedding( ntypes, ): - aux = {} - aux['tebd_dim'] = 8 - return TypeEmbedNet(ntypes, aux['tebd_dim']), aux - + aux = {} + aux["tebd_dim"] = 8 + return TypeEmbedNet(ntypes, aux["tebd_dim"]), aux diff --git a/deepmd_pt/model/descriptor/dpa1.py b/deepmd_pt/model/descriptor/dpa1.py index 7e984a29..28a4efb6 100644 --- a/deepmd_pt/model/descriptor/dpa1.py +++ b/deepmd_pt/model/descriptor/dpa1.py @@ -1,162 +1,152 @@ -import numpy as np -import torch -import logging -try: - from typing import Final -except: - from torch.jit import Final +# SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, Tuple, + List, + Optional, +) + +import torch + +from deepmd_pt.model.descriptor import ( + Descriptor, ) -from deepmd_pt.utils import env -from deepmd_pt.utils.utils import get_activation_fn, ActivationFn -from deepmd_pt.model.descriptor import prod_env_mat_se_a, Descriptor, compute_std -from deepmd_pt.model.network import ( - TypeEmbedNet, SimpleLinear, Identity, Linear +from deepmd_pt.model.network.network import ( + TypeEmbedNet, +) + +from .se_atten import ( + DescrptBlockSeAtten, ) -from deepmd_pt.utils.nlist import build_multiple_neighbor_list -from .se_atten import analyze_descrpt -from .se_atten import DescrptBlockSeAtten @Descriptor.register("dpa1") @Descriptor.register("se_atten") class DescrptDPA1(Descriptor): - def __init__( - self, - rcut, - rcut_smth, - sel, - ntypes: int, - neuron: list = [25, 50, 100], - axis_neuron: int = 16, - tebd_dim: int = 8, - tebd_input_mode: str = 'concat', - # set_davg_zero: bool = False, - set_davg_zero: bool = True, # TODO - attn: int = 128, - attn_layer: int = 2, - attn_dotr: bool = True, - attn_mask: bool = False, - post_ln=True, - ffn=False, - ffn_embed_dim=1024, - activation="tanh", - scaling_factor=1.0, - head_num=1, - normalize=True, - temperature=None, - return_rot=False, - concat_output_tebd: bool = True, - type: Optional[str] = None, - ): - super(DescrptDPA1, self).__init__() - del type - self.se_atten = DescrptBlockSeAtten( - rcut, rcut_smth, sel, ntypes, - neuron=neuron, - axis_neuron=axis_neuron, - tebd_dim=tebd_dim, - tebd_input_mode=tebd_input_mode, - set_davg_zero=set_davg_zero, - attn=attn, - attn_layer=attn_layer, - attn_dotr=attn_dotr, - attn_mask=attn_mask, - post_ln=post_ln, - ffn=ffn, - ffn_embed_dim=ffn_embed_dim, - activation=activation, - scaling_factor=scaling_factor, - head_num=head_num, - normalize=normalize, - temperature=temperature, - return_rot=return_rot, - ) - self.type_embedding = TypeEmbedNet(ntypes, tebd_dim) - self.tebd_dim = tebd_dim - self.concat_output_tebd = concat_output_tebd - - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ - return self.se_atten.get_rcut() - - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ - return self.se_atten.get_nsel() - - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ - return self.se_atten.get_sel() - - def get_ntype(self)->int: - """ - Returns the number of element types - """ - return self.se_atten.get_ntype() - - def get_dim_out(self)->int: - """ - Returns the output dimension - """ - ret = self.se_atten.get_dim_out() - if self.concat_output_tebd: - ret += self.tebd_dim - return ret - - @property - def dim_out(self): - return self.get_dim_out() - - @property - def dim_emb(self): - return self.se_atten.dim_emb - - - def compute_input_stats(self, merged): - return self.se_atten.compute_input_stats(merged) - - def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - self.se_atten.init_desc_stat(sumr, suma, sumn, sumr2, suma2) - - @classmethod - def get_stat_name(cls, config): - descrpt_type = config["type"] - assert descrpt_type in ["dpa1", "se_atten"] - return f'stat_file_dpa1_rcut{config["rcut"]:.2f}_smth{config["rcut_smth"]:.2f}_sel{config["sel"]}.npz' - - @classmethod - def get_data_process_key(cls, config): - descrpt_type = config["type"] - assert descrpt_type in ["dpa1", "se_atten"] - return {"sel": config["sel"], "rcut": config["rcut"]} - - def forward( + def __init__( + self, + rcut, + rcut_smth, + sel, + ntypes: int, + neuron: list = [25, 50, 100], + axis_neuron: int = 16, + tebd_dim: int = 8, + tebd_input_mode: str = "concat", + # set_davg_zero: bool = False, + set_davg_zero: bool = True, # TODO + attn: int = 128, + attn_layer: int = 2, + attn_dotr: bool = True, + attn_mask: bool = False, + post_ln=True, + ffn=False, + ffn_embed_dim=1024, + activation="tanh", + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + return_rot=False, + concat_output_tebd: bool = True, + type: Optional[str] = None, + ): + super().__init__() + del type + self.se_atten = DescrptBlockSeAtten( + rcut, + rcut_smth, + sel, + ntypes, + neuron=neuron, + axis_neuron=axis_neuron, + tebd_dim=tebd_dim, + tebd_input_mode=tebd_input_mode, + set_davg_zero=set_davg_zero, + attn=attn, + attn_layer=attn_layer, + attn_dotr=attn_dotr, + attn_mask=attn_mask, + post_ln=post_ln, + ffn=ffn, + ffn_embed_dim=ffn_embed_dim, + activation=activation, + scaling_factor=scaling_factor, + head_num=head_num, + normalize=normalize, + temperature=temperature, + return_rot=return_rot, + ) + self.type_embedding = TypeEmbedNet(ntypes, tebd_dim) + self.tebd_dim = tebd_dim + self.concat_output_tebd = concat_output_tebd + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.se_atten.get_rcut() + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return self.se_atten.get_nsel() + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.se_atten.get_sel() + + def get_ntype(self) -> int: + """Returns the number of element types.""" + return self.se_atten.get_ntype() + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + ret = self.se_atten.get_dim_out() + if self.concat_output_tebd: + ret += self.tebd_dim + return ret + + @property + def dim_out(self): + return self.get_dim_out() + + @property + def dim_emb(self): + return self.se_atten.dim_emb + + def compute_input_stats(self, merged): + return self.se_atten.compute_input_stats(merged) + + def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): + self.se_atten.init_desc_stat(sumr, suma, sumn, sumr2, suma2) + + @classmethod + def get_stat_name(cls, config): + descrpt_type = config["type"] + assert descrpt_type in ["dpa1", "se_atten"] + return f'stat_file_dpa1_rcut{config["rcut"]:.2f}_smth{config["rcut_smth"]:.2f}_sel{config["sel"]}.npz' + + @classmethod + def get_data_process_key(cls, config): + descrpt_type = config["type"] + assert descrpt_type in ["dpa1", "se_atten"] + return {"sel": config["sel"], "rcut": config["rcut"]} + + def forward( self, extended_coord: torch.Tensor, extended_atype: torch.Tensor, nlist: torch.Tensor, mapping: Optional[torch.Tensor] = None, - ): - del mapping - nframes, nloc, nnei = nlist.shape - nall = extended_coord.view(nframes, -1).shape[1] // 3 - g1_ext = self.type_embedding(extended_atype) - g1_inp = g1_ext[:,:nloc,:] - g1, env_mat, diff, rot_mat, sw = self.se_atten( - nlist, - extended_coord, - extended_atype, - g1_ext, mapping=None, - ) - if self.concat_output_tebd: - g1 = torch.cat([g1, g1_inp], dim=-1) - return g1, env_mat, diff, rot_mat, sw - - + ): + del mapping + nframes, nloc, nnei = nlist.shape + nall = extended_coord.view(nframes, -1).shape[1] // 3 + g1_ext = self.type_embedding(extended_atype) + g1_inp = g1_ext[:, :nloc, :] + g1, env_mat, diff, rot_mat, sw = self.se_atten( + nlist, + extended_coord, + extended_atype, + g1_ext, + mapping=None, + ) + if self.concat_output_tebd: + g1 = torch.cat([g1, g1_inp], dim=-1) + return g1, env_mat, diff, rot_mat, sw diff --git a/deepmd_pt/model/descriptor/dpa2.py b/deepmd_pt/model/descriptor/dpa2.py index 5a6987ac..38281172 100644 --- a/deepmd_pt/model/descriptor/dpa2.py +++ b/deepmd_pt/model/descriptor/dpa2.py @@ -1,358 +1,375 @@ -import numpy as np -import torch -import logging -try: - from typing import Final -except: - from torch.jit import Final +# SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, Optional, Tuple, + List, + Optional, +) + +import torch + +from deepmd_pt.model.descriptor import ( + Descriptor, ) -from deepmd_pt.utils import env -from deepmd_pt.utils.utils import get_activation_fn, ActivationFn -from deepmd_pt.model.descriptor import prod_env_mat_se_a, Descriptor, compute_std -from deepmd_pt.model.network import ( - TypeEmbedNet, SimpleLinear, Identity, Linear +from deepmd_pt.model.network.network import ( + Identity, + Linear, + TypeEmbedNet, ) from deepmd_pt.utils.nlist import ( - get_multiple_nlist_key, build_multiple_neighbor_list + build_multiple_neighbor_list, + get_multiple_nlist_key, ) -from .se_atten import analyze_descrpt -from .se_atten import DescrptBlockSeAtten -from .repformers import DescrptBlockRepformers +from .repformers import ( + DescrptBlockRepformers, +) +from .se_atten import ( + DescrptBlockSeAtten, +) @Descriptor.register("dpa2") class DescrptDPA2(Descriptor): - def __init__( - self, - ntypes: int, - repinit_rcut: float, - repinit_rcut_smth: float, - repinit_nsel: int, - repformer_rcut: float, - repformer_rcut_smth: float, - repformer_nsel: int, - # kwargs - tebd_dim: int = 8, - concat_output_tebd: bool = True, - repinit_neuron: List[int] = [25, 50, 100], - repinit_axis_neuron: int = 16, - repinit_set_davg_zero: bool = True, # TODO - repinit_activation="tanh", - # repinit still unclear: - # ffn, ffn_embed_dim, scaling_factor, normalize, - repformer_nlayers : int = 3, - repformer_g1_dim: int = 128, - repformer_g2_dim: int = 16, - repformer_axis_dim: int = 4, - repformer_do_bn_mode: str = 'no', - repformer_bn_momentum: float = 0.1, - repformer_update_g1_has_conv: bool = True, - repformer_update_g1_has_drrd: bool = True, - repformer_update_g1_has_grrg: bool = True, - repformer_update_g1_has_attn: bool = True, - repformer_update_g2_has_g1g1: bool = True, - repformer_update_g2_has_attn: bool = True, - repformer_update_h2: bool = False, - repformer_attn1_hidden: int = 64, - repformer_attn1_nhead: int = 4, - repformer_attn2_hidden: int = 16, - repformer_attn2_nhead: int = 4, - repformer_attn2_has_gate: bool = False, - repformer_activation: str = "tanh", - repformer_update_style: str = "res_avg", - repformer_set_davg_zero: bool = True, # TODO - repformer_add_type_ebd_to_seq: bool = False, - type: Optional[str] = None, # work around the bad design in get_trainer and DpLoaderSet! - rcut: Optional[float] = None, # work around the bad design in get_trainer and DpLoaderSet! - rcut_smth: Optional[float] = None,# work around the bad design in get_trainer and DpLoaderSet! - sel: Optional[int] = None, # work around the bad design in get_trainer and DpLoaderSet! - ): - """ The DPA-2 descriptor. see https://arxiv.org/abs/2312.15492 - - Parameters - ---------- - ntypes : int - Number of atom types - repinit_rcut : float - The cut-off radius of the repinit block - repinit_rcut_smth : float - From this position the inverse distance smoothly decays - to 0 at the cut-off. Use in the repinit block. - repinit_nsel: int - Maximally possible number of neighbors for repinit block. - repformer_rcut : float - The cut-off radius of the repformer block - repformer_rcut_smth : float - From this position the inverse distance smoothly decays - to 0 at the cut-off. Use in the repformer block. - repformer_nsel: int - Maximally possible number of neighbors for repformer block. - tebd_dim: int - The dimension of atom type embedding - concat_output_tebd: bool - Whether to concat type embedding at the output of the descriptor. - repinit_neuron: List[int] - repinit block: the number of neurons in the embedding net. - repinit_axis_neuron: int - repinit block: the number of dimension of split in the - symmetrization op. - repinit_activation: str - repinit block: the activation function in the embedding net - repformer_nlayers: int - repformers block: the number of repformer layers - repformer_g1_dim: int - repformers block: the dimension of single-atom rep - repformer_g2_dim: int - repformers block: the dimension of invariant pair-atom rep - repformer_axis_dim: int - repformers block: the number of dimension of split in the - symmetrization ops. - repformer_do_bn_mode: bool - repformers block: do batch norm in the repformer layers - repformer_bn_momentum: float - repformers block: moment in the batch normalization - repformer_update_g1_has_conv: bool - repformers block: update the g1 rep with convolution term - repformer_update_g1_has_drrd: bool - repformers block: update the g1 rep with the drrd term - repformer_update_g1_has_grrg: bool - repformers block: update the g1 rep with the grrg term - repformer_update_g1_has_attn: bool - repformers block: update the g1 rep with the localized - self-attention - repformer_update_g2_has_g1g1: bool - repformers block: update the g2 rep with the g1xg1 term - repformer_update_g2_has_attn: bool - repformers block: update the g2 rep with the gated self-attention - repformer_update_h2: bool - repformers block: update the h2 rep - repformer_attn1_hidden: int - repformers block: the hidden dimension of localized self-attention - repformer_attn1_nhead: int - repformers block: the number of heads in localized self-attention - repformer_attn2_hidden: int - repformers block: the hidden dimension of gated self-attention - repformer_attn2_nhead: int - repformers block: the number of heads in gated self-attention - repformer_attn2_has_gate: bool - repformers block: has gate in the gated self-attention - repformer_activation: str - repformers block: the activation function in the MLPs. - repformer_update_style: str - repformers block: style of update a rep. - can be res_avg or res_incr. - res_avg updates a rep `u` with: - u = 1/\sqrt{n+1} (u + u_1 + u_2 + ... + u_n) - res_incr updates a rep `u` with: - u = u + 1/\sqrt{n} (u_1 + u_2 + ... + u_n) - repformer_set_davg_zero: bool - repformers block: set the avg to zero in statistics - repformer_add_type_ebd_to_seq: bool - repformers block: concatenate the type embedding at the output. + def __init__( + self, + ntypes: int, + repinit_rcut: float, + repinit_rcut_smth: float, + repinit_nsel: int, + repformer_rcut: float, + repformer_rcut_smth: float, + repformer_nsel: int, + # kwargs + tebd_dim: int = 8, + concat_output_tebd: bool = True, + repinit_neuron: List[int] = [25, 50, 100], + repinit_axis_neuron: int = 16, + repinit_set_davg_zero: bool = True, # TODO + repinit_activation="tanh", + # repinit still unclear: + # ffn, ffn_embed_dim, scaling_factor, normalize, + repformer_nlayers: int = 3, + repformer_g1_dim: int = 128, + repformer_g2_dim: int = 16, + repformer_axis_dim: int = 4, + repformer_do_bn_mode: str = "no", + repformer_bn_momentum: float = 0.1, + repformer_update_g1_has_conv: bool = True, + repformer_update_g1_has_drrd: bool = True, + repformer_update_g1_has_grrg: bool = True, + repformer_update_g1_has_attn: bool = True, + repformer_update_g2_has_g1g1: bool = True, + repformer_update_g2_has_attn: bool = True, + repformer_update_h2: bool = False, + repformer_attn1_hidden: int = 64, + repformer_attn1_nhead: int = 4, + repformer_attn2_hidden: int = 16, + repformer_attn2_nhead: int = 4, + repformer_attn2_has_gate: bool = False, + repformer_activation: str = "tanh", + repformer_update_style: str = "res_avg", + repformer_set_davg_zero: bool = True, # TODO + repformer_add_type_ebd_to_seq: bool = False, + type: Optional[ + str + ] = None, # work around the bad design in get_trainer and DpLoaderSet! + rcut: Optional[ + float + ] = None, # work around the bad design in get_trainer and DpLoaderSet! + rcut_smth: Optional[ + float + ] = None, # work around the bad design in get_trainer and DpLoaderSet! + sel: Optional[ + int + ] = None, # work around the bad design in get_trainer and DpLoaderSet! + ): + r"""The DPA-2 descriptor. see https://arxiv.org/abs/2312.15492. + + Parameters + ---------- + ntypes : int + Number of atom types + repinit_rcut : float + The cut-off radius of the repinit block + repinit_rcut_smth : float + From this position the inverse distance smoothly decays + to 0 at the cut-off. Use in the repinit block. + repinit_nsel : int + Maximally possible number of neighbors for repinit block. + repformer_rcut : float + The cut-off radius of the repformer block + repformer_rcut_smth : float + From this position the inverse distance smoothly decays + to 0 at the cut-off. Use in the repformer block. + repformer_nsel : int + Maximally possible number of neighbors for repformer block. + tebd_dim : int + The dimension of atom type embedding + concat_output_tebd : bool + Whether to concat type embedding at the output of the descriptor. + repinit_neuron : List[int] + repinit block: the number of neurons in the embedding net. + repinit_axis_neuron : int + repinit block: the number of dimension of split in the + symmetrization op. + repinit_activation : str + repinit block: the activation function in the embedding net + repformer_nlayers : int + repformers block: the number of repformer layers + repformer_g1_dim : int + repformers block: the dimension of single-atom rep + repformer_g2_dim : int + repformers block: the dimension of invariant pair-atom rep + repformer_axis_dim : int + repformers block: the number of dimension of split in the + symmetrization ops. + repformer_do_bn_mode : bool + repformers block: do batch norm in the repformer layers + repformer_bn_momentum : float + repformers block: moment in the batch normalization + repformer_update_g1_has_conv : bool + repformers block: update the g1 rep with convolution term + repformer_update_g1_has_drrd : bool + repformers block: update the g1 rep with the drrd term + repformer_update_g1_has_grrg : bool + repformers block: update the g1 rep with the grrg term + repformer_update_g1_has_attn : bool + repformers block: update the g1 rep with the localized + self-attention + repformer_update_g2_has_g1g1 : bool + repformers block: update the g2 rep with the g1xg1 term + repformer_update_g2_has_attn : bool + repformers block: update the g2 rep with the gated self-attention + repformer_update_h2 : bool + repformers block: update the h2 rep + repformer_attn1_hidden : int + repformers block: the hidden dimension of localized self-attention + repformer_attn1_nhead : int + repformers block: the number of heads in localized self-attention + repformer_attn2_hidden : int + repformers block: the hidden dimension of gated self-attention + repformer_attn2_nhead : int + repformers block: the number of heads in gated self-attention + repformer_attn2_has_gate : bool + repformers block: has gate in the gated self-attention + repformer_activation : str + repformers block: the activation function in the MLPs. + repformer_update_style : str + repformers block: style of update a rep. + can be res_avg or res_incr. + res_avg updates a rep `u` with: + u = 1/\sqrt{n+1} (u + u_1 + u_2 + ... + u_n) + res_incr updates a rep `u` with: + u = u + 1/\sqrt{n} (u_1 + u_2 + ... + u_n) + repformer_set_davg_zero : bool + repformers block: set the avg to zero in statistics + repformer_add_type_ebd_to_seq : bool + repformers block: concatenate the type embedding at the output. - Returns - ------- - descriptor: torch.Tensor - the descriptor of shape nb x nloc x g1_dim. - invariant single-atom representation. - g2: torch.Tensor - invariant pair-atom representation. - h2: torch.Tensor - equivariant pair-atom representation. - rot_mat: torch.Tensor - rotation matrix for equivariant fittings - sw: torch.Tensor - The switch function for decaying inverse distance. + Returns + ------- + descriptor: torch.Tensor + the descriptor of shape nb x nloc x g1_dim. + invariant single-atom representation. + g2: torch.Tensor + invariant pair-atom representation. + h2: torch.Tensor + equivariant pair-atom representation. + rot_mat: torch.Tensor + rotation matrix for equivariant fittings + sw: torch.Tensor + The switch function for decaying inverse distance. - """ - super(DescrptDPA2, self).__init__() - del type, rcut, rcut_smth, sel - self.repinit = DescrptBlockSeAtten( - repinit_rcut, - repinit_rcut_smth, - repinit_nsel, - ntypes, - attn_layer=0, - neuron=repinit_neuron, - axis_neuron=repinit_axis_neuron, - tebd_dim=tebd_dim, - tebd_input_mode='concat', - # tebd_input_mode='dot_residual_s', - set_davg_zero=repinit_set_davg_zero, - activation=repinit_activation, - ) - self.repformers = DescrptBlockRepformers( - repformer_rcut, - repformer_rcut_smth, - repformer_nsel, - ntypes, - nlayers=repformer_nlayers, - g1_dim=repformer_g1_dim, - g2_dim=repformer_g2_dim, - axis_dim=repformer_axis_dim, - direct_dist=False, - do_bn_mode=repformer_do_bn_mode, - bn_momentum=repformer_bn_momentum, - update_g1_has_conv=repformer_update_g1_has_conv, - update_g1_has_drrd=repformer_update_g1_has_drrd, - update_g1_has_grrg=repformer_update_g1_has_grrg, - update_g1_has_attn=repformer_update_g1_has_attn, - update_g2_has_g1g1=repformer_update_g2_has_g1g1, - update_g2_has_attn=repformer_update_g2_has_attn, - update_h2=repformer_update_h2, - attn1_hidden=repformer_attn1_hidden, - attn1_nhead=repformer_attn1_nhead, - attn2_hidden=repformer_attn2_hidden, - attn2_nhead=repformer_attn2_nhead, - attn2_has_gate=repformer_attn2_has_gate, - activation=repformer_activation, - update_style=repformer_update_style, - set_davg_zero=repformer_set_davg_zero, - smooth=True, - add_type_ebd_to_seq=repformer_add_type_ebd_to_seq, - ) - self.type_embedding = TypeEmbedNet(ntypes, tebd_dim) - if self.repinit.dim_out == self.repformers.dim_in: - self.g1_shape_tranform = Identity() - else: - self.g1_shape_tranform = Linear( - self.repinit.dim_out, - self.repformers.dim_in, - bias=False, - init="glorot", - ) - assert self.repinit.rcut > self.repformers.rcut - assert self.repinit.sel[0] > self.repformers.sel[0] - self.concat_output_tebd = concat_output_tebd - self.tebd_dim = tebd_dim - self.rcut = self.repinit.get_rcut() - self.ntypes = ntypes - self.sel = self.repinit.sel + """ + super().__init__() + del type, rcut, rcut_smth, sel + self.repinit = DescrptBlockSeAtten( + repinit_rcut, + repinit_rcut_smth, + repinit_nsel, + ntypes, + attn_layer=0, + neuron=repinit_neuron, + axis_neuron=repinit_axis_neuron, + tebd_dim=tebd_dim, + tebd_input_mode="concat", + # tebd_input_mode='dot_residual_s', + set_davg_zero=repinit_set_davg_zero, + activation=repinit_activation, + ) + self.repformers = DescrptBlockRepformers( + repformer_rcut, + repformer_rcut_smth, + repformer_nsel, + ntypes, + nlayers=repformer_nlayers, + g1_dim=repformer_g1_dim, + g2_dim=repformer_g2_dim, + axis_dim=repformer_axis_dim, + direct_dist=False, + do_bn_mode=repformer_do_bn_mode, + bn_momentum=repformer_bn_momentum, + update_g1_has_conv=repformer_update_g1_has_conv, + update_g1_has_drrd=repformer_update_g1_has_drrd, + update_g1_has_grrg=repformer_update_g1_has_grrg, + update_g1_has_attn=repformer_update_g1_has_attn, + update_g2_has_g1g1=repformer_update_g2_has_g1g1, + update_g2_has_attn=repformer_update_g2_has_attn, + update_h2=repformer_update_h2, + attn1_hidden=repformer_attn1_hidden, + attn1_nhead=repformer_attn1_nhead, + attn2_hidden=repformer_attn2_hidden, + attn2_nhead=repformer_attn2_nhead, + attn2_has_gate=repformer_attn2_has_gate, + activation=repformer_activation, + update_style=repformer_update_style, + set_davg_zero=repformer_set_davg_zero, + smooth=True, + add_type_ebd_to_seq=repformer_add_type_ebd_to_seq, + ) + self.type_embedding = TypeEmbedNet(ntypes, tebd_dim) + if self.repinit.dim_out == self.repformers.dim_in: + self.g1_shape_tranform = Identity() + else: + self.g1_shape_tranform = Linear( + self.repinit.dim_out, + self.repformers.dim_in, + bias=False, + init="glorot", + ) + assert self.repinit.rcut > self.repformers.rcut + assert self.repinit.sel[0] > self.repformers.sel[0] + self.concat_output_tebd = concat_output_tebd + self.tebd_dim = tebd_dim + self.rcut = self.repinit.get_rcut() + self.ntypes = ntypes + self.sel = self.repinit.sel - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ - return self.rcut + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ - return sum(self.sel) + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ - return self.sel + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel - def get_ntype(self)->int: - """ - Returns the number of element types - """ - return self.ntypes + def get_ntype(self) -> int: + """Returns the number of element types.""" + return self.ntypes - def get_dim_out(self)->int: - """ - Returns the output dimension of this descriptor - """ - ret = self.repformers.dim_out - if self.concat_output_tebd: - ret += self.tebd_dim - return ret + def get_dim_out(self) -> int: + """Returns the output dimension of this descriptor.""" + ret = self.repformers.dim_out + if self.concat_output_tebd: + ret += self.tebd_dim + return ret - @property - def dim_out(self): - return self.get_dim_out() + @property + def dim_out(self): + return self.get_dim_out() - @property - def dim_emb(self): - """ - Returns the embedding dimension g2 - """ - return self.repformers.dim_emb + @property + def dim_emb(self): + """Returns the embedding dimension g2.""" + return self.repformers.dim_emb - def compute_input_stats(self, merged): - sumr, suma, sumn, sumr2, suma2 = [], [], [], [], [] - for ii, descrpt in enumerate([self.repinit, self.repformers]): - merged_tmp = [ - {key: item[key] if not isinstance(item[key], list) else item[key][ii] \ - for key in item} \ - for item in merged - ] - sumr_tmp, suma_tmp, sumn_tmp, sumr2_tmp, suma2_tmp = descrpt.compute_input_stats(merged_tmp) - sumr.append(sumr_tmp) - suma.append(suma_tmp) - sumn.append(sumn_tmp) - sumr2.append(sumr2_tmp) - suma2.append(suma2_tmp) - return sumr, suma, sumn, sumr2, suma2 - + def compute_input_stats(self, merged): + sumr, suma, sumn, sumr2, suma2 = [], [], [], [], [] + for ii, descrpt in enumerate([self.repinit, self.repformers]): + merged_tmp = [ + { + key: item[key] if not isinstance(item[key], list) else item[key][ii] + for key in item + } + for item in merged + ] + ( + sumr_tmp, + suma_tmp, + sumn_tmp, + sumr2_tmp, + suma2_tmp, + ) = descrpt.compute_input_stats(merged_tmp) + sumr.append(sumr_tmp) + suma.append(suma_tmp) + sumn.append(sumn_tmp) + sumr2.append(sumr2_tmp) + suma2.append(suma2_tmp) + return sumr, suma, sumn, sumr2, suma2 - def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - for ii, descrpt in enumerate([self.repinit, self.repformers]): - descrpt.init_desc_stat(sumr[ii], suma[ii], sumn[ii], sumr2[ii], suma2[ii]) + def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): + for ii, descrpt in enumerate([self.repinit, self.repformers]): + descrpt.init_desc_stat(sumr[ii], suma[ii], sumn[ii], sumr2[ii], suma2[ii]) - @classmethod - def get_stat_name(cls, config): - descrpt_type = config["type"] - assert descrpt_type in ["dpa2"] - return f'stat_file_dpa2_repinit_rcut{config["repinit_rcut"]:.2f}_smth{config["repinit_rcut_smth"]:.2f}_sel{config["repinit_nsel"]}' \ - f'_repformer_rcut{config["repformer_rcut"]:.2f}_smth{config["repformer_rcut_smth"]:.2f}_sel{config["repformer_nsel"]}.npz' + @classmethod + def get_stat_name(cls, config): + descrpt_type = config["type"] + assert descrpt_type in ["dpa2"] + return ( + f'stat_file_dpa2_repinit_rcut{config["repinit_rcut"]:.2f}_smth{config["repinit_rcut_smth"]:.2f}_sel{config["repinit_nsel"]}' + f'_repformer_rcut{config["repformer_rcut"]:.2f}_smth{config["repformer_rcut_smth"]:.2f}_sel{config["repformer_nsel"]}.npz' + ) - @classmethod - def get_data_process_key(cls, config): - descrpt_type = config["type"] - assert descrpt_type in ["dpa2"] - return {"sel": [config["repinit_nsel"], config["repformer_nsel"]], "rcut": [config["repinit_rcut"], config["repformer_rcut"]]} + @classmethod + def get_data_process_key(cls, config): + descrpt_type = config["type"] + assert descrpt_type in ["dpa2"] + return { + "sel": [config["repinit_nsel"], config["repformer_nsel"]], + "rcut": [config["repinit_rcut"], config["repformer_rcut"]], + } - def forward( + def forward( self, extended_coord: torch.Tensor, extended_atype: torch.Tensor, nlist: torch.Tensor, mapping: Optional[torch.Tensor] = None, - ): - nframes, nloc, nnei = nlist.shape - nall = extended_coord.view(nframes, -1).shape[1] // 3 - # nlists - nlist_dict = build_multiple_neighbor_list( - extended_coord, - nlist, - [self.repformers.get_rcut(), self.repinit.get_rcut()], - [self.repformers.get_nsel(), self.repinit.get_nsel()], - ) - # repinit - g1_ext = self.type_embedding(extended_atype) - g1_inp = g1_ext[:,:nloc,:] - g1, _, _, _, _ = self.repinit( - nlist_dict[get_multiple_nlist_key( - self.repinit.get_rcut(), self.repinit.get_nsel())], - extended_coord, - extended_atype, - g1_ext, mapping, - ) - # linear to change shape - g1 = self.g1_shape_tranform(g1) - # mapping g1 - assert mapping is not None - mapping_ext = mapping.view(nframes, nall)\ - .unsqueeze(-1)\ - .expand(-1, -1, g1.shape[-1]) - g1_ext = torch.gather(g1, 1, mapping_ext) - # repformer - g1, g2, h2, rot_mat, sw = self.repformers( - nlist_dict[get_multiple_nlist_key( - self.repformers.get_rcut(), self.repformers.get_nsel())], - extended_coord, - extended_atype, - g1_ext, mapping, - ) - if self.concat_output_tebd: - g1 = torch.cat([g1, g1_inp], dim=-1) - return g1, g2, h2, rot_mat, sw - + ): + nframes, nloc, nnei = nlist.shape + nall = extended_coord.view(nframes, -1).shape[1] // 3 + # nlists + nlist_dict = build_multiple_neighbor_list( + extended_coord, + nlist, + [self.repformers.get_rcut(), self.repinit.get_rcut()], + [self.repformers.get_nsel(), self.repinit.get_nsel()], + ) + # repinit + g1_ext = self.type_embedding(extended_atype) + g1_inp = g1_ext[:, :nloc, :] + g1, _, _, _, _ = self.repinit( + nlist_dict[ + get_multiple_nlist_key(self.repinit.get_rcut(), self.repinit.get_nsel()) + ], + extended_coord, + extended_atype, + g1_ext, + mapping, + ) + # linear to change shape + g1 = self.g1_shape_tranform(g1) + # mapping g1 + assert mapping is not None + mapping_ext = ( + mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, g1.shape[-1]) + ) + g1_ext = torch.gather(g1, 1, mapping_ext) + # repformer + g1, g2, h2, rot_mat, sw = self.repformers( + nlist_dict[ + get_multiple_nlist_key( + self.repformers.get_rcut(), self.repformers.get_nsel() + ) + ], + extended_coord, + extended_atype, + g1_ext, + mapping, + ) + if self.concat_output_tebd: + g1 = torch.cat([g1, g1_inp], dim=-1) + return g1, g2, h2, rot_mat, sw diff --git a/deepmd_pt/model/descriptor/env_mat.py b/deepmd_pt/model/descriptor/env_mat.py index e6e7bef6..58caf036 100644 --- a/deepmd_pt/model/descriptor/env_mat.py +++ b/deepmd_pt/model/descriptor/env_mat.py @@ -1,7 +1,9 @@ -import logging -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import torch -from deepmd_pt.utils.preprocess import compute_smooth_weight + +from deepmd_pt.utils.preprocess import ( + compute_smooth_weight, +) def _make_env_mat_se_a(nlist, coord, rcut: float, ruct_smth: float): @@ -19,15 +21,15 @@ def _make_env_mat_se_a(nlist, coord, rcut: float, ruct_smth: float): # for index 0 nloc atom length = length + ~mask.unsqueeze(-1) t0 = 1 / length - t1 = diff / length ** 2 + t1 = diff / length**2 weight = compute_smooth_weight(length, ruct_smth, rcut) env_mat_se_a = torch.cat([t0, t1], dim=-1) * weight * mask.unsqueeze(-1) return env_mat_se_a, diff * mask.unsqueeze(-1), weight def prod_env_mat_se_a( - extended_coord, nlist, atype, - mean, stddev, rcut: float, rcut_smth: float): + extended_coord, nlist, atype, mean, stddev, rcut: float, rcut_smth: float +): """Generate smooth environment matrix from atom coordinates and other context. Args: @@ -41,11 +43,14 @@ def prod_env_mat_se_a( - rcut: Cut-off radius. - rcut_smth: Smooth hyper-parameter for pair force & energy. - Returns: + Returns + ------- - env_mat_se_a: Shape is [nframes, natoms[1]*nnei*4]. """ nframes = extended_coord.shape[0] # 样本数量 - _env_mat_se_a, diff, switch = _make_env_mat_se_a(nlist, extended_coord, rcut, rcut_smth) # shape [n_atom, dim, 4] + _env_mat_se_a, diff, switch = _make_env_mat_se_a( + nlist, extended_coord, rcut, rcut_smth + ) # shape [n_atom, dim, 4] t_avg = mean[atype] # [n_atom, dim, 4] t_std = stddev[atype] # [n_atom, dim, 4] env_mat_se_a = (_env_mat_se_a - t_avg) / t_std diff --git a/deepmd_pt/model/descriptor/gaussian_lcc.py b/deepmd_pt/model/descriptor/gaussian_lcc.py index f05421e7..9507a002 100644 --- a/deepmd_pt/model/descriptor/gaussian_lcc.py +++ b/deepmd_pt/model/descriptor/gaussian_lcc.py @@ -1,46 +1,49 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import torch import torch.nn as nn -from deepmd_pt.utils import env -from deepmd_pt.model.descriptor import prod_env_mat_se_a, Descriptor, compute_std - -try: - from typing import Final -except: - from torch.jit import Final - -from deepmd_pt.model.network import TypeFilter, TypeEmbedNet, GaussianEmbedding, Evoformer3bEncoder +from deepmd_pt.model.descriptor import ( + Descriptor, +) +from deepmd_pt.model.network.network import ( + Evoformer3bEncoder, + GaussianEmbedding, + TypeEmbedNet, +) +from deepmd_pt.utils import ( + env, +) class DescrptGaussianLcc(Descriptor): - - def __init__(self, - rcut, - rcut_smth, - sel: int, - ntypes: int, - num_pair: int, - embed_dim: int = 768, - kernel_num: int = 128, - pair_embed_dim: int = 64, - num_block: int = 1, - layer_num: int = 12, - attn_head: int = 48, - pair_hidden_dim: int = 16, - ffn_embedding_dim: int = 768, - dropout: float = 0.0, - droppath_prob: float = 0.1, - pair_dropout: float = 0.25, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - pre_ln: bool = True, - do_tag_embedding: bool = False, - tag_ener_pref: bool = False, - atomic_sum_gbf: bool = False, - pre_add_seq: bool = True, - tri_update: bool = True, - **kwargs): + def __init__( + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + num_pair: int, + embed_dim: int = 768, + kernel_num: int = 128, + pair_embed_dim: int = 64, + num_block: int = 1, + layer_num: int = 12, + attn_head: int = 48, + pair_hidden_dim: int = 16, + ffn_embedding_dim: int = 768, + dropout: float = 0.0, + droppath_prob: float = 0.1, + pair_dropout: float = 0.25, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + pre_ln: bool = True, + do_tag_embedding: bool = False, + tag_ener_pref: bool = False, + atomic_sum_gbf: bool = False, + pre_add_seq: bool = True, + tri_update: bool = True, + **kwargs, + ): """Construct a descriptor of Gaussian Based Local Cluster. Args: @@ -67,7 +70,7 @@ def __init__(self, - atomic_sum_gbf: Add sum of gaussian outputs to atomic representation or not. - pre_add_seq: Add output of other descriptor (if has) to the atomic representation before attention. """ - super(DescrptGaussianLcc, self).__init__() + super().__init__() self.rcut = rcut self.rcut_smth = rcut_smth self.embed_dim = embed_dim @@ -103,64 +106,72 @@ def __init__(self, self.tag_encoder = nn.Embedding(3, self.embed_dim) self.tag_encoder2 = nn.Embedding(2, self.embed_dim) self.tag_type_embedding = TypeEmbedNet(10, pair_embed_dim) - self.edge_type_embedding = nn.Embedding((ntypes + 1) * (ntypes + 1), - pair_embed_dim, padding_idx=(ntypes + 1) * (ntypes + 1) - 1, - dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.gaussian_encoder = GaussianEmbedding(rcut, kernel_num, num_pair, embed_dim, - pair_embed_dim, sel, ntypes, atomic_sum_gbf) - self.backbone = Evoformer3bEncoder(self.nnei, - layer_num=self.layer_num, - attn_head=self.attention_heads, - atomic_dim=self.embed_dim, - pair_dim=self.pair_embed_dim, - pair_hidden_dim=self.pair_hidden_dim, - ffn_embedding_dim=self.ffn_embedding_dim, - dropout=self.dropout, - droppath_prob=self.droppath_prob, - pair_dropout=self.pair_dropout, - attention_dropout=self.attention_dropout, - activation_dropout=self.activation_dropout, - pre_ln=self.pre_ln, - tri_update=self.tri_update) + self.edge_type_embedding = nn.Embedding( + (ntypes + 1) * (ntypes + 1), + pair_embed_dim, + padding_idx=(ntypes + 1) * (ntypes + 1) - 1, + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + ) + self.gaussian_encoder = GaussianEmbedding( + rcut, + kernel_num, + num_pair, + embed_dim, + pair_embed_dim, + sel, + ntypes, + atomic_sum_gbf, + ) + self.backbone = Evoformer3bEncoder( + self.nnei, + layer_num=self.layer_num, + attn_head=self.attention_heads, + atomic_dim=self.embed_dim, + pair_dim=self.pair_embed_dim, + pair_hidden_dim=self.pair_hidden_dim, + ffn_embedding_dim=self.ffn_embedding_dim, + dropout=self.dropout, + droppath_prob=self.droppath_prob, + pair_dropout=self.pair_dropout, + attention_dropout=self.attention_dropout, + activation_dropout=self.activation_dropout, + pre_ln=self.pre_ln, + tri_update=self.tri_update, + ) @property def dim_out(self): - """ - Returns the output dimension of atomic representation. - """ + """Returns the output dimension of atomic representation.""" return self.embed_dim @property def dim_in(self): - """ - Returns the atomic input dimension of this descriptor - """ + """Returns the atomic input dimension of this descriptor.""" return self.embed_dim @property def dim_emb(self): - """ - Returns the output dimension of pair representation. - """ + """Returns the output dimension of pair representation.""" return self.pair_embed_dim def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ + """Update mean and stddev for descriptor elements.""" return [], [], [], [], [] def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): pass - def forward(self, - extended_coord, - nlist, - atype, - nlist_type, - nlist_loc=None, - atype_tebd=None, - nlist_tebd=None, - seq_input=None): + def forward( + self, + extended_coord, + nlist, + atype, + nlist_type, + nlist_loc=None, + atype_tebd=None, + nlist_tebd=None, + seq_input=None, + ): """Calculate the atomic and pair representations of this descriptor. Args: @@ -174,18 +185,31 @@ def forward(self, - seq_input: The sequential input from other descriptor with shape [nframes, nloc, tebd_dim] or [nframes * nloc, 1 + nnei, tebd_dim] - Returns: + Returns + ------- - result: descriptor with shape [nframes, nloc, self.filter_neuron[-1] * self.axis_neuron]. - ret: environment matrix with shape [nframes, nloc, self.neei, out_size] """ nframes, nloc = nlist.shape[:2] nall = extended_coord.shape[1] nlist2 = torch.cat( - [torch.arange(0, nloc, device=nlist.device).reshape(1, nloc, 1).expand(nframes, -1, -1), - nlist], dim=-1) + [ + torch.arange(0, nloc, device=nlist.device) + .reshape(1, nloc, 1) + .expand(nframes, -1, -1), + nlist, + ], + dim=-1, + ) nlist_loc2 = torch.cat( - [torch.arange(0, nloc, device=nlist_loc.device).reshape(1, nloc, 1).expand(nframes, -1, -1), - nlist_loc], dim=-1) + [ + torch.arange(0, nloc, device=nlist_loc.device) + .reshape(1, nloc, 1) + .expand(nframes, -1, -1), + nlist_loc, + ], + dim=-1, + ) nlist_type2 = torch.cat([atype.reshape(nframes, nloc, 1), nlist_type], dim=-1) nnei2_mask = nlist2 != -1 padding_mask = nlist2 == -1 @@ -195,22 +219,29 @@ def forward(self, # nframes x nloc x (1 + nnei2) x (1 + nnei2) pair_mask = nnei2_mask.unsqueeze(-1) * nnei2_mask.unsqueeze(-2) # nframes x nloc x (1 + nnei2) x (1 + nnei2) x head - attn_mask = torch.zeros([nframes, nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads], - device=nlist.device, - dtype=extended_coord.dtype) - attn_mask.masked_fill_( - padding_mask.unsqueeze(2).unsqueeze(-1), - float("-inf") + attn_mask = torch.zeros( + [nframes, nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads], + device=nlist.device, + dtype=extended_coord.dtype, ) + attn_mask.masked_fill_(padding_mask.unsqueeze(2).unsqueeze(-1), float("-inf")) # (nframes x nloc) x head x (1 + nnei2) x (1 + nnei2) - attn_mask = attn_mask.reshape( - nframes * nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads - ).permute(0, 3, 1, 2).contiguous() + attn_mask = ( + attn_mask.reshape( + nframes * nloc, 1 + self.nnei, 1 + self.nnei, self.attention_heads + ) + .permute(0, 3, 1, 2) + .contiguous() + ) # Atomic feature # [(nframes x nloc) x (1 + nnei2) x tebd_dim] atom_feature = torch.gather( - atype_tebd, dim=1, index=nlist_loc2.reshape(nframes, -1).unsqueeze(-1).expand(-1, -1, self.embed_dim) + atype_tebd, + dim=1, + index=nlist_loc2.reshape(nframes, -1) + .unsqueeze(-1) + .expand(-1, -1, self.embed_dim), ).reshape(nframes * nloc, 1 + self.nnei, self.embed_dim) if self.pre_add_seq and seq_input is not None: first_dim = seq_input.shape[0] @@ -218,43 +249,67 @@ def forward(self, atom_feature += seq_input elif first_dim == nframes: atom_feature_seq = torch.gather( - seq_input, dim=1, index=nlist_loc2.reshape(nframes, -1).unsqueeze(-1).expand(-1, -1, self.embed_dim) + seq_input, + dim=1, + index=nlist_loc2.reshape(nframes, -1) + .unsqueeze(-1) + .expand(-1, -1, self.embed_dim), ).reshape(nframes * nloc, 1 + self.nnei, self.embed_dim) atom_feature += atom_feature_seq else: raise RuntimeError - atom_feature = atom_feature * nnei2_mask.reshape(nframes * nloc, 1 + self.nnei, 1) + atom_feature = atom_feature * nnei2_mask.reshape( + nframes * nloc, 1 + self.nnei, 1 + ) # Pair feature # [(nframes x nloc) x (1 + nnei2)] nlist_type2_reshape = nlist_type2.reshape(nframes * nloc, 1 + self.nnei) # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2)] - edge_type = nlist_type2_reshape.unsqueeze(-1) * (self.ntypes + 1) + nlist_type2_reshape.unsqueeze(-2) + edge_type = nlist_type2_reshape.unsqueeze(-1) * ( + self.ntypes + 1 + ) + nlist_type2_reshape.unsqueeze(-2) # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x pair_dim] edge_feature = self.edge_type_embedding(edge_type) # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x 2] edge_type_2dim = torch.cat( - [nlist_type2_reshape.view(nframes * nloc, 1 + self.nnei, 1, 1).expand(-1, -1, 1 + self.nnei, -1), - nlist_type2_reshape.view(nframes * nloc, 1, 1 + self.nnei, 1).expand(-1, 1 + self.nnei, -1, - -1) + self.ntypes], + [ + nlist_type2_reshape.view(nframes * nloc, 1 + self.nnei, 1, 1).expand( + -1, -1, 1 + self.nnei, -1 + ), + nlist_type2_reshape.view(nframes * nloc, 1, 1 + self.nnei, 1).expand( + -1, 1 + self.nnei, -1, -1 + ) + + self.ntypes, + ], dim=-1, ) # [(nframes x nloc) x (1 + nnei2) x 3] coord_selected = torch.gather( - extended_coord.unsqueeze(1).expand(-1, nloc, -1, -1).reshape(nframes * nloc, nall, 3), - dim=1, index=nlist2.reshape(nframes * nloc, 1 + self.nnei, 1).expand(-1, -1, 3)) + extended_coord.unsqueeze(1) + .expand(-1, nloc, -1, -1) + .reshape(nframes * nloc, nall, 3), + dim=1, + index=nlist2.reshape(nframes * nloc, 1 + self.nnei, 1).expand(-1, -1, 3), + ) # Update pair features (or and atomic features) with gbf features # delta_pos: [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x 3]. - atomic_feature, pair_feature, delta_pos = self.gaussian_encoder(coord_selected, atom_feature, - edge_type_2dim, edge_feature) + atomic_feature, pair_feature, delta_pos = self.gaussian_encoder( + coord_selected, atom_feature, edge_type_2dim, edge_feature + ) # [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x pair_dim] attn_bias = pair_feature # output: [(nframes x nloc) x (1 + nnei2) x tebd_dim] # pair: [(nframes x nloc) x (1 + nnei2) x (1 + nnei2) x pair_dim] - output, pair = self.backbone(atomic_feature, pair=attn_bias, attn_mask=attn_mask, pair_mask=pair_mask, - atom_mask=nnei2_mask.reshape(nframes * nloc, 1 + self.nnei)) + output, pair = self.backbone( + atomic_feature, + pair=attn_bias, + attn_mask=attn_mask, + pair_mask=pair_mask, + atom_mask=nnei2_mask.reshape(nframes * nloc, 1 + self.nnei), + ) return output, pair, delta_pos, None diff --git a/deepmd_pt/model/descriptor/hybrid.py b/deepmd_pt/model/descriptor/hybrid.py index c360c09e..6eac4212 100644 --- a/deepmd_pt/model/descriptor/hybrid.py +++ b/deepmd_pt/model/descriptor/hybrid.py @@ -1,44 +1,49 @@ -import numpy as np -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, +) -from deepmd_pt.utils import env -from deepmd_pt.model.descriptor import prod_env_mat_se_a, DescriptorBlock, compute_std -from deepmd_pt.model.network import Identity, Linear -from typing import Optional, List +import torch -try: - from typing import Final -except: - from torch.jit import Final +from deepmd_pt.model.descriptor import ( + DescriptorBlock, +) +from deepmd_pt.model.network.network import ( + Identity, + Linear, +) @DescriptorBlock.register("hybrid") class DescrptBlockHybrid(DescriptorBlock): - - def __init__(self, - list, - ntypes: int, - tebd_dim: int = 8, - tebd_input_mode: str = 'concat', - hybrid_mode: str = "concat", - **kwargs): + def __init__( + self, + list, + ntypes: int, + tebd_dim: int = 8, + tebd_input_mode: str = "concat", + hybrid_mode: str = "concat", + **kwargs, + ): """Construct a hybrid descriptor. Args: - descriptor_list: list of descriptors. - descriptor_param: descriptor configs. """ - super(DescrptBlockHybrid, self).__init__() - supported_descrpt = ['se_atten', 'se_uni'] + super().__init__() + supported_descrpt = ["se_atten", "se_uni"] descriptor_list = [] for descriptor_param_item in list: - descriptor_type_tmp = descriptor_param_item['type'] - assert descriptor_type_tmp in supported_descrpt, \ - f'Only descriptors in {supported_descrpt} are supported for `hybrid` descriptor!' - descriptor_param_item['ntypes'] = ntypes + descriptor_type_tmp = descriptor_param_item["type"] + assert ( + descriptor_type_tmp in supported_descrpt + ), f"Only descriptors in {supported_descrpt} are supported for `hybrid` descriptor!" + descriptor_param_item["ntypes"] = ntypes if descriptor_type_tmp == "se_atten": - descriptor_param_item['tebd_dim'] = tebd_dim - descriptor_param_item['tebd_input_mode'] = tebd_input_mode + descriptor_param_item["tebd_dim"] = tebd_dim + descriptor_param_item["tebd_input_mode"] = tebd_input_mode descriptor_list.append(DescriptorBlock(**descriptor_param_item)) self.descriptor_list = torch.nn.ModuleList(descriptor_list) self.descriptor_param = list @@ -46,7 +51,9 @@ def __init__(self, self.sec = [descrpt.sec for descrpt in self.descriptor_list] self.sel = [descrpt.sel for descrpt in self.descriptor_list] self.split_sel = [sum(ii) for ii in self.sel] - self.local_cluster_list = [descrpt.local_cluster for descrpt in self.descriptor_list] + self.local_cluster_list = [ + descrpt.local_cluster for descrpt in self.descriptor_list + ] self.local_cluster = True in self.local_cluster_list self.hybrid_mode = hybrid_mode self.tebd_dim = tebd_dim @@ -57,53 +64,45 @@ def __init__(self, if descriptor_list[ii].dim_out == descriptor_list[ii + 1].dim_in: sequential_transform.append(Identity()) else: - sequential_transform.append(Linear(descriptor_list[ii].dim_out, descriptor_list[ii + 1].dim_in, - bias=False, init="glorot")) + sequential_transform.append( + Linear( + descriptor_list[ii].dim_out, + descriptor_list[ii + 1].dim_in, + bias=False, + init="glorot", + ) + ) sequential_transform.append(Identity()) self.sequential_transform = torch.nn.ModuleList(sequential_transform) self.ntypes = ntypes - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ - return self.rcut - - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ - return [sum(ii) for ii in self.get_sel()] - - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ - return self.sel - - def get_ntype(self)->int: - """ - Returns the number of element types - """ - return self.ntypes - - def get_dim_out(self)->int: - """ - Returns the output dimension - """ - return self.dim_out - - def get_dim_in(self)->int: - """ - Returns the input dimension - """ - return self.dim_in + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return [sum(ii) for ii in self.get_sel()] + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntype(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in @property def dim_out(self): - """ - Returns the output dimension of this descriptor - """ + """Returns the output dimension of this descriptor.""" if self.hybrid_mode == "concat": return sum([descrpt.dim_out for descrpt in self.descriptor_list]) elif self.hybrid_mode == "sequential": @@ -113,16 +112,12 @@ def dim_out(self): @property def dim_emb_list(self) -> List[int]: - """ - Returns the output dimension list of embeddings - """ + """Returns the output dimension list of embeddings.""" return [descrpt.dim_emb for descrpt in self.descriptor_list] @property def dim_emb(self): - """ - Returns the output dimension of embedding - """ + """Returns the output dimension of embedding.""" if self.hybrid_mode == "concat": return sum(self.dim_emb_list) elif self.hybrid_mode == "sequential": @@ -131,23 +126,37 @@ def dim_emb(self): raise RuntimeError def share_params(self, base_class, shared_level, resume=False): - assert self.__class__ == base_class.__class__, "Only descriptors of the same type can share params!" + assert ( + self.__class__ == base_class.__class__ + ), "Only descriptors of the same type can share params!" if shared_level == 0: for ii, des in enumerate(self.descriptor_list): - self.descriptor_list[ii].share_params(base_class.descriptor_list[ii], shared_level, resume=resume) + self.descriptor_list[ii].share_params( + base_class.descriptor_list[ii], shared_level, resume=resume + ) if self.hybrid_mode == "sequential": self.sequential_transform = base_class.sequential_transform else: raise NotImplementedError def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ + """Update mean and stddev for descriptor elements.""" sumr, suma, sumn, sumr2, suma2 = [], [], [], [], [] for ii, descrpt in enumerate(self.descriptor_list): - merged_tmp = [{key: item[key] if not isinstance(item[key], list) else item[key][ii] for key in item} for - item in merged] - sumr_tmp, suma_tmp, sumn_tmp, sumr2_tmp, suma2_tmp = descrpt.compute_input_stats(merged_tmp) + merged_tmp = [ + { + key: item[key] if not isinstance(item[key], list) else item[key][ii] + for key in item + } + for item in merged + ] + ( + sumr_tmp, + suma_tmp, + sumn_tmp, + sumr2_tmp, + suma2_tmp, + ) = descrpt.compute_input_stats(merged_tmp) sumr.append(sumr_tmp) suma.append(suma_tmp) sumn.append(sumn_tmp) @@ -177,27 +186,27 @@ def forward( - atype_tebd: Tell simulation box with shape [nframes, 9]. - nlist_tebd: Tell simulation box with shape [nframes, 9]. - Returns: + Returns + ------- - result: descriptor with shape [nframes, nloc, self.filter_neuron[-1] * self.axis_neuron]. - ret: environment matrix with shape [nframes, nloc, self.neei, out_size] """ nlist_list = list(torch.split(nlist, self.split_sel, -1)) nframes, nloc, nnei = nlist.shape concat_rot_mat = True - if self.hybrid_mode == 'concat': + if self.hybrid_mode == "concat": out_descriptor = [] # out_env_mat = [] out_rot_mat_list = [] # out_diff = [] for ii, descrpt in enumerate(self.descriptor_list): - descriptor, env_mat, diff, rot_mat, sw = \ - descrpt( - nlist_list[ii], + descriptor, env_mat, diff, rot_mat, sw = descrpt( + nlist_list[ii], extended_coord, extended_atype, extended_atype_embd, mapping, - ) + ) if descriptor.shape[0] == nframes * nloc: # [nframes * nloc, 1 + nnei, emb_dim] descriptor = descriptor[:, 0, :].reshape(nframes, nloc, -1) @@ -213,28 +222,33 @@ def forward( else: out_rot_mat = None return out_descriptor, None, None, out_rot_mat, sw - elif self.hybrid_mode == 'sequential': + elif self.hybrid_mode == "sequential": assert extended_atype_embd is not None assert mapping is not None nframes, nloc, nnei = nlist.shape nall = extended_coord.view(nframes, -1).shape[1] // 3 seq_input_ext = extended_atype_embd - seq_input = seq_input_ext[:, :nloc, :] if len(self.descriptor_list) == 0 else None + seq_input = ( + seq_input_ext[:, :nloc, :] if len(self.descriptor_list) == 0 else None + ) env_mat, diff, rot_mat, sw = None, None, None, None env_mat_list, diff_list = [], [] - for ii, (descrpt, seq_transform) in enumerate(zip(self.descriptor_list, self.sequential_transform)): - seq_output, env_mat, diff, rot_mat, sw = \ - descrpt( - nlist_list[ii], + for ii, (descrpt, seq_transform) in enumerate( + zip(self.descriptor_list, self.sequential_transform) + ): + seq_output, env_mat, diff, rot_mat, sw = descrpt( + nlist_list[ii], extended_coord, extended_atype, seq_input_ext, mapping, - ) + ) seq_input = seq_transform(seq_output) - mapping_ext = mapping.view(nframes, nall)\ - .unsqueeze(-1)\ - .expand(-1, -1, seq_input.shape[-1]) + mapping_ext = ( + mapping.view(nframes, nall) + .unsqueeze(-1) + .expand(-1, -1, seq_input.shape[-1]) + ) seq_input_ext = torch.gather(seq_input, 1, mapping_ext) env_mat_list.append(env_mat) diff_list.append(diff) diff --git a/deepmd_pt/model/descriptor/repformer_layer.py b/deepmd_pt/model/descriptor/repformer_layer.py index 8ea24b03..56e6fad8 100644 --- a/deepmd_pt/model/descriptor/repformer_layer.py +++ b/deepmd_pt/model/descriptor/repformer_layer.py @@ -1,28 +1,31 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + List, +) + import torch -import logging -try: - from typing import Final -except: - from torch.jit import Final -from typing import ( - List, Optional, Tuple, Callable +from deepmd_pt.model.network.network import ( + SimpleLinear, +) +from deepmd_pt.utils import ( + env, ) -from deepmd_pt.utils import env -from deepmd_pt.utils.utils import get_activation_fn, ActivationFn -from deepmd_pt.model.network import ( - TypeEmbedNet, SimpleLinear +from deepmd_pt.utils.utils import ( + get_activation_fn, ) def torch_linear(*args, **kwargs): - return torch.nn.Linear(*args, **kwargs, dtype= env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + return torch.nn.Linear( + *args, **kwargs, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) def _make_nei_g1( - g1_ext: torch.Tensor, - nlist: torch.Tensor, + g1_ext: torch.Tensor, + nlist: torch.Tensor, ) -> torch.Tensor: # nlist: nb x nloc x nnei nb, nloc, nnei = nlist.shape @@ -38,25 +41,22 @@ def _make_nei_g1( def _apply_nlist_mask( - gg: torch.Tensor, - nlist_mask: torch.Tensor, + gg: torch.Tensor, + nlist_mask: torch.Tensor, ) -> torch.Tensor: # gg: nf x nloc x nnei x ng # msk: nf x nloc x nnei - return gg.masked_fill(~nlist_mask.unsqueeze(-1), float(0.)) + return gg.masked_fill(~nlist_mask.unsqueeze(-1), 0.0) -def _apply_switch( - gg: torch.Tensor, - sw: torch.Tensor -) -> torch.Tensor: +def _apply_switch(gg: torch.Tensor, sw: torch.Tensor) -> torch.Tensor: # gg: nf x nloc x nnei x ng # sw: nf x nloc x nnei return gg * sw.unsqueeze(-1) def _apply_h_norm( - hh: torch.Tensor, # nf x nloc x nnei x 3 + hh: torch.Tensor, # nf x nloc x nnei x 3 ) -> torch.Tensor: """Normalize h by the std of vector length. do not have an idea if this is a good way. @@ -67,21 +67,21 @@ def _apply_h_norm( # nf x nloc std = torch.std(normh, dim=-1) # nf x nloc x nnei x 3 - hh = hh[:, :, :, :] / (1. + std[:, :, None, None]) + hh = hh[:, :, :, :] / (1.0 + std[:, :, None, None]) return hh class Atten2Map(torch.nn.Module): def __init__( - self, - ni: int, - nd: int, - nh: int, - has_gate: bool = False, # apply gate to attn map - smooth: bool = True, - attnw_shift: float = 20.0, + self, + ni: int, + nd: int, + nh: int, + has_gate: bool = False, # apply gate to attn map + smooth: bool = True, + attnw_shift: float = 20.0, ): - super(Atten2Map, self).__init__() + super().__init__() self.ni = ni self.nd = nd self.nh = nh @@ -91,13 +91,18 @@ def __init__( self.attnw_shift = attnw_shift def forward( - self, - g2: torch.Tensor, # nb x nloc x nnei x ng2 - h2: torch.Tensor, # nb x nloc x nnei x 3 - nlist_mask: torch.Tensor, # nb x nloc x nnei - sw: torch.Tensor # nb x nloc x nnei + self, + g2: torch.Tensor, # nb x nloc x nnei x ng2 + h2: torch.Tensor, # nb x nloc x nnei x 3 + nlist_mask: torch.Tensor, # nb x nloc x nnei + sw: torch.Tensor, # nb x nloc x nnei ) -> torch.Tensor: - nb, nloc, nnei, _, = g2.shape + ( + nb, + nloc, + nnei, + _, + ) = g2.shape nd, nh = self.nd, self.nh # nb x nloc x nnei x nd x (nh x 2) g2qk = self.mapqk(g2).view(nb, nloc, nnei, nd, nh * 2) @@ -108,7 +113,7 @@ def forward( # g2q = torch.nn.functional.normalize(g2q, dim=-1) # g2k = torch.nn.functional.normalize(g2k, dim=-1) # nb x nloc x nh x nnei x nnei - attnw = torch.matmul(g2q, torch.transpose(g2k, -1, -2)) / nd ** 0.5 + attnw = torch.matmul(g2q, torch.transpose(g2k, -1, -2)) / nd**0.5 if self.has_gate: gate = torch.matmul(h2, torch.transpose(h2, -1, -2)).unsqueeze(-3) attnw = attnw * gate @@ -117,17 +122,28 @@ def forward( # mask the attenmap, nb x nloc x 1 x nnei x 1 attnw_mask_c = ~nlist_mask.unsqueeze(2).unsqueeze(-1) if self.smooth: - attnw = (attnw + self.attnw_shift) * sw[:,:,None,:,None] * sw[:,:,None,None,:] - self.attnw_shift + attnw = (attnw + self.attnw_shift) * sw[:, :, None, :, None] * sw[ + :, :, None, None, : + ] - self.attnw_shift else: - attnw = attnw.masked_fill(attnw_mask, float("-inf"), ) + attnw = attnw.masked_fill( + attnw_mask, + float("-inf"), + ) attnw = torch.softmax(attnw, dim=-1) - attnw = attnw.masked_fill(attnw_mask, float(0.0), ) + attnw = attnw.masked_fill( + attnw_mask, + 0.0, + ) # nb x nloc x nh x nnei x nnei - attnw = attnw.masked_fill(attnw_mask_c, float(0.0), ) + attnw = attnw.masked_fill( + attnw_mask_c, + 0.0, + ) if self.smooth: - attnw = attnw * sw[:,:,None,:,None] * sw[:,:,None,None,:] + attnw = attnw * sw[:, :, None, :, None] * sw[:, :, None, None, :] # nb x nloc x nnei x nnei - h2h2t = torch.matmul(h2, torch.transpose(h2, -1, -2)) / 3. ** 0.5 + h2h2t = torch.matmul(h2, torch.transpose(h2, -1, -2)) / 3.0**0.5 # nb x nloc x nh x nnei x nnei ret = attnw * h2h2t[:, :, None, :, :] # ret = torch.softmax(g2qk, dim=-1) @@ -138,20 +154,20 @@ def forward( class Atten2MultiHeadApply(torch.nn.Module): def __init__( - self, - ni: int, - nh: int, + self, + ni: int, + nh: int, ): - super(Atten2MultiHeadApply, self).__init__() + super().__init__() self.ni = ni self.nh = nh self.mapv = SimpleLinear(ni, ni * nh, bias=False) self.head_map = SimpleLinear(ni * nh, ni) def forward( - self, - AA: torch.Tensor, # nf x nloc x nnei x nnei x nh - g2: torch.Tensor, # nf x nloc x nnei x ng2 + self, + AA: torch.Tensor, # nf x nloc x nnei x nnei x nh + g2: torch.Tensor, # nf x nloc x nnei x ng2 ) -> torch.Tensor: nf, nloc, nnei, ng2 = g2.shape nh = self.nh @@ -172,19 +188,19 @@ def forward( class Atten2EquiVarApply(torch.nn.Module): def __init__( - self, - ni: int, - nh: int, + self, + ni: int, + nh: int, ): - super(Atten2EquiVarApply, self).__init__() + super().__init__() self.ni = ni self.nh = nh self.head_map = SimpleLinear(nh, 1, bias=False) def forward( - self, - AA: torch.Tensor, # nf x nloc x nnei x nnei x nh - h2: torch.Tensor, # nf x nloc x nnei x 3 + self, + AA: torch.Tensor, # nf x nloc x nnei x nnei x nh + h2: torch.Tensor, # nf x nloc x nnei x 3 ) -> torch.Tensor: nf, nloc, nnei, _ = h2.shape nh = self.nh @@ -203,14 +219,14 @@ def forward( class LocalAtten(torch.nn.Module): def __init__( - self, - ni: int, - nd: int, - nh: int, - smooth: bool = True, - attnw_shift: float = 20.0, + self, + ni: int, + nd: int, + nh: int, + smooth: bool = True, + attnw_shift: float = 20.0, ): - super(LocalAtten, self).__init__() + super().__init__() self.ni = ni self.nd = nd self.nh = nh @@ -221,11 +237,11 @@ def __init__( self.attnw_shift = attnw_shift def forward( - self, - g1: torch.Tensor, # nb x nloc x ng1 - gg1: torch.Tensor, # nb x nloc x nnei x ng1 - nlist_mask: torch.Tensor, # nb x nloc x nnei - sw: torch.Tensor, # nb x nloc x nnei + self, + g1: torch.Tensor, # nb x nloc x ng1 + gg1: torch.Tensor, # nb x nloc x nnei x ng1 + nlist_mask: torch.Tensor, # nb x nloc x nnei + sw: torch.Tensor, # nb x nloc x nnei ) -> torch.Tensor: nb, nloc, nnei = nlist_mask.shape ni, nd, nh = self.ni, self.nd, self.nh @@ -242,9 +258,7 @@ def forward( gg1k, gg1v = torch.split(gg1kv, [nd, ni], dim=-1) # nb x nloc x nh x 1 x nnei - attnw = torch.matmul( - g1q.unsqueeze(-2), - torch.transpose(gg1k, -1, -2)) / nd ** 0.5 + attnw = torch.matmul(g1q.unsqueeze(-2), torch.transpose(gg1k, -1, -2)) / nd**0.5 # nb x nloc x nh x nnei attnw = attnw.squeeze(-2) # mask the attenmap, nb x nloc x 1 x nnei @@ -253,16 +267,22 @@ def forward( if self.smooth: attnw = (attnw + self.attnw_shift) * sw.unsqueeze(-2) - self.attnw_shift else: - attnw = attnw.masked_fill(attnw_mask, float("-inf"), ) + attnw = attnw.masked_fill( + attnw_mask, + float("-inf"), + ) attnw = torch.softmax(attnw, dim=-1) - attnw = attnw.masked_fill(attnw_mask, float(0.0), ) + attnw = attnw.masked_fill( + attnw_mask, + 0.0, + ) if self.smooth: attnw = attnw * sw.unsqueeze(-2) # nb x nloc x nh x ng1 - ret = torch.matmul(attnw.unsqueeze(-2), gg1v) \ - .squeeze(-2) \ - .view(nb, nloc, nh * ni) + ret = ( + torch.matmul(attnw.unsqueeze(-2), gg1v).squeeze(-2).view(nb, nloc, nh * ni) + ) # nb x nloc x ng1 ret = self.head_map(ret) return ret @@ -270,35 +290,35 @@ def forward( class RepformerLayer(torch.nn.Module): def __init__( - self, - rcut, - rcut_smth, - sel: int, - ntypes: int, - g1_dim=128, - g2_dim=16, - axis_dim: int = 4, - update_chnnl_2: bool = True, - do_bn_mode: str = 'no', - bn_momentum: float = 0.1, - update_g1_has_conv: bool = True, - update_g1_has_drrd: bool = True, - update_g1_has_grrg: bool = True, - update_g1_has_attn: bool = True, - update_g2_has_g1g1: bool = True, - update_g2_has_attn: bool = True, - update_h2: bool = False, - attn1_hidden: int = 64, - attn1_nhead: int = 4, - attn2_hidden: int = 16, - attn2_nhead: int = 4, - attn2_has_gate: bool = False, - activation: str = "tanh", - update_style: str = "res_avg", - set_davg_zero: bool = True, # TODO - smooth: bool = True, + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + g1_dim=128, + g2_dim=16, + axis_dim: int = 4, + update_chnnl_2: bool = True, + do_bn_mode: str = "no", + bn_momentum: float = 0.1, + update_g1_has_conv: bool = True, + update_g1_has_drrd: bool = True, + update_g1_has_grrg: bool = True, + update_g1_has_attn: bool = True, + update_g2_has_g1g1: bool = True, + update_g2_has_attn: bool = True, + update_h2: bool = False, + attn1_hidden: int = 64, + attn1_nhead: int = 4, + attn2_hidden: int = 16, + attn2_nhead: int = 4, + attn2_has_gate: bool = False, + activation: str = "tanh", + update_style: str = "res_avg", + set_davg_zero: bool = True, # TODO + smooth: bool = True, ): - super(RepformerLayer, self).__init__() + super().__init__() self.epsilon = 1e-4 # protection of 1./nnei self.rcut = rcut self.rcut_smth = rcut_smth @@ -346,33 +366,36 @@ def __init__( if self.update_g2_has_g1g1: self.proj_g1g1g2 = SimpleLinear(g1_dim, g2_dim, bias=False) if self.update_g2_has_attn: - self.attn2g_map = Atten2Map(g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth) + self.attn2g_map = Atten2Map( + g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth + ) self.attn2_mh_apply = Atten2MultiHeadApply(g2_dim, attn2_nhead) self.attn2_lm = torch.nn.LayerNorm( - g2_dim, elementwise_affine=True, device=env.DEVICE, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + g2_dim, + elementwise_affine=True, + device=env.DEVICE, + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + ) if self.update_h2: - self.attn2h_map = Atten2Map(g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth) + self.attn2h_map = Atten2Map( + g2_dim, attn2_hidden, attn2_nhead, attn2_has_gate, self.smooth + ) self.attn2_ev_apply = Atten2EquiVarApply(g2_dim, attn2_nhead) if self.update_g1_has_attn: self.loc_attn = LocalAtten(g1_dim, attn1_hidden, attn1_nhead, self.smooth) - if self.do_bn_mode == 'uniform': + if self.do_bn_mode == "uniform": self.bn1 = self._bn_layer() self.bn2 = self._bn_layer() - elif self.do_bn_mode == 'component': + elif self.do_bn_mode == "component": self.bn1 = self._bn_layer(nf=g1_dim) self.bn2 = self._bn_layer(nf=g2_dim) - elif self.do_bn_mode == 'no': + elif self.do_bn_mode == "no": self.bn1, self.bn2 = None, None else: raise RuntimeError(f"unknown bn_mode {self.do_bn_mode}") - def cal_1_dim( - self, - g1d: int, - g2d: int, - ax: int - ) -> int: + def cal_1_dim(self, g1d: int, g2d: int, ax: int) -> int: ret = g1d if self.update_g1_has_grrg: ret += g2d * ax @@ -383,11 +406,11 @@ def cal_1_dim( return ret def _update_h2( - self, - g2: torch.Tensor, - h2: torch.Tensor, - nlist_mask: torch.Tensor, - sw: torch.Tensor + self, + g2: torch.Tensor, + h2: torch.Tensor, + nlist_mask: torch.Tensor, + sw: torch.Tensor, ) -> torch.Tensor: assert self.attn2h_map is not None assert self.attn2_ev_apply is not None @@ -402,11 +425,11 @@ def _update_h2( return h2_1 def _update_g1_conv( - self, - gg1: torch.Tensor, - g2: torch.Tensor, - nlist_mask: torch.Tensor, - sw: torch.Tensor + self, + gg1: torch.Tensor, + g2: torch.Tensor, + nlist_mask: torch.Tensor, + sw: torch.Tensor, ) -> torch.Tensor: assert self.proj_g1g2 is not None nb, nloc, nnei, _ = g2.shape @@ -419,20 +442,22 @@ def _update_g1_conv( if not self.smooth: # normalized by number of neighbors, not smooth # nb x nloc x 1 - invnnei = 1. / (self.epsilon + torch.sum(nlist_mask, dim=-1)).unsqueeze(-1) + invnnei = 1.0 / (self.epsilon + torch.sum(nlist_mask, dim=-1)).unsqueeze(-1) else: gg1 = _apply_switch(gg1, sw) - invnnei = (1. / float(nnei)) * torch.ones((nb, nloc, 1), dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + invnnei = (1.0 / float(nnei)) * torch.ones( + (nb, nloc, 1), dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) # nb x nloc x ng2 g1_11 = torch.sum(g2 * gg1, dim=2) * invnnei return g1_11 def _cal_h2g2( - self, - g2: torch.Tensor, - h2: torch.Tensor, - nlist_mask: torch.Tensor, - sw: torch.Tensor + self, + g2: torch.Tensor, + h2: torch.Tensor, + nlist_mask: torch.Tensor, + sw: torch.Tensor, ) -> torch.Tensor: # g2: nf x nloc x nnei x ng2 # h2: nf x nloc x nnei x 3 @@ -443,38 +468,35 @@ def _cal_h2g2( g2 = _apply_nlist_mask(g2, nlist_mask) if not self.smooth: # nb x nloc - invnnei = 1. / (self.epsilon + torch.sum(nlist_mask, dim=-1)) + invnnei = 1.0 / (self.epsilon + torch.sum(nlist_mask, dim=-1)) # nb x nloc x 1 x 1 invnnei = invnnei.unsqueeze(-1).unsqueeze(-1) else: g2 = _apply_switch(g2, sw) - invnnei = (1. / float(nnei)) * torch.ones((nb, nloc, 1, 1), dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + invnnei = (1.0 / float(nnei)) * torch.ones( + (nb, nloc, 1, 1), dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) # nb x nloc x 3 x ng2 - h2g2 = torch.matmul( - torch.transpose(h2, -1, -2), g2) * invnnei + h2g2 = torch.matmul(torch.transpose(h2, -1, -2), g2) * invnnei return h2g2 - def _cal_grrg( - self, - h2g2: torch.Tensor - ) -> torch.Tensor: + def _cal_grrg(self, h2g2: torch.Tensor) -> torch.Tensor: # nb x nloc x 3 x ng2 nb, nloc, _, ng2 = h2g2.shape # nb x nloc x 3 x axis h2g2m = torch.split(h2g2, self.axis_dim, dim=-1)[0] # nb x nloc x axis x ng2 - g1_13 = torch.matmul( - torch.transpose(h2g2m, -1, -2), h2g2) / (float(3.) ** 1) + g1_13 = torch.matmul(torch.transpose(h2g2m, -1, -2), h2g2) / (3.0**1) # nb x nloc x (axisxng2) g1_13 = g1_13.view(nb, nloc, self.axis_dim * ng2) return g1_13 def _update_g1_grrg( - self, - g2: torch.Tensor, - h2: torch.Tensor, - nlist_mask: torch.Tensor, - sw: torch.Tensor + self, + g2: torch.Tensor, + h2: torch.Tensor, + nlist_mask: torch.Tensor, + sw: torch.Tensor, ) -> torch.Tensor: # g2: nf x nloc x nnei x ng2 # h2: nf x nloc x nnei x 3 @@ -488,11 +510,11 @@ def _update_g1_grrg( return g1_13 def _update_g2_g1g1( - self, - g1: torch.Tensor, # nb x nloc x ng1 - gg1: torch.Tensor, # nb x nloc x nnei x ng1 - nlist_mask: torch.Tensor, # nb x nloc x nnei - sw: torch.Tensor, # nb x nloc x nnei + self, + g1: torch.Tensor, # nb x nloc x ng1 + gg1: torch.Tensor, # nb x nloc x nnei x ng1 + nlist_mask: torch.Tensor, # nb x nloc x nnei + sw: torch.Tensor, # nb x nloc x nnei ) -> torch.Tensor: ret = g1.unsqueeze(-2) * gg1 # nb x nloc x nnei x ng1 @@ -502,22 +524,18 @@ def _update_g2_g1g1( return ret def _apply_bn( - self, - bn_number: int, - gg: torch.Tensor, + self, + bn_number: int, + gg: torch.Tensor, ): - if self.do_bn_mode == 'uniform': + if self.do_bn_mode == "uniform": return self._apply_bn_uni(bn_number, gg) - elif self.do_bn_mode == 'component': + elif self.do_bn_mode == "component": return self._apply_bn_comp(bn_number, gg) else: return gg - def _apply_nb_1( - self, - bn_number: int, - gg: torch.Tensor - ) -> torch.Tensor: + def _apply_nb_1(self, bn_number: int, gg: torch.Tensor) -> torch.Tensor: nb, nl, nf = gg.shape gg = gg.view([nb, 1, nl * nf]) if bn_number == 1: @@ -529,9 +547,9 @@ def _apply_nb_1( return gg.view([nb, nl, nf]) def _apply_nb_2( - self, - bn_number: int, - gg: torch.Tensor, + self, + bn_number: int, + gg: torch.Tensor, ) -> torch.Tensor: nb, nl, nnei, nf = gg.shape gg = gg.view([nb, 1, nl * nnei * nf]) @@ -544,22 +562,22 @@ def _apply_nb_2( return gg.view([nb, nl, nnei, nf]) def _apply_bn_uni( - self, - bn_number: int, - gg: torch.Tensor, - mode: str = '1', + self, + bn_number: int, + gg: torch.Tensor, + mode: str = "1", ) -> torch.Tensor: if len(gg.shape) == 3: return self._apply_nb_1(bn_number, gg) elif len(gg.shape) == 4: return self._apply_nb_2(bn_number, gg) else: - raise RuntimeError(f'unsupported input shape {gg.shape}') + raise RuntimeError(f"unsupported input shape {gg.shape}") def _apply_bn_comp( - self, - bn_number: int, - gg: torch.Tensor, + self, + bn_number: int, + gg: torch.Tensor, ) -> torch.Tensor: ss = gg.shape nf = ss[-1] @@ -573,33 +591,40 @@ def _apply_bn_comp( return gg def forward( - self, - g1_ext: torch.Tensor, # nf x nall x ng1 - g2: torch.Tensor, # nf x nloc x nnei x ng2 - h2: torch.Tensor, # nf x nloc x nnei x 3 - nlist: torch.Tensor, # nf x nloc x nnei - nlist_mask: torch.Tensor, # nf x nloc x nnei - sw: torch.Tensor, # switch func, nf x nloc x nnei + self, + g1_ext: torch.Tensor, # nf x nall x ng1 + g2: torch.Tensor, # nf x nloc x nnei x ng2 + h2: torch.Tensor, # nf x nloc x nnei x 3 + nlist: torch.Tensor, # nf x nloc x nnei + nlist_mask: torch.Tensor, # nf x nloc x nnei + sw: torch.Tensor, # switch func, nf x nloc x nnei ): """ - Parameters: - g1_ext: nf x nall x ng1 extended single-atom chanel - g2: nf x nloc x nnei x ng2 pair-atom channel, invariant - h2: nf x nloc x nnei x 3 pair-atom channel, equivariant - nlist: nf x nloc x nnei neighbor list (padded neis are set to 0) - nlist_mask: nf x nloc x nnei masks of the neighbor list. real nei 1 otherwise 0 - sw: nf x nloc x nnei switch function - - Returns: + Parameters + ---------- + g1_ext : nf x nall x ng1 extended single-atom chanel + g2 : nf x nloc x nnei x ng2 pair-atom channel, invariant + h2 : nf x nloc x nnei x 3 pair-atom channel, equivariant + nlist : nf x nloc x nnei neighbor list (padded neis are set to 0) + nlist_mask : nf x nloc x nnei masks of the neighbor list. real nei 1 otherwise 0 + sw : nf x nloc x nnei switch function + + Returns + ------- g1: nf x nloc x ng1 updated single-atom chanel g2: nf x nloc x nnei x ng2 updated pair-atom channel, invariant h2: nf x nloc x nnei x 3 updated pair-atom channel, equivariant """ - cal_gg1 = self.update_g1_has_drrd or self.update_g1_has_conv or self.update_g1_has_attn or self.update_g2_has_g1g1 + cal_gg1 = ( + self.update_g1_has_drrd + or self.update_g1_has_conv + or self.update_g1_has_attn + or self.update_g2_has_g1g1 + ) nb, nloc, nnei, _ = g2.shape nall = g1_ext.shape[1] - g1,_ = torch.split(g1_ext, [nloc,nall-nloc], dim=1) + g1, _ = torch.split(g1_ext, [nloc, nall - nloc], dim=1) assert (nb, nloc) == g1.shape[:2] assert (nb, nloc, nnei) == h2.shape[:3] ng1 = g1.shape[-1] @@ -632,8 +657,9 @@ def forward( if self.update_g2_has_g1g1: assert gg1 is not None assert self.proj_g1g1g2 is not None - g2_update.append(self.proj_g1g1g2( - self._update_g2_g1g1(g1, gg1, nlist_mask, sw))) + g2_update.append( + self.proj_g1g1g2(self._update_g2_g1g1(g1, gg1, nlist_mask, sw)) + ) if self.update_g2_has_attn: assert self.attn2g_map is not None @@ -662,9 +688,7 @@ def forward( # nb x nloc x [ng1+ng2+(axisxng2)+(axisxng1)] # conv grrg drrd - g1_1 = self.act(self.linear1( - torch.cat(g1_mlp, dim=-1) - )) + g1_1 = self.act(self.linear1(torch.cat(g1_mlp, dim=-1))) g1_update.append(g1_1) if self.update_g1_has_attn: @@ -683,9 +707,9 @@ def forward( @torch.jit.export def list_update_res_avg( - self, - update_list: List[torch.Tensor], - )-> torch.Tensor: + self, + update_list: List[torch.Tensor], + ) -> torch.Tensor: nitem = len(update_list) uu = update_list[0] for ii in range(1, nitem): @@ -693,22 +717,16 @@ def list_update_res_avg( return uu / (float(nitem) ** 0.5) @torch.jit.export - def list_update_res_incr( - self, - update_list: List[torch.Tensor] - ) -> torch.Tensor: + def list_update_res_incr(self, update_list: List[torch.Tensor]) -> torch.Tensor: nitem = len(update_list) uu = update_list[0] - scale = 1. / (float(nitem - 1) ** 0.5) if nitem > 1 else 0. + scale = 1.0 / (float(nitem - 1) ** 0.5) if nitem > 1 else 0.0 for ii in range(1, nitem): uu = uu + scale * update_list[ii] return uu @torch.jit.export - def list_update( - self, - update_list: List[torch.Tensor] - ) -> torch.Tensor: + def list_update(self, update_list: List[torch.Tensor]) -> torch.Tensor: if self.update_style == "res_avg": return self.list_update_res_avg(update_list) elif self.update_style == "res_incr": @@ -717,9 +735,15 @@ def list_update( raise RuntimeError(f"unknown update style {self.update_style}") def _bn_layer( - self, - nf: int = 1, + self, + nf: int = 1, ) -> Callable: return torch.nn.BatchNorm1d( - nf, eps=1e-5, momentum=self.bn_momentum, affine=False, - track_running_stats=True, device=env.DEVICE, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + nf, + eps=1e-5, + momentum=self.bn_momentum, + affine=False, + track_running_stats=True, + device=env.DEVICE, + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + ) diff --git a/deepmd_pt/model/descriptor/repformers.py b/deepmd_pt/model/descriptor/repformers.py index 7f20c65c..81e668f1 100644 --- a/deepmd_pt/model/descriptor/repformers.py +++ b/deepmd_pt/model/descriptor/repformers.py @@ -1,326 +1,348 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, +) + import numpy as np import torch -import logging -try: - from typing import Final -except: - from torch.jit import Final -from typing import ( - List, Optional, Tuple, + +from deepmd_pt.model.descriptor.descriptor import ( + DescriptorBlock, + compute_std, +) +from deepmd_pt.model.descriptor.env_mat import ( + prod_env_mat_se_a, ) -from deepmd_pt.utils import env -from deepmd_pt.utils.utils import get_activation_fn, ActivationFn -from deepmd_pt.model.descriptor import prod_env_mat_se_a, DescriptorBlock, compute_std -from deepmd_pt.model.network import ( - TypeEmbedNet, SimpleLinear +from deepmd_pt.model.network.network import ( + SimpleLinear, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.nlist import ( + build_neighbor_list, +) +from deepmd_pt.utils.utils import ( + get_activation_fn, +) + +from .repformer_layer import ( + RepformerLayer, +) +from .se_atten import ( + analyze_descrpt, ) -from .se_atten import analyze_descrpt -from .repformer_layer import RepformerLayer -from deepmd_pt.utils.nlist import extend_coord_with_ghosts, build_neighbor_list mydtype = env.GLOBAL_PT_FLOAT_PRECISION mydev = env.DEVICE + def torch_linear(*args, **kwargs): - return torch.nn.Linear(*args, **kwargs, dtype=mydtype, device=mydev) -simple_linear = SimpleLinear + return torch.nn.Linear(*args, **kwargs, dtype=mydtype, device=mydev) + + +simple_linear = SimpleLinear mylinear = simple_linear @DescriptorBlock.register("se_repformer") @DescriptorBlock.register("se_uni") class DescrptBlockRepformers(DescriptorBlock): - def __init__( - self, - rcut, - rcut_smth, - sel: int, - ntypes: int, - nlayers: int = 3, - g1_dim = 128, - g2_dim = 16, - axis_dim: int = 4, - direct_dist: bool = False, - do_bn_mode: str = 'no', - bn_momentum: float = 0.1, - update_g1_has_conv: bool = True, - update_g1_has_drrd: bool = True, - update_g1_has_grrg: bool = True, - update_g1_has_attn: bool = True, - update_g2_has_g1g1: bool = True, - update_g2_has_attn: bool = True, - update_h2: bool = False, - attn1_hidden: int = 64, - attn1_nhead: int = 4, - attn2_hidden: int = 16, - attn2_nhead: int = 4, - attn2_has_gate: bool = False, - activation: str = "tanh", - update_style: str = "res_avg", - set_davg_zero: bool = True, # TODO - smooth: bool = True, - add_type_ebd_to_seq: bool = False, - type: Optional[str] = None, - ): - """ - smooth: - If strictly smooth, cannot be used with update_g1_has_attn - add_type_ebd_to_seq: - At the presence of seq_input (optional input to forward), - whether or not add an type embedding to seq_input. - If no seq_input is given, it has no effect. - """ - super(DescrptBlockRepformers, self).__init__() - del type - self.epsilon = 1e-4 # protection of 1./nnei - self.rcut = rcut - self.rcut_smth = rcut_smth - self.ntypes = ntypes - self.nlayers = nlayers - sel = [sel] if isinstance(sel, int) else sel - self.nnei = sum(sel) # 总的邻居数量 - assert len(sel) == 1 - self.sel = sel # 每种元素在邻居中的位移 - self.sec = self.sel - self.split_sel = self.sel - self.axis_dim = axis_dim - self.set_davg_zero = set_davg_zero - self.g1_dim = g1_dim - self.g2_dim = g2_dim - self.act = get_activation_fn(activation) - self.direct_dist = direct_dist - self.add_type_ebd_to_seq = add_type_ebd_to_seq + def __init__( + self, + rcut, + rcut_smth, + sel: int, + ntypes: int, + nlayers: int = 3, + g1_dim=128, + g2_dim=16, + axis_dim: int = 4, + direct_dist: bool = False, + do_bn_mode: str = "no", + bn_momentum: float = 0.1, + update_g1_has_conv: bool = True, + update_g1_has_drrd: bool = True, + update_g1_has_grrg: bool = True, + update_g1_has_attn: bool = True, + update_g2_has_g1g1: bool = True, + update_g2_has_attn: bool = True, + update_h2: bool = False, + attn1_hidden: int = 64, + attn1_nhead: int = 4, + attn2_hidden: int = 16, + attn2_nhead: int = 4, + attn2_has_gate: bool = False, + activation: str = "tanh", + update_style: str = "res_avg", + set_davg_zero: bool = True, # TODO + smooth: bool = True, + add_type_ebd_to_seq: bool = False, + type: Optional[str] = None, + ): + """ + smooth: + If strictly smooth, cannot be used with update_g1_has_attn + add_type_ebd_to_seq: + At the presence of seq_input (optional input to forward), + whether or not add an type embedding to seq_input. + If no seq_input is given, it has no effect. + """ + super().__init__() + del type + self.epsilon = 1e-4 # protection of 1./nnei + self.rcut = rcut + self.rcut_smth = rcut_smth + self.ntypes = ntypes + self.nlayers = nlayers + sel = [sel] if isinstance(sel, int) else sel + self.nnei = sum(sel) # 总的邻居数量 + assert len(sel) == 1 + self.sel = sel # 每种元素在邻居中的位移 + self.sec = self.sel + self.split_sel = self.sel + self.axis_dim = axis_dim + self.set_davg_zero = set_davg_zero + self.g1_dim = g1_dim + self.g2_dim = g2_dim + self.act = get_activation_fn(activation) + self.direct_dist = direct_dist + self.add_type_ebd_to_seq = add_type_ebd_to_seq - self.g2_embd = mylinear(1, self.g2_dim) - layers = [] - for ii in range(nlayers): - layers.append( - RepformerLayer( - rcut, rcut_smth, sel, ntypes, self.g1_dim, self.g2_dim, - axis_dim=self.axis_dim, - update_chnnl_2=(ii != nlayers - 1), - do_bn_mode=do_bn_mode, - bn_momentum=bn_momentum, - update_g1_has_conv=update_g1_has_conv, - update_g1_has_drrd=update_g1_has_drrd, - update_g1_has_grrg=update_g1_has_grrg, - update_g1_has_attn=update_g1_has_attn, - update_g2_has_g1g1=update_g2_has_g1g1, - update_g2_has_attn=update_g2_has_attn, - update_h2=update_h2, - attn1_hidden=attn1_hidden, - attn1_nhead=attn1_nhead, - attn2_has_gate=attn2_has_gate, - attn2_hidden=attn2_hidden, - attn2_nhead=attn2_nhead, - activation=activation, - update_style=update_style, - smooth=smooth, - )) - self.layers = torch.nn.ModuleList(layers) + self.g2_embd = mylinear(1, self.g2_dim) + layers = [] + for ii in range(nlayers): + layers.append( + RepformerLayer( + rcut, + rcut_smth, + sel, + ntypes, + self.g1_dim, + self.g2_dim, + axis_dim=self.axis_dim, + update_chnnl_2=(ii != nlayers - 1), + do_bn_mode=do_bn_mode, + bn_momentum=bn_momentum, + update_g1_has_conv=update_g1_has_conv, + update_g1_has_drrd=update_g1_has_drrd, + update_g1_has_grrg=update_g1_has_grrg, + update_g1_has_attn=update_g1_has_attn, + update_g2_has_g1g1=update_g2_has_g1g1, + update_g2_has_attn=update_g2_has_attn, + update_h2=update_h2, + attn1_hidden=attn1_hidden, + attn1_nhead=attn1_nhead, + attn2_has_gate=attn2_has_gate, + attn2_hidden=attn2_hidden, + attn2_nhead=attn2_nhead, + activation=activation, + update_style=update_style, + smooth=smooth, + ) + ) + self.layers = torch.nn.ModuleList(layers) - sshape = (self.ntypes, self.nnei, 4) - mean = torch.zeros(sshape, dtype=mydtype, device=mydev) - stddev = torch.ones(sshape, dtype=mydtype, device=mydev) - self.register_buffer('mean', mean) - self.register_buffer('stddev', stddev) + sshape = (self.ntypes, self.nnei, 4) + mean = torch.zeros(sshape, dtype=mydtype, device=mydev) + stddev = torch.ones(sshape, dtype=mydtype, device=mydev) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ - return self.rcut + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ - return sum(self.sel) + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ - return self.sel + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel - def get_ntype(self)->int: - """ - Returns the number of element types - """ - return self.ntypes + def get_ntype(self) -> int: + """Returns the number of element types.""" + return self.ntypes - def get_dim_out(self)->int: - """ - Returns the output dimension - """ - return self.dim_out + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out - def get_dim_in(self)->int: - """ - Returns the input dimension - """ - return self.dim_in + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in - @property - def dim_out(self): - """ - Returns the output dimension of this descriptor - """ - return self.g1_dim + @property + def dim_out(self): + """Returns the output dimension of this descriptor.""" + return self.g1_dim - @property - def dim_in(self): - """ - Returns the atomic input dimension of this descriptor - """ - return self.g1_dim + @property + def dim_in(self): + """Returns the atomic input dimension of this descriptor.""" + return self.g1_dim - @property - def dim_emb(self): - """ - Returns the embedding dimension g2 - """ - return self.g2_dim + @property + def dim_emb(self): + """Returns the embedding dimension g2.""" + return self.g2_dim - def forward( + def forward( self, nlist: torch.Tensor, extended_coord: torch.Tensor, extended_atype: torch.Tensor, extended_atype_embd: Optional[torch.Tensor] = None, mapping: Optional[torch.Tensor] = None, - ): - - """ - extended_coord: [nb, nloc x 3] - atype: [nb, nloc] - """ - assert mapping is not None - assert extended_atype_embd is not None - nframes, nloc, nnei = nlist.shape - nall = extended_coord.view(nframes, -1).shape[1] // 3 - atype = extended_atype[:,:nloc] - # nb x nloc x nnei x 4, nb x nloc x nnei x 3, nb x nloc x nnei x 1 - dmatrix, diff, sw = prod_env_mat_se_a( - extended_coord, nlist, atype, - self.mean, self.stddev, - self.rcut, self.rcut_smth) - nlist_mask = (nlist != -1) - sw = torch.squeeze(sw, -1) - # beyond the cutoff sw should be 0.0 - sw = sw.masked_fill(~nlist_mask, float(0.0)) + ): + assert mapping is not None + assert extended_atype_embd is not None + nframes, nloc, nnei = nlist.shape + nall = extended_coord.view(nframes, -1).shape[1] // 3 + atype = extended_atype[:, :nloc] + # nb x nloc x nnei x 4, nb x nloc x nnei x 3, nb x nloc x nnei x 1 + dmatrix, diff, sw = prod_env_mat_se_a( + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + ) + nlist_mask = nlist != -1 + sw = torch.squeeze(sw, -1) + # beyond the cutoff sw should be 0.0 + sw = sw.masked_fill(~nlist_mask, 0.0) - # [nframes, nloc, tebd_dim] - atype_embd = extended_atype_embd[:,:nloc,:] - assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim] + # [nframes, nloc, tebd_dim] + atype_embd = extended_atype_embd[:, :nloc, :] + assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim] - g1 = self.act(atype_embd) - # nb x nloc x nnei x 1, nb x nloc x nnei x 3 - if not self.direct_dist: - g2, h2 = torch.split(dmatrix, [1, 3], dim=-1) - else: - g2, h2 = torch.linalg.norm(diff, dim=-1, keepdim=True), diff - g2 = g2 / self.rcut - h2 = h2 / self.rcut - # nb x nloc x nnei x ng2 - g2 = self.act(self.g2_embd(g2)) + g1 = self.act(atype_embd) + # nb x nloc x nnei x 1, nb x nloc x nnei x 3 + if not self.direct_dist: + g2, h2 = torch.split(dmatrix, [1, 3], dim=-1) + else: + g2, h2 = torch.linalg.norm(diff, dim=-1, keepdim=True), diff + g2 = g2 / self.rcut + h2 = h2 / self.rcut + # nb x nloc x nnei x ng2 + g2 = self.act(self.g2_embd(g2)) - # set all padding positions to index of 0 - # if the a neighbor is real or not is indicated by nlist_mask - nlist[nlist == -1] = 0 - # nb x nall x ng1 - mapping = mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, self.g1_dim) - for idx,ll in enumerate(self.layers): - # g1: nb x nloc x ng1 - # g1_ext: nb x nall x ng1 - g1_ext = torch.gather(g1, 1, mapping) - g1, g2, h2 = ll.forward(g1_ext, g2, h2, nlist, nlist_mask, sw,) + # set all padding positions to index of 0 + # if the a neighbor is real or not is indicated by nlist_mask + nlist[nlist == -1] = 0 + # nb x nall x ng1 + mapping = mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, self.g1_dim) + for idx, ll in enumerate(self.layers): + # g1: nb x nloc x ng1 + # g1_ext: nb x nall x ng1 + g1_ext = torch.gather(g1, 1, mapping) + g1, g2, h2 = ll.forward( + g1_ext, + g2, + h2, + nlist, + nlist_mask, + sw, + ) - # uses the last layer. - # nb x nloc x 3 x ng2 - h2g2 = ll._cal_h2g2(g2, h2, nlist_mask, sw) - # (nb x nloc) x ng2 x 3 - rot_mat = torch.permute(h2g2, (0, 1, 3, 2)) + # uses the last layer. + # nb x nloc x 3 x ng2 + h2g2 = ll._cal_h2g2(g2, h2, nlist_mask, sw) + # (nb x nloc) x ng2 x 3 + rot_mat = torch.permute(h2g2, (0, 1, 3, 2)) - return g1, g2, h2, rot_mat.view(-1, self.dim_emb, 3), sw + return g1, g2, h2, rot_mat.view(-1, self.dim_emb, 3), sw - def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ - ndescrpt = self.nnei * 4 # 描述符的元素数量 - sumr = [] - suma = [] - sumn = [] - sumr2 = [] - suma2 = [] - mixed_type = 'real_natoms_vec' in merged[0] - for system in merged: # 逐个 system 的分析 - index = system['mapping'].unsqueeze(-1).expand(-1, -1, 3) - extended_coord = torch.gather(system['coord'], dim=1, index=index) - extended_coord = extended_coord - system['shift'] - index = system['mapping'] - extended_atype = torch.gather(system['atype'], dim=1, index=index) - nloc = system['atype'].shape[-1] - ####################################################### - # dirty hack here! the interface of dataload should be - # redesigned to support descriptors like dpa2 - ####################################################### - nlist = build_neighbor_list( - extended_coord, - extended_atype, - nloc, - self.rcut, - self.get_sel(), - distinguish_types=False, - ) - env_mat, _, _ = prod_env_mat_se_a( - extended_coord, nlist, system['atype'], - self.mean, self.stddev, - self.rcut, self.rcut_smth, - ) - if not mixed_type: - sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( - env_mat.detach().cpu().numpy(), ndescrpt, - system['natoms']) - else: - sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( - env_mat.detach().cpu().numpy(), ndescrpt, - system['real_natoms_vec'], mixed_type=mixed_type, - real_atype=system['atype'].detach().cpu().numpy()) - sumr.append(sysr) - suma.append(sysa) - sumn.append(sysn) - sumr2.append(sysr2) - suma2.append(sysa2) - sumr = np.sum(sumr, axis=0) - suma = np.sum(suma, axis=0) - sumn = np.sum(sumn, axis=0) - sumr2 = np.sum(sumr2, axis=0) - suma2 = np.sum(suma2, axis=0) - return sumr, suma, sumn, sumr2, suma2 + def compute_input_stats(self, merged): + """Update mean and stddev for descriptor elements.""" + ndescrpt = self.nnei * 4 # 描述符的元素数量 + sumr = [] + suma = [] + sumn = [] + sumr2 = [] + suma2 = [] + mixed_type = "real_natoms_vec" in merged[0] + for system in merged: # 逐个 system 的分析 + index = system["mapping"].unsqueeze(-1).expand(-1, -1, 3) + extended_coord = torch.gather(system["coord"], dim=1, index=index) + extended_coord = extended_coord - system["shift"] + index = system["mapping"] + extended_atype = torch.gather(system["atype"], dim=1, index=index) + nloc = system["atype"].shape[-1] + ####################################################### + # dirty hack here! the interface of dataload should be + # redesigned to support descriptors like dpa2 + ####################################################### + nlist = build_neighbor_list( + extended_coord, + extended_atype, + nloc, + self.rcut, + self.get_sel(), + distinguish_types=False, + ) + env_mat, _, _ = prod_env_mat_se_a( + extended_coord, + nlist, + system["atype"], + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + ) + if not mixed_type: + sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( + env_mat.detach().cpu().numpy(), ndescrpt, system["natoms"] + ) + else: + sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( + env_mat.detach().cpu().numpy(), + ndescrpt, + system["real_natoms_vec"], + mixed_type=mixed_type, + real_atype=system["atype"].detach().cpu().numpy(), + ) + sumr.append(sysr) + suma.append(sysa) + sumn.append(sysn) + sumr2.append(sysr2) + suma2.append(sysa2) + sumr = np.sum(sumr, axis=0) + suma = np.sum(suma, axis=0) + sumn = np.sum(sumn, axis=0) + sumr2 = np.sum(sumr2, axis=0) + suma2 = np.sum(suma2, axis=0) + return sumr, suma, sumn, sumr2, suma2 - def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - all_davg = [] - all_dstd = [] - for type_i in range(self.ntypes): - davgunit = [[sumr[type_i] / (sumn[type_i] + 1e-15), 0, 0, 0]] - dstdunit = [[ - compute_std(sumr2[type_i], sumr[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut) - ]] - davg = np.tile(davgunit, [self.nnei, 1]) - dstd = np.tile(dstdunit, [self.nnei, 1]) - all_davg.append(davg) - all_dstd.append(dstd) - self.sumr = sumr - self.suma = suma - self.sumn = sumn - self.sumr2 = sumr2 - self.suma2 = suma2 - if not self.set_davg_zero: - mean = np.stack(all_davg) - self.mean.copy_(torch.tensor(mean, device=env.DEVICE)) - stddev = np.stack(all_dstd) - self.stddev.copy_(torch.tensor(stddev, device=env.DEVICE)) + def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): + all_davg = [] + all_dstd = [] + for type_i in range(self.ntypes): + davgunit = [[sumr[type_i] / (sumn[type_i] + 1e-15), 0, 0, 0]] + dstdunit = [ + [ + compute_std(sumr2[type_i], sumr[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + ] + ] + davg = np.tile(davgunit, [self.nnei, 1]) + dstd = np.tile(dstdunit, [self.nnei, 1]) + all_davg.append(davg) + all_dstd.append(dstd) + self.sumr = sumr + self.suma = suma + self.sumn = sumn + self.sumr2 = sumr2 + self.suma2 = suma2 + if not self.set_davg_zero: + mean = np.stack(all_davg) + self.mean.copy_(torch.tensor(mean, device=env.DEVICE)) + stddev = np.stack(all_dstd) + self.stddev.copy_(torch.tensor(stddev, device=env.DEVICE)) diff --git a/deepmd_pt/model/descriptor/se_a.py b/deepmd_pt/model/descriptor/se_a.py index 549022ff..3ddc9d9c 100644 --- a/deepmd_pt/model/descriptor/se_a.py +++ b/deepmd_pt/model/descriptor/se_a.py @@ -1,24 +1,41 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + ClassVar, + List, + Optional, +) + import numpy as np import torch -from typing import Optional, List, Dict -from deepmd_pt.utils import env -from deepmd_pt.model.descriptor import prod_env_mat_se_a, Descriptor, DescriptorBlock, compute_std +from deepmd_pt.model.descriptor import ( + Descriptor, + DescriptorBlock, + compute_std, + prod_env_mat_se_a, +) +from deepmd_pt.utils import ( + env, +) from deepmd_pt.utils.env import ( - PRECISION_DICT, - DEFAULT_PRECISION, + PRECISION_DICT, ) try: - from typing import Final -except: + from typing import ( + Final, + ) +except ImportError: from torch.jit import Final -from deepmd_pt.model.network import TypeFilter -from deepmd_pt.model.network.mlp import EmbeddingNet, NetworkCollection +from deepmd.model_format import EnvMat as DPEnvMat -from deepmd_utils.model_format import ( - EnvMat as DPEnvMat, +from deepmd_pt.model.network.mlp import ( + EmbeddingNet, + NetworkCollection, +) +from deepmd_pt.model.network.network import ( + TypeFilter, ) @@ -38,57 +55,52 @@ def __init__( old_impl: bool = False, **kwargs, ): - super(DescrptSeA, self).__init__() - self.sea = DescrptBlockSeA( - rcut, rcut_smth, sel, neuron, axis_neuron, set_davg_zero, - activation_function, precision, resnet_dt, old_impl, - **kwargs, - ) - - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ - return self.sea.get_rcut() - - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ - return self.sea.get_nsel() - - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ - return self.sea.get_sel() - - def get_ntype(self)->int: - """ - Returns the number of element types - """ - return self.sea.get_ntype() - - def get_dim_out(self)->int: - """ - Returns the output dimension - """ - return self.sea.get_dim_out() + super().__init__() + self.sea = DescrptBlockSeA( + rcut, + rcut_smth, + sel, + neuron, + axis_neuron, + set_davg_zero, + activation_function, + precision, + resnet_dt, + old_impl, + **kwargs, + ) + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.sea.get_rcut() + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return self.sea.get_nsel() + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sea.get_sel() + + def get_ntype(self) -> int: + """Returns the number of element types.""" + return self.sea.get_ntype() + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.sea.get_dim_out() @property def dim_out(self): - """ - Returns the output dimension of this descriptor - """ + """Returns the output dimension of this descriptor.""" return self.sea.dim_out def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ + """Update mean and stddev for descriptor elements.""" return self.sea.compute_input_stats(merged) def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): - self.sea.init_desc_stat( sumr, suma, sumn, sumr2, suma2) + self.sea.init_desc_stat(sumr, suma, sumn, sumr2, suma2) @classmethod def get_stat_name(cls, config): @@ -109,73 +121,76 @@ def forward( nlist: torch.Tensor, mapping: Optional[torch.Tensor] = None, ): - return self.sea.forward( - nlist, extended_coord, extended_atype, None, mapping) + return self.sea.forward(nlist, extended_coord, extended_atype, None, mapping) def set_stat_mean_and_stddev( self, mean: torch.Tensor, stddev: torch.Tensor, - )->None: - self.sea.mean = mean - self.sea.stddev = stddev - - - def serialize(self)->dict: - obj = self.sea - return { - "rcut": obj.rcut, - "rcut_smth": obj.rcut_smth, - "sel": obj.sel, - "neuron": obj.neuron, - "axis_neuron": obj.axis_neuron, - "resnet_dt": obj.resnet_dt, - "set_davg_zero": obj.set_davg_zero, - "activation_function": obj.activation_function, - "precision": obj.precision, - "embeddings": obj.filter_layers.serialize(), - "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), - "@variables" : { - "davg" : obj["davg"].detach().cpu().numpy(), - "dstd" : obj["dstd"].detach().cpu().numpy(), - }, - ## to be updated when the options are supported. - "trainable": True, - "type_one_side": True, - "exclude_types": [], - "spin": None, - } - + ) -> None: + self.sea.mean = mean + self.sea.stddev = stddev + + def serialize(self) -> dict: + obj = self.sea + return { + "rcut": obj.rcut, + "rcut_smth": obj.rcut_smth, + "sel": obj.sel, + "neuron": obj.neuron, + "axis_neuron": obj.axis_neuron, + "resnet_dt": obj.resnet_dt, + "set_davg_zero": obj.set_davg_zero, + "activation_function": obj.activation_function, + "precision": obj.precision, + "embeddings": obj.filter_layers.serialize(), + "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), + "@variables": { + "davg": obj["davg"].detach().cpu().numpy(), + "dstd": obj["dstd"].detach().cpu().numpy(), + }, + ## to be updated when the options are supported. + "trainable": True, + "type_one_side": True, + "exclude_types": [], + "spin": None, + } + @classmethod - def deserialize(cls, data: dict)->"DescrptSeA": - variables = data.pop("@variables") - embeddings = data.pop("embeddings") - env_mat = data.pop("env_mat") - obj = cls(**data) - t_cvt = lambda xx: torch.tensor(xx, dtype=obj.sea.prec, device=env.DEVICE) - obj.sea["davg"] = t_cvt(variables["davg"]) - obj.sea["dstd"] = t_cvt(variables["dstd"]) - obj.sea.filter_layers = NetworkCollection.deserialize(embeddings) - return obj + def deserialize(cls, data: dict) -> "DescrptSeA": + variables = data.pop("@variables") + embeddings = data.pop("embeddings") + env_mat = data.pop("env_mat") + obj = cls(**data) + + def t_cvt(xx): + return torch.tensor(xx, dtype=obj.sea.prec, device=env.DEVICE) + + obj.sea["davg"] = t_cvt(variables["davg"]) + obj.sea["dstd"] = t_cvt(variables["dstd"]) + obj.sea.filter_layers = NetworkCollection.deserialize(embeddings) + return obj @DescriptorBlock.register("se_e2_a") class DescrptBlockSeA(DescriptorBlock): ndescrpt: Final[int] - __constants__ = ['ndescrpt'] - - def __init__(self, - rcut, - rcut_smth, - sel, - neuron=[25, 50, 100], - axis_neuron=16, - set_davg_zero: bool = False, - activation_function: str = "tanh", - precision: str = "float64", - resnet_dt: bool = False, - old_impl: bool = False, - **kwargs): + __constants__: ClassVar[list] = ["ndescrpt"] + + def __init__( + self, + rcut, + rcut_smth, + sel, + neuron=[25, 50, 100], + axis_neuron=16, + set_davg_zero: bool = False, + activation_function: str = "tanh", + precision: str = "float64", + resnet_dt: bool = False, + old_impl: bool = False, + **kwargs, + ): """Construct an embedding net of type `se_a`. Args: @@ -185,7 +200,7 @@ def __init__(self, - filter_neuron: Number of neurons in each hidden layers of the embedding net. - axis_neuron: Number of columns of the sub-matrix of the embedding matrix. """ - super(DescrptBlockSeA, self).__init__() + super().__init__() self.rcut = rcut self.rcut_smth = rcut_smth self.neuron = neuron @@ -200,7 +215,9 @@ def __init__(self, self.ntypes = len(sel) # 元素数量 self.sel = sel # 每种元素在邻居中的位移 - self.sec = torch.tensor(np.append([0], np.cumsum(self.sel)), dtype=int, device=env.DEVICE) + self.sec = torch.tensor( + np.append([0], np.cumsum(self.sel)), dtype=int, device=env.DEVICE + ) self.split_sel = self.sel self.nnei = sum(sel) # 总的邻居数量 self.ndescrpt = self.nnei * 4 # 描述符的元素数量 @@ -208,83 +225,67 @@ def __init__(self, wanted_shape = (self.ntypes, self.nnei, 4) mean = torch.zeros(wanted_shape, dtype=self.prec, device=env.DEVICE) stddev = torch.ones(wanted_shape, dtype=self.prec, device=env.DEVICE) - self.register_buffer('mean', mean) - self.register_buffer('stddev', stddev) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) self.filter_layers_old = None self.filter_layers = None - if self.old_impl: - filter_layers = [] - # TODO: remove - start_index = 0 - for type_i in range(self.ntypes): - one = TypeFilter(start_index, sel[type_i], self.filter_neuron) - filter_layers.append(one) - start_index += sel[type_i] - self.filter_layers_old = torch.nn.ModuleList(filter_layers) + filter_layers = [] + # TODO: remove + start_index = 0 + for type_i in range(self.ntypes): + one = TypeFilter(start_index, sel[type_i], self.filter_neuron) + filter_layers.append(one) + start_index += sel[type_i] + self.filter_layers_old = torch.nn.ModuleList(filter_layers) else: - filter_layers = NetworkCollection(ndim=1, ntypes=len(sel), network_type="embedding_network") - # TODO: ndim=2 if type_one_side=False - for ii in range(self.ntypes): - filter_layers[(ii,)] = EmbeddingNet( - 1, - self.filter_neuron, - activation_function=self.activation_function, - precision=self.precision, - resnet_dt=self.resnet_dt, - ) - self.filter_layers = filter_layers - - - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ - return self.rcut - - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ - return sum(self.sel) - - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ - return self.sel - - def get_ntype(self)->int: - """ - Returns the number of element types - """ - return self.ntypes - - def get_dim_out(self)->int: - """ - Returns the output dimension - """ - return self.dim_out - - def get_dim_in(self)->int: - """ - Returns the input dimension - """ - return self.dim_in + filter_layers = NetworkCollection( + ndim=1, ntypes=len(sel), network_type="embedding_network" + ) + # TODO: ndim=2 if type_one_side=False + for ii in range(self.ntypes): + filter_layers[(ii,)] = EmbeddingNet( + 1, + self.filter_neuron, + activation_function=self.activation_function, + precision=self.precision, + resnet_dt=self.resnet_dt, + ) + self.filter_layers = filter_layers + + def get_rcut(self) -> float: + """Returns the cut-off radius.""" + return self.rcut + + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" + return sum(self.sel) + + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" + return self.sel + + def get_ntype(self) -> int: + """Returns the number of element types.""" + return self.ntypes + + def get_dim_out(self) -> int: + """Returns the output dimension.""" + return self.dim_out + + def get_dim_in(self) -> int: + """Returns the input dimension.""" + return self.dim_in @property def dim_out(self): - """ - Returns the output dimension of this descriptor - """ + """Returns the output dimension of this descriptor.""" return self.filter_neuron[-1] * self.axis_neuron @property def dim_in(self): - """ - Returns the atomic input dimension of this descriptor - """ + """Returns the atomic input dimension of this descriptor.""" return 0 def __setitem__(self, key, value): @@ -303,26 +304,29 @@ def __getitem__(self, key): else: raise KeyError(key) - def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ + """Update mean and stddev for descriptor elements.""" sumr = [] suma = [] sumn = [] sumr2 = [] suma2 = [] for system in merged: # 逐个 system 的分析 - index = system['mapping'].unsqueeze(-1).expand(-1, -1, 3) - extended_coord = torch.gather(system['coord'], dim=1, index=index) - extended_coord = extended_coord - system['shift'] + index = system["mapping"].unsqueeze(-1).expand(-1, -1, 3) + extended_coord = torch.gather(system["coord"], dim=1, index=index) + extended_coord = extended_coord - system["shift"] env_mat, _, _ = prod_env_mat_se_a( - extended_coord, system['nlist'], system['atype'], - self.mean, self.stddev, - self.rcut, self.rcut_smth, + extended_coord, + system["nlist"], + system["atype"], + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, + ) + sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( + env_mat.detach().cpu().numpy(), self.ndescrpt, system["natoms"] ) - sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt(env_mat.detach().cpu().numpy(), self.ndescrpt, - system['natoms']) sumr.append(sysr) suma.append(sysa) sumn.append(sysn) @@ -340,12 +344,14 @@ def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): all_dstd = [] for type_i in range(self.ntypes): davgunit = [[sumr[type_i] / (sumn[type_i] + 1e-15), 0, 0, 0]] - dstdunit = [[ - compute_std(sumr2[type_i], sumr[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut) - ]] + dstdunit = [ + [ + compute_std(sumr2[type_i], sumr[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + ] + ] davg = np.tile(davgunit, [self.nnei, 1]) dstd = np.tile(dstdunit, [self.nnei, 1]) all_davg.append(davg) @@ -361,9 +367,8 @@ def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): stddev = np.stack(all_dstd) self.stddev.copy_(torch.tensor(stddev, device=env.DEVICE)) - def forward( - self, + self, nlist: torch.Tensor, extended_coord: torch.Tensor, extended_atype: torch.Tensor, @@ -378,51 +383,69 @@ def forward( - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. - box: Tell simulation box with shape [nframes, 9]. - Returns: + Returns + ------- - `torch.Tensor`: descriptor matrix with shape [nframes, natoms[0]*self.filter_neuron[-1]*self.axis_neuron]. """ del extended_atype_embd, mapping nloc = nlist.shape[1] atype = extended_atype[:, :nloc] dmatrix, diff, _ = prod_env_mat_se_a( - extended_coord, nlist, atype, - self.mean, self.stddev, - self.rcut, self.rcut_smth, + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, ) if self.old_impl: - assert self.filter_layers_old is not None - dmatrix = dmatrix.view(-1, self.ndescrpt) # shape is [nframes*nall, self.ndescrpt] - xyz_scatter = torch.empty(1, ) - ret = self.filter_layers_old[0](dmatrix) - xyz_scatter = ret - for ii, transform in enumerate(self.filter_layers_old[1:]): - # shape is [nframes*nall, 4, self.filter_neuron[-1]] - ret = transform.forward(dmatrix) - xyz_scatter = xyz_scatter + ret + assert self.filter_layers_old is not None + dmatrix = dmatrix.view( + -1, self.ndescrpt + ) # shape is [nframes*nall, self.ndescrpt] + xyz_scatter = torch.empty( + 1, + ) + ret = self.filter_layers_old[0](dmatrix) + xyz_scatter = ret + for ii, transform in enumerate(self.filter_layers_old[1:]): + # shape is [nframes*nall, 4, self.filter_neuron[-1]] + ret = transform.forward(dmatrix) + xyz_scatter = xyz_scatter + ret else: - assert self.filter_layers is not None - dmatrix = dmatrix.view(-1, self.nnei, 4) - nfnl = dmatrix.shape[0] - # pre-allocate a shape to pass jit - xyz_scatter = torch.zeros([nfnl, 4, self.filter_neuron[-1]], dtype=self.prec, device=env.DEVICE) - for ii,ll in enumerate(self.filter_layers.networks): - # nfnl x nt x 4 - rr = dmatrix[:, self.sec[ii]:self.sec[ii+1], :] - ss = rr[:,:,:1] - # nfnl x nt x ng - gg = ll.forward(ss) - # nfnl x 4 x ng - gr = torch.matmul(rr.permute(0,2,1), gg) - xyz_scatter += gr + assert self.filter_layers is not None + dmatrix = dmatrix.view(-1, self.nnei, 4) + nfnl = dmatrix.shape[0] + # pre-allocate a shape to pass jit + xyz_scatter = torch.zeros( + [nfnl, 4, self.filter_neuron[-1]], dtype=self.prec, device=env.DEVICE + ) + for ii, ll in enumerate(self.filter_layers.networks): + # nfnl x nt x 4 + rr = dmatrix[:, self.sec[ii] : self.sec[ii + 1], :] + ss = rr[:, :, :1] + # nfnl x nt x ng + gg = ll.forward(ss) + # nfnl x 4 x ng + gr = torch.matmul(rr.permute(0, 2, 1), gg) + xyz_scatter += gr xyz_scatter /= self.nnei xyz_scatter_1 = xyz_scatter.permute(0, 2, 1) rot_mat = xyz_scatter_1[:, :, 1:4] - xyz_scatter_2 = xyz_scatter[:, :, 0:self.axis_neuron] - result = torch.matmul(xyz_scatter_1, - xyz_scatter_2) # shape is [nframes*nall, self.filter_neuron[-1], self.axis_neuron] - return result.view(-1, nloc, self.filter_neuron[-1] * self.axis_neuron), None, None, None, None + xyz_scatter_2 = xyz_scatter[:, :, 0 : self.axis_neuron] + result = torch.matmul( + xyz_scatter_1, xyz_scatter_2 + ) # shape is [nframes*nall, self.filter_neuron[-1], self.axis_neuron] + return ( + result.view(-1, nloc, self.filter_neuron[-1] * self.axis_neuron), + None, + None, + None, + None, + ) def analyze_descrpt(matrix, ndescrpt, natoms): @@ -438,14 +461,16 @@ def analyze_descrpt(matrix, ndescrpt, natoms): end_index = start_index + natoms[0, 2 + type_i] dd = matrix[:, start_index:end_index] # 本元素所有原子的 descriptor start_index = end_index - dd = np.reshape(dd, [-1, 4]) # Shape is [nframes*natoms[2+type_id]*self.nnei, 4] + dd = np.reshape( + dd, [-1, 4] + ) # Shape is [nframes*natoms[2+type_id]*self.nnei, 4] ddr = dd[:, :1] # 径向值 dda = dd[:, 1:] # XYZ 轴分量 sumr = np.sum(ddr) - suma = np.sum(dda) / 3. + suma = np.sum(dda) / 3.0 sumn = dd.shape[0] # Value is nframes*natoms[2+type_id]*self.nnei sumr2 = np.sum(np.multiply(ddr, ddr)) - suma2 = np.sum(np.multiply(dda, dda)) / 3. + suma2 = np.sum(np.multiply(dda, dda)) / 3.0 sysr.append(sumr) sysa.append(suma) sysn.append(sumn) diff --git a/deepmd_pt/model/descriptor/se_atten.py b/deepmd_pt/model/descriptor/se_atten.py index 26ea594a..1c2ba575 100644 --- a/deepmd_pt/model/descriptor/se_atten.py +++ b/deepmd_pt/model/descriptor/se_atten.py @@ -1,16 +1,26 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, +) + import numpy as np import torch -from typing import Optional, List, Dict -from deepmd_pt.utils import env -from deepmd_pt.model.descriptor import prod_env_mat_se_a, DescriptorBlock, compute_std - -try: - from typing import Final -except: - from torch.jit import Final -from typing import Any, Union, Tuple, List -from deepmd_pt.model.network import TypeFilter, NeighborWiseAttention +from deepmd_pt.model.descriptor.descriptor import ( + DescriptorBlock, + compute_std, +) +from deepmd_pt.model.descriptor.env_mat import ( + prod_env_mat_se_a, +) +from deepmd_pt.model.network.network import ( + NeighborWiseAttention, + TypeFilter, +) +from deepmd_pt.utils import ( + env, +) @DescriptorBlock.register("se_atten") @@ -24,7 +34,7 @@ def __init__( neuron: list = [25, 50, 100], axis_neuron: int = 16, tebd_dim: int = 8, - tebd_input_mode: str = 'concat', + tebd_input_mode: str = "concat", # set_davg_zero: bool = False, set_davg_zero: bool = True, # TODO attn: int = 128, @@ -51,7 +61,7 @@ def __init__( - filter_neuron: Number of neurons in each hidden layers of the embedding net. - axis_neuron: Number of columns of the sub-matrix of the embedding matrix. """ - super(DescrptBlockSeAtten, self).__init__() + super().__init__() del type self.rcut = rcut self.rcut_smth = rcut_smth @@ -83,107 +93,118 @@ def __init__( self.split_sel = self.sel self.nnei = sum(sel) # 总的邻居数量 self.ndescrpt = self.nnei * 4 # 描述符的元素数量 - self.dpa1_attention = NeighborWiseAttention(self.attn_layer, self.nnei, self.filter_neuron[-1], self.attn_dim, - dotr=self.attn_dotr, do_mask=self.attn_mask, post_ln=self.post_ln, - ffn=self.ffn, ffn_embed_dim=self.ffn_embed_dim, - activation=self.activation, scaling_factor=self.scaling_factor, - head_num=self.head_num, normalize=self.normalize, - temperature=self.temperature) + self.dpa1_attention = NeighborWiseAttention( + self.attn_layer, + self.nnei, + self.filter_neuron[-1], + self.attn_dim, + dotr=self.attn_dotr, + do_mask=self.attn_mask, + post_ln=self.post_ln, + ffn=self.ffn, + ffn_embed_dim=self.ffn_embed_dim, + activation=self.activation, + scaling_factor=self.scaling_factor, + head_num=self.head_num, + normalize=self.normalize, + temperature=self.temperature, + ) wanted_shape = (self.ntypes, self.nnei, 4) - mean = torch.zeros(wanted_shape, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) - stddev = torch.ones(wanted_shape, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) - self.register_buffer('mean', mean) - self.register_buffer('stddev', stddev) + mean = torch.zeros( + wanted_shape, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) + stddev = torch.ones( + wanted_shape, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) + self.register_buffer("mean", mean) + self.register_buffer("stddev", stddev) filter_layers = [] - one = TypeFilter(0, self.nnei, self.filter_neuron, return_G=True, tebd_dim=self.tebd_dim, use_tebd=True, - tebd_mode=self.tebd_input_mode) + one = TypeFilter( + 0, + self.nnei, + self.filter_neuron, + return_G=True, + tebd_dim=self.tebd_dim, + use_tebd=True, + tebd_mode=self.tebd_input_mode, + ) filter_layers.append(one) self.filter_layers = torch.nn.ModuleList(filter_layers) - def get_rcut(self)->float: - """ - Returns the cut-off radius - """ + def get_rcut(self) -> float: + """Returns the cut-off radius.""" return self.rcut - def get_nsel(self)->int: - """ - Returns the number of selected atoms in the cut-off radius - """ + def get_nsel(self) -> int: + """Returns the number of selected atoms in the cut-off radius.""" return sum(self.sel) - def get_sel(self)->List[int]: - """ - Returns the number of selected atoms for each type. - """ + def get_sel(self) -> List[int]: + """Returns the number of selected atoms for each type.""" return self.sel - def get_ntype(self)->int: - """ - Returns the number of element types - """ + def get_ntype(self) -> int: + """Returns the number of element types.""" return self.ntypes - def get_dim_in(self)->int: - """ - Returns the output dimension - """ + def get_dim_in(self) -> int: + """Returns the output dimension.""" return self.dim_in - def get_dim_out(self)->int: - """ - Returns the output dimension - """ + def get_dim_out(self) -> int: + """Returns the output dimension.""" return self.dim_out @property def dim_out(self): - """ - Returns the output dimension of this descriptor - """ + """Returns the output dimension of this descriptor.""" return self.filter_neuron[-1] * self.axis_neuron @property def dim_in(self): - """ - Returns the atomic input dimension of this descriptor - """ + """Returns the atomic input dimension of this descriptor.""" return self.tebd_dim @property def dim_emb(self): - """ - Returns the output dimension of embedding - """ + """Returns the output dimension of embedding.""" return self.filter_neuron[-1] def compute_input_stats(self, merged): - """Update mean and stddev for descriptor elements. - """ + """Update mean and stddev for descriptor elements.""" sumr = [] suma = [] sumn = [] sumr2 = [] suma2 = [] - mixed_type = 'real_natoms_vec' in merged[0] + mixed_type = "real_natoms_vec" in merged[0] for system in merged: # 逐个 system 的分析 - index = system['mapping'].unsqueeze(-1).expand(-1, -1, 3) - extended_coord = torch.gather(system['coord'], dim=1, index=index) - extended_coord = extended_coord - system['shift'] + index = system["mapping"].unsqueeze(-1).expand(-1, -1, 3) + extended_coord = torch.gather(system["coord"], dim=1, index=index) + extended_coord = extended_coord - system["shift"] env_mat, _, _ = prod_env_mat_se_a( - extended_coord, system['nlist'], system['atype'], - self.mean, self.stddev, - self.rcut, self.rcut_smth, + extended_coord, + system["nlist"], + system["atype"], + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, ) if not mixed_type: - sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt(env_mat.detach().cpu().numpy(), self.ndescrpt, - system['natoms']) + sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( + env_mat.detach().cpu().numpy(), self.ndescrpt, system["natoms"] + ) else: - sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt(env_mat.detach().cpu().numpy(), self.ndescrpt, - system['real_natoms_vec'], mixed_type=mixed_type, - real_atype=system['atype'].detach().cpu().numpy()) + sysr, sysr2, sysa, sysa2, sysn = analyze_descrpt( + env_mat.detach().cpu().numpy(), + self.ndescrpt, + system["real_natoms_vec"], + mixed_type=mixed_type, + real_atype=system["atype"].detach().cpu().numpy(), + ) sumr.append(sysr) suma.append(sysa) sumn.append(sysn) @@ -201,12 +222,14 @@ def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): all_dstd = [] for type_i in range(self.ntypes): davgunit = [[sumr[type_i] / (sumn[type_i] + 1e-15), 0, 0, 0]] - dstdunit = [[ - compute_std(sumr2[type_i], sumr[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), - compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut) - ]] + dstdunit = [ + [ + compute_std(sumr2[type_i], sumr[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + compute_std(suma2[type_i], suma[type_i], sumn[type_i], self.rcut), + ] + ] davg = np.tile(davgunit, [self.nnei, 1]) dstd = np.tile(dstdunit, [self.nnei, 1]) all_davg.append(davg) @@ -223,7 +246,7 @@ def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2): self.stddev.copy_(torch.tensor(stddev, device=env.DEVICE)) def forward( - self, + self, nlist: torch.Tensor, extended_coord: torch.Tensor, extended_atype: torch.Tensor, @@ -238,29 +261,36 @@ def forward( - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. - box: Tell simulation box with shape [nframes, 9]. - Returns: + Returns + ------- - result: descriptor with shape [nframes, nloc, self.filter_neuron[-1] * self.axis_neuron]. - ret: environment matrix with shape [nframes, nloc, self.neei, out_size] """ del mapping assert extended_atype_embd is not None nframes, nloc, nnei = nlist.shape - atype = extended_atype[:,:nloc] + atype = extended_atype[:, :nloc] nb = nframes nall = extended_coord.view(nb, -1, 3).shape[1] dmatrix, diff, sw = prod_env_mat_se_a( - extended_coord, nlist, atype, - self.mean, self.stddev, - self.rcut, self.rcut_smth, + extended_coord, + nlist, + atype, + self.mean, + self.stddev, + self.rcut, + self.rcut_smth, ) - dmatrix = dmatrix.view(-1, self.ndescrpt) # shape is [nframes*nall, self.ndescrpt] - nlist_mask = (nlist != -1) + dmatrix = dmatrix.view( + -1, self.ndescrpt + ) # shape is [nframes*nall, self.ndescrpt] + nlist_mask = nlist != -1 nlist[nlist == -1] = 0 sw = torch.squeeze(sw, -1) # beyond the cutoff sw should be 0.0 - sw = sw.masked_fill(~nlist_mask, float(0.0)) + sw = sw.masked_fill(~nlist_mask, 0.0) # nf x nloc x nt -> nf x nloc x nnei x nt - atype_tebd = extended_atype_embd[:,:nloc,:] + atype_tebd = extended_atype_embd[:, :nloc, :] atype_tebd_nnei = atype_tebd.unsqueeze(2).expand(-1, -1, self.nnei, -1) # nf x nall x nt nt = extended_atype_embd.shape[-1] @@ -272,23 +302,36 @@ def forward( # nb x nloc x nnei x nt atype_tebd_nlist = atype_tebd_nlist.view(nb, nloc, nnei, nt) ret = self.filter_layers[0]( - dmatrix, - atype_tebd=atype_tebd_nnei, - nlist_tebd=atype_tebd_nlist, + dmatrix, + atype_tebd=atype_tebd_nnei, + nlist_tebd=atype_tebd_nlist, ) # shape is [nframes*nall, self.neei, out_size] - input_r = torch.nn.functional.normalize(dmatrix.reshape(-1, self.nnei, 4)[:, :, 1:4], dim=-1) - ret = self.dpa1_attention(ret, nlist_mask, input_r=input_r, sw=sw) # shape is [nframes*nloc, self.neei, out_size] - inputs_reshape = dmatrix.view(-1, self.nnei, 4).permute(0, 2, 1) # shape is [nframes*natoms[0], 4, self.neei] - xyz_scatter = torch.matmul(inputs_reshape, ret) # shape is [nframes*natoms[0], 4, out_size] + input_r = torch.nn.functional.normalize( + dmatrix.reshape(-1, self.nnei, 4)[:, :, 1:4], dim=-1 + ) + ret = self.dpa1_attention( + ret, nlist_mask, input_r=input_r, sw=sw + ) # shape is [nframes*nloc, self.neei, out_size] + inputs_reshape = dmatrix.view(-1, self.nnei, 4).permute( + 0, 2, 1 + ) # shape is [nframes*natoms[0], 4, self.neei] + xyz_scatter = torch.matmul( + inputs_reshape, ret + ) # shape is [nframes*natoms[0], 4, out_size] xyz_scatter = xyz_scatter / self.nnei xyz_scatter_1 = xyz_scatter.permute(0, 2, 1) rot_mat = xyz_scatter_1[:, :, 1:4] - xyz_scatter_2 = xyz_scatter[:, :, 0:self.axis_neuron] - result = torch.matmul(xyz_scatter_1, - xyz_scatter_2) # shape is [nframes*nloc, self.filter_neuron[-1], self.axis_neuron] - return result.view(-1, nloc, self.filter_neuron[-1] * self.axis_neuron), \ - ret.view(-1, nloc, self.nnei, self.filter_neuron[-1]), diff, \ - rot_mat.view(-1, self.filter_neuron[-1], 3), sw + xyz_scatter_2 = xyz_scatter[:, :, 0 : self.axis_neuron] + result = torch.matmul( + xyz_scatter_1, xyz_scatter_2 + ) # shape is [nframes*nloc, self.filter_neuron[-1], self.axis_neuron] + return ( + result.view(-1, nloc, self.filter_neuron[-1] * self.axis_neuron), + ret.view(-1, nloc, self.nnei, self.filter_neuron[-1]), + diff, + rot_mat.view(-1, self.filter_neuron[-1], 3), + sw, + ) def analyze_descrpt(matrix, ndescrpt, natoms, mixed_type=False, real_atype=None): @@ -305,14 +348,16 @@ def analyze_descrpt(matrix, ndescrpt, natoms, mixed_type=False, real_atype=None) end_index = start_index + natoms[0, 2 + type_i] dd = matrix[:, start_index:end_index] # 本元素所有原子的 descriptor start_index = end_index - dd = np.reshape(dd, [-1, 4]) # Shape is [nframes*natoms[2+type_id]*self.nnei, 4] + dd = np.reshape( + dd, [-1, 4] + ) # Shape is [nframes*natoms[2+type_id]*self.nnei, 4] ddr = dd[:, :1] # 径向值 dda = dd[:, 1:] # XYZ 轴分量 sumr = np.sum(ddr) - suma = np.sum(dda) / 3. + suma = np.sum(dda) / 3.0 sumn = dd.shape[0] # Value is nframes*natoms[2+type_id]*self.nnei sumr2 = np.sum(np.multiply(ddr, ddr)) - suma2 = np.sum(np.multiply(dda, dda)) / 3. + suma2 = np.sum(np.multiply(dda, dda)) / 3.0 sysr.append(sumr) sysa.append(suma) sysn.append(sumn) diff --git a/deepmd_pt/model/model/__init__.py b/deepmd_pt/model/model/__init__.py index 62f44ad1..a3db3dbd 100644 --- a/deepmd_pt/model/model/__init__.py +++ b/deepmd_pt/model/model/__init__.py @@ -1,14 +1,27 @@ -from .model import BaseModel -from .ener import EnergyModel +# SPDX-License-Identifier: LGPL-3.0-or-later +from .ener import ( + EnergyModel, +) +from .model import ( + BaseModel, +) + def get_model(model_params, sampled=None): return EnergyModel( - descriptor=model_params['descriptor'], - fitting_net=model_params.get('fitting_net', None), - type_map=model_params['type_map'], - type_embedding=model_params.get('type_embedding', None), - resuming=model_params.get("resuming", False), - stat_file_dir=model_params.get("stat_file_dir", None), - stat_file_path=model_params.get("stat_file_path", None), - sampled=sampled, + descriptor=model_params["descriptor"], + fitting_net=model_params.get("fitting_net", None), + type_map=model_params["type_map"], + type_embedding=model_params.get("type_embedding", None), + resuming=model_params.get("resuming", False), + stat_file_dir=model_params.get("stat_file_dir", None), + stat_file_path=model_params.get("stat_file_path", None), + sampled=sampled, ) + + +__all__ = [ + "BaseModel", + "EnergyModel", + "get_model", +] diff --git a/deepmd_pt/model/model/atomic_model.py b/deepmd_pt/model/model/atomic_model.py index 3ec741d5..41020cbd 100644 --- a/deepmd_pt/model/model/atomic_model.py +++ b/deepmd_pt/model/model/atomic_model.py @@ -1,70 +1,77 @@ -from abc import ABC, abstractmethod -import torch -import logging -import os +# SPDX-License-Identifier: LGPL-3.0-or-later +from abc import ( + ABC, + abstractmethod, +) from typing import ( - Optional, - Dict, - List, + Dict, + List, + Optional, +) + +import torch +from deepmd.model_format import ( + FittingOutputDef, +) + +from deepmd_pt.model.task import ( + Fitting, ) -from deepmd_utils.model_format import FittingOutputDef -from deepmd_pt.model.task import Fitting class AtomicModel(ABC): + @abstractmethod + def get_fitting_net(self) -> Fitting: + raise NotImplementedError + + @abstractmethod + def get_fitting_output_def(self) -> FittingOutputDef: + raise NotImplementedError - @abstractmethod - def get_fitting_net(self)->Fitting: - raise NotImplementedError + @abstractmethod + def get_rcut(self) -> float: + raise NotImplementedError - @abstractmethod - def get_fitting_output_def(self)->FittingOutputDef: - raise NotImplementedError + @abstractmethod + def get_sel(self) -> List[int]: + raise NotImplementedError - @abstractmethod - def get_rcut(self)->float: - raise NotImplementedError + @abstractmethod + def distinguish_types(self) -> bool: + raise NotImplementedError - @abstractmethod - def get_sel(self)->List[int]: - raise NotImplementedError + @abstractmethod + def forward_atomic( + self, + extended_coord, + extended_atype, + nlist, + mapping: Optional[torch.Tensor] = None, + do_atomic_virial: bool = False, + ) -> Dict[str, torch.Tensor]: + raise NotImplementedError - @abstractmethod - def distinguish_types(self)->bool: - raise NotImplementedError + def do_grad( + self, + var_name: Optional[str] = None, + ) -> bool: + """Tell if the output variable `var_name` is differentiable. + if var_name is None, returns if any of the variable is differentiable. - @abstractmethod - def forward_atomic( - self, - extended_coord, - extended_atype, - nlist, - mapping: Optional[torch.Tensor] = None, - do_atomic_virial: bool = False, - ) -> Dict[str, torch.Tensor]: - raise NotImplementedError - - def do_grad( - self, - var_name: Optional[str] = None, - )->bool: - """Tell if the output variable `var_name` is differentiable. - if var_name is None, returns if any of the variable is differentiable. - - """ - odef = self.get_fitting_output_def() - if var_name is None: - require: List[bool] = [] - for vv in odef.keys(): - require.append(self.do_grad_(vv)) - return any(require) - else: - return self.do_grad_(var_name) + """ + odef = self.get_fitting_output_def() + if var_name is None: + require: List[bool] = [] + for vv in odef.keys(): + require.append(self.do_grad_(vv)) + return any(require) + else: + return self.do_grad_(var_name) - def do_grad_( - self, - var_name: str, - )->bool: - """Tell if the output variable `var_name` is differentiable.""" - assert var_name is not None - return self.get_fitting_output_def()[var_name].differentiable + def do_grad_( + self, + var_name: str, + ) -> bool: + """Tell if the output variable `var_name` is differentiable.""" + assert var_name is not None + return self.get_fitting_output_def()[var_name].differentiable diff --git a/deepmd_pt/model/model/dp_atomic_model.py b/deepmd_pt/model/model/dp_atomic_model.py index 9f388bb3..ebe9bbaa 100644 --- a/deepmd_pt/model/model/dp_atomic_model.py +++ b/deepmd_pt/model/model/dp_atomic_model.py @@ -1,195 +1,214 @@ -import numpy as np -import torch -import logging -import os +# SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - Optional, - Dict, - List, + Dict, + List, + Optional, +) + +import torch +from deepmd.model_format import ( + FittingOutputDef, +) + +from deepmd_pt.model.descriptor.descriptor import ( + Descriptor, +) +from deepmd_pt.model.task import ( + DenoiseNet, + Fitting, ) -from deepmd_utils.model_format import FittingOutputDef -from deepmd_pt.model.descriptor import Descriptor -from deepmd_pt.model.task import Fitting, DenoiseNet -from deepmd_pt.model.network import TypeEmbedNet -from .model import BaseModel -from .atomic_model import AtomicModel +from .atomic_model import ( + AtomicModel, +) +from .model import ( + BaseModel, +) class DPAtomicModel(BaseModel, AtomicModel): - """Model give atomic prediction of some physical property. - - Parameters - ---------- - descriptor - Descriptor - fitting_net - Fitting net - type_map - Mapping atom type to the name (str) of the type. - For example `type_map[1]` gives the name of the type 1. - type_embedding - Type embedding net - resuming - Whether to resume/fine-tune from checkpoint or not. - stat_file_dir - The directory to the state files. - stat_file_path - The path to the state files. - sampled - Sampled frames to compute the statistics. - """ - - def __init__( - self, - descriptor: dict, - fitting_net: dict, - type_map: Optional[List[str]], - type_embedding: Optional[dict] = None, - resuming: bool = False, - stat_file_dir=None, - stat_file_path=None, - sampled=None, - **kwargs, - ): - super().__init__() - # Descriptor + Type Embedding Net (Optional) - ntypes = len(type_map) - self.type_map = type_map - self.ntypes = ntypes - descriptor['ntypes'] = ntypes - self.combination = descriptor.get('combination',False) - if(self.combination): - self.prefactor=descriptor.get('prefactor', [0.5,0.5]) - self.descriptor_type = descriptor['type'] - - self.type_split = True - if self.descriptor_type not in ['se_e2_a']: - self.type_split = False - - self.descriptor = Descriptor(**descriptor) - self.rcut = self.descriptor.get_rcut() - self.sel = self.descriptor.get_sel() - self.split_nlist = False - - # Statistics - self.compute_or_load_stat(fitting_net, ntypes, - resuming=resuming, - type_map=type_map, - stat_file_dir=stat_file_dir, - stat_file_path=stat_file_path, - sampled=sampled) - - # Fitting - if fitting_net: - fitting_net['type'] = fitting_net.get('type', 'ener') - if self.descriptor_type not in ['se_e2_a']: - fitting_net['ntypes'] = 1 - else: - fitting_net['ntypes'] = self.descriptor.get_ntype() - fitting_net['use_tebd'] = False - fitting_net['embedding_width'] = self.descriptor.dim_out - - self.grad_force = 'direct' not in fitting_net['type'] - if not self.grad_force: - fitting_net['out_dim'] = self.descriptor.dim_emb - if 'ener' in fitting_net['type']: - fitting_net['return_energy'] = True - self.fitting_net = Fitting(**fitting_net) - else: - self.fitting_net = None - self.grad_force = False - if not self.split_nlist: - self.coord_denoise_net = DenoiseNet(self.descriptor.dim_out, self.ntypes - 1, self.descriptor.dim_emb) - elif self.combination: - self.coord_denoise_net = DenoiseNet(self.descriptor.dim_out, self.ntypes - 1, self.descriptor.dim_emb_list, self.prefactor) - else: - self.coord_denoise_net = DenoiseNet(self.descriptor.dim_out, self.ntypes - 1, self.descriptor.dim_emb) - - - def get_fitting_net(self)->Fitting: - """Get the fitting net.""" - return ( - self.fitting_net - if self.fitting_net is not None - else self.coord_denoise_net - ) - - def get_fitting_output_def(self)->FittingOutputDef: - """Get the output def of the fitting net.""" - return ( - self.fitting_net.output_def() - if self.fitting_net is not None - else self.coord_denoise_net.output_def() - ) - - def get_rcut(self)->float: - """Get the cut-off radius.""" - return self.rcut - - def get_sel(self)->List[int]: - """Get the neighbor selection.""" - return self.sel - - def distinguish_types(self)->bool: - """If distinguish different types by sorting.""" - return self.type_split - - - def forward_atomic( - self, - extended_coord, - extended_atype, - nlist, - mapping: Optional[torch.Tensor] = None, - ) -> Dict[str, torch.Tensor]: - """Return atomic prediction. + """Model give atomic prediction of some physical property. Parameters ---------- - extended_coord - coodinates in extended region - extended_atype - atomic type in extended region - nlist - neighbor list. nf x nloc x nsel - mapping - mapps the extended indices to local indices - - Return - ------ - result_dict - the result dict, defined by the fitting net output def. - + descriptor + Descriptor + fitting_net + Fitting net + type_map + Mapping atom type to the name (str) of the type. + For example `type_map[1]` gives the name of the type 1. + type_embedding + Type embedding net + resuming + Whether to resume/fine-tune from checkpoint or not. + stat_file_dir + The directory to the state files. + stat_file_path + The path to the state files. + sampled + Sampled frames to compute the statistics. """ - nframes, nloc, nnei = nlist.shape - atype = extended_atype[:, :nloc] - if self.do_grad(): - extended_coord.requires_grad_(True) - descriptor, env_mat, diff, rot_mat, sw = \ - self.descriptor( + + def __init__( + self, + descriptor: dict, + fitting_net: dict, + type_map: Optional[List[str]], + type_embedding: Optional[dict] = None, + resuming: bool = False, + stat_file_dir=None, + stat_file_path=None, + sampled=None, + **kwargs, + ): + super().__init__() + # Descriptor + Type Embedding Net (Optional) + ntypes = len(type_map) + self.type_map = type_map + self.ntypes = ntypes + descriptor["ntypes"] = ntypes + self.combination = descriptor.get("combination", False) + if self.combination: + self.prefactor = descriptor.get("prefactor", [0.5, 0.5]) + self.descriptor_type = descriptor["type"] + + self.type_split = True + if self.descriptor_type not in ["se_e2_a"]: + self.type_split = False + + self.descriptor = Descriptor(**descriptor) + self.rcut = self.descriptor.get_rcut() + self.sel = self.descriptor.get_sel() + self.split_nlist = False + + # Statistics + self.compute_or_load_stat( + fitting_net, + ntypes, + resuming=resuming, + type_map=type_map, + stat_file_dir=stat_file_dir, + stat_file_path=stat_file_path, + sampled=sampled, + ) + + # Fitting + if fitting_net: + fitting_net["type"] = fitting_net.get("type", "ener") + if self.descriptor_type not in ["se_e2_a"]: + fitting_net["ntypes"] = 1 + else: + fitting_net["ntypes"] = self.descriptor.get_ntype() + fitting_net["use_tebd"] = False + fitting_net["embedding_width"] = self.descriptor.dim_out + + self.grad_force = "direct" not in fitting_net["type"] + if not self.grad_force: + fitting_net["out_dim"] = self.descriptor.dim_emb + if "ener" in fitting_net["type"]: + fitting_net["return_energy"] = True + self.fitting_net = Fitting(**fitting_net) + else: + self.fitting_net = None + self.grad_force = False + if not self.split_nlist: + self.coord_denoise_net = DenoiseNet( + self.descriptor.dim_out, self.ntypes - 1, self.descriptor.dim_emb + ) + elif self.combination: + self.coord_denoise_net = DenoiseNet( + self.descriptor.dim_out, + self.ntypes - 1, + self.descriptor.dim_emb_list, + self.prefactor, + ) + else: + self.coord_denoise_net = DenoiseNet( + self.descriptor.dim_out, self.ntypes - 1, self.descriptor.dim_emb + ) + + def get_fitting_net(self) -> Fitting: + """Get the fitting net.""" + return ( + self.fitting_net if self.fitting_net is not None else self.coord_denoise_net + ) + + def get_fitting_output_def(self) -> FittingOutputDef: + """Get the output def of the fitting net.""" + return ( + self.fitting_net.output_def() + if self.fitting_net is not None + else self.coord_denoise_net.output_def() + ) + + def get_rcut(self) -> float: + """Get the cut-off radius.""" + return self.rcut + + def get_sel(self) -> List[int]: + """Get the neighbor selection.""" + return self.sel + + def distinguish_types(self) -> bool: + """If distinguish different types by sorting.""" + return self.type_split + + def forward_atomic( + self, extended_coord, extended_atype, nlist, - mapping=mapping, - ) - assert descriptor is not None - # energy, force - if self.fitting_net is not None: - fit_ret = self.fitting_net(descriptor, atype, atype_tebd=None, rot_mat=rot_mat) - # denoise - else: - nlist_list = [nlist] - if not self.split_nlist: - nnei_mask = nlist != -1 - elif self.combination: - nnei_mask = [] - for item in nlist_list: - nnei_mask_item = item != -1 - nnei_mask.append(nnei_mask_item) + mapping: Optional[torch.Tensor] = None, + ) -> Dict[str, torch.Tensor]: + """Return atomic prediction. + + Parameters + ---------- + extended_coord + coodinates in extended region + extended_atype + atomic type in extended region + nlist + neighbor list. nf x nloc x nsel + mapping + mapps the extended indices to local indices + + Returns + ------- + result_dict + the result dict, defined by the fitting net output def. + + """ + nframes, nloc, nnei = nlist.shape + atype = extended_atype[:, :nloc] + if self.do_grad(): + extended_coord.requires_grad_(True) + descriptor, env_mat, diff, rot_mat, sw = self.descriptor( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + assert descriptor is not None + # energy, force + if self.fitting_net is not None: + fit_ret = self.fitting_net( + descriptor, atype, atype_tebd=None, rot_mat=rot_mat + ) + # denoise else: - env_mat = env_mat[-1] - diff = diff[-1] - nnei_mask = nlist_list[-1] != -1 - fit_ret = self.coord_denoise_net(env_mat, diff, nnei_mask, descriptor, sw) - return fit_ret + nlist_list = [nlist] + if not self.split_nlist: + nnei_mask = nlist != -1 + elif self.combination: + nnei_mask = [] + for item in nlist_list: + nnei_mask_item = item != -1 + nnei_mask.append(nnei_mask_item) + else: + env_mat = env_mat[-1] + diff = diff[-1] + nnei_mask = nlist_list[-1] != -1 + fit_ret = self.coord_denoise_net(env_mat, diff, nnei_mask, descriptor, sw) + return fit_ret diff --git a/deepmd_pt/model/model/ener.py b/deepmd_pt/model/model/ener.py index 60cb9b9c..c316c99a 100644 --- a/deepmd_pt/model/model/ener.py +++ b/deepmd_pt/model/model/ener.py @@ -1,27 +1,23 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + List, + Optional, +) + import torch -from typing import Optional, List, Union, Dict -from deepmd_pt.model.model import BaseModel -from deepmd_pt.utils.nlist import extend_coord_with_ghosts, build_neighbor_list -from deepmd_pt.utils.region import normalize_coord -from deepmd_pt.model.descriptor import make_default_type_embedding -from deepmd_pt.model.model.transform_output import ( - fit_output_to_model_output, - communicate_extended_output, +from .dp_atomic_model import ( + DPAtomicModel, ) -from deepmd_utils.model_format import ( - model_check_output, - ModelOutputDef, +from .make_model import ( + make_model, ) -from .dp_atomic_model import DPAtomicModel -from .make_model import make_model - DPModel = make_model(DPAtomicModel) class EnergyModel(DPModel): - model_type = "ener" def __init__( @@ -35,10 +31,12 @@ def forward( self, coord, atype, - box: Optional[torch.Tensor] = None, + box: Optional[torch.Tensor] = None, do_atomic_virial: bool = False, ) -> Dict[str, torch.Tensor]: - model_ret = self.forward_common(coord, atype, box, do_atomic_virial=do_atomic_virial) + model_ret = self.forward_common( + coord, atype, box, do_atomic_virial=do_atomic_virial + ) if self.fitting_net is not None: model_predict = {} model_predict["atom_energy"] = model_ret["energy"] @@ -46,48 +44,54 @@ def forward( if self.do_grad("energy"): model_predict["force"] = model_ret["energy_derv_r"].squeeze(-2) if do_atomic_virial: - model_predict["atomic_virial"] = model_ret["energy_derv_c"].squeeze(-3) + model_predict["atomic_virial"] = model_ret["energy_derv_c"].squeeze( + -3 + ) model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-3) else: - model_predict['force'] = model_ret['dforce'] + model_predict["force"] = model_ret["dforce"] else: model_predict = model_ret - model_predict['updated_coord'] += coord + model_predict["updated_coord"] += coord return model_predict - def forward_lower( - self, - extended_coord, - extended_atype, + self, + extended_coord, + extended_atype, nlist, mapping: Optional[torch.Tensor] = None, do_atomic_virial: bool = False, ): model_ret = self.common_forward_lower( - extended_coord, extended_atype, nlist, mapping, - do_atomic_virial=do_atomic_virial) + extended_coord, + extended_atype, + nlist, + mapping, + do_atomic_virial=do_atomic_virial, + ) if self.fitting_net is not None: model_predict = {} model_predict["atom_energy"] = model_ret["energy"] model_predict["energy"] = model_ret["energy_redu"] if self.do_grad("energy"): - model_predict['extended_force'] = model_ret['energy_derv_r'].squeeze(-2) + model_predict["extended_force"] = model_ret["energy_derv_r"].squeeze(-2) if do_atomic_virial: - model_predict['extended_virial'] = model_ret['energy_derv_c'].squeeze(-3) + model_predict["extended_virial"] = model_ret[ + "energy_derv_c" + ].squeeze(-3) else: assert model_ret["dforce"] is not None - model_predict["dforce"] = model_ret["dforce"] + model_predict["dforce"] = model_ret["dforce"] else: model_predict = model_ret return model_predict - # should be a stand-alone function!!!! def process_nlist( - nlist, - extended_atype, + nlist, + extended_atype, mapping: Optional[torch.Tensor] = None, ): # process the nlist_type and nlist_loc @@ -96,25 +100,26 @@ def process_nlist( nlist[nmask] = 0 if mapping is not None: nlist_loc = torch.gather( - mapping, - dim=1, - index=nlist.reshape(nframes, -1), + mapping, + dim=1, + index=nlist.reshape(nframes, -1), ).reshape(nframes, nloc, -1) nlist_loc[nmask] = -1 else: nlist_loc = None nlist_type = torch.gather( - extended_atype, - dim=1, - index=nlist.reshape(nframes, -1), - ).reshape(nframes, nloc,-1) + extended_atype, + dim=1, + index=nlist.reshape(nframes, -1), + ).reshape(nframes, nloc, -1) nlist_type[nmask] = -1 nlist[nmask] = -1 return nlist_loc, nlist_type, nframes, nloc + def process_nlist_gathered( - nlist, - extended_atype, + nlist, + extended_atype, split_sel: List[int], mapping: Optional[torch.Tensor] = None, ): @@ -126,15 +131,14 @@ def process_nlist_gathered( nmask = nlist_item == -1 nlist_item[nmask] = 0 if mapping is not None: - nlist_loc_item = torch.gather(mapping, dim=1, index=nlist_item.reshape(nframes, -1)).reshape( - nframes, nloc, - -1) + nlist_loc_item = torch.gather( + mapping, dim=1, index=nlist_item.reshape(nframes, -1) + ).reshape(nframes, nloc, -1) nlist_loc_item[nmask] = -1 nlist_loc_list.append(nlist_loc_item) - nlist_type_item = torch.gather(extended_atype, dim=1, index=nlist_item.reshape(nframes, -1)).reshape( - nframes, - nloc, - -1) + nlist_type_item = torch.gather( + extended_atype, dim=1, index=nlist_item.reshape(nframes, -1) + ).reshape(nframes, nloc, -1) nlist_type_item[nmask] = -1 nlist_type_list.append(nlist_type_item) nlist_item[nmask] = -1 diff --git a/deepmd_pt/model/model/make_model.py b/deepmd_pt/model/model/make_model.py index 80c02fb2..680a7787 100644 --- a/deepmd_pt/model/model/make_model.py +++ b/deepmd_pt/model/model/make_model.py @@ -1,42 +1,48 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + Optional, +) + import torch -from typing import Optional, List, Union, Dict -from deepmd_pt.utils.nlist import extend_coord_with_ghosts, build_neighbor_list -from deepmd_pt.utils.region import normalize_coord +from deepmd.model_format import ( + ModelOutputDef, +) + from deepmd_pt.model.model.transform_output import ( - fit_output_to_model_output, - communicate_extended_output, + communicate_extended_output, + fit_output_to_model_output, ) -from deepmd_utils.model_format import ( - model_check_output, - ModelOutputDef, +from deepmd_pt.utils.nlist import ( + build_neighbor_list, + extend_coord_with_ghosts, +) +from deepmd_pt.utils.region import ( + normalize_coord, ) def make_model(T_AtomicModel): - class CM(T_AtomicModel): - def __init__( self, *args, **kwargs, ): super().__init__( - *args, - **kwargs, + *args, + **kwargs, ) def get_model_output_def(self): - return ModelOutputDef( - self.get_fitting_output_def() - ) + return ModelOutputDef(self.get_fitting_output_def()) # cannot use the name forward. torch script does not work def forward_common( self, coord, atype, - box: Optional[torch.Tensor] = None, + box: Optional[torch.Tensor] = None, do_atomic_virial: bool = False, ) -> Dict[str, torch.Tensor]: """Return total energy of the system. @@ -46,7 +52,9 @@ def forward_common( - natoms: Atom statisics with shape [self.ntypes+2]. - box: Simulation box with shape [nframes, 9]. - atomic_virial: Whether or not compoute the atomic virial. - Returns: + + Returns + ------- - energy: Energy per atom. - force: XYZ force per atom. """ @@ -56,31 +64,36 @@ def forward_common( else: coord_normalized = coord.clone() extended_coord, extended_atype, mapping = extend_coord_with_ghosts( - coord_normalized, atype, box, self.get_rcut()) + coord_normalized, atype, box, self.get_rcut() + ) nlist = build_neighbor_list( - extended_coord, extended_atype, nloc, - self.get_rcut(), self.get_sel(), distinguish_types=self.distinguish_types()) + extended_coord, + extended_atype, + nloc, + self.get_rcut(), + self.get_sel(), + distinguish_types=self.distinguish_types(), + ) extended_coord = extended_coord.reshape(nframes, -1, 3) model_predict_lower = self.forward_common_lower( - extended_coord, - extended_atype, - nlist, - mapping, - do_atomic_virial=do_atomic_virial, + extended_coord, + extended_atype, + nlist, + mapping, + do_atomic_virial=do_atomic_virial, ) model_predict = communicate_extended_output( - model_predict_lower, - self.get_model_output_def(), - mapping, - do_atomic_virial=do_atomic_virial, + model_predict_lower, + self.get_model_output_def(), + mapping, + do_atomic_virial=do_atomic_virial, ) return model_predict - def forward_common_lower( - self, - extended_coord, - extended_atype, + self, + extended_coord, + extended_atype, nlist, mapping: Optional[torch.Tensor] = None, do_atomic_virial: bool = False, @@ -90,33 +103,33 @@ def forward_common_lower( Parameters ---------- extended_coord - coodinates in extended region + coodinates in extended region extended_atype - atomic type in extended region + atomic type in extended region nlist - neighbor list. nf x nloc x nsel + neighbor list. nf x nloc x nsel mapping - mapps the extended indices to local indices + mapps the extended indices to local indices do_atomic_virial - whether do atomic virial + whether do atomic virial - Return - ------ + Returns + ------- result_dict - the result dict, defined by the fitting net output def. + the result dict, defined by the fitting net output def. """ atomic_ret = self.forward_atomic( - extended_coord, - extended_atype, - nlist, - mapping=mapping, + extended_coord, + extended_atype, + nlist, + mapping=mapping, ) model_predict = fit_output_to_model_output( - atomic_ret, - self.get_fitting_output_def(), - extended_coord, - do_atomic_virial=do_atomic_virial, + atomic_ret, + self.get_fitting_output_def(), + extended_coord, + do_atomic_virial=do_atomic_virial, ) return model_predict diff --git a/deepmd_pt/model/model/model.py b/deepmd_pt/model/model/model.py index 2c363549..9883a472 100644 --- a/deepmd_pt/model/model/model.py +++ b/deepmd_pt/model/model/model.py @@ -1,26 +1,39 @@ -import numpy as np -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later import logging import os -from deepmd_pt.utils import env -from deepmd_pt.utils.stat import compute_output_stats, make_stat_input +import numpy as np +import torch + +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.stat import ( + compute_output_stats, +) -class BaseModel(torch.nn.Module): +class BaseModel(torch.nn.Module): def __init__(self): - """Construct a basic model for different tasks. - """ - super(BaseModel, self).__init__() + """Construct a basic model for different tasks.""" + super().__init__() def forward(self, *args, **kwargs): - """Model output. - """ + """Model output.""" raise NotImplementedError - def compute_or_load_stat(self, fitting_param, ntypes, - resuming=False, type_map=None, stat_file_dir=None, stat_file_path=None, sampled=None): - if fitting_param is None: fitting_param = {} + def compute_or_load_stat( + self, + fitting_param, + ntypes, + resuming=False, + type_map=None, + stat_file_dir=None, + stat_file_path=None, + sampled=None, + ): + if fitting_param is None: + fitting_param = {} if not resuming: if sampled is not None: # compute stat for sys in sampled: @@ -30,76 +43,108 @@ def compute_or_load_stat(self, fitting_param, ntypes, else: if sys[key] is not None: sys[key] = sys[key].to(env.DEVICE) - sumr, suma, sumn, sumr2, suma2 = self.descriptor.compute_input_stats(sampled) + sumr, suma, sumn, sumr2, suma2 = self.descriptor.compute_input_stats( + sampled + ) - energy = [item['energy'] for item in sampled] - mixed_type = 'real_natoms_vec' in sampled[0] + energy = [item["energy"] for item in sampled] + mixed_type = "real_natoms_vec" in sampled[0] if mixed_type: - input_natoms = [item['real_natoms_vec'] for item in sampled] + input_natoms = [item["real_natoms_vec"] for item in sampled] else: - input_natoms = [item['natoms'] for item in sampled] + input_natoms = [item["natoms"] for item in sampled] tmp = compute_output_stats(energy, input_natoms) - fitting_param['bias_atom_e'] = tmp[:, 0] + fitting_param["bias_atom_e"] = tmp[:, 0] if stat_file_path is not None: if not os.path.exists(stat_file_dir): os.mkdir(stat_file_dir) if not isinstance(stat_file_path, list): - logging.info(f'Saving stat file to {stat_file_path}') - np.savez_compressed(stat_file_path, - sumr=sumr, suma=suma, sumn=sumn, sumr2=sumr2, suma2=suma2, - bias_atom_e=fitting_param['bias_atom_e'], type_map=type_map) + logging.info(f"Saving stat file to {stat_file_path}") + np.savez_compressed( + stat_file_path, + sumr=sumr, + suma=suma, + sumn=sumn, + sumr2=sumr2, + suma2=suma2, + bias_atom_e=fitting_param["bias_atom_e"], + type_map=type_map, + ) else: for ii, file_path in enumerate(stat_file_path): - logging.info(f'Saving stat file to {file_path}') - np.savez_compressed(file_path, - sumr=sumr[ii], suma=suma[ii], sumn=sumn[ii], sumr2=sumr2[ii], suma2=suma2[ii], - bias_atom_e=fitting_param['bias_atom_e'], - type_map=type_map) + logging.info(f"Saving stat file to {file_path}") + np.savez_compressed( + file_path, + sumr=sumr[ii], + suma=suma[ii], + sumn=sumn[ii], + sumr2=sumr2[ii], + suma2=suma2[ii], + bias_atom_e=fitting_param["bias_atom_e"], + type_map=type_map, + ) else: # load stat target_type_map = type_map if not isinstance(stat_file_path, list): - logging.info(f'Loading stat file from {stat_file_path}') + logging.info(f"Loading stat file from {stat_file_path}") stats = np.load(stat_file_path) stat_type_map = list(stats["type_map"]) - missing_type = [i for i in target_type_map if i not in stat_type_map] - assert not missing_type, \ - f"These type are not in stat file {stat_file_path}: {missing_type}! Please change the stat file path!" + missing_type = [ + i for i in target_type_map if i not in stat_type_map + ] + assert not missing_type, f"These type are not in stat file {stat_file_path}: {missing_type}! Please change the stat file path!" idx_map = [stat_type_map.index(i) for i in target_type_map] if stats["sumr"].size: - sumr, suma, sumn, sumr2, suma2 = stats["sumr"][idx_map], stats["suma"][idx_map], \ - stats["sumn"][idx_map], stats["sumr2"][idx_map], \ - stats["suma2"][idx_map] + sumr, suma, sumn, sumr2, suma2 = ( + stats["sumr"][idx_map], + stats["suma"][idx_map], + stats["sumn"][idx_map], + stats["sumr2"][idx_map], + stats["suma2"][idx_map], + ) else: sumr, suma, sumn, sumr2, suma2 = [], [], [], [], [] - fitting_param['bias_atom_e'] = stats["bias_atom_e"][idx_map] + fitting_param["bias_atom_e"] = stats["bias_atom_e"][idx_map] else: sumr, suma, sumn, sumr2, suma2 = [], [], [], [], [] id_bias_atom_e = None for ii, file_path in enumerate(stat_file_path): - logging.info(f'Loading stat file from {file_path}') + logging.info(f"Loading stat file from {file_path}") stats = np.load(file_path) stat_type_map = list(stats["type_map"]) - missing_type = [i for i in target_type_map if i not in stat_type_map] - assert not missing_type, \ - f"These type are not in stat file {file_path}: {missing_type}! Please change the stat file path!" + missing_type = [ + i for i in target_type_map if i not in stat_type_map + ] + assert not missing_type, f"These type are not in stat file {file_path}: {missing_type}! Please change the stat file path!" idx_map = [stat_type_map.index(i) for i in target_type_map] if stats["sumr"].size: - sumr_tmp, suma_tmp, sumn_tmp, sumr2_tmp, suma2_tmp = stats["sumr"][idx_map], stats["suma"][idx_map], \ - stats["sumn"][idx_map], stats["sumr2"][idx_map], \ - stats["suma2"][idx_map] + sumr_tmp, suma_tmp, sumn_tmp, sumr2_tmp, suma2_tmp = ( + stats["sumr"][idx_map], + stats["suma"][idx_map], + stats["sumn"][idx_map], + stats["sumr2"][idx_map], + stats["suma2"][idx_map], + ) else: - sumr_tmp, suma_tmp, sumn_tmp, sumr2_tmp, suma2_tmp = [], [], [], [], [] + sumr_tmp, suma_tmp, sumn_tmp, sumr2_tmp, suma2_tmp = ( + [], + [], + [], + [], + [], + ) sumr.append(sumr_tmp) suma.append(suma_tmp) sumn.append(sumn_tmp) sumr2.append(sumr2_tmp) suma2.append(suma2_tmp) - fitting_param['bias_atom_e'] = stats["bias_atom_e"][idx_map] + fitting_param["bias_atom_e"] = stats["bias_atom_e"][idx_map] if id_bias_atom_e is None: - id_bias_atom_e = fitting_param['bias_atom_e'] + id_bias_atom_e = fitting_param["bias_atom_e"] else: - assert (id_bias_atom_e == fitting_param['bias_atom_e']).all(), \ - "bias_atom_e in stat files are not consistent!" + assert ( + id_bias_atom_e == fitting_param["bias_atom_e"] + ).all(), "bias_atom_e in stat files are not consistent!" self.descriptor.init_desc_stat(sumr, suma, sumn, sumr2, suma2) else: # resuming for checkpoint; init model params from scratch - fitting_param['bias_atom_e'] = [0.0] * ntypes + fitting_param["bias_atom_e"] = [0.0] * ntypes diff --git a/deepmd_pt/model/model/transform_output.py b/deepmd_pt/model/model/transform_output.py index 5ed310d0..a2a410bd 100644 --- a/deepmd_pt/model/model/transform_output.py +++ b/deepmd_pt/model/model/transform_output.py @@ -1,43 +1,54 @@ -import torch -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later from typing import ( - List, - Optional, - Dict, + Dict, + List, + Optional, ) -from deepmd_utils.model_format import ( - get_reduce_name, - get_deriv_name, - OutputVariableDef, - FittingOutputDef, - ModelOutputDef, +import torch +from deepmd.model_format import ( + FittingOutputDef, + ModelOutputDef, + OutputVariableDef, + get_deriv_name, + get_reduce_name, ) def atomic_virial_corr( - extended_coord: torch.Tensor, + extended_coord: torch.Tensor, atom_energy: torch.Tensor, ): - nall = extended_coord.shape[1] - nloc = atom_energy.shape[1] - coord, _ = torch.split(extended_coord, [nloc, nall-nloc], dim=1) - # no derivative with respect to the loc coord. - coord = coord.detach() - ce = coord * atom_energy - sumce0, sumce1, sumce2 = torch.split(torch.sum(ce, dim=1), [1,1,1], dim=-1) - faked_grad = torch.ones_like(sumce0) - lst = torch.jit.annotate(List[Optional[torch.Tensor]], [faked_grad]) - extended_virial_corr0 = torch.autograd.grad([sumce0], [extended_coord], grad_outputs=lst, create_graph=True)[0] - assert extended_virial_corr0 is not None - extended_virial_corr1 = torch.autograd.grad([sumce1], [extended_coord], grad_outputs=lst, create_graph=True)[0] - assert extended_virial_corr1 is not None - extended_virial_corr2 = torch.autograd.grad([sumce2], [extended_coord], grad_outputs=lst, create_graph=True)[0] - assert extended_virial_corr2 is not None - extended_virial_corr = torch.concat([extended_virial_corr0.unsqueeze(-1), - extended_virial_corr1.unsqueeze(-1), - extended_virial_corr2.unsqueeze(-1)], dim=-1) - return extended_virial_corr + nall = extended_coord.shape[1] + nloc = atom_energy.shape[1] + coord, _ = torch.split(extended_coord, [nloc, nall - nloc], dim=1) + # no derivative with respect to the loc coord. + coord = coord.detach() + ce = coord * atom_energy + sumce0, sumce1, sumce2 = torch.split(torch.sum(ce, dim=1), [1, 1, 1], dim=-1) + faked_grad = torch.ones_like(sumce0) + lst = torch.jit.annotate(List[Optional[torch.Tensor]], [faked_grad]) + extended_virial_corr0 = torch.autograd.grad( + [sumce0], [extended_coord], grad_outputs=lst, create_graph=True + )[0] + assert extended_virial_corr0 is not None + extended_virial_corr1 = torch.autograd.grad( + [sumce1], [extended_coord], grad_outputs=lst, create_graph=True + )[0] + assert extended_virial_corr1 is not None + extended_virial_corr2 = torch.autograd.grad( + [sumce2], [extended_coord], grad_outputs=lst, create_graph=True + )[0] + assert extended_virial_corr2 is not None + extended_virial_corr = torch.concat( + [ + extended_virial_corr0.unsqueeze(-1), + extended_virial_corr1.unsqueeze(-1), + extended_virial_corr2.unsqueeze(-1), + ], + dim=-1, + ) + return extended_virial_corr def task_deriv_one( @@ -46,35 +57,37 @@ def task_deriv_one( extended_coord: torch.Tensor, do_atomic_virial: bool = False, ): - faked_grad = torch.ones_like(energy) - lst = torch.jit.annotate(List[Optional[torch.Tensor]], [faked_grad]) - extended_force = torch.autograd.grad([energy], [extended_coord], grad_outputs=lst, create_graph=True)[0] - assert extended_force is not None - extended_force = -extended_force - extended_virial = extended_force.unsqueeze(-1) @ extended_coord.unsqueeze(-2) - # the correction sums to zero, which does not contribute to global virial - if do_atomic_virial: - extended_virial_corr = atomic_virial_corr(extended_coord, atom_energy) - extended_virial = extended_virial + extended_virial_corr - return extended_force, extended_virial + faked_grad = torch.ones_like(energy) + lst = torch.jit.annotate(List[Optional[torch.Tensor]], [faked_grad]) + extended_force = torch.autograd.grad( + [energy], [extended_coord], grad_outputs=lst, create_graph=True + )[0] + assert extended_force is not None + extended_force = -extended_force + extended_virial = extended_force.unsqueeze(-1) @ extended_coord.unsqueeze(-2) + # the correction sums to zero, which does not contribute to global virial + if do_atomic_virial: + extended_virial_corr = atomic_virial_corr(extended_coord, atom_energy) + extended_virial = extended_virial + extended_virial_corr + return extended_force, extended_virial def get_leading_dims( - vv: torch.Tensor, + vv: torch.Tensor, vdef: OutputVariableDef, ): - """Get the dimensions of nf x nloc. - """ - vshape = vv.shape - return list(vshape[:(len(vshape) - len(vdef.shape))]) + """Get the dimensions of nf x nloc.""" + vshape = vv.shape + return list(vshape[: (len(vshape) - len(vdef.shape))]) + def get_atom_axis( vdef: torch.Tensor, ): - """Get the axis of atoms - """ - atom_axis = -(len(vdef.shape) + 1) - return atom_axis + """Get the axis of atoms.""" + atom_axis = -(len(vdef.shape) + 1) + return atom_axis + def take_deriv( vv: torch.Tensor, @@ -83,105 +96,118 @@ def take_deriv( coord_ext: torch.Tensor, do_atomic_virial: bool = False, ): - size = 1 - for ii in vdef.shape: - size *= ii - vv1 = vv.view(get_leading_dims(vv, vdef) + [size]) - svv1 = svv.view(get_leading_dims(svv, vdef) + [size]) - split_vv1 = torch.split(vv1, [1]*size, dim=-1) - split_svv1 = torch.split(svv1, [1]*size, dim=-1) - split_ff, split_avir = [], [] - for vvi, svvi in zip(split_vv1, split_svv1): - # nf x nloc x 3, nf x nloc x 3 x 3 - ffi, aviri = task_deriv_one(vvi, svvi, coord_ext, do_atomic_virial=do_atomic_virial) - # nf x nloc x 1 x 3, nf x nloc x 1 x 3 x 3 - ffi = ffi.unsqueeze(-2) - aviri = aviri.unsqueeze(-3) - split_ff.append(ffi) - split_avir.append(aviri) - # nf x nloc x v_dim x 3, nf x nloc x v_dim x 3 x 3 - ff = torch.concat(split_ff, dim=-2) - avir = torch.concat(split_avir, dim=-3) - return ff, avir + size = 1 + for ii in vdef.shape: + size *= ii + vv1 = vv.view(list(get_leading_dims(vv, vdef)) + [size]) # noqa: RUF005 + svv1 = svv.view(list(get_leading_dims(svv, vdef)) + [size]) # noqa: RUF005 + split_vv1 = torch.split(vv1, [1] * size, dim=-1) + split_svv1 = torch.split(svv1, [1] * size, dim=-1) + split_ff, split_avir = [], [] + for vvi, svvi in zip(split_vv1, split_svv1): + # nf x nloc x 3, nf x nloc x 3 x 3 + ffi, aviri = task_deriv_one( + vvi, svvi, coord_ext, do_atomic_virial=do_atomic_virial + ) + # nf x nloc x 1 x 3, nf x nloc x 1 x 3 x 3 + ffi = ffi.unsqueeze(-2) + aviri = aviri.unsqueeze(-3) + split_ff.append(ffi) + split_avir.append(aviri) + # nf x nloc x v_dim x 3, nf x nloc x v_dim x 3 x 3 + ff = torch.concat(split_ff, dim=-2) + avir = torch.concat(split_avir, dim=-3) + return ff, avir def fit_output_to_model_output( - fit_ret : Dict[str, torch.Tensor], + fit_ret: Dict[str, torch.Tensor], fit_output_def: FittingOutputDef, coord_ext: torch.Tensor, do_atomic_virial: bool = False, ) -> Dict[str, torch.Tensor]: - """Transform the output of the fitting network to - the model output. - - """ - model_ret = {kk:vv for kk,vv in fit_ret.items()} - for kk, vv in fit_ret.items(): - vdef = fit_output_def[kk] - shap = vdef.shape - atom_axis = -(len(shap) + 1) - if vdef.reduciable: - kk_redu = get_reduce_name(kk) - model_ret[kk_redu] = torch.sum(vv, dim=atom_axis) - if vdef.differentiable: - kk_derv_r, kk_derv_c = get_deriv_name(kk) - dr, dc = take_deriv( - vv, model_ret[kk_redu], vdef, coord_ext, do_atomic_virial=do_atomic_virial) - model_ret[kk_derv_r] = dr - model_ret[kk_derv_c] = dc - return model_ret + """Transform the output of the fitting network to + the model output. + + """ + model_ret = dict(fit_ret.items()) + for kk, vv in fit_ret.items(): + vdef = fit_output_def[kk] + shap = vdef.shape + atom_axis = -(len(shap) + 1) + if vdef.reduciable: + kk_redu = get_reduce_name(kk) + model_ret[kk_redu] = torch.sum(vv, dim=atom_axis) + if vdef.differentiable: + kk_derv_r, kk_derv_c = get_deriv_name(kk) + dr, dc = take_deriv( + vv, + model_ret[kk_redu], + vdef, + coord_ext, + do_atomic_virial=do_atomic_virial, + ) + model_ret[kk_derv_r] = dr + model_ret[kk_derv_c] = dc + return model_ret def communicate_extended_output( model_ret: Dict[str, torch.Tensor], model_output_def: ModelOutputDef, - mapping: torch.Tensor, # nf x nloc + mapping: torch.Tensor, # nf x nloc do_atomic_virial: bool = False, ) -> Dict[str, torch.Tensor]: - """Transform the output of the model network defined on - local and ghost (extended) atoms to local atoms. - - """ - new_ret = {} - for kk in model_output_def.keys_outp(): - vv = model_ret[kk] - vdef = model_output_def[kk] - new_ret[kk] = vv - if vdef.reduciable: - kk_redu = get_reduce_name(kk) - new_ret[kk_redu] = model_ret[kk_redu] - if vdef.differentiable: - # nf x nloc - vldims = get_leading_dims(vv, vdef) - # nf x nall - mldims = list(mapping.shape) - kk_derv_r, kk_derv_c = get_deriv_name(kk) - # vdim x 3 - derv_r_ext_dims = vdef.shape + [3] - mapping = mapping\ - .view(mldims + [1] * len(derv_r_ext_dims))\ - .expand([-1]*len(mldims) + derv_r_ext_dims) - force = torch.zeros(vldims + derv_r_ext_dims, dtype=vv.dtype, device=vv.device) - # nf x nloc x 1 x 3 - new_ret[kk_derv_r] = torch.scatter_reduce( - force, 1, - index=mapping, - src=model_ret[kk_derv_r], - reduce='sum', - ) - mapping = mapping\ - .unsqueeze(-1)\ - .expand([-1]*(len(mldims) + len(derv_r_ext_dims)) + [3]) - virial = torch.zeros(vldims + derv_r_ext_dims + [3], dtype=vv.dtype, device=vv.device) - # nf x nloc x 1 x 3 - new_ret[kk_derv_c] = torch.scatter_reduce( - virial, 1, - index=mapping, - src=model_ret[kk_derv_c], - reduce='sum', - ) - new_ret[kk_derv_c+'_redu'] = torch.sum(new_ret[kk_derv_c], dim=1) - if not do_atomic_virial: - # pop atomic virial, because it is not correctly calculated. - new_ret.pop(kk_derv_c) - return new_ret + """Transform the output of the model network defined on + local and ghost (extended) atoms to local atoms. + + """ + new_ret = {} + for kk in model_output_def.keys_outp(): + vv = model_ret[kk] + vdef = model_output_def[kk] + new_ret[kk] = vv + if vdef.reduciable: + kk_redu = get_reduce_name(kk) + new_ret[kk_redu] = model_ret[kk_redu] + if vdef.differentiable: + # nf x nloc + vldims = get_leading_dims(vv, vdef) + # nf x nall + mldims = list(mapping.shape) + kk_derv_r, kk_derv_c = get_deriv_name(kk) + # vdim x 3 + derv_r_ext_dims = list(vdef.shape) + [3] # noqa:RUF005 + mapping = mapping.view(mldims + [1] * len(derv_r_ext_dims)).expand( + [-1] * len(mldims) + derv_r_ext_dims + ) + force = torch.zeros( + vldims + derv_r_ext_dims, dtype=vv.dtype, device=vv.device + ) + # nf x nloc x 1 x 3 + new_ret[kk_derv_r] = torch.scatter_reduce( + force, + 1, + index=mapping, + src=model_ret[kk_derv_r], + reduce="sum", + ) + mapping = mapping.unsqueeze(-1).expand( + [-1] * (len(mldims) + len(derv_r_ext_dims)) + [3] + ) + virial = torch.zeros( + vldims + derv_r_ext_dims + [3], dtype=vv.dtype, device=vv.device + ) + # nf x nloc x 1 x 3 + new_ret[kk_derv_c] = torch.scatter_reduce( + virial, + 1, + index=mapping, + src=model_ret[kk_derv_c], + reduce="sum", + ) + new_ret[kk_derv_c + "_redu"] = torch.sum(new_ret[kk_derv_c], dim=1) + if not do_atomic_virial: + # pop atomic virial, because it is not correctly calculated. + new_ret.pop(kk_derv_c) + return new_ret diff --git a/deepmd_pt/model/network/__init__.py b/deepmd_pt/model/network/__init__.py index 6cf34ce8..6ceb116d 100644 --- a/deepmd_pt/model/network/__init__.py +++ b/deepmd_pt/model/network/__init__.py @@ -1 +1 @@ -from .network import * +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd_pt/model/network/mlp.py b/deepmd_pt/model/network/mlp.py index 4b7c6648..95f85297 100644 --- a/deepmd_pt/model/network/mlp.py +++ b/deepmd_pt/model/network/mlp.py @@ -1,31 +1,40 @@ -from typing import ClassVar, Dict, Optional, List, Union -from deepmd_utils.model_format.network import NativeNet +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + ClassVar, + Dict, + Optional, +) + import numpy as np import torch import torch.nn as nn -from deepmd_pt.utils import env + +from deepmd_pt.utils import ( + env, +) device = env.DEVICE -from deepmd_pt.utils.env import ( - PRECISION_DICT, - DEFAULT_PRECISION, +from deepmd.model_format import ( + NativeLayer, +) +from deepmd.model_format import NetworkCollection as DPNetworkCollection +from deepmd.model_format import ( + make_embedding_network, + make_fitting_network, + make_multilayer_network, ) -from deepmd_utils.model_format import ( - NativeLayer, - NativeNet, - NetworkCollection as DPNetworkCollection, - load_dp_model, - save_dp_model, - make_multilayer_network, - make_embedding_network, - make_fitting_network, +from deepmd_pt.utils.env import ( + DEFAULT_PRECISION, + PRECISION_DICT, +) +from deepmd_pt.utils.utils import ( + ActivationFn, ) -from deepmd_pt.utils.utils import get_activation_fn, ActivationFn try: - from deepmd_utils._version import version as __version__ + from deepmd._version import version as __version__ except ImportError: __version__ = "unknown" @@ -33,160 +42,161 @@ def empty_t(shape, precision): return torch.empty(shape, dtype=precision, device=device) + class MLPLayer(nn.Module): - def __init__( - self, - num_in, - num_out, - bias: bool = True, - use_timestep: bool = False, - activation_function: Optional[str] = None, - resnet: bool = False, - bavg: float = 0., - stddev: float = 1., - precision: str = DEFAULT_PRECISION, - ): - super(MLPLayer, self).__init__() - self.use_timestep = use_timestep - self.activate_name = activation_function - self.activate = ActivationFn(self.activate_name) - self.precision = precision - self.prec = PRECISION_DICT[self.precision] - self.matrix = nn.Parameter( - data=empty_t((num_in, num_out), self.prec) - ) - nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) - if bias: - self.bias = nn.Parameter( - data=empty_t([num_out], self.prec), - ) - nn.init.normal_(self.bias.data, mean=bavg, std=stddev) - else: - self.bias = None - if self.use_timestep: - self.idt = nn.Parameter( - data=empty_t([num_out], self.prec) - ) - nn.init.normal_(self.idt.data, mean=0.1, std=0.001) - else: - self.idt = None - self.resnet = resnet - - - def check_type_consistency(self): - precision = self.precision - - def check_var(var): - if var is not None: - # assertion "float64" == "double" would fail - assert PRECISION_DICT[var.dtype.name] is PRECISION_DICT[precision] - - check_var(self.w) - check_var(self.b) - check_var(self.idt) - - - def dim_in(self) -> int: - return self.matrix.shape[0] - - def dim_out(self) -> int: - return self.matrix.shape[1] - - - def forward( - self, - xx: torch.Tensor, - )->torch.Tensor: - """One MLP layer used by DP model. - - Parameters - ---------- - xx: torch.Tensor - The input. - - Returns - ------- - yy: torch.Tensor - The output. - """ - yy = ( - torch.matmul(xx, self.matrix) + self.bias \ - if self.bias is not None \ - else torch.matmul(xx, self.matrix) - ) - yy = self.activate(yy).clone() - yy = yy * self.idt if self.idt is not None else yy - if self.resnet: - if xx.shape[-1] == yy.shape[-1]: - yy += xx - elif 2 * xx.shape[-1] == yy.shape[-1]: - yy += torch.concat([xx, xx], dim=-1) - else: - yy = yy - return yy - - - def serialize(self) -> dict: - """Serialize the layer to a dict. - - Returns - ------- - dict - The serialized layer. - """ - nl = NativeLayer( - self.matrix.shape[0], - self.matrix.shape[1], - bias=self.bias is not None, - use_timestep=self.idt is not None, - activation_function=self.activate_name, - resnet=self.resnet, - precision=self.precision, - ) - nl.w, nl.b, nl.idt = ( - self.matrix.detach().cpu().numpy(), - self.bias.detach().cpu().numpy() if self.bias is not None else None, - self.idt.detach().cpu().numpy() if self.idt is not None else None, - ) - return nl.serialize() - - @classmethod - def deserialize(cls, data:dict)->"MLPLayer": - """Deserialize the layer from a dict. - - Parameters - ---------- - data : dict - The dict to deserialize from. - """ - nl = NativeLayer.deserialize(data) - obj = cls( - nl["matrix"].shape[0], - nl["matrix"].shape[1], - bias=nl["bias"] is not None, - use_timestep=nl["idt"] is not None, - activation_function=nl["activation_function"], - resnet=nl["resnet"], - precision=nl["precision"], - ) - prec = PRECISION_DICT[obj.precision] - check_load_param = \ - lambda ss: nn.Parameter(data=torch.tensor(nl[ss], dtype=prec, device=device)) \ - if nl[ss] is not None else None - obj.matrix = check_load_param("matrix") - obj.bias = check_load_param("bias") - obj.idt = check_load_param("idt") - return obj + def __init__( + self, + num_in, + num_out, + bias: bool = True, + use_timestep: bool = False, + activation_function: Optional[str] = None, + resnet: bool = False, + bavg: float = 0.0, + stddev: float = 1.0, + precision: str = DEFAULT_PRECISION, + ): + super().__init__() + self.use_timestep = use_timestep + self.activate_name = activation_function + self.activate = ActivationFn(self.activate_name) + self.precision = precision + self.prec = PRECISION_DICT[self.precision] + self.matrix = nn.Parameter(data=empty_t((num_in, num_out), self.prec)) + nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) + if bias: + self.bias = nn.Parameter( + data=empty_t([num_out], self.prec), + ) + nn.init.normal_(self.bias.data, mean=bavg, std=stddev) + else: + self.bias = None + if self.use_timestep: + self.idt = nn.Parameter(data=empty_t([num_out], self.prec)) + nn.init.normal_(self.idt.data, mean=0.1, std=0.001) + else: + self.idt = None + self.resnet = resnet + + def check_type_consistency(self): + precision = self.precision + + def check_var(var): + if var is not None: + # assertion "float64" == "double" would fail + assert PRECISION_DICT[var.dtype.name] is PRECISION_DICT[precision] + + check_var(self.w) + check_var(self.b) + check_var(self.idt) + + def dim_in(self) -> int: + return self.matrix.shape[0] + + def dim_out(self) -> int: + return self.matrix.shape[1] + + def forward( + self, + xx: torch.Tensor, + ) -> torch.Tensor: + """One MLP layer used by DP model. + + Parameters + ---------- + xx : torch.Tensor + The input. + + Returns + ------- + yy: torch.Tensor + The output. + """ + yy = ( + torch.matmul(xx, self.matrix) + self.bias + if self.bias is not None + else torch.matmul(xx, self.matrix) + ) + yy = self.activate(yy).clone() + yy = yy * self.idt if self.idt is not None else yy + if self.resnet: + if xx.shape[-1] == yy.shape[-1]: + yy += xx + elif 2 * xx.shape[-1] == yy.shape[-1]: + yy += torch.concat([xx, xx], dim=-1) + else: + yy = yy + return yy + + def serialize(self) -> dict: + """Serialize the layer to a dict. + + Returns + ------- + dict + The serialized layer. + """ + nl = NativeLayer( + self.matrix.shape[0], + self.matrix.shape[1], + bias=self.bias is not None, + use_timestep=self.idt is not None, + activation_function=self.activate_name, + resnet=self.resnet, + precision=self.precision, + ) + nl.w, nl.b, nl.idt = ( + self.matrix.detach().cpu().numpy(), + self.bias.detach().cpu().numpy() if self.bias is not None else None, + self.idt.detach().cpu().numpy() if self.idt is not None else None, + ) + return nl.serialize() + + @classmethod + def deserialize(cls, data: dict) -> "MLPLayer": + """Deserialize the layer from a dict. + + Parameters + ---------- + data : dict + The dict to deserialize from. + """ + nl = NativeLayer.deserialize(data) + obj = cls( + nl["matrix"].shape[0], + nl["matrix"].shape[1], + bias=nl["bias"] is not None, + use_timestep=nl["idt"] is not None, + activation_function=nl["activation_function"], + resnet=nl["resnet"], + precision=nl["precision"], + ) + prec = PRECISION_DICT[obj.precision] + + def check_load_param(ss): + return ( + nn.Parameter(data=torch.tensor(nl[ss], dtype=prec, device=device)) + if nl[ss] is not None + else None + ) + + obj.matrix = check_load_param("matrix") + obj.bias = check_load_param("bias") + obj.idt = check_load_param("idt") + return obj MLP_ = make_multilayer_network(MLPLayer, nn.Module) + class MLP(MLP_): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.layers = torch.nn.ModuleList(self.layers) + forward = MLP_.call + EmbeddingNet = make_embedding_network(MLP, MLPLayer) FittingNet = make_fitting_network(EmbeddingNet, MLP, MLPLayer) @@ -194,6 +204,7 @@ def __init__(self, *args, **kwargs): class NetworkCollection(DPNetworkCollection, nn.Module): """PyTorch implementation of NetworkCollection.""" + NETWORK_TYPE_MAP: ClassVar[Dict[str, type]] = { "network": MLP, "embedding_network": EmbeddingNet, @@ -201,7 +212,7 @@ class NetworkCollection(DPNetworkCollection, nn.Module): } def __init__(self, *args, **kwargs): - # init both two base classes + # init both two base classes DPNetworkCollection.__init__(self, *args, **kwargs) nn.Module.__init__(self) self.networks = self._networks = torch.nn.ModuleList(self._networks) diff --git a/deepmd_pt/model/network/network.py b/deepmd_pt/model/network/network.py index 99f74b16..d6ff6acc 100644 --- a/deepmd_pt/model/network/network.py +++ b/deepmd_pt/model/network/network.py @@ -1,19 +1,34 @@ -from typing import Optional +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, +) + import numpy as np import torch import torch.nn as nn import torch.nn.functional as F -from deepmd_pt.utils import env +from deepmd_pt.utils import ( + env, +) try: - from typing import Final -except: + from typing import ( + Final, + ) +except ImportError: from torch.jit import Final -from deepmd_pt.utils.utils import get_activation_fn, ActivationFn +from functools import ( + partial, +) + import torch.utils.checkpoint -from functools import partial + +from deepmd_pt.utils.utils import ( + ActivationFn, + get_activation_fn, +) def Tensor(*shape): @@ -44,7 +59,7 @@ class DropPath(torch.nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, prob=None): - super(DropPath, self).__init__() + super().__init__() self.drop_prob = prob def forward(self, x): @@ -52,7 +67,7 @@ def forward(self, x): return x keep_prob = 1 - self.drop_prob shape = (x.shape[0],) + (1,) * ( - x.ndim - 1 + x.ndim - 1 ) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize @@ -63,7 +78,9 @@ def extra_repr(self) -> str: return f"prob={self.drop_prob}" -def softmax_dropout(input_x, dropout_prob, is_training=True, mask=None, bias=None, inplace=True): +def softmax_dropout( + input_x, dropout_prob, is_training=True, mask=None, bias=None, inplace=True +): input_x = input_x.contiguous() if not inplace: input_x = input_x.clone() @@ -107,7 +124,7 @@ def wrap_exec(*a): class ResidualLinear(nn.Module): resnet: Final[int] - def __init__(self, num_in, num_out, bavg=0., stddev=1., resnet_dt=False): + def __init__(self, num_in, num_out, bavg=0.0, stddev=1.0, resnet_dt=False): """Construct a residual linear layer. Args: @@ -115,7 +132,7 @@ def __init__(self, num_in, num_out, bavg=0., stddev=1., resnet_dt=False): - num_out: Width of output tensor. - resnet_dt: Using time-step in the ResNet construction. """ - super(ResidualLinear, self).__init__() + super().__init__() self.num_in = num_in self.num_out = num_out self.resnet = resnet_dt @@ -126,7 +143,7 @@ def __init__(self, num_in, num_out, bavg=0., stddev=1., resnet_dt=False): nn.init.normal_(self.bias.data, mean=bavg, std=stddev) if self.resnet: self.idt = nn.Parameter(data=Tensor(1, num_out)) - nn.init.normal_(self.idt.data, mean=1., std=0.001) + nn.init.normal_(self.idt.data, mean=1.0, std=0.001) def forward(self, inputs): """Return X ?+ X*W+b.""" @@ -146,7 +163,16 @@ class TypeFilter(nn.Module): use_tebd: Final[bool] tebd_mode: Final[str] - def __init__(self, offset, length, neuron, return_G=False, tebd_dim=0, use_tebd=False, tebd_mode='concat'): + def __init__( + self, + offset, + length, + neuron, + return_G=False, + tebd_dim=0, + use_tebd=False, + tebd_mode="concat", + ): """Construct a filter on the given element as neighbor. Args: @@ -154,18 +180,20 @@ def __init__(self, offset, length, neuron, return_G=False, tebd_dim=0, use_tebd= - length: Atom count of this element. - neuron: Number of neurons in each hidden layers of the embedding net. """ - super(TypeFilter, self).__init__() + super().__init__() self.offset = offset self.length = length self.tebd_dim = tebd_dim self.use_tebd = use_tebd self.tebd_mode = tebd_mode - supported_tebd_mode = ['concat', 'dot', 'dot_residual_s', 'dot_residual_t'] - assert tebd_mode in supported_tebd_mode, f'Unknown tebd_mode {tebd_mode}! Supported are {supported_tebd_mode}.' - if use_tebd and tebd_mode == 'concat': - self.neuron = [1 + tebd_dim * 2] + neuron + supported_tebd_mode = ["concat", "dot", "dot_residual_s", "dot_residual_t"] + assert ( + tebd_mode in supported_tebd_mode + ), f"Unknown tebd_mode {tebd_mode}! Supported are {supported_tebd_mode}." + if use_tebd and tebd_mode == "concat": + self.neuron = [1 + tebd_dim * 2, *neuron] else: - self.neuron = [1] + neuron + self.neuron = [1, *neuron] deep_layers = [] for ii in range(1, len(self.neuron)): @@ -174,8 +202,8 @@ def __init__(self, offset, length, neuron, return_G=False, tebd_dim=0, use_tebd= self.deep_layers = nn.ModuleList(deep_layers) deep_layers_t = [] - if use_tebd and tebd_mode in ['dot', 'dot_residual_s', 'dot_residual_t']: - self.neuron_t = [tebd_dim * 2] + neuron + if use_tebd and tebd_mode in ["dot", "dot_residual_s", "dot_residual_t"]: + self.neuron_t = [tebd_dim * 2, *neuron] for ii in range(1, len(self.neuron_t)): one = ResidualLinear(self.neuron_t[ii - 1], self.neuron_t[ii]) deep_layers_t.append(one) @@ -183,21 +211,29 @@ def __init__(self, offset, length, neuron, return_G=False, tebd_dim=0, use_tebd= self.return_G = return_G - def forward(self, inputs, atype_tebd: Optional[torch.Tensor]=None, nlist_tebd: Optional[torch.Tensor]=None): + def forward( + self, + inputs, + atype_tebd: Optional[torch.Tensor] = None, + nlist_tebd: Optional[torch.Tensor] = None, + ): """Calculate decoded embedding for each atom. Args: - inputs: Descriptor matrix. Its shape is [nframes*natoms[0], len_descriptor]. - Returns: + Returns + ------- - `torch.Tensor`: Embedding contributed by me. Its shape is [nframes*natoms[0], 4, self.neuron[-1]]. """ - inputs_i = inputs[:, self.offset * 4:(self.offset + self.length) * 4] - inputs_reshape = inputs_i.reshape(-1, 4) # shape is [nframes*natoms[0]*self.length, 4] + inputs_i = inputs[:, self.offset * 4 : (self.offset + self.length) * 4] + inputs_reshape = inputs_i.reshape( + -1, 4 + ) # shape is [nframes*natoms[0]*self.length, 4] xyz_scatter = inputs_reshape[:, 0:1] # concat the tebd as input - if self.use_tebd and self.tebd_mode == 'concat': + if self.use_tebd and self.tebd_mode == "concat": assert nlist_tebd is not None and atype_tebd is not None nlist_tebd = nlist_tebd.reshape(-1, self.tebd_dim) atype_tebd = atype_tebd.reshape(-1, self.tebd_dim) @@ -209,7 +245,11 @@ def forward(self, inputs, atype_tebd: Optional[torch.Tensor]=None, nlist_tebd: O # [nframes * nloc * nnei, out_size] # dot the tebd output - if self.use_tebd and self.tebd_mode in ['dot', 'dot_residual_s', 'dot_residual_t']: + if self.use_tebd and self.tebd_mode in [ + "dot", + "dot_residual_s", + "dot_residual_t", + ]: assert nlist_tebd is not None and atype_tebd is not None nlist_tebd = nlist_tebd.reshape(-1, self.tebd_dim) atype_tebd = atype_tebd.reshape(-1, self.tebd_dim) @@ -218,15 +258,16 @@ def forward(self, inputs, atype_tebd: Optional[torch.Tensor]=None, nlist_tebd: O for linear in self.deep_layers_t: two_side_tebd = linear(two_side_tebd) # [nframes * nloc * nnei, out_size] - if self.tebd_mode == 'dot': + if self.tebd_mode == "dot": xyz_scatter = xyz_scatter * two_side_tebd - elif self.tebd_mode == 'dot_residual_s': + elif self.tebd_mode == "dot_residual_s": xyz_scatter = xyz_scatter * two_side_tebd + xyz_scatter - elif self.tebd_mode == 'dot_residual_t': + elif self.tebd_mode == "dot_residual_t": xyz_scatter = xyz_scatter * two_side_tebd + two_side_tebd - xyz_scatter = xyz_scatter.view(-1, self.length, - self.neuron[-1]) # shape is [nframes*natoms[0], self.length, self.neuron[-1]] + xyz_scatter = xyz_scatter.view( + -1, self.length, self.neuron[-1] + ) # shape is [nframes*natoms[0], self.length, self.neuron[-1]] if self.return_G: return xyz_scatter else: @@ -238,15 +279,16 @@ def forward(self, inputs, atype_tebd: Optional[torch.Tensor]=None, nlist_tebd: O class SimpleLinear(nn.Module): use_timestep: Final[bool] - def __init__(self, - num_in, - num_out, - bavg=0., - stddev=1., - use_timestep=False, - activate=None, - bias: bool = True, - ): + def __init__( + self, + num_in, + num_out, + bavg=0.0, + stddev=1.0, + use_timestep=False, + activate=None, + bias: bool = True, + ): """Construct a linear layer. Args: @@ -255,7 +297,7 @@ def __init__(self, - use_timestep: Apply time-step to weight. - activate: type of activate func. """ - super(SimpleLinear, self).__init__() + super().__init__() self.num_in = num_in self.num_out = num_out self.use_timestep = use_timestep @@ -264,16 +306,16 @@ def __init__(self, self.matrix = nn.Parameter(data=Tensor(num_in, num_out)) nn.init.normal_(self.matrix.data, std=stddev / np.sqrt(num_out + num_in)) if bias: - self.bias = nn.Parameter(data=Tensor(1, num_out)) - nn.init.normal_(self.bias.data, mean=bavg, std=stddev) + self.bias = nn.Parameter(data=Tensor(1, num_out)) + nn.init.normal_(self.bias.data, mean=bavg, std=stddev) else: - self.bias = None + self.bias = None if self.use_timestep: self.idt = nn.Parameter(data=Tensor(1, num_out)) nn.init.normal_(self.idt.data, mean=0.1, std=0.001) def forward(self, inputs): - """Return X*W+b.""" + """Return X*W+b.""" xw = torch.matmul(inputs, self.matrix) hidden = xw + self.bias if self.bias is not None else xw hidden = self.activate(hidden) @@ -284,13 +326,13 @@ def forward(self, inputs): class Linear(nn.Linear): def __init__( - self, - d_in: int, - d_out: int, - bias: bool = True, - init: str = "default", + self, + d_in: int, + d_out: int, + bias: bool = True, + init: str = "default", ): - super(Linear, self).__init__(d_in, d_out, bias=bias, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + super().__init__(d_in, d_out, bias=bias, dtype=env.GLOBAL_PT_FLOAT_PRECISION) self.use_bias = bias @@ -318,7 +360,7 @@ def _trunc_normal_init(self, scale=1.0): TRUNCATED_NORMAL_STDDEV_FACTOR = 0.87962566103423978 _, fan_in = self.weight.shape scale = scale / max(1, fan_in) - std = (scale ** 0.5) / TRUNCATED_NORMAL_STDDEV_FACTOR + std = (scale**0.5) / TRUNCATED_NORMAL_STDDEV_FACTOR nn.init.trunc_normal_(self.weight, mean=0.0, std=std) def _glorot_uniform_init(self): @@ -337,8 +379,7 @@ def _normal_init(self): class Transition(nn.Module): def __init__(self, d_in, n, dropout=0.0): - - super(Transition, self).__init__() + super().__init__() self.d_in = d_in self.n = n @@ -359,20 +400,19 @@ def forward( self, x: torch.Tensor, ) -> torch.Tensor: - x = self._transition(x=x) return x class Embedding(nn.Embedding): def __init__( - self, - num_embeddings: int, - embedding_dim: int, - padding_idx: int = None, - dtype=torch.float64, + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + dtype=torch.float64, ): - super(Embedding, self).__init__( + super().__init__( num_embeddings, embedding_dim, padding_idx=padding_idx, dtype=dtype ) self._normal_init() @@ -385,12 +425,8 @@ def _normal_init(self, std=0.02): class NonLinearHead(nn.Module): - def __init__(self, - input_dim, - out_dim, - activation_fn, - hidden=None): - super(NonLinearHead, self).__init__() + def __init__(self, input_dim, out_dim, activation_fn, hidden=None): + super().__init__() hidden = input_dim if not hidden else hidden self.linear1 = SimpleLinear(input_dim, hidden, activate=activation_fn) self.linear2 = SimpleLinear(hidden, out_dim) @@ -403,7 +439,7 @@ def forward(self, x): class NonLinear(nn.Module): def __init__(self, input, output_size, hidden=None): - super(NonLinear, self).__init__() + super().__init__() if hidden is None: hidden = input @@ -432,11 +468,15 @@ def __init__(self, embed_dim, output_dim, activation_fn, weight=None): self.layer_norm = nn.LayerNorm(embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) if weight is None: - weight = nn.Linear(embed_dim, output_dim, bias=False, dtype=env.GLOBAL_PT_FLOAT_PRECISION).weight + weight = nn.Linear( + embed_dim, output_dim, bias=False, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ).weight self.weight = weight - self.bias = nn.Parameter(torch.zeros(output_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION)) + self.bias = nn.Parameter( + torch.zeros(output_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + ) - def forward(self, features, masked_tokens: Optional[torch.Tensor]=None, **kwargs): + def forward(self, features, masked_tokens: Optional[torch.Tensor] = None, **kwargs): # Only project the masked tokens while training, # saves both memory and computation if masked_tokens is not None: @@ -451,8 +491,9 @@ def forward(self, features, masked_tokens: Optional[torch.Tensor]=None, **kwargs class ResidualDeep(nn.Module): - - def __init__(self, type_id, embedding_width, neuron, bias_atom_e, out_dim=1, resnet_dt=False): + def __init__( + self, type_id, embedding_width, neuron, bias_atom_e, out_dim=1, resnet_dt=False + ): """Construct a filter on the given element as neighbor. Args: @@ -461,9 +502,9 @@ def __init__(self, type_id, embedding_width, neuron, bias_atom_e, out_dim=1, res - neuron: Number of neurons in each hidden layers of the embedding net. - resnet_dt: Using time-step in the ResNet construction. """ - super(ResidualDeep, self).__init__() + super().__init__() self.type_id = type_id - self.neuron = [embedding_width] + neuron + self.neuron = [embedding_width, *neuron] self.out_dim = out_dim deep_layers = [] @@ -471,7 +512,9 @@ def __init__(self, type_id, embedding_width, neuron, bias_atom_e, out_dim=1, res one = SimpleLinear( num_in=self.neuron[ii - 1], num_out=self.neuron[ii], - use_timestep=(resnet_dt and ii > 1 and self.neuron[ii - 1] == self.neuron[ii]), + use_timestep=( + resnet_dt and ii > 1 and self.neuron[ii - 1] == self.neuron[ii] + ), activate="tanh", ) deep_layers.append(one) @@ -486,7 +529,8 @@ def forward(self, inputs): Args: - inputs: Embedding net output per atom. Its shape is [nframes*nloc, self.embedding_width]. - Returns: + Returns + ------- - `torch.Tensor`: Output layer with shape [nframes*nloc, self.neuron[-1]]. """ outputs = inputs @@ -500,29 +544,33 @@ def forward(self, inputs): class TypeEmbedNet(nn.Module): - def __init__(self, type_nums, embed_dim, bavg=0.0, stddev=1.0): - """Construct a type embedding net. - """ - super(TypeEmbedNet, self).__init__() - self.embedding = nn.Embedding(type_nums + 1, embed_dim, padding_idx=type_nums, - dtype=env.GLOBAL_PT_FLOAT_PRECISION) + """Construct a type embedding net.""" + super().__init__() + self.embedding = nn.Embedding( + type_nums + 1, + embed_dim, + padding_idx=type_nums, + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + ) # nn.init.normal_(self.embedding.weight[:-1], mean=bavg, std=stddev) def forward(self, atype): """ - Args: - atype: Type of each input, [nframes, nloc] or [nframes, nloc, nnei] + atype: Type of each input, [nframes, nloc] or [nframes, nloc, nnei]. - Returns: - type_embedding: + Returns + ------- + type_embedding: """ return self.embedding(atype) def share_params(self, base_class, shared_level, resume=False): - assert self.__class__ == base_class.__class__, "Only TypeEmbedNet of the same type can share params!" + assert ( + self.__class__ == base_class.__class__ + ), "Only TypeEmbedNet of the same type can share params!" if shared_level == 0: # the following will successfully link all the params except buffers, which need manually link. for item in self._modules: @@ -548,8 +596,12 @@ def __init__(self, K=128, num_pair=512, std_width=1.0, start=0.0, stop=9.0): mean = torch.linspace(start, stop, K, dtype=env.GLOBAL_PT_FLOAT_PRECISION) self.std = (std_width * (mean[1] - mean[0])).item() self.register_buffer("mean", mean) - self.mul = Embedding(num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.bias = Embedding(num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.mul = Embedding( + num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) + self.bias = Embedding( + num_pair + 1, 1, padding_idx=num_pair, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) nn.init.constant_(self.bias.weight, 0) nn.init.constant_(self.mul.weight, 1.0) @@ -564,7 +616,17 @@ def forward(self, x, atom_pair): class GaussianEmbedding(nn.Module): - def __init__(self, rcut, kernel_num, num_pair, embed_dim, pair_embed_dim, sel, ntypes, atomic_sum_gbf): + def __init__( + self, + rcut, + kernel_num, + num_pair, + embed_dim, + pair_embed_dim, + sel, + ntypes, + atomic_sum_gbf, + ): """Construct a gaussian kernel based embedding of pair representation. Args: @@ -576,7 +638,7 @@ def __init__(self, rcut, kernel_num, num_pair, embed_dim, pair_embed_dim, sel, n sel: Number of neighbors. ntypes: Number of atom types. """ - super(GaussianEmbedding, self).__init__() + super().__init__() self.gbf = GaussianKernel(K=kernel_num, num_pair=num_pair, stop=rcut) self.gbf_proj = NonLinear(kernel_num, pair_embed_dim) self.embed_dim = embed_dim @@ -584,7 +646,9 @@ def __init__(self, rcut, kernel_num, num_pair, embed_dim, pair_embed_dim, sel, n self.atomic_sum_gbf = atomic_sum_gbf if self.atomic_sum_gbf: if kernel_num != self.embed_dim: - self.edge_proj = torch.nn.Linear(kernel_num, self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.edge_proj = torch.nn.Linear( + kernel_num, self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) else: self.edge_proj = None self.ntypes = ntypes @@ -599,10 +663,11 @@ def forward(self, coord_selected, atom_feature, edge_type_2dim, edge_feature): edge_type_2dim: Edge index for gbf calculation with shape [nframes*nloc, natoms, natoms, 2]. edge_feature: Previous calculated edge features with shape [nframes*nloc, natoms, natoms, pair_dim]. - Returns: - atom_feature: Updated atomic features with shape [nframes*nloc, natoms, embed_dim]. - attn_bias: Updated edge features as attention bias with shape [nframes*nloc, natoms, natoms, pair_dim]. - delta_pos: Delta position for force/vector prediction with shape [nframes*nloc, natoms, natoms, 3]. + Returns + ------- + atom_feature: Updated atomic features with shape [nframes*nloc, natoms, embed_dim]. + attn_bias: Updated edge features as attention bias with shape [nframes*nloc, natoms, natoms, pair_dim]. + delta_pos: Delta position for force/vector prediction with shape [nframes*nloc, natoms, natoms, 3]. """ ncluster, natoms, _ = coord_selected.shape # ncluster x natoms x natoms x 3 @@ -628,42 +693,63 @@ def forward(self, coord_selected, atom_feature, edge_type_2dim, edge_feature): class NeighborWiseAttention(nn.Module): - def __init__(self, layer_num, nnei, embed_dim, hidden_dim, dotr=False, do_mask=False, post_ln=True, - ffn=False, ffn_embed_dim=1024, activation="tanh", scaling_factor=1.0, - head_num=1, normalize=True, temperature=None): - """Construct a neighbor-wise attention net. - """ - super(NeighborWiseAttention, self).__init__() + def __init__( + self, + layer_num, + nnei, + embed_dim, + hidden_dim, + dotr=False, + do_mask=False, + post_ln=True, + ffn=False, + ffn_embed_dim=1024, + activation="tanh", + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + ): + """Construct a neighbor-wise attention net.""" + super().__init__() self.layer_num = layer_num attention_layers = [] for i in range(self.layer_num): - attention_layers.append(NeighborWiseAttentionLayer(nnei, embed_dim, hidden_dim, - dotr=dotr, do_mask=do_mask, - post_ln=post_ln, ffn=ffn, - ffn_embed_dim=ffn_embed_dim, - activation=activation, - scaling_factor=scaling_factor, - head_num=head_num, - normalize=normalize, - temperature=temperature)) + attention_layers.append( + NeighborWiseAttentionLayer( + nnei, + embed_dim, + hidden_dim, + dotr=dotr, + do_mask=do_mask, + post_ln=post_ln, + ffn=ffn, + ffn_embed_dim=ffn_embed_dim, + activation=activation, + scaling_factor=scaling_factor, + head_num=head_num, + normalize=normalize, + temperature=temperature, + ) + ) self.attention_layers = nn.ModuleList(attention_layers) def forward( - self, + self, input_G, nei_mask, - input_r: Optional[torch.Tensor]=None, - sw: Optional[torch.Tensor]=None, + input_r: Optional[torch.Tensor] = None, + sw: Optional[torch.Tensor] = None, ): """ - Args: - input_G: Input G, [nframes * nloc, nnei, embed_dim] - nei_mask: neighbor mask, [nframes * nloc, nnei] - input_r: normalized radial, [nframes, nloc, nei, 3] + input_G: Input G, [nframes * nloc, nnei, embed_dim]. + nei_mask: neighbor mask, [nframes * nloc, nnei]. + input_r: normalized radial, [nframes, nloc, nei, 3]. - Returns: - out: Output G, [nframes * nloc, nnei, embed_dim] + Returns + ------- + out: Output G, [nframes * nloc, nnei, embed_dim] """ out = input_G @@ -676,12 +762,24 @@ def forward( class NeighborWiseAttentionLayer(nn.Module): ffn: Final[bool] - def __init__(self, nnei, embed_dim, hidden_dim, dotr=False, do_mask=False, post_ln=True, - ffn=False, ffn_embed_dim=1024, activation="tanh", scaling_factor=1.0, - head_num=1, normalize=True, temperature=None): - """Construct a neighbor-wise attention layer. - """ - super(NeighborWiseAttentionLayer, self).__init__() + def __init__( + self, + nnei, + embed_dim, + hidden_dim, + dotr=False, + do_mask=False, + post_ln=True, + ffn=False, + ffn_embed_dim=1024, + activation="tanh", + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + ): + """Construct a neighbor-wise attention layer.""" + super().__init__() self.nnei = nnei self.embed_dim = embed_dim self.hidden_dim = hidden_dim @@ -689,23 +787,39 @@ def __init__(self, nnei, embed_dim, hidden_dim, dotr=False, do_mask=False, post_ self.do_mask = do_mask self.post_ln = post_ln self.ffn = ffn - self.attention_layer = GatedSelfAttetion(nnei, embed_dim, hidden_dim, dotr=dotr, do_mask=do_mask, - scaling_factor=scaling_factor, head_num=head_num, normalize=normalize, - temperature=temperature) - self.attn_layer_norm = nn.LayerNorm(self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.attention_layer = GatedSelfAttetion( + nnei, + embed_dim, + hidden_dim, + dotr=dotr, + do_mask=do_mask, + scaling_factor=scaling_factor, + head_num=head_num, + normalize=normalize, + temperature=temperature, + ) + self.attn_layer_norm = nn.LayerNorm( + self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) if self.ffn: self.ffn_embed_dim = ffn_embed_dim - self.fc1 = nn.Linear(self.embed_dim, self.ffn_embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.fc1 = nn.Linear( + self.embed_dim, self.ffn_embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) self.activation_fn = get_activation_fn(activation) - self.fc2 = nn.Linear(self.ffn_embed_dim, self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.final_layer_norm = nn.LayerNorm(self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.fc2 = nn.Linear( + self.ffn_embed_dim, self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) + self.final_layer_norm = nn.LayerNorm( + self.embed_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) def forward( self, x, nei_mask, - input_r: Optional[torch.Tensor]=None, - sw: Optional[torch.Tensor]=None, + input_r: Optional[torch.Tensor] = None, + sw: Optional[torch.Tensor] = None, ): residual = x if not self.post_ln: @@ -728,11 +842,22 @@ def forward( class GatedSelfAttetion(nn.Module): - def __init__(self, nnei, embed_dim, hidden_dim, dotr=False, do_mask=False, scaling_factor=1.0, - head_num=1, normalize=True, temperature=None, bias=True, smooth=True): - """Construct a neighbor-wise attention net. - """ - super(GatedSelfAttetion, self).__init__() + def __init__( + self, + nnei, + embed_dim, + hidden_dim, + dotr=False, + do_mask=False, + scaling_factor=1.0, + head_num=1, + normalize=True, + temperature=None, + bias=True, + smooth=True, + ): + """Construct a neighbor-wise attention net.""" + super().__init__() self.nnei = nnei self.embed_dim = embed_dim self.hidden_dim = hidden_dim @@ -744,27 +869,36 @@ def __init__(self, nnei, embed_dim, hidden_dim, dotr=False, do_mask=False, scali else: self.scaling = temperature self.normalize = normalize - self.in_proj = SimpleLinear(embed_dim, hidden_dim * 3, bavg=0., stddev=1., use_timestep=False, bias=bias) - self.out_proj = SimpleLinear(hidden_dim, embed_dim, bavg=0., stddev=1., use_timestep=False, bias=bias) + self.in_proj = SimpleLinear( + embed_dim, + hidden_dim * 3, + bavg=0.0, + stddev=1.0, + use_timestep=False, + bias=bias, + ) + self.out_proj = SimpleLinear( + hidden_dim, embed_dim, bavg=0.0, stddev=1.0, use_timestep=False, bias=bias + ) self.smooth = smooth def forward( self, query, nei_mask, - input_r: Optional[torch.Tensor]=None, - sw: Optional[torch.Tensor]=None, + input_r: Optional[torch.Tensor] = None, + sw: Optional[torch.Tensor] = None, attnw_shift: float = 20.0, ): """ - Args: - query: input G, [nframes * nloc, nnei, embed_dim] - nei_mask: neighbor mask, [nframes * nloc, nnei] - input_r: normalized radial, [nframes, nloc, nei, 3] + query: input G, [nframes * nloc, nnei, embed_dim]. + nei_mask: neighbor mask, [nframes * nloc, nnei]. + input_r: normalized radial, [nframes, nloc, nei, 3]. - Returns: - type_embedding: + Returns + ------- + type_embedding: """ q, k, v = self.in_proj(query).chunk(3, dim=-1) @@ -786,14 +920,18 @@ def forward( # [nframes * nloc, nnei] assert sw is not None sw = sw.view([-1, self.nnei]) - attn_weights = (attn_weights + attnw_shift) * sw[:,:,None] * sw[:,None,:] - attnw_shift + attn_weights = (attn_weights + attnw_shift) * sw[:, :, None] * sw[ + :, None, : + ] - attnw_shift else: - attn_weights = attn_weights.masked_fill(~nei_mask.unsqueeze(1), float("-inf")) + attn_weights = attn_weights.masked_fill( + ~nei_mask.unsqueeze(1), float("-inf") + ) attn_weights = F.softmax(attn_weights, dim=-1) - attn_weights = attn_weights.masked_fill(~nei_mask.unsqueeze(-1), float(0.0)) - if self.smooth: + attn_weights = attn_weights.masked_fill(~nei_mask.unsqueeze(-1), 0.0) + if self.smooth: assert sw is not None - attn_weights = attn_weights * sw[:,:,None] * sw[:,None,:] + attn_weights = attn_weights * sw[:, :, None] * sw[:, None, :] if self.dotr: assert input_r is not None, "input_r must be provided when dotr is True!" angular_weight = torch.bmm(input_r, input_r.transpose(1, 2)) @@ -805,29 +943,39 @@ def forward( class LocalSelfMultiheadAttention(nn.Module): def __init__(self, feature_dim, attn_head, scaling_factor=1.0): - super(LocalSelfMultiheadAttention, self).__init__() + super().__init__() self.feature_dim = feature_dim self.attn_head = attn_head self.head_dim = feature_dim // attn_head - assert feature_dim % attn_head == 0, f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" + assert ( + feature_dim % attn_head == 0 + ), f"feature_dim {feature_dim} must be divided by attn_head {attn_head}!" self.scaling = (self.head_dim * scaling_factor) ** -0.5 self.in_proj = SimpleLinear(self.feature_dim, self.feature_dim * 3) # TODO debug # self.out_proj = SimpleLinear(self.feature_dim, self.feature_dim) - def forward(self, query, attn_bias: Optional[torch.Tensor]=None, nlist_mask: Optional[torch.Tensor]=None, nlist: Optional[torch.Tensor]=None, return_attn=True): + def forward( + self, + query, + attn_bias: Optional[torch.Tensor] = None, + nlist_mask: Optional[torch.Tensor] = None, + nlist: Optional[torch.Tensor] = None, + return_attn=True, + ): nframes, nloc, feature_dim = query.size() _, _, nnei = nlist.size() assert feature_dim == self.feature_dim # [nframes, nloc, feature_dim] q, k, v = self.in_proj(query).chunk(3, dim=-1) # [nframes * attn_head * nloc, 1, head_dim] - q = (q.view(nframes, nloc, self.attn_head, self.head_dim) - .transpose(1, 2) - .contiguous() - .view(nframes * self.attn_head * nloc, 1, self.head_dim) - * self.scaling - ) + q = ( + q.view(nframes, nloc, self.attn_head, self.head_dim) + .transpose(1, 2) + .contiguous() + .view(nframes * self.attn_head * nloc, 1, self.head_dim) + * self.scaling + ) # [nframes, nloc, feature_dim] --> [nframes, nloc + 1, feature_dim] # with nlist [nframes, nloc, nnei] --> [nframes, nloc, nnei, feature_dim] # padding = torch.zeros(feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION).to(k.device) @@ -840,36 +988,44 @@ def forward(self, query, attn_bias: Optional[torch.Tensor]=None, nlist_mask: Opt # [nframes, nloc * nnei, feature_dim] v = torch.gather(v, dim=1, index=index) # [nframes * attn_head * nloc, nnei, head_dim] - k = (k.view(nframes, nloc, nnei, self.attn_head, self.head_dim) - .permute(0, 3, 1, 2, 4) - .contiguous() - .view(nframes * self.attn_head * nloc, nnei, self.head_dim)) - v = (v.view(nframes, nloc, nnei, self.attn_head, self.head_dim) - .permute(0, 3, 1, 2, 4) - .contiguous() - .view(nframes * self.attn_head * nloc, nnei, self.head_dim)) + k = ( + k.view(nframes, nloc, nnei, self.attn_head, self.head_dim) + .permute(0, 3, 1, 2, 4) + .contiguous() + .view(nframes * self.attn_head * nloc, nnei, self.head_dim) + ) + v = ( + v.view(nframes, nloc, nnei, self.attn_head, self.head_dim) + .permute(0, 3, 1, 2, 4) + .contiguous() + .view(nframes * self.attn_head * nloc, nnei, self.head_dim) + ) # [nframes * attn_head * nloc, 1, nnei] attn_weights = torch.bmm(q, k.transpose(1, 2)) # maskfill # [nframes, attn_head, nloc, nnei] - attn_weights = (attn_weights.view(nframes, self.attn_head, nloc, nnei) - .masked_fill(~nlist_mask.unsqueeze(1), float("-inf"))) + attn_weights = attn_weights.view( + nframes, self.attn_head, nloc, nnei + ).masked_fill(~nlist_mask.unsqueeze(1), float("-inf")) # add bias if return_attn: attn_weights = attn_weights + attn_bias # softmax # [nframes * attn_head * nloc, 1, nnei] - attn = F.softmax(attn_weights, dim=-1).view(nframes * self.attn_head * nloc, 1, nnei) + attn = F.softmax(attn_weights, dim=-1).view( + nframes * self.attn_head * nloc, 1, nnei + ) # bmm # [nframes * attn_head * nloc, 1, head_dim] o = torch.bmm(attn, v) assert list(o.size()) == [nframes * self.attn_head * nloc, 1, self.head_dim] # [nframes, nloc, feature_dim] - o = (o.view(nframes, self.attn_head, nloc, self.head_dim) - .transpose(1, 2) - .contiguous() - .view(nframes, nloc, self.feature_dim) - ) + o = ( + o.view(nframes, self.attn_head, nloc, self.head_dim) + .transpose(1, 2) + .contiguous() + .view(nframes, nloc, self.feature_dim) + ) # out ## TODO debug: # o = self.out_proj(o) @@ -895,7 +1051,7 @@ def __init__( self.v_proj = Linear(embed_dim, embed_dim, bias=False, init="glorot") self.num_heads = num_head self.head_dim = embed_dim // num_head - self.scaling = self.head_dim ** -0.5 + self.scaling = self.head_dim**-0.5 self.force_proj = Linear(embed_dim, 1, init="final", bias=False) self.linear_bias = Linear(pair_dim, num_head) self.dropout = 0.1 @@ -917,12 +1073,22 @@ def forward( # [ncluster, attn_head, natoms, head_dim] q = ( - self.q_proj(query).view(ncluster, natoms, self.num_heads, -1).transpose(1, 2) + self.q_proj(query) + .view(ncluster, natoms, self.num_heads, -1) + .transpose(1, 2) * self.scaling ) # [ncluster, attn_head, natoms, head_dim] - k = self.k_proj(query).view(ncluster, natoms, self.num_heads, -1).transpose(1, 2) - v = self.v_proj(query).view(ncluster, natoms, self.num_heads, -1).transpose(1, 2) + k = ( + self.k_proj(query) + .view(ncluster, natoms, self.num_heads, -1) + .transpose(1, 2) + ) + v = ( + self.v_proj(query) + .view(ncluster, natoms, self.num_heads, -1) + .transpose(1, 2) + ) # [ncluster, attn_head, natoms, natoms] attn = q @ k.transpose(-1, -2) del q, k @@ -974,14 +1140,18 @@ def forward(self, x): class OuterProduct(nn.Module): def __init__(self, d_atom, d_pair, d_hid=32): - super(OuterProduct, self).__init__() + super().__init__() self.d_atom = d_atom self.d_pair = d_pair self.d_hid = d_hid - self.linear_in = nn.Linear(d_atom, d_hid*2, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.linear_out = nn.Linear(d_hid**2, d_pair, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.linear_in = nn.Linear( + d_atom, d_hid * 2, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) + self.linear_out = nn.Linear( + d_hid**2, d_pair, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) self.act = nn.GELU() def _opm(self, a, b): @@ -1013,16 +1183,16 @@ def forward( class Attention(nn.Module): def __init__( - self, - q_dim: int, - k_dim: int, - v_dim: int, - head_dim: int, - num_heads: int, - gating: bool = False, - dropout: float = 0.0, + self, + q_dim: int, + k_dim: int, + v_dim: int, + head_dim: int, + num_heads: int, + gating: bool = False, + dropout: float = 0.0, ): - super(Attention, self).__init__() + super().__init__() self.num_heads = num_heads self.head_dim = head_dim @@ -1038,16 +1208,16 @@ def __init__( if self.gating: self.linear_g = Linear(q_dim, total_dim, init="gating") # precompute the 1/sqrt(head_dim) - self.norm = head_dim ** -0.5 + self.norm = head_dim**-0.5 self.dropout = dropout def forward( - self, - q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - bias: torch.Tensor, - mask: torch.Tensor = None, + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + bias: torch.Tensor, + mask: torch.Tensor = None, ) -> torch.Tensor: nframes, nloc, embed_dim = q.size() g = None @@ -1104,17 +1274,17 @@ def forward( class AtomAttention(nn.Module): def __init__( - self, - q_dim: int, - k_dim: int, - v_dim: int, - pair_dim: int, - head_dim: int, - num_heads: int, - gating: bool = False, - dropout: float = 0.0, + self, + q_dim: int, + k_dim: int, + v_dim: int, + pair_dim: int, + head_dim: int, + num_heads: int, + gating: bool = False, + dropout: float = 0.0, ): - super(AtomAttention, self).__init__() + super().__init__() self.mha = Attention( q_dim, k_dim, v_dim, head_dim, num_heads, gating=gating, dropout=dropout @@ -1123,13 +1293,13 @@ def __init__( self.linear_bias = Linear(pair_dim, num_heads) def forward( - self, - q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - nlist: torch.Tensor, - pair: torch.Tensor, - mask: torch.Tensor = None, + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + nlist: torch.Tensor, + pair: torch.Tensor, + mask: torch.Tensor = None, ) -> torch.Tensor: pair = self.layer_norm(pair) bias = self.linear_bias(pair).permute(0, 3, 1, 2).contiguous() @@ -1138,7 +1308,7 @@ def forward( class TriangleMultiplication(nn.Module): def __init__(self, d_pair, d_hid): - super(TriangleMultiplication, self).__init__() + super().__init__() self.linear_ab_p = Linear(d_pair, d_hid * 2) self.linear_ab_g = Linear(d_pair, d_hid * 2, init="gating") @@ -1153,7 +1323,6 @@ def forward( z: torch.Tensor, mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: - # z : [nframes, nloc, nloc, pair_dim] # [nframes, nloc, nloc, pair_dim] @@ -1193,29 +1362,44 @@ def forward( class EvoformerEncoderLayer(nn.Module): - def __init__(self, - feature_dim: int = 768, - ffn_dim: int = 2048, - attn_head: int = 8, - activation_fn: str = "gelu", - post_ln: bool = False): - super(EvoformerEncoderLayer, self).__init__() + def __init__( + self, + feature_dim: int = 768, + ffn_dim: int = 2048, + attn_head: int = 8, + activation_fn: str = "gelu", + post_ln: bool = False, + ): + super().__init__() self.feature_dim = feature_dim self.ffn_dim = ffn_dim self.attn_head = attn_head - self.activation_fn = get_activation_fn(activation_fn) if activation_fn is not None else None + self.activation_fn = ( + get_activation_fn(activation_fn) if activation_fn is not None else None + ) self.post_ln = post_ln - self.self_attn_layer_norm = nn.LayerNorm(self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.self_attn_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) self.self_attn = LocalSelfMultiheadAttention( self.feature_dim, self.attn_head, ) - self.final_layer_norm = nn.LayerNorm(self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.final_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) self.fc1 = SimpleLinear(self.feature_dim, self.ffn_dim) self.fc2 = SimpleLinear(self.ffn_dim, self.feature_dim) - def forward(self, x, attn_bias: Optional[torch.Tensor]=None, nlist_mask: Optional[torch.Tensor]=None, nlist: Optional[torch.Tensor]=None, return_attn=True): + def forward( + self, + x, + attn_bias: Optional[torch.Tensor] = None, + nlist_mask: Optional[torch.Tensor] = None, + nlist: Optional[torch.Tensor] = None, + return_attn=True, + ): residual = x if not self.post_ln: x = self.self_attn_layer_norm(x) @@ -1249,23 +1433,25 @@ def forward(self, x, attn_bias: Optional[torch.Tensor]=None, nlist_mask: Optiona # output: atomic_rep, transformed_atomic_rep, pair_rep, delta_pair_rep, norm_x, norm_delta_pair_rep, class Evoformer2bEncoder(nn.Module): - def __init__(self, - nnei: int, - layer_num: int = 6, - attn_head: int = 8, - atomic_dim: int = 1024, - pair_dim: int = 100, - feature_dim: int = 1024, - ffn_dim: int = 2048, - post_ln: bool = False, - final_layer_norm: bool = True, - final_head_layer_norm: bool = False, - emb_layer_norm: bool = False, - atomic_residual: bool = False, - evo_residual: bool = False, - residual_factor: float = 1.0, - activation_function: str = "gelu"): - super(Evoformer2bEncoder, self).__init__() + def __init__( + self, + nnei: int, + layer_num: int = 6, + attn_head: int = 8, + atomic_dim: int = 1024, + pair_dim: int = 100, + feature_dim: int = 1024, + ffn_dim: int = 2048, + post_ln: bool = False, + final_layer_norm: bool = True, + final_head_layer_norm: bool = False, + emb_layer_norm: bool = False, + atomic_residual: bool = False, + evo_residual: bool = False, + residual_factor: float = 1.0, + activation_function: str = "gelu", + ): + super().__init__() self.nnei = nnei self.layer_num = layer_num self.attn_head = attn_head @@ -1284,29 +1470,49 @@ def __init__(self, self.atomic_residual = True else: self.atomic_residual = False - self.in_proj = SimpleLinear(self.atomic_dim, self.feature_dim, bavg=0., stddev=1., use_timestep=False, - activate='tanh') # TODO - self.out_proj = SimpleLinear(self.feature_dim, self.atomic_dim, bavg=0., stddev=1., use_timestep=False, - activate='tanh') + self.in_proj = SimpleLinear( + self.atomic_dim, + self.feature_dim, + bavg=0.0, + stddev=1.0, + use_timestep=False, + activate="tanh", + ) # TODO + self.out_proj = SimpleLinear( + self.feature_dim, + self.atomic_dim, + bavg=0.0, + stddev=1.0, + use_timestep=False, + activate="tanh", + ) if self._emb_layer_norm: - self.emb_layer_norm = nn.LayerNorm(self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.emb_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) ## TODO debug : self.in_proj_pair = NonLinearHead(self.pair_dim, self.attn_head, activation_fn=None) self.in_proj_pair = SimpleLinear(self.pair_dim, self.attn_head, activate=None) evoformer_encoder_layers = [] for i in range(self.layer_num): - evoformer_encoder_layers.append(EvoformerEncoderLayer( - feature_dim=self.feature_dim, - ffn_dim=self.ffn_dim, - attn_head=self.attn_head, - activation_fn=self.activation_function, - post_ln=self.post_ln) + evoformer_encoder_layers.append( + EvoformerEncoderLayer( + feature_dim=self.feature_dim, + ffn_dim=self.ffn_dim, + attn_head=self.attn_head, + activation_fn=self.activation_function, + post_ln=self.post_ln, + ) ) self.evoformer_encoder_layers = nn.ModuleList(evoformer_encoder_layers) if self._final_layer_norm: - self.final_layer_norm = nn.LayerNorm(self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.final_layer_norm = nn.LayerNorm( + self.feature_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) if self._final_head_layer_norm: - self.final_head_layer_norm = nn.LayerNorm(self.attn_head, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.final_head_layer_norm = nn.LayerNorm( + self.attn_head, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): """Encoder the atomic and pair representations. @@ -1318,7 +1524,8 @@ def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): - nlist_type: Neighbor types with shape [nframes, nloc, nnei]. - nlist_mask: Neighbor mask with shape [nframes, nloc, nnei], `False` if blank. - Returns: + Returns + ------- - atomic_rep: Atomic representation after encoder with shape [nframes, nloc, feature_dim]. - transformed_atomic_rep: Transformed atomic representation after encoder with shape [nframes, nloc, atomic_dim]. - pair_rep: Pair representation after encoder with shape [nframes, nloc, nnei, attn_head]. @@ -1349,19 +1556,23 @@ def forward(self, atomic_rep, pair_rep, nlist, nlist_type, nlist_mask): for i in range(self.layer_num): atomic_rep, pair_rep, _ = self.evoformer_encoder_layers[i]( - atomic_rep, attn_bias=pair_rep, nlist_mask=nlist_mask, nlist=nlist, return_attn=True + atomic_rep, + attn_bias=pair_rep, + nlist_mask=nlist_mask, + nlist=nlist, + return_attn=True, ) def norm_loss(x, eps=1e-10, tolerance=1.0): # x = x.float() max_norm = x.shape[-1] ** 0.5 - norm = torch.sqrt(torch.sum(x ** 2, dim=-1) + eps) + norm = torch.sqrt(torch.sum(x**2, dim=-1) + eps) error = F.relu((norm - max_norm).abs() - tolerance) return error def masked_mean(mask, value, dim=-1, eps=1e-10): return ( - torch.sum(mask * value, dim=dim) / (eps + torch.sum(mask, dim=dim)) + torch.sum(mask * value, dim=dim) / (eps + torch.sum(mask, dim=dim)) ).mean() # atomic_rep shape: [nframes, nloc, feature_dim] @@ -1374,9 +1585,11 @@ def masked_mean(mask, value, dim=-1, eps=1e-10): delta_pair_rep = pair_rep - input_pair_rep delta_pair_rep = delta_pair_rep.masked_fill(~nlist_mask.unsqueeze(1), 0) # [nframes, nloc, nnei, attn_head] - delta_pair_rep = (delta_pair_rep.view(nframes, self.attn_head, nloc, nnei) - .permute(0, 2, 3, 1) - .contiguous()) + delta_pair_rep = ( + delta_pair_rep.view(nframes, self.attn_head, nloc, nnei) + .permute(0, 2, 3, 1) + .contiguous() + ) # [nframes, nloc, nnei] norm_delta_pair_rep = norm_loss(delta_pair_rep) @@ -1390,28 +1603,38 @@ def masked_mean(mask, value, dim=-1, eps=1e-10): transformed_atomic_rep = self.out_proj(atomic_rep) if self.evo_residual: - transformed_atomic_rep = (self.residual_factor * transformed_atomic_rep + input_atomic_rep) * (1/np.sqrt(2)) - - return atomic_rep, transformed_atomic_rep, pair_rep, delta_pair_rep, norm_x, norm_delta_pair_rep + transformed_atomic_rep = ( + self.residual_factor * transformed_atomic_rep + input_atomic_rep + ) * (1 / np.sqrt(2)) + + return ( + atomic_rep, + transformed_atomic_rep, + pair_rep, + delta_pair_rep, + norm_x, + norm_delta_pair_rep, + ) class Evoformer3bEncoderLayer(nn.Module): - def __init__(self, - nnei, - embedding_dim: int = 768, - pair_dim: int = 64, - pair_hidden_dim: int = 32, - ffn_embedding_dim: int = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - droppath_prob: float = 0.0, - pair_dropout: float = 0.25, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - pre_ln: bool = True, - tri_update: bool = True - ): - super(Evoformer3bEncoderLayer, self).__init__() + def __init__( + self, + nnei, + embedding_dim: int = 768, + pair_dim: int = 64, + pair_hidden_dim: int = 32, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + droppath_prob: float = 0.0, + pair_dropout: float = 0.25, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + pre_ln: bool = True, + tri_update: bool = True, + ): + super().__init__() # Initialize parameters self.nnei = nnei self.embedding_dim = embedding_dim @@ -1429,21 +1652,40 @@ def __init__(self, # self.self_attn = AtomAttentionLocal(embedding_dim, embedding_dim, embedding_dim, pair_dim, # embedding_dim // num_attention_heads, num_attention_heads, # gating=False, dropout=attention_dropout) - self.self_attn = AtomAttention(embedding_dim, embedding_dim, embedding_dim, pair_dim, - embedding_dim // num_attention_heads, num_attention_heads, - gating=False, dropout=attention_dropout) + self.self_attn = AtomAttention( + embedding_dim, + embedding_dim, + embedding_dim, + pair_dim, + embedding_dim // num_attention_heads, + num_attention_heads, + gating=False, + dropout=attention_dropout, + ) # layer norm associated with the self attention layer self.pre_ln = pre_ln - self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.final_layer_norm = nn.LayerNorm(self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.self_attn_layer_norm = nn.LayerNorm( + self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) + self.fc1 = nn.Linear( + self.embedding_dim, ffn_embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) + self.fc2 = nn.Linear( + ffn_embedding_dim, self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) + self.final_layer_norm = nn.LayerNorm( + self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) - self.x_layer_norm_opm = nn.LayerNorm(self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.x_layer_norm_opm = nn.LayerNorm( + self.embedding_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) # self.opm = OuterProductLocal(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) self.opm = OuterProduct(self.embedding_dim, pair_dim, d_hid=pair_hidden_dim) # self.pair_layer_norm_opm = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) - self.pair_layer_norm_ffn = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.pair_layer_norm_ffn = nn.LayerNorm( + pair_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) self.pair_ffn = Transition( pair_dim, 1, @@ -1452,7 +1694,9 @@ def __init__(self, self.pair_dropout = pair_dropout self.tri_update = tri_update if self.tri_update: - self.pair_layer_norm_trimul = nn.LayerNorm(pair_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION) + self.pair_layer_norm_trimul = nn.LayerNorm( + pair_dim, dtype=env.GLOBAL_PT_FLOAT_PRECISION + ) self.pair_tri_mul = TriangleMultiplication(pair_dim, pair_hidden_dim) def update_pair( @@ -1467,7 +1711,9 @@ def update_pair( # [nframes, nloc, nnei, pair_dim] # global: # [nframes, nloc, nloc, pair_dim] - pair = pair + self.dropout_module(self.opm(self.x_layer_norm_opm(x), nlist, op_mask, op_norm)) + pair = pair + self.dropout_module( + self.opm(self.x_layer_norm_opm(x), nlist, op_mask, op_norm) + ) if not self.pre_ln: pair = self.pair_layer_norm_opm(pair) return x, pair @@ -1479,15 +1725,16 @@ def shared_dropout(self, x, shared_dim, dropout): mask = x.new_ones(shape) return F.dropout(mask, p=dropout, training=self.training) * x - def forward(self, - x: torch.Tensor, - pair: torch.Tensor, - nlist: torch.Tensor = None, - attn_mask: Optional[torch.Tensor] = None, - pair_mask: Optional[torch.Tensor] = None, - op_mask: float = 1.0, - op_norm: float = 1.0, - ): + def forward( + self, + x: torch.Tensor, + pair: torch.Tensor, + nlist: torch.Tensor = None, + attn_mask: Optional[torch.Tensor] = None, + pair_mask: Optional[torch.Tensor] = None, + op_mask: float = 1.0, + op_norm: float = 1.0, + ): """Encoder the atomic and pair representations. Args: @@ -1496,7 +1743,6 @@ def forward(self, - attn_mask: Attention mask with shape [ncluster, head, natoms, natoms]. - pair_mask: Neighbor mask with shape [ncluster, natoms, natoms]. - Returns: """ # [ncluster, natoms, embed_dim] residual = x @@ -1533,11 +1779,11 @@ def forward(self, block = [ partial( - self.update_pair, - nlist=nlist, - op_mask=op_mask, - op_norm=op_norm, - ) + self.update_pair, + nlist=nlist, + op_mask=op_mask, + op_norm=op_norm, + ) ] x, pair = checkpoint_sequential( @@ -1568,24 +1814,25 @@ def forward(self, class Evoformer3bEncoder(nn.Module): - def __init__(self, - nnei, - layer_num=6, - attn_head=8, - atomic_dim=768, - pair_dim=64, - pair_hidden_dim=32, - ffn_embedding_dim=3072, - dropout: float = 0.1, - droppath_prob: float = 0.0, - pair_dropout: float = 0.25, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - pre_ln: bool = True, - tri_update: bool = True, - **kwargs, - ): - super(Evoformer3bEncoder, self).__init__() + def __init__( + self, + nnei, + layer_num=6, + attn_head=8, + atomic_dim=768, + pair_dim=64, + pair_hidden_dim=32, + ffn_embedding_dim=3072, + dropout: float = 0.1, + droppath_prob: float = 0.0, + pair_dropout: float = 0.25, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + pre_ln: bool = True, + tri_update: bool = True, + **kwargs, + ): + super().__init__() self.nnei = nnei if droppath_prob > 0: droppath_probs = [ @@ -1596,20 +1843,26 @@ def __init__(self, self.layers = nn.ModuleList( [ - Evoformer3bEncoderLayer(nnei, atomic_dim, pair_dim, pair_hidden_dim, ffn_embedding_dim, - num_attention_heads=attn_head, - dropout=dropout, droppath_prob=droppath_probs[_], pair_dropout=pair_dropout, - attention_dropout=attention_dropout, activation_dropout=activation_dropout, - pre_ln=pre_ln, tri_update=tri_update) + Evoformer3bEncoderLayer( + nnei, + atomic_dim, + pair_dim, + pair_hidden_dim, + ffn_embedding_dim, + num_attention_heads=attn_head, + dropout=dropout, + droppath_prob=droppath_probs[_], + pair_dropout=pair_dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + pre_ln=pre_ln, + tri_update=tri_update, + ) for _ in range(layer_num) ] ) - def forward( - self, - x, - pair, attn_mask=None, pair_mask=None, atom_mask=None - ): + def forward(self, x, pair, attn_mask=None, pair_mask=None, atom_mask=None): """Encoder the atomic and pair representations. Args: @@ -1619,9 +1872,10 @@ def forward( pair_mask: Pair mask (with 1 for real atom pair and 0 for padding) with shape [ncluster, natoms, natoms]. atom_mask: Atom mask (with 1 for real atom and 0 for padding) with shape [ncluster, natoms]. - Returns: - x: Atomic representation with shape [ncluster, natoms, atomic_dim]. - pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. + Returns + ------- + x: Atomic representation with shape [ncluster, natoms, atomic_dim]. + pair: Pair representation with shape [ncluster, natoms, natoms, pair_dim]. """ # [ncluster, natoms, 1] @@ -1638,6 +1892,6 @@ def forward( attn_mask=attn_mask, pair_mask=pair_mask, op_mask=op_mask, - op_norm=op_norm + op_norm=op_norm, ) return x, pair diff --git a/deepmd_pt/model/task/__init__.py b/deepmd_pt/model/task/__init__.py index 46308580..fcf46632 100644 --- a/deepmd_pt/model/task/__init__.py +++ b/deepmd_pt/model/task/__init__.py @@ -1,7 +1,34 @@ -from .task import TaskBaseMethod -from .fitting import Fitting -from .ener import EnergyFittingNet, EnergyFittingNetDirect -from .denoise import DenoiseNet -from .type_predict import TypePredictNet -from .dipole import DipoleFittingNetType -from .atten_lcc import FittingNetAttenLcc +# SPDX-License-Identifier: LGPL-3.0-or-later +from .atten_lcc import ( + FittingNetAttenLcc, +) +from .denoise import ( + DenoiseNet, +) +from .dipole import ( + DipoleFittingNetType, +) +from .ener import ( + EnergyFittingNet, + EnergyFittingNetDirect, +) +from .fitting import ( + Fitting, +) +from .task import ( + TaskBaseMethod, +) +from .type_predict import ( + TypePredictNet, +) + +__all__ = [ + "FittingNetAttenLcc", + "DenoiseNet", + "DipoleFittingNetType", + "EnergyFittingNet", + "EnergyFittingNetDirect", + "Fitting", + "TaskBaseMethod", + "TypePredictNet", +] diff --git a/deepmd_pt/model/task/atten_lcc.py b/deepmd_pt/model/task/atten_lcc.py index b4d78bac..be7c0a00 100644 --- a/deepmd_pt/model/task/atten_lcc.py +++ b/deepmd_pt/model/task/atten_lcc.py @@ -1,57 +1,38 @@ -import logging -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import torch import torch.nn as nn -from deepmd_pt.utils import env - -try: - from typing_extensions import Final -except: - from torch.jit import Final - -from deepmd_pt.model.network import EnergyHead, NodeTaskHead -from deepmd_pt.model.task import TaskBaseMethod +from deepmd_pt.model.network.network import ( + EnergyHead, + NodeTaskHead, +) +from deepmd_pt.model.task.task import ( + TaskBaseMethod, +) +from deepmd_pt.utils import ( + env, +) class FittingNetAttenLcc(TaskBaseMethod): - - def __init__(self, embedding_width, bias_atom_e, pair_embed_dim, attention_heads, **kwargs): - """ - - Args: - embedding_width: - bias_atom_e: - pair_embed_dim: - attention_heads: - **kwargs: - """ - super(FittingNetAttenLcc, self).__init__() + def __init__( + self, embedding_width, bias_atom_e, pair_embed_dim, attention_heads, **kwargs + ): + super().__init__() self.embedding_width = embedding_width self.engergy_proj = EnergyHead(self.embedding_width, 1) self.energe_agg_factor = nn.Embedding(4, 1, dtype=env.GLOBAL_PT_FLOAT_PRECISION) nn.init.normal_(self.energe_agg_factor.weight, 0, 0.01) bias_atom_e = torch.tensor(bias_atom_e) - self.register_buffer('bias_atom_e', bias_atom_e) + self.register_buffer("bias_atom_e", bias_atom_e) self.pair_embed_dim = pair_embed_dim self.attention_heads = attention_heads - self.node_proc = NodeTaskHead(self.embedding_width, self.pair_embed_dim, self.attention_heads) + self.node_proc = NodeTaskHead( + self.embedding_width, self.pair_embed_dim, self.attention_heads + ) self.node_proc.zero_init() def forward(self, output, pair, delta_pos, atype, nframes, nloc): - """ - - Args: - output: - pair: - delta_pos: - atype: - nframes: - nloc: - - Returns: - - """ # [nframes x nloc x tebd_dim] output_nloc = (output[:, 0, :]).reshape(nframes, nloc, self.embedding_width) # Optional: GRRG or mean of gbf TODO @@ -60,7 +41,9 @@ def forward(self, output, pair, delta_pos, atype, nframes, nloc): # [nframes, nloc] energy_out = self.engergy_proj(output_nloc).view(nframes, nloc) # [nframes, nloc] - energy_factor = self.energe_agg_factor(torch.zeros_like(atype)).view(nframes, nloc) + energy_factor = self.energe_agg_factor(torch.zeros_like(atype)).view( + nframes, nloc + ) energy_out = (energy_out * energy_factor) + self.bias_atom_e[atype] energy_out = energy_out.sum(dim=-1) @@ -70,5 +53,3 @@ def forward(self, output, pair, delta_pos, atype, nframes, nloc): # predict_force_nloc: [nframes x nloc x 3] predict_force_nloc = (predict_force[:, 0, :]).reshape(nframes, nloc, 3) return energy_out, predict_force_nloc - - diff --git a/deepmd_pt/model/task/denoise.py b/deepmd_pt/model/task/denoise.py index d0b20d07..598b46d3 100644 --- a/deepmd_pt/model/task/denoise.py +++ b/deepmd_pt/model/task/denoise.py @@ -1,34 +1,38 @@ -import logging -from typing import Optional -import numpy as np -import torch - -from deepmd_pt.utils import env -try: - from typing_extensions import Final -except: - from torch.jit import Final +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, +) -from deepmd_pt.model.network import NonLinearHead, MaskLMHead -from deepmd_pt.model.task import TaskBaseMethod +import torch +from deepmd.model_format import ( + FittingOutputDef, + OutputVariableDef, + fitting_check_output, +) -from deepmd_utils.model_format import ( - FittingOutputDef, - OutputVariableDef, - fitting_check_output, +from deepmd_pt.model.network.network import ( + MaskLMHead, + NonLinearHead, +) +from deepmd_pt.model.task.task import ( + TaskBaseMethod, +) +from deepmd_pt.utils import ( + env, ) @fitting_check_output class DenoiseNet(TaskBaseMethod): - - def __init__(self, - feature_dim, - ntypes, - attn_head=8, - prefactor=[0.5,0.5], - activation_function="gelu", - **kwargs): + def __init__( + self, + feature_dim, + ntypes, + attn_head=8, + prefactor=[0.5, 0.5], + activation_function="gelu", + **kwargs, + ): """Construct a denoise net. Args: @@ -42,34 +46,52 @@ def __init__(self, self.feature_dim = feature_dim self.ntypes = ntypes self.attn_head = attn_head - self.prefactor = torch.tensor(prefactor,dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) + self.prefactor = torch.tensor( + prefactor, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) self.lm_head = MaskLMHead( - embed_dim=self.feature_dim, - output_dim=ntypes, - activation_fn=activation_function, - weight=None, - ) + embed_dim=self.feature_dim, + output_dim=ntypes, + activation_fn=activation_function, + weight=None, + ) if not isinstance(self.attn_head, list): - self.pair2coord_proj = NonLinearHead(self.attn_head, 1, activation_fn=activation_function) + self.pair2coord_proj = NonLinearHead( + self.attn_head, 1, activation_fn=activation_function + ) else: self.pair2coord_proj = [] self.ndescriptor = len(self.attn_head) for ii in range(self.ndescriptor): - _pair2coord_proj = NonLinearHead(self.attn_head[ii], 1, activation_fn=activation_function) + _pair2coord_proj = NonLinearHead( + self.attn_head[ii], 1, activation_fn=activation_function + ) self.pair2coord_proj.append(_pair2coord_proj) self.pair2coord_proj = torch.nn.ModuleList(self.pair2coord_proj) - def output_def(self): - return FittingOutputDef([ - OutputVariableDef("updated_coord", [3], reduciable=False, differentiable=False), - OutputVariableDef("logits", [-1], reduciable=False, differentiable=False), - ]) - + return FittingOutputDef( + [ + OutputVariableDef( + "updated_coord", [3], reduciable=False, differentiable=False + ), + OutputVariableDef( + "logits", [-1], reduciable=False, differentiable=False + ), + ] + ) - def forward(self, pair_weights, diff, nlist_mask, features, sw, masked_tokens: Optional[torch.Tensor]=None): + def forward( + self, + pair_weights, + diff, + nlist_mask, + features, + sw, + masked_tokens: Optional[torch.Tensor] = None, + ): """Calculate the updated coord. Args: - coord: Input noisy coord with shape [nframes, nloc, 3]. @@ -77,26 +99,31 @@ def forward(self, pair_weights, diff, nlist_mask, features, sw, masked_tokens: O - diff: Input pair relative coord list with shape [nframes, nloc, nnei, 3]. - nlist_mask: Input nlist mask with shape [nframes, nloc, nnei]. - Returns: + Returns + ------- - denoised_coord: Denoised updated coord with shape [nframes, nloc, 3]. """ # [nframes, nloc, nnei, 1] logits = self.lm_head(features, masked_tokens=masked_tokens) if not isinstance(self.attn_head, list): attn_probs = self.pair2coord_proj(pair_weights) - out_coord = (attn_probs * diff).sum(dim=-2) / (sw.sum(dim=-1).unsqueeze(-1)+1e-6) + out_coord = (attn_probs * diff).sum(dim=-2) / ( + sw.sum(dim=-1).unsqueeze(-1) + 1e-6 + ) else: assert len(self.prefactor) == self.ndescriptor all_coord_update = [] assert len(pair_weights) == len(diff) == len(nlist_mask) == self.ndescriptor for ii in range(self.ndescriptor): _attn_probs = self.pair2coord_proj[ii](pair_weights[ii]) - _coord_update = (_attn_probs * diff[ii]).sum(dim=-2) / (nlist_mask[ii].sum(dim=-1).unsqueeze(-1)+1e-6) + _coord_update = (_attn_probs * diff[ii]).sum(dim=-2) / ( + nlist_mask[ii].sum(dim=-1).unsqueeze(-1) + 1e-6 + ) all_coord_update.append(_coord_update) out_coord = self.prefactor[0] * all_coord_update[0] - for ii in range(self.ndescriptor-1): - out_coord += self.prefactor[ii+1] * all_coord_update[ii+1] + for ii in range(self.ndescriptor - 1): + out_coord += self.prefactor[ii + 1] * all_coord_update[ii + 1] return { - "updated_coord": out_coord, + "updated_coord": out_coord, "logits": logits, } diff --git a/deepmd_pt/model/task/dipole.py b/deepmd_pt/model/task/dipole.py index 822d210a..cf806bc6 100644 --- a/deepmd_pt/model/task/dipole.py +++ b/deepmd_pt/model/task/dipole.py @@ -1,19 +1,20 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging -import numpy as np -import torch -from deepmd_pt.utils import env -try: - from typing_extensions import Final -except: - from torch.jit import Final +import torch -from deepmd_pt.model.network import ResidualDeep -from deepmd_pt.model.task import TaskBaseMethod +from deepmd_pt.model.network.network import ( + ResidualDeep, +) +from deepmd_pt.model.task.task import ( + TaskBaseMethod, +) class DipoleFittingNetType(TaskBaseMethod): - def __init__(self, ntypes, embedding_width, neuron, out_dim, resnet_dt=True, **kwargs): + def __init__( + self, ntypes, embedding_width, neuron, out_dim, resnet_dt=True, **kwargs + ): """Construct a fitting net for dipole. Args: @@ -23,19 +24,21 @@ def __init__(self, ntypes, embedding_width, neuron, out_dim, resnet_dt=True, **k - bias_atom_e: Average enery per atom for each element. - resnet_dt: Using time-step in the ResNet construction. """ - super(DipoleFittingNetType, self).__init__() + super().__init__() self.ntypes = ntypes self.embedding_width = embedding_width self.out_dim = out_dim filter_layers = [] - one = ResidualDeep(0, embedding_width, neuron, 0.0, out_dim=self.out_dim, resnet_dt=resnet_dt) + one = ResidualDeep( + 0, embedding_width, neuron, 0.0, out_dim=self.out_dim, resnet_dt=resnet_dt + ) filter_layers.append(one) self.filter_layers = torch.nn.ModuleList(filter_layers) - if 'seed' in kwargs: - logging.info('Set seed to %d in fitting net.', kwargs['seed']) - torch.manual_seed(kwargs['seed']) + if "seed" in kwargs: + logging.info("Set seed to %d in fitting net.", kwargs["seed"]) + torch.manual_seed(kwargs["seed"]) def forward(self, inputs, atype, atype_tebd, rot_mat): """Based on embedding net output, alculate total energy. @@ -46,14 +49,17 @@ def forward(self, inputs, atype, atype_tebd, rot_mat): - atype_tebd: Atom type embedding. Its shape is [nframes, nloc, tebd_dim] - rot_mat: GR during descriptor calculation. Its shape is [nframes * nloc, m1, 3]. - Returns: + Returns + ------- - vec_out: output vector. Its shape is [nframes, nloc, 3]. """ nframes, nloc, _ = inputs.size() if atype_tebd is not None: - inputs = torch.concat([inputs, atype_tebd], dim=-1) + inputs = torch.concat([inputs, atype_tebd], dim=-1) vec_out = self.filter_layers[0](inputs) # Shape is [nframes, nloc, m1] assert list(vec_out.size()) == [nframes, nloc, self.out_dim] vec_out = vec_out.view(-1, 1, self.out_dim) - vec_out = torch.bmm(vec_out, rot_mat).squeeze(-2).view(nframes, nloc, 3) # Shape is [nframes, nloc, 3] + vec_out = ( + torch.bmm(vec_out, rot_mat).squeeze(-2).view(nframes, nloc, 3) + ) # Shape is [nframes, nloc, 3] return vec_out diff --git a/deepmd_pt/model/task/ener.py b/deepmd_pt/model/task/ener.py index 82285c77..ea26c20f 100644 --- a/deepmd_pt/model/task/ener.py +++ b/deepmd_pt/model/task/ener.py @@ -1,34 +1,38 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging -import torch - -from deepmd_pt.utils import env -try: - from typing_extensions import Final -except: - from torch.jit import Final +from typing import ( + Optional, + Tuple, +) -from typing import Optional, List, Dict, Tuple -from deepmd_pt.model.network import ResidualDeep -from deepmd_pt.model.task import Fitting +import torch +from deepmd.model_format import ( + FittingOutputDef, + OutputVariableDef, + fitting_check_output, +) -from deepmd_utils.model_format import ( - FittingOutputDef, - OutputVariableDef, - fitting_check_output, +from deepmd_pt.model.network.network import ( + ResidualDeep, +) +from deepmd_pt.model.task.fitting import ( + Fitting, +) +from deepmd_pt.utils import ( + env, ) @Fitting.register("ener") @fitting_check_output class EnergyFittingNet(Fitting): - def __init__( - self, - ntypes, - embedding_width, - neuron, - bias_atom_e, - resnet_dt=True, + self, + ntypes, + embedding_width, + neuron, + bias_atom_e, + resnet_dt=True, use_tebd=True, **kwargs, ): @@ -46,45 +50,54 @@ def __init__( self.embedding_width = embedding_width self.use_tebd = use_tebd if not use_tebd: - assert self.ntypes == len(bias_atom_e), 'Element count mismatches!' + assert self.ntypes == len(bias_atom_e), "Element count mismatches!" bias_atom_e = torch.tensor(bias_atom_e) - self.register_buffer('bias_atom_e', bias_atom_e) + self.register_buffer("bias_atom_e", bias_atom_e) filter_layers = [] for type_i in range(self.ntypes): bias_type = 0.0 - one = ResidualDeep(type_i, embedding_width, neuron, bias_type, resnet_dt=resnet_dt) + one = ResidualDeep( + type_i, embedding_width, neuron, bias_type, resnet_dt=resnet_dt + ) filter_layers.append(one) self.filter_layers = torch.nn.ModuleList(filter_layers) - if 'seed' in kwargs: - logging.info('Set seed to %d in fitting net.', kwargs['seed']) - torch.manual_seed(kwargs['seed']) + if "seed" in kwargs: + logging.info("Set seed to %d in fitting net.", kwargs["seed"]) + torch.manual_seed(kwargs["seed"]) def output_def(self): - return FittingOutputDef([ - OutputVariableDef("energy", [1], reduciable=True, differentiable=True), - ]) - - def forward(self, - inputs: torch.Tensor, - atype: torch.Tensor, - atype_tebd: Optional[torch.Tensor] = None, - rot_mat: Optional[torch.Tensor] = None): + return FittingOutputDef( + [ + OutputVariableDef("energy", [1], reduciable=True, differentiable=True), + ] + ) + + def forward( + self, + inputs: torch.Tensor, + atype: torch.Tensor, + atype_tebd: Optional[torch.Tensor] = None, + rot_mat: Optional[torch.Tensor] = None, + ): """Based on embedding net output, alculate total energy. Args: - inputs: Embedding matrix. Its shape is [nframes, natoms[0], self.embedding_width]. - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. - Returns: + Returns + ------- - `torch.Tensor`: Total energy with shape [nframes, natoms[0]]. """ outs = torch.zeros_like(atype).unsqueeze(-1) # jit assertion if self.use_tebd: if atype_tebd is not None: inputs = torch.concat([inputs, atype_tebd], dim=-1) - atom_energy = self.filter_layers[0](inputs) + self.bias_atom_e[atype].unsqueeze(-1) + atom_energy = self.filter_layers[0](inputs) + self.bias_atom_e[ + atype + ].unsqueeze(-1) outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] else: for type_i, filter_layer in enumerate(self.filter_layers): @@ -92,18 +105,26 @@ def forward(self, atom_energy = filter_layer(inputs) atom_energy = atom_energy + self.bias_atom_e[type_i] atom_energy = atom_energy * mask.unsqueeze(-1) - outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] - return { - "energy": outs.to(env.GLOBAL_PT_FLOAT_PRECISION) - } + outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] + return {"energy": outs.to(env.GLOBAL_PT_FLOAT_PRECISION)} @Fitting.register("direct_force") @Fitting.register("direct_force_ener") @fitting_check_output class EnergyFittingNetDirect(Fitting): - - def __init__(self, ntypes, embedding_width, neuron, bias_atom_e, out_dim=1, resnet_dt=True, use_tebd=True, return_energy=False, **kwargs): + def __init__( + self, + ntypes, + embedding_width, + neuron, + bias_atom_e, + out_dim=1, + resnet_dt=True, + use_tebd=True, + return_energy=False, + **kwargs, + ): """Construct a fitting net for energy. Args: @@ -119,13 +140,20 @@ def __init__(self, ntypes, embedding_width, neuron, bias_atom_e, out_dim=1, resn self.use_tebd = use_tebd self.out_dim = out_dim if not use_tebd: - assert self.ntypes == len(bias_atom_e), 'Element count mismatches!' + assert self.ntypes == len(bias_atom_e), "Element count mismatches!" bias_atom_e = torch.tensor(bias_atom_e) - self.register_buffer('bias_atom_e', bias_atom_e) + self.register_buffer("bias_atom_e", bias_atom_e) filter_layers_dipole = [] for type_i in range(self.ntypes): - one = ResidualDeep(type_i, embedding_width, neuron, 0.0, out_dim=out_dim, resnet_dt=resnet_dt) + one = ResidualDeep( + type_i, + embedding_width, + neuron, + 0.0, + out_dim=out_dim, + resnet_dt=resnet_dt, + ) filter_layers_dipole.append(one) self.filter_layers_dipole = torch.nn.ModuleList(filter_layers_dipole) @@ -134,56 +162,70 @@ def __init__(self, ntypes, embedding_width, neuron, bias_atom_e, out_dim=1, resn if self.return_energy: 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) + one = ResidualDeep( + type_i, embedding_width, neuron, bias_type, resnet_dt=resnet_dt + ) filter_layers.append(one) self.filter_layers = torch.nn.ModuleList(filter_layers) - if 'seed' in kwargs: - logging.info('Set seed to %d in fitting net.', kwargs['seed']) - torch.manual_seed(kwargs['seed']) - + if "seed" in kwargs: + logging.info("Set seed to %d in fitting net.", kwargs["seed"]) + torch.manual_seed(kwargs["seed"]) def output_def(self): - return FittingOutputDef([ - OutputVariableDef("energy", [1], reduciable=True, differentiable=False), - OutputVariableDef("dforce", [3], reduciable=False, differentiable=False), - ]) - - def forward(self, - inputs: torch.Tensor, - atype: torch.Tensor, - atype_tebd: Optional[torch.Tensor] = None, - rot_mat: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, None]: + return FittingOutputDef( + [ + OutputVariableDef("energy", [1], reduciable=True, differentiable=False), + OutputVariableDef( + "dforce", [3], reduciable=False, differentiable=False + ), + ] + ) + + def forward( + self, + inputs: torch.Tensor, + atype: torch.Tensor, + atype_tebd: Optional[torch.Tensor] = None, + rot_mat: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, None]: """Based on embedding net output, alculate total energy. Args: - inputs: Embedding matrix. Its shape is [nframes, natoms[0], self.embedding_width]. - natoms: Tell atom count and element count. Its shape is [2+self.ntypes]. - Returns: + Returns + ------- - `torch.Tensor`: Total energy with shape [nframes, natoms[0]]. """ nframes, nloc, _ = inputs.size() if self.use_tebd: if atype_tebd is not None: inputs = torch.concat([inputs, atype_tebd], dim=-1) - vec_out = self.filter_layers_dipole[0](inputs) # Shape is [nframes, nloc, m1] + vec_out = self.filter_layers_dipole[0]( + inputs + ) # Shape is [nframes, nloc, m1] assert list(vec_out.size()) == [nframes, nloc, self.out_dim] vec_out = vec_out.view(-1, 1, self.out_dim) assert rot_mat is not None - vec_out = torch.bmm(vec_out, rot_mat).squeeze(-2).view(nframes, nloc, 3) # Shape is [nframes, nloc, 3] + vec_out = ( + torch.bmm(vec_out, rot_mat).squeeze(-2).view(nframes, nloc, 3) + ) # Shape is [nframes, nloc, 3] else: vec_out = torch.zeros_like(atype).unsqueeze(-1) # jit assertion for type_i, filter_layer in enumerate(self.filter_layers_dipole): mask = atype == type_i vec_out_type = filter_layer(inputs) # Shape is [nframes, nloc, m1] vec_out_type = vec_out_type * mask.unsqueeze(-1) - vec_out = vec_out + vec_out_type # Shape is [nframes, natoms[0], 1] + vec_out = vec_out + vec_out_type # Shape is [nframes, natoms[0], 1] outs = torch.zeros_like(atype).unsqueeze(-1) # jit assertion if self.return_energy: if self.use_tebd: - atom_energy = self.filter_layers[0](inputs) + self.bias_atom_e[atype].unsqueeze(-1) + atom_energy = self.filter_layers[0](inputs) + self.bias_atom_e[ + atype + ].unsqueeze(-1) outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] else: for type_i, filter_layer in enumerate(self.filter_layers): @@ -192,9 +234,8 @@ def forward(self, if not env.ENERGY_BIAS_TRAINABLE: atom_energy = atom_energy + self.bias_atom_e[type_i] atom_energy = atom_energy * mask.unsqueeze(-1) - outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] + outs = outs + atom_energy # Shape is [nframes, natoms[0], 1] return { - "energy": outs.to(env.GLOBAL_PT_FLOAT_PRECISION), - "dforce": vec_out, + "energy": outs.to(env.GLOBAL_PT_FLOAT_PRECISION), + "dforce": vec_out, } - diff --git a/deepmd_pt/model/task/fitting.py b/deepmd_pt/model/task/fitting.py index 1de5d937..82928d41 100644 --- a/deepmd_pt/model/task/fitting.py +++ b/deepmd_pt/model/task/fitting.py @@ -1,17 +1,33 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging +from typing import ( + Callable, +) + import numpy as np import torch -from deepmd_pt.utils.env import DEVICE -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input -from deepmd_pt.model.task import TaskBaseMethod -from deepmd_pt.utils.plugin import Plugin, PluginVariant -from typing import Callable -from deepmd_utils.model_format import FittingOutputDef, fitting_check_output +from deepmd.model_format import ( + FittingOutputDef, +) +from deepmd_pt.model.task.task import ( + TaskBaseMethod, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.env import ( + DEVICE, +) +from deepmd_pt.utils.plugin import ( + Plugin, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) -class Fitting(TaskBaseMethod): +class Fitting(TaskBaseMethod): __plugins = Plugin() @staticmethod @@ -49,20 +65,20 @@ def __new__(cls, *args, **kwargs): return super().__new__(cls) def output_def(self) -> FittingOutputDef: - """Definition for the task Output. - """ + """Definition for the task Output.""" raise NotImplementedError def forward(self, **kwargs): - """Task Output. - """ + """Task Output.""" raise NotImplementedError def share_params(self, base_class, shared_level, resume=False): - assert self.__class__ == base_class.__class__, "Only fitting nets of the same type can share params!" + assert ( + self.__class__ == base_class.__class__ + ), "Only fitting nets of the same type can share params!" if shared_level == 0: # link buffers - if hasattr(self, 'bias_atom_e'): + if hasattr(self, "bias_atom_e"): self.bias_atom_e = base_class.bias_atom_e # the following will successfully link all the params except buffers, which need manually link. for item in self._modules: @@ -75,15 +91,21 @@ def share_params(self, base_class, shared_level, resume=False): elif shared_level == 2: # share all the layers before final layer # the following will successfully link all the params except buffers, which need manually link. - self._modules['filter_layers'][0].deep_layers = base_class._modules['filter_layers'][0].deep_layers + self._modules["filter_layers"][0].deep_layers = base_class._modules[ + "filter_layers" + ][0].deep_layers elif shared_level == 3: # share the first layers # the following will successfully link all the params except buffers, which need manually link. - self._modules['filter_layers'][0].deep_layers[0] = base_class._modules['filter_layers'][0].deep_layers[0] + self._modules["filter_layers"][0].deep_layers[0] = base_class._modules[ + "filter_layers" + ][0].deep_layers[0] else: raise NotImplementedError - def change_energy_bias(self, config, model, old_type_map, new_type_map, bias_shift='delta', ntest=10): + def change_energy_bias( + self, config, model, old_type_map, new_type_map, bias_shift="delta", ntest=10 + ): """Change the energy bias according to the input data and the pretrained model. Parameters @@ -109,8 +131,10 @@ def change_energy_bias(self, config, model, old_type_map, new_type_map, bias_shi "(this step may take long time)".format(str(new_type_map)) ) # data - systems = config['training']['training_data']['systems'] - finetune_data = DpLoaderSet(systems, ntest, config["model"], type_split=False, noise_settings=None) + systems = config["training"]["training_data"]["systems"] + finetune_data = DpLoaderSet( + systems, ntest, config["model"], type_split=False, noise_settings=None + ) sampled = make_stat_input(finetune_data.systems, finetune_data.dataloaders, 1) # map sorter = np.argsort(old_type_map) @@ -121,7 +145,7 @@ def change_energy_bias(self, config, model, old_type_map, new_type_map, bias_shi numb_type = len(old_type_map) type_numbs, energy_ground_truth, energy_predict = [], [], [] for test_data in sampled: - nframes = test_data['energy'].shape[0] + nframes = test_data["energy"].shape[0] if mixed_type: atype = test_data["atype"].detach().cpu().numpy() else: @@ -129,9 +153,7 @@ def change_energy_bias(self, config, model, old_type_map, new_type_map, bias_shi assert np.array( [i.item() in idx_type_map for i in list(set(atype.reshape(-1)))] ).all(), "Some types are not in 'type_map'!" - energy_ground_truth.append( - test_data["energy"].cpu().numpy() - ) + energy_ground_truth.append(test_data["energy"].cpu().numpy()) if mixed_type: type_numbs.append( np.array( @@ -148,10 +170,16 @@ def change_energy_bias(self, config, model, old_type_map, new_type_map, bias_shi ) if bias_shift == "delta": coord = test_data["coord"].to(DEVICE) - atype = test_data['atype'].to(DEVICE) - box = test_data['box'].to(DEVICE) if test_data['box'] is not None else None + atype = test_data["atype"].to(DEVICE) + box = ( + test_data["box"].to(DEVICE) + if test_data["box"] is not None + else None + ) ret = model(coord, atype, box) - energy_predict.append(ret['energy'].reshape([nframes, 1]).detach().cpu().numpy()) + energy_predict.append( + ret["energy"].reshape([nframes, 1]).detach().cpu().numpy() + ) type_numbs = np.concatenate(type_numbs) energy_ground_truth = np.concatenate(energy_ground_truth) old_bias = self.bias_atom_e[idx_type_map] @@ -168,22 +196,28 @@ def change_energy_bias(self, config, model, old_type_map, new_type_map, bias_shi ) ) ) - self.bias_atom_e[idx_type_map] += torch.from_numpy(delta_bias.reshape(-1)).to(DEVICE) + self.bias_atom_e[idx_type_map] += torch.from_numpy( + delta_bias.reshape(-1) + ).to(DEVICE) logging.info( - "RMSE of atomic energy after linear regression is: {:10.5e} eV/atom.".format( - rmse_ae - ) + f"RMSE of atomic energy after linear regression is: {rmse_ae:10.5e} eV/atom." ) elif bias_shift == "statistic": statistic_bias = np.linalg.lstsq( type_numbs, energy_ground_truth, rcond=None )[0] - self.bias_atom_e[idx_type_map] = torch.from_numpy(statistic_bias.reshape(-1)).type_as(self.bias_atom_e[idx_type_map]).to(DEVICE) + self.bias_atom_e[idx_type_map] = ( + torch.from_numpy(statistic_bias.reshape(-1)) + .type_as(self.bias_atom_e[idx_type_map]) + .to(DEVICE) + ) else: raise RuntimeError("Unknown bias_shift mode: " + bias_shift) logging.info( "Change energy bias of {} from {} to {}.".format( - str(new_type_map), str(old_bias.detach().cpu().numpy()), str(self.bias_atom_e[idx_type_map].detach().cpu().numpy()) + str(new_type_map), + str(old_bias.detach().cpu().numpy()), + str(self.bias_atom_e[idx_type_map].detach().cpu().numpy()), ) ) return None diff --git a/deepmd_pt/model/task/task.py b/deepmd_pt/model/task/task.py index 8d6bb4a3..a9b2efeb 100644 --- a/deepmd_pt/model/task/task.py +++ b/deepmd_pt/model/task/task.py @@ -1,14 +1,12 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import torch class TaskBaseMethod(torch.nn.Module): - def __init__(self, **kwargs): - """Construct a basic head for different tasks. - """ - super(TaskBaseMethod, self).__init__() + """Construct a basic head for different tasks.""" + super().__init__() def forward(self, **kwargs): - """Task Output. - """ + """Task Output.""" raise NotImplementedError diff --git a/deepmd_pt/model/task/type_predict.py b/deepmd_pt/model/task/type_predict.py index 9512d976..7b7016f3 100644 --- a/deepmd_pt/model/task/type_predict.py +++ b/deepmd_pt/model/task/type_predict.py @@ -1,25 +1,20 @@ -import logging -from typing import Optional -import numpy as np -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Optional, +) -from deepmd_pt.utils import env -try: - from typing_extensions import Final -except: - from torch.jit import Final +import torch -from deepmd_pt.model.network import MaskLMHead -from deepmd_pt.model.task import TaskBaseMethod +from deepmd_pt.model.network.network import ( + MaskLMHead, +) +from deepmd_pt.model.task import ( + TaskBaseMethod, +) class TypePredictNet(TaskBaseMethod): - - def __init__(self, - feature_dim, - ntypes, - activation_function="gelu", - **kwargs): + def __init__(self, feature_dim, ntypes, activation_function="gelu", **kwargs): """Construct a type predict net. Args: @@ -27,23 +22,24 @@ def __init__(self, - ntypes: Numer of types to predict. - activation_function: Activate function. """ - super(TypePredictNet, self).__init__() + super().__init__() self.feature_dim = feature_dim self.ntypes = ntypes self.lm_head = MaskLMHead( - embed_dim=self.feature_dim, - output_dim=ntypes, - activation_fn=activation_function, - weight=None, - ) + embed_dim=self.feature_dim, + output_dim=ntypes, + activation_fn=activation_function, + weight=None, + ) - def forward(self, features, masked_tokens: Optional[torch.Tensor]=None): + def forward(self, features, masked_tokens: Optional[torch.Tensor] = None): """Calculate the predicted logits. Args: - features: Input features with shape [nframes, nloc, feature_dim]. - masked_tokens: Input masked tokens with shape [nframes, nloc]. - Returns: + Returns + ------- - logits: Predicted probs with shape [nframes, nloc, ntypes]. """ # [nframes, nloc, ntypes] diff --git a/deepmd_pt/optimizer/KFWrapper.py b/deepmd_pt/optimizer/KFWrapper.py index a32f569c..3ab7ffe7 100644 --- a/deepmd_pt/optimizer/KFWrapper.py +++ b/deepmd_pt/optimizer/KFWrapper.py @@ -1,9 +1,13 @@ -import torch -import torch.nn as nn -from torch.optim.optimizer import Optimizer +# SPDX-License-Identifier: LGPL-3.0-or-later +import math + import numpy as np +import torch import torch.distributed as dist -import math +import torch.nn as nn +from torch.optim.optimizer import ( + Optimizer, +) class KFOptimizerWrapper: @@ -25,7 +29,7 @@ def update_energy( self, inputs: dict, Etot_label: torch.Tensor, update_prefactor: float = 1 ) -> None: model_pred, _, _ = self.model(**inputs, inference_only=True) - Etot_predict = model_pred['energy'] + Etot_predict = model_pred["energy"] natoms_sum = int(inputs["atype"].shape[-1]) self.optimizer.set_grad_prefactor(natoms_sum) @@ -63,9 +67,9 @@ def update_force( for i in range(index.shape[0]): self.optimizer.zero_grad() model_pred, _, _ = self.model(**inputs, inference_only=True) - Etot_predict = model_pred['energy'] + Etot_predict = model_pred["energy"] natoms_sum = int(inputs["atype"].shape[-1]) - force_predict = model_pred['force'] + force_predict = model_pred["force"] error_tmp = Force_label[:, index[i]] - force_predict[:, index[i]] error_tmp = update_prefactor * error_tmp mask = error_tmp < 0 @@ -86,7 +90,12 @@ def update_force( return Etot_predict, force_predict def update_denoise_coord( - self, inputs: dict, clean_coord: torch.Tensor, update_prefactor: float = 1, mask_loss_coord: bool = True, coord_mask: torch.Tensor = None + self, + inputs: dict, + clean_coord: torch.Tensor, + update_prefactor: float = 1, + mask_loss_coord: bool = True, + coord_mask: torch.Tensor = None, ) -> None: natoms_sum = int(inputs["atype"].shape[-1]) bs = clean_coord.shape[0] @@ -97,7 +106,7 @@ def update_denoise_coord( for i in range(index.shape[0]): self.optimizer.zero_grad() model_pred, _, _ = self.model(**inputs, inference_only=True) - updated_coord = model_pred['updated_coord'] + updated_coord = model_pred["updated_coord"] natoms_sum = int(inputs["atype"].shape[-1]) error_tmp = clean_coord[:, index[i]] - updated_coord[:, index[i]] error_tmp = update_prefactor * error_tmp @@ -126,7 +135,8 @@ def __sample( if atoms_selected % atoms_per_group: raise Exception("divider") index = range(natoms) - res = np.random.choice(index, atoms_selected).reshape(-1, atoms_per_group) + rng = np.random.default_rng() + res = rng.choice(index, atoms_selected).reshape(-1, atoms_per_group) return res diff --git a/deepmd_pt/optimizer/LKF.py b/deepmd_pt/optimizer/LKF.py index e246ae31..5e18797c 100644 --- a/deepmd_pt/optimizer/LKF.py +++ b/deepmd_pt/optimizer/LKF.py @@ -1,8 +1,12 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging -import torch -from torch.optim.optimizer import Optimizer import math +import torch +from torch.optim.optimizer import ( + Optimizer, +) + class LKFOptimizer(Optimizer): def __init__( @@ -12,14 +16,12 @@ def __init__( kalman_nue=0.9987, block_size=5120, ): - - defaults = dict( - lr=0.1, - kalman_nue=kalman_nue, - block_size=block_size, - ) - - super(LKFOptimizer, self).__init__(params, defaults) + defaults = { + "lr": 0.1, + "kalman_nue": kalman_nue, + "block_size": block_size, + } + super().__init__(params, defaults) self._params = self.param_groups[0]["params"] @@ -36,7 +38,6 @@ def __init__( self.__init_P() def __init_P(self): - param_nums = [] param_sum = 0 block_size = self.__get_blocksize() @@ -81,7 +82,7 @@ def __init_P(self): ) ) params_packed_index.append(param_num - block_size * i) - else: + else: P.append(torch.eye(param_num, dtype=data_type, device=device)) params_packed_index.append(param_num) @@ -169,7 +170,6 @@ def set_grad_prefactor(self, grad_prefactor): self.grad_prefactor = grad_prefactor def step(self, error): - params_packed_index = self._state.get("params_packed_index") weights = [] diff --git a/deepmd_pt/optimizer/__init__.py b/deepmd_pt/optimizer/__init__.py index 4f34809c..db340b3b 100644 --- a/deepmd_pt/optimizer/__init__.py +++ b/deepmd_pt/optimizer/__init__.py @@ -1,2 +1,9 @@ -from .KFWrapper import KFOptimizerWrapper -from .LKF import LKFOptimizer +# SPDX-License-Identifier: LGPL-3.0-or-later +from .KFWrapper import ( + KFOptimizerWrapper, +) +from .LKF import ( + LKFOptimizer, +) + +__all__ = ["KFOptimizerWrapper", "LKFOptimizer"] diff --git a/deepmd_pt/train/__init__.py b/deepmd_pt/train/__init__.py index e69de29b..6ceb116d 100644 --- a/deepmd_pt/train/__init__.py +++ b/deepmd_pt/train/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd_pt/train/training.py b/deepmd_pt/train/training.py index 9ce84be9..9ec4ad09 100644 --- a/deepmd_pt/train/training.py +++ b/deepmd_pt/train/training.py @@ -1,45 +1,83 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging import os -import torch import time -import math -from copy import deepcopy +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) +from typing import ( + Any, + Dict, +) -from typing import Any, Dict import numpy as np -from deepmd_pt.utils import dp_random -from deepmd_pt.utils.env import DEVICE, JIT, LOCAL_RANK, DISABLE_TQDM, SAMPLER_RECORD, NUM_WORKERS -from deepmd_pt.optimizer import KFOptimizerWrapper, LKFOptimizer -from deepmd_pt.utils.learning_rate import LearningRateExp -from deepmd_pt.loss import EnergyStdLoss, DenoiseLoss -from deepmd_pt.model.model import get_model -from deepmd_pt.train.wrapper import ModelWrapper -from deepmd_pt.utils.dataloader import BufferedIterator, get_weighted_sampler -from pathlib import Path -from tqdm import tqdm -from tqdm.contrib.logging import logging_redirect_tqdm - +import torch import wandb as wb +from tqdm import ( + tqdm, +) +from tqdm.contrib.logging import ( + logging_redirect_tqdm, +) + +from deepmd_pt.loss import ( + DenoiseLoss, + EnergyStdLoss, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.optimizer import ( + KFOptimizerWrapper, + LKFOptimizer, +) +from deepmd_pt.train.wrapper import ( + ModelWrapper, +) +from deepmd_pt.utils import ( + dp_random, +) +from deepmd_pt.utils.dataloader import ( + BufferedIterator, + get_weighted_sampler, +) +from deepmd_pt.utils.env import ( + DEVICE, + DISABLE_TQDM, + JIT, + LOCAL_RANK, + NUM_WORKERS, + SAMPLER_RECORD, +) +from deepmd_pt.utils.learning_rate import ( + LearningRateExp, +) if torch.__version__.startswith("2"): import torch._dynamo -from torch.nn.parallel import DistributedDataParallel as DDP + import torch.distributed as dist -from torch.utils.data import DataLoader +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import ( + DataLoader, +) -class Trainer(object): +class Trainer: def __init__( - self, - config: Dict[str, Any], - training_data, - sampled, - validation_data=None, - init_model=None, - restart_model=None, - finetune_model=None, - force_load=False, - shared_links=None, + self, + config: Dict[str, Any], + training_data, + sampled, + validation_data=None, + init_model=None, + restart_model=None, + finetune_model=None, + force_load=False, + shared_links=None, ): """Construct a DeePMD trainer. @@ -51,8 +89,12 @@ def __init__( model_params = config["model"] training_params = config["training"] self.multi_task = "model_dict" in model_params - self.finetune_multi_task = model_params.pop("finetune_multi_task", False) # should use pop for next finetune - self.model_keys = [key for key in model_params["model_dict"]] if self.multi_task else ["Default"] + self.finetune_multi_task = model_params.pop( + "finetune_multi_task", False + ) # should use pop for next finetune + self.model_keys = ( + list(model_params["model_dict"]) if self.multi_task else ["Default"] + ) self.rank = dist.get_rank() if dist.is_initialized() else 0 self.world_size = dist.get_world_size() if dist.is_initialized() else 1 self.num_model = len(self.model_keys) @@ -71,11 +113,11 @@ def __init__( if self.wandb_enabled: entity = self.wandb_config.get("entity", None) assert ( - entity is not None + entity is not None ), "The parameter 'entity' of wandb must be specified." project = self.wandb_config.get("project", None) assert ( - project is not None + project is not None ), "The parameter 'project' of wandb must be specified." job_name = self.wandb_config.get("job_name", None) if job_name is None: @@ -92,31 +134,46 @@ def __init__( def get_opt_param(params): opt_type = params.get("opt_type", "Adam") - opt_param = {'kf_blocksize': params.get("kf_blocksize", 5120), - 'kf_start_pref_e': params.get("kf_start_pref_e", 1), - 'kf_limit_pref_e': params.get("kf_limit_pref_e", 1), - 'kf_start_pref_f': params.get("kf_start_pref_f", 1), - 'kf_limit_pref_f': params.get("kf_limit_pref_f", 1)} + opt_param = { + "kf_blocksize": params.get("kf_blocksize", 5120), + "kf_start_pref_e": params.get("kf_start_pref_e", 1), + "kf_limit_pref_e": params.get("kf_limit_pref_e", 1), + "kf_start_pref_f": params.get("kf_start_pref_f", 1), + "kf_limit_pref_f": params.get("kf_limit_pref_f", 1), + } return opt_type, opt_param def get_data_loader(_training_data, _validation_data, _training_params): - if 'auto_prob' in _training_params['training_data']: - train_sampler = get_weighted_sampler(_training_data, _training_params['training_data']['auto_prob']) - elif 'sys_probs' in _training_params['training_data']: - train_sampler = get_weighted_sampler(_training_data, _training_params['training_data']['sys_probs'],sys_prob=True) + if "auto_prob" in _training_params["training_data"]: + train_sampler = get_weighted_sampler( + _training_data, _training_params["training_data"]["auto_prob"] + ) + elif "sys_probs" in _training_params["training_data"]: + train_sampler = get_weighted_sampler( + _training_data, + _training_params["training_data"]["sys_probs"], + sys_prob=True, + ) else: - train_sampler = get_weighted_sampler(_training_data, 'prob_sys_size') - + train_sampler = get_weighted_sampler(_training_data, "prob_sys_size") - if 'auto_prob' in _training_params['validation_data']: - valid_sampler = get_weighted_sampler(_validation_data, _training_params['validation_data']['auto_prob']) - elif 'sys_probs' in _training_params['validation_data']: - valid_sampler = get_weighted_sampler(_validation_data, _training_params['validation_data']['sys_probs'],sys_prob=True) + if "auto_prob" in _training_params["validation_data"]: + valid_sampler = get_weighted_sampler( + _validation_data, _training_params["validation_data"]["auto_prob"] + ) + elif "sys_probs" in _training_params["validation_data"]: + valid_sampler = get_weighted_sampler( + _validation_data, + _training_params["validation_data"]["sys_probs"], + sys_prob=True, + ) else: - valid_sampler = get_weighted_sampler(_validation_data, 'prob_sys_size') + valid_sampler = get_weighted_sampler(_validation_data, "prob_sys_size") - if train_sampler == None or valid_sampler == None: - logging.warning("Sampler not specified!")#None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration. + if train_sampler is None or valid_sampler is None: + logging.warning( + "Sampler not specified!" + ) # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration. training_dataloader = DataLoader( _training_data, sampler=train_sampler, @@ -142,26 +199,33 @@ def get_data_loader(_training_data, _validation_data, _training_params): ) else: valid_numb_batch = 1 - return training_dataloader, training_data_buffered, \ - validation_dataloader, validation_data_buffered, valid_numb_batch + return ( + training_dataloader, + training_data_buffered, + validation_dataloader, + validation_data_buffered, + valid_numb_batch, + ) def get_single_model(_model_params, _sampled): model = get_model(deepcopy(_model_params), _sampled).to(DEVICE) return model def get_lr(lr_params): - assert lr_params.get("type", "exp") == "exp", "Only learning rate `exp` is supported!" + assert ( + lr_params.get("type", "exp") == "exp" + ), "Only learning rate `exp` is supported!" lr_params["stop_steps"] = self.num_steps - self.warmup_steps lr_exp = LearningRateExp(**lr_params) return lr_exp def get_loss(loss_params, start_lr, _ntypes): loss_type = loss_params.get("type", "ener") - if loss_type == 'ener': + if loss_type == "ener": loss_params["starter_learning_rate"] = start_lr return EnergyStdLoss(**loss_params) - elif loss_type == 'denoise': - loss_params['ntypes'] = _ntypes + elif loss_type == "denoise": + loss_params["ntypes"] = _ntypes return DenoiseLoss(**loss_params) else: raise NotImplementedError @@ -169,39 +233,63 @@ def get_loss(loss_params, start_lr, _ntypes): # Optimizer if self.multi_task and training_params.get("optim_dict", None) is not None: self.optim_dict = training_params.get("optim_dict") - missing_keys = [key for key in self.model_keys if key not in self.optim_dict] - assert not missing_keys, f"These keys are not in optim_dict: {missing_keys}!" + missing_keys = [ + key for key in self.model_keys if key not in self.optim_dict + ] + assert ( + not missing_keys + ), f"These keys are not in optim_dict: {missing_keys}!" self.opt_type = {} self.opt_param = {} for model_key in self.model_keys: - self.opt_type[model_key], self.opt_param[model_key] = get_opt_param(self.optim_dict[model_key]) + self.opt_type[model_key], self.opt_param[model_key] = get_opt_param( + self.optim_dict[model_key] + ) else: self.opt_type, self.opt_param = get_opt_param(training_params) # Data + Model dp_random.seed(training_params["seed"]) if not self.multi_task: - self.training_dataloader, self.training_data, \ - self.validation_dataloader, self.validation_data, self.valid_numb_batch = get_data_loader(training_data, - validation_data, - training_params) + ( + self.training_dataloader, + self.training_data, + self.validation_dataloader, + self.validation_data, + self.valid_numb_batch, + ) = get_data_loader(training_data, validation_data, training_params) self.model = get_single_model(model_params, sampled) else: - self.training_dataloader, self.training_data, \ - self.validation_dataloader, self.validation_data, \ - self.valid_numb_batch, self.model = {}, {}, {}, {}, {}, {} + ( + self.training_dataloader, + self.training_data, + self.validation_dataloader, + self.validation_data, + self.valid_numb_batch, + self.model, + ) = {}, {}, {}, {}, {}, {} for model_key in self.model_keys: - self.training_dataloader[model_key], self.training_data[model_key], \ - self.validation_dataloader[model_key], self.validation_data[model_key], \ - self.valid_numb_batch[model_key] = get_data_loader(training_data[model_key], - validation_data[model_key], - training_params['data_dict'][model_key]) - self.model[model_key] = get_single_model(model_params['model_dict'][model_key], sampled[model_key]) + ( + self.training_dataloader[model_key], + self.training_data[model_key], + self.validation_dataloader[model_key], + self.validation_data[model_key], + self.valid_numb_batch[model_key], + ) = get_data_loader( + training_data[model_key], + validation_data[model_key], + training_params["data_dict"][model_key], + ) + self.model[model_key] = get_single_model( + model_params["model_dict"][model_key], sampled[model_key] + ) # Learning rate self.warmup_steps = training_params.get("warmup_steps", 0) - self.gradient_max_norm = training_params.get("gradient_max_norm", 0.) - assert self.num_steps - self.warmup_steps > 0, "Warm up steps must be less than total training steps!" + self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0) + assert ( + self.num_steps - self.warmup_steps > 0 + ), "Warm up steps must be less than total training steps!" if self.multi_task and config.get("learning_rate_dict", None) is not None: self.lr_exp = {} for model_key in self.model_keys: @@ -211,7 +299,11 @@ def get_loss(loss_params, start_lr, _ntypes): # Loss if not self.multi_task: - self.loss = get_loss(config["loss"], config["learning_rate"]["start_lr"], len(model_params['type_map'])) + self.loss = get_loss( + config["loss"], + config["learning_rate"]["start_lr"], + len(model_params["type_map"]), + ) else: self.loss = {} for model_key in self.model_keys: @@ -220,7 +312,7 @@ def get_loss(loss_params, start_lr, _ntypes): lr_param = config["learning_rate_dict"][model_key]["start_lr"] else: lr_param = config["learning_rate"]["start_lr"] - ntypes = len(model_params['model_dict'][model_key]['type_map']) + ntypes = len(model_params["model_dict"][model_key]["type_map"]) self.loss[model_key] = get_loss(loss_param, lr_param, ntypes) # JIT @@ -235,18 +327,28 @@ def get_loss(loss_params, start_lr, _ntypes): optimizer_state_dict = None if model_params["resuming"]: ntest = model_params.get("data_bias_nsample", 1) - origin_model = finetune_model if finetune_model is not None else resume_model + origin_model = ( + finetune_model if finetune_model is not None else resume_model + ) logging.info(f"Resuming from {origin_model}.") state_dict = torch.load(origin_model, map_location=DEVICE) if "model" in state_dict: - optimizer_state_dict = state_dict["optimizer"] if finetune_model is None else None + optimizer_state_dict = ( + state_dict["optimizer"] if finetune_model is None else None + ) state_dict = state_dict["model"] - self.start_step = state_dict['_extra_state']['train_infos']['step'] if self.restart_training else 0 + self.start_step = ( + state_dict["_extra_state"]["train_infos"]["step"] + if self.restart_training + else 0 + ) if self.rank == 0: if force_load: input_keys = list(state_dict.keys()) target_keys = list(self.wrapper.state_dict().keys()) - missing_keys = [item for item in target_keys if item not in input_keys] + missing_keys = [ + item for item in target_keys if item not in input_keys + ] if missing_keys: target_state_dict = self.wrapper.state_dict() slim_keys = [] @@ -258,35 +360,49 @@ def get_loss(loss_params, start_lr, _ntypes): new_key = False break if new_key: - tmp_keys = '.'.join(item.split('.')[:3]) + tmp_keys = ".".join(item.split(".")[:3]) slim_keys.append(tmp_keys) - slim_keys = [i + '.*' for i in slim_keys] + slim_keys = [i + ".*" for i in slim_keys] logging.warning( - f"Force load mode allowed! These keys are not in ckpt and will re-init: {slim_keys}") + f"Force load mode allowed! These keys are not in ckpt and will re-init: {slim_keys}" + ) elif self.finetune_multi_task: new_state_dict = {} - model_branch_chosen = model_params.pop('model_branch_chosen') - new_fitting = model_params.pop('new_fitting', False) + model_branch_chosen = model_params.pop("model_branch_chosen") + new_fitting = model_params.pop("new_fitting", False) target_state_dict = self.wrapper.state_dict() - target_keys = [i for i in target_state_dict.keys() if i != '_extra_state'] + target_keys = [ + i for i in target_state_dict.keys() if i != "_extra_state" + ] for item_key in target_keys: - if new_fitting and '.fitting_net.' in item_key: + if new_fitting and ".fitting_net." in item_key: # print(f'Keep {item_key} in old model!') - new_state_dict[item_key] = target_state_dict[item_key].clone().detach() + new_state_dict[item_key] = ( + target_state_dict[item_key].clone().detach() + ) else: - new_key = item_key.replace('.Default.', f'.{model_branch_chosen}.') + new_key = item_key.replace( + ".Default.", f".{model_branch_chosen}." + ) # print(f'Replace {item_key} with {new_key} in pretrained_model!') - new_state_dict[item_key] = state_dict[new_key].clone().detach() + new_state_dict[item_key] = ( + state_dict[new_key].clone().detach() + ) state_dict = new_state_dict if finetune_model is not None: - state_dict['_extra_state'] = self.wrapper.state_dict()['_extra_state'] + state_dict["_extra_state"] = self.wrapper.state_dict()[ + "_extra_state" + ] self.wrapper.load_state_dict(state_dict) # finetune - if finetune_model is not None and model_params["fitting_net"].get("type", "ener") in ['ener', - 'direct_force_ener', - 'atten_vec_lcc']: - old_type_map, new_type_map = model_params['type_map'], model_params['new_type_map'] + if finetune_model is not None and model_params["fitting_net"].get( + "type", "ener" + ) in ["ener", "direct_force_ener", "atten_vec_lcc"]: + old_type_map, new_type_map = ( + model_params["type_map"], + model_params["new_type_map"], + ) self.model.fitting_net.change_energy_bias( config, self.model, @@ -310,7 +426,7 @@ def get_loss(loss_params, start_lr, _ntypes): self.wrapper, device_ids=[LOCAL_RANK], find_unused_parameters=True, - output_device=LOCAL_RANK + output_device=LOCAL_RANK, ) # TODO ZD add lr warmups for multitask @@ -333,7 +449,7 @@ def warm_up_linear(step, warmup_steps): ) elif self.opt_type == "LKF": self.optimizer = LKFOptimizer( - self.wrapper.parameters(), 0.98, 0.99870, self.opt_param['kf_blocksize'] + self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"] ) else: raise ValueError("Not supported optimizer type '%s'" % self.opt_type) @@ -341,8 +457,8 @@ def warm_up_linear(step, warmup_steps): # Get model prob for multi-task if self.multi_task: self.model_prob = np.array([0.0 for key in self.model_keys]) - if training_params.get('model_prob', None) is not None: - model_prob = training_params['model_prob'] + if training_params.get("model_prob", None) is not None: + model_prob = training_params["model_prob"] for ii, model_key in enumerate(self.model_keys): if model_key in model_prob: self.model_prob[ii] += float(model_prob[model_key]) @@ -350,18 +466,16 @@ def warm_up_linear(step, warmup_steps): for ii, model_key in enumerate(self.model_keys): self.model_prob[ii] += float(len(self.training_data[model_key])) sum_prob = np.sum(self.model_prob) - assert sum_prob > 0., "Sum of model prob must be larger than 0!" + assert sum_prob > 0.0, "Sum of model prob must be larger than 0!" self.model_prob = self.model_prob / sum_prob def run(self): fout = ( open(self.disp_file, mode="w", buffering=1) if self.rank == 0 else None ) # line buffered - if SAMPLER_RECORD : + if SAMPLER_RECORD: record_file = f"Sample_rank_{self.rank}.txt" - fout1 = ( - open(record_file, mode="w", buffering=1) - ) + fout1 = open(record_file, mode="w", buffering=1) logging.info("Start to train %d steps.", self.num_steps) if dist.is_initialized(): logging.info(f"Rank: {dist.get_rank()}/{dist.get_world_size()}") @@ -375,8 +489,10 @@ def step(_step_id, task_key="Default"): cur_lr = _lr.value(_step_id) pref_lr = cur_lr self.optimizer.zero_grad(set_to_none=True) - input_dict, label_dict,log_dict = self.get_data(is_train=True, task_key=task_key) - if SAMPLER_RECORD : + input_dict, label_dict, log_dict = self.get_data( + is_train=True, task_key=task_key + ) + if SAMPLER_RECORD: print_str = f"Step {_step_id}: sample system{log_dict['sid']} frame{log_dict['fid']}\n" fout1.write(print_str) fout1.flush() @@ -390,8 +506,10 @@ def step(_step_id, task_key="Default"): **input_dict, cur_lr=pref_lr, label=label_dict, task_key=task_key ) loss.backward() - if self.gradient_max_norm > 0.: - grad_norm = torch.nn.utils.clip_grad_norm_(self.wrapper.parameters(), self.gradient_max_norm) + if self.gradient_max_norm > 0.0: + grad_norm = torch.nn.utils.clip_grad_norm_( + self.wrapper.parameters(), self.gradient_max_norm + ) if not torch.isfinite(grad_norm).all(): # check local gradnorm single GPU case, trigger NanDetector raise FloatingPointError("gradients are Nan/Inf") @@ -402,32 +520,50 @@ def step(_step_id, task_key="Default"): KFOptWrapper = KFOptimizerWrapper( self.wrapper, self.optimizer, 24, 6, dist.is_initialized() ) - pref_e = self.opt_param['kf_start_pref_e'] * ( - self.opt_param['kf_limit_pref_e'] / self.opt_param['kf_start_pref_e']) ** ( - _step_id / self.num_steps) - _ = KFOptWrapper.update_energy(input_dict, label_dict["energy"], pref_e) - pref_f = self.opt_param['kf_start_pref_f'] * ( - self.opt_param['kf_limit_pref_f'] / self.opt_param['kf_start_pref_f']) ** ( - _step_id / self.num_steps) + pref_e = self.opt_param["kf_start_pref_e"] * ( + self.opt_param["kf_limit_pref_e"] + / self.opt_param["kf_start_pref_e"] + ) ** (_step_id / self.num_steps) + _ = KFOptWrapper.update_energy( + input_dict, label_dict["energy"], pref_e + ) + pref_f = self.opt_param["kf_start_pref_f"] * ( + self.opt_param["kf_limit_pref_f"] + / self.opt_param["kf_start_pref_f"] + ) ** (_step_id / self.num_steps) p_energy, p_force = KFOptWrapper.update_force( input_dict, label_dict["force"], pref_f ) # [coord, atype, natoms, mapping, shift, nlist, box] model_pred = {"energy": p_energy, "force": p_force} - module = self.wrapper.module if dist.is_initialized() else self.wrapper + module = ( + self.wrapper.module if dist.is_initialized() else self.wrapper + ) loss, more_loss = module.loss[task_key]( - model_pred, label_dict, int(input_dict["atype"].shape[-1]), learning_rate=pref_lr + model_pred, + label_dict, + int(input_dict["atype"].shape[-1]), + learning_rate=pref_lr, ) elif isinstance(self.loss, DenoiseLoss): KFOptWrapper = KFOptimizerWrapper( self.wrapper, self.optimizer, 24, 6, dist.is_initialized() ) - module = self.wrapper.module if dist.is_initialized() else self.wrapper - model_pred = KFOptWrapper.update_denoise_coord(input_dict, label_dict["clean_coord"], 1, - module.loss[task_key].mask_loss_coord, - label_dict["coord_mask"]) + module = ( + self.wrapper.module if dist.is_initialized() else self.wrapper + ) + model_pred = KFOptWrapper.update_denoise_coord( + input_dict, + label_dict["clean_coord"], + 1, + module.loss[task_key].mask_loss_coord, + label_dict["coord_mask"], + ) loss, more_loss = module.loss[task_key]( - model_pred, label_dict, input_dict["natoms"], learning_rate=pref_lr + model_pred, + label_dict, + input_dict["natoms"], + learning_rate=pref_lr, ) else: raise ValueError("Not supported optimizer type '%s'" % self.opt_type) @@ -437,34 +573,40 @@ def step(_step_id, task_key="Default"): self.wrapper.eval() msg = f"step={_step_id}, lr={cur_lr:.2e}" - def log_loss_train(_loss, _more_loss, _task_key='Default'): + def log_loss_train(_loss, _more_loss, _task_key="Default"): results = {} if not self.multi_task: - suffix = '' + suffix = "" else: - suffix = f'_{_task_key}' + suffix = f"_{_task_key}" _msg = f"loss{suffix}={_loss:.4f}" rmse_val = { - item: _more_loss[item] for item in _more_loss if 'l2_' not in item + item: _more_loss[item] + for item in _more_loss + if "l2_" not in item } - for item in sorted(list(rmse_val.keys())): + for item in sorted(rmse_val.keys()): _msg += f", {item}_train{suffix}={rmse_val[item]:.4f}" results[item] = rmse_val[item] - self.wandb_log({item: rmse_val[item]}, _step_id, f"_train{suffix}") + self.wandb_log( + {item: rmse_val[item]}, _step_id, f"_train{suffix}" + ) return _msg, results - def log_loss_valid(_task_key='Default'): + def log_loss_valid(_task_key="Default"): single_results = {} sum_natoms = 0 if not self.multi_task: - suffix = '' + suffix = "" valid_numb_batch = self.valid_numb_batch else: - suffix = f'_{_task_key}' + suffix = f"_{_task_key}" valid_numb_batch = self.valid_numb_batch[_task_key] for ii in range(valid_numb_batch): self.optimizer.zero_grad() - input_dict, label_dict,_ = self.get_data(is_train=False, task_key=_task_key) + input_dict, label_dict, _ = self.get_data( + is_train=False, task_key=_task_key + ) _, loss, more_loss = self.wrapper( **input_dict, cur_lr=pref_lr, @@ -475,22 +617,22 @@ def log_loss_valid(_task_key='Default'): natoms = int(input_dict["atype"].shape[-1]) sum_natoms += natoms for k, v in more_loss.items(): - if 'l2_' not in k: + if "l2_" not in k: single_results[k] = ( - single_results.get(k, 0.0) + v * natoms + single_results.get(k, 0.0) + v * natoms ) - results = { - k: v / sum_natoms for k, v in single_results.items() - } + results = {k: v / sum_natoms for k, v in single_results.items()} _msg = "" - for item in sorted(list(results.keys())): + for item in sorted(results.keys()): _msg += f", {item}_valid{suffix}={results[item]:.4f}" - self.wandb_log({item: results[item]}, _step_id, f"_valid{suffix}") + self.wandb_log( + {item: results[item]}, _step_id, f"_valid{suffix}" + ) return _msg, results if not self.multi_task: temp_msg, train_results = log_loss_train(loss, more_loss) - msg += '\n' + temp_msg + msg += "\n" + temp_msg temp_msg, valid_results = log_loss_valid() msg += temp_msg else: @@ -498,20 +640,28 @@ def log_loss_valid(_task_key='Default'): valid_results = {_key: {} for _key in self.model_keys} train_msg = {} valid_msg = {} - train_msg[task_key], train_results[task_key] = log_loss_train(loss, more_loss, _task_key=task_key) + train_msg[task_key], train_results[task_key] = log_loss_train( + loss, more_loss, _task_key=task_key + ) for _key in self.model_keys: if _key != task_key: self.optimizer.zero_grad() - input_dict, label_dict,_ = self.get_data(is_train=True, task_key=_key) + input_dict, label_dict, _ = self.get_data( + is_train=True, task_key=_key + ) _, loss, more_loss = self.wrapper( **input_dict, cur_lr=pref_lr, label=label_dict, task_key=_key, ) - train_msg[_key], train_results[_key] = log_loss_train(loss, more_loss, _task_key=_key) - valid_msg[_key], valid_results[_key] = log_loss_valid(_task_key=_key) - msg += '\n' + train_msg[_key] + train_msg[_key], train_results[_key] = log_loss_train( + loss, more_loss, _task_key=_key + ) + valid_msg[_key], valid_results[_key] = log_loss_valid( + _task_key=_key + ) + msg += "\n" + train_msg[_key] msg += valid_msg[_key] train_time = time.time() - self.t0 @@ -524,16 +674,19 @@ def log_loss_valid(_task_key='Default'): if self.lcurve_should_print_header: self.print_header(fout, train_results, valid_results) self.lcurve_should_print_header = False - self.print_on_training(fout, _step_id, cur_lr, train_results, valid_results) + self.print_on_training( + fout, _step_id, cur_lr, train_results, valid_results + ) if ( - ((_step_id + 1) % self.save_freq == 0 and _step_id != self.start_step) - or (_step_id + 1) == self.num_steps + ((_step_id + 1) % self.save_freq == 0 and _step_id != self.start_step) + or (_step_id + 1) == self.num_steps ) and (self.rank == 0 or dist.get_rank() == 0): # Handle the case if rank 0 aborted and re-assigned self.latest_model = Path(self.save_ckpt) self.latest_model = self.latest_model.with_name( - f"{self.latest_model.stem}_{_step_id + 1}{self.latest_model.suffix}") + f"{self.latest_model.stem}_{_step_id + 1}{self.latest_model.suffix}" + ) module = self.wrapper.module if dist.is_initialized() else self.wrapper self.save_model(self.latest_model, lr=cur_lr, step=_step_id) logging.info(f"Saved model to {self.latest_model}") @@ -541,14 +694,19 @@ def log_loss_valid(_task_key='Default'): self.t0 = time.time() with logging_redirect_tqdm(): for step_id in tqdm( - range(self.num_steps), disable=(bool(dist.get_rank()) if dist.is_initialized() else False) or DISABLE_TQDM + range(self.num_steps), + disable=(bool(dist.get_rank()) if dist.is_initialized() else False) + or DISABLE_TQDM, ): # set to None to disable on non-TTY; disable on not rank 0 if step_id < self.start_step: continue if self.multi_task: chosen_index_list = dp_random.choice( - np.arange(self.num_model), p=np.array(self.model_prob), - size=self.world_size, replace=True) + np.arange(self.num_model), + p=np.array(self.model_prob), + size=self.world_size, + replace=True, + ) assert chosen_index_list.size == self.world_size model_index = chosen_index_list[self.rank] model_key = self.model_keys[model_index] @@ -559,12 +717,16 @@ def log_loss_valid(_task_key='Default'): break if ( - self.rank == 0 or dist.get_rank() == 0 + self.rank == 0 or dist.get_rank() == 0 ): # Handle the case if rank 0 aborted and re-assigned if JIT: - pth_model_path = "frozen_model.pth" # We use .pth to denote the frozen model + pth_model_path = ( + "frozen_model.pth" # We use .pth to denote the frozen model + ) self.model.save(pth_model_path) - logging.info(f"Frozen model for inferencing has been saved to {pth_model_path}") + logging.info( + f"Frozen model for inferencing has been saved to {pth_model_path}" + ) try: os.symlink(self.latest_model, self.save_ckpt) except OSError: @@ -573,14 +735,17 @@ def log_loss_valid(_task_key='Default'): if fout: fout.close() - if SAMPLER_RECORD : + if SAMPLER_RECORD: fout1.close() - def save_model(self, save_path, lr=0., step=0): + def save_model(self, save_path, lr=0.0, step=0): module = self.wrapper.module if dist.is_initialized() else self.wrapper - module.train_infos['lr'] = lr - module.train_infos['step'] = step - torch.save({"model": module.state_dict(), "optimizer": self.optimizer.state_dict()}, save_path) + module.train_infos["lr"] = lr + module.train_infos["step"] = step + torch.save( + {"model": module.state_dict(), "optimizer": self.optimizer.state_dict()}, + save_path, + ) def get_data(self, is_train=True, task_key="Default"): if not self.multi_task: @@ -589,7 +754,9 @@ def get_data(self, is_train=True, task_key="Default"): batch_data = next(iter(self.training_data)) except StopIteration: # Refresh the status of the dataloader to start from a new epoch - self.training_data = BufferedIterator(iter(self.training_dataloader)) + self.training_data = BufferedIterator( + iter(self.training_dataloader) + ) batch_data = next(iter(self.training_data)) else: try: @@ -605,7 +772,9 @@ def get_data(self, is_train=True, task_key="Default"): batch_data = next(iter(self.training_data[task_key])) except StopIteration: # Refresh the status of the dataloader to start from a new epoch - self.training_data[task_key] = BufferedIterator(iter(self.training_dataloader[task_key])) + self.training_data[task_key] = BufferedIterator( + iter(self.training_dataloader[task_key]) + ) batch_data = next(iter(self.training_data[task_key])) else: try: @@ -617,12 +786,12 @@ def get_data(self, is_train=True, task_key="Default"): batch_data = next(iter(self.validation_data[task_key])) for key in batch_data.keys(): - if key == 'sid' or key == 'fid': + if key == "sid" or key == "fid": continue elif not isinstance(batch_data[key], list): - if batch_data[key] is not None : + if batch_data[key] is not None: batch_data[key] = batch_data[key].to(DEVICE) - else : + else: batch_data[key] = [item.to(DEVICE) for item in batch_data[key]] input_dict = {} for item in [ @@ -635,14 +804,22 @@ def get_data(self, is_train=True, task_key="Default"): else: input_dict[item] = None label_dict = {} - for item in ["energy", "force", "virial", "clean_coord", "clean_type", "coord_mask", "type_mask"]: + for item in [ + "energy", + "force", + "virial", + "clean_coord", + "clean_type", + "coord_mask", + "type_mask", + ]: if item in batch_data: label_dict[item] = batch_data[item] log_dict = {} if "fid" in batch_data: - log_dict['fid'] = batch_data['fid'] - log_dict['sid'] = batch_data['sid'] - return input_dict, label_dict,log_dict + log_dict["fid"] = batch_data["fid"] + log_dict["sid"] = batch_data["sid"] + return input_dict, label_dict, log_dict def wandb_log(self, data: dict, step, type_suffix=""): if not self.wandb_enabled or self.rank != 0: @@ -651,7 +828,7 @@ def wandb_log(self, data: dict, step, type_suffix=""): wb.log({k + type_suffix: v}, step=step) def print_header(self, fout, train_results, valid_results): - train_keys = sorted(list(train_results.keys())) + train_keys = sorted(train_results.keys()) print_str = "" print_str += "# %5s" % "step" if not self.multi_task: @@ -667,18 +844,21 @@ def print_header(self, fout, train_results, valid_results): for model_key in self.model_keys: if valid_results[model_key] is not None: prop_fmt = " %11s %11s" - for k in sorted(list(train_results[model_key].keys())): - print_str += prop_fmt % (k + f"_val_{model_key}", k + f"_trn_{model_key}") + for k in sorted(train_results[model_key].keys()): + print_str += prop_fmt % ( + k + f"_val_{model_key}", + k + f"_trn_{model_key}", + ) else: prop_fmt = " %11s" - for k in sorted(list(train_results[model_key].keys())): + for k in sorted(train_results[model_key].keys()): print_str += prop_fmt % (k + f"_trn_{model_key}") print_str += " %8s\n" % "lr" fout.write(print_str) fout.flush() def print_on_training(self, fout, step_id, cur_lr, train_results, valid_results): - train_keys = sorted(list(train_results.keys())) + train_keys = sorted(train_results.keys()) print_str = "" print_str += "%7d" % step_id if not self.multi_task: @@ -694,14 +874,14 @@ def print_on_training(self, fout, step_id, cur_lr, train_results, valid_results) for model_key in self.model_keys: if valid_results[model_key] is not None: prop_fmt = " %11.2e %11.2e" - for k in sorted(list(valid_results[model_key].keys())): + for k in sorted(valid_results[model_key].keys()): print_str += prop_fmt % ( valid_results[model_key][k], train_results[model_key][k], ) else: prop_fmt = " %11.2e" - for k in sorted(list(train_results[model_key].keys())): + for k in sorted(train_results[model_key].keys()): print_str += prop_fmt % (train_results[model_key][k]) print_str += " %8.1e\n" % cur_lr fout.write(print_str) diff --git a/deepmd_pt/train/wrapper.py b/deepmd_pt/train/wrapper.py index aff8ca5e..fe423e63 100644 --- a/deepmd_pt/train/wrapper.py +++ b/deepmd_pt/train/wrapper.py @@ -1,32 +1,34 @@ -import logging -import os +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + Optional, + Union, +) + import torch -from typing import Dict, Optional, Union -from deepmd_pt.model.descriptor import * -from deepmd_pt.model.task import * -from deepmd_pt.model.network import TypeEmbedNet if torch.__version__.startswith("2"): import torch._dynamo class ModelWrapper(torch.nn.Module): - - def __init__(self, - model: Union[torch.nn.Module, Dict], - loss: Union[torch.nn.Module, Dict] = None, - model_params=None, - shared_links=None): + def __init__( + self, + model: Union[torch.nn.Module, Dict], + loss: Union[torch.nn.Module, Dict] = None, + model_params=None, + shared_links=None, + ): """Construct a DeePMD model wrapper. Args: - config: The Dict-like configuration with training options. """ - super(ModelWrapper, self).__init__() + super().__init__() self.model_params = model_params if model_params is not None else {} self.train_infos = { - 'lr': 0, - 'step': 0, + "lr": 0, + "step": 0, } self.multi_task = False self.model = torch.nn.ModuleDict() @@ -36,8 +38,9 @@ def __init__(self, elif isinstance(model, dict): self.multi_task = True for task_key in model: - assert isinstance(model[task_key], torch.nn.Module), \ - f"{task_key} in model_dict is not a torch.nn.Module!" + assert isinstance( + model[task_key], torch.nn.Module + ), f"{task_key} in model_dict is not a torch.nn.Module!" self.model[task_key] = model[task_key] # Loss self.loss = None @@ -47,8 +50,9 @@ def __init__(self, self.loss["Default"] = loss elif isinstance(loss, dict): for task_key in loss: - assert isinstance(loss[task_key], torch.nn.Module), \ - f"{task_key} in loss_dict is not a torch.nn.Module!" + assert isinstance( + loss[task_key], torch.nn.Module + ), f"{task_key} in loss_dict is not a torch.nn.Module!" self.loss[task_key] = loss[task_key] self.inference_only = self.loss is None @@ -62,9 +66,16 @@ def set_trainable_params(self): trainable = self.model_params[net_type].get("trainable", True) else: if net_type in self.model_params["model_dict"][model_item]: - trainable = self.model_params["model_dict"][model_item][net_type].get("trainable", True) - if hasattr(self.model[model_item], net_type) and getattr(self.model[model_item], net_type) is not None: - for param in self.model[model_item].__getattr__(net_type).parameters(): + trainable = self.model_params["model_dict"][model_item][ + net_type + ].get("trainable", True) + if ( + hasattr(self.model[model_item], net_type) + and getattr(self.model[model_item], net_type) is not None + ): + for param in ( + self.model[model_item].__getattr__(net_type).parameters() + ): param.requires_grad = trainable def share_params(self, shared_links, resume=False): @@ -77,28 +88,45 @@ def share_params(self, shared_links, resume=False): shared_level_base = shared_base["shared_level"] if "descriptor" in class_type_base: if class_type_base == "descriptor": - base_class = self.model[model_key_base].__getattr__('descriptor') + base_class = self.model[model_key_base].__getattr__("descriptor") elif "hybrid" in class_type_base: - hybrid_index = int(class_type_base.split('_')[-1]) - base_class = self.model[model_key_base].__getattr__('descriptor').descriptor_list[hybrid_index] + hybrid_index = int(class_type_base.split("_")[-1]) + base_class = ( + self.model[model_key_base] + .__getattr__("descriptor") + .descriptor_list[hybrid_index] + ) else: raise RuntimeError(f"Unknown class_type {class_type_base}!") for link_item in shared_links[shared_item]["links"][1:]: class_type_link = link_item["shared_type"] model_key_link = link_item["model_key"] shared_level_link = int(link_item["shared_level"]) - assert shared_level_link >= shared_level_base, "The shared_links must be sorted by shared_level!" - assert "descriptor" in class_type_link, f"Class type mismatched: {class_type_base} vs {class_type_link}!" + assert ( + shared_level_link >= shared_level_base + ), "The shared_links must be sorted by shared_level!" + assert ( + "descriptor" in class_type_link + ), f"Class type mismatched: {class_type_base} vs {class_type_link}!" if class_type_link == "descriptor": - link_class = self.model[model_key_link].__getattr__('descriptor') + link_class = self.model[model_key_link].__getattr__( + "descriptor" + ) elif "hybrid" in class_type_link: - hybrid_index = int(class_type_link.split('_')[-1]) - link_class = self.model[model_key_link].__getattr__('descriptor').descriptor_list[hybrid_index] + hybrid_index = int(class_type_link.split("_")[-1]) + link_class = ( + self.model[model_key_link] + .__getattr__("descriptor") + .descriptor_list[hybrid_index] + ) else: raise RuntimeError(f"Unknown class_type {class_type_link}!") - link_class.share_params(base_class, shared_level_link, resume=resume) + link_class.share_params( + base_class, shared_level_link, resume=resume + ) print( - f'Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!') + f"Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!" + ) else: if hasattr(self.model[model_key_base], class_type_base): base_class = self.model[model_key_base].__getattr__(class_type_base) @@ -106,38 +134,59 @@ def share_params(self, shared_links, resume=False): class_type_link = link_item["shared_type"] model_key_link = link_item["model_key"] shared_level_link = int(link_item["shared_level"]) - assert shared_level_link >= shared_level_base, "The shared_links must be sorted by shared_level!" - assert class_type_base == class_type_link, f"Class type mismatched: {class_type_base} vs {class_type_link}!" - link_class = self.model[model_key_link].__getattr__(class_type_link) - link_class.share_params(base_class, shared_level_link, resume=resume) + assert ( + shared_level_link >= shared_level_base + ), "The shared_links must be sorted by shared_level!" + assert ( + class_type_base == class_type_link + ), f"Class type mismatched: {class_type_base} vs {class_type_link}!" + link_class = self.model[model_key_link].__getattr__( + class_type_link + ) + link_class.share_params( + base_class, shared_level_link, resume=resume + ) print( - f'Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!') + f"Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!" + ) - def forward(self, coord, atype, box: Optional[torch.Tensor] = None, - cur_lr: Optional[torch.Tensor] = None, label: Optional[torch.Tensor] = None, - task_key: Optional[torch.Tensor] = None, inference_only=False, - do_atomic_virial=False): + def forward( + self, + coord, + atype, + box: Optional[torch.Tensor] = None, + cur_lr: Optional[torch.Tensor] = None, + label: Optional[torch.Tensor] = None, + task_key: Optional[torch.Tensor] = None, + inference_only=False, + do_atomic_virial=False, + ): if not self.multi_task: task_key = "Default" else: - assert task_key is not None, \ - f"Multitask model must specify the inference task! Supported tasks are {list(self.model.keys())}." - model_pred = self.model[task_key](coord, atype, box=box, do_atomic_virial=do_atomic_virial) + assert ( + task_key is not None + ), f"Multitask model must specify the inference task! Supported tasks are {list(self.model.keys())}." + model_pred = self.model[task_key]( + coord, atype, box=box, do_atomic_virial=do_atomic_virial + ) natoms = atype.shape[-1] if not self.inference_only and not inference_only: - loss, more_loss = self.loss[task_key](model_pred, label, natoms=natoms, learning_rate=cur_lr) + loss, more_loss = self.loss[task_key]( + model_pred, label, natoms=natoms, learning_rate=cur_lr + ) return model_pred, loss, more_loss else: return model_pred, None, None def set_extra_state(self, state: Dict): - self.model_params = state['model_params'] - self.train_infos = state['train_infos'] + self.model_params = state["model_params"] + self.train_infos = state["train_infos"] return None - + def get_extra_state(self) -> Dict: state = { - 'model_params': self.model_params, - 'train_infos': self.train_infos, + "model_params": self.model_params, + "train_infos": self.train_infos, } return state diff --git a/deepmd_pt/utils/__init__.py b/deepmd_pt/utils/__init__.py index e69de29b..6ceb116d 100644 --- a/deepmd_pt/utils/__init__.py +++ b/deepmd_pt/utils/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/deepmd_pt/utils/ase_calc.py b/deepmd_pt/utils/ase_calc.py index 77b4f33d..7730bd0b 100644 --- a/deepmd_pt/utils/ase_calc.py +++ b/deepmd_pt/utils/ase_calc.py @@ -1,17 +1,33 @@ -from ase import Atoms -from ase.calculators.calculator import Calculator, PropertyNotImplementedError -from deepmd_pt.infer.deep_eval import DeepPot -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + ClassVar, +) + import dpdata +import numpy as np +from ase import ( + Atoms, +) +from ase.calculators.calculator import ( + Calculator, + PropertyNotImplementedError, +) + +from deepmd_pt.infer.deep_eval import ( + DeepPot, +) class DPCalculator(Calculator): - implemented_properties = ["energy", "free_energy", "forces", "virial", "stress"] + implemented_properties: ClassVar[list] = [ + "energy", + "free_energy", + "forces", + "virial", + "stress", + ] - def __init__( - self, - model - ): + def __init__(self, model): Calculator.__init__(self) self.dp = DeepPot(model) self.type_map = self.dp.type_map @@ -19,16 +35,18 @@ def __init__( def calculate(self, atoms: Atoms, properties, system_changes) -> None: Calculator.calculate(self, atoms, properties, system_changes) system = dpdata.System(atoms, fmt="ase/structure") - type_trans = np.array([self.type_map.index(i) for i in system.data['atom_names']]) - input_coords = system.data['coords'] - input_cells = system.data['cells'] - input_types = list(type_trans[system.data['atom_types']]) + type_trans = np.array( + [self.type_map.index(i) for i in system.data["atom_names"]] + ) + input_coords = system.data["coords"] + input_cells = system.data["cells"] + input_types = list(type_trans[system.data["atom_types"]]) model_predict = self.dp.eval(input_coords, input_cells, input_types) self.results = { "energy": model_predict[0].item(), "free_energy": model_predict[0].item(), "forces": model_predict[1].reshape(-1, 3), - "virial": model_predict[2].reshape(3, 3) + "virial": model_predict[2].reshape(3, 3), } # convert virial into stress for lattice relaxation @@ -36,7 +54,11 @@ def calculate(self, atoms: Atoms, properties, system_changes) -> None: if sum(atoms.get_pbc()) > 0 or (atoms.cell is not None): # the usual convention (tensile stress is positive) # stress = -virial / volume - stress = -0.5 * (self.results["virial"].copy() + self.results["virial"].copy().T) / atoms.get_volume() + stress = ( + -0.5 + * (self.results["virial"].copy() + self.results["virial"].copy().T) + / atoms.get_volume() + ) # Voigt notation self.results["stress"] = stress.flat[[0, 4, 8, 5, 2, 1]] else: diff --git a/deepmd_pt/utils/auto_batch_size.py b/deepmd_pt/utils/auto_batch_size.py index 049bad4c..04cbfa9b 100644 --- a/deepmd_pt/utils/auto_batch_size.py +++ b/deepmd_pt/utils/auto_batch_size.py @@ -1,7 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import torch - -from deepmd_utils.utils.batch_size import AutoBatchSize as AutoBatchSizeBase +from deepmd.utils.batch_size import AutoBatchSize as AutoBatchSizeBase class AutoBatchSize(AutoBatchSizeBase): diff --git a/deepmd_pt/utils/cache.py b/deepmd_pt/utils/cache.py index cea7ce28..c40c4050 100644 --- a/deepmd_pt/utils/cache.py +++ b/deepmd_pt/utils/cache.py @@ -1,7 +1,6 @@ -import functools +# SPDX-License-Identifier: LGPL-3.0-or-later import copy as copy_lib -import time -import queue +import functools def lru_cache(maxsize=16, typed=False, copy=False, deepcopy=False): diff --git a/deepmd_pt/utils/dataloader.py b/deepmd_pt/utils/dataloader.py index 2ba7dad6..72006b73 100644 --- a/deepmd_pt/utils/dataloader.py +++ b/deepmd_pt/utils/dataloader.py @@ -1,27 +1,45 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging import os import queue import time -import numpy as np -from threading import Thread -from typing import Callable, Dict, List, Tuple, Type, Union -from multiprocessing.dummy import Pool +from multiprocessing.dummy import ( + Pool, +) +from threading import ( + Thread, +) +from typing import ( + List, +) import h5py import torch import torch.distributed as dist -from deepmd_pt.utils import env -from deepmd_pt.utils.dataset import DeepmdDataSetForLoader -from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler -from torch.utils.data.distributed import DistributedSampler -from deepmd_pt.model.descriptor import Descriptor -from tqdm import tqdm import torch.multiprocessing - -from deepmd_utils.utils.data_system import ( +from deepmd.utils.data_system import ( prob_sys_size_ext, - process_sys_probs + process_sys_probs, +) +from torch.utils.data import ( + DataLoader, + Dataset, + WeightedRandomSampler, +) +from torch.utils.data.distributed import ( + DistributedSampler, +) + +from deepmd_pt.model.descriptor import ( + Descriptor, ) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataset import ( + DeepmdDataSetForLoader, +) + torch.multiprocessing.set_sharing_strategy("file_system") @@ -94,15 +112,15 @@ def construct_dataset(system): self.sampler_list.append(system_sampler) else: system_sampler = None - if isinstance(batch_size,str): + if isinstance(batch_size, str): if batch_size == "auto": rule = 32 elif batch_size.startswith("auto:"): rule = int(batch_size.split(":")[1]) else: rule = None - logging.error(f"Unsupported batch size type") - self.batch_size = rule // system._natoms + logging.error("Unsupported batch size type") + self.batch_size = rule // system._natoms if self.batch_size * system._natoms < rule: self.batch_size += 1 else: @@ -122,7 +140,7 @@ def construct_dataset(system): self.iters = [] for item in self.dataloaders: self.iters.append(iter(item)) - + def set_noise(self, noise_settings): # noise_settings['noise_type'] # "trunc_normal", "normal", "uniform" # noise_settings['noise'] # float, default 1.0 @@ -137,17 +155,16 @@ def __len__(self): return len(self.dataloaders) def __getitem__(self, idx): - #logging.warning(str(torch.distributed.get_rank())+" idx: "+str(idx)+" index: "+str(self.index[idx])) + # logging.warning(str(torch.distributed.get_rank())+" idx: "+str(idx)+" index: "+str(self.index[idx])) try: batch = next(self.iters[idx]) except StopIteration: self.iters[idx] = iter(self.dataloaders[idx]) batch = next(self.iters[idx]) - batch['sid'] = idx + batch["sid"] = idx return batch - _sentinel = object() QUEUESIZE = 32 @@ -167,7 +184,7 @@ def run(self): self._queue.put(_sentinel) -class BufferedIterator(object): +class BufferedIterator: def __init__(self, iterable): self._queue = queue.Queue(QUEUESIZE) self._iterable = iterable @@ -235,8 +252,12 @@ def collate_tensor_fn(batch): # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum(x.numel() for x in tmp_batch) - storage = hybrid_item._typed_storage()._new_shared(numel, device=hybrid_item.device) - out = hybrid_item.new(storage).resize_(len(tmp_batch), *list(hybrid_item.size())) + storage = hybrid_item._typed_storage()._new_shared( + numel, device=hybrid_item.device + ) + out = hybrid_item.new(storage).resize_( + len(tmp_batch), *list(hybrid_item.size()) + ) out_hybrid.append(torch.stack(tmp_batch, 0, out=out)) return out_hybrid @@ -277,21 +298,22 @@ def collate_batch(batch): result[key] = collate_tensor_fn([d[key] for d in batch]) return result -def get_weighted_sampler(training_data,prob_style,sys_prob=False): - if sys_prob == False: + +def get_weighted_sampler(training_data, prob_style, sys_prob=False): + if sys_prob is False: if prob_style == "prob_uniform": prob_v = 1.0 / float(training_data.__len__()) probs = [prob_v for ii in range(training_data.__len__())] - else:#prob_sys_size;A:B:p1;C:D:p2 or prob_sys_size = prob_sys_size;0:nsys:1.0 + else: # prob_sys_size;A:B:p1;C:D:p2 or prob_sys_size = prob_sys_size;0:nsys:1.0 if prob_style == "prob_sys_size": - style = "prob_sys_size;0:{}:1.0".format(len(training_data)) + style = f"prob_sys_size;0:{len(training_data)}:1.0" else: style = prob_style - probs = prob_sys_size_ext(style,len(training_data),training_data.index) + probs = prob_sys_size_ext(style, len(training_data), training_data.index) else: - probs = process_sys_probs(prob_style,training_data.index) - logging.info("Generated weighted sampler with prob array: "+str(probs)) - #training_data.total_batch is the size of one epoch, you can increase it to avoid too many rebuilding of iteraters + probs = process_sys_probs(prob_style, training_data.index) + logging.info("Generated weighted sampler with prob array: " + str(probs)) + # training_data.total_batch is the size of one epoch, you can increase it to avoid too many rebuilding of iteraters len_sampler = training_data.total_batch * env.NUM_WORKERS - sampler = WeightedRandomSampler(probs,len_sampler, replacement = True) + sampler = WeightedRandomSampler(probs, len_sampler, replacement=True) return sampler diff --git a/deepmd_pt/utils/dataset.py b/deepmd_pt/utils/dataset.py index d3c2033b..4dae575a 100644 --- a/deepmd_pt/utils/dataset.py +++ b/deepmd_pt/utils/dataset.py @@ -1,41 +1,67 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import glob -import logging -import numpy as np import os -import torch -from typing import List -from torch.utils.data import Dataset -from deepmd_pt.utils import env, dp_random -from deepmd_pt.utils.preprocess import Region3D, normalize_coord, make_env_mat -from tqdm import trange +from typing import ( + List, + Optional, +) + import h5py +import numpy as np +import torch import torch.distributed as dist -from deepmd_pt.utils.cache import lru_cache - - -class DeepmdDataSystem(object): - - def __init__(self, sys_path: str, rcut, sec, type_map: List[str] = None, type_split=True, noise_settings=None, shuffle=True): +from torch.utils.data import ( + Dataset, +) +from tqdm import ( + trange, +) + +from deepmd_pt.utils import ( + dp_random, + env, +) +from deepmd_pt.utils.cache import ( + lru_cache, +) +from deepmd_pt.utils.preprocess import ( + Region3D, + make_env_mat, + normalize_coord, +) + + +class DeepmdDataSystem: + def __init__( + self, + sys_path: str, + rcut, + sec, + type_map: Optional[List[str]] = None, + type_split=True, + noise_settings=None, + shuffle=True, + ): """Construct DeePMD-style frame collection of one system. Args: - sys_path: Paths to the system. - type_map: Atom types. """ - sys_path = sys_path.replace('#', '') - if '.hdf5' in sys_path: + sys_path = sys_path.replace("#", "") + if ".hdf5" in sys_path: tmp = sys_path.split("/") path = "/".join(tmp[:-1]) sys = tmp[-1] self.file = h5py.File(path)[sys] self._dirs = [] for item in self.file.keys(): - if 'set.' in item: + if "set." in item: self._dirs.append(item) self._dirs.sort() else: self.file = None - self._dirs = glob.glob(os.path.join(sys_path, 'set.*')) + self._dirs = glob.glob(os.path.join(sys_path, "set.*")) self._dirs.sort() self.type_split = type_split self.noise_settings = noise_settings @@ -72,7 +98,9 @@ def __init__(self, sys_path: str, rcut, sec, type_map: List[str] = None, type_sp self.enforce_type_map = False if type_map is not None and self._type_map is not None: if not self.mixed_type: - atom_type = [type_map.index(self._type_map[ii]) for ii in self._atom_type] + atom_type = [ + type_map.index(self._type_map[ii]) for ii in self._atom_type + ] self._atom_type = np.array(atom_type, dtype=np.int32) else: @@ -91,11 +119,11 @@ def __init__(self, sys_path: str, rcut, sec, type_map: List[str] = None, type_sp self._idx_map = _make_idx_map(self._atom_type) self._data_dict = {} - self.add('box', 9, must=self.pbc) - self.add('coord', 3, atomic=True, must=True) - self.add('energy', 1, atomic=False, must=False, high_prec=True) - self.add('force', 3, atomic=True, must=False, high_prec=False) - self.add('virial', 9, atomic=False, must=False, high_prec=False) + self.add("box", 9, must=self.pbc) + self.add("coord", 3, atomic=True, must=True) + self.add("energy", 1, atomic=False, must=False, high_prec=True) + self.add("force", 3, atomic=True, must=False, high_prec=False) + self.add("virial", 9, atomic=False, must=False, high_prec=False) self._sys_path = sys_path self.rcut = rcut @@ -119,7 +147,7 @@ def __init__(self, sys_path: str, rcut, sec, type_map: List[str] = None, type_sp def _check_pbc(self, sys_path): pbc = True - if os.path.isfile(os.path.join(sys_path, 'nopbc')): + if os.path.isfile(os.path.join(sys_path, "nopbc")): pbc = False self.pbc = pbc @@ -140,13 +168,14 @@ def set_noise(self, noise_settings): self.mask_prob = float(noise_settings.get("mask_prob", 0.15)) self.same_mask = noise_settings.get("noise_type", False) - def add(self, - key: str, - ndof: int, - atomic: bool = False, - must: bool = False, - high_prec: bool = False - ): + def add( + self, + key: str, + ndof: int, + atomic: bool = False, + must: bool = False, + high_prec: bool = False, + ): """Add a data item that to be loaded. Args: @@ -157,10 +186,10 @@ def add(self, - high_prec: Load the data and store in float64, otherwise in float32. """ self._data_dict[key] = { - 'ndof': ndof, - 'atomic': atomic, - 'must': must, - 'high_prec': high_prec + "ndof": ndof, + "atomic": atomic, + "must": must, + "high_prec": high_prec, } # deprecated TODO @@ -170,11 +199,11 @@ def get_batch_for_train(self, batch_size: int): Args: - batch_size: Frame count. """ - if not hasattr(self, '_frames'): + if not hasattr(self, "_frames"): self.set_size = 0 self._set_count = 0 self._iterator = 0 - if batch_size == 'auto': + if batch_size == "auto": batch_size = -(-32 // self._natoms) if self._iterator + batch_size > self.set_size: set_idx = self._set_count % len(self._dirs) @@ -194,12 +223,12 @@ def get_batch_for_train(self, batch_size: int): if dist.is_initialized(): world_size = dist.get_world_size() rank = dist.get_rank() - ssize = self._frames['coord'].shape[0] + ssize = self._frames["coord"].shape[0] subsize = ssize // world_size self._iterator = rank * subsize self.set_size = min((rank + 1) * subsize, ssize) else: - self.set_size = self._frames['coord'].shape[0] + self.set_size = self._frames["coord"].shape[0] self._iterator = 0 self._set_count += 1 iterator = min(self._iterator + batch_size, self.set_size) @@ -213,11 +242,11 @@ def get_batch(self, batch_size: int): Args: - batch_size: Frame count. """ - if not hasattr(self, '_frames'): + if not hasattr(self, "_frames"): self.set_size = 0 self._set_count = 0 self._iterator = 0 - if batch_size == 'auto': + if batch_size == "auto": batch_size = -(-32 // self._natoms) if self._iterator + batch_size > self.set_size: set_idx = self._set_count % len(self._dirs) @@ -226,7 +255,7 @@ def get_batch(self, batch_size: int): frames = self.preprocess(frames) cnt = 0 for item in self.sets: - if not item is None: + if item is not None: cnt += 1 if cnt < env.CACHE_PER_SYS: self.sets[set_idx] = frames @@ -234,7 +263,7 @@ def get_batch(self, batch_size: int): frames = self.sets[set_idx] self._frames = frames self._shuffle_data() - self.set_size = self._frames['coord'].shape[0] + self.set_size = self._frames["coord"].shape[0] self._iterator = 0 self._set_count += 1 iterator = min(self._iterator + batch_size, self.set_size) @@ -264,20 +293,22 @@ def get_natoms_vec(self, ntypes: int): return tmp.astype(np.int32) def _load_type(self, sys_path): - if not self.file is None: - return self.file['type.raw'][:] + if self.file is not None: + return self.file["type.raw"][:] else: - return np.loadtxt(os.path.join(sys_path, 'type.raw'), dtype=np.int32, ndmin=1) + return np.loadtxt( + os.path.join(sys_path, "type.raw"), dtype=np.int32, ndmin=1 + ) def _load_type_map(self, sys_path): - if not self.file is None: - tmp = self.file['type_map.raw'][:].tolist() - tmp = [item.decode('ascii') for item in tmp] + if self.file is not None: + tmp = self.file["type_map.raw"][:].tolist() + tmp = [item.decode("ascii") for item in tmp] return tmp else: - fname = os.path.join(sys_path, 'type_map.raw') + fname = os.path.join(sys_path, "type_map.raw") if os.path.isfile(fname): - with open(fname, 'r') as fin: + with open(fname) as fin: content = fin.read() return content.split() else: @@ -295,33 +326,35 @@ def _load_type_mix(self, set_name): def _load_set(self, set_name, fast=False): if self.file is None: path = os.path.join(set_name, "coord.npy") - if self._data_dict['coord']['high_prec']: + if self._data_dict["coord"]["high_prec"]: coord = np.load(path).astype(env.GLOBAL_ENER_FLOAT_PRECISION) else: coord = np.load(path).astype(env.GLOBAL_NP_FLOAT_PRECISION) if coord.ndim == 1: coord = coord.reshape([1, -1]) - assert (coord.shape[1] == self._data_dict['coord']['ndof'] * self._natoms) + assert coord.shape[1] == self._data_dict["coord"]["ndof"] * self._natoms nframes = coord.shape[0] if fast: return nframes - data = {'type': np.tile(self._atom_type[self._idx_map], (nframes, 1))} + data = {"type": np.tile(self._atom_type[self._idx_map], (nframes, 1))} for kk in self._data_dict.keys(): - data['find_' + kk], data[kk] = self._load_data( + data["find_" + kk], data[kk] = self._load_data( set_name, kk, nframes, - self._data_dict[kk]['ndof'], - atomic=self._data_dict[kk]['atomic'], - high_prec=self._data_dict[kk]['high_prec'], - must=self._data_dict[kk]['must'] + self._data_dict[kk]["ndof"], + atomic=self._data_dict[kk]["atomic"], + high_prec=self._data_dict[kk]["high_prec"], + must=self._data_dict[kk]["must"], ) if self.mixed_type: # nframes x natoms atom_type_mix = self._load_type_mix(set_name) if self.enforce_type_map: try: - atom_type_mix_ = self.type_idx_map[atom_type_mix].astype(np.int32) + atom_type_mix_ = self.type_idx_map[atom_type_mix].astype( + np.int32 + ) except IndexError as e: raise IndexError( "some types in 'real_atom_types.npy' of set {} are not contained in {} types!".format( @@ -342,13 +375,15 @@ def _load_set(self, set_name, fast=False): dtype=np.int32, ).T assert ( - atom_type_nums.sum(axis=-1) + ghost_nums.sum(axis=-1) == natoms + atom_type_nums.sum(axis=-1) + ghost_nums.sum(axis=-1) == natoms ).all(), "some types in 'real_atom_types.npy' of set {} are not contained in {} types!".format( set_name, self.get_ntypes() ) data["real_natoms_vec"] = np.concatenate( ( - np.tile(np.array([natoms, natoms], dtype=np.int32), (nframes, 1)), + np.tile( + np.array([natoms, natoms], dtype=np.int32), (nframes, 1) + ), atom_type_nums, ), axis=-1, @@ -357,19 +392,23 @@ def _load_set(self, set_name, fast=False): return data else: data = {} - nframes = self.file[set_name][f"coord.npy"].shape[0] + nframes = self.file[set_name]["coord.npy"].shape[0] if fast: return nframes - for key in ['coord', 'energy', 'force', 'box']: + for key in ["coord", "energy", "force", "box"]: data[key] = self.file[set_name][f"{key}.npy"][:] - if self._data_dict[key]['atomic']: - data[key] = data[key].reshape(nframes, self._natoms, -1)[:, self._idx_map, :] + if self._data_dict[key]["atomic"]: + data[key] = data[key].reshape(nframes, self._natoms, -1)[ + :, self._idx_map, : + ] if self.mixed_type: # nframes x natoms atom_type_mix = self._load_type_mix(set_name) if self.enforce_type_map: try: - atom_type_mix_ = self.type_idx_map[atom_type_mix].astype(np.int32) + atom_type_mix_ = self.type_idx_map[atom_type_mix].astype( + np.int32 + ) except IndexError as e: raise IndexError( "some types in 'real_atom_types.npy' of set {} are not contained in {} types!".format( @@ -390,25 +429,29 @@ def _load_set(self, set_name, fast=False): dtype=np.int32, ).T assert ( - atom_type_nums.sum(axis=-1) + ghost_nums.sum(axis=-1) == natoms + atom_type_nums.sum(axis=-1) + ghost_nums.sum(axis=-1) == natoms ).all(), "some types in 'real_atom_types.npy' of set {} are not contained in {} types!".format( set_name, self.get_ntypes() ) data["real_natoms_vec"] = np.concatenate( ( - np.tile(np.array([natoms, natoms], dtype=np.int32), (nframes, 1)), + np.tile( + np.array([natoms, natoms], dtype=np.int32), (nframes, 1) + ), atom_type_nums, ), axis=-1, ) else: - data['type'] = np.tile(self._atom_type[self._idx_map], (nframes, 1)) + data["type"] = np.tile(self._atom_type[self._idx_map], (nframes, 1)) return data - def _load_data(self, set_name, key, nframes, ndof, atomic=False, must=True, high_prec=False): + def _load_data( + self, set_name, key, nframes, ndof, atomic=False, must=True, high_prec=False + ): if atomic: ndof *= self._natoms - path = os.path.join(set_name, key + '.npy') + path = os.path.join(set_name, key + ".npy") # logging.info('Loading data from: %s', path) if os.path.isfile(path): if high_prec: @@ -432,27 +475,35 @@ def _load_data(self, set_name, key, nframes, ndof, atomic=False, must=True, high # deprecated TODO def preprocess(self, batch): - n_frames = batch['coord'].shape[0] + n_frames = batch["coord"].shape[0] for kk in self._data_dict.keys(): if "find_" in kk: pass else: - batch[kk] = torch.tensor(batch[kk], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.PREPROCESS_DEVICE) - if self._data_dict[kk]['atomic']: - batch[kk] = batch[kk].view(n_frames, -1, self._data_dict[kk]['ndof']) + batch[kk] = torch.tensor( + batch[kk], + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + device=env.PREPROCESS_DEVICE, + ) + if self._data_dict[kk]["atomic"]: + batch[kk] = batch[kk].view( + n_frames, -1, self._data_dict[kk]["ndof"] + ) - for kk in ['type', 'real_natoms_vec']: + for kk in ["type", "real_natoms_vec"]: if kk in batch.keys(): - batch[kk] = torch.tensor(batch[kk], dtype=torch.long, device=env.PREPROCESS_DEVICE) - batch['atype'] = batch.pop('type') + batch[kk] = torch.tensor( + batch[kk], dtype=torch.long, device=env.PREPROCESS_DEVICE + ) + batch["atype"] = batch.pop("type") - keys = ['nlist', 'nlist_loc', 'nlist_type', 'shift', 'mapping'] - coord = batch['coord'] - atype = batch['atype'] - box = batch['box'] + keys = ["nlist", "nlist_loc", "nlist_type", "shift", "mapping"] + coord = batch["coord"] + atype = batch["atype"] + box = batch["box"] rcut = self.rcut sec = self.sec - assert batch['atype'].max() < len(self._type_map) + assert batch["atype"].max() < len(self._type_map) nlist, nlist_loc, nlist_type, shift, mapping = [], [], [], [], [] for sid in trange(n_frames, disable=env.DISABLE_TQDM): @@ -460,7 +511,9 @@ def preprocess(self, batch): nloc = atype[sid].shape[0] _coord = normalize_coord(coord[sid], region, nloc) coord[sid] = _coord - a, b, c, d, e = make_env_mat(_coord, atype[sid], region, rcut, sec, type_split=self.type_split) + a, b, c, d, e = make_env_mat( + _coord, atype[sid], region, rcut, sec, type_split=self.type_split + ) nlist.append(a) nlist_loc.append(b) nlist_type.append(c) @@ -469,21 +522,26 @@ def preprocess(self, batch): nlist = torch.stack(nlist) nlist_loc = torch.stack(nlist_loc) nlist_type = torch.stack(nlist_type) - batch['nlist'] = nlist - batch['nlist_loc'] = nlist_loc - batch['nlist_type'] = nlist_type + batch["nlist"] = nlist + batch["nlist_loc"] = nlist_loc + batch["nlist_type"] = nlist_type natoms_extended = max([item.shape[0] for item in shift]) - batch['shift'] = torch.zeros((n_frames, natoms_extended, 3), dtype=env.GLOBAL_PT_FLOAT_PRECISION, - device=env.PREPROCESS_DEVICE) - batch['mapping'] = torch.zeros((n_frames, natoms_extended), dtype=torch.long, device=env.PREPROCESS_DEVICE) + batch["shift"] = torch.zeros( + (n_frames, natoms_extended, 3), + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + device=env.PREPROCESS_DEVICE, + ) + batch["mapping"] = torch.zeros( + (n_frames, natoms_extended), dtype=torch.long, device=env.PREPROCESS_DEVICE + ) for i in range(len(shift)): natoms_tmp = shift[i].shape[0] - batch['shift'][i, :natoms_tmp] = shift[i] - batch['mapping'][i, :natoms_tmp] = mapping[i] + batch["shift"][i, :natoms_tmp] = shift[i] + batch["mapping"][i, :natoms_tmp] = mapping[i] return batch def _shuffle_data(self): - nframes = self._frames['coord'].shape[0] + nframes = self._frames["coord"].shape[0] idx = np.arange(nframes) if self.shuffle: dp_random.shuffle(idx) @@ -495,7 +553,7 @@ def _get_subdata(self, idx=None): new_data = {} for ii in data: dd = data[ii] - if 'find_' in ii: + if "find_" in ii: new_data[ii] = dd else: if idx is not None: @@ -510,59 +568,70 @@ def single_preprocess(self, batch, sid): if "find_" in kk: pass else: - batch[kk] = torch.tensor(batch[kk][sid], dtype=env.GLOBAL_PT_FLOAT_PRECISION, - device=env.PREPROCESS_DEVICE) - if self._data_dict[kk]['atomic']: - batch[kk] = batch[kk].view(-1, self._data_dict[kk]['ndof']) - for kk in ['type', 'real_natoms_vec']: + batch[kk] = torch.tensor( + batch[kk][sid], + dtype=env.GLOBAL_PT_FLOAT_PRECISION, + device=env.PREPROCESS_DEVICE, + ) + if self._data_dict[kk]["atomic"]: + batch[kk] = batch[kk].view(-1, self._data_dict[kk]["ndof"]) + for kk in ["type", "real_natoms_vec"]: if kk in batch.keys(): - batch[kk] = torch.tensor(batch[kk][sid], dtype=torch.long, device=env.PREPROCESS_DEVICE) - clean_coord = batch.pop('coord') - clean_type = batch.pop('type') + batch[kk] = torch.tensor( + batch[kk][sid], dtype=torch.long, device=env.PREPROCESS_DEVICE + ) + clean_coord = batch.pop("coord") + clean_type = batch.pop("type") nloc = clean_type.shape[0] rcut = self.rcut sec = self.sec nlist, nlist_loc, nlist_type, shift, mapping = [], [], [], [], [] if self.pbc: - box = batch['box'] + box = batch["box"] region = Region3D(box) else: box = None - batch['box'] = None + batch["box"] = None region = None if self.noise_settings is None: - batch['atype'] = clean_type - batch['coord'] = clean_coord + batch["atype"] = clean_type + batch["coord"] = clean_coord coord = clean_coord - atype = batch['atype'] + atype = batch["atype"] if self.pbc: _coord = normalize_coord(coord, region, nloc) else: _coord = coord.clone() - batch['coord'] = _coord - nlist, nlist_loc, nlist_type, shift, mapping = make_env_mat(_coord, atype, region, rcut, sec, - pbc=self.pbc, - type_split=self.type_split) - batch['nlist'] = nlist - batch['nlist_loc'] = nlist_loc - batch['nlist_type'] = nlist_type - batch['shift'] = shift - batch['mapping'] = mapping + batch["coord"] = _coord + nlist, nlist_loc, nlist_type, shift, mapping = make_env_mat( + _coord, + atype, + region, + rcut, + sec, + pbc=self.pbc, + type_split=self.type_split, + ) + batch["nlist"] = nlist + batch["nlist_loc"] = nlist_loc + batch["nlist_type"] = nlist_type + batch["shift"] = shift + batch["mapping"] = mapping return batch else: - batch['clean_type'] = clean_type + batch["clean_type"] = clean_type if self.pbc: _clean_coord = normalize_coord(clean_coord, region, nloc) else: _clean_coord = clean_coord.clone() - batch['clean_coord'] = _clean_coord + batch["clean_coord"] = _clean_coord # add noise for i in range(self.max_fail_num): mask_num = 0 if self.noise_mode == "fix_num": mask_num = self.mask_num - if(len(batch["clean_type"])= stop_steps: self.decay_steps = default_ds - self.decay_rate = np.exp(np.log(stop_lr / self.start_lr) / (stop_steps / self.decay_steps)) - if 'decay_rate' in kwargs: - self.decay_rate = kwargs['decay_rate'] - if 'min_lr' in kwargs: - self.min_lr = kwargs['min_lr'] + self.decay_rate = np.exp( + np.log(stop_lr / self.start_lr) / (stop_steps / self.decay_steps) + ) + if "decay_rate" in kwargs: + self.decay_rate = kwargs["decay_rate"] + if "min_lr" in kwargs: + self.min_lr = kwargs["min_lr"] else: self.min_lr = 3e-10 diff --git a/deepmd_pt/utils/multi_task.py b/deepmd_pt/utils/multi_task.py index ea3e480c..2b98149b 100644 --- a/deepmd_pt/utils/multi_task.py +++ b/deepmd_pt/utils/multi_task.py @@ -1,27 +1,42 @@ -from deepmd_pt.model.descriptor import * -from deepmd_pt.model.task import * -from deepmd_pt.model.network import TypeEmbedNet -from copy import deepcopy +# SPDX-License-Identifier: LGPL-3.0-or-later +from copy import ( + deepcopy, +) + +from deepmd_pt.model.descriptor import ( + DescrptDPA1, + DescrptDPA2, + DescrptSeA, +) +from deepmd_pt.model.network.network import ( + TypeEmbedNet, +) +from deepmd_pt.model.task import ( + EnergyFittingNet, + EnergyFittingNetDirect, + FittingNetAttenLcc, +) def preprocess_shared_params(model_config): - """ Preprocess the model params for multitask model, and generate the links dict for further sharing. + """Preprocess the model params for multitask model, and generate the links dict for further sharing. Args: model_config: Model params of multitask model. - Returns: - model_config: Preprocessed model params of multitask model. - Those string names are replaced with real params in `shared_dict` of model params. - shared_links: Dict of link infos for further sharing. - Each item, whose key must be in `shared_dict`, is a dict with following keys: - - "type": The real class type of this item. - - "links": List of shared settings, each sub-item is a dict with following keys: - - "model_key": Model key in the `model_dict` to share this item. - - "shared_type": Type of this shard item. - - "shared_level": Shared level (int) of this item in this model. - Lower for more params to share, 0 means to share all params in this item. - This list are sorted by "shared_level". + Returns + ------- + model_config: Preprocessed model params of multitask model. + Those string names are replaced with real params in `shared_dict` of model params. + shared_links: Dict of link infos for further sharing. + Each item, whose key must be in `shared_dict`, is a dict with following keys: + - "type": The real class type of this item. + - "links": List of shared settings, each sub-item is a dict with following keys: + - "model_key": Model key in the `model_dict` to share this item. + - "shared_type": Type of this shard item. + - "shared_level": Shared level (int) of this item in this model. + Lower for more params to share, 0 means to share all params in this item. + This list are sorted by "shared_level". """ assert "model_dict" in model_config, "only multi-task model can use this method!" supported_types = ["type_map", "type_embedding", "descriptor", "fitting_net"] @@ -29,15 +44,16 @@ def preprocess_shared_params(model_config): shared_links = {} type_map_keys = [] - def replace_one_item(params_dict, key_type, key_in_dict, suffix='', index=None): + def replace_one_item(params_dict, key_type, key_in_dict, suffix="", index=None): shared_type = key_type shared_key = key_in_dict shared_level = 0 if ":" in key_in_dict: shared_key = key_in_dict.split(":")[0] shared_level = int(key_in_dict.split(":")[1]) - assert shared_key in shared_dict, \ - f"Appointed {shared_type} {shared_key} are not in the shared_dict! Please check the input params." + assert ( + shared_key in shared_dict + ), f"Appointed {shared_type} {shared_key} are not in the shared_dict! Please check the input params." if index is None: params_dict[shared_type] = deepcopy(shared_dict[shared_key]) else: @@ -49,9 +65,11 @@ def replace_one_item(params_dict, key_type, key_in_dict, suffix='', index=None): if shared_key not in shared_links: class_name = get_class_name(shared_type, shared_dict[key_in_dict]) shared_links[shared_key] = {"type": class_name, "links": []} - link_item = {"model_key": model_key, - "shared_type": shared_type + suffix, - "shared_level": shared_level} + link_item = { + "model_key": model_key, + "shared_type": shared_type + suffix, + "shared_level": shared_level, + } shared_links[shared_key]["links"].append(link_item) for model_key in model_config["model_dict"]: @@ -64,10 +82,17 @@ def replace_one_item(params_dict, key_type, key_in_dict, suffix='', index=None): elif item_params.get("type", "") == "hybrid": for ii, hybrid_item in enumerate(item_params["list"]): if isinstance(hybrid_item, str): - replace_one_item(model_params_item[item_key]["list"], item_key, hybrid_item, - suffix=f"_hybrid_{ii}", index=ii) + replace_one_item( + model_params_item[item_key]["list"], + item_key, + hybrid_item, + suffix=f"_hybrid_{ii}", + index=ii, + ) for shared_key in shared_links: - shared_links[shared_key]["links"] = sorted(shared_links[shared_key]["links"], key=lambda x: x['shared_level']) + shared_links[shared_key]["links"] = sorted( + shared_links[shared_key]["links"], key=lambda x: x["shared_level"] + ) assert len(type_map_keys) == 1, "Multitask model must have only one type_map!" return model_config, shared_links @@ -79,14 +104,15 @@ def get_class_name(item_key, item_params): item_type = item_params.get("type", "se_e2_a") if item_type == "se_e2_a": return DescrptSeA.__name__ - elif item_type == "se_atten": - return DescrptSeAtten.__name__ - elif item_type == "se_uni": - return DescrptSeUni.__name__ - elif item_type == "gaussian_lcc": - return DescrptGaussianLcc.__name__ - elif item_type == "hybrid": - return DescrptHybrid.__name__ + elif item_type in ["se_atten", "dpa1"]: + return DescrptDPA1.__name__ + elif item_type in ["dpa2"]: + return DescrptDPA2.__name__ + # todo add support for other combination + # elif item_type == "gaussian_lcc": + # return DescrptGaussianLcc.__name__ + # elif item_type == "hybrid": + # return DescrptHybrid.__name__ else: raise RuntimeError(f"Unknown descriptor type {item_type}") elif item_key == "fitting_net": diff --git a/deepmd_pt/utils/nlist.py b/deepmd_pt/utils/nlist.py index 7f315c55..afdc69c3 100644 --- a/deepmd_pt/utils/nlist.py +++ b/deepmd_pt/utils/nlist.py @@ -1,17 +1,18 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Dict, + List, + Optional, + Union, +) + import torch -import itertools -import numpy as np -from deepmd_pt.utils import env -from deepmd_pt.utils.region import ( - phys2inter, - inter2phys, - to_face_distance, + +from deepmd_pt.utils import ( + env, ) -from typing import ( - List, - Union, - Optional, - Dict, +from deepmd_pt.utils.region import ( + to_face_distance, ) @@ -23,29 +24,29 @@ def _build_neighbor_list( rmin: float = 1e-10, cut_nearest: bool = True, ) -> torch.Tensor: - """build neightbor list for a single frame. keeps nsel neighbors. - coord1 : [nall x 3] - - ret: [nloc x nsel] stores indexes of coord1 - """ - nall = coord1.shape[-1] // 3 - coord0 = torch.split(coord1, [nloc*3, (nall-nloc)*3])[0] - # nloc x nall x 3 - diff = coord1.view([-1,3])[None,:,:] - coord0.view([-1,3])[:,None,:] - assert(list(diff.shape) == [nloc, nall, 3]) - # nloc x nall - rr = torch.linalg.norm(diff, dim=-1) - rr, nlist = torch.sort(rr, dim=-1) - if cut_nearest: - # nloc x (nall-1) - rr = torch.split(rr, [1,nall-1], dim=-1)[-1] - nlist = torch.split(nlist, [1,nall-1], dim=-1)[-1] - # nloc x nsel - nnei = rr.shape[1] - rr = torch.split(rr, [nsel,nnei-nsel], dim=-1)[0] - nlist = torch.split(nlist, [nsel,nnei-nsel], dim=-1)[0] - nlist = nlist.masked_fill((rr > rcut), -1) - return nlist + """Build neightbor list for a single frame. keeps nsel neighbors. + coord1 : [nall x 3]. + + ret: [nloc x nsel] stores indexes of coord1. + """ + nall = coord1.shape[-1] // 3 + coord0 = torch.split(coord1, [nloc * 3, (nall - nloc) * 3])[0] + # nloc x nall x 3 + diff = coord1.view([-1, 3])[None, :, :] - coord0.view([-1, 3])[:, None, :] + assert list(diff.shape) == [nloc, nall, 3] + # nloc x nall + rr = torch.linalg.norm(diff, dim=-1) + rr, nlist = torch.sort(rr, dim=-1) + if cut_nearest: + # nloc x (nall-1) + rr = torch.split(rr, [1, nall - 1], dim=-1)[-1] + nlist = torch.split(nlist, [1, nall - 1], dim=-1)[-1] + # nloc x nsel + nnei = rr.shape[1] + rr = torch.split(rr, [nsel, nnei - nsel], dim=-1)[0] + nlist = torch.split(nlist, [nsel, nnei - nsel], dim=-1)[0] + nlist = nlist.masked_fill((rr > rcut), -1) + return nlist def build_neighbor_list_lower( @@ -56,30 +57,31 @@ def build_neighbor_list_lower( sel: Union[int, List[int]], distinguish_types: bool = True, ) -> torch.Tensor: - """build neightbor list for a single frame. keeps nsel neighbors. - Parameters - ---------- - coord1 : torch.Tensor + """Build neightbor list for a single frame. keeps nsel neighbors. + + Parameters + ---------- + coord1 : torch.Tensor exptended coordinates of shape [nall x 3] - atype : torch.Tensor + atype : torch.Tensor extended atomic types of shape [nall] - nloc: int + nloc : int number of local atoms. - rcut: float + rcut : float cut-off radius - nsel: int or List[int] + sel : int or List[int] maximal number of neighbors (of each type). - if distinguish_types==True, nsel should be list and - the length of nsel should be equal to number of + if distinguish_types==True, nsel should be list and + the length of nsel should be equal to number of types. - distinguish_types: bool - distinguish different types. - - Returns - ------- - neighbor_list : torch.Tensor + distinguish_types : bool + distinguish different types. + + Returns + ------- + neighbor_list : torch.Tensor Neighbor list of shape [nloc, nsel], the neighbors - are stored in an ascending order. If the number of + are stored in an ascending order. If the number of neighbors is less than nsel, the positions are masked with -1. The neighbor list of an atom looks like |------ nsel ------| @@ -88,61 +90,67 @@ def build_neighbor_list_lower( |---- nsel[0] -----| |---- nsel[1] -----| xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1 - """ - nall = coord1.shape[0]//3 - if isinstance(sel, int): - sel = [sel] - nsel = sum(sel) - # nloc x 3 - coord0 = coord1[:nloc * 3] - # nloc x nall x 3 - diff = coord1.view([-1, 3]).unsqueeze(0) - coord0.view([-1, 3]).unsqueeze(1) - assert(list(diff.shape) == [nloc, nall, 3]) - # nloc x nall - rr = torch.linalg.norm(diff, dim=-1) - rr, nlist = torch.sort(rr, dim=-1) - # nloc x (nall-1) - rr = rr[:, 1:] - nlist = nlist[:, 1:] - # nloc x nsel - nnei = rr.shape[1] - if nsel <= nnei: - rr = rr[:, :nsel] - nlist = nlist[:, :nsel] - else: - rr = torch.cat([rr, torch.ones([nloc, nsel-nnei]).to(rr.device) + rcut], dim=-1) - nlist = torch.cat([nlist, torch.ones([nloc, nsel-nnei], dtype=torch.long).to(rr.device)], dim=-1) - assert (list(nlist.shape) == [nloc, nsel]) - nlist = nlist.masked_fill((rr > rcut), -1) - - if not distinguish_types: - return nlist - else: - ret_nlist = [] + """ + nall = coord1.shape[0] // 3 + if isinstance(sel, int): + sel = [sel] + nsel = sum(sel) + # nloc x 3 + coord0 = coord1[: nloc * 3] + # nloc x nall x 3 + diff = coord1.view([-1, 3]).unsqueeze(0) - coord0.view([-1, 3]).unsqueeze(1) + assert list(diff.shape) == [nloc, nall, 3] # nloc x nall - tmp_atype = torch.tile(atype.unsqueeze(0), [nloc,1]) - mask = (nlist == -1) - # nloc x s(nsel) - tnlist = torch.gather( - tmp_atype, 1, nlist.masked_fill(mask, 0), - ) - tnlist = tnlist.masked_fill(mask, -1) - snsel = tnlist.shape[1] - for ii,ss in enumerate(sel): - # nloc x s(nsel) - # to int because bool cannot be sort on GPU - pick_mask = (tnlist == ii).to(torch.int32) - # nloc x s(nsel), stable sort, nearer neighbors first - pick_mask, imap = torch.sort( - pick_mask, dim=-1, descending=True, stable=True) - # nloc x s(nsel) - inlist = torch.gather(nlist, 1, imap) - inlist = inlist.masked_fill(~(pick_mask.to(torch.bool)), -1) - # nloc x nsel[ii] - ret_nlist.append( - torch.split(inlist, [ss,snsel-ss], dim=-1)[0] - ) - return torch.concat(ret_nlist, dim=-1) + rr = torch.linalg.norm(diff, dim=-1) + rr, nlist = torch.sort(rr, dim=-1) + # nloc x (nall-1) + rr = rr[:, 1:] + nlist = nlist[:, 1:] + # nloc x nsel + nnei = rr.shape[1] + if nsel <= nnei: + rr = rr[:, :nsel] + nlist = nlist[:, :nsel] + else: + rr = torch.cat( + [rr, torch.ones([nloc, nsel - nnei]).to(rr.device) + rcut], dim=-1 + ) + nlist = torch.cat( + [nlist, torch.ones([nloc, nsel - nnei], dtype=torch.long).to(rr.device)], + dim=-1, + ) + assert list(nlist.shape) == [nloc, nsel] + nlist = nlist.masked_fill((rr > rcut), -1) + + if not distinguish_types: + return nlist + else: + ret_nlist = [] + # nloc x nall + tmp_atype = torch.tile(atype.unsqueeze(0), [nloc, 1]) + mask = nlist == -1 + # nloc x s(nsel) + tnlist = torch.gather( + tmp_atype, + 1, + nlist.masked_fill(mask, 0), + ) + tnlist = tnlist.masked_fill(mask, -1) + snsel = tnlist.shape[1] + for ii, ss in enumerate(sel): + # nloc x s(nsel) + # to int because bool cannot be sort on GPU + pick_mask = (tnlist == ii).to(torch.int32) + # nloc x s(nsel), stable sort, nearer neighbors first + pick_mask, imap = torch.sort( + pick_mask, dim=-1, descending=True, stable=True + ) + # nloc x s(nsel) + inlist = torch.gather(nlist, 1, imap) + inlist = inlist.masked_fill(~(pick_mask.to(torch.bool)), -1) + # nloc x nsel[ii] + ret_nlist.append(torch.split(inlist, [ss, snsel - ss], dim=-1)[0]) + return torch.concat(ret_nlist, dim=-1) def build_neighbor_list( @@ -153,30 +161,31 @@ def build_neighbor_list( sel: Union[int, List[int]], distinguish_types: bool = True, ) -> torch.Tensor: - """build neightbor list for a single frame. keeps nsel neighbors. - Parameters - ---------- - coord1 : torch.Tensor + """Build neightbor list for a single frame. keeps nsel neighbors. + + Parameters + ---------- + coord1 : torch.Tensor exptended coordinates of shape [batch_size, nall x 3] - atype : torch.Tensor + atype : torch.Tensor extended atomic types of shape [batch_size, nall] - nloc: int + nloc : int number of local atoms. - rcut: float + rcut : float cut-off radius - nsel: int or List[int] + sel : int or List[int] maximal number of neighbors (of each type). - if distinguish_types==True, nsel should be list and - the length of nsel should be equal to number of + if distinguish_types==True, nsel should be list and + the length of nsel should be equal to number of types. - distinguish_types: bool - distinguish different types. - - Returns - ------- - neighbor_list : torch.Tensor + distinguish_types : bool + distinguish different types. + + Returns + ------- + neighbor_list : torch.Tensor Neighbor list of shape [batch_size, nloc, nsel], the neighbors - are stored in an ascending order. If the number of + are stored in an ascending order. If the number of neighbors is less than nsel, the positions are masked with -1. The neighbor list of an atom looks like |------ nsel ------| @@ -185,77 +194,91 @@ def build_neighbor_list( |---- nsel[0] -----| |---- nsel[1] -----| xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1 - """ - batch_size = coord1.shape[0] - coord1 = coord1.view(batch_size, -1) - nall = coord1.shape[1]//3 - if isinstance(sel, int): - sel = [sel] - nsel = sum(sel) - # nloc x 3 - coord0 = coord1[: , :nloc * 3] - # nloc x nall x 3 - diff = coord1.view([batch_size, -1, 3]).unsqueeze(1) - coord0.view([batch_size, -1, 3]).unsqueeze(2) - assert(list(diff.shape) == [batch_size,nloc, nall, 3]) - # nloc x nall - rr = torch.linalg.norm(diff, dim=-1) - rr, nlist = torch.sort(rr, dim=-1) - # nloc x (nall-1) - rr = rr[: , :, 1:] - nlist = nlist[:, :, 1:] - # nloc x nsel - nnei = rr.shape[2] - if nsel <= nnei: - rr = rr[:, :, :nsel] - nlist = nlist[:, :, :nsel] - else: - rr = torch.cat([rr, torch.ones([batch_size, nloc, nsel-nnei]).to(rr.device) + rcut], dim=-1) - nlist = torch.cat([nlist, torch.ones([batch_size, nloc, nsel-nnei], dtype=torch.long).to(rr.device)], dim=-1) - assert (list(nlist.shape) == [batch_size, nloc, nsel]) - nlist = nlist.masked_fill((rr > rcut), -1) - - if not distinguish_types: - return nlist - else: - ret_nlist = [] + """ + batch_size = coord1.shape[0] + coord1 = coord1.view(batch_size, -1) + nall = coord1.shape[1] // 3 + if isinstance(sel, int): + sel = [sel] + nsel = sum(sel) + # nloc x 3 + coord0 = coord1[:, : nloc * 3] + # nloc x nall x 3 + diff = coord1.view([batch_size, -1, 3]).unsqueeze(1) - coord0.view( + [batch_size, -1, 3] + ).unsqueeze(2) + assert list(diff.shape) == [batch_size, nloc, nall, 3] # nloc x nall - tmp_atype = torch.tile(atype.unsqueeze(1), [1,nloc,1]) - mask = (nlist == -1) - # nloc x s(nsel) - tnlist = torch.gather( - tmp_atype, 2, nlist.masked_fill(mask, 0), - ) - tnlist = tnlist.masked_fill(mask, -1) - snsel = tnlist.shape[2] - for ii,ss in enumerate(sel): - - # nloc x s(nsel) - # to int because bool cannot be sort on GPU - pick_mask = (tnlist == ii).to(torch.int32) - # nloc x s(nsel), stable sort, nearer neighbors first - pick_mask, imap = torch.sort( - pick_mask, dim=-1, descending=True, stable=True) - # nloc x s(nsel) - inlist = torch.gather(nlist, 2, imap) - inlist = inlist.masked_fill(~(pick_mask.to(torch.bool)), -1) - # nloc x nsel[ii] - ret_nlist.append( - torch.split(inlist, [ss,snsel-ss], dim=-1)[0] - ) - return torch.concat(ret_nlist, dim=-1) + rr = torch.linalg.norm(diff, dim=-1) + rr, nlist = torch.sort(rr, dim=-1) + # nloc x (nall-1) + rr = rr[:, :, 1:] + nlist = nlist[:, :, 1:] + # nloc x nsel + nnei = rr.shape[2] + if nsel <= nnei: + rr = rr[:, :, :nsel] + nlist = nlist[:, :, :nsel] + else: + rr = torch.cat( + [rr, torch.ones([batch_size, nloc, nsel - nnei]).to(rr.device) + rcut], + dim=-1, + ) + nlist = torch.cat( + [ + nlist, + torch.ones([batch_size, nloc, nsel - nnei], dtype=torch.long).to( + rr.device + ), + ], + dim=-1, + ) + assert list(nlist.shape) == [batch_size, nloc, nsel] + nlist = nlist.masked_fill((rr > rcut), -1) + + if not distinguish_types: + return nlist + else: + ret_nlist = [] + # nloc x nall + tmp_atype = torch.tile(atype.unsqueeze(1), [1, nloc, 1]) + mask = nlist == -1 + # nloc x s(nsel) + tnlist = torch.gather( + tmp_atype, + 2, + nlist.masked_fill(mask, 0), + ) + tnlist = tnlist.masked_fill(mask, -1) + snsel = tnlist.shape[2] + for ii, ss in enumerate(sel): + # nloc x s(nsel) + # to int because bool cannot be sort on GPU + pick_mask = (tnlist == ii).to(torch.int32) + # nloc x s(nsel), stable sort, nearer neighbors first + pick_mask, imap = torch.sort( + pick_mask, dim=-1, descending=True, stable=True + ) + # nloc x s(nsel) + inlist = torch.gather(nlist, 2, imap) + inlist = inlist.masked_fill(~(pick_mask.to(torch.bool)), -1) + # nloc x nsel[ii] + ret_nlist.append(torch.split(inlist, [ss, snsel - ss], dim=-1)[0]) + return torch.concat(ret_nlist, dim=-1) # build_neighbor_list = torch.vmap( -# build_neighbor_list_lower, -# in_dims=(0,0,None,None,None), +# build_neighbor_list_lower, +# in_dims=(0,0,None,None,None), # out_dims=(0), # ) + def get_multiple_nlist_key( rcut: float, nsel: int, -)->str: - return str(rcut) + "_" + str(nsel) +) -> str: + return str(rcut) + "_" + str(nsel) def build_multiple_neighbor_list( @@ -264,142 +287,145 @@ def build_multiple_neighbor_list( rcuts: List[float], nsels: List[int], ) -> Dict[str, torch.Tensor]: - """Input one neighbor list, and produce multiple neighbor lists with - different cutoff radius and numbers of selection out of it. The - required rcuts and nsels should be smaller or equal to the input nlist. + """Input one neighbor list, and produce multiple neighbor lists with + different cutoff radius and numbers of selection out of it. The + required rcuts and nsels should be smaller or equal to the input nlist. - Parameters - ---------- - coord: torch.Tensor + Parameters + ---------- + coord : torch.Tensor exptended coordinates of shape [batch_size, nall x 3] - nlist: torch.Tensor + nlist : torch.Tensor Neighbor list of shape [batch_size, nloc, nsel], the neighbors - should be stored in an ascending order. - nloc: int - number of local atoms. - rcuts: List[float] + should be stored in an ascending order. + rcuts : List[float] list of cut-off radius in ascending order. - nsels: List[int] + nsels : List[int] maximal number of neighbors in ascending order. - - Returns - ------- - nlist_dict : Dict[str, torch.Tensor] + + Returns + ------- + nlist_dict : Dict[str, torch.Tensor] A dict of nlists, key given by get_multiple_nlist_key(rc, nsel) value being the corresponding nlist. - """ - assert len(rcuts) == len(nsels) - if len(rcuts) == 0: - return {} - nb, nloc, nsel = nlist.shape - if nsel < nsels[-1]: - pad = -1 * torch.ones( - [nb, nloc, nsels[-1] - nsel], - dtype=nlist.dtype, - device=nlist.device, + """ + assert len(rcuts) == len(nsels) + if len(rcuts) == 0: + return {} + nb, nloc, nsel = nlist.shape + if nsel < nsels[-1]: + pad = -1 * torch.ones( + [nb, nloc, nsels[-1] - nsel], + dtype=nlist.dtype, + device=nlist.device, + ) + # nb x nloc x nsel + nlist = torch.cat([nlist, pad], dim=-1) + nsel = nsels[-1] + # nb x nall x 3 + coord1 = coord.view(nb, -1, 3) + nall = coord1.shape[1] + # nb x nloc x 3 + coord0 = coord1[:, :nloc, :] + nlist_mask = nlist == -1 + # nb x (nloc x nsel) x 3 + index = ( + nlist.masked_fill(nlist_mask, 0) + .view(nb, nloc * nsel) + .unsqueeze(-1) + .expand(-1, -1, 3) ) + # nb x nloc x nsel x 3 + coord2 = torch.gather(coord1, dim=1, index=index).view(nb, nloc, nsel, 3) + # nb x nloc x nsel x 3 + diff = coord2 - coord0[:, :, None, :] # nb x nloc x nsel - nlist = torch.cat([nlist, pad], dim=-1) - nsel = nsels[-1] - # nb x nall x 3 - coord1 = coord.view(nb, -1, 3) - nall = coord1.shape[1] - # nb x nloc x 3 - coord0 = coord1[:, :nloc, :] - nlist_mask = (nlist == -1) - # nb x (nloc x nsel) x 3 - index = nlist.masked_fill(nlist_mask, 0)\ - .view(nb, nloc*nsel)\ - .unsqueeze(-1)\ - .expand(-1,-1,3) - # nb x nloc x nsel x 3 - coord2 = torch.gather(coord1, dim=1, index=index).view(nb,nloc,nsel,3) - # nb x nloc x nsel x 3 - diff = coord2 - coord0[:,:,None,:] - # nb x nloc x nsel - rr = torch.linalg.norm(diff, dim=-1) - rr.masked_fill(nlist_mask, float("inf")) - nlist0 = nlist - ret = {} - for rc, ns in zip(rcuts[::-1], nsels[::-1]): - nlist0 = nlist0[:,:,:ns].masked_fill( rr[:,:,:ns] > rc, int(-1) ) - ret[get_multiple_nlist_key(rc, ns)] = nlist0 - return ret + rr = torch.linalg.norm(diff, dim=-1) + rr.masked_fill(nlist_mask, float("inf")) + nlist0 = nlist + ret = {} + for rc, ns in zip(rcuts[::-1], nsels[::-1]): + nlist0 = nlist0[:, :, :ns].masked_fill(rr[:, :, :ns] > rc, int(-1)) + ret[get_multiple_nlist_key(rc, ns)] = nlist0 + return ret def extend_coord_with_ghosts( - coord : torch.Tensor, - atype : torch.Tensor, - cell : Optional[torch.Tensor], - rcut : float, + coord: torch.Tensor, + atype: torch.Tensor, + cell: Optional[torch.Tensor], + rcut: float, ): - """Extend the coordinates of the atoms by appending peridoc images. - The number of images is large enough to ensure all the neighbors - within rcut are appended. + """Extend the coordinates of the atoms by appending peridoc images. + The number of images is large enough to ensure all the neighbors + within rcut are appended. - Parameters - ---------- - coord: torch.Tensor + Parameters + ---------- + coord : torch.Tensor original coordinates of shape [-1, nloc*3]. - atype: torch.Tensor + atype : torch.Tensor atom type of shape [-1, nloc]. - cell: torch.Tensor + cell : torch.Tensor simulation cell tensor of shape [-1, 9]. - Returns - ------- - extended_coord: torch.Tensor + Returns + ------- + extended_coord: torch.Tensor extended coordinates of shape [-1, nall*3]. - extended_atype: torch.Tensor + extended_atype: torch.Tensor extended atom type of shape [-1, nall]. - index_mapping: torch.Tensor + index_mapping: torch.Tensor maping extended index to the local index - - """ - nf, nloc = atype.shape - aidx = torch.tile(torch.arange(nloc).unsqueeze(0), [nf, 1]) - if cell is None: - nall = nloc - extend_coord = coord.clone() - extend_atype = atype.clone() - extend_aidx = aidx.clone() - else: - coord = coord.view([nf, nloc, 3]) - cell = cell.view([nf, 3, 3]) - # nf x 3 - to_face = to_face_distance(cell) - # nf x 3 - # *2: ghost copies on + and - directions - # +1: central cell - nbuff = torch.ceil(rcut / to_face).to(torch.long) - # 3 - nbuff = torch.max(nbuff, dim=0, keepdim=False).values - xi = torch.arange(-nbuff[0], nbuff[0] + 1, 1, device=env.DEVICE) - yi = torch.arange(-nbuff[1], nbuff[1] + 1, 1, device=env.DEVICE) - zi = torch.arange(-nbuff[2], nbuff[2] + 1, 1, device=env.DEVICE) - xyz = xi.view(-1, 1, 1, 1) * torch.tensor([1, 0, 0], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) - xyz = xyz + yi.view(1, -1, 1, 1) * torch.tensor([0, 1, 0], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) - xyz = xyz + zi.view(1, 1, -1, 1) * torch.tensor([0, 0, 1], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE) - xyz = xyz.view(-1, 3) - # ns x 3 - shift_idx = xyz[torch.argsort(torch.norm(xyz, dim=1))] - ns, _ = shift_idx.shape - nall = ns * nloc - # nf x ns x 3 - shift_vec = torch.einsum("sd,fdk->fsk", shift_idx, cell) - # nf x ns x nloc x 3 - extend_coord = coord[:,None,:,:] + shift_vec[:,:,None,:] - # nf x ns x nloc - extend_atype = torch.tile(atype.unsqueeze(-2), [1, ns, 1]) - # nf x ns x nloc - extend_aidx = torch.tile(aidx.unsqueeze(-2), [1, ns, 1]) - - return ( - extend_coord.reshape([nf, nall*3]).to(env.DEVICE), - extend_atype.view([nf, nall]).to(env.DEVICE), - extend_aidx.view([nf, nall]).to(env.DEVICE), - ) - + """ + nf, nloc = atype.shape + aidx = torch.tile(torch.arange(nloc).unsqueeze(0), [nf, 1]) + if cell is None: + nall = nloc + extend_coord = coord.clone() + extend_atype = atype.clone() + extend_aidx = aidx.clone() + else: + coord = coord.view([nf, nloc, 3]) + cell = cell.view([nf, 3, 3]) + # nf x 3 + to_face = to_face_distance(cell) + # nf x 3 + # *2: ghost copies on + and - directions + # +1: central cell + nbuff = torch.ceil(rcut / to_face).to(torch.long) + # 3 + nbuff = torch.max(nbuff, dim=0, keepdim=False).values + xi = torch.arange(-nbuff[0], nbuff[0] + 1, 1, device=env.DEVICE) + yi = torch.arange(-nbuff[1], nbuff[1] + 1, 1, device=env.DEVICE) + zi = torch.arange(-nbuff[2], nbuff[2] + 1, 1, device=env.DEVICE) + xyz = xi.view(-1, 1, 1, 1) * torch.tensor( + [1, 0, 0], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) + xyz = xyz + yi.view(1, -1, 1, 1) * torch.tensor( + [0, 1, 0], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) + xyz = xyz + zi.view(1, 1, -1, 1) * torch.tensor( + [0, 0, 1], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE + ) + xyz = xyz.view(-1, 3) + # ns x 3 + shift_idx = xyz[torch.argsort(torch.norm(xyz, dim=1))] + ns, _ = shift_idx.shape + nall = ns * nloc + # nf x ns x 3 + shift_vec = torch.einsum("sd,fdk->fsk", shift_idx, cell) + # nf x ns x nloc x 3 + extend_coord = coord[:, None, :, :] + shift_vec[:, :, None, :] + # nf x ns x nloc + extend_atype = torch.tile(atype.unsqueeze(-2), [1, ns, 1]) + # nf x ns x nloc + extend_aidx = torch.tile(aidx.unsqueeze(-2), [1, ns, 1]) + return ( + extend_coord.reshape([nf, nall * 3]).to(env.DEVICE), + extend_atype.view([nf, nall]).to(env.DEVICE), + extend_aidx.view([nf, nall]).to(env.DEVICE), + ) diff --git a/deepmd_pt/utils/plugin.py b/deepmd_pt/utils/plugin.py index dbc02375..c24f36f5 100644 --- a/deepmd_pt/utils/plugin.py +++ b/deepmd_pt/utils/plugin.py @@ -1,6 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later """Base of plugin systems.""" -from deepmd_utils.utils.plugin import ( +from deepmd.utils.plugin import ( Plugin, PluginVariant, VariantABCMeta, diff --git a/deepmd_pt/utils/preprocess.py b/deepmd_pt/utils/preprocess.py index 58db6041..e535b6e8 100644 --- a/deepmd_pt/utils/preprocess.py +++ b/deepmd_pt/utils/preprocess.py @@ -1,12 +1,17 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging -import numpy as np +from typing import ( + Union, +) + import torch -from deepmd_pt.utils import env -from typing import Union +from deepmd_pt.utils import ( + env, +) -class Region3D(object): +class Region3D: def __init__(self, boxt): """Construct a simulation box.""" boxt = boxt.reshape([3, 3]) @@ -56,7 +61,6 @@ def compute_serial_cid(cell_offset, ncell): - cell_offset: shape is [3] - ncell: shape is [3] """ - cell_offset[:, 0] *= ncell[1] * ncell[2] cell_offset[:, 1] *= ncell[2] return cell_offset.sum(-1) @@ -65,8 +69,12 @@ def compute_serial_cid(cell_offset, ncell): def compute_pbc_shift(cell_offset, ncell): """Tell shift count to move the atom into region.""" shift = torch.zeros_like(cell_offset) - shift = shift + (cell_offset < 0) * -(torch.div(cell_offset, ncell, rounding_mode='floor')) - shift = shift + (cell_offset >= ncell) * -(torch.div((cell_offset - ncell), ncell, rounding_mode='floor') + 1) + shift = shift + (cell_offset < 0) * -( + torch.div(cell_offset, ncell, rounding_mode="floor") + ) + shift = shift + (cell_offset >= ncell) * -( + torch.div((cell_offset - ncell), ncell, rounding_mode="floor") + 1 + ) assert torch.all(cell_offset + shift * ncell >= 0) assert torch.all(cell_offset + shift * ncell < ncell) return shift @@ -81,7 +89,7 @@ def build_inside_clist(coord, region: Region3D, ncell): """ loc_ncell = int(torch.prod(ncell)) # 模拟区域内的 Cell 数量 nloc = coord.numel() // 3 # 原子数量 - inter_cell_size = 1. / ncell + inter_cell_size = 1.0 / ncell inter_cood = region.phys2inter(coord.view(-1, 3)) cell_offset = torch.floor(inter_cood / inter_cell_size).to(torch.long) @@ -91,14 +99,14 @@ def build_inside_clist(coord, region: Region3D, ncell): delta = cell_offset - ncell a2c = compute_serial_cid(cell_offset, ncell) # cell id of atoms arange = torch.arange(0, loc_ncell, 1, device=env.PREPROCESS_DEVICE) - cellid = (a2c == arange.unsqueeze(-1)) # one hot cellid + cellid = a2c == arange.unsqueeze(-1) # one hot cellid c2a = cellid.nonzero() lst = [] cnt = 0 bincount = torch.bincount(a2c, minlength=loc_ncell) for i in range(loc_ncell): n = bincount[i] - lst.append(c2a[cnt: cnt + n, 1]) + lst.append(c2a[cnt : cnt + n, 1]) cnt += n return a2c, lst @@ -116,16 +124,24 @@ def append_neighbors(coord, region: Region3D, atype, rcut: float): ncell = torch.floor(to_face / rcut).to(torch.long) ncell[ncell == 0] = 1 # 模拟区域内的 Cell 数量 cell_size = to_face / ncell - ngcell = torch.floor(rcut / cell_size).to(torch.long) + 1 # 模拟区域外的 Cell 数量，存储的是 Ghost 原子 + ngcell = ( + torch.floor(rcut / cell_size).to(torch.long) + 1 + ) # 模拟区域外的 Cell 数量, 存储的是 Ghost 原子 # 借助 Cell 列表添加边界外的 Ghost 原子 a2c, c2a = build_inside_clist(coord, region, ncell) xi = torch.arange(-ngcell[0], ncell[0] + ngcell[0], 1, device=env.PREPROCESS_DEVICE) yi = torch.arange(-ngcell[1], ncell[1] + ngcell[1], 1, device=env.PREPROCESS_DEVICE) zi = torch.arange(-ngcell[2], ncell[2] + ngcell[2], 1, device=env.PREPROCESS_DEVICE) - xyz = xi.view(-1, 1, 1, 1) * torch.tensor([1, 0, 0], dtype=torch.long, device=env.PREPROCESS_DEVICE) - xyz = xyz + yi.view(1, -1, 1, 1) * torch.tensor([0, 1, 0], dtype=torch.long, device=env.PREPROCESS_DEVICE) - xyz = xyz + zi.view(1, 1, -1, 1) * torch.tensor([0, 0, 1], dtype=torch.long, device=env.PREPROCESS_DEVICE) + xyz = xi.view(-1, 1, 1, 1) * torch.tensor( + [1, 0, 0], dtype=torch.long, device=env.PREPROCESS_DEVICE + ) + xyz = xyz + yi.view(1, -1, 1, 1) * torch.tensor( + [0, 1, 0], dtype=torch.long, device=env.PREPROCESS_DEVICE + ) + xyz = xyz + zi.view(1, 1, -1, 1) * torch.tensor( + [0, 0, 1], dtype=torch.long, device=env.PREPROCESS_DEVICE + ) xyz = xyz.view(-1, 3) mask_a = (xyz >= 0).all(dim=-1) mask_b = (xyz < ncell).all(dim=-1) @@ -139,7 +155,7 @@ def append_neighbors(coord, region: Region3D, atype, rcut: float): n_atoms = coord.shape[0] aid = [c2a[ci] + i * n_atoms for i, ci in enumerate(cid)] aid = torch.cat(aid) - tmp = torch.div(aid, n_atoms, rounding_mode='trunc') + tmp = torch.div(aid, n_atoms, rounding_mode="trunc") aid = aid % n_atoms tmp_coord = coord[aid] - coord_shift[tmp] tmp_atype = atype[aid] @@ -148,11 +164,15 @@ def append_neighbors(coord, region: Region3D, atype, rcut: float): merged_coord = torch.cat([coord, tmp_coord]) merged_coord_shift = torch.cat([torch.zeros_like(coord), coord_shift[tmp]]) merged_atype = torch.cat([atype, tmp_atype]) - merged_mapping = torch.cat([torch.arange(atype.numel(), device=env.PREPROCESS_DEVICE), aid]) + merged_mapping = torch.cat( + [torch.arange(atype.numel(), device=env.PREPROCESS_DEVICE), aid] + ) return merged_coord_shift, merged_atype, merged_mapping -def build_neighbor_list(nloc: int, coord, atype, rcut: float, sec, mapping, type_split=True, min_check=False): +def build_neighbor_list( + nloc: int, coord, atype, rcut: float, sec, mapping, type_split=True, min_check=False +): """For each atom inside region, build its neighbor list. Args: @@ -167,7 +187,9 @@ def build_neighbor_list(nloc: int, coord, atype, rcut: float, sec, mapping, type distance = coord_l - coord_r distance = torch.linalg.norm(distance, dim=-1) DISTANCE_INF = distance.max().detach() + rcut - distance[:nloc, :nloc] += torch.eye(nloc, dtype=torch.bool, device=env.PREPROCESS_DEVICE) * DISTANCE_INF + distance[:nloc, :nloc] += ( + torch.eye(nloc, dtype=torch.bool, device=env.PREPROCESS_DEVICE) * DISTANCE_INF + ) if min_check: if distance.min().abs() < 1e-6: RuntimeError("Atom dist too close!") @@ -175,8 +197,12 @@ def build_neighbor_list(nloc: int, coord, atype, rcut: float, sec, mapping, type sec = sec[-1:] lst = [] nlist = torch.zeros((nloc, sec[-1].item()), device=env.PREPROCESS_DEVICE).long() - 1 - nlist_loc = torch.zeros((nloc, sec[-1].item()), device=env.PREPROCESS_DEVICE).long() - 1 - nlist_type = torch.zeros((nloc, sec[-1].item()), device=env.PREPROCESS_DEVICE).long() - 1 + nlist_loc = ( + torch.zeros((nloc, sec[-1].item()), device=env.PREPROCESS_DEVICE).long() - 1 + ) + nlist_type = ( + torch.zeros((nloc, sec[-1].item()), device=env.PREPROCESS_DEVICE).long() - 1 + ) for i, nnei in enumerate(sec): if i > 0: nnei = nnei - sec[i - 1] @@ -190,10 +216,15 @@ def build_neighbor_list(nloc: int, coord, atype, rcut: float, sec, mapping, type else: # when nnei > nall indices = torch.zeros((nloc, nnei), device=env.PREPROCESS_DEVICE).long() - 1 - _sorted = torch.ones((nloc, nnei), device=env.PREPROCESS_DEVICE).long() * DISTANCE_INF - _sorted_nnei, indices_nnei = torch.topk(tmp, tmp.shape[1], dim=1, largest=False) - _sorted[:, :tmp.shape[1]] = _sorted_nnei - indices[:, :tmp.shape[1]] = indices_nnei + _sorted = ( + torch.ones((nloc, nnei), device=env.PREPROCESS_DEVICE).long() + * DISTANCE_INF + ) + _sorted_nnei, indices_nnei = torch.topk( + tmp, tmp.shape[1], dim=1, largest=False + ) + _sorted[:, : tmp.shape[1]] = _sorted_nnei + indices[:, : tmp.shape[1]] = indices_nnei mask = (_sorted < rcut).to(torch.long) indices_loc = mapping[indices] indices = indices * mask + -1 * (1 - mask) # -1 for padding @@ -219,50 +250,67 @@ def compute_smooth_weight(distance, rmin: float, rmax: float): return vv * mid_mask + min_mask -def make_env_mat(coord, - atype, - region, - rcut: Union[float, list], - sec, - pbc=True, - type_split=True, - min_check=False): +def make_env_mat( + coord, + atype, + region, + rcut: Union[float, list], + sec, + pbc=True, + type_split=True, + min_check=False, +): """Based on atom coordinates, return environment matrix. Returns - nlist: nlist, [nloc, nnei] - merged_coord_shift: shift on nall atoms, [nall, 3] - merged_mapping: mapping from nall index to nloc index, [nall] + ------- + nlist: nlist, [nloc, nnei] + merged_coord_shift: shift on nall atoms, [nall, 3] + merged_mapping: mapping from nall index to nloc index, [nall] """ - # 将盒子外的原子，通过镜像挪入盒子内 + # 将盒子外的原子, 通过镜像挪入盒子内 hybrid = isinstance(rcut, list) _rcut = rcut if hybrid: _rcut = max(rcut) if pbc: - merged_coord_shift, merged_atype, merged_mapping = append_neighbors(coord, region, atype, _rcut) + merged_coord_shift, merged_atype, merged_mapping = append_neighbors( + coord, region, atype, _rcut + ) merged_coord = coord[merged_mapping] - merged_coord_shift if merged_coord.shape[0] <= coord.shape[0]: - logging.warning('No ghost atom is added for system ') + logging.warning("No ghost atom is added for system ") else: merged_coord_shift = torch.zeros_like(coord) merged_atype = atype.clone() merged_mapping = torch.arange(atype.numel(), device=env.PREPROCESS_DEVICE) merged_coord = coord.clone() - # 构建邻居列表，并按 sel_a 筛选 + # 构建邻居列表, 并按 sel_a 筛选 if not hybrid: - nlist, nlist_loc, nlist_type = build_neighbor_list(coord.shape[0], merged_coord, merged_atype, rcut, sec, - merged_mapping, type_split=type_split, min_check=min_check) + nlist, nlist_loc, nlist_type = build_neighbor_list( + coord.shape[0], + merged_coord, + merged_atype, + rcut, + sec, + merged_mapping, + type_split=type_split, + min_check=min_check, + ) else: nlist, nlist_loc, nlist_type = [], [], [] for ii, single_rcut in enumerate(rcut): - nlist_tmp, nlist_loc_tmp, nlist_type_tmp = build_neighbor_list(coord.shape[0], merged_coord, - merged_atype, - single_rcut, sec[ii], - merged_mapping, - type_split=type_split, - min_check=min_check) + nlist_tmp, nlist_loc_tmp, nlist_type_tmp = build_neighbor_list( + coord.shape[0], + merged_coord, + merged_atype, + single_rcut, + sec[ii], + merged_mapping, + type_split=type_split, + min_check=min_check, + ) nlist.append(nlist_tmp) nlist_loc.append(nlist_loc_tmp) nlist_type.append(nlist_type_tmp) diff --git a/deepmd_pt/utils/region.py b/deepmd_pt/utils/region.py index e90c461b..b07d2f73 100644 --- a/deepmd_pt/utils/region.py +++ b/deepmd_pt/utils/region.py @@ -1,113 +1,116 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import torch -from deepmd_pt.utils.env import DEVICE + def phys2inter( - coord : torch.Tensor, - cell : torch.Tensor, + coord: torch.Tensor, + cell: torch.Tensor, ) -> torch.Tensor: - """convert physical coordinates to internal(direct) coordinates + """Convert physical coordinates to internal(direct) coordinates. - Parameters - ---------- - coord: torch.Tensor + Parameters + ---------- + coord : torch.Tensor physical coordinates of shape [*, na, 3]. - cell: torch.Tensor + cell : torch.Tensor simulation cell tensor of shape [*, 3, 3]. - - Returns - ------- - inter_coord: torch.Tensor + + Returns + ------- + inter_coord: torch.Tensor the internal coordinates - """ - rec_cell = torch.linalg.inv(cell) - return torch.matmul(coord, rec_cell) + """ + rec_cell = torch.linalg.inv(cell) + return torch.matmul(coord, rec_cell) + def inter2phys( - coord : torch.Tensor, - cell : torch.Tensor, + coord: torch.Tensor, + cell: torch.Tensor, ) -> torch.Tensor: - """convert internal(direct) coordinates to physical coordinates + """Convert internal(direct) coordinates to physical coordinates. - Parameters - ---------- - coord: torch.Tensor + Parameters + ---------- + coord : torch.Tensor internal coordinates of shape [*, na, 3]. - cell: torch.Tensor + cell : torch.Tensor simulation cell tensor of shape [*, 3, 3]. - - Returns - ------- - phys_coord: torch.Tensor + + Returns + ------- + phys_coord: torch.Tensor the physical coordinates - """ - return torch.matmul(coord, cell) + """ + return torch.matmul(coord, cell) + def to_face_distance( cell: torch.Tensor, ) -> torch.Tensor: - """compute the to-face-distance of the simulation cell + """Compute the to-face-distance of the simulation cell. - Parameters - ---------- - cell: torch.Tensor + Parameters + ---------- + cell : torch.Tensor simulation cell tensor of shape [*, 3, 3]. - - Returns - ------- - dist: torch.Tensor + + Returns + ------- + dist: torch.Tensor the to face distances of shape [*, 3] - """ - cshape = cell.shape - dist = b_to_face_distance(cell.view([-1, 3, 3])) - return dist.view(list(cshape[:-2])+[3]) + """ + cshape = cell.shape + dist = b_to_face_distance(cell.view([-1, 3, 3])) + return dist.view(list(cshape[:-2]) + [3]) # noqa:RUF005 def _to_face_distance(cell): - volume = torch.linalg.det(cell) - c_yz = torch.cross(cell[1], cell[2]) - _h2yz = volume / torch.linalg.norm(c_yz) - c_zx = torch.cross(cell[2], cell[0]) - _h2zx = volume / torch.linalg.norm(c_zx) - c_xy = torch.cross(cell[0], cell[1]) - _h2xy = volume / torch.linalg.norm(c_xy) - return torch.stack([_h2yz, _h2zx, _h2xy]) + volume = torch.linalg.det(cell) + c_yz = torch.cross(cell[1], cell[2]) + _h2yz = volume / torch.linalg.norm(c_yz) + c_zx = torch.cross(cell[2], cell[0]) + _h2zx = volume / torch.linalg.norm(c_zx) + c_xy = torch.cross(cell[0], cell[1]) + _h2xy = volume / torch.linalg.norm(c_xy) + return torch.stack([_h2yz, _h2zx, _h2xy]) + def b_to_face_distance(cell): - volume = torch.linalg.det(cell) - c_yz = torch.cross(cell[:,1], cell[:,2],dim=-1) - _h2yz = volume / torch.linalg.norm(c_yz,dim=-1) - c_zx = torch.cross(cell[:,2], cell[:,0],dim=-1) - _h2zx = volume / torch.linalg.norm(c_zx,dim=-1) - c_xy = torch.cross(cell[:,0], cell[:,1],dim=-1) - _h2xy = volume / torch.linalg.norm(c_xy,dim=-1) - return torch.stack([_h2yz, _h2zx, _h2xy],dim=1) + volume = torch.linalg.det(cell) + c_yz = torch.cross(cell[:, 1], cell[:, 2], dim=-1) + _h2yz = volume / torch.linalg.norm(c_yz, dim=-1) + c_zx = torch.cross(cell[:, 2], cell[:, 0], dim=-1) + _h2zx = volume / torch.linalg.norm(c_zx, dim=-1) + c_xy = torch.cross(cell[:, 0], cell[:, 1], dim=-1) + _h2xy = volume / torch.linalg.norm(c_xy, dim=-1) + return torch.stack([_h2yz, _h2zx, _h2xy], dim=1) # b_to_face_distance = torch.vmap( # _to_face_distance, in_dims=(0), out_dims=(0)) + def normalize_coord( - coord : torch.Tensor, - cell : torch.Tensor, + coord: torch.Tensor, + cell: torch.Tensor, ) -> torch.Tensor: - """Apply PBC according to the atomic coordinates + """Apply PBC according to the atomic coordinates. - Parameters - ---------- - coord: torch.Tensor + Parameters + ---------- + coord : torch.Tensor orignal coordinates of shape [*, na, 3]. - Returns - ------- - wrapped_coord: torch.Tensor + Returns + ------- + wrapped_coord: torch.Tensor wrapped coordinates of shape [*, na, 3]. - """ - icoord = phys2inter(coord, cell) - icoord = torch.remainder(icoord, 1.0) - return inter2phys(icoord, cell) - - + """ + icoord = phys2inter(coord, cell) + icoord = torch.remainder(icoord, 1.0) + return inter2phys(icoord, cell) diff --git a/deepmd_pt/utils/stat.py b/deepmd_pt/utils/stat.py index 60dcc344..baff2822 100644 --- a/deepmd_pt/utils/stat.py +++ b/deepmd_pt/utils/stat.py @@ -1,11 +1,15 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import logging -from collections import defaultdict import numpy as np import torch -from deepmd_pt.utils import env -from deepmd_pt.utils.dataloader import BufferedIterator -from tqdm import trange +from tqdm import ( + trange, +) + +from deepmd_pt.utils import ( + env, +) def make_stat_input(datasets, dataloaders, nbatches): @@ -15,7 +19,8 @@ def make_stat_input(datasets, dataloaders, nbatches): - dataset: A list of dataset to analyze. - nbatches: Batch count for collecting stats. - Returns: + Returns + ------- - a list of dicts, each of which contains data from a system """ lst = [] @@ -94,7 +99,8 @@ def compute_output_stats(energy, natoms, rcond=None): - energy: Batched energy with shape [nframes, 1]. - natoms: Batched atom statisics with shape [self.ntypes+2]. - Returns: + Returns + ------- - energy_coef: Average enery per atom for each element. """ for i in range(len(energy)): diff --git a/deepmd_pt/utils/utils.py b/deepmd_pt/utils/utils.py index ee314e19..780dbf7e 100644 --- a/deepmd_pt/utils/utils.py +++ b/deepmd_pt/utils/utils.py @@ -1,11 +1,15 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Callable, + Optional, +) + import torch import torch.nn.functional as F -from typing import List, Callable, Any, Dict, Optional def get_activation_fn(activation: str) -> Callable: - """ Returns the activation function corresponding to `activation` """ - + """Returns the activation function corresponding to `activation`.""" if activation.lower() == "relu": return F.relu elif activation.lower() == "gelu": @@ -21,10 +25,10 @@ def get_activation_fn(activation: str) -> Callable: class ActivationFn(torch.nn.Module): def __init__(self, activation: Optional[str]): super().__init__() - self.activation : str = activation if activation is not None else "linear" + self.activation: str = activation if activation is not None else "linear" def forward(self, x: torch.Tensor) -> torch.Tensor: - """ Returns the tensor after applying activation function corresponding to `activation` """ + """Returns the tensor after applying activation function corresponding to `activation`.""" # See jit supported types: https://pytorch.org/docs/stable/jit_language_reference.html#supported-type if self.activation.lower() == "relu": diff --git a/deploy/CMakeLists.txt b/deploy/CMakeLists.txt deleted file mode 100644 index f04f8cd1..00000000 --- a/deploy/CMakeLists.txt +++ /dev/null @@ -1,7 +0,0 @@ -cmake_minimum_required(VERSION 3.16 FATAL_ERROR) -project(test) -set(CMAKE_CXX_STANDARD 14) -set(CMAKE_CXX_STANDARD_REQUIRED ON) -find_package(Torch REQUIRED) -add_executable(test test_model.cpp) -target_link_libraries(test ${TORCH_LIBRARIES}) diff --git a/deploy/test.cpp b/deploy/test.cpp deleted file mode 100644 index abb342d0..00000000 --- a/deploy/test.cpp +++ /dev/null @@ -1,35 +0,0 @@ -#include // One-stop header. - -#include -#include - -int main(int argc, const char* argv[]) { - if (argc != 2) { - std::cerr << "usage: example-app \n"; - return -1; - } - - torch::jit::script::Module module; - try { - // Deserialize the ScriptModule from a file using torch::jit::load(). - module = torch::jit::load(argv[1]); - } - catch (const c10::Error& e) { - std::cerr << "error loading the model\n"; - return -1; - } - auto device = torch::kCUDA; - //auto device = torch::kCPU; - // Deserialize the ScriptModule from a file using torch::jit::load(). - module.to(device); - // Create a vector of inputs. - std::vector inputs; - auto options = torch::TensorOptions().dtype(torch::kFloat64).requires_grad(true); - inputs.push_back(torch::ones({10, 3}, options).to(device)); - - // Execute the model and turn its output into a tensor. - auto outputs = module.forward(inputs).toTensorVector(); - at::Tensor energy = outputs[0]; - at::Tensor force = outputs[1]; - std::cout < // One-stop header. -#include -#include -#define SEL 40 - -int main(int argc, const char* argv[]) { - if (argc != 2) { - std::cerr << "usage: example-app \n"; - return -1; - } - auto device = torch::kCUDA; - //auto device = torch::kCPU; - torch::jit::script::Module module; - // Deserialize the ScriptModule from a file using torch::jit::load(). - module = torch::jit::load(argv[1]); - // Move the model to GPU. - module.to(device); - auto options = torch::TensorOptions(); - auto int_options = torch::TensorOptions().dtype(torch::kInt64); - // Create coord, atype, natoms, mapping, shift, selected - double coord_value[9] = {1, 0, 0, 0, 1, 0, 0, 0, 1}; - at::Tensor coord = torch::from_blob(coord_value, {1, 3, 3}, options).to(device); - long atype_value[3] = {0, 1, 1}; - at::Tensor atype = torch::from_blob(atype_value, {1, 3}, int_options).to(device); - long natoms_value[4] = {3, 3, 1, 2}; - at::Tensor natoms = torch::from_blob(natoms_value, {1, 4}, int_options).to(device); - long mapping_value[3] = {0, 1, 2}; - at::Tensor mapping = torch::from_blob(mapping_value, {1, 3}, int_options).to(device); - at::Tensor shift = at::zeros({1, 3, 3}, options).to(device); - long selected_value[3*SEL] = {}; - for (int i=0; i<3; i++) - { - for (int j=0; j inputs; - inputs.push_back(coord); - inputs.push_back(atype); - inputs.push_back(natoms); - inputs.push_back(mapping); - inputs.push_back(shift); - inputs.push_back(selected); - inputs.push_back(selected*0); //selected_type, unused for se_a - inputs.push_back(selected*0); //selected_loc, unused for se_a - inputs.push_back(box); - c10::Dict outputs = module.forward(inputs).toGenericDict(); - c10::IValue energy = outputs.at("energy"); - c10::IValue force = outputs.at("force"); - c10::IValue virial = outputs.at("virial"); - std::cout << energy<< force<< virial<<"ok\n"; -} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 016c010f..02952a53 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,3 +40,38 @@ write_to = "deepmd_pt/_version.py" [tool.cibuildwheel] test-command = ["dp_pt -h",] + +[tool.isort] +profile = "black" +force_grid_wrap = 1 + +[tool.ruff] +select = [ + "E", # errors + "F", # pyflakes + "D", # pydocstyle + "UP", # pyupgrade + "C4", # flake8-comprehensions + "RUF", # ruff + "NPY", # numpy +] + +ignore = [ + "E501", # line too long + "F841", # local variable is assigned to but never used + "E741", # ambiguous variable name + "E402", # module level import not at top of file + "D100", # TODO: missing docstring in public module + "D101", # TODO: missing docstring in public class + "D102", # TODO: missing docstring in public method + "D103", # TODO: missing docstring in public function + "D104", # TODO: missing docstring in public package + "D105", # TODO: missing docstring in magic method + "D205", # 1 blank line required between summary line and description + "D401", # TODO: first line should be in imperative mood + "D404", # TODO: first word of the docstring should not be This +] 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z7-Ney(#PCuHGu1Z2)k3&2KXGLp~2dTA2w}mZCLy>lkzrUhgDKg_iZU1*D43_4wDND z3n|=fw~kuKt!^>=+1q5=1_hOxnVSzb&hw`tE@hPTZ4pj`5Nit4kEteQWo1$N66vZ~ ziXvaj_MB+I|LH>!U);SIW@>tY z$S*?y%6Gl)M2}Z)gE)|Xo$Y^qZ8$4w0GznyyLT zfUJd+eJ{tXprBx1a4;X1IF*{3`sUmCk5LTFG+#cN`}{i1Rq233vpz zxyoAsMQ@J8w7F^E8~Xh5vhHAav=$a4y1sTP7SAPhU4$DsM^&(pR~fkLKxnAX*tdQN z0+XHcSsIWM4doFLA%e)-rz5WorzBWc-hNi)p!y>Sw$s3 zzOt%{nS{#o=Jjienwr|(Voj^F|Ee-DUt3dPrciZstRNpxEH5voXs=o0 zlEg!EwjfsCbNb}&>7{IYskeQ;d#ziN1InLb+=kp9bhi-Bb0FyFY+pwveR95e-nFe| zyFG_P5RJakv9Tl>dyaxwio#@%RDK_92k=`7ze$fI$^--41<5hyNj1{{cPz?+mNsnx}Hw{LNa42dXHlysDy8th9n*lc!9f8)G^WBqqx`(t1i zNQ!@Mf*0eB?uSM)zYl+Qf0j-LlNn 1: - local_rank = os.environ['SLURM_LOCALID'] - output += " && export LOCAL_RANK='{}'".format(local_rank, local_rank) - - print(output) diff --git a/slurm/distributed_data_parallel_slurm_setup.sbatch b/slurm/distributed_data_parallel_slurm_setup.sbatch deleted file mode 100644 index dc79e68e..00000000 --- a/slurm/distributed_data_parallel_slurm_setup.sbatch +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash -#SBATCH -o job.%j.out -#SBATCH --partition= #your_partition -#SBATCH -J job_name -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=2 -#SBATCH --ntasks=2 -#SBATCH --gres=gpu:2 - -#setup your environment here - -###### - -# --- Setup In Master --- -# Master will be the one running this bash script (SLURM runs this only once) -# Get hostname and port on first node first process -# For the port see: https://unix.stackexchange.com/questions/55913/whats-the-easiest-way-to-find-an-unused-local-port -master_addr=$(hostname -i) -master_port=$(python -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()') - - -# --- Call the Script which the User will Edit --- -# With srun this will be run on all nodes for all processes -srun ./distributed_data_parallel_slurm_run.bash --master_addr $master_addr --master_port $master_port diff --git a/source/api_cc/build.sh b/source/api_cc/build.sh index 02ea1892..0032fa61 100644 --- a/source/api_cc/build.sh +++ b/source/api_cc/build.sh @@ -1,7 +1,7 @@ -rm -rf build +rm -rf build mkdir build cd build export Eigen3_DIR=/opt/mamba/envs/dppt/share/eigen3/cmake -export CMAKE_PREFIX_PATH=`python -c "import torch;print(torch.__path__[0])"`/share/cmake:$CMAKE_PREFIX_PATH +export CMAKE_PREFIX_PATH=$(python -c "import torch;print(torch.__path__[0])")/share/cmake:$CMAKE_PREFIX_PATH cmake .. make diff --git a/source/api_cc/include/DeepPot.h b/source/api_cc/include/DeepPot.h index 17d3e03e..a8c0c017 100644 --- a/source/api_cc/include/DeepPot.h +++ b/source/api_cc/include/DeepPot.h @@ -1,3 +1,4 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later #include "common.h" namespace deepmd { @@ -5,7 +6,7 @@ namespace deepmd { * @brief Deep Potential. **/ class DeepPot { - public: +public: // cublasHandle_t handle; /** * @brief DP constructor without initialization. @@ -16,8 +17,7 @@ class DeepPot { * @brief Initialize the DP. * @param[in] model The name of the frozen model file. **/ - template - void init(const std::string& model); + template void init(const std::string &model); /** * @brief Evaluate the energy, force and virial by using this DP. @@ -31,32 +31,27 @@ class DeepPot { *x 9. **/ template - void compute(ENERGYVTYPE& ener, - std::vector& force, - std::vector& virial, - const std::vector& coord, - const std::vector& atype, - const std::vector& box); - private: + void + compute(ENERGYVTYPE &ener, std::vector &force, + std::vector &virial, const std::vector &coord, + const std::vector &atype, const std::vector &box); + +private: torch::jit::script::Module module; }; -template -void DeepPot::init(const std::string& model) { - // cublasCreate(&handle); - - try { - module = torch::jit::load(model); - } - catch (const c10::Error& e) { - std::cerr << "Error loading the model\n"; - } - +template void DeepPot::init(const std::string &model) { + // cublasCreate(&handle); + try { + module = torch::jit::load(model); + } catch (const c10::Error &e) { + std::cerr << "Error loading the model\n"; + } } class DeepPotModelDevi { - public: +public: /** * @brief DP model deviation constructor without initialization. **/ @@ -67,7 +62,7 @@ class DeepPotModelDevi { * @param[in] models The name of the frozen model files. **/ template - void init(const std::vector& models); + void init(const std::vector &models); /** * @brief Evaluate the energy, force and virial by using this DP. @@ -81,28 +76,28 @@ class DeepPotModelDevi { *x 9. **/ template - void compute(std::vector& all_ener, - std::vector>& all_force, - std::vector>& all_virial, - const std::vector& coord, - const std::vector& atype, - const std::vector& box); + void compute(std::vector &all_ener, + std::vector> &all_force, + std::vector> &all_virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box); /** * @brief Compute the average energy. * @param[out] dener The average energy. * @param[in] all_energy The energies of all models. **/ template - void compute_avg(VALUETYPE& dener, const std::vector& all_energy); + void compute_avg(VALUETYPE &dener, const std::vector &all_energy); /** * @brief Compute the average of vectors. * @param[out] avg The average of vectors. * @param[in] xx The vectors of all models. **/ template - void compute_avg(std::vector& avg, - const std::vector >& xx); - /** + void compute_avg(std::vector &avg, + const std::vector> &xx); + /** * @brief Compute the standard deviation of vectors. * @param[out] std The standard deviation of vectors. * @param[in] avg The average of vectors. @@ -110,10 +105,9 @@ class DeepPotModelDevi { * @param[in] stride The stride to compute the deviation. **/ template - void compute_std(std::vector& std, - const std::vector& avg, - const std::vector >& xx, - const int& stride); + void + compute_std(std::vector &std, const std::vector &avg, + const std::vector> &xx, const int &stride); /** * @brief Compute the relative standard deviation of vectors. * @param[out] std The standard deviation of vectors. @@ -122,10 +116,9 @@ class DeepPotModelDevi { * @param[in] stride The stride to compute the deviation. **/ template - void compute_relative_std(std::vector& std, - const std::vector& avg, - const VALUETYPE eps, - const int& stride); + void compute_relative_std(std::vector &std, + const std::vector &avg, + const VALUETYPE eps, const int &stride); /** * @brief Compute the standard deviation of forces. * @param[out] std The standard deviation of forces. @@ -133,9 +126,9 @@ class DeepPotModelDevi { * @param[in] xx The vectors of all forces. **/ template - void compute_std_f(std::vector& std, - const std::vector& avg, - const std::vector >& xx); + void compute_std_f(std::vector &std, + const std::vector &avg, + const std::vector> &xx); /** * @brief Compute the relative standard deviation of forces. * @param[out] std The relative standard deviation of forces. @@ -143,29 +136,28 @@ class DeepPotModelDevi { * @param[in] eps The level parameter for computing the deviation. **/ template - void compute_relative_std_f(std::vector& std, - const std::vector& avg, + void compute_relative_std_f(std::vector &std, + const std::vector &avg, const VALUETYPE eps); - private: +private: unsigned numb_models; std::vector modules; }; template -void DeepPotModelDevi::init(const std::vector& models) { - numb_models = models.size(); - modules.resize(numb_models); - // cublasCreate(&handle); +void DeepPotModelDevi::init(const std::vector &models) { + numb_models = models.size(); + modules.resize(numb_models); + // cublasCreate(&handle); - try { - for (int ii=0; ii #ifndef COMMON_H #define COMMON_H -#include -#include +#include #include +#include +#include #include -#include template -void TensortoVec(c10::IValue in, std::vector& out) { +void TensortoVec(c10::IValue in, std::vector &out) { std::vector values; if (in.isTensorList()) { - auto tensor_list = in.toTensorList().vec(); // Extract the underlying vector - for (const at::Tensor& tensor : tensor_list) { - values.push_back(tensor.item()); - } + auto tensor_list = in.toTensorList().vec(); // Extract the underlying vector + for (const at::Tensor &tensor : tensor_list) { + values.push_back(tensor.item()); + } } else { - std::cerr << "in is not a tensor list\n"; + std::cerr << "in is not a tensor list\n"; } out.resize(values.size()); - for (int ii=0; ii(values[ii]); + for (int ii = 0; ii < values.size(); ii++) { + out[ii] = static_cast(values[ii]); } } -template -class Region3D { - public: +template class Region3D { +public: Region3D(); ~Region3D() {} - Region3D(const std::vector& box){ - Eigen::Vector3d a1((std::vector(box.begin(), box.begin()+3)).data()); - Eigen::Vector3d a2((std::vector(box.begin()+3, box.begin()+6)).data()); - Eigen::Vector3d a3((std::vector(box.begin()+6, box.begin()+9)).data()); + Region3D(const std::vector &box) { + Eigen::Vector3d a1( + (std::vector(box.begin(), box.begin() + 3)).data()); + Eigen::Vector3d a2( + (std::vector(box.begin() + 3, box.begin() + 6)).data()); + Eigen::Vector3d a3( + (std::vector(box.begin() + 6, box.begin() + 9)).data()); boxt << a1, a2, a3; rec_boxt = boxt.inverse(); volume = boxt.determinant(); @@ -43,43 +46,51 @@ class Region3D { _h2yz = volume / c_yz.norm(); c_zx = a3.cross(a1); _h2zx = volume / c_zx.norm(); -} + } VALUETYPE volume; - std::vector get_face_distance(){ + std::vector get_face_distance() { std::vector temp(3); temp[0] = _h2yz; temp[1] = _h2zx; temp[2] = _h2xy; return temp; -} + } - void phys2inter(std::vector& inner_coord, const std::vector& coord){ - int natom = coord.size()/3; - inner_coord.resize(natom*3); - for (int ii=0; ii(coord.begin()+(3*ii), coord.begin()+(3*(ii+1)))).data()); - Eigen::RowVector3d tmp_inner_coord = tmp_coord * rec_boxt; - for (int jj=0; jj<3; jj++) { - inner_coord[3*ii+jj] = tmp_inner_coord[jj]; - } + void phys2inter(std::vector &inner_coord, + const std::vector &coord) { + int natom = coord.size() / 3; + inner_coord.resize(natom * 3); + for (int ii = 0; ii < natom; ii++) { + Eigen::RowVector3d tmp_coord( + (std::vector(coord.begin() + (3 * ii), + coord.begin() + (3 * (ii + 1)))) + .data()); + Eigen::RowVector3d tmp_inner_coord = tmp_coord * rec_boxt; + for (int jj = 0; jj < 3; jj++) { + inner_coord[3 * ii + jj] = tmp_inner_coord[jj]; + } } -} + } - void inter2phys(std::vector& coord, const std::vector& inner_coord){ - int natom = inner_coord.size()/3; - coord.resize(natom*3); - for (int ii=0; ii(inner_coord.begin()+(3*ii), inner_coord.begin()+(3*(ii+1)))).data()); - Eigen::RowVector3d tmp_inner_coord = tmp_coord * boxt; - for (int jj=0; jj<3; jj++) { - coord[3*ii+jj] = tmp_inner_coord[jj]; - } + void inter2phys(std::vector &coord, + const std::vector &inner_coord) { + int natom = inner_coord.size() / 3; + coord.resize(natom * 3); + for (int ii = 0; ii < natom; ii++) { + Eigen::RowVector3d tmp_coord( + (std::vector(inner_coord.begin() + (3 * ii), + inner_coord.begin() + (3 * (ii + 1)))) + .data()); + Eigen::RowVector3d tmp_inner_coord = tmp_coord * boxt; + for (int jj = 0; jj < 3; jj++) { + coord[3 * ii + jj] = tmp_inner_coord[jj]; + } } -} + } - private: +private: Eigen::Matrix3d boxt; Eigen::Matrix3d rec_boxt; Eigen::Vector3d c_xy, c_yz, c_zx; @@ -87,176 +98,192 @@ class Region3D { }; template -void normalize_coord(std::vector& coord, - Region3D& region){ - std::vector inner_coord; - region.phys2inter(inner_coord, coord); - for (int ii=0; ii &coord, + Region3D ®ion) { + std::vector inner_coord; + region.phys2inter(inner_coord, coord); + for (int ii = 0; ii < inner_coord.size(); ii++) { + inner_coord[ii] = std::fmod(inner_coord[ii], 1.0); + if (inner_coord[ii] < 0) + inner_coord[ii] += 1; + } + region.inter2phys(coord, inner_coord); } -int compute_serial_id(const std::vector& cid, const std::vector& ncell); +int compute_serial_id(const std::vector &cid, + const std::vector &ncell); template -void append_neighbors(std::vector& merged_coord_shift, - std::vector& merged_atype, - std::vector& merged_mapping, - std::vector& a2c, - std::map, int>& region_id_map_serial, - std::vector>& cell_list, - const std::vector& ncell, - const std::vector& coord, - const std::vector& atype, - Region3D& region, - VALUETYPE rcut){ - int natom = coord.size()/3; - a2c.resize(natom); - int nlocal_region = ncell[0]*ncell[1]*ncell[2]; - int nghost_region = (ncell[0]+2)*(ncell[1]+2)*(ncell[2]+2)-ncell[0]*ncell[1]*ncell[2]; - cell_list.resize(nlocal_region+nghost_region); - - std::vector> c2a(nlocal_region); - build_inside_clist(a2c, c2a, coord, region, ncell); - - for (int ii=0; ii &merged_coord_shift, + std::vector &merged_atype, + std::vector &merged_mapping, std::vector &a2c, + std::map, int> ®ion_id_map_serial, + std::vector> &cell_list, + const std::vector &ncell, + const std::vector &coord, + const std::vector &atype, + Region3D ®ion, VALUETYPE rcut) { + int natom = coord.size() / 3; + a2c.resize(natom); + int nlocal_region = ncell[0] * ncell[1] * ncell[2]; + int nghost_region = (ncell[0] + 2) * (ncell[1] + 2) * (ncell[2] + 2) - + ncell[0] * ncell[1] * ncell[2]; + cell_list.resize(nlocal_region + nghost_region); + + std::vector> c2a(nlocal_region); + build_inside_clist(a2c, c2a, coord, region, ncell); + + for (int ii = 0; ii < nlocal_region; ii++) { + cell_list[ii].assign(c2a[ii].begin(), c2a[ii].end()); + } - std::vector> ghost_region_id(nghost_region); - std::vector> mirror_region_id(nghost_region); - // std::vector ghost_region_serial_id(nghost_region); - std::vector mirror_region_serial_id(nghost_region); - std::vector> ghost_region_shift_period_to_inside(nghost_region); - int m = 0; - for (int ii=-1; ii=ncell[0] || jj<0 || jj>=ncell[1] || kk<0 || kk>=ncell[2]) { - ghost_region_id[m].resize(3); - ghost_region_id[m][0] = ii; - ghost_region_id[m][1] = jj; - ghost_region_id[m][2] = kk; - region_id_map_serial[ghost_region_id[m]] = nlocal_region + m; - // ghost_region_serial_id[m] = nlocal_region + m; - - mirror_region_id[m].resize(3); - mirror_region_id[m][0] = ii % ncell[0]; - mirror_region_id[m][1] = jj % ncell[1]; - mirror_region_id[m][2] = kk % ncell[2]; - - for (int ll = 0; ll < 3; ll++) { - if (mirror_region_id[m][ll] < 0) mirror_region_id[m][ll] += ncell[ll]; - } - mirror_region_serial_id[m] = compute_serial_id(mirror_region_id[m], ncell); - - ghost_region_shift_period_to_inside[m].resize(3); - ghost_region_shift_period_to_inside[m][0] = -int(std::floor(VALUETYPE(ii)/VALUETYPE(ncell[0]))); - ghost_region_shift_period_to_inside[m][1] = -int(std::floor(VALUETYPE(jj)/VALUETYPE(ncell[1]))); - ghost_region_shift_period_to_inside[m][2] = -int(std::floor(VALUETYPE(kk)/VALUETYPE(ncell[2]))); - m++; - } - else { - std::vector tmp_region_id = {ii, jj, kk}; - region_id_map_serial[tmp_region_id] = compute_serial_id(tmp_region_id, ncell); - } - } + std::vector> ghost_region_id(nghost_region); + std::vector> mirror_region_id(nghost_region); + // std::vector ghost_region_serial_id(nghost_region); + std::vector mirror_region_serial_id(nghost_region); + std::vector> ghost_region_shift_period_to_inside( + nghost_region); + int m = 0; + for (int ii = -1; ii < ncell[0] + 1; ii++) { + for (int jj = -1; jj < ncell[1] + 1; jj++) { + for (int kk = -1; kk < ncell[2] + 1; kk++) { + if (ii < 0 || ii >= ncell[0] || jj < 0 || jj >= ncell[1] || kk < 0 || + kk >= ncell[2]) { + ghost_region_id[m].resize(3); + ghost_region_id[m][0] = ii; + ghost_region_id[m][1] = jj; + ghost_region_id[m][2] = kk; + region_id_map_serial[ghost_region_id[m]] = nlocal_region + m; + // ghost_region_serial_id[m] = nlocal_region + m; + + mirror_region_id[m].resize(3); + mirror_region_id[m][0] = ii % ncell[0]; + mirror_region_id[m][1] = jj % ncell[1]; + mirror_region_id[m][2] = kk % ncell[2]; + + for (int ll = 0; ll < 3; ll++) { + if (mirror_region_id[m][ll] < 0) + mirror_region_id[m][ll] += ncell[ll]; + } + mirror_region_serial_id[m] = + compute_serial_id(mirror_region_id[m], ncell); + + ghost_region_shift_period_to_inside[m].resize(3); + ghost_region_shift_period_to_inside[m][0] = + -int(std::floor(VALUETYPE(ii) / VALUETYPE(ncell[0]))); + ghost_region_shift_period_to_inside[m][1] = + -int(std::floor(VALUETYPE(jj) / VALUETYPE(ncell[1]))); + ghost_region_shift_period_to_inside[m][2] = + -int(std::floor(VALUETYPE(kk) / VALUETYPE(ncell[2]))); + m++; + } else { + std::vector tmp_region_id = {ii, jj, kk}; + region_id_map_serial[tmp_region_id] = + compute_serial_id(tmp_region_id, ncell); } + } } - // printf("ghost region serial id:\n"); - // for (int ii=0; ii nghost_each_region(nghost_region); - for (int ii=0; ii ghost_atom_mirror_region_id(nghost); - std::vector ghost_atom_ghost_region_idx(nghost); // Note: the elements in this vector range from [0, # ghost regions) - m = 0; - for (int ii=0; ii> coord_shift(nghost); - merged_coord_shift.resize(3*nall); - merged_atype.resize(nall); - merged_mapping.resize(nall); - for (int ii=0; ii inner_int = ghost_region_shift_period_to_inside[ghost_atom_ghost_region_idx[ii]]; - std::vector inner_coord_shift(inner_int.begin(), inner_int.end()); - std::vector shift(3); - region.inter2phys(shift, inner_coord_shift); - std::copy(shift.begin(), shift.end(), merged_coord_shift.begin()+3*(natom+ii)); - } + } + // printf("ghost region serial id:\n"); + // for (int ii=0; ii nghost_each_region(nghost_region); + for (int ii = 0; ii < nghost_region; ii++) { + nghost_each_region[ii] = c2a[mirror_region_serial_id[ii]].size(); + nghost += nghost_each_region[ii]; + } + int nall = natom + nghost; + std::vector ghost_atom_mirror_region_id(nghost); + std::vector ghost_atom_ghost_region_idx( + nghost); // Note: the elements in this vector range from [0, # ghost + // regions) + m = 0; + for (int ii = 0; ii < nghost_region; ii++) { + std::fill( + ghost_atom_ghost_region_idx.begin() + m, + ghost_atom_ghost_region_idx.begin() + (m + nghost_each_region[ii]), ii); + m += nghost_each_region[ii]; + } - for (int ii=0; ii tmp_mapping = c2a[mirror_region_serial_id[ii]]; - std::vector tmp_id(tmp_mapping.size()); - for (int jj=0; jj> coord_shift(nghost); + merged_coord_shift.resize(3 * nall); + merged_atype.resize(nall); + merged_mapping.resize(nall); + for (int ii = 0; ii < nghost; ii++) { + std::vector inner_int = + ghost_region_shift_period_to_inside[ghost_atom_ghost_region_idx[ii]]; + std::vector inner_coord_shift(inner_int.begin(), + inner_int.end()); + std::vector shift(3); + region.inter2phys(shift, inner_coord_shift); + std::copy(shift.begin(), shift.end(), + merged_coord_shift.begin() + 3 * (natom + ii)); + } - for (int ii=0; ii tmp_mapping = c2a[mirror_region_serial_id[ii]]; + std::vector tmp_id(tmp_mapping.size()); + for (int jj = 0; jj < tmp_id.size(); jj++) { + tmp_id[jj] = natom + last_size + jj; } + cell_list[nlocal_region + ii].assign(tmp_id.begin(), tmp_id.end()); + std::copy(tmp_mapping.begin(), tmp_mapping.end(), + merged_mapping.begin() + (natom + last_size)); + last_size += tmp_mapping.size(); + } + + for (int ii = 0; ii < nall; ii++) { + merged_atype[ii] = atype[merged_mapping[ii]]; + } } template -void build_inside_clist(std::vector& a2c, - std::vector>& c2a, - const std::vector& coord, - Region3D& region, - const std::vector& ncell){ - std::vector inter_cell_size(3); - for (int ii=0; ii<3; ii++) inter_cell_size[ii] = 1./ncell[ii]; - int natom = coord.size()/3; - std::vector inner_coord; - std::vector cell_offset(natom*3); - region.phys2inter(inner_coord, coord); - for (int ii=0; ii(cell_offset.begin()+(3*ii), cell_offset.begin()+(3*ii+3)), ncell); - c2a[a2c[ii]].push_back(ii); +void build_inside_clist(std::vector &a2c, + std::vector> &c2a, + const std::vector &coord, + Region3D ®ion, + const std::vector &ncell) { + std::vector inter_cell_size(3); + for (int ii = 0; ii < 3; ii++) + inter_cell_size[ii] = 1. / ncell[ii]; + int natom = coord.size() / 3; + std::vector inner_coord; + std::vector cell_offset(natom * 3); + region.phys2inter(inner_coord, coord); + for (int ii = 0; ii < natom; ii++) { + for (int jj = 0; jj < 3; jj++) { + cell_offset[3 * ii + jj] = + std::floor(inner_coord[3 * ii + jj] / inter_cell_size[jj]); + if (cell_offset[3 * ii + jj] < 0) + cell_offset[3 * ii + jj] = 0; } + } + for (int ii = 0; ii < natom; ii++) { + a2c[ii] = + compute_serial_id(std::vector(cell_offset.begin() + (3 * ii), + cell_offset.begin() + (3 * ii + 3)), + ncell); + c2a[a2c[ii]].push_back(ii); + } } template -void build_neighbor_list(std::vector>& nlist, - std::vector>& nlist_loc, - std::vector>& nlist_atype, - int nloc, - const std::vector& coord, - const std::vector& atype, - VALUETYPE rcut, - const std::vector& ncell, - const std::vector& sec, - const std::vector& merged_mapping, - const std::vector& a2c, - const std::map, int>& region_id_map_serial, - const std::vector>& cell_list, - bool type_split = true, - bool min_check = false) -{ +void build_neighbor_list( + std::vector> &nlist, + std::vector> &nlist_loc, + std::vector> &nlist_atype, int nloc, + const std::vector &coord, const std::vector &atype, + VALUETYPE rcut, const std::vector &ncell, const std::vector &sec, + const std::vector &merged_mapping, const std::vector &a2c, + const std::map, int> ®ion_id_map_serial, + const std::vector> &cell_list, bool type_split = true, + bool min_check = false) { int nall = merged_mapping.size(); nlist.resize(nloc); nlist_loc.resize(nloc); @@ -266,40 +293,47 @@ void build_neighbor_list(std::vector>& nlist, std::vector sec_offset(ntype); sec_[0] = sec[0]; sec_offset[0] = 0; - for (int i=1; i region_id(3); int region_serial_id = a2c[i]; - region_id[0] = int(region_serial_id/(ncell[1]*ncell[2])); - region_id[1] = int((region_serial_id - region_id[0]*ncell[1]*ncell[2])/ncell[2]); - region_id[2] = region_serial_id - region_id[0]*ncell[1]*ncell[2] - region_id[1]*ncell[2]; + region_id[0] = int(region_serial_id / (ncell[1] * ncell[2])); + region_id[1] = + int((region_serial_id - region_id[0] * ncell[1] * ncell[2]) / ncell[2]); + region_id[2] = region_serial_id - region_id[0] * ncell[1] * ncell[2] - + region_id[1] * ncell[2]; // printf("%d %d %d\n", region_id[0], region_id[1], region_id[2]); std::vector neighbor_regions(27); int m = 0; - for (int ii=-1; ii<2; ii++) { - for (int jj=-1; jj<2; jj++) { - for (int kk=-1; kk<2; kk++) { + for (int ii = -1; ii < 2; ii++) { + for (int jj = -1; jj < 2; jj++) { + for (int kk = -1; kk < 2; kk++) { // if (! (ii==0 && jj==0 && kk==0)) { - std::vector neighbor_region_id = {region_id[0]+ii, region_id[1]+jj, region_id[2]+kk}; - int neighbor_region_serial_id = region_id_map_serial.find(neighbor_region_id)->second; - neighbor_regions[m] = neighbor_region_serial_id; - m++; - // std::vector neighbor_atoms = cell_list[neighbor_region_serial_id]; + std::vector neighbor_region_id = { + region_id[0] + ii, region_id[1] + jj, region_id[2] + kk}; + int neighbor_region_serial_id = + region_id_map_serial.find(neighbor_region_id)->second; + neighbor_regions[m] = neighbor_region_serial_id; + m++; + // std::vector neighbor_atoms = + // cell_list[neighbor_region_serial_id]; // } } } } - + std::vector neighbor_atoms; // std::vector neighbor_atoms_atype; - for (int ii=0; ii<27; ii++) { - neighbor_atoms.insert(neighbor_atoms.end(), cell_list[neighbor_regions[ii]].begin(), cell_list[neighbor_regions[ii]].end()); + for (int ii = 0; ii < 27; ii++) { + neighbor_atoms.insert(neighbor_atoms.end(), + cell_list[neighbor_regions[ii]].begin(), + cell_list[neighbor_regions[ii]].end()); } auto iter = std::find(neighbor_atoms.begin(), neighbor_atoms.end(), i); neighbor_atoms.erase(iter); @@ -308,14 +342,18 @@ void build_neighbor_list(std::vector>& nlist, std::vector neighbor_atype_in_rcut; std::vector neighbor_distances_in_rcut; - int n=0; - for (int j=0; j coord_i(3); std::vector coord_j(3); int idx_neighbor = neighbor_atoms[j]; - std::copy(coord.begin()+(3*i), coord.begin()+(3*i+3), coord_i.begin()); - std::copy(coord.begin()+(3*idx_neighbor), coord.begin()+(3*idx_neighbor+3), coord_j.begin()); - VALUETYPE tmp_distance = std::sqrt(std::pow(coord_j[0] - coord_i[0], 2) + std::pow(coord_j[1] - coord_i[1], 2) + std::pow(coord_j[2] - coord_i[2], 2)); + std::copy(coord.begin() + (3 * i), coord.begin() + (3 * i + 3), + coord_i.begin()); + std::copy(coord.begin() + (3 * idx_neighbor), + coord.begin() + (3 * idx_neighbor + 3), coord_j.begin()); + VALUETYPE tmp_distance = std::sqrt(std::pow(coord_j[0] - coord_i[0], 2) + + std::pow(coord_j[1] - coord_i[1], 2) + + std::pow(coord_j[2] - coord_i[2], 2)); if (tmp_distance < rcut) { neighbor_atoms_in_rcut.push_back(idx_neighbor); neighbor_distances_in_rcut.push_back(tmp_distance); @@ -337,14 +375,17 @@ void build_neighbor_list(std::vector>& nlist, // std::vector neighbor_atoms_each_type(ntype); std::vector neighbor_atoms_onetype; std::vector neighbor_distances_onetype; - for (int ii=0; ii>& nlist, } std::vector indices(neighbor_atoms_onetype.size()); std::iota(indices.begin(), indices.end(), 0); - std::sort(indices.begin(), indices.end(), [&neighbor_distances_onetype](size_t i1, size_t i2) { return neighbor_distances_onetype[i1] < neighbor_distances_onetype[i2]; }); + std::sort(indices.begin(), indices.end(), + [&neighbor_distances_onetype](size_t i1, size_t i2) { + return neighbor_distances_onetype[i1] < + neighbor_distances_onetype[i2]; + }); // std::cout << indices << std::endl; @@ -360,18 +405,21 @@ void build_neighbor_list(std::vector>& nlist, // std::vector sorted_distances(neighbor_atoms_onetype.size()); // printf("i = %d ii = %d indices.size: %ld\n", i, ii, indices.size()); - for (int jj=0; jj>& nlist, } // if(i==0) { std::cout << neighbor_atoms << std::endl; } // std::cout << neighbor_atoms_each_type << std::endl; - - // std::cout << std::count(neighbor_atoms_atype.begin(), neighbor_atoms_atype.end(), 0) << " " << std::count(neighbor_atoms_atype.begin(), neighbor_atoms_atype.end(), 1) << std::endl; - // std::cout << neighbor_atoms.size() << std::endl; + + // std::cout << std::count(neighbor_atoms_atype.begin(), + // neighbor_atoms_atype.end(), 0) << " " << + // std::count(neighbor_atoms_atype.begin(), neighbor_atoms_atype.end(), 1) + // << std::endl; std::cout << neighbor_atoms.size() << std::endl; } - + // printf("iterated over all local atoms\n"); } - template -void make_env_mat(std::vector>& nlist, - std::vector>& nlist_loc, - std::vector>& nlist_type, - std::vector& merged_coord_shift, - std::vector& merged_mapping, - std::vector& coord_wrapped, - const std::vector& coord, - const std::vector& atype, - const std::vector& box, - VALUETYPE rcut, - const std::vector& sec) -{ +void make_env_mat(std::vector> &nlist, + std::vector> &nlist_loc, + std::vector> &nlist_type, + std::vector &merged_coord_shift, + std::vector &merged_mapping, + std::vector &coord_wrapped, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, VALUETYPE rcut, + const std::vector &sec) { Region3D region(box); coord_wrapped = coord; normalize_coord(coord_wrapped, region); @@ -418,44 +465,53 @@ void make_env_mat(std::vector>& nlist, std::vector face_distance(3); face_distance = region.get_face_distance(); std::vector ncell(3); - for (int ii=0; ii<3; ii++) { - ncell[ii] = std::floor(face_distance[ii]/rcut); - if(ncell[ii] == 0) ncell[ii] = 1; + for (int ii = 0; ii < 3; ii++) { + ncell[ii] = std::floor(face_distance[ii] / rcut); + if (ncell[ii] == 0) + ncell[ii] = 1; } - append_neighbors(merged_coord_shift, merged_atype, merged_mapping, a2c, region_id_map_serial, cell_list, ncell, coord_wrapped, atype, region, rcut); - -// for (std::map, int>::iterator iter=region_id_map_serial.begin(); iter!=region_id_map_serial.end(); iter++){ -// std::cout << iter->first << " " << iter->second << std::endl; -// } -// for (int ii=0; ii<64; ii++) { -// std::cout << cell_list[ii] << std::endl; -// } + append_neighbors(merged_coord_shift, merged_atype, merged_mapping, a2c, + region_id_map_serial, cell_list, ncell, coord_wrapped, atype, + region, rcut); + + // for (std::map, int>::iterator + // iter=region_id_map_serial.begin(); iter!=region_id_map_serial.end(); + // iter++){ + // std::cout << iter->first << " " << iter->second << std::endl; + // } + // for (int ii=0; ii<64; ii++) { + // std::cout << cell_list[ii] << std::endl; + // } int nall = merged_mapping.size(); - std::vector merged_coord(3*nall); - for (int ii=0; ii merged_coord(3 * nall); + for (int ii = 0; ii < nall; ii++) { // std::vector shifted_coord(3); int idx = merged_mapping[ii]; - for (int jj=0; jj<3; jj++) { - // shifted_coord[jj] = coord[3*idx+jj] - merged_coord_shift[3*ii+jj]; - merged_coord[3*ii+jj] = coord_wrapped[3*idx+jj] - merged_coord_shift[3*ii+jj]; + for (int jj = 0; jj < 3; jj++) { + // shifted_coord[jj] = coord[3*idx+jj] - merged_coord_shift[3*ii+jj]; + merged_coord[3 * ii + jj] = + coord_wrapped[3 * idx + jj] - merged_coord_shift[3 * ii + jj]; } } -// for (int ii=0; ii(const std::string &model); +template +void DeepPot::compute(ENERGYVTYPE &ener, std::vector &force, + std::vector &virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box) { + auto device = torch::kCUDA; + module.to(device); + std::vector coord_wrapped = coord; + int natoms = atype.size(); + // make_env_mat(nlist, nlist_loc, nlist_type, merged_coord_shift, + // merged_mapping, coord_wrapped, coord, atype, box, rcut, sec); + // auto device = torch::kCPU; + auto options = torch::TensorOptions().dtype(torch::kFloat64); + auto int_options = torch::TensorOptions().dtype(torch::kInt64); + std::vector inputs; + at::Tensor coord_wrapped_Tensor = + torch::from_blob(coord_wrapped.data(), {1, natoms, 3}, options) + .to(device); + // for (int ii=0; ii atype_64(atype.begin(), atype.end()); + at::Tensor atype_Tensor = + torch::from_blob(atype_64.data(), {1, natoms}, int_options).to(device); + inputs.push_back(atype_Tensor); + at::Tensor box_Tensor = + torch::from_blob(const_cast(box.data()), {1, 9}, options) + .to(device); + inputs.push_back(box_Tensor); + c10::Dict outputs = + module.forward(inputs).toGenericDict(); + + c10::IValue energy_ = outputs.at("energy"); + c10::IValue force_ = outputs.at("force"); + c10::IValue virial_ = outputs.at("virial"); + // std::cout << energy_ << std::endl; + // std::cout << force_ << std::endl; + ener = energy_.toTensor().item(); + + torch::Tensor flat_force_ = force_.toTensor().view({-1}); + torch::Tensor cpu_force_ = flat_force_.to(torch::kCPU); + force.assign(cpu_force_.data_ptr(), + cpu_force_.data_ptr() + cpu_force_.numel()); + + torch::Tensor flat_virial_ = virial_.toTensor().view({-1}); + torch::Tensor cpu_virial_ = flat_virial_.to(torch::kCPU); + virial.assign(cpu_virial_.data_ptr(), + cpu_virial_.data_ptr() + cpu_virial_.numel()); +} +template void DeepPot::compute(double &ener, + std::vector &force, + std::vector &virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box); + +DeepPotModelDevi::DeepPotModelDevi() {} + +DeepPotModelDevi::~DeepPotModelDevi() {} -template void DeepPot::init(const std::string& model); +template void +DeepPotModelDevi::init(const std::vector &models); template -void DeepPot::compute(ENERGYVTYPE& ener, - std::vector& force, - std::vector& virial, - const std::vector& coord, - const std::vector& atype, - const std::vector& box) -{ - auto device = torch::kCUDA; - module.to(device); - std::vector coord_wrapped = coord; - int natoms = atype.size(); - //make_env_mat(nlist, nlist_loc, nlist_type, merged_coord_shift, merged_mapping, coord_wrapped, coord, atype, box, rcut, sec); - // auto device = torch::kCPU; - auto options = torch::TensorOptions().dtype(torch::kFloat64); - auto int_options = torch::TensorOptions().dtype(torch::kInt64); - std::vector inputs; - at::Tensor coord_wrapped_Tensor = torch::from_blob(coord_wrapped.data(), {1, natoms, 3}, options).to(device); - // for (int ii=0; ii atype_64(atype.begin(), atype.end()); - at::Tensor atype_Tensor = torch::from_blob(atype_64.data(), {1, natoms}, int_options).to(device); - inputs.push_back(atype_Tensor); - at::Tensor box_Tensor = torch::from_blob(const_cast(box.data()), {1, 9}, options).to(device); - inputs.push_back(box_Tensor); - c10::Dict outputs = module.forward(inputs).toGenericDict(); +void DeepPotModelDevi::compute(std::vector &all_energy, + std::vector> &all_force, + std::vector> &all_virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box) { + all_energy.resize(numb_models); + all_force.resize(numb_models); + all_virial.resize(numb_models); + + auto device = torch::kCUDA; + for (int ii = 0; ii < numb_models; ii++) { + modules[ii].to(device); + } + std::vector coord_wrapped = coord; + + int natoms = atype.size(); + + // make_env_mat(nlist, nlist_loc, nlist_type, merged_coord_shift, + // merged_mapping, coord_wrapped, coord, atype, box, rcut, sec); + // auto device = torch::kCPU; + auto options = torch::TensorOptions().dtype(torch::kFloat64); + auto int_options = torch::TensorOptions().dtype(torch::kInt64); + std::vector inputs; + + at::Tensor coord_wrapped_Tensor = + torch::from_blob(coord_wrapped.data(), {1, natoms, 3}, options) + .to(device); + // for (int ii=0; ii atype_64(atype.begin(), atype.end()); + at::Tensor atype_Tensor = + torch::from_blob(atype_64.data(), {1, natoms}, int_options).to(device); + inputs.push_back(atype_Tensor); + + at::Tensor box_Tensor = + torch::from_blob(const_cast(box.data()), {1, 9}, options) + .to(device); + inputs.push_back(box_Tensor); + for (int ii = 0; ii < numb_models; ii++) { + c10::Dict outputs = + modules[ii].forward(inputs).toGenericDict(); c10::IValue energy_ = outputs.at("energy"); c10::IValue force_ = outputs.at("force"); c10::IValue virial_ = outputs.at("virial"); // std::cout << energy_ << std::endl; // std::cout << force_ << std::endl; - ener = energy_.toTensor().item(); + all_energy[ii] = energy_.toTensor().item(); torch::Tensor flat_force_ = force_.toTensor().view({-1}); torch::Tensor cpu_force_ = flat_force_.to(torch::kCPU); - force.assign(cpu_force_.data_ptr(), cpu_force_.data_ptr() + cpu_force_.numel()); + all_force[ii].assign(cpu_force_.data_ptr(), + cpu_force_.data_ptr() + cpu_force_.numel()); torch::Tensor flat_virial_ = virial_.toTensor().view({-1}); torch::Tensor cpu_virial_ = flat_virial_.to(torch::kCPU); - virial.assign(cpu_virial_.data_ptr(), cpu_virial_.data_ptr() + cpu_virial_.numel()); - -} -template void DeepPot::compute(double& ener, - std::vector& force, - std::vector& virial, - const std::vector& coord, - const std::vector& atype, - const std::vector& box); - -DeepPotModelDevi::DeepPotModelDevi() { } - -DeepPotModelDevi::~DeepPotModelDevi() { } - -template void DeepPotModelDevi::init(const std::vector& models); - -template -void DeepPotModelDevi::compute(std::vector& all_energy, - std::vector>& all_force, - std::vector>& all_virial, - const std::vector& coord, - const std::vector& atype, - const std::vector& box) -{ - all_energy.resize(numb_models); - all_force.resize(numb_models); - all_virial.resize(numb_models); - - auto device = torch::kCUDA; - for (int ii=0; ii coord_wrapped = coord; - - int natoms = atype.size(); - - //make_env_mat(nlist, nlist_loc, nlist_type, merged_coord_shift, merged_mapping, coord_wrapped, coord, atype, box, rcut, sec); - // auto device = torch::kCPU; - auto options = torch::TensorOptions().dtype(torch::kFloat64); - auto int_options = torch::TensorOptions().dtype(torch::kInt64); - std::vector inputs; - - at::Tensor coord_wrapped_Tensor = torch::from_blob(coord_wrapped.data(), {1, natoms, 3}, options).to(device); - // for (int ii=0; ii atype_64(atype.begin(), atype.end()); - at::Tensor atype_Tensor = torch::from_blob(atype_64.data(), {1, natoms}, int_options).to(device); - inputs.push_back(atype_Tensor); - - - at::Tensor box_Tensor = torch::from_blob(const_cast(box.data()), {1, 9}, options).to(device); - inputs.push_back(box_Tensor); - for (int ii=0; ii outputs = modules[ii].forward(inputs).toGenericDict(); - - c10::IValue energy_ = outputs.at("energy"); - c10::IValue force_ = outputs.at("force"); - c10::IValue virial_ = outputs.at("virial"); - // std::cout << energy_ << std::endl; - // std::cout << force_ << std::endl; - all_energy[ii] = energy_.toTensor().item(); - - torch::Tensor flat_force_ = force_.toTensor().view({-1}); - torch::Tensor cpu_force_ = flat_force_.to(torch::kCPU); - all_force[ii].assign(cpu_force_.data_ptr(), cpu_force_.data_ptr() + cpu_force_.numel()); - - torch::Tensor flat_virial_ = virial_.toTensor().view({-1}); - torch::Tensor cpu_virial_ = flat_virial_.to(torch::kCPU); - all_virial[ii].assign(cpu_virial_.data_ptr(), cpu_virial_.data_ptr() + cpu_virial_.numel()); - } - - + all_virial[ii].assign(cpu_virial_.data_ptr(), + cpu_virial_.data_ptr() + cpu_virial_.numel()); + } } -template void DeepPotModelDevi::compute(std::vector& all_energy, - std::vector>& all_force, - std::vector>& all_virial, - const std::vector& coord, - const std::vector& atype, - const std::vector& box); - +template void DeepPotModelDevi::compute( + std::vector &all_energy, + std::vector> &all_force, + std::vector> &all_virial, + const std::vector &coord, const std::vector &atype, + const std::vector &box); template -void DeepPotModelDevi::compute_avg(VALUETYPE& dener, - const std::vector& all_energy) { +void DeepPotModelDevi::compute_avg(VALUETYPE &dener, + const std::vector &all_energy) { assert(all_energy.size() == numb_models); - if (numb_models == 0) return; + if (numb_models == 0) + return; dener = 0; for (unsigned ii = 0; ii < numb_models; ++ii) { @@ -146,18 +160,21 @@ void DeepPotModelDevi::compute_avg(VALUETYPE& dener, dener /= (VALUETYPE)(numb_models); } -template void DeepPotModelDevi::compute_avg( - double& dener, const std::vector& all_energy); +template void +DeepPotModelDevi::compute_avg(double &dener, + const std::vector &all_energy); -template void DeepPotModelDevi::compute_avg( - float& dener, const std::vector& all_energy); +template void +DeepPotModelDevi::compute_avg(float &dener, + const std::vector &all_energy); template void DeepPotModelDevi::compute_avg( - std::vector& avg, - const std::vector>& xx) { + std::vector &avg, + const std::vector> &xx) { assert(xx.size() == numb_models); - if (numb_models == 0) return; + if (numb_models == 0) + return; avg.resize(xx[0].size()); fill(avg.begin(), avg.end(), VALUETYPE(0.)); @@ -174,19 +191,19 @@ void DeepPotModelDevi::compute_avg( } template void DeepPotModelDevi::compute_avg( - std::vector& avg, const std::vector>& xx); + std::vector &avg, const std::vector> &xx); -template void DeepPotModelDevi::compute_avg( - std::vector& avg, const std::vector>& xx); +template void +DeepPotModelDevi::compute_avg(std::vector &avg, + const std::vector> &xx); template void DeepPotModelDevi::compute_std( - std::vector& std, - const std::vector& avg, - const std::vector>& xx, - const int& stride) { + std::vector &std, const std::vector &avg, + const std::vector> &xx, const int &stride) { assert(xx.size() == numb_models); - if (numb_models == 0) return; + if (numb_models == 0) + return; unsigned ndof = avg.size(); unsigned nloc = ndof / stride; @@ -197,8 +214,8 @@ void DeepPotModelDevi::compute_std( for (unsigned ii = 0; ii < numb_models; ++ii) { for (unsigned jj = 0; jj < nloc; ++jj) { - const VALUETYPE* tmp_f = &(xx[ii][jj * stride]); - const VALUETYPE* tmp_avg = &(avg[jj * stride]); + const VALUETYPE *tmp_f = &(xx[ii][jj * stride]); + const VALUETYPE *tmp_avg = &(avg[jj * stride]); for (unsigned dd = 0; dd < stride; ++dd) { VALUETYPE vdiff = tmp_f[dd] - tmp_avg[dd]; std[jj] += vdiff * vdiff; @@ -212,46 +229,39 @@ void DeepPotModelDevi::compute_std( } template void DeepPotModelDevi::compute_std( - std::vector& std, - const std::vector& avg, - const std::vector>& xx, - const int& stride); + std::vector &std, const std::vector &avg, + const std::vector> &xx, const int &stride); template void DeepPotModelDevi::compute_std( - std::vector& std, - const std::vector& avg, - const std::vector>& xx, - const int& stride); + std::vector &std, const std::vector &avg, + const std::vector> &xx, const int &stride); template void DeepPotModelDevi::compute_std_f( - std::vector& std, - const std::vector& avg, - const std::vector>& xx) { + std::vector &std, const std::vector &avg, + const std::vector> &xx) { compute_std(std, avg, xx, 3); } template void DeepPotModelDevi::compute_std_f( - std::vector& std, - const std::vector& avg, - const std::vector>& xx); + std::vector &std, const std::vector &avg, + const std::vector> &xx); template void DeepPotModelDevi::compute_std_f( - std::vector& std, - const std::vector& avg, - const std::vector>& xx); - + std::vector &std, const std::vector &avg, + const std::vector> &xx); + template -void DeepPotModelDevi::compute_relative_std(std::vector& std, - const std::vector& avg, +void DeepPotModelDevi::compute_relative_std(std::vector &std, + const std::vector &avg, const VALUETYPE eps, - const int& stride) { + const int &stride) { unsigned ndof = avg.size(); unsigned nloc = std.size(); assert(nloc * stride == ndof); for (unsigned ii = 0; ii < nloc; ++ii) { - const VALUETYPE* tmp_avg = &(avg[ii * stride]); + const VALUETYPE *tmp_avg = &(avg[ii * stride]); VALUETYPE f_norm = 0.0; for (unsigned dd = 0; dd < stride; ++dd) { f_norm += tmp_avg[dd] * tmp_avg[dd]; @@ -262,28 +272,24 @@ void DeepPotModelDevi::compute_relative_std(std::vector& std, } template void DeepPotModelDevi::compute_relative_std( - std::vector& std, - const std::vector& avg, - const double eps, - const int& stride); + std::vector &std, const std::vector &avg, const double eps, + const int &stride); template void DeepPotModelDevi::compute_relative_std( - std::vector& std, - const std::vector& avg, - const float eps, - const int& stride); + std::vector &std, const std::vector &avg, const float eps, + const int &stride); template -void DeepPotModelDevi::compute_relative_std_f(std::vector& std, - const std::vector& avg, +void DeepPotModelDevi::compute_relative_std_f(std::vector &std, + const std::vector &avg, const VALUETYPE eps) { compute_relative_std(std, avg, eps, 3); } template void DeepPotModelDevi::compute_relative_std_f( - std::vector& std, const std::vector& avg, const double eps); + std::vector &std, const std::vector &avg, const double eps); template void DeepPotModelDevi::compute_relative_std_f( - std::vector& std, const std::vector& avg, const float eps); + std::vector &std, const std::vector &avg, const float eps); -} \ No newline at end of file +} // namespace deepmd diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index bfdc7ff8..dbf594de 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -1,57 +1,46 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later #include "common.h" -template void make_env_mat(std::vector>& nlist, - std::vector>& nlist_loc, - std::vector>& nlist_type, - std::vector& merged_coord_shift, - std::vector& merged_mapping, - std::vector& coord_wrapped, - const std::vector& coord, - const std::vector& atype, - const std::vector& box, - double rcut, - const std::vector& sec); - -int compute_serial_id(const std::vector& cid, const std::vector& ncell) -{ - return cid[0]*ncell[1]*ncell[2] + cid[1]*ncell[2] + cid[2]; +template void make_env_mat(std::vector> &nlist, + std::vector> &nlist_loc, + std::vector> &nlist_type, + std::vector &merged_coord_shift, + std::vector &merged_mapping, + std::vector &coord_wrapped, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, double rcut, + const std::vector &sec); + +int compute_serial_id(const std::vector &cid, + const std::vector &ncell) { + return cid[0] * ncell[1] * ncell[2] + cid[1] * ncell[2] + cid[2]; } - -template void normalize_coord(std::vector& coord, - Region3D& region); - - -template void append_neighbors(std::vector& merged_coord_shift, - std::vector& merged_atype, - std::vector& merged_mapping, - std::vector& a2c, - std::map, int>& region_id_map_serial, - std::vector>& cell_list, - const std::vector& ncell, - const std::vector& coord, - const std::vector& atype, - Region3D& region, - double rcut); - -template void build_inside_clist(std::vector& a2c, - std::vector>& c2a, - const std::vector& coord, - Region3D& region, - const std::vector& ncell); - -template void build_neighbor_list(std::vector>& nlist, - std::vector>& nlist_loc, - std::vector>& nlist_atype, - int nloc, - const std::vector& coord, - const std::vector& atype, - double rcut, - const std::vector& ncell, - const std::vector& sec, - const std::vector& merged_mapping, - const std::vector& a2c, - const std::map, int>& region_id_map_serial, - const std::vector>& cell_list, - bool type_split = true, - bool min_check = false); \ No newline at end of file +template void normalize_coord(std::vector &coord, + Region3D ®ion); + +template void append_neighbors( + std::vector &merged_coord_shift, std::vector &merged_atype, + std::vector &merged_mapping, std::vector &a2c, + std::map, int> ®ion_id_map_serial, + std::vector> &cell_list, const std::vector &ncell, + const std::vector &coord, const std::vector &atype, + Region3D ®ion, double rcut); + +template void build_inside_clist(std::vector &a2c, + std::vector> &c2a, + const std::vector &coord, + Region3D ®ion, + const std::vector &ncell); + +template void build_neighbor_list( + std::vector> &nlist, + std::vector> &nlist_loc, + std::vector> &nlist_atype, int nloc, + const std::vector &coord, const std::vector &atype, + double rcut, const std::vector &ncell, const std::vector &sec, + const std::vector &merged_mapping, const std::vector &a2c, + const std::map, int> ®ion_id_map_serial, + const std::vector> &cell_list, bool type_split = true, + bool min_check = false); diff --git a/source/api_cc/test/cppapi/CMakeLists.txt b/source/api_cc/test/cppapi/CMakeLists.txt index 709d49ad..faf8f149 100644 --- a/source/api_cc/test/cppapi/CMakeLists.txt +++ b/source/api_cc/test/cppapi/CMakeLists.txt @@ -3,11 +3,12 @@ project(test) set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(SOURCE_PATH /root/Softwares/deepmd-pytorch/) -find_package (Eigen3 REQUIRED) +find_package(Eigen3 REQUIRED) find_package(Torch REQUIRED) add_executable(test test.cpp ${SOURCE_PATH}/source/api_cc/src/common.cc) add_library(deepmd SHARED ${SOURCE_PATH}/source/api_cc/src/DeepPot.cc) -#target_link_libraries(test ${TORCH_LIBRARIES}) -include_directories(${SOURCE_PATH}/source/api_cc/include /opt/mamba/envs/dppt/include/eigen3/) +# target_link_libraries(test ${TORCH_LIBRARIES}) +include_directories(${SOURCE_PATH}/source/api_cc/include + /opt/mamba/envs/dppt/include/eigen3/) target_link_libraries(deepmd ${TORCH_LIBRARIES} Eigen3::Eigen) target_link_libraries(test ${TORCH_LIBRARIES} deepmd) diff --git a/source/api_cc/test/cppapi/test.cpp b/source/api_cc/test/cppapi/test.cpp index 214c8043..b7fe64f3 100644 --- a/source/api_cc/test/cppapi/test.cpp +++ b/source/api_cc/test/cppapi/test.cpp @@ -1,31 +1,32 @@ -#include "common.h" +// SPDX-License-Identifier: LGPL-3.0-or-later #include "DeepPot.h" +#include "common.h" #include -#include #include +#include using namespace deepmd; template -std::vector read_numbers(const std::string& filename) { - std::ifstream file(filename); - if (!file.is_open()) { - throw std::runtime_error("Failed to open file: " + filename); - } +std::vector read_numbers(const std::string &filename) { + std::ifstream file(filename); + if (!file.is_open()) { + throw std::runtime_error("Failed to open file: " + filename); + } - std::vector numbers; - std::string line; - while (std::getline(file, line)) { - try { - VALUETYPE num = std::stod(line); - numbers.push_back(num); - } catch (const std::exception& e) { - std::cerr << "Failed to parse number: " << line - << ". Skipping..." << std::endl; - } + std::vector numbers; + std::string line; + while (std::getline(file, line)) { + try { + VALUETYPE num = std::stod(line); + numbers.push_back(num); + } catch (const std::exception &e) { + std::cerr << "Failed to parse number: " << line << ". Skipping..." + << std::endl; } + } - return numbers; + return numbers; } int main() { @@ -41,16 +42,16 @@ int main() { printf("energy: %.6f\n", energy); printf("force:\n"); - for (int ii=0; ii @@ -43,19 +44,18 @@ static bool is_key(const string &input) { return false; } - -static void ana_st(double &max, - double &min, - double &sum, - const vector &vec, - const int &nloc) { - if (nloc == 0) return; +static void ana_st(double &max, double &min, double &sum, + const vector &vec, const int &nloc) { + if (nloc == 0) + return; max = vec[0]; min = vec[0]; sum = vec[0]; for (unsigned ii = 1; ii < nloc; ++ii) { - if (vec[ii] > max) max = vec[ii]; - if (vec[ii] < min) min = vec[ii]; + if (vec[ii] > max) + max = vec[ii]; + if (vec[ii] < min) + min = vec[ii]; sum += vec[ii]; } } @@ -70,7 +70,6 @@ PairDeepMD::PairDeepMD(LAMMPS *lmp) "Pair deepmd requires metal unit, please set it by \"units metal\""); } numb_models = 0; - } /* ---------------------------------------------------------------------- */ @@ -104,22 +103,19 @@ void PairDeepMD::settings(int narg, char **arg) { numb_models = models.size(); if (numb_models == 1) { // try { - deep_pot.init(std::string(arg[0])); + deep_pot.init(std::string(arg[0])); // } catch (deepmd_compat::deepmd_exception &e) { - // error->one(FLERR, e.what()); + // error->one(FLERR, e.what()); // } - } - else { + } else { // try { - deep_pot.init(std::string(arg[0])); - deep_pot_model_devi.init(models); + deep_pot.init(std::string(arg[0])); + deep_pot_model_devi.init(models); // } catch (deepmd_compat::deepmd_exception &e) { - // error->one(FLERR, e.what()); + // error->one(FLERR, e.what()); // } - } - out_freq = 100; out_file = "model_devi.out"; out_each = 0; @@ -133,11 +129,13 @@ void PairDeepMD::settings(int narg, char **arg) { "Illegal pair_style command\nwrong number of parameters\n"); } if (string(arg[iarg]) == string("out_freq")) { - if (iarg + 1 >= narg) error->all(FLERR, "Illegal out_freq, not provided"); + if (iarg + 1 >= narg) + error->all(FLERR, "Illegal out_freq, not provided"); out_freq = atoi(arg[iarg + 1]); iarg += 2; } else if (string(arg[iarg]) == string("out_file")) { - if (iarg + 1 >= narg) error->all(FLERR, "Illegal out_file, not provided"); + if (iarg + 1 >= narg) + error->all(FLERR, "Illegal out_file, not provided"); out_file = string(arg[iarg + 1]); iarg += 2; } else if (string(arg[iarg]) == string("fparam")) { @@ -186,9 +184,8 @@ void PairDeepMD::settings(int narg, char **arg) { else if (string(arg[iarg]) == string("fparam_from_compute")) { for (int ii = 0; ii < 1; ++ii) { if (iarg + 1 + ii >= narg || is_key(arg[iarg + 1 + ii])) { - error->all(FLERR, - "invalid fparam_from_compute key: should be " - "fparam_from_compute compute_id(str)"); + error->all(FLERR, "invalid fparam_from_compute key: should be " + "fparam_from_compute compute_id(str)"); } } do_compute = true; @@ -222,7 +219,8 @@ void PairDeepMD::settings(int narg, char **arg) { } } - if (out_freq < 0) error->all(FLERR, "Illegal out_freq, should be >= 0"); + if (out_freq < 0) + error->all(FLERR, "Illegal out_freq, should be >= 0"); if (do_ttm && aparam.size() > 0) { error->all(FLERR, "aparam and ttm should NOT be set simultaneously"); } @@ -276,20 +274,20 @@ void PairDeepMD::compute(int eflag, int vflag) { vector dtype(nlocal); vector dbox(9, 0); - for (int ii=0; iih[0]; // xx - dbox[4] = domain->h[1]; // yy - dbox[8] = domain->h[2]; // zz - dbox[7] = domain->h[3]; // zy - dbox[6] = domain->h[4]; // zx - dbox[3] = domain->h[5]; // yx + dbox[0] = domain->h[0]; // xx + dbox[4] = domain->h[1]; // yy + dbox[8] = domain->h[2]; // zz + dbox[7] = domain->h[3]; // zy + dbox[6] = domain->h[4]; // zx + dbox[3] = domain->h[5]; // yx // int ago = numb_models > 1 ? 0 : neighbor->ago; int ago = neighbor->ago; @@ -310,12 +308,13 @@ void PairDeepMD::compute(int eflag, int vflag) { (numb_models > 1 && (out_freq > 0 && update->ntimestep % out_freq == 0)); if (single_model || multi_models_no_mod_devi) { - deep_pot.compute(dener, dforce, dvirial, dcoord, dtype, dbox); - } - else if (multi_models_mod_devi) { + deep_pot.compute(dener, dforce, dvirial, dcoord, dtype, + dbox); + } else if (multi_models_mod_devi) { vector> all_virial; vector all_energy; - deep_pot_model_devi.compute(all_energy, all_force, all_virial, dcoord, dtype, dbox); + deep_pot_model_devi.compute( + all_energy, all_force, all_virial, dcoord, dtype, dbox); dener = all_energy[0]; dforce = all_force[0]; dvirial = all_virial[0]; @@ -346,7 +345,7 @@ void PairDeepMD::compute(int eflag, int vflag) { } } MPI_Reduce(&send_v[0], &recv_v[0], 9 * numb_models, MPI_DOUBLE, MPI_SUM, - 0, world); + 0, world); std::vector> all_virial_1(numb_models); std::vector avg_virial, std_virial; for (int kk = 0; kk < numb_models; ++kk) { @@ -356,14 +355,14 @@ void PairDeepMD::compute(int eflag, int vflag) { } } double all_v_min = numeric_limits::max(), all_v_max = 0, - all_v_avg = 0; + all_v_avg = 0; if (rank == 0) { deep_pot_model_devi.compute_avg(avg_virial, all_virial_1); deep_pot_model_devi.compute_std(std_virial, avg_virial, all_virial_1, 1); if (out_rel_v == 1) { deep_pot_model_devi.compute_relative_std(std_virial, avg_virial, - eps_v, 1); + eps_v, 1); } for (int ii = 0; ii < 9; ++ii) { if (std_virial[ii] > all_v_max) { @@ -378,9 +377,9 @@ void PairDeepMD::compute(int eflag, int vflag) { } if (rank == 0) { fp << setw(12) << update->ntimestep << " " << setw(18) << all_v_max - << " " << setw(18) << all_v_min << " " << setw(18) << all_v_avg - << " " << setw(18) << all_f_max << " " << setw(18) << all_f_min - << " " << setw(18) << all_f_avg; + << " " << setw(18) << all_v_min << " " << setw(18) << all_v_avg + << " " << setw(18) << all_f_max << " " << setw(18) << all_f_min + << " " << setw(18) << all_f_avg; } if (out_each == 1) { vector std_f_all(all_nlocal); @@ -414,10 +413,9 @@ void PairDeepMD::compute(int eflag, int vflag) { } } - - for (int ii=0; ii 5) error->all(FLERR, "Incorrect args for pair coefficients"); - if (!allocated) allocate(); + // if (narg < 4 || narg > 5) error->all(FLERR, "Incorrect args for pair + // coefficients"); + if (!allocated) + allocate(); int ilo, ihi, jlo, jhi; utils::bounds(FLERR, arg[0], 1, atom->ntypes, ilo, ihi, error); @@ -458,10 +458,10 @@ void PairDeepMD::coeff(int narg, char **arg) { } } - if (count == 0) error->all(FLERR, "Incorrect args for pair coefficients"); + if (count == 0) + error->all(FLERR, "Incorrect args for pair coefficients"); } - /* ---------------------------------------------------------------------- */ void PairDeepMD::allocate() { diff --git a/source/lmp/pair_deepmd.h b/source/lmp/pair_deepmd.h index d6313c72..2621f7e0 100644 --- a/source/lmp/pair_deepmd.h +++ b/source/lmp/pair_deepmd.h @@ -1,3 +1,4 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later #ifdef PAIR_CLASS // clang-format off PairStyle(deepmd,PairDeepMD); @@ -7,16 +8,15 @@ PairStyle(deepmd,PairDeepMD); #ifndef LMP_PAIR_DEEPMD_H #define LMP_PAIR_DEEPMD_H -#include "pair.h" #include "DeepPot.h" +#include "pair.h" namespace deepmd_compat = deepmd; - namespace LAMMPS_NS { class PairDeepMD : public Pair { - public: +public: PairDeepMD(class LAMMPS *); ~PairDeepMD() override; void compute(int, int) override; @@ -55,18 +55,18 @@ class PairDeepMD : public Pair { // std::vector get_file_content( // const std::vector &models); - protected: +protected: virtual void allocate(); double **scale; - private: +private: deepmd_compat::DeepPot deep_pot; deepmd_compat::DeepPotModelDevi deep_pot_model_devi; unsigned numb_models; double cutoff; int numb_types; int numb_types_spin; - std::vector > all_force; + std::vector> all_force; std::ofstream fp; int out_freq; std::string out_file; @@ -84,7 +84,7 @@ class PairDeepMD : public Pair { int extend_inum; std::vector extend_ilist; std::vector extend_numneigh; - std::vector > extend_neigh; + std::vector> extend_neigh; std::vector extend_firstneigh; std::vector extend_dcoord; std::vector extend_dtype; @@ -112,7 +112,7 @@ class PairDeepMD : public Pair { std::vector type_idx_map; }; -} // namespace LAMMPS_NS +} // namespace LAMMPS_NS #endif #endif diff --git a/tests/AlMgCu/input_AlCuMg.json b/tests/AlMgCu/input_AlCuMg.json index 12ea903a..0d0ccc39 100644 --- a/tests/AlMgCu/input_AlCuMg.json +++ b/tests/AlMgCu/input_AlCuMg.json @@ -1,7509 +1,7511 @@ { - "model": { - "descriptor": { - "type": "se_e2_a", - "sel": [ - 40, 40, 40 - ], - "rcut_smth": 2.0, - "rcut": 9.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 12, - "seed": 1801819940, - "_activation_function": "tanh" + "model": { + "descriptor": { + "type": "se_e2_a", + "sel": [ + 40, + 40, + 40 + ], + "rcut_smth": 2.0, + "rcut": 9.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 12, + "seed": 1801819940, + "_activation_function": "tanh" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "_coord_norm": true, + "_type_fitting_net": false, + "seed": 2375417769, + "_activation_function": "tanh" + }, + "type_map": [ + "Al", + "Cu", + "Mg" + ] }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "_coord_norm": true, - "_type_fitting_net": false, - "seed": 2375417769, - "_activation_function": "tanh" + "learning_rate": { + "type": "exp", + "start_lr": 0.001, + "decay_steps": 80000, + "stop_lr": 5e-08, + "_decay_rate": 0.95 }, - "type_map": [ - "Al", - "Cu", - "Mg" - ] - }, - "learning_rate": { - "type": "exp", - "start_lr": 0.001, - "decay_steps": 80000, - "stop_lr": 5e-08, - "_decay_rate": 0.95 - }, - "loss": { - "start_pref_e": 0.02, - "limit_pref_e": 2, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0.01, - "limit_pref_v": 1 - }, - "training": { - "training_data": { - "systems": [ - "data/AlMgCu/data/init.000", - "data/AlMgCu/data/init.001", - "data/AlMgCu/data/init.002", - "data/AlMgCu/data/init.003", - "data/AlMgCu/data/init.004", - "data/AlMgCu/data/init.005", - "data/AlMgCu/data/init.006", - "data/AlMgCu/data/init.007", - "data/AlMgCu/data/init.009", - "data/AlMgCu/data/init.010", - "data/AlMgCu/data/init.011", - "data/AlMgCu/data/init.012", - "data/AlMgCu/data/init.014", - "data/AlMgCu/data/init.015", - "data/AlMgCu/data/init.016", - "data/AlMgCu/data/init.017", - "data/AlMgCu/data/init.018", - "data/AlMgCu/data/init.019", - "data/AlMgCu/data/init.020", - "data/AlMgCu/data/init.021", - "data/AlMgCu/data/init.022", - "data/AlMgCu/data/init.023", - "data/AlMgCu/data/init.024", - "data/AlMgCu/data/init.025", - "data/AlMgCu/data/init.026", - "data/AlMgCu/data/init.027", - "data/AlMgCu/data/init.028", - "data/AlMgCu/data/init.030", - "data/AlMgCu/data/init.032", - "data/AlMgCu/data/init.033", - "data/AlMgCu/data/init.034", - "data/AlMgCu/data/init.035", - "data/AlMgCu/data/init.036", - "data/AlMgCu/data/init.037", - "data/AlMgCu/data/init.038", - "data/AlMgCu/data/init.039", - "data/AlMgCu/data/init.040", - "data/AlMgCu/data/init.041", - "data/AlMgCu/data/init.042", - "data/AlMgCu/data/init.043", - "data/AlMgCu/data/init.044", - "data/AlMgCu/data/init.045", - "data/AlMgCu/data/init.046", - "data/AlMgCu/data/init.047", - "data/AlMgCu/data/init.048", - "data/AlMgCu/data/init.049", - "data/AlMgCu/data/init.050", - "data/AlMgCu/data/init.051", - "data/AlMgCu/data/init.056", - "data/AlMgCu/data/init.057", - "data/AlMgCu/data/init.058", - "data/AlMgCu/data/init.059", - "data/AlMgCu/data/init.060", - "data/AlMgCu/data/init.061", - "data/AlMgCu/data/init.062", - "data/AlMgCu/data/init.063", - "data/AlMgCu/data/init.064", - "data/AlMgCu/data/init.065", - "data/AlMgCu/data/init.067", - "data/AlMgCu/data/init.068", - "data/AlMgCu/data/init.069", - "data/AlMgCu/data/init.070", - "data/AlMgCu/data/init.071", - "data/AlMgCu/data/init.072", - "data/AlMgCu/data/init.073", - "data/AlMgCu/data/init.074", - "data/AlMgCu/data/init.075", - "data/AlMgCu/data/init.076", - "data/AlMgCu/data/init.078", - "data/AlMgCu/data/init.079", - "data/AlMgCu/data/init.080", - "data/AlMgCu/data/init.082", - 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"data/AlMgCu/data/init.1016", - "data/AlMgCu/data/init.1017", - "data/AlMgCu/data/init.1018", - "data/AlMgCu/data/init.1019", - "data/AlMgCu/data/init.102", - "data/AlMgCu/data/init.1020", - "data/AlMgCu/data/init.1021", - "data/AlMgCu/data/init.1023", - "data/AlMgCu/data/init.1025", - "data/AlMgCu/data/init.1026", - "data/AlMgCu/data/init.1027", - "data/AlMgCu/data/init.1028", - "data/AlMgCu/data/init.1029", - "data/AlMgCu/data/init.103", - "data/AlMgCu/data/init.1030", - "data/AlMgCu/data/init.1031", - "data/AlMgCu/data/init.1032", - "data/AlMgCu/data/init.1033", - "data/AlMgCu/data/init.1035", - "data/AlMgCu/data/init.1037", - "data/AlMgCu/data/init.1038", - "data/AlMgCu/data/init.1039", - "data/AlMgCu/data/init.104", - "data/AlMgCu/data/init.1040", - "data/AlMgCu/data/init.1041", - "data/AlMgCu/data/init.1042", - "data/AlMgCu/data/init.1043", - "data/AlMgCu/data/init.1044", - "data/AlMgCu/data/init.1045", - "data/AlMgCu/data/init.1046", - "data/AlMgCu/data/init.1048", - 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false, + "profiling_file": "timeline.json" + } +} diff --git a/tests/AlMgCu/job.json b/tests/AlMgCu/job.json index 2ccb1783..5f5f5984 100644 --- a/tests/AlMgCu/job.json +++ b/tests/AlMgCu/job.json @@ -1,11 +1,14 @@ { - "job_name": "torch AlMgCu", - "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/AlMgCu/input_AlCuMg.json >tmp_log 2>&1", - "log_file": "tmp_log", - "backward_files": ["lcurve.out", "model.ckpt"], - "project_id": 11176, - "platform": "ali", - "machine_type": "c12_m92_1 * NVIDIA V100", - "job_type": "container", - "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" -} \ No newline at end of file + "job_name": "torch AlMgCu", + "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/AlMgCu/input_AlCuMg.json >tmp_log 2>&1", + "log_file": "tmp_log", + "backward_files": [ + "lcurve.out", + "model.ckpt" + ], + "project_id": 11176, + "platform": "ali", + "machine_type": "c12_m92_1 * NVIDIA V100", + "job_type": "container", + "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" +} diff --git a/tests/Cu/job.json b/tests/Cu/job.json index ea883101..e716b68c 100644 --- a/tests/Cu/job.json +++ b/tests/Cu/job.json @@ -1,11 +1,14 @@ { - "job_name": "torch Cu", - "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/Cu/se_e2_a.json >tmp_log 2>&1", - "log_file": "tmp_log", - "backward_files": ["lcurve.out", "model.ckpt"], - "project_id": 11176, - "platform": "ali", - "machine_type": "c12_m92_1 * NVIDIA V100", - "job_type": "container", - "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" -} \ No newline at end of file + "job_name": "torch Cu", + "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/Cu/se_e2_a.json >tmp_log 2>&1", + "log_file": "tmp_log", + "backward_files": [ + "lcurve.out", + "model.ckpt" + ], + "project_id": 11176, + "platform": "ali", + "machine_type": "c12_m92_1 * NVIDIA V100", + "job_type": "container", + "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" +} diff --git a/tests/Cu/se_e2_a.json b/tests/Cu/se_e2_a.json index 8e1433b6..6c4b271e 100644 --- a/tests/Cu/se_e2_a.json +++ b/tests/Cu/se_e2_a.json @@ -1,71 +1,71 @@ { - "model": { - "type_map": [ - "Cu" - ], - "descriptor": { - "type": "se_e2_a", - "sel": [ - 138 - ], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 20, - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 20, - "_comment": " that's all" - }, - "data_stat_nbatch": 20, - "_comment": " that's all" + "model": { + "type_map": [ + "Cu" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 138 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 20, + "_comment": " that's all" }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 20, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "_comment": " that's all" + "data_stat_nbatch": 20, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": "data/cu_train.hdf5", + "batch_size": "auto", + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": "data/cu_train.hdf5", - "batch_size": "auto", - "_comment": "that's all" - }, - "validation_data": { - "systems": "data/cu_test.hdf5", - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "numb_steps": 1000000, - "seed": 20, - "disp_file": "lcurve.out", - "disp_freq": 2, - "save_freq": 10000, - "_comment": "that's all" + "validation_data": { + "systems": "data/cu_test.hdf5", + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" }, + "numb_steps": 1000000, + "seed": 20, + "disp_file": "lcurve.out", + "disp_freq": 2, + "save_freq": 10000, "_comment": "that's all" + }, + "_comment": "that's all" } diff --git a/tests/LiGePS/0310.json b/tests/LiGePS/0310.json index 2c75e23f..15f1c199 100644 --- a/tests/LiGePS/0310.json +++ b/tests/LiGePS/0310.json @@ -1,119 +1,124 @@ { - "model": { - "descriptor": { - "type": "se_e2_a", - "sel": [30, 30, 30, 30], - "rcut_smth": 2.0, - "rcut": 9.0, - "neuron": [ - 36, - 72, - 144 - ], - "resnet_dt": false, - "axis_neuron": 12, - "attn": 128, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "seed": 10, - "_activation_function": "tanh" + "model": { + "descriptor": { + "type": "se_e2_a", + "sel": [ + 30, + 30, + 30, + 30 + ], + "rcut_smth": 2.0, + "rcut": 9.0, + "neuron": [ + 36, + 72, + 144 + ], + "resnet_dt": false, + "axis_neuron": 12, + "attn": 128, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "seed": 10, + "_activation_function": "tanh" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "_coord_norm": true, + "_type_fitting_net": false, + "seed": 10, + "_activation_function": "tanh" + }, + "type_map": [ + "Li", + "Ge", + "P", + "S" + ] }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "_coord_norm": true, - "_type_fitting_net": false, - "seed": 10, - "_activation_function": "tanh" + "learning_rate": { + "type": "exp", + "start_lr": 0.001, + "decay_steps": 80000, + "stop_lr": 5e-08, + "_decay_rate": 0.95 }, - "type_map": [ - "Li", - "Ge", - "P", - "S" - ] - }, - "learning_rate": { - "type": "exp", - "start_lr": 0.001, - "decay_steps": 80000, - "stop_lr": 5e-08, - "_decay_rate": 0.95 - }, - "loss": { - "start_pref_e": 0.02, - "limit_pref_e": 2, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0.01, - "limit_pref_v": 1 - }, - "training": { - "training_data": { - "systems": [ - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000000-data.000", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000001-data.001", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000002-data.002", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000003-data.003", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000007-data.007", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000008-data.008", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000008-data.016", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000009-data.009", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000010-data.010", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000010-data.018", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000011-data.011", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000011-data.019", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.012", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023", - "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.000", - "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.001", - "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.002", - "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.003", - "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.000", - "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.001", - "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.002", - "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.003", - "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.000", - "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.001", - "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.002", - "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.003" - ], - "batch_size": "auto" + "loss": { + "start_pref_e": 0.02, + "limit_pref_e": 2, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0.01, + "limit_pref_v": 1 }, - "validation_data": { - "systems": [ - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000004-data.004", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000005-data.005", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000006-data.006", - "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000009-data.017" - ], - "batch_size": 1, - "numb_btch": 4, - "_comment": "that's all" - }, - "numb_steps": 2000000, - "seed": 10, - "_comment": "that's all", - "disp_file": "lcurve.out", - "disp_freq": 2000, - "numb_test": 1, - "save_freq": 2000, - "save_ckpt": "model.ckpt", - "disp_training": true, - "time_training": true, - "profiling": false, - "profiling_file": "timeline.json" - } + "training": { + "training_data": { + "systems": [ + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000000-data.000", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000001-data.001", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000002-data.002", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000003-data.003", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000007-data.007", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000008-data.008", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000008-data.016", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000009-data.009", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000010-data.010", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000010-data.018", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000011-data.011", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000011-data.019", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.012", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023", + "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.000", + "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.001", + "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.002", + "data/LiGePS-SSE-PBE/data/iter.000000/02.fp/data.003", + "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.000", + "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.001", + "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.002", + "data/LiGePS-SSE-PBE/data/iter.000001/02.fp/data.003", + "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.000", + "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.001", + "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.002", + "data/LiGePS-SSE-PBE/data/iter.000002/02.fp/data.003" + ], + "batch_size": "auto" + }, + "validation_data": { + "systems": [ + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000004-data.004", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000005-data.005", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000006-data.006", + "data/LiGePS-SSE-PBE/data/home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000009-data.017" + ], + "batch_size": 1, + "numb_btch": 4, + "_comment": "that's all" + }, + "numb_steps": 2000000, + "seed": 10, + "_comment": "that's all", + "disp_file": "lcurve.out", + "disp_freq": 2000, + "numb_test": 1, + "save_freq": 2000, + "save_ckpt": "model.ckpt", + "disp_training": true, + "time_training": true, + "profiling": false, + "profiling_file": "timeline.json" + } } diff --git a/tests/LiGePS/job.json b/tests/LiGePS/job.json index a801e065..b7ddec56 100644 --- a/tests/LiGePS/job.json +++ b/tests/LiGePS/job.json @@ -1,11 +1,14 @@ { - "job_name": "torch LiGePS", - "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/LiGePS/0310.json >tmp_log 2>&1", - "log_file": "tmp_log", - "backward_files": ["lcurve.out", "model.ckpt"], - "project_id": 11176, - "platform": "ali", - "machine_type": "c12_m92_1 * NVIDIA V100", - "job_type": "container", - "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" -} \ No newline at end of file + "job_name": "torch LiGePS", + "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/LiGePS/0310.json >tmp_log 2>&1", + "log_file": "tmp_log", + "backward_files": [ + "lcurve.out", + "model.ckpt" + ], + "project_id": 11176, + "platform": "ali", + "machine_type": "c12_m92_1 * NVIDIA V100", + "job_type": "container", + "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" +} diff --git a/tests/__init__.py b/tests/__init__.py index e69de29b..6ceb116d 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/tests/models/dpa1.json b/tests/models/dpa1.json index 56b39ba2..dd838ac6 100644 --- a/tests/models/dpa1.json +++ b/tests/models/dpa1.json @@ -1,35 +1,39 @@ { "type_map": [ - "O", - "H" + "O", + "H" ], "descriptor": { - "type": "se_atten", - "sel": 30, - "rcut_smth": 2.0, - "rcut": 6.0, - "neuron": [2, 4, 8 ], - "axis_neuron": 4, - "attn": 5, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 10, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0 + "type": "se_atten", + "sel": 30, + "rcut_smth": 2.0, + "rcut": 6.0, + "neuron": [ + 2, + 4, + 8 + ], + "axis_neuron": 4, + "attn": 5, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 10, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0 }, "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1 + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 } } diff --git a/tests/models/dpa2.json b/tests/models/dpa2.json index 3ef7dc5f..8b9c7358 100644 --- a/tests/models/dpa2.json +++ b/tests/models/dpa2.json @@ -1,44 +1,48 @@ { "type_map": [ - "O", - "H" + "O", + "H" ], "descriptor": { - "type": "dpa2", - "repinit_rcut": 6.0, - "repinit_rcut_smth": 2.0, - "repinit_nsel": 30, - "repformer_rcut": 4.0, - "repformer_rcut_smth": 0.5, - "repformer_nsel": 10, - "repinit_neuron": [2, 4, 8], - "repinit_axis_neuron": 4, - "repinit_activation": "tanh", - "repformer_nlayers": 12, - "repformer_g1_dim": 8, - "repformer_g2_dim": 5, - "repformer_attn2_hidden": 3, - "repformer_attn2_nhead": 1, - "repformer_attn1_hidden": 5, - "repformer_attn1_nhead": 1, - "repformer_axis_dim": 4, - "repformer_update_h2": false, - "repformer_update_g1_has_conv": true, - "repformer_update_g1_has_grrg": true, - "repformer_update_g1_has_drrd": true, - "repformer_update_g1_has_attn": true, - "repformer_update_g2_has_g1g1": true, - "repformer_update_g2_has_attn": true, - "repformer_attn2_has_gate": true, - "repformer_add_type_ebd_to_seq": false + "type": "dpa2", + "repinit_rcut": 6.0, + "repinit_rcut_smth": 2.0, + "repinit_nsel": 30, + "repformer_rcut": 4.0, + "repformer_rcut_smth": 0.5, + "repformer_nsel": 10, + "repinit_neuron": [ + 2, + 4, + 8 + ], + "repinit_axis_neuron": 4, + "repinit_activation": "tanh", + "repformer_nlayers": 12, + "repformer_g1_dim": 8, + "repformer_g2_dim": 5, + "repformer_attn2_hidden": 3, + "repformer_attn2_nhead": 1, + "repformer_attn1_hidden": 5, + "repformer_attn1_nhead": 1, + "repformer_axis_dim": 4, + "repformer_update_h2": false, + "repformer_update_g1_has_conv": true, + "repformer_update_g1_has_grrg": true, + "repformer_update_g1_has_drrd": true, + "repformer_update_g1_has_attn": true, + "repformer_update_g2_has_g1g1": true, + "repformer_update_g2_has_attn": true, + "repformer_attn2_has_gate": true, + "repformer_add_type_ebd_to_seq": false }, "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1 + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 } } diff --git a/tests/models/dpa2_hyb.json b/tests/models/dpa2_hyb.json index c3bf0cac..b5d53b02 100644 --- a/tests/models/dpa2_hyb.json +++ b/tests/models/dpa2_hyb.json @@ -1,69 +1,69 @@ { "type_map": [ - "O", - "H" + "O", + "H" ], "descriptor": { - "type": "hybrid", - "hybrid_mode": "sequential", - "list": [ - { - "type": "se_atten", - "sel": 30, - "rcut_smth": 2.0, - "rcut": 6.0, - "neuron": [ - 2, - 4, - 8 - ], - "axis_neuron": 4, - "attn": 5, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 10, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": true, - "temperature": 1.0 - }, - { - "type": "se_uni", - "sel": 10, - "rcut_smth": 0.5, - "rcut": 4.0, - "nlayers": 12, - "g1_dim": 8, - "g2_dim": 5, - "attn2_hidden": 3, - "attn2_nhead": 1, - "attn1_hidden": 5, - "attn1_nhead": 1, - "axis_dim": 4, - "update_h2": false, - "update_g1_has_conv": true, - "update_g1_has_grrg": true, - "update_g1_has_drrd": true, - "update_g1_has_attn": true, - "update_g2_has_g1g1": true, - "update_g2_has_attn": true, - "attn2_has_gate": true, - "add_type_ebd_to_seq": false, - "smooth": true - } - ] + "type": "hybrid", + "hybrid_mode": "sequential", + "list": [ + { + "type": "se_atten", + "sel": 30, + "rcut_smth": 2.0, + "rcut": 6.0, + "neuron": [ + 2, + 4, + 8 + ], + "axis_neuron": 4, + "attn": 5, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false, + "post_ln": true, + "ffn": false, + "ffn_embed_dim": 10, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": true, + "temperature": 1.0 + }, + { + "type": "se_uni", + "sel": 10, + "rcut_smth": 0.5, + "rcut": 4.0, + "nlayers": 12, + "g1_dim": 8, + "g2_dim": 5, + "attn2_hidden": 3, + "attn2_nhead": 1, + "attn1_hidden": 5, + "attn1_nhead": 1, + "axis_dim": 4, + "update_h2": false, + "update_g1_has_conv": true, + "update_g1_has_grrg": true, + "update_g1_has_drrd": true, + "update_g1_has_attn": true, + "update_g2_has_g1g1": true, + "update_g2_has_attn": true, + "attn2_has_gate": true, + "add_type_ebd_to_seq": false, + "smooth": true + } + ] }, "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1 + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 } } diff --git a/tests/test_LKF.py b/tests/test_LKF.py index 5c0adecb..393a6799 100644 --- a/tests/test_LKF.py +++ b/tests/test_LKF.py @@ -1,6 +1,9 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest -from deepmd_pt.entrypoints.main import main +from deepmd_pt.entrypoints.main import ( + main, +) class TestLKF(unittest.TestCase): @@ -8,5 +11,5 @@ def test_lkf(self): main(["train", "tests/water/lkf.json"]) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_autodiff.py b/tests/test_autodiff.py index 6c915c4b..442603cb 100644 --- a/tests/test_autodiff.py +++ b/tests/test_autodiff.py @@ -1,20 +1,28 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import copy -import torch import unittest + import numpy as np -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model +import torch + +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) dtype = torch.float64 from .test_permutation import ( - model_se_e2_a, - model_dpa1, - model_dpa2, - eval_model, - make_sample, + eval_model, + make_sample, + model_dpa1, + model_dpa2, + model_se_e2_a, ) + # from deepmd-kit repo def finite_difference(f, x, delta=1e-6): in_shape = x.shape @@ -29,6 +37,7 @@ def finite_difference(f, x, delta=1e-6): res[(Ellipsis, *idx)] = (y1p - y1n) / (2 * delta) return res + def stretch_box(old_coord, old_box, new_box): ocoord = old_coord.reshape(-1, 3) obox = old_box.reshape(3, 3) @@ -37,111 +46,145 @@ def stretch_box(old_coord, old_box, new_box): return ncoord.reshape(old_coord.shape) -class TestForce(): - def test( - self, - ): - places = 8 - delta = 1e-5 - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype) - cell = (cell + cell.T) + 5. * torch.eye(3) - coord = torch.rand([natoms, 3], dtype=dtype) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]) - # assumes input to be numpy tensor - coord = coord.numpy() - def np_infer( - coord, +class TestForce: + def test( + self, ): - e0, f0, v0 = eval_model(self.model, torch.tensor(coord).unsqueeze(0), cell.unsqueeze(0), atype) - ret = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - # detach - ret = {kk: ret[kk].detach().cpu().numpy() for kk in ret} - return ret - ff = lambda coord: np_infer(coord)["energy"] - fdf = -finite_difference(ff, coord, delta=delta).squeeze() - rff = np_infer(coord)["force"] - np.testing.assert_almost_equal(fdf, rff, decimal=places) - - -class TestVirial(): - def test( - self, - ): - places = 8 - delta = 1e-4 - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype) - cell = (cell) + 5. * torch.eye(3) - coord = torch.rand([natoms, 3], dtype=dtype) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]) - # assumes input to be numpy tensor - coord = coord.numpy() - cell = cell.numpy() - def np_infer( - new_cell, + places = 8 + delta = 1e-5 + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype) + cell = (cell + cell.T) + 5.0 * torch.eye(3) + coord = torch.rand([natoms, 3], dtype=dtype) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]) + # assumes input to be numpy tensor + coord = coord.numpy() + + def np_infer( + coord, + ): + e0, f0, v0 = eval_model( + self.model, torch.tensor(coord).unsqueeze(0), cell.unsqueeze(0), atype + ) + ret = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + # detach + ret = {kk: ret[kk].detach().cpu().numpy() for kk in ret} + return ret + + def ff(_coord): + return np_infer(_coord)["energy"] + + fdf = -finite_difference(ff, coord, delta=delta).squeeze() + rff = np_infer(coord)["force"] + np.testing.assert_almost_equal(fdf, rff, decimal=places) + + +class TestVirial: + def test( + self, ): - e0, f0, v0 = eval_model(self.model, torch.tensor(stretch_box(coord, cell, new_cell)).unsqueeze(0), torch.tensor(new_cell).unsqueeze(0), atype) - ret = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - # detach - ret = {kk: ret[kk].detach().cpu().numpy() for kk in ret} - return ret - ff = lambda bb: np_infer(bb)["energy"] - fdv = -(finite_difference(ff, cell, delta=delta)\ - .transpose(0, 2, 1) @ cell).squeeze() - rfv = np_infer(cell)["virial"] - np.testing.assert_almost_equal(fdv, rfv, decimal=places) - + places = 8 + delta = 1e-4 + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype) + cell = (cell) + 5.0 * torch.eye(3) + coord = torch.rand([natoms, 3], dtype=dtype) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]) + # assumes input to be numpy tensor + coord = coord.numpy() + cell = cell.numpy() + + def np_infer( + new_cell, + ): + e0, f0, v0 = eval_model( + self.model, + torch.tensor(stretch_box(coord, cell, new_cell)).unsqueeze(0), + torch.tensor(new_cell).unsqueeze(0), + atype, + ) + ret = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + # detach + ret = {kk: ret[kk].detach().cpu().numpy() for kk in ret} + return ret + + def ff(bb): + return np_infer(bb)["energy"] + + fdv = -( + finite_difference(ff, cell, delta=delta).transpose(0, 2, 1) @ cell + ).squeeze() + rfv = np_infer(cell)["virial"] + np.testing.assert_almost_equal(fdv, rfv, decimal=places) + class TestEnergyModelSeAForce(unittest.TestCase, TestForce): - def setUp(self): - model_params = copy.deepcopy(model_se_e2_a) - sampled = make_sample(model_params) - self.type_split = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + sampled = make_sample(model_params) + self.type_split = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestEnergyModelSeAVirial(unittest.TestCase, TestVirial): - def setUp(self): - model_params = copy.deepcopy(model_se_e2_a) - sampled = make_sample(model_params) - self.type_split = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + sampled = make_sample(model_params) + self.type_split = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestEnergyModelDPA1Force(unittest.TestCase, TestForce): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestEnergyModelDPA1Virial(unittest.TestCase, TestVirial): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestEnergyModelDPA2Force(unittest.TestCase, TestForce): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - -class TestEnergyModelDPAUniVirial(unittest.TestCase, TestVirial): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) +class TestEnergyModelDPAUniVirial(unittest.TestCase, TestVirial): + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) diff --git a/tests/test_calculator.py b/tests/test_calculator.py index 7cf4fe07..7018b4ac 100644 --- a/tests/test_calculator.py +++ b/tests/test_calculator.py @@ -1,12 +1,18 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import json -import os -import torch import unittest -from copy import deepcopy +from copy import ( + deepcopy, +) + +import torch -from deepmd_pt.entrypoints.main import get_trainer -from deepmd_pt.infer import inference -from deepmd_pt.utils.ase_calc import DPCalculator +from deepmd_pt.entrypoints.main import ( + get_trainer, +) +from deepmd_pt.utils.ase_calc import ( + DPCalculator, +) dtype = torch.float64 @@ -14,11 +20,13 @@ class TestCalculator(unittest.TestCase): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 - self.config["training"]["validation_data"]["systems"] = ["tests/water/data/single"] + self.config["training"]["validation_data"]["systems"] = [ + "tests/water/data/single" + ] self.input_json = "test_dp_test.json" with open(self.input_json, "w") as fp: json.dump(self.config, fp, indent=4) @@ -32,7 +40,9 @@ def setUp(self): self.calculator = DPCalculator("model.pt") def test_calculator(self): - from ase import Atoms + from ase import ( + Atoms, + ) natoms = 5 cell = torch.eye(3, dtype=dtype) * 10 @@ -53,7 +63,10 @@ def test_calculator(self): calculator=self.calculator, ) e0, f0 = ase_atoms0.get_potential_energy(), ase_atoms0.get_forces() - s0, v0 = ase_atoms0.get_stress(voigt=True), -ase_atoms0.get_stress(voigt=False) * ase_atoms0.get_volume() + s0, v0 = ( + ase_atoms0.get_stress(voigt=True), + -ase_atoms0.get_stress(voigt=False) * ase_atoms0.get_volume(), + ) ase_atoms1 = Atoms( numbers=[atomic_numbers[i] for i in idx_perm], @@ -63,9 +76,12 @@ def test_calculator(self): calculator=self.calculator, ) e1, f1 = ase_atoms1.get_potential_energy(), ase_atoms1.get_forces() - s1, v1 = ase_atoms1.get_stress(voigt=True), -ase_atoms1.get_stress(voigt=False) * ase_atoms1.get_volume() + s1, v1 = ( + ase_atoms1.get_stress(voigt=True), + -ase_atoms1.get_stress(voigt=False) * ase_atoms1.get_volume(), + ) - assert type(e0) == float + assert isinstance(e0, float) assert f0.shape == (natoms, 3) assert v0.shape == (3, 3) torch.testing.assert_close(e0, e1, rtol=low_prec, atol=prec) diff --git a/tests/test_deeppot.py b/tests/test_deeppot.py index 3f678d08..3749e842 100644 --- a/tests/test_deeppot.py +++ b/tests/test_deeppot.py @@ -1,22 +1,36 @@ -from copy import deepcopy +# SPDX-License-Identifier: LGPL-3.0-or-later import json import unittest -from pathlib import Path +from copy import ( + deepcopy, +) +from pathlib import ( + Path, +) import numpy as np -from deepmd_pt.entrypoints.main import get_trainer -from deepmd_pt.infer.deep_eval import DeepPot + +from deepmd_pt.entrypoints.main import ( + get_trainer, +) +from deepmd_pt.infer.deep_eval import ( + DeepPot, +) class TestDeepPot(unittest.TestCase): def setUp(self): input_json = str(Path(__file__).parent / "water/se_atten.json") - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 - self.config["training"]["training_data"]["systems"] = [str(Path(__file__).parent / "water/data/single")] - self.config["training"]["validation_data"]["systems"] = [str(Path(__file__).parent / "water/data/single")] + self.config["training"]["training_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] + self.config["training"]["validation_data"]["systems"] = [ + str(Path(__file__).parent / "water/data/single") + ] self.input_json = "test_dp_test.json" with open(self.input_json, "w") as fp: json.dump(self.config, fp, indent=4) @@ -30,14 +44,38 @@ def setUp(self): def test_dp_test(self): dp = DeepPot(str(self.model)) - cell = np.array([ - 5.122106549439247480e+00,4.016537340154059388e-01,6.951654033828678081e-01, - 4.016537340154059388e-01,6.112136112297989143e+00,8.178091365465004481e-01, - 6.951654033828678081e-01,8.178091365465004481e-01,6.159552512682983760e+00, - ]).reshape(1, 3, 3) - coord = np.array([ - 2.978060152121375648e+00,3.588469695887098077e+00,2.792459820604495491e+00,3.895592322591093115e+00,2.712091020667753760e+00,1.366836847133650501e+00,9.955616170888935690e-01,4.121324820711413039e+00,1.817239061889086571e+00,3.553661462345699906e+00,5.313046969500791583e+00,6.635182659098815883e+00,6.088601018589653080e+00,6.575011420004332585e+00,6.825240650611076099e+00 - ]).reshape(1, -1, 3) + cell = np.array( + [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + ).reshape(1, 3, 3) + coord = np.array( + [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + ).reshape(1, -1, 3) atype = np.array([0, 0, 0, 1, 1]).reshape(1, -1) e, f, v, ae, av = dp.eval(coord, cell, atype, atomic=True) diff --git a/tests/test_descriptor.py b/tests/test_descriptor.py index cc2ccc00..0c902da4 100644 --- a/tests/test_descriptor.py +++ b/tests/test_descriptor.py @@ -1,21 +1,38 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import os -import torch import unittest +import numpy as np import tensorflow.compat.v1 as tf +import torch tf.disable_eager_execution() -from deepmd.env import op_module - -from deepmd_pt.utils import dp_random -from deepmd_pt.utils.dataset import DeepmdDataSet -from deepmd_pt.model.descriptor import prod_env_mat_se_a -from deepmd_pt.utils.env import * -from deepmd.common import expand_sys_str import json +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.env import ( + op_module, +) + +from deepmd_pt.model.descriptor import ( + prod_env_mat_se_a, +) +from deepmd_pt.utils import ( + dp_random, +) +from deepmd_pt.utils.dataset import ( + DeepmdDataSet, +) +from deepmd_pt.utils.env import ( + DEVICE, + GLOBAL_NP_FLOAT_PRECISION, + GLOBAL_PT_FLOAT_PRECISION, + TEST_CONFIG, +) + CUR_DIR = os.path.dirname(__file__) @@ -27,80 +44,91 @@ def base_se_a(rcut, rcut_smth, sel, batch, mean, stddev): atype = tf.placeholder(tf.int32, [None, None]) natoms_vec = tf.placeholder(tf.int32, [None]) default_mesh = tf.placeholder(tf.int32, [None]) - stat_descrpt, descrpt_deriv, rij, nlist \ - = op_module.prod_env_mat_a(coord, - atype, - natoms_vec, - box, - default_mesh, - tf.constant(mean), - tf.constant(stddev), - rcut_a=-1., - rcut_r=rcut, - rcut_r_smth=rcut_smth, - sel_a=sel, - sel_r=[0 for i in sel]) + stat_descrpt, descrpt_deriv, rij, nlist = op_module.prod_env_mat_a( + coord, + atype, + natoms_vec, + box, + default_mesh, + tf.constant(mean), + tf.constant(stddev), + rcut_a=-1.0, + rcut_r=rcut, + rcut_r_smth=rcut_smth, + sel_a=sel, + sel_r=[0 for i in sel], + ) net_deriv_reshape = tf.ones_like(stat_descrpt) - force = op_module.prod_force_se_a(net_deriv_reshape, - descrpt_deriv, - nlist, - natoms_vec, - n_a_sel=sum(sel), - n_r_sel=0) + force = op_module.prod_force_se_a( + net_deriv_reshape, + descrpt_deriv, + nlist, + natoms_vec, + n_a_sel=sum(sel), + n_r_sel=0, + ) with tf.Session(graph=g) as sess: - return sess.run([stat_descrpt, force, nlist], feed_dict={ - coord: batch['coord'], - box: batch['box'], - natoms_vec: batch['natoms'], - atype: batch['atype'], - default_mesh: np.array([0, 0, 0, 2, 2, 2]) - }) + return sess.run( + [stat_descrpt, force, nlist], + feed_dict={ + coord: batch["coord"], + box: batch["box"], + natoms_vec: batch["natoms"], + atype: batch["atype"], + default_mesh: np.array([0, 0, 0, 2, 2, 2]), + }, + ) class TestSeA(unittest.TestCase): - def setUp(self): dp_random.seed(20) - with open(TEST_CONFIG, 'r') as fin: + with open(TEST_CONFIG) as fin: content = fin.read() config = json.loads(content) - model_config = config['model'] - self.rcut = model_config['descriptor']['rcut'] - self.rcut_smth = model_config['descriptor']['rcut_smth'] - self.sel = model_config['descriptor']['sel'] - self.bsz = config['training']['training_data']['batch_size'] - self.systems = config['training']['validation_data']['systems'] + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.bsz = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] if isinstance(self.systems, str): self.systems = expand_sys_str(self.systems) - ds = DeepmdDataSet(self.systems, self.bsz, model_config['type_map'], self.rcut, self.sel) + ds = DeepmdDataSet( + self.systems, self.bsz, model_config["type_map"], self.rcut, self.sel + ) self.np_batch, self.pt_batch = ds.get_batch() self.sec = np.cumsum(self.sel) self.ntypes = len(self.sel) self.nnei = sum(self.sel) def test_consistency(self): - avg_zero = torch.zeros([self.ntypes, self.nnei * 4], dtype=GLOBAL_PT_FLOAT_PRECISION) - std_ones = torch.ones([self.ntypes, self.nnei * 4], dtype=GLOBAL_PT_FLOAT_PRECISION) + avg_zero = torch.zeros( + [self.ntypes, self.nnei * 4], dtype=GLOBAL_PT_FLOAT_PRECISION + ) + std_ones = torch.ones( + [self.ntypes, self.nnei * 4], dtype=GLOBAL_PT_FLOAT_PRECISION + ) base_d, base_force, nlist = base_se_a( rcut=self.rcut, rcut_smth=self.rcut_smth, sel=self.sel, batch=self.np_batch, mean=avg_zero, - stddev=std_ones + stddev=std_ones, ) - pt_coord = self.pt_batch['coord'] + pt_coord = self.pt_batch["coord"] pt_coord.requires_grad_(True) - index = self.pt_batch['mapping'].unsqueeze(-1).expand(-1, -1, 3) + index = self.pt_batch["mapping"].unsqueeze(-1).expand(-1, -1, 3) extended_coord = torch.gather(pt_coord, dim=1, index=index) - extended_coord = extended_coord - self.pt_batch['shift'] + extended_coord = extended_coord - self.pt_batch["shift"] my_d, _, _ = prod_env_mat_se_a( extended_coord.to(DEVICE), - self.pt_batch['nlist'], - self.pt_batch['atype'], + self.pt_batch["nlist"], + self.pt_batch["atype"], avg_zero.reshape([-1, self.nnei, 4]).to(DEVICE), std_ones.reshape([-1, self.nnei, 4]).to(DEVICE), self.rcut, @@ -114,8 +142,8 @@ def test_consistency(self): my_d = my_d.view(bsz, -1, self.nnei, 4).cpu().detach().numpy() nlist = nlist.reshape(bsz, -1, self.nnei) - mapping = self.pt_batch['mapping'].cpu() - my_nlist = self.pt_batch['nlist'].view(bsz, -1).cpu() + mapping = self.pt_batch["mapping"].cpu() + my_nlist = self.pt_batch["nlist"].view(bsz, -1).cpu() mask = my_nlist == -1 my_nlist = my_nlist * ~mask my_nlist = torch.gather(mapping, dim=-1, index=my_nlist) @@ -128,5 +156,5 @@ def test_consistency(self): self.assertTrue(np.allclose(base_force, -my_force)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_descriptor_dpa1.py b/tests/test_descriptor_dpa1.py index a0957dbf..f68b6af7 100644 --- a/tests/test_descriptor_dpa1.py +++ b/tests/test_descriptor_dpa1.py @@ -1,17 +1,30 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import os -import torch import unittest -import json -from typing import Optional -from pathlib import Path +from pathlib import ( + Path, +) -from deepmd_pt.model.descriptor import DescrptBlockSeAtten, DescrptDPA1 -from deepmd_pt.utils import env -from deepmd_pt.utils.region import normalize_coord -from deepmd_pt.utils.nlist import extend_coord_with_ghosts, build_neighbor_list -from deepmd_pt.model.network import TypeEmbedNet -from deepmd_pt.model.model.ener import process_nlist, process_nlist_gathered +import torch + +from deepmd_pt.model.descriptor import ( + DescrptBlockSeAtten, + DescrptDPA1, +) +from deepmd_pt.model.network.network import ( + TypeEmbedNet, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.nlist import ( + build_neighbor_list, + extend_coord_with_ghosts, +) +from deepmd_pt.utils.region import ( + normalize_coord, +) dtype = torch.float64 torch.set_default_dtype(dtype) @@ -20,133 +33,318 @@ class TestDPA1(unittest.TestCase): - def setUp(self): - cell = [ - 5.122106549439247480e+00,4.016537340154059388e-01,6.951654033828678081e-01, - 4.016537340154059388e-01,6.112136112297989143e+00,8.178091365465004481e-01, - 6.951654033828678081e-01,8.178091365465004481e-01,6.159552512682983760e+00, - ] - self.cell = torch.Tensor(cell).view(1,3,3).to(env.DEVICE) - coord = [ - 2.978060152121375648e+00,3.588469695887098077e+00,2.792459820604495491e+00,3.895592322591093115e+00,2.712091020667753760e+00,1.366836847133650501e+00,9.955616170888935690e-01,4.121324820711413039e+00,1.817239061889086571e+00,3.553661462345699906e+00,5.313046969500791583e+00,6.635182659098815883e+00,6.088601018589653080e+00,6.575011420004332585e+00,6.825240650611076099e+00 - ] - self.coord = torch.Tensor(coord).view(1,-1,3).to(env.DEVICE) - self.atype = torch.IntTensor([0, 0, 0, 1, 1]).view(1,-1).to(env.DEVICE) - self.ref_d = torch.Tensor([ - 8.382518544113587780e-03,-3.390120566088597812e-03,6.145981571114964362e-03,-4.880300873973819273e-03,-3.390120566088597812e-03,1.372540996564941464e-03,-2.484163690574096341e-03,1.972313058658722688e-03,6.145981571114964362e-03,-2.484163690574096341e-03,4.507748738021747671e-03,-3.579717194906019764e-03,-4.880300873973819273e-03,1.972313058658722688e-03,-3.579717194906019764e-03,2.842794615687799838e-03,6.733043802494966066e-04,-2.721540313345096771e-04,4.936158526085561134e-04,-3.919743287822345223e-04,-1.311123004527576900e-02,5.301179352601203924e-03,-9.614612349318877454e-03,7.634884975521277241e-03,8.877088452901006621e-03,-3.590945566653638409e-03,6.508042782015627942e-03,-5.167671664327699171e-03,-2.697241463040870365e-03,1.091350446825975137e-03,-1.976895708961905022e-03,1.569671412121975348e-03,8.645131636261189911e-03,-3.557395265621639355e-03,6.298048561552698106e-03,-4.999272007935521948e-03,-3.557395265621639355e-03,1.467866637220284964e-03,-2.587004431651147504e-03,2.052752235601402672e-03,6.298048561552698106e-03,-2.587004431651147504e-03,4.594085551315935101e-03,-3.647656549789176847e-03,-4.999272007935521948e-03,2.052752235601402672e-03,-3.647656549789176847e-03,2.896359275520481256e-03,6.689620176492027878e-04,-2.753606422414641049e-04,4.864958810186969444e-04,-3.860599754167503119e-04,-1.349238259226558101e-02,5.547478630961994242e-03,-9.835472300819447095e-03,7.808197926069362048e-03,9.220744348752592245e-03,-3.795799103392961601e-03,6.716516319358462918e-03,-5.331265718473574867e-03,-2.783836698392940304e-03,1.147461939123531121e-03,-2.025013030986024063e-03,1.606944814423778541e-03,9.280385723343491378e-03,-3.515852178447095942e-03,7.085282215778941628e-03,-5.675852414643783178e-03,-3.515852178447095942e-03,1.337760635271160884e-03,-2.679428786337713451e-03,2.145400621815936413e-03,7.085282215778941628e-03,-2.679428786337713451e-03,5.414439648102228192e-03,-4.338426468139268931e-03,-5.675852414643783178e-03,2.145400621815936413e-03,-4.338426468139268931e-03,3.476467482674507146e-03,7.166961981167455130e-04,-2.697932188839837972e-04,5.474643906631899504e-04,-4.386556623669893621e-04,-1.480434821331240956e-02,5.604647062899507579e-03,-1.130745349141585449e-02,9.059113563516829268e-03,9.758791063112262978e-03,-3.701477720487638626e-03,7.448215522796466058e-03,-5.966057584545172120e-03,-2.845102393948158344e-03,1.078743584169829543e-03,-2.170093031447992756e-03,1.738010461687942770e-03,9.867599071916231118e-03,-3.811041717688905522e-03,7.121877634386481262e-03,-5.703120290113914553e-03,-3.811041717688905522e-03,1.474046183772771213e-03,-2.747386907428428938e-03,2.199711055637492037e-03,7.121877634386481262e-03,-2.747386907428428938e-03,5.145050639440944609e-03,-4.120642824501622239e-03,-5.703120290113914553e-03,2.199711055637492037e-03,-4.120642824501622239e-03,3.300262321758350853e-03,1.370499995344566383e-03,-5.313041843655797901e-04,9.860110343046961986e-04,-7.892505817954784597e-04,-1.507686316307561489e-02,5.818961290579217904e-03,-1.088774506142304276e-02,8.719460408506790952e-03,9.764630842803939323e-03,-3.770134041110058572e-03,7.049438389985595785e-03,-5.645302934019884485e-03,-3.533582373572779437e-03,1.367148320603491559e-03,-2.546602904764623705e-03,2.038882844528267305e-03,7.448297038731285964e-03,-2.924276815200288742e-03,5.355960540523636154e-03,-4.280386435083473329e-03,-2.924276815200288742e-03,1.150311064893848757e-03,-2.100635980860638373e-03,1.678427895009850001e-03,5.355960540523636154e-03,-2.100635980860638373e-03,3.853607053247790071e-03,-3.080076301871465493e-03,-4.280386435083473329e-03,1.678427895009850001e-03,-3.080076301871465493e-03,2.461876613756722523e-03,9.730712866459405395e-04,-3.821759579990726546e-04,6.994242056622360787e-04,-5.589662297882965055e-04,-1.138916742131982317e-02,4.469391132927387489e-03,-8.192016282448397885e-03,6.547234460517113892e-03,7.460070829043288082e-03,-2.929867802018087421e-03,5.363646855497249989e-03,-4.286347242903034739e-03,-2.643569023340565718e-03,1.038826463247002245e-03,-1.899910089750410976e-03,1.518237240362583541e-03 - ]).to(env.DEVICE) - with open(Path(CUR_DIR)/"models"/"dpa1.json") as fp: - self.model_json = json.load(fp) - self.file_model_param = Path(CUR_DIR)/"models"/"dpa1.pth" - self.file_type_embed = Path(CUR_DIR)/"models"/"dpa2_tebd.pth" - - def test_descriptor_block(self): - # torch.manual_seed(0) - model_dpa1 = self.model_json - dparams = model_dpa1["descriptor"] - ntypes = len(model_dpa1["type_map"]) - assert "se_atten" == dparams.pop("type") - dparams["ntypes"] = ntypes - des = DescrptBlockSeAtten( - **dparams, - ) - des.load_state_dict(torch.load(self.file_model_param)) - rcut = dparams["rcut"] - nsel = dparams["sel"] - coord = self.coord - atype = self.atype - box = self.cell - nf, nloc = coord.shape[:2] - coord_normalized = normalize_coord( - coord, box.reshape(-1, 3, 3)) - extended_coord, extended_atype, mapping = extend_coord_with_ghosts( - coord_normalized, atype, box, rcut) - # single nlist - nlist = build_neighbor_list( - extended_coord, extended_atype, nloc, - rcut, nsel, distinguish_types=False) - # handel type_embedding - type_embedding = TypeEmbedNet(ntypes, 8) - type_embedding.load_state_dict(torch.load(self.file_type_embed)) + def setUp(self): + cell = [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + self.cell = torch.Tensor(cell).view(1, 3, 3).to(env.DEVICE) + coord = [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + self.coord = torch.Tensor(coord).view(1, -1, 3).to(env.DEVICE) + self.atype = torch.IntTensor([0, 0, 0, 1, 1]).view(1, -1).to(env.DEVICE) + self.ref_d = torch.Tensor( + [ + 8.382518544113587780e-03, + -3.390120566088597812e-03, + 6.145981571114964362e-03, + -4.880300873973819273e-03, + -3.390120566088597812e-03, + 1.372540996564941464e-03, + -2.484163690574096341e-03, + 1.972313058658722688e-03, + 6.145981571114964362e-03, + -2.484163690574096341e-03, + 4.507748738021747671e-03, + -3.579717194906019764e-03, + -4.880300873973819273e-03, + 1.972313058658722688e-03, + -3.579717194906019764e-03, + 2.842794615687799838e-03, + 6.733043802494966066e-04, + -2.721540313345096771e-04, + 4.936158526085561134e-04, + -3.919743287822345223e-04, + -1.311123004527576900e-02, + 5.301179352601203924e-03, + -9.614612349318877454e-03, + 7.634884975521277241e-03, + 8.877088452901006621e-03, + -3.590945566653638409e-03, + 6.508042782015627942e-03, + -5.167671664327699171e-03, + -2.697241463040870365e-03, + 1.091350446825975137e-03, + -1.976895708961905022e-03, + 1.569671412121975348e-03, + 8.645131636261189911e-03, + -3.557395265621639355e-03, + 6.298048561552698106e-03, + -4.999272007935521948e-03, + -3.557395265621639355e-03, + 1.467866637220284964e-03, + -2.587004431651147504e-03, + 2.052752235601402672e-03, + 6.298048561552698106e-03, + -2.587004431651147504e-03, + 4.594085551315935101e-03, + -3.647656549789176847e-03, + -4.999272007935521948e-03, + 2.052752235601402672e-03, + -3.647656549789176847e-03, + 2.896359275520481256e-03, + 6.689620176492027878e-04, + -2.753606422414641049e-04, + 4.864958810186969444e-04, + -3.860599754167503119e-04, + -1.349238259226558101e-02, + 5.547478630961994242e-03, + -9.835472300819447095e-03, + 7.808197926069362048e-03, + 9.220744348752592245e-03, + -3.795799103392961601e-03, + 6.716516319358462918e-03, + -5.331265718473574867e-03, + -2.783836698392940304e-03, + 1.147461939123531121e-03, + -2.025013030986024063e-03, + 1.606944814423778541e-03, + 9.280385723343491378e-03, + -3.515852178447095942e-03, + 7.085282215778941628e-03, + -5.675852414643783178e-03, + -3.515852178447095942e-03, + 1.337760635271160884e-03, + -2.679428786337713451e-03, + 2.145400621815936413e-03, + 7.085282215778941628e-03, + -2.679428786337713451e-03, + 5.414439648102228192e-03, + -4.338426468139268931e-03, + -5.675852414643783178e-03, + 2.145400621815936413e-03, + -4.338426468139268931e-03, + 3.476467482674507146e-03, + 7.166961981167455130e-04, + -2.697932188839837972e-04, + 5.474643906631899504e-04, + -4.386556623669893621e-04, + -1.480434821331240956e-02, + 5.604647062899507579e-03, + -1.130745349141585449e-02, + 9.059113563516829268e-03, + 9.758791063112262978e-03, + -3.701477720487638626e-03, + 7.448215522796466058e-03, + -5.966057584545172120e-03, + -2.845102393948158344e-03, + 1.078743584169829543e-03, + -2.170093031447992756e-03, + 1.738010461687942770e-03, + 9.867599071916231118e-03, + -3.811041717688905522e-03, + 7.121877634386481262e-03, + -5.703120290113914553e-03, + -3.811041717688905522e-03, + 1.474046183772771213e-03, + -2.747386907428428938e-03, + 2.199711055637492037e-03, + 7.121877634386481262e-03, + -2.747386907428428938e-03, + 5.145050639440944609e-03, + -4.120642824501622239e-03, + -5.703120290113914553e-03, + 2.199711055637492037e-03, + -4.120642824501622239e-03, + 3.300262321758350853e-03, + 1.370499995344566383e-03, + -5.313041843655797901e-04, + 9.860110343046961986e-04, + -7.892505817954784597e-04, + -1.507686316307561489e-02, + 5.818961290579217904e-03, + -1.088774506142304276e-02, + 8.719460408506790952e-03, + 9.764630842803939323e-03, + -3.770134041110058572e-03, + 7.049438389985595785e-03, + -5.645302934019884485e-03, + -3.533582373572779437e-03, + 1.367148320603491559e-03, + -2.546602904764623705e-03, + 2.038882844528267305e-03, + 7.448297038731285964e-03, + -2.924276815200288742e-03, + 5.355960540523636154e-03, + -4.280386435083473329e-03, + -2.924276815200288742e-03, + 1.150311064893848757e-03, + -2.100635980860638373e-03, + 1.678427895009850001e-03, + 5.355960540523636154e-03, + -2.100635980860638373e-03, + 3.853607053247790071e-03, + -3.080076301871465493e-03, + -4.280386435083473329e-03, + 1.678427895009850001e-03, + -3.080076301871465493e-03, + 2.461876613756722523e-03, + 9.730712866459405395e-04, + -3.821759579990726546e-04, + 6.994242056622360787e-04, + -5.589662297882965055e-04, + -1.138916742131982317e-02, + 4.469391132927387489e-03, + -8.192016282448397885e-03, + 6.547234460517113892e-03, + 7.460070829043288082e-03, + -2.929867802018087421e-03, + 5.363646855497249989e-03, + -4.286347242903034739e-03, + -2.643569023340565718e-03, + 1.038826463247002245e-03, + -1.899910089750410976e-03, + 1.518237240362583541e-03, + ] + ).to(env.DEVICE) + with open(Path(CUR_DIR) / "models" / "dpa1.json") as fp: + self.model_json = json.load(fp) + self.file_model_param = Path(CUR_DIR) / "models" / "dpa1.pth" + self.file_type_embed = Path(CUR_DIR) / "models" / "dpa2_tebd.pth" - ## to save model parameters - # torch.save(des.state_dict(), 'model_weights.pth') - # torch.save(type_embedding.state_dict(), 'model_weights.pth') - descriptor, env_mat, diff, rot_mat, sw = \ - des( - nlist, - extended_coord, - extended_atype, - type_embedding(extended_atype), - mapping=None, - ) - # np.savetxt('tmp.out', descriptor.detach().numpy().reshape(1,-1), delimiter=",") - self.assertEqual(descriptor.shape[-1], des.get_dim_out()) - self.assertAlmostEqual(6., des.get_rcut()) - self.assertEqual(30, des.get_nsel()) - self.assertEqual(2, des.get_ntype()) - torch.testing.assert_close(descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10) + def test_descriptor_block(self): + # torch.manual_seed(0) + model_dpa1 = self.model_json + dparams = model_dpa1["descriptor"] + ntypes = len(model_dpa1["type_map"]) + assert "se_atten" == dparams.pop("type") + dparams["ntypes"] = ntypes + des = DescrptBlockSeAtten( + **dparams, + ) + des.load_state_dict(torch.load(self.file_model_param)) + rcut = dparams["rcut"] + nsel = dparams["sel"] + coord = self.coord + atype = self.atype + box = self.cell + nf, nloc = coord.shape[:2] + coord_normalized = normalize_coord(coord, box.reshape(-1, 3, 3)) + extended_coord, extended_atype, mapping = extend_coord_with_ghosts( + coord_normalized, atype, box, rcut + ) + # single nlist + nlist = build_neighbor_list( + extended_coord, extended_atype, nloc, rcut, nsel, distinguish_types=False + ) + # handel type_embedding + type_embedding = TypeEmbedNet(ntypes, 8) + type_embedding.load_state_dict(torch.load(self.file_type_embed)) + ## to save model parameters + # torch.save(des.state_dict(), 'model_weights.pth') + # torch.save(type_embedding.state_dict(), 'model_weights.pth') + descriptor, env_mat, diff, rot_mat, sw = des( + nlist, + extended_coord, + extended_atype, + type_embedding(extended_atype), + mapping=None, + ) + # np.savetxt('tmp.out', descriptor.detach().numpy().reshape(1,-1), delimiter=",") + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + self.assertAlmostEqual(6.0, des.get_rcut()) + self.assertEqual(30, des.get_nsel()) + self.assertEqual(2, des.get_ntype()) + torch.testing.assert_close( + descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10 + ) - def test_descriptor(self): - with open(Path(CUR_DIR)/"models"/"dpa1.json") as fp: - self.model_json = json.load(fp) - model_dpa2 = self.model_json - ntypes = len(model_dpa2["type_map"]) - dparams = model_dpa2["descriptor"] - dparams["ntypes"] = ntypes - assert dparams.pop("type") == "se_atten" - dparams["concat_output_tebd"] = False - des = DescrptDPA1( - **dparams, - ) - target_dict = des.state_dict() - source_dict = torch.load(self.file_model_param) - type_embd_dict = torch.load(self.file_type_embed) - target_dict = translate_se_atten_and_type_embd_dicts_to_dpa1( - target_dict, - source_dict, - type_embd_dict, - ) - des.load_state_dict(target_dict) + def test_descriptor(self): + with open(Path(CUR_DIR) / "models" / "dpa1.json") as fp: + self.model_json = json.load(fp) + model_dpa2 = self.model_json + ntypes = len(model_dpa2["type_map"]) + dparams = model_dpa2["descriptor"] + dparams["ntypes"] = ntypes + assert dparams.pop("type") == "se_atten" + dparams["concat_output_tebd"] = False + des = DescrptDPA1( + **dparams, + ) + target_dict = des.state_dict() + source_dict = torch.load(self.file_model_param) + type_embd_dict = torch.load(self.file_type_embed) + target_dict = translate_se_atten_and_type_embd_dicts_to_dpa1( + target_dict, + source_dict, + type_embd_dict, + ) + des.load_state_dict(target_dict) - coord = self.coord - atype = self.atype - box = self.cell - nf, nloc = coord.shape[:2] - coord_normalized = normalize_coord( - coord, box.reshape(-1, 3, 3)) - extended_coord, extended_atype, mapping = extend_coord_with_ghosts( - coord_normalized, atype, box, des.get_rcut()) - nlist = build_neighbor_list( - extended_coord, extended_atype, nloc, - des.get_rcut(), des.get_nsel(), distinguish_types=False, - ) - descriptor, env_mat, diff, rot_mat, sw = \ - des( - extended_coord, - extended_atype, - nlist, - mapping=mapping, - ) - self.assertEqual(descriptor.shape[-1], des.get_dim_out()) - self.assertAlmostEqual(6., des.get_rcut()) - self.assertEqual(30, des.get_nsel()) - self.assertEqual(2, des.get_ntype()) - torch.testing.assert_close(descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10) + coord = self.coord + atype = self.atype + box = self.cell + nf, nloc = coord.shape[:2] + coord_normalized = normalize_coord(coord, box.reshape(-1, 3, 3)) + extended_coord, extended_atype, mapping = extend_coord_with_ghosts( + coord_normalized, atype, box, des.get_rcut() + ) + nlist = build_neighbor_list( + extended_coord, + extended_atype, + nloc, + des.get_rcut(), + des.get_nsel(), + distinguish_types=False, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + self.assertAlmostEqual(6.0, des.get_rcut()) + self.assertEqual(30, des.get_nsel()) + self.assertEqual(2, des.get_ntype()) + torch.testing.assert_close( + descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10 + ) - dparams["concat_output_tebd"] = True - des = DescrptDPA1( - **dparams, - ) - descriptor, env_mat, diff, rot_mat, sw = \ - des( - extended_coord, - extended_atype, - nlist, - mapping=mapping, - ) - self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + dparams["concat_output_tebd"] = True + des = DescrptDPA1( + **dparams, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) def translate_se_atten_and_type_embd_dicts_to_dpa1( @@ -154,16 +352,16 @@ def translate_se_atten_and_type_embd_dicts_to_dpa1( source_dict, type_embd_dict, ): - all_keys = list(target_dict.keys()) - record = [False for ii in all_keys] - for kk, vv in source_dict.items(): - tk = "se_atten." + kk + all_keys = list(target_dict.keys()) + record = [False for ii in all_keys] + for kk, vv in source_dict.items(): + tk = "se_atten." + kk + record[all_keys.index(tk)] = True + target_dict[tk] = vv + assert len(type_embd_dict.keys()) == 1 + kk = next(iter(type_embd_dict.keys())) + tk = "type_embedding." + kk record[all_keys.index(tk)] = True - target_dict[tk] = vv - assert len(type_embd_dict.keys()) == 1 - kk = [ii for ii in type_embd_dict.keys()][0] - tk = "type_embedding." + kk - record[all_keys.index(tk)] = True - target_dict[tk] = type_embd_dict[kk] - assert(all(record)) - return target_dict + target_dict[tk] = type_embd_dict[kk] + assert all(record) + return target_dict diff --git a/tests/test_descriptor_dpa2.py b/tests/test_descriptor_dpa2.py index 37228808..d1154623 100644 --- a/tests/test_descriptor_dpa2.py +++ b/tests/test_descriptor_dpa2.py @@ -1,17 +1,30 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import os -import torch import unittest -import json -from typing import Optional -from pathlib import Path +from pathlib import ( + Path, +) + +import torch -from deepmd_pt.model.descriptor import DescrptBlockHybrid, DescrptDPA2 -from deepmd_pt.utils import env -from deepmd_pt.utils.region import normalize_coord -from deepmd_pt.utils.nlist import extend_coord_with_ghosts, build_neighbor_list -from deepmd_pt.model.network import TypeEmbedNet -from deepmd_pt.model.model.ener import process_nlist, process_nlist_gathered +from deepmd_pt.model.descriptor import ( + DescrptBlockHybrid, + DescrptDPA2, +) +from deepmd_pt.model.network.network import ( + TypeEmbedNet, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.nlist import ( + build_neighbor_list, + extend_coord_with_ghosts, +) +from deepmd_pt.utils.region import ( + normalize_coord, +) dtype = torch.float64 torch.set_default_dtype(dtype) @@ -20,146 +33,213 @@ class TestDPA2(unittest.TestCase): - def setUp(self): - cell = [ - 5.122106549439247480e+00,4.016537340154059388e-01,6.951654033828678081e-01, - 4.016537340154059388e-01,6.112136112297989143e+00,8.178091365465004481e-01, - 6.951654033828678081e-01,8.178091365465004481e-01,6.159552512682983760e+00, - ] - self.cell = torch.Tensor(cell).view(1,3,3).to(env.DEVICE) - coord = [ - 2.978060152121375648e+00,3.588469695887098077e+00,2.792459820604495491e+00,3.895592322591093115e+00,2.712091020667753760e+00,1.366836847133650501e+00,9.955616170888935690e-01,4.121324820711413039e+00,1.817239061889086571e+00,3.553661462345699906e+00,5.313046969500791583e+00,6.635182659098815883e+00,6.088601018589653080e+00,6.575011420004332585e+00,6.825240650611076099e+00 - ] - self.coord = torch.Tensor(coord).view(1,-1,3).to(env.DEVICE) - self.atype = torch.IntTensor([0, 0, 0, 1, 1]).view(1,-1).to(env.DEVICE) - self.ref_d = torch.Tensor([ - 8.435412613327306630e-01,-4.717109614540972440e-01,-1.812643456954206256e+00,-2.315248767961955167e-01,-7.112973006771171613e-01,-4.162041919507591392e-01,-1.505159810095323181e+00,-1.191652416985768403e-01,8.439214937875325617e-01,-4.712976890460106594e-01,-1.812605149396642856e+00,-2.307222236291133766e-01,-7.115427800870099961e-01,-4.164729253167227530e-01,-1.505483119125936797e+00,-1.191288524278367872e-01,8.286420823261241297e-01,-4.535033763979030574e-01,-1.787877160970498425e+00,-1.961763875645104460e-01,-7.475459187804838201e-01,-5.231446874663764346e-01,-1.488399984491664219e+00,-3.974117581747104583e-02,8.283793431613817315e-01,-4.551551577556525729e-01,-1.789253136645859943e+00,-1.977673627726055372e-01,-7.448826048241211639e-01,-5.161350182531234676e-01,-1.487589463573479209e+00,-4.377376017839779143e-02,8.295404560710329944e-01,-4.492219258475603216e-01,-1.784484611185287450e+00,-1.901182059718481143e-01,-7.537407667483000395e-01,-5.384371277650709109e-01,-1.490368056268364549e+00,-3.073744832541754762e-02 - ]).to(env.DEVICE) - with open(Path(CUR_DIR)/"models"/"dpa2_hyb.json") as fp: - self.model_json = json.load(fp) - self.file_model_param = Path(CUR_DIR)/"models"/"dpa2.pth" - self.file_type_embed = Path(CUR_DIR)/"models"/"dpa2_tebd.pth" + def setUp(self): + cell = [ + 5.122106549439247480e00, + 4.016537340154059388e-01, + 6.951654033828678081e-01, + 4.016537340154059388e-01, + 6.112136112297989143e00, + 8.178091365465004481e-01, + 6.951654033828678081e-01, + 8.178091365465004481e-01, + 6.159552512682983760e00, + ] + self.cell = torch.Tensor(cell).view(1, 3, 3).to(env.DEVICE) + coord = [ + 2.978060152121375648e00, + 3.588469695887098077e00, + 2.792459820604495491e00, + 3.895592322591093115e00, + 2.712091020667753760e00, + 1.366836847133650501e00, + 9.955616170888935690e-01, + 4.121324820711413039e00, + 1.817239061889086571e00, + 3.553661462345699906e00, + 5.313046969500791583e00, + 6.635182659098815883e00, + 6.088601018589653080e00, + 6.575011420004332585e00, + 6.825240650611076099e00, + ] + self.coord = torch.Tensor(coord).view(1, -1, 3).to(env.DEVICE) + self.atype = torch.IntTensor([0, 0, 0, 1, 1]).view(1, -1).to(env.DEVICE) + self.ref_d = torch.Tensor( + [ + 8.435412613327306630e-01, + -4.717109614540972440e-01, + -1.812643456954206256e00, + -2.315248767961955167e-01, + -7.112973006771171613e-01, + -4.162041919507591392e-01, + -1.505159810095323181e00, + -1.191652416985768403e-01, + 8.439214937875325617e-01, + -4.712976890460106594e-01, + -1.812605149396642856e00, + -2.307222236291133766e-01, + -7.115427800870099961e-01, + -4.164729253167227530e-01, + -1.505483119125936797e00, + -1.191288524278367872e-01, + 8.286420823261241297e-01, + -4.535033763979030574e-01, + -1.787877160970498425e00, + -1.961763875645104460e-01, + -7.475459187804838201e-01, + -5.231446874663764346e-01, + -1.488399984491664219e00, + -3.974117581747104583e-02, + 8.283793431613817315e-01, + -4.551551577556525729e-01, + -1.789253136645859943e00, + -1.977673627726055372e-01, + -7.448826048241211639e-01, + -5.161350182531234676e-01, + -1.487589463573479209e00, + -4.377376017839779143e-02, + 8.295404560710329944e-01, + -4.492219258475603216e-01, + -1.784484611185287450e00, + -1.901182059718481143e-01, + -7.537407667483000395e-01, + -5.384371277650709109e-01, + -1.490368056268364549e00, + -3.073744832541754762e-02, + ] + ).to(env.DEVICE) + with open(Path(CUR_DIR) / "models" / "dpa2_hyb.json") as fp: + self.model_json = json.load(fp) + self.file_model_param = Path(CUR_DIR) / "models" / "dpa2.pth" + self.file_type_embed = Path(CUR_DIR) / "models" / "dpa2_tebd.pth" - def test_descriptor_hyb(self): - # torch.manual_seed(0) - model_hybrid_dpa2 = self.model_json - dparams = model_hybrid_dpa2["descriptor"] - ntypes = len(model_hybrid_dpa2["type_map"]) - dlist = dparams.pop("list") - des = DescrptBlockHybrid( - dlist, - ntypes, - hybrid_mode=dparams["hybrid_mode"], - ) - model_dict = torch.load(self.file_model_param) - # type_embd of repformer is removed - model_dict.pop("descriptor_list.1.type_embd.embedding.weight") - des.load_state_dict(model_dict) - all_rcut = [ii["rcut"] for ii in dlist] - all_nsel = [ii["sel"] for ii in dlist] - rcut_max = max(all_rcut) - coord = self.coord - atype = self.atype - box = self.cell - nf, nloc = coord.shape[:2] - coord_normalized = normalize_coord( - coord, box.reshape(-1, 3, 3)) - extended_coord, extended_atype, mapping = extend_coord_with_ghosts( - coord_normalized, atype, box, rcut_max) - ## single nlist - # nlist = build_neighbor_list( - # extended_coord, extended_atype, nloc, - # rcut_max, nsel, distinguish_types=False) - nlist_list = [] - for rcut, sel in zip(all_rcut, all_nsel): - nlist_list.append( - build_neighbor_list( - extended_coord, - extended_atype, - nloc, - rcut, - sel, - distinguish_types=False, - )) - nlist = torch.cat(nlist_list, -1) - # handel type_embedding - type_embedding = TypeEmbedNet(ntypes, 8) - type_embedding.load_state_dict(torch.load(self.file_type_embed)) + def test_descriptor_hyb(self): + # torch.manual_seed(0) + model_hybrid_dpa2 = self.model_json + dparams = model_hybrid_dpa2["descriptor"] + ntypes = len(model_hybrid_dpa2["type_map"]) + dlist = dparams.pop("list") + des = DescrptBlockHybrid( + dlist, + ntypes, + hybrid_mode=dparams["hybrid_mode"], + ) + model_dict = torch.load(self.file_model_param) + # type_embd of repformer is removed + model_dict.pop("descriptor_list.1.type_embd.embedding.weight") + des.load_state_dict(model_dict) + all_rcut = [ii["rcut"] for ii in dlist] + all_nsel = [ii["sel"] for ii in dlist] + rcut_max = max(all_rcut) + coord = self.coord + atype = self.atype + box = self.cell + nf, nloc = coord.shape[:2] + coord_normalized = normalize_coord(coord, box.reshape(-1, 3, 3)) + extended_coord, extended_atype, mapping = extend_coord_with_ghosts( + coord_normalized, atype, box, rcut_max + ) + ## single nlist + # nlist = build_neighbor_list( + # extended_coord, extended_atype, nloc, + # rcut_max, nsel, distinguish_types=False) + nlist_list = [] + for rcut, sel in zip(all_rcut, all_nsel): + nlist_list.append( + build_neighbor_list( + extended_coord, + extended_atype, + nloc, + rcut, + sel, + distinguish_types=False, + ) + ) + nlist = torch.cat(nlist_list, -1) + # handel type_embedding + type_embedding = TypeEmbedNet(ntypes, 8) + type_embedding.load_state_dict(torch.load(self.file_type_embed)) - ## to save model parameters - # torch.save(des.state_dict(), 'model_weights.pth') - # torch.save(type_embedding.state_dict(), 'model_weights.pth') - descriptor, env_mat, diff, rot_mat, sw = \ - des( - nlist, - extended_coord, - extended_atype, - type_embedding(extended_atype), - mapping=mapping, - ) - torch.testing.assert_close(descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10) + ## to save model parameters + # torch.save(des.state_dict(), 'model_weights.pth') + # torch.save(type_embedding.state_dict(), 'model_weights.pth') + descriptor, env_mat, diff, rot_mat, sw = des( + nlist, + extended_coord, + extended_atype, + type_embedding(extended_atype), + mapping=mapping, + ) + torch.testing.assert_close( + descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10 + ) + def test_descriptor(self): + with open(Path(CUR_DIR) / "models" / "dpa2.json") as fp: + self.model_json = json.load(fp) + model_dpa2 = self.model_json + ntypes = len(model_dpa2["type_map"]) + dparams = model_dpa2["descriptor"] + dparams["ntypes"] = ntypes + assert dparams.pop("type") == "dpa2" + dparams["concat_output_tebd"] = False + des = DescrptDPA2( + **dparams, + ) + target_dict = des.state_dict() + source_dict = torch.load(self.file_model_param) + # type_embd of repformer is removed + source_dict.pop("descriptor_list.1.type_embd.embedding.weight") + type_embd_dict = torch.load(self.file_type_embed) + target_dict = translate_hybrid_and_type_embd_dicts_to_dpa2( + target_dict, + source_dict, + type_embd_dict, + ) + des.load_state_dict(target_dict) - def test_descriptor(self): - with open(Path(CUR_DIR)/"models"/"dpa2.json") as fp: - self.model_json = json.load(fp) - model_dpa2 = self.model_json - ntypes = len(model_dpa2["type_map"]) - dparams = model_dpa2["descriptor"] - dparams["ntypes"] = ntypes - assert dparams.pop("type") == "dpa2" - dparams["concat_output_tebd"] = False - des = DescrptDPA2( - **dparams, - ) - target_dict = des.state_dict() - source_dict = torch.load(self.file_model_param) - # type_embd of repformer is removed - source_dict.pop("descriptor_list.1.type_embd.embedding.weight") - type_embd_dict = torch.load(self.file_type_embed) - target_dict = translate_hybrid_and_type_embd_dicts_to_dpa2( - target_dict, - source_dict, - type_embd_dict, - ) - des.load_state_dict(target_dict) - - coord = self.coord - atype = self.atype - box = self.cell - nf, nloc = coord.shape[:2] - coord_normalized = normalize_coord( - coord, box.reshape(-1, 3, 3)) - extended_coord, extended_atype, mapping = extend_coord_with_ghosts( - coord_normalized, atype, box, des.repinit.rcut) - nlist = build_neighbor_list( - extended_coord, extended_atype, nloc, - des.repinit.rcut, des.repinit.sel, distinguish_types=False) - descriptor, env_mat, diff, rot_mat, sw = \ - des( - extended_coord, - extended_atype, - nlist, - mapping=mapping, - ) - self.assertEqual(descriptor.shape[-1], des.get_dim_out()) - self.assertAlmostEqual(6., des.get_rcut()) - self.assertEqual(30, des.get_nsel()) - self.assertEqual(2, des.get_ntype()) - torch.testing.assert_close(descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10) + coord = self.coord + atype = self.atype + box = self.cell + nf, nloc = coord.shape[:2] + coord_normalized = normalize_coord(coord, box.reshape(-1, 3, 3)) + extended_coord, extended_atype, mapping = extend_coord_with_ghosts( + coord_normalized, atype, box, des.repinit.rcut + ) + nlist = build_neighbor_list( + extended_coord, + extended_atype, + nloc, + des.repinit.rcut, + des.repinit.sel, + distinguish_types=False, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + self.assertAlmostEqual(6.0, des.get_rcut()) + self.assertEqual(30, des.get_nsel()) + self.assertEqual(2, des.get_ntype()) + torch.testing.assert_close( + descriptor.view(-1), self.ref_d, atol=1e-10, rtol=1e-10 + ) - dparams["concat_output_tebd"] = True - des = DescrptDPA2( - **dparams, - ) - descriptor, env_mat, diff, rot_mat, sw = \ - des( - extended_coord, - extended_atype, - nlist, - mapping=mapping, - ) - self.assertEqual(descriptor.shape[-1], des.get_dim_out()) + dparams["concat_output_tebd"] = True + des = DescrptDPA2( + **dparams, + ) + descriptor, env_mat, diff, rot_mat, sw = des( + extended_coord, + extended_atype, + nlist, + mapping=mapping, + ) + self.assertEqual(descriptor.shape[-1], des.get_dim_out()) def translate_hybrid_and_type_embd_dicts_to_dpa2( @@ -167,18 +247,18 @@ def translate_hybrid_and_type_embd_dicts_to_dpa2( source_dict, type_embd_dict, ): - all_keys = list(target_dict.keys()) - record = [False for ii in all_keys] - for kk, vv in source_dict.items(): - tk = kk.replace("descriptor_list.1", "repformers") - tk = tk.replace("descriptor_list.0", "repinit") - tk = tk.replace("sequential_transform.0", "g1_shape_tranform") + all_keys = list(target_dict.keys()) + record = [False for ii in all_keys] + for kk, vv in source_dict.items(): + tk = kk.replace("descriptor_list.1", "repformers") + tk = tk.replace("descriptor_list.0", "repinit") + tk = tk.replace("sequential_transform.0", "g1_shape_tranform") + record[all_keys.index(tk)] = True + target_dict[tk] = vv + assert len(type_embd_dict.keys()) == 1 + kk = next(iter(type_embd_dict.keys())) + tk = "type_embedding." + kk record[all_keys.index(tk)] = True - target_dict[tk] = vv - assert len(type_embd_dict.keys()) == 1 - kk = [ii for ii in type_embd_dict.keys()][0] - tk = "type_embedding." + kk - record[all_keys.index(tk)] = True - target_dict[tk] = type_embd_dict[kk] - assert(all(record)) - return target_dict + target_dict[tk] = type_embd_dict[kk] + assert all(record) + return target_dict diff --git a/tests/test_dp_test.py b/tests/test_dp_test.py index 3484c85f..d66e1bde 100644 --- a/tests/test_dp_test.py +++ b/tests/test_dp_test.py @@ -1,22 +1,32 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import json import os import shutil import unittest -from copy import deepcopy +from copy import ( + deepcopy, +) import numpy as np -from deepmd_pt.entrypoints.main import get_trainer -from deepmd_pt.infer import inference + +from deepmd_pt.entrypoints.main import ( + get_trainer, +) +from deepmd_pt.infer import ( + inference, +) class TestDPTest(unittest.TestCase): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 - self.config["training"]["validation_data"]["systems"] = ["tests/water/data/single"] + self.config["training"]["validation_data"]["systems"] = [ + "tests/water/data/single" + ] self.input_json = "test_dp_test.json" with open(self.input_json, "w") as fp: json.dump(self.config, fp, indent=4) @@ -25,7 +35,7 @@ def test_dp_test(self): trainer = get_trainer(deepcopy(self.config)) trainer.run() - input_dict, label_dict,_ = trainer.get_data(is_train=False) + input_dict, label_dict, _ = trainer.get_data(is_train=False) _, _, more_loss = trainer.wrapper(**input_dict, label=label_dict, cur_lr=1.0) tester = inference.Tester("model.pt", input_script=self.input_json) @@ -37,7 +47,9 @@ def test_dp_test(self): for k, v in res.items(): if k == "rmse" or "mae" in k or k not in more_loss: continue - np.testing.assert_allclose(v, more_loss[k].cpu().detach().numpy(), rtol=1e-04, atol=1e-07) + np.testing.assert_allclose( + v, more_loss[k].cpu().detach().numpy(), rtol=1e-04, atol=1e-07 + ) def tearDown(self): for f in os.listdir("."): @@ -50,5 +62,5 @@ def tearDown(self): os.remove(self.input_json) -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_embedding_net.py b/tests/test_embedding_net.py index 78ac963a..791ce245 100644 --- a/tests/test_embedding_net.py +++ b/tests/test_embedding_net.py @@ -1,21 +1,34 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import os import re -import torch -import json import unittest +import numpy as np import tensorflow.compat.v1 as tf +import torch tf.disable_eager_execution() -from deepmd.descriptor import DescrptSeA as DescrptSeA_tf - -from deepmd_pt.utils import dp_random -from deepmd_pt.utils.dataset import DeepmdDataSet -from deepmd_pt.model.descriptor import DescrptSeA -from deepmd_pt.utils.env import GLOBAL_NP_FLOAT_PRECISION, DEVICE, TEST_CONFIG -from deepmd.common import expand_sys_str +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.descriptor import DescrptSeA as DescrptSeA_tf + +from deepmd_pt.model.descriptor import ( + DescrptSeA, +) +from deepmd_pt.utils import ( + dp_random, +) +from deepmd_pt.utils.dataset import ( + DeepmdDataSet, +) +from deepmd_pt.utils.env import ( + DEVICE, + GLOBAL_NP_FLOAT_PRECISION, + TEST_CONFIG, +) CUR_DIR = os.path.dirname(__file__) @@ -27,18 +40,26 @@ def gen_key(worb, depth, elemid): def base_se_a(descriptor, coord, atype, natoms, box): g = tf.Graph() with g.as_default(): - name_pfx = 'd_sea_' - t_coord = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + 't_coord') - t_atype = tf.placeholder(tf.int32, [None, None], name=name_pfx + 't_type') - t_natoms = tf.placeholder(tf.int32, [descriptor.ntypes + 2], name=name_pfx + 't_natoms') - t_box = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + 't_box') - t_default_mesh = tf.placeholder(tf.int32, [None], name=name_pfx + 't_mesh') - t_embedding = descriptor.build(t_coord, t_atype, t_natoms, t_box, t_default_mesh, input_dict={}) + name_pfx = "d_sea_" + t_coord = tf.placeholder( + GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + "t_coord" + ) + t_atype = tf.placeholder(tf.int32, [None, None], name=name_pfx + "t_type") + t_natoms = tf.placeholder( + tf.int32, [descriptor.ntypes + 2], name=name_pfx + "t_natoms" + ) + t_box = tf.placeholder( + GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + "t_box" + ) + t_default_mesh = tf.placeholder(tf.int32, [None], name=name_pfx + "t_mesh") + t_embedding = descriptor.build( + t_coord, t_atype, t_natoms, t_box, t_default_mesh, input_dict={} + ) fake_energy = tf.reduce_sum(t_embedding) t_force = descriptor.prod_force_virial(fake_energy, t_natoms)[0] t_vars = {} for var in tf.global_variables(): - ms = re.findall(r'([a-z]+)_(\d)_(\d)', var.name) + ms = re.findall(r"([a-z]+)_(\d)_(\d)", var.name) if len(ms) == 1: m = ms[0] key = gen_key(worb=m[0], depth=int(m[1]), elemid=int(m[2])) @@ -47,34 +68,38 @@ def base_se_a(descriptor, coord, atype, natoms, box): with tf.Session(graph=g) as sess: sess.run(init_op) - embedding, force, values = sess.run([t_embedding, t_force, t_vars], feed_dict={ - t_coord: coord, - t_atype: atype, - t_natoms: natoms, - t_box: box, - t_default_mesh: np.array([0, 0, 0, 2, 2, 2]) - }) + embedding, force, values = sess.run( + [t_embedding, t_force, t_vars], + feed_dict={ + t_coord: coord, + t_atype: atype, + t_natoms: natoms, + t_box: box, + t_default_mesh: np.array([0, 0, 0, 2, 2, 2]), + }, + ) return embedding, force, values class TestSeA(unittest.TestCase): - def setUp(self): dp_random.seed(0) - with open(TEST_CONFIG, 'r') as fin: + with open(TEST_CONFIG) as fin: content = fin.read() config = json.loads(content) - model_config = config['model'] - self.rcut = model_config['descriptor']['rcut'] - self.rcut_smth = model_config['descriptor']['rcut_smth'] - self.sel = model_config['descriptor']['sel'] - self.bsz = config['training']['training_data']['batch_size'] - self.systems = config['training']['validation_data']['systems'] + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.bsz = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] if isinstance(self.systems, str): self.systems = expand_sys_str(self.systems) - ds = DeepmdDataSet(self.systems, self.bsz, model_config['type_map'], self.rcut, self.sel) - self.filter_neuron = model_config['descriptor']['neuron'] - self.axis_neuron = model_config['descriptor']['axis_neuron'] + ds = DeepmdDataSet( + self.systems, self.bsz, model_config["type_map"], self.rcut, self.sel + ) + self.filter_neuron = model_config["descriptor"]["neuron"] + self.axis_neuron = model_config["descriptor"]["axis_neuron"] self.np_batch, self.torch_batch = ds.get_batch() def test_consistency(self): @@ -88,25 +113,27 @@ def test_consistency(self): ) dp_embedding, dp_force, dp_vars = base_se_a( descriptor=dp_d, - coord=self.np_batch['coord'], - atype=self.np_batch['atype'], - natoms=self.np_batch['natoms'], - box=self.np_batch['box'], + coord=self.np_batch["coord"], + atype=self.np_batch["atype"], + natoms=self.np_batch["natoms"], + box=self.np_batch["box"], ) # Reproduced old_impl = False descriptor = DescrptSeA( - self.rcut, self.rcut_smth, self.sel, - neuron=self.filter_neuron, + self.rcut, + self.rcut_smth, + self.sel, + neuron=self.filter_neuron, axis_neuron=self.axis_neuron, old_impl=old_impl, ).to(DEVICE) for name, param in descriptor.named_parameters(): if old_impl: - ms = re.findall(r'(\d)\.deep_layers\.(\d)\.([a-z]+)', name) + ms = re.findall(r"(\d)\.deep_layers\.(\d)\.([a-z]+)", name) else: - ms = re.findall(r'(\d)\.layers\.(\d)\.([a-z]+)', name) + ms = re.findall(r"(\d)\.layers\.(\d)\.([a-z]+)", name) if len(ms) == 1: m = ms[0] key = gen_key(worb=m[2], depth=int(m[1]) + 1, elemid=int(m[0])) @@ -115,16 +142,18 @@ def test_consistency(self): # Keep parameter value consistency between 2 implentations param.data.copy_(torch.from_numpy(var)) - pt_coord = self.torch_batch['coord'] + pt_coord = self.torch_batch["coord"] pt_coord.requires_grad_(True) - index = self.torch_batch['mapping'].unsqueeze(-1).expand(-1, -1, 3) + index = self.torch_batch["mapping"].unsqueeze(-1).expand(-1, -1, 3) extended_coord = torch.gather(pt_coord, dim=1, index=index) - extended_coord = extended_coord - self.torch_batch['shift'] - extended_atype = torch.gather(self.torch_batch['atype'], dim=1, index=self.torch_batch['mapping']) + extended_coord = extended_coord - self.torch_batch["shift"] + extended_atype = torch.gather( + self.torch_batch["atype"], dim=1, index=self.torch_batch["mapping"] + ) descriptor_out, _, _, _, _ = descriptor( extended_coord, extended_atype, - self.torch_batch['nlist'], + self.torch_batch["nlist"], ) my_embedding = descriptor_out.cpu().detach().numpy() fake_energy = torch.sum(descriptor_out) @@ -137,5 +166,5 @@ def test_consistency(self): np.testing.assert_allclose(dp_force, my_force) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_env_mat.py b/tests/test_env_mat.py index 94cc0396..0033d416 100644 --- a/tests/test_env_mat.py +++ b/tests/test_env_mat.py @@ -1,24 +1,31 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest -import itertools + import numpy as np +import torch + +try: + from deepmd.model_format import ( + EnvMat, + ) -try : - from deepmd_utils.model_format import ( - EnvMat, - ) - support_env_mat = True + support_env_mat = True except ModuleNotFoundError: - support_env_mat = False + support_env_mat = False except ImportError: - support_env_mat = False + support_env_mat = False -from deepmd_pt.model.descriptor.env_mat import prod_env_mat_se_a -from deepmd_pt.utils import env +from deepmd_pt.model.descriptor.env_mat import ( + prod_env_mat_se_a, +) +from deepmd_pt.utils import ( + env, +) dtype = env.GLOBAL_PT_FLOAT_PRECISION -class TestCaseSingleFrameWithNlist(): + +class TestCaseSingleFrameWithNlist: def setUp(self): # nloc == 3, nall == 4 self.nloc = 3 @@ -45,30 +52,33 @@ def setUp(self): dtype=int, ).reshape([1, self.nloc, sum(self.sel)]) self.rcut = 0.4 - self.rcut_smth = 2.2 + self.rcut_smth = 2.2 + # to be merged with the tf test case @unittest.skipIf(not support_env_mat, "EnvMat not supported") class TestEnvMat(unittest.TestCase, TestCaseSingleFrameWithNlist): - def setUp(self): - TestCaseSingleFrameWithNlist.setUp(self) - - def test_consistency( - self, - ): - rng = np.random.default_rng() - nf, nloc, nnei = self.nlist.shape - davg = rng.normal(size=(self.nt, nnei, 4)) - dstd = rng.normal(size=(self.nt, nnei, 4)) - dstd = 0.1 + np.abs(dstd) - em0 = EnvMat(self.rcut, self.rcut_smth) - mm0, ww0 = em0.call(self.coord_ext, self.atype_ext, self.nlist, davg, dstd) - mm1, _, ww1 = prod_env_mat_se_a( - torch.tensor(self.coord_ext, dtype=dtype), - torch.tensor(self.nlist, dtype=int), - torch.tensor(self.atype_ext[:,:nloc], dtype=int), - davg, dstd, self.rcut, self.rcut_smth - ) - np.testing.assert_allclose(mm0, mm1) - np.testing.assert_allclose(ww0, ww1) + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) + def test_consistency( + self, + ): + rng = np.random.default_rng() + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + em0 = EnvMat(self.rcut, self.rcut_smth) + mm0, ww0 = em0.call(self.coord_ext, self.atype_ext, self.nlist, davg, dstd) + mm1, _, ww1 = prod_env_mat_se_a( + torch.tensor(self.coord_ext, dtype=dtype), + torch.tensor(self.nlist, dtype=int), + torch.tensor(self.atype_ext[:, :nloc], dtype=int), + davg, + dstd, + self.rcut, + self.rcut_smth, + ) + np.testing.assert_allclose(mm0, mm1) + np.testing.assert_allclose(ww0, ww1) diff --git a/tests/test_fitting_net.py b/tests/test_fitting_net.py index 4f03e812..aed474c0 100644 --- a/tests/test_fitting_net.py +++ b/tests/test_fitting_net.py @@ -1,21 +1,26 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import re -import torch import unittest -import json +import numpy as np import tensorflow.compat.v1 as tf +import torch tf.disable_eager_execution() -from deepmd.fit.ener import EnerFitting - -from deepmd_pt.utils.env import GLOBAL_NP_FLOAT_PRECISION, TEST_CONFIG -from deepmd_pt.model.task import EnergyFittingNet +from deepmd.tf.fit.ener import ( + EnerFitting, +) +from deepmd_pt.model.task import ( + EnergyFittingNet, +) +from deepmd_pt.utils.env import ( + GLOBAL_NP_FLOAT_PRECISION, +) -class FakeDescriptor(object): +class FakeDescriptor: def __init__(self, ntypes, embedding_width): self._ntypes = ntypes self._dim_out = embedding_width @@ -37,62 +42,85 @@ def base_fitting_net(dp_fn, embedding, natoms, atype): t_embedding = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None]) t_natoms = tf.placeholder(tf.int32, [None]) t_atype = tf.placeholder(tf.int32, [None, None]) - t_energy = dp_fn.build(t_embedding, t_natoms, {'atype': t_atype}) + t_energy = dp_fn.build(t_embedding, t_natoms, {"atype": t_atype}) init_op = tf.global_variables_initializer() t_vars = {} for var in tf.global_variables(): key = None - matched = re.match(r'layer_(\d)_type_(\d)/([a-z]+)', var.name) + matched = re.match(r"layer_(\d)_type_(\d)/([a-z]+)", var.name) if matched: - key = gen_key(type_id=matched.group(2), layer_id=matched.group(1), w_or_b=matched.group(3)) + key = gen_key( + type_id=matched.group(2), + layer_id=matched.group(1), + w_or_b=matched.group(3), + ) else: - matched = re.match(r'final_layer_type_(\d)/([a-z]+)', var.name) + matched = re.match(r"final_layer_type_(\d)/([a-z]+)", var.name) if matched: - key = gen_key(type_id=matched.group(1), layer_id=-1, w_or_b=matched.group(2)) + key = gen_key( + type_id=matched.group(1), layer_id=-1, w_or_b=matched.group(2) + ) if key is not None: t_vars[key] = var with tf.Session(graph=g) as sess: sess.run(init_op) - energy, values = sess.run([t_energy, t_vars], feed_dict={ - t_embedding: embedding, - t_natoms: natoms, - t_atype: atype, - }) + energy, values = sess.run( + [t_energy, t_vars], + feed_dict={ + t_embedding: embedding, + t_natoms: natoms, + t_atype: atype, + }, + ) return energy, values class TestFittingNet(unittest.TestCase): - def setUp(self): nloc = 7 self.embedding_width = 30 self.natoms = np.array([nloc, nloc, 2, 5], dtype=np.int32) - self.embedding = np.random.uniform(size=[4, nloc * self.embedding_width]) + rng = np.random.default_rng() + self.embedding = rng.uniform(size=[4, nloc * self.embedding_width]) self.ntypes = self.natoms.size - 2 self.n_neuron = [32, 32, 32] self.atype = np.zeros([4, nloc], dtype=np.int32) cnt = 0 for i in range(self.ntypes): - self.atype[:, cnt:cnt + self.natoms[i + 2]] = i + self.atype[:, cnt : cnt + self.natoms[i + 2]] = i cnt += self.natoms[i + 2] fake_d = FakeDescriptor(2, 30) self.dp_fn = EnerFitting(fake_d, self.n_neuron) - self.dp_fn.bias_atom_e = np.random.uniform(size=[self.ntypes]) + self.dp_fn.bias_atom_e = rng.uniform(size=[self.ntypes]) def test_consistency(self): - dp_energy, values = base_fitting_net(self.dp_fn, self.embedding, self.natoms, self.atype) - my_fn = EnergyFittingNet(self.ntypes, self.embedding_width, self.n_neuron, self.dp_fn.bias_atom_e, use_tebd=False) + dp_energy, values = base_fitting_net( + self.dp_fn, self.embedding, self.natoms, self.atype + ) + my_fn = EnergyFittingNet( + self.ntypes, + self.embedding_width, + self.n_neuron, + self.dp_fn.bias_atom_e, + use_tebd=False, + ) for name, param in my_fn.named_parameters(): - matched = re.match('filter_layers\.(\d).deep_layers\.(\d)\.([a-z]+)', name) + matched = re.match("filter_layers\.(\d).deep_layers\.(\d)\.([a-z]+)", name) key = None if matched: - key = gen_key(type_id=matched.group(1), layer_id=matched.group(2), w_or_b=matched.group(3)) + key = gen_key( + type_id=matched.group(1), + layer_id=matched.group(2), + w_or_b=matched.group(3), + ) else: - matched = re.match('filter_layers\.(\d).final_layer\.([a-z]+)', name) + matched = re.match("filter_layers\.(\d).final_layer\.([a-z]+)", name) if matched: - key = gen_key(type_id=matched.group(1), layer_id=-1, w_or_b=matched.group(2)) + key = gen_key( + type_id=matched.group(1), layer_id=-1, w_or_b=matched.group(2) + ) assert key is not None var = values[key] with torch.no_grad(): @@ -107,5 +135,5 @@ def test_consistency(self): self.assertTrue(np.allclose(dp_energy, my_energy.numpy().reshape([-1]))) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_force_grad.py b/tests/test_force_grad.py index e3b3cdf4..191f62a8 100644 --- a/tests/test_force_grad.py +++ b/tests/test_force_grad.py @@ -1,45 +1,67 @@ -from typing import List -import unittest -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import json +import unittest +from typing import ( + List, + Optional, +) + import numpy as np -import copy +import torch -from deepmd_pt.utils import env -from deepmd_pt.utils.dataset import DeepmdDataSystem -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.dataset import ( + DeepmdDataSystem, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) class CheckSymmetry(DeepmdDataSystem): - def __init__(self, sys_path: str, rcut, sec, type_map: List[str] = None, type_split=True): + def __init__( + self, + sys_path: str, + rcut, + sec, + type_map: Optional[List[str]] = None, + type_split=True, + ): super().__init__(sys_path, rcut, sec, type_map, type_split) def get_disturb(self, index, atom_index, axis_index, delta): - for i in range(0, - len(self._dirs) + 1): # note: if different sets can be merged, prefix sum is unused to calculate + for i in range( + 0, len(self._dirs) + 1 + ): # note: if different sets can be merged, prefix sum is unused to calculate if index < self.prefix_sum[i]: break frames = self._load_set(self._dirs[i - 1]) - tmp = copy.deepcopy(frames['coord'].reshape(self.nframes, -1, 3)) + tmp = copy.deepcopy(frames["coord"].reshape(self.nframes, -1, 3)) tmp[:, atom_index, axis_index] += delta - frames['coord'] = tmp + frames["coord"] = tmp frame = self.single_preprocess(frames, index - self.prefix_sum[i - 1]) return frame def get_data(batch): inputs = {} - for key in ['coord', 'atype', 'box']: + for key in ["coord", "atype", "box"]: inputs[key] = batch[key].unsqueeze(0).to(env.DEVICE) return inputs class TestForceGrad(unittest.TestCase): def setUp(self): - np.random.seed(20) - with open(env.TEST_CONFIG, 'r') as fin: + with open(env.TEST_CONFIG) as fin: self.config = json.load(fin) self.system_index = 0 self.batch_index = 0 @@ -47,36 +69,48 @@ def setUp(self): self.get_model() def get_model(self): - training_systems = self.config['training']['training_data']['systems'] - model_params = self.config['model'] - data_stat_nbatch = model_params.get('data_stat_nbatch', 10) - train_data = DpLoaderSet(training_systems, self.config['training']['training_data']['batch_size'], model_params) - sampled = make_stat_input(train_data.systems, train_data.dataloaders, data_stat_nbatch) - self.model = get_model(self.config['model'], sampled).to(env.DEVICE) + training_systems = self.config["training"]["training_data"]["systems"] + model_params = self.config["model"] + data_stat_nbatch = model_params.get("data_stat_nbatch", 10) + train_data = DpLoaderSet( + training_systems, + self.config["training"]["training_data"]["batch_size"], + model_params, + ) + sampled = make_stat_input( + train_data.systems, train_data.dataloaders, data_stat_nbatch + ) + self.model = get_model(self.config["model"], sampled).to(env.DEVICE) def get_dataset(self, system_index=0, batch_index=0): - systems = self.config['training']['training_data']['systems'] - rcut = self.config['model']['descriptor']['rcut'] - sel = self.config['model']['descriptor']['sel'] + systems = self.config["training"]["training_data"]["systems"] + rcut = self.config["model"]["descriptor"]["rcut"] + sel = self.config["model"]["descriptor"]["sel"] sec = torch.cumsum(torch.tensor(sel), dim=0) - type_map = self.config['model']['type_map'] - self.dpdatasystem = CheckSymmetry(sys_path=systems[system_index], rcut=rcut, sec=sec, type_map=type_map) + type_map = self.config["model"]["type_map"] + self.dpdatasystem = CheckSymmetry( + sys_path=systems[system_index], rcut=rcut, sec=sec, type_map=type_map + ) self.origin_batch = self.dpdatasystem._get_item(batch_index) - def test_force_grad(self, threshold=1E-3, delta0=1E-6, seed=20): + def test_force_grad(self, threshold=1e-3, delta0=1e-6, seed=20): result0 = self.model(**get_data(self.origin_batch)) - np.random.seed(seed) + np.random.default_rng(seed) errors = np.zeros((self.dpdatasystem._natoms, 3)) for atom_index in range(self.dpdatasystem._natoms): for axis_index in range(3): delta = np.random.random() * delta0 - disturb_batch = self.dpdatasystem.get_disturb(self.batch_index, atom_index, axis_index, delta) + disturb_batch = self.dpdatasystem.get_disturb( + self.batch_index, atom_index, axis_index, delta + ) disturb_result = self.model(**get_data(disturb_batch)) - disturb_force = -(disturb_result['energy'] - result0['energy']) / delta - disturb_error = result0['force'][0, atom_index, axis_index] - disturb_force + disturb_force = -(disturb_result["energy"] - result0["energy"]) / delta + disturb_error = ( + result0["force"][0, atom_index, axis_index] - disturb_force + ) errors[atom_index, axis_index] = disturb_error.detach().cpu().numpy() self.assertTrue(np.abs(errors).max() < threshold) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_jit.py b/tests/test_jit.py index 3610d0b2..c0f65887 100644 --- a/tests/test_jit.py +++ b/tests/test_jit.py @@ -1,19 +1,26 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import json import os import shutil import unittest -from copy import deepcopy -from pathlib import Path +from copy import ( + deepcopy, +) + import torch -import numpy as np -from deepmd_pt.entrypoints.main import get_trainer -from deepmd_pt.infer import inference +from deepmd_pt.entrypoints.main import ( + get_trainer, +) +from deepmd_pt.infer import ( + inference, +) + from .test_permutation import ( - model_se_e2_a, - model_dpa1, - model_dpa2, - model_hybrid, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, ) @@ -21,8 +28,8 @@ class TestJIT: def test_jit(self): trainer = get_trainer(deepcopy(self.config)) trainer.run() - model = torch.jit.script(inference.Tester('./model.pt', numb_test=1).model) - torch.jit.save(model, './frozen_model.pth', {}) + model = torch.jit.script(inference.Tester("./model.pt", numb_test=1).model) + torch.jit.save(model, "./frozen_model.pth", {}) def tearDown(self): for f in os.listdir("."): @@ -37,12 +44,12 @@ def tearDown(self): class TestEnergyModelSeA(unittest.TestCase, TestJIT): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_se_e2_a) self.config["training"]["numb_steps"] = 10 self.config["training"]["save_freq"] = 10 - + def tearDown(self): TestJIT.tearDown(self) @@ -50,7 +57,7 @@ def tearDown(self): class TestEnergyModelDPA1(unittest.TestCase, TestJIT): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_dpa1) self.config["training"]["numb_steps"] = 10 @@ -63,12 +70,18 @@ def tearDown(self): class TestEnergyModelDPA2(unittest.TestCase, TestJIT): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) - self.config["model"] = deepcopy(model_dpa2) - self.config["model"]["descriptor"]["rcut"] = self.config["model"]["descriptor"]["repinit_rcut"] - self.config["model"]["descriptor"]["rcut_smth"] = self.config["model"]["descriptor"]["repinit_rcut_smth"] - self.config["model"]["descriptor"]["sel"] = self.config["model"]["descriptor"]["repinit_nsel"] + self.config["model"] = deepcopy(model_dpa2) + self.config["model"]["descriptor"]["rcut"] = self.config["model"]["descriptor"][ + "repinit_rcut" + ] + self.config["model"]["descriptor"]["rcut_smth"] = self.config["model"][ + "descriptor" + ]["repinit_rcut_smth"] + self.config["model"]["descriptor"]["sel"] = self.config["model"]["descriptor"][ + "repinit_nsel" + ] self.config["training"]["numb_steps"] = 10 self.config["training"]["save_freq"] = 10 @@ -77,17 +90,18 @@ def setUp(self): class TestEnergyModelHybrid(unittest.TestCase, TestJIT): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_hybrid) self.config["training"]["numb_steps"] = 10 self.config["training"]["save_freq"] = 10 + @unittest.skip("hybrid not supported at the moment") class TestEnergyModelHybrid2(unittest.TestCase, TestJIT): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_hybrid) self.config["model"]["descriptor"]["hybrid_mode"] = "sequential" @@ -95,5 +109,5 @@ def setUp(self): self.config["training"]["save_freq"] = 10 -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_loss.py b/tests/test_loss.py index b075a111..e7e57891 100644 --- a/tests/test_loss.py +++ b/tests/test_loss.py @@ -1,64 +1,88 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import os -import torch import unittest -import json +import numpy as np import tensorflow.compat.v1 as tf +import torch tf.disable_eager_execution() -from deepmd.loss.ener import EnerStdLoss -from deepmd.common import expand_sys_str +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.loss.ener import ( + EnerStdLoss, +) -from deepmd_pt.loss import EnergyStdLoss -from deepmd_pt.utils.env import TEST_CONFIG -from deepmd_pt.utils.dataset import DeepmdDataSet +from deepmd_pt.loss import ( + EnergyStdLoss, +) +from deepmd_pt.utils.dataset import ( + DeepmdDataSet, +) +from deepmd_pt.utils.env import ( + TEST_CONFIG, +) CUR_DIR = os.path.dirname(__file__) def get_batch(): - with open(TEST_CONFIG, 'r') as fin: + with open(TEST_CONFIG) as fin: content = fin.read() config = json.loads(content) - model_config = config['model'] - rcut = model_config['descriptor']['rcut'] + model_config = config["model"] + rcut = model_config["descriptor"]["rcut"] # self.rcut_smth = model_config['descriptor']['rcut_smth'] - sel = model_config['descriptor']['sel'] - batch_size = config['training']['training_data']['batch_size'] - systems = config['training']['validation_data']['systems'] + sel = model_config["descriptor"]["sel"] + batch_size = config["training"]["training_data"]["batch_size"] + systems = config["training"]["validation_data"]["systems"] if isinstance(systems, str): systems = expand_sys_str(systems) - dataset = DeepmdDataSet(systems, - batch_size, model_config['type_map'], rcut, sel) + dataset = DeepmdDataSet(systems, batch_size, model_config["type_map"], rcut, sel) np_batch, pt_batch = dataset.get_batch() return np_batch, pt_batch class TestLearningRate(unittest.TestCase): - def setUp(self): self.start_lr = 1.1 self.start_pref_e = 0.02 - self.limit_pref_e = 1. - self.start_pref_f = 1000. - self.limit_pref_f = 1. + self.limit_pref_e = 1.0 + self.start_pref_f = 1000.0 + self.limit_pref_f = 1.0 self.start_pref_v = 0.02 - self.limit_pref_v = 1. + self.limit_pref_v = 1.0 self.cur_lr = 1.2 # data np_batch, pt_batch = get_batch() - natoms = np_batch['natoms'] + natoms = np_batch["natoms"] self.nloc = natoms[0] - l_energy, l_force, l_virial = np_batch['energy'], np_batch['force'], np_batch['virial'] - p_energy, p_force, p_virial = np.ones_like(l_energy), np.ones_like(l_force), np.ones_like(l_virial) + l_energy, l_force, l_virial = ( + np_batch["energy"], + np_batch["force"], + np_batch["virial"], + ) + p_energy, p_force, p_virial = ( + np.ones_like(l_energy), + np.ones_like(l_force), + np.ones_like(l_virial), + ) nloc = natoms[0] - batch_size = pt_batch['coord'].shape[0] + batch_size = pt_batch["coord"].shape[0] atom_energy = np.zeros(shape=[batch_size, nloc]) atom_pref = np.zeros(shape=[batch_size, nloc * 3]) # tf - base = EnerStdLoss(self.start_lr, self.start_pref_e, self.limit_pref_e, self.start_pref_f, self.limit_pref_f, - self.start_pref_v, self.limit_pref_v) + base = EnerStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + ) self.g = tf.Graph() with self.g.as_default(): t_cur_lr = tf.placeholder(shape=[], dtype=tf.float64) @@ -72,30 +96,32 @@ def setUp(self): t_lvirial = tf.placeholder(shape=[None, 9], dtype=tf.float64) t_latom_energy = tf.placeholder(shape=[None, None], dtype=tf.float64) t_atom_pref = tf.placeholder(shape=[None, None], dtype=tf.float64) - find_energy = tf.constant(1., dtype=tf.float64) - find_force = tf.constant(1., dtype=tf.float64) - find_virial = tf.constant(1., dtype=tf.float64) - find_atom_energy = tf.constant(0., dtype=tf.float64) - find_atom_pref = tf.constant(0., dtype=tf.float64) + find_energy = tf.constant(1.0, dtype=tf.float64) + find_force = tf.constant(1.0, dtype=tf.float64) + find_virial = tf.constant(1.0, dtype=tf.float64) + find_atom_energy = tf.constant(0.0, dtype=tf.float64) + find_atom_pref = tf.constant(0.0, dtype=tf.float64) model_dict = { - 'energy': t_penergy, - 'force': t_pforce, - 'virial': t_pvirial, - 'atom_ener': t_patom_energy + "energy": t_penergy, + "force": t_pforce, + "virial": t_pvirial, + "atom_ener": t_patom_energy, } label_dict = { - 'energy': t_lenergy, - 'force': t_lforce, - 'virial': t_lvirial, - 'atom_ener': t_latom_energy, - 'atom_pref': t_atom_pref, - 'find_energy': find_energy, - 'find_force': find_force, - 'find_virial': find_virial, - 'find_atom_ener': find_atom_energy, - 'find_atom_pref': find_atom_pref + "energy": t_lenergy, + "force": t_lforce, + "virial": t_lvirial, + "atom_ener": t_latom_energy, + "atom_pref": t_atom_pref, + "find_energy": find_energy, + "find_force": find_force, + "find_virial": find_virial, + "find_atom_ener": find_atom_energy, + "find_atom_pref": find_atom_pref, } - self.base_loss_sess = base.build(t_cur_lr, t_natoms, model_dict, label_dict, '') + self.base_loss_sess = base.build( + t_cur_lr, t_natoms, model_dict, label_dict, "" + ) # torch self.feed_dict = { t_cur_lr: self.cur_lr, @@ -108,23 +134,34 @@ def setUp(self): t_lforce: l_force, t_lvirial: l_virial.reshape(-1, 9), t_latom_energy: atom_energy, - t_atom_pref: atom_pref + t_atom_pref: atom_pref, } - self.model_pred = {'energy': torch.from_numpy(p_energy), - 'force': torch.from_numpy(p_force), - 'virial': torch.from_numpy(p_virial), - } - self.label = {'energy': torch.from_numpy(l_energy), - 'force': torch.from_numpy(l_force), - 'virial': torch.from_numpy(l_virial), - } - self.natoms = pt_batch['natoms'] + self.model_pred = { + "energy": torch.from_numpy(p_energy), + "force": torch.from_numpy(p_force), + "virial": torch.from_numpy(p_virial), + } + self.label = { + "energy": torch.from_numpy(l_energy), + "force": torch.from_numpy(l_force), + "virial": torch.from_numpy(l_virial), + } + self.natoms = pt_batch["natoms"] def test_consistency(self): with tf.Session(graph=self.g) as sess: - base_loss, base_more_loss = sess.run(self.base_loss_sess, feed_dict=self.feed_dict) - mine = EnergyStdLoss(self.start_lr, self.start_pref_e, self.limit_pref_e, self.start_pref_f, self.limit_pref_f, - self.start_pref_v, self.limit_pref_v) + base_loss, base_more_loss = sess.run( + self.base_loss_sess, feed_dict=self.feed_dict + ) + mine = EnergyStdLoss( + self.start_lr, + self.start_pref_e, + self.limit_pref_e, + self.start_pref_f, + self.limit_pref_f, + self.start_pref_v, + self.limit_pref_v, + ) my_loss, my_more_loss = mine( self.label, self.model_pred, @@ -133,9 +170,13 @@ def test_consistency(self): ) my_loss = my_loss.detach().cpu() self.assertTrue(np.allclose(base_loss, my_loss.numpy())) - for key in ['ener', 'force', 'virial']: - self.assertTrue(np.allclose(base_more_loss['l2_%s_loss' % key], my_more_loss['l2_%s_loss' % key])) + for key in ["ener", "force", "virial"]: + self.assertTrue( + np.allclose( + base_more_loss["l2_%s_loss" % key], my_more_loss["l2_%s_loss" % key] + ) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_lr.py b/tests/test_lr.py index adeb8fe9..d1ede612 100644 --- a/tests/test_lr.py +++ b/tests/test_lr.py @@ -1,17 +1,21 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest +import numpy as np import tensorflow.compat.v1 as tf tf.disable_eager_execution() -from deepmd.utils import learning_rate +from deepmd.tf.utils import ( + learning_rate, +) -from deepmd_pt.utils.learning_rate import LearningRateExp +from deepmd_pt.utils.learning_rate import ( + LearningRateExp, +) class TestLearningRate(unittest.TestCase): - def setUp(self): self.start_lr = 0.001 self.stop_lr = 3.51e-8 @@ -26,19 +30,30 @@ def test_consistency(self): self.judge_it() def judge_it(self): - base_lr = learning_rate.LearningRateExp(self.start_lr, self.stop_lr, self.decay_step) + base_lr = learning_rate.LearningRateExp( + self.start_lr, self.stop_lr, self.decay_step + ) g = tf.Graph() with g.as_default(): global_step = tf.placeholder(shape=[], dtype=tf.int32) t_lr = base_lr.build(global_step, self.stop_step) - my_lr = LearningRateExp(self.start_lr, self.stop_lr, self.decay_step, self.stop_step) + my_lr = LearningRateExp( + self.start_lr, self.stop_lr, self.decay_step, self.stop_step + ) with tf.Session(graph=g) as sess: - base_vals = [sess.run(t_lr, feed_dict={global_step: step_id}) for step_id in range(self.stop_step) if - step_id % self.decay_step != 0] - my_vals = [my_lr.value(step_id) for step_id in range(self.stop_step) if step_id % self.decay_step != 0] + base_vals = [ + sess.run(t_lr, feed_dict={global_step: step_id}) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] + my_vals = [ + my_lr.value(step_id) + for step_id in range(self.stop_step) + if step_id % self.decay_step != 0 + ] self.assertTrue(np.allclose(base_vals, my_vals)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_mlp.py b/tests/test_mlp.py index 0b068cf5..c5511482 100644 --- a/tests/test_mlp.py +++ b/tests/test_mlp.py @@ -1,294 +1,321 @@ -import torch, copy -import unittest +# SPDX-License-Identifier: LGPL-3.0-or-later import itertools +import unittest + import numpy as np +import torch + from deepmd_pt.utils.env import ( - PRECISION_DICT, + PRECISION_DICT, ) try: - from deepmd_pt.model.network.mlp import ( - MLPLayer, - MLP, - ) - support_native_net = True + from deepmd_pt.model.network.mlp import ( + MLP, + MLPLayer, + ) + + support_native_net = True except ModuleNotFoundError: - support_native_net = False + support_native_net = False try: - from deepmd_pt.model.network.mlp import ( - EmbeddingNet, - ) - support_embedding_net = True + from deepmd_pt.model.network.mlp import ( + EmbeddingNet, + ) + + support_embedding_net = True except ModuleNotFoundError: - support_embedding_net = False + support_embedding_net = False try: - from deepmd_pt.model.network.mlp import ( - FittingNet, - ) - support_fitting_net = True + from deepmd_pt.model.network.mlp import ( + FittingNet, + ) + + support_fitting_net = True except ModuleNotFoundError: - support_fitting_net = False + support_fitting_net = False -try : - from deepmd_utils.model_format import ( - NativeLayer, - NativeNet, - ) - support_native_net = True +try: + from deepmd.model_format import ( + NativeLayer, + NativeNet, + ) + + support_native_net = True except ModuleNotFoundError: - support_native_net = False + support_native_net = False except ImportError: - support_native_net = False + support_native_net = False -try : - from deepmd_utils.model_format import ( - EmbeddingNet as DPEmbeddingNet, - ) - support_embedding_net = True +try: + from deepmd.model_format import EmbeddingNet as DPEmbeddingNet + + support_embedding_net = True except ModuleNotFoundError: - support_embedding_net = False + support_embedding_net = False except ImportError: - support_embedding_net = False + support_embedding_net = False + +try: + from deepmd.model_format import FittingNet as DPFittingNet -try : - from deepmd_utils.model_format import ( - FittingNet as DPFittingNet, - ) - support_fitting_net = True + support_fitting_net = True except ModuleNotFoundError: - support_fitting_net = False + support_fitting_net = False except ImportError: - support_fitting_net = False - + support_fitting_net = False def get_tols(prec): - if prec in ["single", "float32"]: - rtol, atol=0., 1e-4 - elif prec in ["double", "float64"]: - rtol, atol=0., 1e-12 - # elif prec in ["half", "float16"]: - # rtol, atol=1e-2, 0 - else: - raise ValueError(f"unknown prec {prec}") - return rtol, atol + if prec in ["single", "float32"]: + rtol, atol = 0.0, 1e-4 + elif prec in ["double", "float64"]: + rtol, atol = 0.0, 1e-12 + # elif prec in ["half", "float16"]: + # rtol, atol=1e-2, 0 + else: + raise ValueError(f"unknown prec {prec}") + return rtol, atol + @unittest.skipIf(not support_native_net, "NativeLayer not supported") class TestMLPLayer(unittest.TestCase): - def setUp(self): - self.test_cases = itertools.product( - [(5, 5), (5, 10), (5, 8), (8, 5)], # inp, out - [True, False], # bias - [True, False], # use time step - ["tanh", "none"], # activation - [True, False], # resnet - [None, [4], [3,2]], # prefix shapes - ["float32", "double"], # precision - ) + def setUp(self): + self.test_cases = itertools.product( + [(5, 5), (5, 10), (5, 8), (8, 5)], # inp, out + [True, False], # bias + [True, False], # use time step + ["tanh", "none"], # activation + [True, False], # resnet + [None, [4], [3, 2]], # prefix shapes + ["float32", "double"], # precision + ) - def test_match_native_layer( - self, - ): - for (ninp, nout), bias, ut, ac, resnet, ashp, prec in self.test_cases: - # input - inp_shap = [ninp] - if ashp is not None: - inp_shap = ashp + inp_shap - rtol, atol = get_tols(prec) - dtype = PRECISION_DICT[prec] - xx = torch.arange(np.prod(inp_shap), dtype=dtype).view(inp_shap) - # def mlp layer - ml = MLPLayer(ninp, nout, bias, ut, ac, resnet, precision=prec) - # check consistency - nl = NativeLayer.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), nl.call(xx.detach().numpy()), - rtol=rtol, atol=atol, - err_msg=f"(i={ninp}, o={nout}) bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}" - ) - # check self-consistency - ml1 = MLPLayer.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), ml1.forward(xx).detach().numpy(), - rtol=rtol, atol=atol, - err_msg=f"(i={ninp}, o={nout}) bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}" - ) - - def test_jit(self): - for (ninp, nout), bias, ut, ac, resnet, _, prec in self.test_cases: - ml = MLPLayer(ninp, nout, bias, ut, ac, resnet, precision=prec) - model = torch.jit.script(ml) - ml1 = MLPLayer.deserialize(ml.serialize()) - model = torch.jit.script(ml1) + def test_match_native_layer( + self, + ): + for (ninp, nout), bias, ut, ac, resnet, ashp, prec in self.test_cases: + # input + inp_shap = [ninp] + if ashp is not None: + inp_shap = ashp + inp_shap + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = torch.arange(np.prod(inp_shap), dtype=dtype).view(inp_shap) + # def mlp layer + ml = MLPLayer(ninp, nout, bias, ut, ac, resnet, precision=prec) + # check consistency + nl = NativeLayer.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + nl.call(xx.detach().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"(i={ninp}, o={nout}) bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + # check self-consistency + ml1 = MLPLayer.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + ml1.forward(xx).detach().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"(i={ninp}, o={nout}) bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + + def test_jit(self): + for (ninp, nout), bias, ut, ac, resnet, _, prec in self.test_cases: + ml = MLPLayer(ninp, nout, bias, ut, ac, resnet, precision=prec) + model = torch.jit.script(ml) + ml1 = MLPLayer.deserialize(ml.serialize()) + model = torch.jit.script(ml1) @unittest.skipIf(not support_native_net, "NativeLayer not supported") class TestMLP(unittest.TestCase): - def setUp(self): - self.test_cases = itertools.product( - [[2, 2, 4, 8], [1, 3, 3] ], # inp and hiddens - [True, False], # bias - [True, False], # use time step - ["tanh", "none"], # activation - [True, False], # resnet - [None, [4], [3,2]], # prefix shapes - ["float32", "double"], # precision - ) + def setUp(self): + self.test_cases = itertools.product( + [[2, 2, 4, 8], [1, 3, 3]], # inp and hiddens + [True, False], # bias + [True, False], # use time step + ["tanh", "none"], # activation + [True, False], # resnet + [None, [4], [3, 2]], # prefix shapes + ["float32", "double"], # precision + ) + + def test_match_native_net( + self, + ): + for ndims, bias, ut, ac, resnet, ashp, prec in self.test_cases: + # input + inp_shap = [ndims[0]] + if ashp is not None: + inp_shap = ashp + inp_shap + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = torch.arange(np.prod(inp_shap), dtype=dtype).view(inp_shap) + # def MLP + layers = [] + for ii in range(1, len(ndims)): + layers.append( + MLPLayer( + ndims[ii - 1], ndims[ii], bias, ut, ac, resnet, precision=prec + ).serialize() + ) + ml = MLP(layers) + # check consistency + nl = NativeNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + nl.call(xx.detach().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"net={ndims} bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) + # check self-consistency + ml1 = MLP.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + ml1.forward(xx).detach().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"net={ndims} bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}", + ) - def test_match_native_net( - self, - ): - for ndims, bias, ut, ac, resnet, ashp, prec in self.test_cases: - # input - inp_shap = [ndims[0]] - if ashp is not None: - inp_shap = ashp + inp_shap - rtol, atol = get_tols(prec) - dtype = PRECISION_DICT[prec] - xx = torch.arange(np.prod(inp_shap), dtype=dtype).view(inp_shap) - # def MLP - layers = [] - for ii in range(1, len(ndims)): - layers.append( - MLPLayer(ndims[ii-1], ndims[ii], bias, ut, ac, resnet, precision=prec).serialize()) - ml = MLP(layers) - # check consistency - nl = NativeNet.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), nl.call(xx.detach().numpy()), - rtol=rtol, atol=atol, - err_msg=f"net={ndims} bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}" - ) - # check self-consistency - ml1 = MLP.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), ml1.forward(xx).detach().numpy(), - rtol=rtol, atol=atol, - err_msg=f"net={ndims} bias={bias} use_dt={ut} act={ac} resnet={resnet} prec={prec}" - ) - - def test_jit(self): - for ndims, bias, ut, ac, resnet, _, prec in self.test_cases: - layers = [] - for ii in range(1, len(ndims)): - ml = layers.append( - MLPLayer(ndims[ii-1], ndims[ii], bias, ut, ac, resnet, precision=prec).serialize()) - ml = MLP(ml) - model = torch.jit.script(ml) - ml1 = MLP.deserialize(ml.serialize()) - model = torch.jit.script(ml1) + def test_jit(self): + for ndims, bias, ut, ac, resnet, _, prec in self.test_cases: + layers = [] + for ii in range(1, len(ndims)): + ml = layers.append( + MLPLayer( + ndims[ii - 1], ndims[ii], bias, ut, ac, resnet, precision=prec + ).serialize() + ) + ml = MLP(ml) + model = torch.jit.script(ml) + ml1 = MLP.deserialize(ml.serialize()) + model = torch.jit.script(ml1) @unittest.skipIf(not support_embedding_net, "EmbeddingNet not supported") class TestEmbeddingNet(unittest.TestCase): - def setUp(self): - self.test_cases = itertools.product( - [1, 3], # inp - [[24, 48, 96], [24, 36]], # and hiddens - ["tanh", "none"], # activation - [True, False], # resnet_dt - ["float32", "double"], # precision - ) + def setUp(self): + self.test_cases = itertools.product( + [1, 3], # inp + [[24, 48, 96], [24, 36]], # and hiddens + ["tanh", "none"], # activation + [True, False], # resnet_dt + ["float32", "double"], # precision + ) - def test_match_embedding_net( - self, - ): - for idim, nn, act, idt, prec in self.test_cases: - # input - rtol, atol = get_tols(prec) - dtype = PRECISION_DICT[prec] - xx = torch.arange(idim, dtype=dtype) - # def MLP - ml = EmbeddingNet(idim, nn, act, idt, prec) - # check consistency - nl = DPEmbeddingNet.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), nl.call(xx.detach().numpy()), - rtol=rtol, atol=atol, - err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}" - ) - # check self-consistency - ml1 = EmbeddingNet.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), ml1.forward(xx).detach().numpy(), - rtol=rtol, atol=atol, - err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}" - ) - - def test_jit( - self, - ): - for idim, nn, act, idt, prec in self.test_cases: - # def MLP - ml = EmbeddingNet(idim, nn, act, idt, prec) - ml1 = EmbeddingNet.deserialize(ml.serialize()) - model = torch.jit.script(ml) - model = torch.jit.script(ml1) + def test_match_embedding_net( + self, + ): + for idim, nn, act, idt, prec in self.test_cases: + # input + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = torch.arange(idim, dtype=dtype) + # def MLP + ml = EmbeddingNet(idim, nn, act, idt, prec) + # check consistency + nl = DPEmbeddingNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + nl.call(xx.detach().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + # check self-consistency + ml1 = EmbeddingNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + ml1.forward(xx).detach().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + def test_jit( + self, + ): + for idim, nn, act, idt, prec in self.test_cases: + # def MLP + ml = EmbeddingNet(idim, nn, act, idt, prec) + ml1 = EmbeddingNet.deserialize(ml.serialize()) + model = torch.jit.script(ml) + model = torch.jit.script(ml1) @unittest.skipIf(not support_fitting_net, "FittingNet not supported") class TestFittingNet(unittest.TestCase): - def setUp(self): - self.test_cases = itertools.product( - [1, 3], # inp - [1, 5], # out - [[24, 48, 96], [24, 36]], # and hiddens - ["tanh", "none"], # activation - [True, False], # resnet_dt - ["float32", "double"], # precision - [True, False], # bias_out - ) + def setUp(self): + self.test_cases = itertools.product( + [1, 3], # inp + [1, 5], # out + [[24, 48, 96], [24, 36]], # and hiddens + ["tanh", "none"], # activation + [True, False], # resnet_dt + ["float32", "double"], # precision + [True, False], # bias_out + ) + + def test_match_fitting_net( + self, + ): + for idim, odim, nn, act, idt, prec, ob in self.test_cases: + # input + rtol, atol = get_tols(prec) + dtype = PRECISION_DICT[prec] + xx = torch.arange(idim, dtype=dtype) + # def MLP + ml = FittingNet( + idim, + odim, + neuron=nn, + activation_function=act, + resnet_dt=idt, + precision=prec, + bias_out=ob, + ) + # check consistency + nl = DPFittingNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + nl.call(xx.detach().numpy()), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) + # check self-consistency + ml1 = FittingNet.deserialize(ml.serialize()) + np.testing.assert_allclose( + ml.forward(xx).detach().numpy(), + ml1.forward(xx).detach().numpy(), + rtol=rtol, + atol=atol, + err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}", + ) - def test_match_fitting_net( - self, - ): - for idim, odim, nn, act, idt, prec, ob in self.test_cases: - # input - rtol, atol = get_tols(prec) - dtype = PRECISION_DICT[prec] - xx = torch.arange(idim, dtype=dtype) - # def MLP - ml = FittingNet( - idim, odim, - neuron=nn, - activation_function=act, - resnet_dt=idt, - precision=prec, - bias_out=ob, - ) - # check consistency - nl = DPFittingNet.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), nl.call(xx.detach().numpy()), - rtol=rtol, atol=atol, - err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}" - ) - # check self-consistency - ml1 = FittingNet.deserialize(ml.serialize()) - np.testing.assert_allclose( - ml.forward(xx).detach().numpy(), ml1.forward(xx).detach().numpy(), - rtol=rtol, atol=atol, - err_msg=f"idim={idim} nn={nn} use_dt={idt} act={act} prec={prec}" - ) - - def test_jit( - self, - ): - for idim, odim, nn, act, idt, prec, ob in self.test_cases: - # def MLP - ml = FittingNet( - idim, odim, - neuron=nn, - activation_function=act, - resnet_dt=idt, - precision=prec, - bias_out=ob, - ) - ml1 = FittingNet.deserialize(ml.serialize()) - model = torch.jit.script(ml) - model = torch.jit.script(ml1) - + def test_jit( + self, + ): + for idim, odim, nn, act, idt, prec, ob in self.test_cases: + # def MLP + ml = FittingNet( + idim, + odim, + neuron=nn, + activation_function=act, + resnet_dt=idt, + precision=prec, + bias_out=ob, + ) + ml1 = FittingNet.deserialize(ml.serialize()) + model = torch.jit.script(ml) + model = torch.jit.script(ml1) diff --git a/tests/test_model.py b/tests/test_model.py index 61ee2c6e..10aefe49 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -1,77 +1,101 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import collections import json import unittest +import numpy as np import tensorflow.compat.v1 as tf +import torch tf.disable_eager_execution() -from deepmd import op -from deepmd.common import data_requirement, expand_sys_str -from deepmd.descriptor import DescrptSeA as DescrptSeA_tf -from deepmd.fit import EnerFitting -from deepmd.loss import EnerStdLoss -from deepmd.model import EnerModel -from deepmd.utils.data_system import DeepmdDataSystem -from deepmd.utils.learning_rate import LearningRateExp - -from deepmd_pt.utils.dataloader import DpLoaderSet +from deepmd.tf.common import ( + data_requirement, + expand_sys_str, +) +from deepmd.tf.descriptor import DescrptSeA as DescrptSeA_tf +from deepmd.tf.fit import ( + EnerFitting, +) +from deepmd.tf.loss import ( + EnerStdLoss, +) +from deepmd.tf.model import ( + EnerModel, +) +from deepmd.tf.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.tf.utils.learning_rate import ( + LearningRateExp, +) + +from deepmd_pt.loss import ( + EnergyStdLoss, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.env import ( + DEVICE, + TEST_CONFIG, +) from deepmd_pt.utils.learning_rate import LearningRateExp as MyLRExp -from deepmd_pt.loss import EnergyStdLoss -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.env import * - -from deepmd_pt.utils.stat import make_stat_input +from deepmd_pt.utils.stat import ( + make_stat_input, +) -VariableState = collections.namedtuple('VariableState', ['value', 'gradient']) +VariableState = collections.namedtuple("VariableState", ["value", "gradient"]) def torch2tf(torch_name): - fields = torch_name.split('.') + fields = torch_name.split(".") offset = int(fields[2] == "networks") element_id = int(fields[2 + offset]) - if fields[0] == 'descriptor': + if fields[0] == "descriptor": layer_id = int(fields[4 + offset]) + 1 weight_type = fields[5 + offset] - return 'filter_type_all/%s_%d_%d:0' % (weight_type, layer_id, element_id) - elif fields[3] == 'deep_layers': + return "filter_type_all/%s_%d_%d:0" % (weight_type, layer_id, element_id) + elif fields[3] == "deep_layers": layer_id = int(fields[4]) weight_type = fields[5] - return 'layer_%d_type_%d/%s:0' % (layer_id, element_id, weight_type) - elif fields[3] == 'final_layer': + return "layer_%d_type_%d/%s:0" % (layer_id, element_id, weight_type) + elif fields[3] == "final_layer": weight_type = fields[4] - return 'final_layer_type_%d/%s:0' % (element_id, weight_type) + return "final_layer_type_%d/%s:0" % (element_id, weight_type) else: - raise RuntimeError('Unexpected parameter name: %s' % torch_name) + raise RuntimeError("Unexpected parameter name: %s" % torch_name) -class DpTrainer(object): - +class DpTrainer: def __init__(self): - with open(TEST_CONFIG, 'r') as fin: + with open(TEST_CONFIG) as fin: content = fin.read() config = json.loads(content) - model_config = config['model'] - self.rcut = model_config['descriptor']['rcut'] - self.rcut_smth = model_config['descriptor']['rcut_smth'] - self.sel = model_config['descriptor']['sel'] - self.systems = config['training']['validation_data']['systems'] + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.systems = config["training"]["validation_data"]["systems"] if isinstance(self.systems, str): self.systems = expand_sys_str(self.systems) - self.batch_size = config['training']['training_data']['batch_size'] - self.type_map = model_config['type_map'] - self.filter_neuron = model_config['descriptor']['neuron'] - self.axis_neuron = model_config['descriptor']['axis_neuron'] - self.n_neuron = model_config['fitting_net']['neuron'] + self.batch_size = config["training"]["training_data"]["batch_size"] + self.type_map = model_config["type_map"] + self.filter_neuron = model_config["descriptor"]["neuron"] + self.axis_neuron = model_config["descriptor"]["axis_neuron"] + self.n_neuron = model_config["fitting_net"]["neuron"] self.data_stat_nbatch = 3 self.start_lr = 0.001 self.stop_lr = 3.51e-8 self.decay_steps = 500 self.stop_steps = 1600 - self.start_pref_e = 1. - self.limit_pref_e = 2. - self.start_pref_f = 2. - self.limit_pref_f = 1. + self.start_pref_e = 1.0 + self.limit_pref_e = 2.0 + self.start_pref_f = 2.0 + self.limit_pref_f = 1.0 self.ntypes = len(self.type_map) def get_intermediate_state(self, num_steps=1): @@ -86,21 +110,21 @@ def get_intermediate_state(self, num_steps=1): with g.as_default(): place_holders = self._get_dp_placeholders(dp_ds) model_pred = dp_model.build( - coord_=place_holders['coord'], - atype_=place_holders['type'], - natoms=place_holders['natoms_vec'], - box=place_holders['box'], - mesh=place_holders['default_mesh'], - input_dict=place_holders + coord_=place_holders["coord"], + atype_=place_holders["type"], + natoms=place_holders["natoms_vec"], + box=place_holders["box"], + mesh=place_holders["default_mesh"], + input_dict=place_holders, ) global_step = tf.train.get_or_create_global_step() learning_rate = dp_lr.build(global_step, self.stop_steps) l2_l, _ = dp_loss.build( learning_rate=learning_rate, - natoms=place_holders['natoms_vec'], + natoms=place_holders["natoms_vec"], model_dict=model_pred, label_dict=place_holders, - suffix='test' + suffix="test", ) t_vars = tf.trainable_variables() optimizer = tf.train.AdamOptimizer(learning_rate) @@ -108,18 +132,18 @@ def get_intermediate_state(self, num_steps=1): train_op = optimizer.apply_gradients(t_grad_and_vars, global_step) init_op = tf.global_variables_initializer() t_heads = { - 'loss': l2_l, - 'energy': model_pred['energy'], - 'force': model_pred['force'], - 'virial': model_pred['virial'], - 'atomic_virial': model_pred['atom_virial'], + "loss": l2_l, + "energy": model_pred["energy"], + "force": model_pred["force"], + "virial": model_pred["virial"], + "atomic_virial": model_pred["atom_virial"], } # Get statistics of each component stat_dict = { - 'descriptor.mean': dp_model.descrpt.davg, - 'descriptor.stddev': dp_model.descrpt.dstd, - 'fitting_net.bias_atom_e': dp_model.fitting.bias_atom_e + "descriptor.mean": dp_model.descrpt.davg, + "descriptor.stddev": dp_model.descrpt.dstd, + "fitting_net.bias_atom_e": dp_model.fitting.bias_atom_e, } # Get variables and their gradients @@ -132,7 +156,9 @@ def get_intermediate_state(self, num_steps=1): batch = dp_ds.get_batch() feeds = self._get_feed_dict(batch, place_holders) - grads_and_vars, head_dict = sess.run([t_grad_and_vars, t_heads], feed_dict=feeds) + grads_and_vars, head_dict = sess.run( + [t_grad_and_vars, t_heads], feed_dict=feeds + ) vs_dict = {} for idx, one in enumerate(t_vars): grad, var = grads_and_vars[idx] @@ -148,7 +174,7 @@ def _get_dp_dataset(self): test_size=1, rcut=self.rcut, type_map=self.type_map, - trn_all_set=True + trn_all_set=True, ) data.add_dict(data_requirement) return data @@ -159,17 +185,14 @@ def _get_dp_model(self): rcut_smth=self.rcut_smth, sel=self.sel, neuron=self.filter_neuron, - axis_neuron=self.axis_neuron - ) - dp_fitting = EnerFitting( - descrpt=dp_descrpt, - neuron=self.n_neuron + axis_neuron=self.axis_neuron, ) + dp_fitting = EnerFitting(descrpt=dp_descrpt, neuron=self.n_neuron) return EnerModel( dp_descrpt, dp_fitting, type_map=self.type_map, - data_stat_nbatch=self.data_stat_nbatch + data_stat_nbatch=self.data_stat_nbatch, ) def _get_dp_loss(self): @@ -178,88 +201,94 @@ def _get_dp_loss(self): start_pref_e=self.start_pref_e, limit_pref_e=self.limit_pref_e, start_pref_f=self.start_pref_f, - limit_pref_f=self.limit_pref_f + limit_pref_f=self.limit_pref_f, ) def _get_dp_lr(self): return LearningRateExp( - start_lr=self.start_lr, - stop_lr=self.stop_lr, - decay_steps=self.decay_steps + start_lr=self.start_lr, stop_lr=self.stop_lr, decay_steps=self.decay_steps ) def _get_dp_placeholders(self, dataset): place_holders = {} data_dict = dataset.get_data_dict() for kk in data_dict.keys(): - if kk == 'type': + if kk == "type": continue prec = tf.float64 - place_holders[kk] = tf.placeholder(prec, [None], name='t_' + kk) - place_holders['find_' + kk] = tf.placeholder(tf.float32, name='t_find_' + kk) - place_holders['type'] = tf.placeholder(tf.int32, [None], name='t_type') - place_holders['natoms_vec'] = tf.placeholder(tf.int32, [self.ntypes + 2], name='t_natoms') - place_holders['default_mesh'] = tf.placeholder(tf.int32, [None], name='t_mesh') - place_holders['is_training'] = tf.placeholder(tf.bool) + place_holders[kk] = tf.placeholder(prec, [None], name="t_" + kk) + place_holders["find_" + kk] = tf.placeholder( + tf.float32, name="t_find_" + kk + ) + place_holders["type"] = tf.placeholder(tf.int32, [None], name="t_type") + place_holders["natoms_vec"] = tf.placeholder( + tf.int32, [self.ntypes + 2], name="t_natoms" + ) + place_holders["default_mesh"] = tf.placeholder(tf.int32, [None], name="t_mesh") + place_holders["is_training"] = tf.placeholder(tf.bool) return place_holders def _get_feed_dict(self, batch, place_holders): feed_dict = {} for kk in batch.keys(): - if kk == 'find_type' or kk == 'type': + if kk == "find_type" or kk == "type": continue - if 'find_' in kk: + if "find_" in kk: feed_dict[place_holders[kk]] = batch[kk] else: feed_dict[place_holders[kk]] = np.reshape(batch[kk], [-1]) - for ii in ['type']: + for ii in ["type"]: feed_dict[place_holders[ii]] = np.reshape(batch[ii], [-1]) - for ii in ['natoms_vec', 'default_mesh']: + for ii in ["natoms_vec", "default_mesh"]: feed_dict[place_holders[ii]] = batch[ii] - feed_dict[place_holders['is_training']] = True + feed_dict[place_holders["is_training"]] = True return feed_dict class TestEnergy(unittest.TestCase): - def setUp(self): self.dp_trainer = DpTrainer() self.wanted_step = 0 for key in dir(self.dp_trainer): - if not key.startswith('_') or key == 'get_intermediate_state': + if not key.startswith("_") or key == "get_intermediate_state": value = getattr(self.dp_trainer, key) setattr(self, key, value) def test_consistency(self): - batch, head_dict, stat_dict, vs_dict = self.dp_trainer.get_intermediate_state(self.wanted_step) + batch, head_dict, stat_dict, vs_dict = self.dp_trainer.get_intermediate_state( + self.wanted_step + ) # Build DeePMD graph - my_ds = DpLoaderSet(self.systems, self.batch_size, - model_params={ - 'descriptor': { - 'type': 'se_e2_a', - 'sel': self.sel, - 'rcut': self.rcut, - }, - 'type_map': self.type_map - }) - sampled = make_stat_input(my_ds.systems, my_ds.dataloaders, self.data_stat_nbatch) - my_model = get_model( + my_ds = DpLoaderSet( + self.systems, + self.batch_size, model_params={ - 'descriptor': { - 'type': 'se_e2_a', - 'sel': self.sel, - 'rcut_smth': self.rcut_smth, - 'rcut': self.rcut, - 'neuron': self.filter_neuron, - 'axis_neuron': self.axis_neuron, + "descriptor": { + "type": "se_e2_a", + "sel": self.sel, + "rcut": self.rcut, }, - 'fitting_net': { - 'neuron': self.n_neuron + "type_map": self.type_map, + }, + ) + sampled = make_stat_input( + my_ds.systems, my_ds.dataloaders, self.data_stat_nbatch + ) + my_model = get_model( + model_params={ + "descriptor": { + "type": "se_e2_a", + "sel": self.sel, + "rcut_smth": self.rcut_smth, + "rcut": self.rcut, + "neuron": self.filter_neuron, + "axis_neuron": self.axis_neuron, }, - 'data_stat_nbatch': self.data_stat_nbatch, - 'type_map': self.type_map, + "fitting_net": {"neuron": self.n_neuron}, + "data_stat_nbatch": self.data_stat_nbatch, + "type_map": self.type_map, }, - sampled=sampled + sampled=sampled, ) my_model.to(DEVICE) my_lr = MyLRExp(self.start_lr, self.stop_lr, self.decay_steps, self.stop_steps) @@ -268,22 +297,26 @@ def test_consistency(self): start_pref_e=self.start_pref_e, limit_pref_e=self.limit_pref_e, start_pref_f=self.start_pref_f, - limit_pref_f=self.limit_pref_f + limit_pref_f=self.limit_pref_f, ) # Keep statistics consistency between 2 implentations my_em = my_model.descriptor - mean = stat_dict['descriptor.mean'].reshape([self.ntypes, my_em.get_nsel(), 4]) - stddev = stat_dict['descriptor.stddev'].reshape([self.ntypes, my_em.get_nsel(), 4]) + mean = stat_dict["descriptor.mean"].reshape([self.ntypes, my_em.get_nsel(), 4]) + stddev = stat_dict["descriptor.stddev"].reshape( + [self.ntypes, my_em.get_nsel(), 4] + ) my_em.set_stat_mean_and_stddev( - torch.tensor(mean, device=DEVICE), - torch.tensor(stddev, device=DEVICE), + torch.tensor(mean, device=DEVICE), + torch.tensor(stddev, device=DEVICE), + ) + my_model.fitting_net.bias_atom_e = torch.tensor( + stat_dict["fitting_net.bias_atom_e"], device=DEVICE ) - my_model.fitting_net.bias_atom_e = torch.tensor(stat_dict['fitting_net.bias_atom_e'], device=DEVICE) # Keep parameter value consistency between 2 implentations for name, param in my_model.named_parameters(): - name = name.replace('sea.', '') + name = name.replace("sea.", "") var_name = torch2tf(name) var = vs_dict[var_name].value with torch.no_grad(): @@ -295,28 +328,64 @@ def test_consistency(self): dst.copy_(src) # Start forward computing batch = my_ds.systems[0]._data_system.preprocess(batch) - batch['coord'].requires_grad_(True) - batch['natoms'] = torch.tensor(batch['natoms_vec'], device=batch['coord'].device).unsqueeze(0) - model_predict = my_model(batch['coord'], batch['atype'], batch['box'], do_atomic_virial=True) - model_predict_1 = my_model(batch['coord'], batch['atype'], batch['box'], do_atomic_virial=False) - p_energy, p_force, p_virial, p_atomic_virial = model_predict['energy'], model_predict['force'], model_predict['virial'], model_predict['atomic_virial'] + batch["coord"].requires_grad_(True) + batch["natoms"] = torch.tensor( + batch["natoms_vec"], device=batch["coord"].device + ).unsqueeze(0) + model_predict = my_model( + batch["coord"], batch["atype"], batch["box"], do_atomic_virial=True + ) + model_predict_1 = my_model( + batch["coord"], batch["atype"], batch["box"], do_atomic_virial=False + ) + p_energy, p_force, p_virial, p_atomic_virial = ( + model_predict["energy"], + model_predict["force"], + model_predict["virial"], + model_predict["atomic_virial"], + ) cur_lr = my_lr.value(self.wanted_step) - model_pred = {'energy': p_energy, - 'force': p_force, - } - label = {'energy': batch['energy'], - 'force': batch['force'], - } - loss, _ = my_loss(model_pred, label, int(batch['natoms'][0, 0]), cur_lr) - np.testing.assert_allclose(head_dict['energy'], p_energy.view(-1).cpu().detach().numpy()) - np.testing.assert_allclose(head_dict['force'], p_force.view(*head_dict['force'].shape).cpu().detach().numpy()) + model_pred = { + "energy": p_energy, + "force": p_force, + } + label = { + "energy": batch["energy"], + "force": batch["force"], + } + loss, _ = my_loss(model_pred, label, int(batch["natoms"][0, 0]), cur_lr) + np.testing.assert_allclose( + head_dict["energy"], p_energy.view(-1).cpu().detach().numpy() + ) + np.testing.assert_allclose( + head_dict["force"], + p_force.view(*head_dict["force"].shape).cpu().detach().numpy(), + ) rtol = 1e-5 atol = 1e-8 - np.testing.assert_allclose(head_dict['loss'], loss.cpu().detach().numpy(), rtol=rtol, atol=atol) - np.testing.assert_allclose(head_dict['virial'], p_virial.view(*head_dict['virial'].shape).cpu().detach().numpy()) - np.testing.assert_allclose(head_dict['virial'], model_predict_1['virial'].view(*head_dict['virial'].shape).cpu().detach().numpy()) - self.assertIsNone(model_predict_1.get('atomic_virial', None)) - np.testing.assert_allclose(head_dict['atomic_virial'], p_atomic_virial.view(*head_dict['atomic_virial'].shape).cpu().detach().numpy()) + np.testing.assert_allclose( + head_dict["loss"], loss.cpu().detach().numpy(), rtol=rtol, atol=atol + ) + np.testing.assert_allclose( + head_dict["virial"], + p_virial.view(*head_dict["virial"].shape).cpu().detach().numpy(), + ) + np.testing.assert_allclose( + head_dict["virial"], + model_predict_1["virial"] + .view(*head_dict["virial"].shape) + .cpu() + .detach() + .numpy(), + ) + self.assertIsNone(model_predict_1.get("atomic_virial", None)) + np.testing.assert_allclose( + head_dict["atomic_virial"], + p_atomic_virial.view(*head_dict["atomic_virial"].shape) + .cpu() + .detach() + .numpy(), + ) optimizer = torch.optim.Adam(my_model.parameters(), lr=cur_lr) optimizer.zero_grad() @@ -328,7 +397,7 @@ def step(step_id): loss.backward() for name, param in my_model.named_parameters(): - name = name.replace('sea.', '') + name = name.replace("sea.", "") var_name = torch2tf(name) var_grad = vs_dict[var_name].gradient param_grad = param.grad.cpu() @@ -336,5 +405,5 @@ def step(step_id): assert np.allclose(var_grad, param_grad, rtol=rtol, atol=atol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_nlist.py b/tests/test_nlist.py index 493bc7eb..1987528e 100644 --- a/tests/test_nlist.py +++ b/tests/test_nlist.py @@ -1,155 +1,212 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest + import torch -from deepmd_pt.utils.preprocess import Region3D -from deepmd_pt.utils import env -from deepmd_pt.utils.region import ( - phys2inter, - inter2phys, - to_face_distance, + +from deepmd_pt.utils import ( + env, ) from deepmd_pt.utils.nlist import ( - extend_coord_with_ghosts, - build_neighbor_list, - build_multiple_neighbor_list, - get_multiple_nlist_key, + build_multiple_neighbor_list, + build_neighbor_list, + extend_coord_with_ghosts, + get_multiple_nlist_key, ) -from deepmd_pt.utils.preprocess import( - build_neighbor_list as legacy_build_neighbor_list, +from deepmd_pt.utils.region import ( + inter2phys, ) dtype = torch.float64 + class TestNeighList(unittest.TestCase): - def setUp(self): - self.nf = 3 - self.nloc = 2 - self.ns = 5 * 5 * 3 - self.nall = self.ns * self.nloc - self.cell = torch.tensor( - [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype).to(env.DEVICE) - self.icoord = torch.tensor([[0, 0, 0], [0.5, 0.5, 0.1]], dtype=dtype).to(env.DEVICE) - self.atype = torch.tensor([0, 1], dtype=torch.int).to(env.DEVICE) - [self.cell, self.icoord, self.atype] = [ - ii.unsqueeze(0) for ii in [self.cell, self.icoord, self.atype]] - self.coord = inter2phys(self.icoord, self.cell).view([-1, self.nloc*3]) - self.cell = self.cell.view([-1, 9]) - [self.cell, self.coord, self.atype] = [ - torch.tile(ii, [self.nf,1]) for ii in [self.cell, self.coord, self.atype]] - self.rcut = 1.01 - self.prec = 1e-10 - self.nsel = [10, 10] - # genrated by preprocess.build_neighbor_list - # ref_nlist, _, _ = legacy_build_neighbor_list( - # 2, ecoord[0], eatype[0], - # self.rcut, - # torch.tensor([10,20], dtype=torch.long), - # mapping[0], type_split=True, ) - self.ref_nlist = torch.tensor( - [[0, 0, 0, 0, 0, 0, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1], - [0, 0, 0, 0, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1]] - ).to(env.DEVICE) - - def test_build_notype(self): - ecoord, eatype, mapping = extend_coord_with_ghosts( - self.coord, self.atype, self.cell, self.rcut) - nlist = build_neighbor_list( - ecoord, eatype, self.nloc, - self.rcut, sum(self.nsel), distinguish_types=False) - torch.testing.assert_close( - nlist[0], nlist[1]) - nlist_mask = nlist[0] == -1 - nlist_loc = mapping[0][nlist[0]] - nlist_loc[nlist_mask] = -1 - torch.testing.assert_close( - torch.sort(nlist_loc, dim=-1)[0], - torch.sort(self.ref_nlist, dim=-1)[0], - ) + def setUp(self): + self.nf = 3 + self.nloc = 2 + self.ns = 5 * 5 * 3 + self.nall = self.ns * self.nloc + self.cell = torch.tensor( + [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype + ).to(env.DEVICE) + self.icoord = torch.tensor([[0, 0, 0], [0.5, 0.5, 0.1]], dtype=dtype).to( + env.DEVICE + ) + self.atype = torch.tensor([0, 1], dtype=torch.int).to(env.DEVICE) + [self.cell, self.icoord, self.atype] = [ + ii.unsqueeze(0) for ii in [self.cell, self.icoord, self.atype] + ] + self.coord = inter2phys(self.icoord, self.cell).view([-1, self.nloc * 3]) + self.cell = self.cell.view([-1, 9]) + [self.cell, self.coord, self.atype] = [ + torch.tile(ii, [self.nf, 1]) for ii in [self.cell, self.coord, self.atype] + ] + self.rcut = 1.01 + self.prec = 1e-10 + self.nsel = [10, 10] + # genrated by preprocess.build_neighbor_list + # ref_nlist, _, _ = legacy_build_neighbor_list( + # 2, ecoord[0], eatype[0], + # self.rcut, + # torch.tensor([10,20], dtype=torch.long), + # mapping[0], type_split=True, ) + self.ref_nlist = torch.tensor( + [ + [0, 0, 0, 0, 0, 0, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1], + ] + ).to(env.DEVICE) - def test_build_type(self): - ecoord, eatype, mapping = extend_coord_with_ghosts( - self.coord, self.atype, self.cell, self.rcut) - nlist = build_neighbor_list( - ecoord, eatype, self.nloc, - self.rcut, self.nsel, distinguish_types=True, - ) - torch.testing.assert_close(nlist[0], nlist[1]) - nlist_mask = nlist[0] == -1 - nlist_loc = mapping[0][nlist[0]] - nlist_loc[nlist_mask] = -1 - for ii in range(2): - torch.testing.assert_close( - torch.sort(torch.split(nlist_loc, self.nsel, dim=-1)[ii], dim=-1)[0], - torch.sort(torch.split(self.ref_nlist, self.nsel, dim=-1)[ii], dim=-1)[0], - ) + def test_build_notype(self): + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + nlist = build_neighbor_list( + ecoord, + eatype, + self.nloc, + self.rcut, + sum(self.nsel), + distinguish_types=False, + ) + torch.testing.assert_close(nlist[0], nlist[1]) + nlist_mask = nlist[0] == -1 + nlist_loc = mapping[0][nlist[0]] + nlist_loc[nlist_mask] = -1 + torch.testing.assert_close( + torch.sort(nlist_loc, dim=-1)[0], + torch.sort(self.ref_nlist, dim=-1)[0], + ) - def test_build_multiple_nlist(self): - rcuts = [1.01, 2.01] - nsels = [20, 80] - ecoord, eatype, mapping = extend_coord_with_ghosts( - self.coord, self.atype, self.cell, max(rcuts)) - nlist1 = build_neighbor_list( - ecoord, eatype, self.nloc, - rcuts[1], nsels[1]-1, - distinguish_types=False, - ) - pad = -1*torch.ones([self.nf,self.nloc,1], dtype=nlist1.dtype, device=nlist1.device) - nlist2 = torch.cat([nlist1, pad], dim=-1) - nlist0 = build_neighbor_list( - ecoord, eatype, self.nloc, - rcuts[0], nsels[0], - distinguish_types=False, - ) - nlists = build_multiple_neighbor_list(ecoord, nlist1, rcuts, nsels) - for dd in range(2): - self.assertEqual( - nlists[get_multiple_nlist_key(rcuts[dd], nsels[dd])].shape[-1], - nsels[dd], - ) - torch.testing.assert_close( - nlists[get_multiple_nlist_key(rcuts[0], nsels[0])], - nlist0, - ) - torch.testing.assert_close( - nlists[get_multiple_nlist_key(rcuts[1], nsels[1])], - nlist2, - ) + def test_build_type(self): + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + nlist = build_neighbor_list( + ecoord, + eatype, + self.nloc, + self.rcut, + self.nsel, + distinguish_types=True, + ) + torch.testing.assert_close(nlist[0], nlist[1]) + nlist_mask = nlist[0] == -1 + nlist_loc = mapping[0][nlist[0]] + nlist_loc[nlist_mask] = -1 + for ii in range(2): + torch.testing.assert_close( + torch.sort(torch.split(nlist_loc, self.nsel, dim=-1)[ii], dim=-1)[0], + torch.sort(torch.split(self.ref_nlist, self.nsel, dim=-1)[ii], dim=-1)[ + 0 + ], + ) - def test_extend_coord(self): - ecoord, eatype, mapping = extend_coord_with_ghosts( - self.coord, self.atype, self.cell, self.rcut) - # expected ncopy x nloc - self.assertEqual(list(ecoord.shape), [self.nf, self.nall*3]) - self.assertEqual(list(eatype.shape), [self.nf, self.nall]) - self.assertEqual(list(mapping.shape), [self.nf, self.nall]) - # check the nloc part is identical with original coord - torch.testing.assert_close( - ecoord[:,:self.nloc*3], self.coord, rtol=self.prec, atol=self.prec) - # check the shift vectors are aligned with grid - shift_vec = \ - ecoord.view([-1, self.ns, self.nloc, 3]) - \ - self.coord.view([-1, self.nloc, 3])[:,None,:,:] - shift_vec = shift_vec.view([-1, self.nall, 3]) - # hack!!! assumes identical cell across frames - shift_vec = torch.matmul(shift_vec, torch.linalg.inv(self.cell.view([self.nf,3,3])[0])) - # nf x nall x 3 - shift_vec = torch.round(shift_vec) - # check: identical shift vecs - torch.testing.assert_close( - shift_vec[0], shift_vec[1], rtol=self.prec, atol=self.prec) - # check: shift idx aligned with grid - mm, cc = torch.unique(shift_vec[0][:,0], dim=-1, return_counts=True) - torch.testing.assert_close( - mm, torch.tensor([-2,-1,0,1,2], dtype=dtype).to(env.DEVICE), rtol=self.prec, atol=self.prec) - torch.testing.assert_close( - cc, torch.tensor([30,30,30,30,30], dtype=torch.long).to(env.DEVICE), rtol=self.prec, atol=self.prec) - mm, cc = torch.unique(shift_vec[1][:,1], dim=-1, return_counts=True) - torch.testing.assert_close( - mm, torch.tensor([-2,-1,0,1,2], dtype=dtype).to(env.DEVICE), rtol=self.prec, atol=self.prec) - torch.testing.assert_close( - cc, torch.tensor([30,30,30,30,30], dtype=torch.long).to(env.DEVICE), rtol=self.prec, atol=self.prec) - mm, cc = torch.unique(shift_vec[1][:,2], dim=-1, return_counts=True) - torch.testing.assert_close( - mm, torch.tensor([-1,0,1], dtype=dtype).to(env.DEVICE), rtol=self.prec, atol=self.prec) - torch.testing.assert_close( - cc, torch.tensor([50,50,50], dtype=torch.long).to(env.DEVICE), rtol=self.prec, atol=self.prec) + def test_build_multiple_nlist(self): + rcuts = [1.01, 2.01] + nsels = [20, 80] + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, max(rcuts) + ) + nlist1 = build_neighbor_list( + ecoord, + eatype, + self.nloc, + rcuts[1], + nsels[1] - 1, + distinguish_types=False, + ) + pad = -1 * torch.ones( + [self.nf, self.nloc, 1], dtype=nlist1.dtype, device=nlist1.device + ) + nlist2 = torch.cat([nlist1, pad], dim=-1) + nlist0 = build_neighbor_list( + ecoord, + eatype, + self.nloc, + rcuts[0], + nsels[0], + distinguish_types=False, + ) + nlists = build_multiple_neighbor_list(ecoord, nlist1, rcuts, nsels) + for dd in range(2): + self.assertEqual( + nlists[get_multiple_nlist_key(rcuts[dd], nsels[dd])].shape[-1], + nsels[dd], + ) + torch.testing.assert_close( + nlists[get_multiple_nlist_key(rcuts[0], nsels[0])], + nlist0, + ) + torch.testing.assert_close( + nlists[get_multiple_nlist_key(rcuts[1], nsels[1])], + nlist2, + ) + def test_extend_coord(self): + ecoord, eatype, mapping = extend_coord_with_ghosts( + self.coord, self.atype, self.cell, self.rcut + ) + # expected ncopy x nloc + self.assertEqual(list(ecoord.shape), [self.nf, self.nall * 3]) + self.assertEqual(list(eatype.shape), [self.nf, self.nall]) + self.assertEqual(list(mapping.shape), [self.nf, self.nall]) + # check the nloc part is identical with original coord + torch.testing.assert_close( + ecoord[:, : self.nloc * 3], self.coord, rtol=self.prec, atol=self.prec + ) + # check the shift vectors are aligned with grid + shift_vec = ( + ecoord.view([-1, self.ns, self.nloc, 3]) + - self.coord.view([-1, self.nloc, 3])[:, None, :, :] + ) + shift_vec = shift_vec.view([-1, self.nall, 3]) + # hack!!! assumes identical cell across frames + shift_vec = torch.matmul( + shift_vec, torch.linalg.inv(self.cell.view([self.nf, 3, 3])[0]) + ) + # nf x nall x 3 + shift_vec = torch.round(shift_vec) + # check: identical shift vecs + torch.testing.assert_close( + shift_vec[0], shift_vec[1], rtol=self.prec, atol=self.prec + ) + # check: shift idx aligned with grid + mm, cc = torch.unique(shift_vec[0][:, 0], dim=-1, return_counts=True) + torch.testing.assert_close( + mm, + torch.tensor([-2, -1, 0, 1, 2], dtype=dtype).to(env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + torch.testing.assert_close( + cc, + torch.tensor([30, 30, 30, 30, 30], dtype=torch.long).to(env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + mm, cc = torch.unique(shift_vec[1][:, 1], dim=-1, return_counts=True) + torch.testing.assert_close( + mm, + torch.tensor([-2, -1, 0, 1, 2], dtype=dtype).to(env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + torch.testing.assert_close( + cc, + torch.tensor([30, 30, 30, 30, 30], dtype=torch.long).to(env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + mm, cc = torch.unique(shift_vec[1][:, 2], dim=-1, return_counts=True) + torch.testing.assert_close( + mm, + torch.tensor([-1, 0, 1], dtype=dtype).to(env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) + torch.testing.assert_close( + cc, + torch.tensor([50, 50, 50], dtype=torch.long).to(env.DEVICE), + rtol=self.prec, + atol=self.prec, + ) diff --git a/tests/test_permutation.py b/tests/test_permutation.py index ff77c464..f33d1c28 100644 --- a/tests/test_permutation.py +++ b/tests/test_permutation.py @@ -1,143 +1,57 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input -from deepmd_pt.infer.deep_eval import eval_model + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) dtype = torch.float64 model_se_e2_a = { - "type_map": ["O", "H", "B"], - "descriptor": { - "type": "se_e2_a", - "sel": [46, 92, 4], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [25, 50, 100], - "resnet_dt": False, - "axis_neuron": 16, - "seed": 1, - }, - "fitting_net": { - "neuron": [24, 24, 24], - "resnet_dt": True, - "seed": 1, - }, - "data_stat_nbatch": 20, + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_e2_a", + "sel": [46, 92, 4], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [25, 50, 100], + "resnet_dt": False, + "axis_neuron": 16, + "seed": 1, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, + "data_stat_nbatch": 20, } model_dpa2 = { - "type_map": ["O", "H", "B"], - "descriptor": { - "type": "dpa2", - "repinit_rcut": 6.0, - "repinit_rcut_smth": 2.0, - "repinit_nsel": 30, - "repformer_rcut": 4.0, - "repformer_rcut_smth": 0.5, - "repformer_nsel": 20, - "repinit_neuron": [2, 4, 8], - "repinit_axis_neuron": 4, - "repinit_activation": "tanh", - "repformer_nlayers": 12, - "repformer_g1_dim": 8, - "repformer_g2_dim": 5, - "repformer_attn2_hidden": 3, - "repformer_attn2_nhead": 1, - "repformer_attn1_hidden": 5, - "repformer_attn1_nhead": 1, - "repformer_axis_dim": 4, - "repformer_update_h2": False, - "repformer_update_g1_has_conv": True, - "repformer_update_g1_has_grrg": True, - "repformer_update_g1_has_drrd": True, - "repformer_update_g1_has_attn": True, - "repformer_update_g2_has_g1g1": True, - "repformer_update_g2_has_attn": True, - "repformer_attn2_has_gate": True, - "repformer_add_type_ebd_to_seq": False - }, - "fitting_net": { - "neuron": [24, 24], - "resnet_dt": True, - "seed": 1, - }, -} - -model_dpa1 = { - "type_map": ["O", "H", "B"], - "descriptor": { - "type": "se_atten", - "sel": 40, - "rcut_smth": 0.5, - "rcut": 4.0, - "neuron": [25, 50, 100], - "axis_neuron": 16, - "attn": 64, - "attn_layer": 2, - "attn_dotr": True, - "attn_mask": False, - "post_ln": True, - "ffn": False, - "ffn_embed_dim": 512, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": False, - "temperature": 1.0, - "set_davg_zero": True - }, - "fitting_net": { - "neuron": [24, 24, 24], - "resnet_dt": True, - "seed": 1, - }, -} - - -model_hybrid = { - "type_map": [ - "O", - "H", - "B" - ], - "descriptor": { - "type": "hybrid", - "list": [ - { - "type": "se_atten", - "sel": 120, - "rcut_smth": 0.5, - "rcut": 6.0, - "neuron": [ - 25, - 50, - 100 - ], - "axis_neuron": 16, - "attn": 128, - "attn_layer": 0, - "attn_dotr": True, - "attn_mask": False, - "post_ln": True, - "ffn": False, - "ffn_embed_dim": 1024, - "activation": "tanh", - "scaling_factor": 1.0, - "head_num": 1, - "normalize": True, - "temperature": 1.0 - }, - { + "type_map": ["O", "H", "B"], + "descriptor": { "type": "dpa2", "repinit_rcut": 6.0, "repinit_rcut_smth": 2.0, "repinit_nsel": 30, "repformer_rcut": 4.0, "repformer_rcut_smth": 0.5, - "repformer_nsel": 10, + "repformer_nsel": 20, "repinit_neuron": [2, 4, 8], "repinit_axis_neuron": 4, "repinit_activation": "tanh", @@ -157,115 +71,232 @@ "repformer_update_g2_has_g1g1": True, "repformer_update_g2_has_attn": True, "repformer_attn2_has_gate": True, - "repformer_add_type_ebd_to_seq": False - }, - ] - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": True, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" + "repformer_add_type_ebd_to_seq": False, + }, + "fitting_net": { + "neuron": [24, 24], + "resnet_dt": True, + "seed": 1, + }, +} + +model_dpa1 = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "se_atten", + "sel": 40, + "rcut_smth": 0.5, + "rcut": 4.0, + "neuron": [25, 50, 100], + "axis_neuron": 16, + "attn": 64, + "attn_layer": 2, + "attn_dotr": True, + "attn_mask": False, + "post_ln": True, + "ffn": False, + "ffn_embed_dim": 512, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": False, + "temperature": 1.0, + "set_davg_zero": True, + }, + "fitting_net": { + "neuron": [24, 24, 24], + "resnet_dt": True, + "seed": 1, + }, +} + + +model_hybrid = { + "type_map": ["O", "H", "B"], + "descriptor": { + "type": "hybrid", + "list": [ + { + "type": "se_atten", + "sel": 120, + "rcut_smth": 0.5, + "rcut": 6.0, + "neuron": [25, 50, 100], + "axis_neuron": 16, + "attn": 128, + "attn_layer": 0, + "attn_dotr": True, + "attn_mask": False, + "post_ln": True, + "ffn": False, + "ffn_embed_dim": 1024, + "activation": "tanh", + "scaling_factor": 1.0, + "head_num": 1, + "normalize": True, + "temperature": 1.0, + }, + { + "type": "dpa2", + "repinit_rcut": 6.0, + "repinit_rcut_smth": 2.0, + "repinit_nsel": 30, + "repformer_rcut": 4.0, + "repformer_rcut_smth": 0.5, + "repformer_nsel": 10, + "repinit_neuron": [2, 4, 8], + "repinit_axis_neuron": 4, + "repinit_activation": "tanh", + "repformer_nlayers": 12, + "repformer_g1_dim": 8, + "repformer_g2_dim": 5, + "repformer_attn2_hidden": 3, + "repformer_attn2_nhead": 1, + "repformer_attn1_hidden": 5, + "repformer_attn1_nhead": 1, + "repformer_axis_dim": 4, + "repformer_update_h2": False, + "repformer_update_g1_has_conv": True, + "repformer_update_g1_has_grrg": True, + "repformer_update_g1_has_drrd": True, + "repformer_update_g1_has_attn": True, + "repformer_update_g2_has_g1g1": True, + "repformer_update_g2_has_attn": True, + "repformer_attn2_has_gate": True, + "repformer_add_type_ebd_to_seq": False, + }, + ], + }, + "fitting_net": { + "neuron": [240, 240, 240], + "resnet_dt": True, + "seed": 1, + "_comment": " that's all", + }, + "_comment": " that's all", } def make_sample(model_params): - training_systems = ["tests/water/data/data_0", ] - data_stat_nbatch = model_params.get('data_stat_nbatch', 10) - train_data = DpLoaderSet( - training_systems, batch_size=4, model_params=model_params.copy(), - ) - sampled = make_stat_input( - train_data.systems, train_data.dataloaders, data_stat_nbatch) - return sampled + training_systems = [ + "tests/water/data/data_0", + ] + data_stat_nbatch = model_params.get("data_stat_nbatch", 10) + train_data = DpLoaderSet( + training_systems, + batch_size=4, + model_params=model_params.copy(), + ) + sampled = make_stat_input( + train_data.systems, train_data.dataloaders, data_stat_nbatch + ) + return sampled class TestPermutation: - def test( - self, - ): - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - idx_perm = [1, 0, 4, 3, 2] - e0, f0, v0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) - ret0 = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - e1, f1, v1 = eval_model(self.model, coord[idx_perm].unsqueeze(0), cell.unsqueeze(0), atype[idx_perm]) - ret1 = {'energy': e1.squeeze(0), 'force': f1.squeeze(0), 'virial': v1.squeeze(0)} - prec = 1e-10 - torch.testing.assert_close(ret0['energy'], ret1['energy'], rtol=prec, atol=prec) - torch.testing.assert_close(ret0['force'][idx_perm], ret1['force'], rtol=prec, atol=prec) - if not hasattr(self, "test_virial") or self.test_virial: - torch.testing.assert_close(ret0['virial'], ret1['virial'], rtol=prec, atol=prec) + def test( + self, + ): + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + idx_perm = [1, 0, 4, 3, 2] + e0, f0, v0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret0 = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + e1, f1, v1 = eval_model( + self.model, coord[idx_perm].unsqueeze(0), cell.unsqueeze(0), atype[idx_perm] + ) + ret1 = { + "energy": e1.squeeze(0), + "force": f1.squeeze(0), + "virial": v1.squeeze(0), + } + prec = 1e-10 + torch.testing.assert_close(ret0["energy"], ret1["energy"], rtol=prec, atol=prec) + torch.testing.assert_close( + ret0["force"][idx_perm], ret1["force"], rtol=prec, atol=prec + ) + if not hasattr(self, "test_virial") or self.test_virial: + torch.testing.assert_close( + ret0["virial"], ret1["virial"], rtol=prec, atol=prec + ) class TestEnergyModelSeA(unittest.TestCase, TestPermutation): - def setUp(self): - model_params = copy.deepcopy(model_se_e2_a) - sampled = make_sample(model_params) - self.type_split = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + sampled = make_sample(model_params) + self.type_split = False + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestEnergyModelDPA1(unittest.TestCase, TestPermutation): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestEnergyModelDPA2(unittest.TestCase, TestPermutation): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestForceModelDPA2(unittest.TestCase, TestPermutation): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - model_params["fitting_net"]["type"] = "direct_force_ener" - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) @unittest.skip("hybrid not supported at the moment") class TestEnergyModelHybrid(unittest.TestCase, TestPermutation): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) @unittest.skip("hybrid not supported at the moment") class TestForceModelHybrid(unittest.TestCase, TestPermutation): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - model_params["fitting_net"]["type"] = "direct_force_ener" - sampled = make_sample(model_params) - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + sampled = make_sample(model_params) + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) # class TestEnergyFoo(unittest.TestCase): @@ -279,10 +310,10 @@ def setUp(self): # cell = (cell + cell.T) + 5. * torch.eye(3) # coord = torch.rand([natoms, 3], dtype=dtype) # coord = torch.matmul(coord, cell) -# atype = torch.IntTensor([0, 0, 0, 1, 1]) -# idx_perm = [1, 0, 4, 3, 2] +# atype = torch.IntTensor([0, 0, 0, 1, 1]) +# idx_perm = [1, 0, 4, 3, 2] # ret0 = infer_model(self.model, coord, cell, atype, type_split=True) -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_permutation_denoise.py b/tests/test_permutation_denoise.py index 77cf74a5..aaff8fd1 100644 --- a/tests/test_permutation_denoise.py +++ b/tests/test_permutation_denoise.py @@ -1,16 +1,24 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input -from deepmd_pt.infer.deep_eval import eval_model -from .test_permutation import ( - make_sample, - model_dpa1, - model_dpa2, - # model_dpau, - model_hybrid, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) + +from .test_permutation import ( # model_dpau, + make_sample, + model_dpa1, + model_dpa2, + model_hybrid, ) dtype = torch.float64 @@ -25,49 +33,70 @@ model_hybrid["type_map"] = ["O", "H", "B", "MASKED_TOKEN"] model_hybrid.pop("fitting_net") + class TestPermutationDenoise: - def test( - self, - ): - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - idx_perm = [1, 0, 4, 3, 2] - updated_c0, logits0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True) - ret0 = {'updated_coord': updated_c0.squeeze(0), 'logits': logits0.squeeze(0)} - updated_c1, logits1 = eval_model(self.model, coord[idx_perm].unsqueeze(0), cell.unsqueeze(0), atype[idx_perm], denoise=True) - ret1 = {'updated_coord': updated_c1.squeeze(0), 'logits': logits1.squeeze(0)} - prec = 1e-10 - torch.testing.assert_close(ret0['updated_coord'][idx_perm], ret1['updated_coord'], rtol=prec, atol=prec) - torch.testing.assert_close(ret0['logits'][idx_perm], ret1['logits'], rtol=prec, atol=prec) + def test( + self, + ): + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + idx_perm = [1, 0, 4, 3, 2] + updated_c0, logits0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret0 = {"updated_coord": updated_c0.squeeze(0), "logits": logits0.squeeze(0)} + updated_c1, logits1 = eval_model( + self.model, + coord[idx_perm].unsqueeze(0), + cell.unsqueeze(0), + atype[idx_perm], + denoise=True, + ) + ret1 = {"updated_coord": updated_c1.squeeze(0), "logits": logits1.squeeze(0)} + prec = 1e-10 + torch.testing.assert_close( + ret0["updated_coord"][idx_perm], ret1["updated_coord"], rtol=prec, atol=prec + ) + torch.testing.assert_close( + ret0["logits"][idx_perm], ret1["logits"], rtol=prec, atol=prec + ) + class TestDenoiseModelDPA1(unittest.TestCase, TestPermutationDenoise): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestDenoiseModelDPA2(unittest.TestCase, TestPermutationDenoise): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + + +# @unittest.skip("hybrid not supported at the moment") +# class TestDenoiseModelHybrid(unittest.TestCase, TestPermutationDenoise): +# def setUp(self): +# model_params = copy.deepcopy(model_hybrid_denoise) +# sampled = make_sample(model_params) +# self.type_split = True +# self.model = get_model(model_params, sampled).to(env.DEVICE) -@unittest.skip("hybrid not supported at the moment") -class TestDenoiseModelHybrid(unittest.TestCase, TestPermutationDenoise): - def setUp(self): - model_params = copy.deepcopy(model_hybrid_denoise) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_region.py b/tests/test_region.py index 669487c0..98d3271e 100644 --- a/tests/test_region.py +++ b/tests/test_region.py @@ -1,69 +1,78 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest + +import numpy as np import torch -from deepmd_pt.utils.preprocess import Region3D + +from deepmd_pt.utils.preprocess import ( + Region3D, +) from deepmd_pt.utils.region import ( - phys2inter, - inter2phys, - to_face_distance, + inter2phys, + to_face_distance, ) -import numpy as np dtype = torch.float64 + class TestRegion(unittest.TestCase): - def setUp(self): - self.cell = torch.tensor( - [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype) - self.cell = self.cell.unsqueeze(0).unsqueeze(0) - self.cell = torch.tile(self.cell, [4, 5, 1, 1]) - self.prec = 1e-8 - - def test_inter_to_phys(self): - inter = torch.rand([4, 5, 3, 3], dtype=dtype) - phys = inter2phys(inter, self.cell) - for ii in range(4): - for jj in range(5): - expected_phys = torch.matmul(inter[ii,jj], self.cell[ii,jj]) - torch.testing.assert_close( - phys[ii,jj], expected_phys, rtol=self.prec, atol=self.prec) + def setUp(self): + self.cell = torch.tensor( + [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype + ) + self.cell = self.cell.unsqueeze(0).unsqueeze(0) + self.cell = torch.tile(self.cell, [4, 5, 1, 1]) + self.prec = 1e-8 - def test_to_face_dist(self): - cell0 = self.cell[0][0].numpy() - vol = np.linalg.det(cell0) - # area of surfaces xy, xz, yz - sxy = np.linalg.norm(np.cross(cell0[0], cell0[1])) - sxz = np.linalg.norm(np.cross(cell0[0], cell0[2])) - syz = np.linalg.norm(np.cross(cell0[1], cell0[2])) - # vol / area gives distance - dz = vol / sxy - dy = vol / sxz - dx = vol / syz - expected = torch.tensor([dx, dy, dz]) - dists = to_face_distance(self.cell) - for ii in range(4): - for jj in range(5): - torch.testing.assert_close( - dists[ii][jj], expected, rtol=self.prec, atol=self.prec) + def test_inter_to_phys(self): + inter = torch.rand([4, 5, 3, 3], dtype=dtype) + phys = inter2phys(inter, self.cell) + for ii in range(4): + for jj in range(5): + expected_phys = torch.matmul(inter[ii, jj], self.cell[ii, jj]) + torch.testing.assert_close( + phys[ii, jj], expected_phys, rtol=self.prec, atol=self.prec + ) + + def test_to_face_dist(self): + cell0 = self.cell[0][0].numpy() + vol = np.linalg.det(cell0) + # area of surfaces xy, xz, yz + sxy = np.linalg.norm(np.cross(cell0[0], cell0[1])) + sxz = np.linalg.norm(np.cross(cell0[0], cell0[2])) + syz = np.linalg.norm(np.cross(cell0[1], cell0[2])) + # vol / area gives distance + dz = vol / sxy + dy = vol / sxz + dx = vol / syz + expected = torch.tensor([dx, dy, dz]) + dists = to_face_distance(self.cell) + for ii in range(4): + for jj in range(5): + torch.testing.assert_close( + dists[ii][jj], expected, rtol=self.prec, atol=self.prec + ) class TestLegacyRegion(unittest.TestCase): - def setUp(self): - self.cell = torch.tensor( - [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype) - self.prec = 1e-6 + def setUp(self): + self.cell = torch.tensor( + [[1, 0, 0], [0.4, 0.8, 0], [0.1, 0.3, 2.1]], dtype=dtype + ) + self.prec = 1e-6 + + def test_inter_to_phys(self): + inter = torch.rand([3, 3], dtype=dtype) + reg = Region3D(self.cell) + phys = reg.inter2phys(inter) + expected_phys = torch.matmul(inter, self.cell) + torch.testing.assert_close(phys, expected_phys, rtol=self.prec, atol=self.prec) - def test_inter_to_phys(self): - inter = torch.rand([3, 3], dtype=dtype) - reg = Region3D(self.cell) - phys = reg.inter2phys(inter) - expected_phys = torch.matmul(inter, self.cell) - torch.testing.assert_close(phys, expected_phys, rtol=self.prec, atol=self.prec) + def test_inter_to_inter(self): + inter = torch.rand([3, 3], dtype=dtype) + reg = Region3D(self.cell) + new_inter = reg.phys2inter(reg.inter2phys(inter)) + torch.testing.assert_close(inter, new_inter, rtol=self.prec, atol=self.prec) - def test_inter_to_inter(self): - inter = torch.rand([3, 3], dtype=dtype) - reg = Region3D(self.cell) - new_inter = reg.phys2inter(reg.inter2phys(inter)) - torch.testing.assert_close(inter, new_inter, rtol=self.prec, atol=self.prec) - - def test_to_face_dist(self): - pass + def test_to_face_dist(self): + pass diff --git a/tests/test_rot.py b/tests/test_rot.py index f21106ed..2e343824 100644 --- a/tests/test_rot.py +++ b/tests/test_rot.py @@ -1,125 +1,181 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils.preprocess import ( - Region3D, make_env_mat, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, ) -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input -from .test_permutation import ( - make_sample, - model_se_e2_a, - model_dpa1, - model_dpa2, - # model_dpau, - model_hybrid, +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) + +from .test_permutation import ( # model_dpau, + make_sample, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 -class TestRot(): - def test( - self, - ): - prec = 1e-10 - natoms = 5 - cell = 10. * torch.eye(3, dtype=dtype).to(env.DEVICE) - coord = 2*torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - shift = torch.tensor([4, 4, 4], dtype=dtype).to(env.DEVICE) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - from scipy.stats import special_ortho_group - rmat = torch.tensor(special_ortho_group.rvs(3), dtype=dtype).to(env.DEVICE) - - # rotate only coord and shift to the center of cell - coord_rot = torch.matmul(coord, rmat) - e0, f0, v0 = eval_model(self.model, (coord + shift).unsqueeze(0), cell.unsqueeze(0), atype) - ret0 = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - e1, f1, v1 = eval_model(self.model, (coord_rot + shift).unsqueeze(0), cell.unsqueeze(0), atype) - ret1 = {'energy': e1.squeeze(0), 'force': f1.squeeze(0), 'virial': v1.squeeze(0)} - torch.testing.assert_close(ret0['energy'], ret1['energy'], rtol=prec, atol=prec) - torch.testing.assert_close(torch.matmul(ret0['force'], rmat), ret1['force'], rtol=prec, atol=prec) - if not hasattr(self, "test_virial") or self.test_virial: - torch.testing.assert_close( - torch.matmul(rmat.T, torch.matmul(ret0['virial'], rmat)), - ret1['virial'], rtol=prec, atol=prec) - - # rotate coord and cell - torch.manual_seed(0) - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - coord_rot = torch.matmul(coord, rmat) - cell_rot = torch.matmul(cell, rmat) - e0, f0, v0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) - ret0 = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - e1, f1, v1 = eval_model(self.model, coord_rot.unsqueeze(0), cell_rot.unsqueeze(0), atype) - ret1 = {'energy': e1.squeeze(0), 'force': f1.squeeze(0), 'virial': v1.squeeze(0)} - torch.testing.assert_close(ret0['energy'], ret1['energy'], rtol=prec, atol=prec) - torch.testing.assert_close(torch.matmul(ret0['force'], rmat), ret1['force'], rtol=prec, atol=prec) - if not hasattr(self, "test_virial") or self.test_virial: - torch.testing.assert_close( - torch.matmul(rmat.T, torch.matmul(ret0['virial'], rmat)), - ret1['virial'], rtol=prec, atol=prec) + +class TestRot: + def test( + self, + ): + prec = 1e-10 + natoms = 5 + cell = 10.0 * torch.eye(3, dtype=dtype).to(env.DEVICE) + coord = 2 * torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + shift = torch.tensor([4, 4, 4], dtype=dtype).to(env.DEVICE) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + from scipy.stats import ( + special_ortho_group, + ) + + rmat = torch.tensor(special_ortho_group.rvs(3), dtype=dtype).to(env.DEVICE) + + # rotate only coord and shift to the center of cell + coord_rot = torch.matmul(coord, rmat) + e0, f0, v0 = eval_model( + self.model, (coord + shift).unsqueeze(0), cell.unsqueeze(0), atype + ) + ret0 = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + e1, f1, v1 = eval_model( + self.model, (coord_rot + shift).unsqueeze(0), cell.unsqueeze(0), atype + ) + ret1 = { + "energy": e1.squeeze(0), + "force": f1.squeeze(0), + "virial": v1.squeeze(0), + } + torch.testing.assert_close(ret0["energy"], ret1["energy"], rtol=prec, atol=prec) + torch.testing.assert_close( + torch.matmul(ret0["force"], rmat), ret1["force"], rtol=prec, atol=prec + ) + if not hasattr(self, "test_virial") or self.test_virial: + torch.testing.assert_close( + torch.matmul(rmat.T, torch.matmul(ret0["virial"], rmat)), + ret1["virial"], + rtol=prec, + atol=prec, + ) + + # rotate coord and cell + torch.manual_seed(0) + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + coord_rot = torch.matmul(coord, rmat) + cell_rot = torch.matmul(cell, rmat) + e0, f0, v0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret0 = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + e1, f1, v1 = eval_model( + self.model, coord_rot.unsqueeze(0), cell_rot.unsqueeze(0), atype + ) + ret1 = { + "energy": e1.squeeze(0), + "force": f1.squeeze(0), + "virial": v1.squeeze(0), + } + torch.testing.assert_close(ret0["energy"], ret1["energy"], rtol=prec, atol=prec) + torch.testing.assert_close( + torch.matmul(ret0["force"], rmat), ret1["force"], rtol=prec, atol=prec + ) + if not hasattr(self, "test_virial") or self.test_virial: + torch.testing.assert_close( + torch.matmul(rmat.T, torch.matmul(ret0["virial"], rmat)), + ret1["virial"], + rtol=prec, + atol=prec, + ) class TestEnergyModelSeA(unittest.TestCase, TestRot): - def setUp(self): - model_params = copy.deepcopy(model_se_e2_a) - sampled = make_sample(model_params) - self.type_split = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + sampled = make_sample(model_params) + self.type_split = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestEnergyModelDPA1(unittest.TestCase, TestRot): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestEnergyModelDPA2(unittest.TestCase, TestRot): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestForceModelDPA2(unittest.TestCase, TestRot): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - model_params["fitting_net"]["type"] = "direct_force_ener" - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + @unittest.skip("hybrid not supported at the moment") class TestEnergyModelHybrid(unittest.TestCase, TestRot): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + @unittest.skip("hybrid not supported at the moment") class TestForceModelHybrid(unittest.TestCase, TestRot): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - model_params["fitting_net"]["type"] = "direct_force_ener" - sampled = make_sample(model_params) - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + sampled = make_sample(model_params) + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_rot_denoise.py b/tests/test_rot_denoise.py index 3c63d2bc..8acb288e 100644 --- a/tests/test_rot_denoise.py +++ b/tests/test_rot_denoise.py @@ -1,86 +1,133 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils.preprocess import ( - Region3D, make_env_mat, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, ) -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input + from .test_permutation_denoise import ( - make_sample, - model_dpa1, - model_dpa2, - model_hybrid, + make_sample, + model_dpa1, + model_dpa2, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 -class TestRotDenoise(): - def test( - self, - ): - prec = 1e-10 - natoms = 5 - cell = 10. * torch.eye(3, dtype=dtype).to(env.DEVICE) - coord = 2*torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - shift = torch.tensor([4, 4, 4], dtype=dtype).to(env.DEVICE) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - from scipy.stats import special_ortho_group - rmat = torch.tensor(special_ortho_group.rvs(3), dtype=dtype).to(env.DEVICE) - - # rotate only coord and shift to the center of cell - coord_rot = torch.matmul(coord, rmat) - update_c0, logits0 = eval_model(self.model, (coord + shift).unsqueeze(0), cell.unsqueeze(0), atype, denoise=True) - update_c0 = update_c0 - (coord + shift).unsqueeze(0) - ret0 = {'updated_coord': update_c0.squeeze(0), 'logits': logits0.squeeze(0)} - update_c1, logits1 = eval_model(self.model, (coord_rot + shift).unsqueeze(0), cell.unsqueeze(0), atype, denoise = True) - update_c1 = update_c1 - (coord_rot + shift).unsqueeze(0) - ret1 = {'updated_coord': update_c1.squeeze(0), 'logits': logits1.squeeze(0)} - torch.testing.assert_close(torch.matmul(ret0['updated_coord'], rmat), ret1['updated_coord'], rtol=prec, atol=prec) - torch.testing.assert_close(ret0['logits'], ret1['logits'], rtol=prec, atol=prec) - - # rotate coord and cell - torch.manual_seed(0) - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - coord_rot = torch.matmul(coord, rmat) - cell_rot = torch.matmul(cell, rmat) - update_c0, logits0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True) - ret0 = {'updated_coord': update_c0.squeeze(0), 'logits': logits0.squeeze(0)} - update_c1, logits1 = eval_model(self.model, coord_rot.unsqueeze(0), cell_rot.unsqueeze(0), atype, denoise=True) - ret1 = {'updated_coord': update_c1.squeeze(0), 'logits': logits1.squeeze(0)} - torch.testing.assert_close(ret0['logits'], ret1['logits'], rtol=prec, atol=prec) - torch.testing.assert_close(torch.matmul(ret0['updated_coord'], rmat), ret1['updated_coord'], rtol=prec, atol=prec) + +class TestRotDenoise: + def test( + self, + ): + prec = 1e-10 + natoms = 5 + cell = 10.0 * torch.eye(3, dtype=dtype).to(env.DEVICE) + coord = 2 * torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + shift = torch.tensor([4, 4, 4], dtype=dtype).to(env.DEVICE) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + from scipy.stats import ( + special_ortho_group, + ) + + rmat = torch.tensor(special_ortho_group.rvs(3), dtype=dtype).to(env.DEVICE) + + # rotate only coord and shift to the center of cell + coord_rot = torch.matmul(coord, rmat) + update_c0, logits0 = eval_model( + self.model, + (coord + shift).unsqueeze(0), + cell.unsqueeze(0), + atype, + denoise=True, + ) + update_c0 = update_c0 - (coord + shift).unsqueeze(0) + ret0 = {"updated_coord": update_c0.squeeze(0), "logits": logits0.squeeze(0)} + update_c1, logits1 = eval_model( + self.model, + (coord_rot + shift).unsqueeze(0), + cell.unsqueeze(0), + atype, + denoise=True, + ) + update_c1 = update_c1 - (coord_rot + shift).unsqueeze(0) + ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} + torch.testing.assert_close( + torch.matmul(ret0["updated_coord"], rmat), + ret1["updated_coord"], + rtol=prec, + atol=prec, + ) + torch.testing.assert_close(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + + # rotate coord and cell + torch.manual_seed(0) + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + coord_rot = torch.matmul(coord, rmat) + cell_rot = torch.matmul(cell, rmat) + update_c0, logits0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret0 = {"updated_coord": update_c0.squeeze(0), "logits": logits0.squeeze(0)} + update_c1, logits1 = eval_model( + self.model, + coord_rot.unsqueeze(0), + cell_rot.unsqueeze(0), + atype, + denoise=True, + ) + ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} + torch.testing.assert_close(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + torch.testing.assert_close( + torch.matmul(ret0["updated_coord"], rmat), + ret1["updated_coord"], + rtol=prec, + atol=prec, + ) + class TestDenoiseModelDPA1(unittest.TestCase, TestRotDenoise): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestDenoiseModelDPA2(unittest.TestCase, TestRotDenoise): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - -@unittest.skip("hybrid not supported at the moment") -class TestEnergyModelHybrid(unittest.TestCase, TestRotDenoise): - def setUp(self): - model_params = copy.deepcopy(model_hybrid_denoise) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - -if __name__ == '__main__': + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + + +# @unittest.skip("hybrid not supported at the moment") +# class TestEnergyModelHybrid(unittest.TestCase, TestRotDenoise): +# def setUp(self): +# model_params = copy.deepcopy(model_hybrid_denoise) +# sampled = make_sample(model_params) +# self.type_split = True +# self.model = get_model(model_params, sampled).to(env.DEVICE) + + +if __name__ == "__main__": unittest.main() diff --git a/tests/test_rotation.py b/tests/test_rotation.py index bec2f888..1eda8908 100644 --- a/tests/test_rotation.py +++ b/tests/test_rotation.py @@ -1,65 +1,103 @@ -from typing import List -import unittest -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later import json -from scipy.stats import special_ortho_group +import unittest +from typing import ( + List, + Optional, +) + import numpy as np +import torch +from scipy.stats import ( + special_ortho_group, +) -from deepmd_pt.utils import env -from deepmd_pt.utils.dataset import DeepmdDataSystem -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.dataset import ( + DeepmdDataSystem, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) class CheckSymmetry(DeepmdDataSystem): - def __init__(self, sys_path: str, rcut, sec, type_map: List[str] = None, type_split=True): + def __init__( + self, + sys_path: str, + rcut, + sec, + type_map: Optional[List[str]] = None, + type_split=True, + ): super().__init__(sys_path, rcut, sec, type_map, type_split) def get_rotation(self, index, rotation_matrix): - for i in range(0, - len(self._dirs) + 1): # note: if different sets can be merged, prefix sum is unused to calculate + for i in range( + 0, len(self._dirs) + 1 + ): # note: if different sets can be merged, prefix sum is unused to calculate if index < self.prefix_sum[i]: break frames = self._load_set(self._dirs[i - 1]) - frames['coord'] = np.dot(rotation_matrix, frames['coord'].reshape(-1, 3).T).T.reshape(self.nframes, -1) - frames['box'] = np.dot(rotation_matrix, frames['box'].reshape(-1, 3).T).T.reshape(self.nframes, -1) - frames['force'] = np.dot(rotation_matrix, frames['force'].reshape(-1, 3).T).T.reshape(self.nframes, -1) + frames["coord"] = np.dot( + rotation_matrix, frames["coord"].reshape(-1, 3).T + ).T.reshape(self.nframes, -1) + frames["box"] = np.dot( + rotation_matrix, frames["box"].reshape(-1, 3).T + ).T.reshape(self.nframes, -1) + frames["force"] = np.dot( + rotation_matrix, frames["force"].reshape(-1, 3).T + ).T.reshape(self.nframes, -1) frame = self.single_preprocess(frames, index - self.prefix_sum[i - 1]) return frame def get_data(batch): inputs = {} - for key in ['coord', 'atype', 'box']: + for key in ["coord", "atype", "box"]: inputs[key] = batch[key].unsqueeze(0).to(env.DEVICE) return inputs class TestRotation(unittest.TestCase): def setUp(self): - np.random.seed(20) - with open(env.TEST_CONFIG, 'r') as fin: + with open(env.TEST_CONFIG) as fin: self.config = json.load(fin) self.rotation = special_ortho_group.rvs(3) self.get_dataset(0) self.get_model() def get_model(self): - training_systems = self.config['training']['training_data']['systems'] - model_params = self.config['model'] - data_stat_nbatch = model_params.get('data_stat_nbatch', 10) - train_data = DpLoaderSet(training_systems, self.config['training']['training_data']['batch_size'], model_params) - sampled = make_stat_input(train_data.systems, train_data.dataloaders, data_stat_nbatch) - self.model = get_model(self.config['model'], sampled).to(env.DEVICE) + training_systems = self.config["training"]["training_data"]["systems"] + model_params = self.config["model"] + data_stat_nbatch = model_params.get("data_stat_nbatch", 10) + train_data = DpLoaderSet( + training_systems, + self.config["training"]["training_data"]["batch_size"], + model_params, + ) + sampled = make_stat_input( + train_data.systems, train_data.dataloaders, data_stat_nbatch + ) + self.model = get_model(self.config["model"], sampled).to(env.DEVICE) def get_dataset(self, system_index=0, batch_index=0): - systems = self.config['training']['training_data']['systems'] - rcut = self.config['model']['descriptor']['rcut'] - sel = self.config['model']['descriptor']['sel'] + systems = self.config["training"]["training_data"]["systems"] + rcut = self.config["model"]["descriptor"]["rcut"] + sel = self.config["model"]["descriptor"]["sel"] sec = torch.cumsum(torch.tensor(sel), dim=0) - type_map = self.config['model']['type_map'] - dpdatasystem = CheckSymmetry(sys_path=systems[system_index], rcut=rcut, sec=sec, type_map=type_map) + type_map = self.config["model"]["type_map"] + dpdatasystem = CheckSymmetry( + sys_path=systems[system_index], rcut=rcut, sec=sec, type_map=type_map + ) self.origin_batch = dpdatasystem._get_item(batch_index) self.rotated_batch = dpdatasystem.get_rotation(batch_index, self.rotation) @@ -67,13 +105,23 @@ def test_rotation(self): result1 = self.model(**get_data(self.origin_batch)) result2 = self.model(**get_data(self.rotated_batch)) rotation = torch.from_numpy(self.rotation).to(env.DEVICE) - self.assertTrue(result1['energy'] == result2['energy']) - if 'force' in result1: - self.assertTrue(torch.allclose(result2['force'][0], torch.matmul(rotation, result1['force'][0].T).T)) - if 'virial' in result1: - self.assertTrue(torch.allclose(result2['virial'][0], - torch.matmul(torch.matmul(rotation, result1['virial'][0].T), rotation.T))) + self.assertTrue(result1["energy"] == result2["energy"]) + if "force" in result1: + self.assertTrue( + torch.allclose( + result2["force"][0], torch.matmul(rotation, result1["force"][0].T).T + ) + ) + if "virial" in result1: + self.assertTrue( + torch.allclose( + result2["virial"][0], + torch.matmul( + torch.matmul(rotation, result1["virial"][0].T), rotation.T + ), + ) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_sampler.py b/tests/test_sampler.py index 84418ce7..37382dc6 100644 --- a/tests/test_sampler.py +++ b/tests/test_sampler.py @@ -1,95 +1,112 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import os import unittest -import json -from deepmd.utils.data_system import DeepmdDataSystem -from deepmd.utils import random as tf_random -from deepmd.common import expand_sys_str -from torch.utils.data import DataLoader -from deepmd_pt.utils.dataloader import DpLoaderSet, get_weighted_sampler -from deepmd_pt.utils import env +import numpy as np +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.utils import random as tf_random +from deepmd.tf.utils.data_system import ( + DeepmdDataSystem, +) +from torch.utils.data import ( + DataLoader, +) -CUR_DIR = os.path.dirname(__file__) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, + get_weighted_sampler, +) +CUR_DIR = os.path.dirname(__file__) class TestSampler(unittest.TestCase): - def setUp(self): - with open(env.TEST_CONFIG, 'r') as fin: + with open(env.TEST_CONFIG) as fin: content = fin.read() config = json.loads(content) - model_config = config['model'] - self.rcut = model_config['descriptor']['rcut'] - self.rcut_smth = model_config['descriptor']['rcut_smth'] - self.sel = model_config['descriptor']['sel'] - self.batch_size = config['training']['training_data']['batch_size'] - self.systems = config['training']['validation_data']['systems'] + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.batch_size = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] if isinstance(self.systems, str): self.systems = expand_sys_str(self.systems) - self.my_dataset = DpLoaderSet(self.systems, self.batch_size, - model_params={ - 'descriptor': { - 'type': 'se_e2_a', - 'sel': self.sel, - 'rcut': self.rcut, - }, - 'type_map': model_config['type_map'] - }, seed=10, shuffle=False) + self.my_dataset = DpLoaderSet( + self.systems, + self.batch_size, + model_params={ + "descriptor": { + "type": "se_e2_a", + "sel": self.sel, + "rcut": self.rcut, + }, + "type_map": model_config["type_map"], + }, + seed=10, + shuffle=False, + ) tf_random.seed(10) self.dp_dataset = DeepmdDataSystem(self.systems, self.batch_size, 1, self.rcut) def test_sampler_debug_info(self): dataloader = DataLoader( - self.my_dataset, - sampler=get_weighted_sampler(self.my_dataset,prob_style='prob_sys_size'), - batch_size=None, - num_workers=1, # setting to 0 diverges the behavior of its iterator; should be >=1 - drop_last=False, - pin_memory=True + self.my_dataset, + sampler=get_weighted_sampler(self.my_dataset, prob_style="prob_sys_size"), + batch_size=None, + num_workers=1, # setting to 0 diverges the behavior of its iterator; should be >=1 + drop_last=False, + pin_memory=True, ) batch_data = next(iter(dataloader)) - sid = batch_data['sid'] - fid = batch_data['fid'][0] - coord = batch_data['coord'].squeeze(0) + sid = batch_data["sid"] + fid = batch_data["fid"][0] + coord = batch_data["coord"].squeeze(0) frame = self.my_dataset.systems[sid].__getitem__(fid) - self.assertTrue(np.allclose(coord,frame['coord'])) - + self.assertTrue(np.allclose(coord, frame["coord"])) def test_auto_prob_uniform(self): - auto_prob_style= 'prob_uniform' - sampler = get_weighted_sampler(self.my_dataset,prob_style=auto_prob_style) + auto_prob_style = "prob_uniform" + sampler = get_weighted_sampler(self.my_dataset, prob_style=auto_prob_style) my_probs = np.array(sampler.weights) self.dp_dataset.set_sys_probs(auto_prob_style=auto_prob_style) dp_probs = np.array(self.dp_dataset.sys_probs) - self.assertTrue(np.allclose(my_probs,dp_probs)) - + self.assertTrue(np.allclose(my_probs, dp_probs)) def test_auto_prob_sys_size(self): - auto_prob_style= 'prob_sys_size' - sampler = get_weighted_sampler(self.my_dataset,prob_style=auto_prob_style) + auto_prob_style = "prob_sys_size" + sampler = get_weighted_sampler(self.my_dataset, prob_style=auto_prob_style) my_probs = np.array(sampler.weights) self.dp_dataset.set_sys_probs(auto_prob_style=auto_prob_style) dp_probs = np.array(self.dp_dataset.sys_probs) - self.assertTrue(np.allclose(my_probs,dp_probs)) + self.assertTrue(np.allclose(my_probs, dp_probs)) def test_auto_prob_sys_size_ext(self): - auto_prob_style= 'prob_sys_size;0:1:0.2;1:3:0.8' - sampler = get_weighted_sampler(self.my_dataset,prob_style=auto_prob_style) + auto_prob_style = "prob_sys_size;0:1:0.2;1:3:0.8" + sampler = get_weighted_sampler(self.my_dataset, prob_style=auto_prob_style) my_probs = np.array(sampler.weights) self.dp_dataset.set_sys_probs(auto_prob_style=auto_prob_style) dp_probs = np.array(self.dp_dataset.sys_probs) - self.assertTrue(np.allclose(my_probs,dp_probs)) + self.assertTrue(np.allclose(my_probs, dp_probs)) def test_sys_probs(self): - sys_probs= [0.1,0.4,0.5] - sampler = get_weighted_sampler(self.my_dataset,prob_style=sys_probs,sys_prob=True) + sys_probs = [0.1, 0.4, 0.5] + sampler = get_weighted_sampler( + self.my_dataset, prob_style=sys_probs, sys_prob=True + ) my_probs = np.array(sampler.weights) self.dp_dataset.set_sys_probs(sys_probs=sys_probs) dp_probs = np.array(self.dp_dataset.sys_probs) - self.assertTrue(np.allclose(my_probs,dp_probs)) + self.assertTrue(np.allclose(my_probs, dp_probs)) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_saveload_dpa1.py b/tests/test_saveload_dpa1.py index 33e11fb4..1554602d 100644 --- a/tests/test_saveload_dpa1.py +++ b/tests/test_saveload_dpa1.py @@ -1,49 +1,73 @@ -import unittest -import torch -import json +# SPDX-License-Identifier: LGPL-3.0-or-later import copy +import json import os +import unittest -from torch.utils.data import DataLoader -from deepmd_pt.utils.dataloader import BufferedIterator, DpLoaderSet -from deepmd.common import expand_sys_str -from deepmd_pt.model.model import get_model -from deepmd_pt.utils import env -from deepmd_pt.utils.stat import make_stat_input -from deepmd_pt.train.wrapper import ModelWrapper -from deepmd_pt.loss import EnergyStdLoss +import torch +from deepmd.tf.common import ( + expand_sys_str, +) +from torch.utils.data import ( + DataLoader, +) + +from deepmd_pt.loss import ( + EnergyStdLoss, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.train.wrapper import ( + ModelWrapper, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + BufferedIterator, + DpLoaderSet, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) def get_dataset(config): - model_config = config['model'] - rcut = model_config['descriptor']['rcut'] - sel = model_config['descriptor']['sel'] - systems = config['training']['validation_data']['systems'] + model_config = config["model"] + rcut = model_config["descriptor"]["rcut"] + sel = model_config["descriptor"]["sel"] + systems = config["training"]["validation_data"]["systems"] if isinstance(systems, str): systems = expand_sys_str(systems) - batch_size = config['training']['training_data']['batch_size'] - type_map = model_config['type_map'] + batch_size = config["training"]["training_data"]["batch_size"] + type_map = model_config["type_map"] - dataset = DpLoaderSet(systems, batch_size, - model_params={ - 'descriptor': { - 'type': 'dpa1', - 'sel': sel, - 'rcut': rcut, - }, - 'type_map': type_map - }) - data_stat_nbatch = model_config.get('data_stat_nbatch', 10) + dataset = DpLoaderSet( + systems, + batch_size, + model_params={ + "descriptor": { + "type": "dpa1", + "sel": sel, + "rcut": rcut, + }, + "type_map": type_map, + }, + ) + data_stat_nbatch = model_config.get("data_stat_nbatch", 10) sampled = make_stat_input(dataset.systems, dataset.dataloaders, data_stat_nbatch) return dataset, sampled class TestSaveLoadDPA1(unittest.TestCase): def setUp(self): - input_json = 'tests/water/se_atten.json' - with open(input_json, 'r') as fin: + input_json = "tests/water/se_atten.json" + with open(input_json) as fin: self.config = json.load(fin) - self.config['loss']['starter_learning_rate'] = self.config['learning_rate']['start_lr'] + self.config["loss"]["starter_learning_rate"] = self.config["learning_rate"][ + "start_lr" + ] self.dataset, self.sampled = get_dataset(self.config) self.training_dataloader = DataLoader( self.dataset, @@ -54,13 +78,13 @@ def setUp(self): pin_memory=True, ) self.training_data = BufferedIterator(iter(self.training_dataloader)) - self.loss = EnergyStdLoss(**self.config['loss']) + self.loss = EnergyStdLoss(**self.config["loss"]) self.cur_lr = 1 self.task_key = "Default" self.input_dict, self.label_dict = self.get_data() - self.start_lr = self.config['learning_rate']['start_lr'] + self.start_lr = self.config["learning_rate"]["start_lr"] - def get_model_result(self, read=False, model_file='tmp_model.pt'): + def get_model_result(self, read=False, model_file="tmp_model.pt"): wrapper = self.create_wrapper(read) optimizer = torch.optim.Adam(wrapper.parameters(), lr=self.start_lr) optimizer.zero_grad() @@ -69,16 +93,23 @@ def get_model_result(self, read=False, model_file='tmp_model.pt'): os.remove(model_file) else: torch.save(wrapper.state_dict(), model_file) - result = wrapper(**self.input_dict, cur_lr=self.cur_lr, label=self.label_dict, task_key=self.task_key)[0] + result = wrapper( + **self.input_dict, + cur_lr=self.cur_lr, + label=self.label_dict, + task_key=self.task_key, + )[0] return result def create_wrapper(self, read: bool): - model_config = copy.deepcopy(self.config['model']) + model_config = copy.deepcopy(self.config["model"]) sampled = copy.deepcopy(self.sampled) model_config["resuming"] = read model_config["stat_file_dir"] = "stat_files" model_config["stat_file"] = "stat.npz" - model_config["stat_file_path"] = os.path.join(model_config["stat_file_dir"], model_config["stat_file"]) + model_config["stat_file_path"] = os.path.join( + model_config["stat_file_dir"], model_config["stat_file"] + ) model = get_model(model_config, sampled).to(env.DEVICE) return ModelWrapper(model, self.loss) @@ -90,13 +121,13 @@ def get_data(self): self.training_data = BufferedIterator(iter(self.training_dataloader)) batch_data = next(iter(self.training_data)) input_dict = {} - for item in ['coord', 'atype', 'box']: + for item in ["coord", "atype", "box"]: if item in batch_data: input_dict[item] = batch_data[item] else: input_dict[item] = None label_dict = {} - for item in ['energy', 'force', 'virial']: + for item in ["energy", "force", "virial"]: if item in batch_data: label_dict[item] = batch_data[item] return input_dict, label_dict @@ -104,9 +135,11 @@ def get_data(self): def test_saveload(self): result1 = self.get_model_result() result2 = self.get_model_result(read=True) - final_result = all([torch.allclose(result1[item], result2[item]) for item in result1]) + final_result = all( + torch.allclose(result1[item], result2[item]) for item in result1 + ) self.assertTrue(final_result) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_saveload_se_e2_a.py b/tests/test_saveload_se_e2_a.py index 8c129840..a149b090 100644 --- a/tests/test_saveload_se_e2_a.py +++ b/tests/test_saveload_se_e2_a.py @@ -1,40 +1,61 @@ -import unittest -import torch -import json +# SPDX-License-Identifier: LGPL-3.0-or-later import copy +import json import os +import unittest + +import torch +from deepmd.tf.common import ( + expand_sys_str, +) +from torch.utils.data import ( + DataLoader, +) -from torch.utils.data import DataLoader -from deepmd_pt.utils.dataloader import BufferedIterator, DpLoaderSet -from deepmd.common import expand_sys_str -from deepmd_pt.utils.dataset import DeepmdDataSet -from deepmd_pt.model.model import get_model -from deepmd_pt.utils import env -from deepmd_pt.utils.stat import make_stat_input -from deepmd_pt.train.wrapper import ModelWrapper -from deepmd_pt.loss import EnergyStdLoss +from deepmd_pt.loss import ( + EnergyStdLoss, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.train.wrapper import ( + ModelWrapper, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + BufferedIterator, + DpLoaderSet, +) +from deepmd_pt.utils.stat import ( + make_stat_input, +) def get_dataset(config): - model_config = config['model'] - rcut = model_config['descriptor']['rcut'] - sel = model_config['descriptor']['sel'] - systems = config['training']['validation_data']['systems'] + model_config = config["model"] + rcut = model_config["descriptor"]["rcut"] + sel = model_config["descriptor"]["sel"] + systems = config["training"]["validation_data"]["systems"] if isinstance(systems, str): systems = expand_sys_str(systems) - batch_size = config['training']['training_data']['batch_size'] - type_map = model_config['type_map'] + batch_size = config["training"]["training_data"]["batch_size"] + type_map = model_config["type_map"] - dataset = DpLoaderSet(systems, batch_size, - model_params={ - 'descriptor': { - 'type': 'se_e2_a', - 'sel': sel, - 'rcut': rcut, - }, - 'type_map': type_map - }) - data_stat_nbatch = model_config.get('data_stat_nbatch', 10) + dataset = DpLoaderSet( + systems, + batch_size, + model_params={ + "descriptor": { + "type": "se_e2_a", + "sel": sel, + "rcut": rcut, + }, + "type_map": type_map, + }, + ) + data_stat_nbatch = model_config.get("data_stat_nbatch", 10) sampled = make_stat_input(dataset.systems, dataset.dataloaders, data_stat_nbatch) return dataset, sampled @@ -42,9 +63,11 @@ def get_dataset(config): class TestSaveLoadDPA1(unittest.TestCase): def setUp(self): input_json = env.TEST_CONFIG - with open(input_json, 'r') as fin: + with open(input_json) as fin: self.config = json.load(fin) - self.config['loss']['starter_learning_rate'] = self.config['learning_rate']['start_lr'] + self.config["loss"]["starter_learning_rate"] = self.config["learning_rate"][ + "start_lr" + ] self.dataset, self.sampled = get_dataset(self.config) self.training_dataloader = DataLoader( self.dataset, @@ -55,13 +78,13 @@ def setUp(self): pin_memory=True, ) self.training_data = BufferedIterator(iter(self.training_dataloader)) - self.loss = EnergyStdLoss(**self.config['loss']) + self.loss = EnergyStdLoss(**self.config["loss"]) self.cur_lr = 1 self.task_key = "Default" self.input_dict, self.label_dict = self.get_data() - self.start_lr = self.config['learning_rate']['start_lr'] + self.start_lr = self.config["learning_rate"]["start_lr"] - def get_model_result(self, read=False, model_file='tmp_model.pt'): + def get_model_result(self, read=False, model_file="tmp_model.pt"): wrapper = self.create_wrapper() optimizer = torch.optim.Adam(wrapper.parameters(), lr=self.start_lr) optimizer.zero_grad() @@ -70,11 +93,16 @@ def get_model_result(self, read=False, model_file='tmp_model.pt'): os.remove(model_file) else: torch.save(wrapper.state_dict(), model_file) - result = wrapper(**self.input_dict, cur_lr=self.cur_lr, label=self.label_dict, task_key=self.task_key)[0] + result = wrapper( + **self.input_dict, + cur_lr=self.cur_lr, + label=self.label_dict, + task_key=self.task_key, + )[0] return result def create_wrapper(self): - model_config = copy.deepcopy(self.config['model']) + model_config = copy.deepcopy(self.config["model"]) sampled = copy.deepcopy(self.sampled) model = get_model(model_config, sampled).to(env.DEVICE) return ModelWrapper(model, self.loss) @@ -87,13 +115,13 @@ def get_data(self): self.training_data = BufferedIterator(iter(self.training_dataloader)) batch_data = next(iter(self.training_data)) input_dict = {} - for item in ['coord', 'atype', 'box']: + for item in ["coord", "atype", "box"]: if item in batch_data: input_dict[item] = batch_data[item] else: input_dict[item] = None label_dict = {} - for item in ['energy', 'force', 'virial']: + for item in ["energy", "force", "virial"]: if item in batch_data: label_dict[item] = batch_data[item] return input_dict, label_dict @@ -101,9 +129,11 @@ def get_data(self): def test_saveload(self): result1 = self.get_model_result() result2 = self.get_model_result(read=True) - final_result = all([torch.allclose(result1[item], result2[item]) for item in result1]) + final_result = all( + torch.allclose(result1[item], result2[item]) for item in result1 + ) self.assertTrue(final_result) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_se_e2_a.py b/tests/test_se_e2_a.py index cec56cbe..987bea51 100644 --- a/tests/test_se_e2_a.py +++ b/tests/test_se_e2_a.py @@ -1,32 +1,38 @@ -import torch, copy -import unittest +# SPDX-License-Identifier: LGPL-3.0-or-later import itertools +import unittest + import numpy as np +import torch -try : - from deepmd_utils.model_format import ( - DescrptSeA as DPDescrptSeA, - PRECISION_DICT as DP_PRECISION_DICT, - ) - support_se_e2_a = True +try: + # from deepmd.model_format import PRECISION_DICT as DP_PRECISION_DICT + from deepmd.model_format import DescrptSeA as DPDescrptSeA + + support_se_e2_a = True except ModuleNotFoundError: - support_se_e2_a = False + support_se_e2_a = False except ImportError: - support_se_e2_a = False + support_se_e2_a = False from deepmd_pt.model.descriptor.se_a import ( - DescrptSeA + DescrptSeA, +) +from deepmd_pt.utils import ( + env, ) from deepmd_pt.utils.env import ( - PRECISION_DICT, - DEFAULT_PRECISION, + PRECISION_DICT, +) + +from .test_mlp import ( + get_tols, ) -from deepmd_pt.utils import env -from .test_mlp import get_tols dtype = env.GLOBAL_PT_FLOAT_PRECISION -class TestCaseSingleFrameWithNlist(): + +class TestCaseSingleFrameWithNlist: def setUp(self): # nloc == 3, nall == 4 self.nloc = 3 @@ -53,117 +59,141 @@ def setUp(self): dtype=int, ).reshape([1, self.nloc, sum(self.sel)]) self.rcut = 0.4 - self.rcut_smth = 2.2 + self.rcut_smth = 2.2 # to be merged with the tf test case @unittest.skipIf(not support_se_e2_a, "EnvMat not supported") class TestDescrptSeA(unittest.TestCase, TestCaseSingleFrameWithNlist): - def setUp(self): - TestCaseSingleFrameWithNlist.setUp(self) - - def test_consistency( - self, - ): - rng = np.random.default_rng() - nf, nloc, nnei = self.nlist.shape - davg = rng.normal(size=(self.nt, nnei, 4)) - dstd = rng.normal(size=(self.nt, nnei, 4)) - dstd = 0.1 + np.abs(dstd) + def setUp(self): + TestCaseSingleFrameWithNlist.setUp(self) - for idt, prec in itertools.product( - [False, True], - ["float64", "float32"], + def test_consistency( + self, ): - dtype = PRECISION_DICT[prec] - rtol, atol = get_tols(prec) - err_msg = f"idt={idt} prec={prec}" - # sea new impl - dd0 = DescrptSeA( - self.rcut, self.rcut_smth, self.sel, - precision=prec, - resnet_dt=idt, - old_impl=False, - ).to(env.DEVICE) - dd0.sea.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE) - dd0.sea.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE) - rd0, _,_,_,_ = dd0( - torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE), - torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE), - torch.tensor(self.nlist, dtype=int, device=env.DEVICE), - ) - # serialization - dd1 = DescrptSeA.deserialize(dd0.serialize()) - rd1, _, _, _, _ = dd1( - torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE), - torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE), - torch.tensor(self.nlist, dtype=int, device=env.DEVICE), - ) - np.testing.assert_allclose( - rd0.detach().cpu().numpy(), rd1.detach().cpu().numpy(), - rtol=rtol, atol=atol, err_msg=err_msg, - ) - # dp impl - dd2 = DPDescrptSeA.deserialize(dd0.serialize()) - rd2 = dd2.call( - self.coord_ext, self.atype_ext, self.nlist, - ) - np.testing.assert_allclose( - rd0.detach().cpu().numpy(), rd2, - rtol=rtol, atol=atol, err_msg=err_msg, - ) - # old impl - if idt == False and prec == "float64": - dd3 = DescrptSeA( - self.rcut, self.rcut_smth, self.sel, - precision=prec, - resnet_dt=idt, - old_impl=True, - ).to(env.DEVICE) - dd0_state_dict = dd0.sea.state_dict() - dd3_state_dict = dd3.sea.state_dict() - for i in dd3_state_dict: - dd3_state_dict[i] = dd0_state_dict[i.replace('.deep_layers.', '.layers.') - .replace('filter_layers_old.', 'filter_layers.networks.')].detach().clone() - if '.bias' in i: - dd3_state_dict[i] = dd3_state_dict[i].unsqueeze(0) - dd3.sea.load_state_dict(dd3_state_dict) + rng = np.random.default_rng() + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) - rd3, _,_,_,_ = dd3( - torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE), - torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE), - torch.tensor(self.nlist, dtype=int, device=env.DEVICE), - ) - np.testing.assert_allclose( - rd0.detach().cpu().numpy(), rd3.detach().cpu().numpy(), - rtol=rtol, atol=atol, err_msg=err_msg, - ) + for idt, prec in itertools.product( + [False, True], + ["float64", "float32"], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # sea new impl + dd0 = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + old_impl=False, + ).to(env.DEVICE) + dd0.sea.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE) + dd0.sea.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE) + rd0, _, _, _, _ = dd0( + torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE), + torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE), + torch.tensor(self.nlist, dtype=int, device=env.DEVICE), + ) + # serialization + dd1 = DescrptSeA.deserialize(dd0.serialize()) + rd1, _, _, _, _ = dd1( + torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE), + torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE), + torch.tensor(self.nlist, dtype=int, device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd1.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # dp impl + dd2 = DPDescrptSeA.deserialize(dd0.serialize()) + rd2 = dd2.call( + self.coord_ext, + self.atype_ext, + self.nlist, + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd2, + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) + # old impl + if idt is False and prec == "float64": + dd3 = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + old_impl=True, + ).to(env.DEVICE) + dd0_state_dict = dd0.sea.state_dict() + dd3_state_dict = dd3.sea.state_dict() + for i in dd3_state_dict: + dd3_state_dict[i] = ( + dd0_state_dict[ + i.replace(".deep_layers.", ".layers.").replace( + "filter_layers_old.", "filter_layers.networks." + ) + ] + .detach() + .clone() + ) + if ".bias" in i: + dd3_state_dict[i] = dd3_state_dict[i].unsqueeze(0) + dd3.sea.load_state_dict(dd3_state_dict) - def test_jit( - self, - ): - rng = np.random.default_rng() - nf, nloc, nnei = self.nlist.shape - davg = rng.normal(size=(self.nt, nnei, 4)) - dstd = rng.normal(size=(self.nt, nnei, 4)) - dstd = 0.1 + np.abs(dstd) + rd3, _, _, _, _ = dd3( + torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE), + torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE), + torch.tensor(self.nlist, dtype=int, device=env.DEVICE), + ) + np.testing.assert_allclose( + rd0.detach().cpu().numpy(), + rd3.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + err_msg=err_msg, + ) - for idt, prec in itertools.product( - [False, True], - ["float64", "float32"], + def test_jit( + self, ): - dtype = PRECISION_DICT[prec] - rtol, atol = get_tols(prec) - err_msg = f"idt={idt} prec={prec}" - # sea new impl - dd0 = DescrptSeA( - self.rcut, self.rcut_smth, self.sel, - precision=prec, - resnet_dt=idt, - old_impl=False, - ) - dd0.sea.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE) - dd0.sea.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE) - dd1 = DescrptSeA.deserialize(dd0.serialize()) - model = torch.jit.script(dd0) - model = torch.jit.script(dd1) + rng = np.random.default_rng() + nf, nloc, nnei = self.nlist.shape + davg = rng.normal(size=(self.nt, nnei, 4)) + dstd = rng.normal(size=(self.nt, nnei, 4)) + dstd = 0.1 + np.abs(dstd) + + for idt, prec in itertools.product( + [False, True], + ["float64", "float32"], + ): + dtype = PRECISION_DICT[prec] + rtol, atol = get_tols(prec) + err_msg = f"idt={idt} prec={prec}" + # sea new impl + dd0 = DescrptSeA( + self.rcut, + self.rcut_smth, + self.sel, + precision=prec, + resnet_dt=idt, + old_impl=False, + ) + dd0.sea.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE) + dd0.sea.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE) + dd1 = DescrptSeA.deserialize(dd0.serialize()) + model = torch.jit.script(dd0) + model = torch.jit.script(dd1) diff --git a/tests/test_smooth.py b/tests/test_smooth.py index 4607256c..b0f36383 100644 --- a/tests/test_smooth.py +++ b/tests/test_smooth.py @@ -1,151 +1,213 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input -from .test_permutation import ( - make_sample, - model_se_e2_a, - model_dpa1, - model_dpa2, - # model_dpau, - model_hybrid, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) + +from .test_permutation import ( # model_dpau, + make_sample, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 class TestSmooth: - def test( - self, - ): - # displacement of atoms - epsilon = 1e-5 if self.epsilon is None else self.epsilon - # required prec. relative prec is not checked. - rprec = 0 - aprec = 1e-5 if self.aprec is None else self.aprec - - natoms = 10 - cell = 8.6 * torch.eye(3, dtype=dtype).to(env.DEVICE) - atype = torch.randint(0, 3, [natoms]) - coord0 = torch.tensor( - [ - 0., 0., 0., - 4.-.5*epsilon, 0., 0., - 0., 4.-.5*epsilon, 0., - ], - dtype=dtype).view([-1, 3]).to(env.DEVICE) - coord1 = torch.rand([natoms-coord0.shape[0], 3], dtype=dtype).to(env.DEVICE) - coord1 = torch.matmul(coord1, cell) - coord = torch.concat([coord0, coord1], dim=0) - - coord0 = torch.clone(coord) - coord1 = torch.clone(coord) - coord1[1][0] += epsilon - coord2 = torch.clone(coord) - coord2[2][1] += epsilon - coord3 = torch.clone(coord) - coord3[1][0] += epsilon - coord3[2][1] += epsilon - - e0, f0, v0 = eval_model(self.model, coord0.unsqueeze(0), cell.unsqueeze(0), atype) - ret0 = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - e1, f1, v1 = eval_model(self.model, coord1.unsqueeze(0), cell.unsqueeze(0), atype) - ret1 = {'energy': e1.squeeze(0), 'force': f1.squeeze(0), 'virial': v1.squeeze(0)} - e2, f2, v2 = eval_model(self.model, coord2.unsqueeze(0), cell.unsqueeze(0), atype) - ret2 = {'energy': e2.squeeze(0), 'force': f2.squeeze(0), 'virial': v2.squeeze(0)} - e3, f3, v3 = eval_model(self.model, coord3.unsqueeze(0), cell.unsqueeze(0), atype) - ret3 = {'energy': e3.squeeze(0), 'force': f3.squeeze(0), 'virial': v3.squeeze(0)} - - def compare(ret0, ret1): - torch.testing.assert_close(ret0['energy'], ret1['energy'], rtol=rprec, atol=aprec) - # plus 1. to avoid the divided-by-zero issue - torch.testing.assert_close(1.+ret0['force'], 1.+ret1['force'], rtol=rprec, atol=aprec) - if not hasattr(self, "test_virial") or self.test_virial: - torch.testing.assert_close(1.+ret0['virial'], 1.+ret1['virial'], rtol=rprec, atol=aprec) - - compare(ret0, ret1) - compare(ret1, ret2) - compare(ret0, ret3) + def test( + self, + ): + # displacement of atoms + epsilon = 1e-5 if self.epsilon is None else self.epsilon + # required prec. relative prec is not checked. + rprec = 0 + aprec = 1e-5 if self.aprec is None else self.aprec + + natoms = 10 + cell = 8.6 * torch.eye(3, dtype=dtype).to(env.DEVICE) + atype = torch.randint(0, 3, [natoms]) + coord0 = ( + torch.tensor( + [ + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + ], + dtype=dtype, + ) + .view([-1, 3]) + .to(env.DEVICE) + ) + coord1 = torch.rand([natoms - coord0.shape[0], 3], dtype=dtype).to(env.DEVICE) + coord1 = torch.matmul(coord1, cell) + coord = torch.concat([coord0, coord1], dim=0) + + coord0 = torch.clone(coord) + coord1 = torch.clone(coord) + coord1[1][0] += epsilon + coord2 = torch.clone(coord) + coord2[2][1] += epsilon + coord3 = torch.clone(coord) + coord3[1][0] += epsilon + coord3[2][1] += epsilon + + e0, f0, v0 = eval_model( + self.model, coord0.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret0 = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + e1, f1, v1 = eval_model( + self.model, coord1.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret1 = { + "energy": e1.squeeze(0), + "force": f1.squeeze(0), + "virial": v1.squeeze(0), + } + e2, f2, v2 = eval_model( + self.model, coord2.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret2 = { + "energy": e2.squeeze(0), + "force": f2.squeeze(0), + "virial": v2.squeeze(0), + } + e3, f3, v3 = eval_model( + self.model, coord3.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret3 = { + "energy": e3.squeeze(0), + "force": f3.squeeze(0), + "virial": v3.squeeze(0), + } + + def compare(ret0, ret1): + torch.testing.assert_close( + ret0["energy"], ret1["energy"], rtol=rprec, atol=aprec + ) + # plus 1. to avoid the divided-by-zero issue + torch.testing.assert_close( + 1.0 + ret0["force"], 1.0 + ret1["force"], rtol=rprec, atol=aprec + ) + if not hasattr(self, "test_virial") or self.test_virial: + torch.testing.assert_close( + 1.0 + ret0["virial"], 1.0 + ret1["virial"], rtol=rprec, atol=aprec + ) + + compare(ret0, ret1) + compare(ret1, ret2) + compare(ret0, ret3) class TestEnergyModelSeA(unittest.TestCase, TestSmooth): - def setUp(self): - model_params = copy.deepcopy(model_se_e2_a) - sampled = make_sample(model_params) - self.type_split = False - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + sampled = make_sample(model_params) + self.type_split = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = None, None # @unittest.skip("dpa-1 not smooth at the moment") class TestEnergyModelDPA1(unittest.TestCase, TestSmooth): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - # less degree of smoothness, - # error can be systematically removed by reducing epsilon - self.epsilon = 1e-5 - self.aprec = 1e-5 + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + # less degree of smoothness, + # error can be systematically removed by reducing epsilon + self.epsilon = 1e-5 + self.aprec = 1e-5 + class TestEnergyModelDPA2(unittest.TestCase, TestSmooth): - def setUp(self): - model_params = copy.deepcopy(model_dpa2) - model_params["descriptor"]["repinit_rcut"] = 8 - model_params["descriptor"]["repinit_rcut_smth"] = 3.5 - model_params_sample = copy.deepcopy(model_params) - ####################################################### - # dirty hack here! the interface of dataload should be - # redesigned to support specifying rcut and sel - ####################################################### - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = 1e-5, 1e-4 + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["descriptor"]["repinit_rcut"] = 8 + model_params["descriptor"]["repinit_rcut_smth"] = 3.5 + model_params_sample = copy.deepcopy(model_params) + ####################################################### + # dirty hack here! the interface of dataload should be + # redesigned to support specifying rcut and sel + ####################################################### + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = 1e-5, 1e-4 class TestEnergyModelDPA2_1(unittest.TestCase, TestSmooth): - def setUp(self): - model_params = copy.deepcopy(model_dpa2) - model_params["fitting_net"]["type"] = "ener" - model_params_sample = copy.deepcopy(model_params) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "ener" + model_params_sample = copy.deepcopy(model_params) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = None, None class TestEnergyModelDPA2_2(unittest.TestCase, TestSmooth): - def setUp(self): - model_params = copy.deepcopy(model_dpa2) - model_params["fitting_net"]["type"] = "ener" - model_params_sample = copy.deepcopy(model_params) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None + def setUp(self): + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "ener" + model_params_sample = copy.deepcopy(model_params) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = None, None @unittest.skip("hybrid not supported at the moment") class TestEnergyModelHybrid(unittest.TestCase, TestSmooth): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = None, None # class TestEnergyFoo(unittest.TestCase): @@ -159,10 +221,10 @@ def setUp(self): # cell = (cell + cell.T) + 5. * torch.eye(3) # coord = torch.rand([natoms, 3], dtype=dtype) # coord = torch.matmul(coord, cell) -# atype = torch.IntTensor([0, 0, 0, 1, 1]) -# idx_perm = [1, 0, 4, 3, 2] +# atype = torch.IntTensor([0, 0, 0, 1, 1]) +# idx_perm = [1, 0, 4, 3, 2] # ret0 = infer_model(self.model, coord, cell, atype, type_split=True) -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_smooth_denoise.py b/tests/test_smooth_denoise.py index eaa3229b..26f32b6f 100644 --- a/tests/test_smooth_denoise.py +++ b/tests/test_smooth_denoise.py @@ -1,108 +1,151 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, +) + from .test_permutation_denoise import ( - make_sample, - model_dpa1, - model_dpa2, - model_hybrid, + make_sample, + model_dpa2, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 + class TestSmoothDenoise: - def test( - self, - ): - # displacement of atoms - epsilon = 1e-5 if self.epsilon is None else self.epsilon - # required prec. relative prec is not checked. - rprec = 0 - aprec = 1e-5 if self.aprec is None else self.aprec - - natoms = 10 - cell = 8.6 * torch.eye(3, dtype=dtype).to(env.DEVICE) - atype = torch.randint(0, 3, [natoms]) - coord0 = torch.tensor( - [ - 0., 0., 0., - 4.-.5*epsilon, 0., 0., - 0., 4.-.5*epsilon, 0., - ], - dtype=dtype).view([-1, 3]).to(env.DEVICE) - coord1 = torch.rand([natoms-coord0.shape[0], 3], dtype=dtype).to(env.DEVICE) - coord1 = torch.matmul(coord1, cell) - coord = torch.concat([coord0, coord1], dim=0) - - coord0 = torch.clone(coord) - coord1 = torch.clone(coord) - coord1[1][0] += epsilon - coord2 = torch.clone(coord) - coord2[2][1] += epsilon - coord3 = torch.clone(coord) - coord3[1][0] += epsilon - coord3[2][1] += epsilon - - update_c0, logits0 = eval_model(self.model, coord0.unsqueeze(0), cell.unsqueeze(0), atype, denoise = True) - ret0 = {'updated_coord': update_c0.squeeze(0), 'logits': logits0.squeeze(0)} - update_c1, logits1 = eval_model(self.model, coord1.unsqueeze(0), cell.unsqueeze(0), atype, denoise = True) - ret1 = {'updated_coord': update_c1.squeeze(0), 'logits': logits1.squeeze(0)} - update_c2, logits2 = eval_model(self.model, coord2.unsqueeze(0), cell.unsqueeze(0), atype, denoise = True) - ret2 = {'updated_coord': update_c2.squeeze(0), 'logits': logits2.squeeze(0)} - update_c3, logits3 = eval_model(self.model, coord3.unsqueeze(0), cell.unsqueeze(0), atype, denoise = True) - ret3 = {'updated_coord': update_c3.squeeze(0), 'logits': logits3.squeeze(0)} - - def compare(ret0, ret1): - torch.testing.assert_close(ret0['updated_coord'], ret1['updated_coord'], rtol=rprec, atol=aprec) - torch.testing.assert_close(ret0['logits'], ret1['logits'], rtol=rprec, atol=aprec) - - compare(ret0, ret1) - compare(ret1, ret2) - compare(ret0, ret3) + def test( + self, + ): + # displacement of atoms + epsilon = 1e-5 if self.epsilon is None else self.epsilon + # required prec. relative prec is not checked. + rprec = 0 + aprec = 1e-5 if self.aprec is None else self.aprec + + natoms = 10 + cell = 8.6 * torch.eye(3, dtype=dtype).to(env.DEVICE) + atype = torch.randint(0, 3, [natoms]) + coord0 = ( + torch.tensor( + [ + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + 0.0, + 0.0, + 4.0 - 0.5 * epsilon, + 0.0, + ], + dtype=dtype, + ) + .view([-1, 3]) + .to(env.DEVICE) + ) + coord1 = torch.rand([natoms - coord0.shape[0], 3], dtype=dtype).to(env.DEVICE) + coord1 = torch.matmul(coord1, cell) + coord = torch.concat([coord0, coord1], dim=0) + + coord0 = torch.clone(coord) + coord1 = torch.clone(coord) + coord1[1][0] += epsilon + coord2 = torch.clone(coord) + coord2[2][1] += epsilon + coord3 = torch.clone(coord) + coord3[1][0] += epsilon + coord3[2][1] += epsilon + + update_c0, logits0 = eval_model( + self.model, coord0.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret0 = {"updated_coord": update_c0.squeeze(0), "logits": logits0.squeeze(0)} + update_c1, logits1 = eval_model( + self.model, coord1.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret1 = {"updated_coord": update_c1.squeeze(0), "logits": logits1.squeeze(0)} + update_c2, logits2 = eval_model( + self.model, coord2.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret2 = {"updated_coord": update_c2.squeeze(0), "logits": logits2.squeeze(0)} + update_c3, logits3 = eval_model( + self.model, coord3.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + ret3 = {"updated_coord": update_c3.squeeze(0), "logits": logits3.squeeze(0)} + + def compare(ret0, ret1): + torch.testing.assert_close( + ret0["updated_coord"], ret1["updated_coord"], rtol=rprec, atol=aprec + ) + torch.testing.assert_close( + ret0["logits"], ret1["logits"], rtol=rprec, atol=aprec + ) + + compare(ret0, ret1) + compare(ret1, ret2) + compare(ret0, ret3) + class TestDenoiseModelDPA2(unittest.TestCase, TestSmoothDenoise): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - model_params["descriptor"]["sel"] = 8 - model_params["descriptor"]["rcut_smth"] = 3.5 - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None - self.epsilon = 1e-7 - self.aprec = 1e-5 + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + model_params["descriptor"]["sel"] = 8 + model_params["descriptor"]["rcut_smth"] = 3.5 + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = None, None + self.epsilon = 1e-7 + self.aprec = 1e-5 + class TestDenoiseModelDPA2_1(unittest.TestCase, TestSmoothDenoise): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - # model_params["descriptor"]["combine_grrg"] = True - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None - self.epsilon = 1e-7 - self.aprec = 1e-5 - -@unittest.skip("hybrid not supported at the moment") -class TestDenoiseModelHybrid(unittest.TestCase, TestSmoothDenoise): - def setUp(self): - model_params = copy.deepcopy(model_hybrid_denoise) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) - self.epsilon, self.aprec = None, None - self.epsilon = 1e-7 - self.aprec = 1e-5 - -if __name__ == '__main__': - unittest.main() + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + # model_params["descriptor"]["combine_grrg"] = True + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + self.epsilon, self.aprec = None, None + self.epsilon = 1e-7 + self.aprec = 1e-5 + + +# @unittest.skip("hybrid not supported at the moment") +# class TestDenoiseModelHybrid(unittest.TestCase, TestSmoothDenoise): +# def setUp(self): +# model_params = copy.deepcopy(model_hybrid_denoise) +# sampled = make_sample(model_params) +# self.type_split = True +# self.model = get_model(model_params, sampled).to(env.DEVICE) +# self.epsilon, self.aprec = None, None +# self.epsilon = 1e-7 +# self.aprec = 1e-5 + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_stat.py b/tests/test_stat.py index 38cb61ee..4bc97826 100644 --- a/tests/test_stat.py +++ b/tests/test_stat.py @@ -1,21 +1,37 @@ -import numpy as np +# SPDX-License-Identifier: LGPL-3.0-or-later +import json import os -import torch import unittest -import json -from deepmd.descriptor.se_a import DescrptSeA as DescrptSeA_tf -from deepmd.fit.ener import EnerFitting -from deepmd.model.model_stat import make_stat_input as dp_make, merge_sys_stat as dp_merge -from deepmd.utils.data_system import DeepmdDataSystem -from deepmd.utils import random as tf_random -from deepmd.common import expand_sys_str - -from deepmd_pt.utils.dataset import DeepmdDataSet -from deepmd_pt.model.descriptor import DescrptSeA -from deepmd_pt.utils.stat import make_stat_input as my_make, compute_output_stats -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils import env +import numpy as np +import torch +from deepmd.tf.common import ( + expand_sys_str, +) +from deepmd.tf.descriptor.se_a import DescrptSeA as DescrptSeA_tf +from deepmd.tf.fit.ener import ( + EnerFitting, +) +from deepmd.tf.model.model_stat import make_stat_input as dp_make +from deepmd.tf.model.model_stat import merge_sys_stat as dp_merge +from deepmd.tf.utils import random as tf_random +from deepmd.tf.utils.data_system import ( + DeepmdDataSystem, +) + +from deepmd_pt.model.descriptor import ( + DescrptSeA, +) +from deepmd_pt.utils import ( + env, +) +from deepmd_pt.utils.dataloader import ( + DpLoaderSet, +) +from deepmd_pt.utils.stat import ( + compute_output_stats, +) +from deepmd_pt.utils.stat import make_stat_input as my_make CUR_DIR = os.path.dirname(__file__) @@ -32,50 +48,55 @@ def compare(ut, base, given): class TestDataset(unittest.TestCase): - def setUp(self): - with open(env.TEST_CONFIG, 'r') as fin: + with open(env.TEST_CONFIG) as fin: content = fin.read() config = json.loads(content) - model_config = config['model'] - self.rcut = model_config['descriptor']['rcut'] - self.rcut_smth = model_config['descriptor']['rcut_smth'] - self.sel = model_config['descriptor']['sel'] - self.batch_size = config['training']['training_data']['batch_size'] - self.systems = config['training']['validation_data']['systems'] + model_config = config["model"] + self.rcut = model_config["descriptor"]["rcut"] + self.rcut_smth = model_config["descriptor"]["rcut_smth"] + self.sel = model_config["descriptor"]["sel"] + self.batch_size = config["training"]["training_data"]["batch_size"] + self.systems = config["training"]["validation_data"]["systems"] if isinstance(self.systems, str): self.systems = expand_sys_str(self.systems) - self.my_dataset = DpLoaderSet(self.systems, self.batch_size, - model_params={ - 'descriptor': { - 'type': 'se_e2_a', - 'sel': self.sel, - 'rcut': self.rcut, - }, - 'type_map': model_config['type_map'] - }, seed=10) - self.filter_neuron = model_config['descriptor']['neuron'] - self.axis_neuron = model_config['descriptor']['axis_neuron'] + self.my_dataset = DpLoaderSet( + self.systems, + self.batch_size, + model_params={ + "descriptor": { + "type": "se_e2_a", + "sel": self.sel, + "rcut": self.rcut, + }, + "type_map": model_config["type_map"], + }, + seed=10, + ) + self.filter_neuron = model_config["descriptor"]["neuron"] + self.axis_neuron = model_config["descriptor"]["axis_neuron"] self.data_stat_nbatch = 2 - self.filter_neuron = model_config['descriptor']['neuron'] - self.axis_neuron = model_config['descriptor']['axis_neuron'] - self.n_neuron = model_config['fitting_net']['neuron'] + self.filter_neuron = model_config["descriptor"]["neuron"] + self.axis_neuron = model_config["descriptor"]["axis_neuron"] + self.n_neuron = model_config["fitting_net"]["neuron"] - self.my_sampled = my_make(self.my_dataset.systems, self.my_dataset.dataloaders, self.data_stat_nbatch) + self.my_sampled = my_make( + self.my_dataset.systems, self.my_dataset.dataloaders, self.data_stat_nbatch + ) tf_random.seed(10) dp_dataset = DeepmdDataSystem(self.systems, self.batch_size, 1, self.rcut) - dp_dataset.add('energy', 1, atomic=False, must=False, high_prec=True) - dp_dataset.add('force', 3, atomic=True, must=False, high_prec=False) + dp_dataset.add("energy", 1, atomic=False, must=False, high_prec=True) + dp_dataset.add("force", 3, atomic=True, must=False, high_prec=False) self.dp_sampled = dp_make(dp_dataset, self.data_stat_nbatch, False) self.dp_merged = dp_merge(self.dp_sampled) - self.dp_mesh = self.dp_merged.pop('default_mesh') + self.dp_mesh = self.dp_merged.pop("default_mesh") self.dp_d = DescrptSeA_tf( rcut=self.rcut, rcut_smth=self.rcut_smth, sel=self.sel, neuron=self.filter_neuron, - axis_neuron=self.axis_neuron + axis_neuron=self.axis_neuron, ) def test_stat_output(self): @@ -85,11 +106,15 @@ def my_merge(energy, natoms): for i in range(len(energy)): for j in range(len(energy[i])): energy_lst.append(torch.tensor(energy[i][j])) - natoms_lst.append(torch.tensor(natoms[i][j]).unsqueeze(0).expand(energy[i][j].shape[0], -1)) + natoms_lst.append( + torch.tensor(natoms[i][j]) + .unsqueeze(0) + .expand(energy[i][j].shape[0], -1) + ) return energy_lst, natoms_lst - energy = self.dp_sampled['energy'] - natoms = self.dp_sampled['natoms_vec'] + energy = self.dp_sampled["energy"] + natoms = self.dp_sampled["natoms_vec"] energy, natoms = my_merge(energy, natoms) dp_fn = EnerFitting(self.dp_d, self.n_neuron) dp_fn.compute_output_stats(self.dp_sampled) @@ -97,7 +122,7 @@ def my_merge(energy, natoms): self.assertTrue(np.allclose(dp_fn.bias_atom_e, bias_atom_e[:, 0])) # temporarily delete this function for performance of seeds in tf and pytorch may be different - ''' + """ def test_stat_input(self): my_sampled = self.my_sampled # list of dicts, each dict contains samples from a system @@ -113,30 +138,51 @@ def test_stat_input(self): for j in range(self.data_stat_nbatch): lst.append(item[key][j*bsz:(j+1)*bsz].cpu().numpy()) compare(self, self.dp_merged[key], lst) - ''' + """ def test_descriptor(self): - coord = self.dp_merged['coord'] - atype = self.dp_merged['type'] - natoms = self.dp_merged['natoms_vec'] - box = self.dp_merged['box'] + coord = self.dp_merged["coord"] + atype = self.dp_merged["type"] + natoms = self.dp_merged["natoms_vec"] + box = self.dp_merged["box"] self.dp_d.compute_input_stats(coord, box, atype, natoms, self.dp_mesh, {}) - my_en = DescrptSeA(self.rcut, self.rcut_smth, self.sel, self.filter_neuron, self.axis_neuron) - my_en = my_en.sea # get the block who has stat as private vars + my_en = DescrptSeA( + self.rcut, self.rcut_smth, self.sel, self.filter_neuron, self.axis_neuron + ) + my_en = my_en.sea # get the block who has stat as private vars sampled = self.my_sampled for sys in sampled: - for key in ['coord', 'force', 'energy', 'atype', 'natoms', 'extended_coord', 'nlist', 'shift', - 'mapping']: + for key in [ + "coord", + "force", + "energy", + "atype", + "natoms", + "extended_coord", + "nlist", + "shift", + "mapping", + ]: if key in sys.keys(): sys[key] = sys[key].to(env.DEVICE) sumr, suma, sumn, sumr2, suma2 = my_en.compute_input_stats(sampled) my_en.init_desc_stat(sumr, suma, sumn, sumr2, suma2) my_en.mean = my_en.mean my_en.stddev = my_en.stddev - self.assertTrue(np.allclose(self.dp_d.davg.reshape([-1]), my_en.mean.cpu().reshape([-1]), rtol=0.01)) - self.assertTrue(np.allclose(self.dp_d.dstd.reshape([-1]), my_en.stddev.cpu().reshape([-1]), rtol=0.01)) + self.assertTrue( + np.allclose( + self.dp_d.davg.reshape([-1]), my_en.mean.cpu().reshape([-1]), rtol=0.01 + ) + ) + self.assertTrue( + np.allclose( + self.dp_d.dstd.reshape([-1]), + my_en.stddev.cpu().reshape([-1]), + rtol=0.01, + ) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_training.py b/tests/test_training.py index bcf5eca8..1567da10 100644 --- a/tests/test_training.py +++ b/tests/test_training.py @@ -1,16 +1,21 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later import json import os import shutil import unittest -from copy import deepcopy +from copy import ( + deepcopy, +) + +from deepmd_pt.entrypoints.main import ( + get_trainer, +) -import numpy as np -from deepmd_pt.entrypoints.main import get_trainer from .test_permutation import ( - model_se_e2_a, - model_dpa1, - model_dpa2, - model_hybrid, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, ) @@ -33,7 +38,7 @@ def tearDown(self): class TestEnergyModelSeA(unittest.TestCase, TestDPTrain): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_se_e2_a) self.config["training"]["numb_steps"] = 1 @@ -43,7 +48,7 @@ def setUp(self): class TestEnergyModelDPA1(unittest.TestCase, TestDPTrain): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_dpa1) self.config["training"]["numb_steps"] = 1 @@ -53,12 +58,18 @@ def setUp(self): class TestEnergyModelDPA2(unittest.TestCase, TestDPTrain): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_dpa2) - self.config["model"]["descriptor"]["rcut"] = self.config["model"]["descriptor"]["repinit_rcut"] - self.config["model"]["descriptor"]["rcut_smth"] = self.config["model"]["descriptor"]["repinit_rcut_smth"] - self.config["model"]["descriptor"]["sel"] = self.config["model"]["descriptor"]["repinit_nsel"] + self.config["model"]["descriptor"]["rcut"] = self.config["model"]["descriptor"][ + "repinit_rcut" + ] + self.config["model"]["descriptor"]["rcut_smth"] = self.config["model"][ + "descriptor" + ]["repinit_rcut_smth"] + self.config["model"]["descriptor"]["sel"] = self.config["model"]["descriptor"][ + "repinit_nsel" + ] self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 @@ -67,12 +78,12 @@ def setUp(self): class TestEnergyModelHybrid(unittest.TestCase, TestDPTrain): def setUp(self): input_json = "tests/water/se_atten.json" - with open(input_json, "r") as f: + with open(input_json) as f: self.config = json.load(f) self.config["model"] = deepcopy(model_hybrid) self.config["training"]["numb_steps"] = 1 self.config["training"]["save_freq"] = 1 -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_trans.py b/tests/test_trans.py index 7b3144ce..4cd76af6 100644 --- a/tests/test_trans.py +++ b/tests/test_trans.py @@ -1,109 +1,137 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils.preprocess import ( - Region3D, make_env_mat, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, ) -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input -from .test_permutation import ( - make_sample, - model_se_e2_a, - model_dpa1, - model_dpa2, - # model_dpau, - model_hybrid, +from deepmd_pt.utils import ( + env, +) + +from .test_permutation import ( # model_dpau, + make_sample, + model_dpa1, + model_dpa2, + model_hybrid, + model_se_e2_a, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 class TestTrans: - def test( - self, - ): - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - shift = (torch.rand([3], dtype=dtype) - .5).to(env.DEVICE) * 2. - coord_s = torch.matmul( - torch.remainder( - torch.matmul(coord + shift, torch.linalg.inv(cell)), 1.), cell) - e0, f0, v0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) - ret0 = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - e1, f1, v1 = eval_model(self.model, coord_s.unsqueeze(0), cell.unsqueeze(0), atype) - ret1 = {'energy': e1.squeeze(0), 'force': f1.squeeze(0), 'virial': v1.squeeze(0)} - prec = 1e-10 - torch.testing.assert_close(ret0['energy'], ret1['energy'], rtol=prec, atol=prec) - torch.testing.assert_close(ret0['force'], ret1['force'], rtol=prec, atol=prec) - if not hasattr(self, "test_virial") or self.test_virial: - torch.testing.assert_close(ret0['virial'], ret1['virial'], rtol=prec, atol=prec) + def test( + self, + ): + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + shift = (torch.rand([3], dtype=dtype) - 0.5).to(env.DEVICE) * 2.0 + coord_s = torch.matmul( + torch.remainder(torch.matmul(coord + shift, torch.linalg.inv(cell)), 1.0), + cell, + ) + e0, f0, v0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret0 = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } + e1, f1, v1 = eval_model( + self.model, coord_s.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret1 = { + "energy": e1.squeeze(0), + "force": f1.squeeze(0), + "virial": v1.squeeze(0), + } + prec = 1e-10 + torch.testing.assert_close(ret0["energy"], ret1["energy"], rtol=prec, atol=prec) + torch.testing.assert_close(ret0["force"], ret1["force"], rtol=prec, atol=prec) + if not hasattr(self, "test_virial") or self.test_virial: + torch.testing.assert_close( + ret0["virial"], ret1["virial"], rtol=prec, atol=prec + ) class TestEnergyModelSeA(unittest.TestCase, TestTrans): - def setUp(self): - model_params = copy.deepcopy(model_se_e2_a) - sampled = make_sample(model_params) - self.type_split = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_se_e2_a) + sampled = make_sample(model_params) + self.type_split = False + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestEnergyModelDPA1(unittest.TestCase, TestTrans): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestEnergyModelDPA2(unittest.TestCase, TestTrans): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) class TestForceModelDPA2(unittest.TestCase, TestTrans): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - model_params["fitting_net"]["type"] = "direct_force_ener" - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + model_params["fitting_net"]["type"] = "direct_force_ener" + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) @unittest.skip("hybrid not supported at the moment") class TestEnergyModelHybrid(unittest.TestCase, TestTrans): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) @unittest.skip("hybrid not supported at the moment") class TestForceModelHybrid(unittest.TestCase, TestTrans): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - model_params["fitting_net"]["type"] = "direct_force_ener" - sampled = make_sample(model_params) - self.type_split = True - self.test_virial = False - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + model_params["fitting_net"]["type"] = "direct_force_ener" + sampled = make_sample(model_params) + self.type_split = True + self.test_virial = False + self.model = get_model(model_params, sampled).to(env.DEVICE) -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_trans_denoise.py b/tests/test_trans_denoise.py index 25f67984..48ee99d6 100644 --- a/tests/test_trans_denoise.py +++ b/tests/test_trans_denoise.py @@ -1,70 +1,92 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils.preprocess import ( - Region3D, make_env_mat, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, ) -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input +from deepmd_pt.utils import ( + env, +) + from .test_permutation_denoise import ( - make_sample, - model_dpa1, - model_dpa2, - model_hybrid, + make_sample, + model_dpa1, + model_dpa2, + model_hybrid, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 + class TestTransDenoise: - def test( - self, - ): - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - shift = (torch.rand([3], dtype=dtype) - .5).to(env.DEVICE) * 2. - coord_s = torch.matmul( - torch.remainder( - torch.matmul(coord + shift, torch.linalg.inv(cell)), 1.), cell) - updated_c0, logits0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True) - updated_c0 = updated_c0 - coord.unsqueeze(0) - ret0 = {'updated_coord': updated_c0.squeeze(0), 'logits': logits0.squeeze(0)} - updated_c1, logits1 = eval_model(self.model, coord_s.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True) - updated_c1 = updated_c1 - coord_s.unsqueeze(0) - ret1 = {'updated_coord': updated_c1.squeeze(0), 'logits': logits1.squeeze(0)} - prec = 1e-10 - torch.testing.assert_close(ret0['updated_coord'], ret1['updated_coord'], rtol=prec, atol=prec) - torch.testing.assert_close(ret0['logits'], ret1['logits'], rtol=prec, atol=prec) + def test( + self, + ): + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + shift = (torch.rand([3], dtype=dtype) - 0.5).to(env.DEVICE) * 2.0 + coord_s = torch.matmul( + torch.remainder(torch.matmul(coord + shift, torch.linalg.inv(cell)), 1.0), + cell, + ) + updated_c0, logits0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + updated_c0 = updated_c0 - coord.unsqueeze(0) + ret0 = {"updated_coord": updated_c0.squeeze(0), "logits": logits0.squeeze(0)} + updated_c1, logits1 = eval_model( + self.model, coord_s.unsqueeze(0), cell.unsqueeze(0), atype, denoise=True + ) + updated_c1 = updated_c1 - coord_s.unsqueeze(0) + ret1 = {"updated_coord": updated_c1.squeeze(0), "logits": logits1.squeeze(0)} + prec = 1e-10 + torch.testing.assert_close( + ret0["updated_coord"], ret1["updated_coord"], rtol=prec, atol=prec + ) + torch.testing.assert_close(ret0["logits"], ret1["logits"], rtol=prec, atol=prec) + class TestDenoiseModelDPA1(unittest.TestCase, TestTransDenoise): - def setUp(self): - model_params = copy.deepcopy(model_dpa1) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_dpa1) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + class TestDenoiseModelDPA2(unittest.TestCase, TestTransDenoise): - def setUp(self): - model_params_sample = copy.deepcopy(model_dpa2) - model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"]["repinit_rcut"] - model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"]["repinit_nsel"] - sampled = make_sample(model_params_sample) - model_params = copy.deepcopy(model_dpa2) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params_sample = copy.deepcopy(model_dpa2) + model_params_sample["descriptor"]["rcut"] = model_params_sample["descriptor"][ + "repinit_rcut" + ] + model_params_sample["descriptor"]["sel"] = model_params_sample["descriptor"][ + "repinit_nsel" + ] + sampled = make_sample(model_params_sample) + model_params = copy.deepcopy(model_dpa2) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + @unittest.skip("hybrid not supported at the moment") class TestDenoiseModelHybrid(unittest.TestCase, TestTransDenoise): - def setUp(self): - model_params = copy.deepcopy(model_hybrid) - sampled = make_sample(model_params) - self.type_split = True - self.model = get_model(model_params, sampled).to(env.DEVICE) + def setUp(self): + model_params = copy.deepcopy(model_hybrid) + sampled = make_sample(model_params) + self.type_split = True + self.model = get_model(model_params, sampled).to(env.DEVICE) + -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_unused_params.py b/tests/test_unused_params.py index 284323b2..aefcda05 100644 --- a/tests/test_unused_params.py +++ b/tests/test_unused_params.py @@ -1,81 +1,98 @@ -import torch, copy +# SPDX-License-Identifier: LGPL-3.0-or-later +import copy import unittest -from deepmd_pt.utils.preprocess import ( - Region3D, make_env_mat, + +import torch + +from deepmd_pt.infer.deep_eval import ( + eval_model, +) +from deepmd_pt.model.model import ( + get_model, +) +from deepmd_pt.utils import ( + env, ) -from deepmd_pt.utils import env -from deepmd_pt.model.model import get_model -from deepmd_pt.utils.dataloader import DpLoaderSet -from deepmd_pt.utils.stat import make_stat_input + from .test_permutation import ( make_sample, model_dpa2, ) -from deepmd_pt.infer.deep_eval import eval_model dtype = torch.float64 class TestUnusedParamsDPA2(unittest.TestCase): - def test_unused(self): - import itertools - for conv, drrd, grrg, attn1, g1g1, attn2, h2 in \ - itertools.product( - [True,False], [True,False], [True,False], [True,False], - [True,False], [True,False], [True,False], + def test_unused(self): + import itertools + + for conv, drrd, grrg, attn1, g1g1, attn2, h2 in itertools.product( + [True, False], + [True, False], + [True, False], + [True, False], + [True, False], + [True, False], + [True, False], ): - if (not drrd) and (not grrg) and h2: - # skip the case h2 is not envolved - continue - if (not grrg) and (not conv): - # skip the case g2 is not envolved - continue - model = copy.deepcopy(model_dpa2) - model["descriptor"]["rcut"] = model["descriptor"]["repinit_rcut"] - model["descriptor"]["sel"] = model["descriptor"]["repinit_nsel"] - model["descriptor"]["repformer_nlayers"] = 2 - # model["descriptor"]["combine_grrg"] = cmbg2 - model["descriptor"]["repformer_update_g1_has_conv"] = conv - model["descriptor"]["repformer_update_g1_has_drrd"] = drrd - model["descriptor"]["repformer_update_g1_has_grrg"] = grrg - model["descriptor"]["repformer_update_g1_has_attn"] = attn1 - model["descriptor"]["repformer_update_g2_has_g1g1"] = g1g1 - model["descriptor"]["repformer_update_g2_has_attn"] = attn2 - model["descriptor"]["repformer_update_h2"] = h2 - model["fitting_net"]["neuron"] = [12, 12, 12] - self._test_unused(model) + if (not drrd) and (not grrg) and h2: + # skip the case h2 is not envolved + continue + if (not grrg) and (not conv): + # skip the case g2 is not envolved + continue + model = copy.deepcopy(model_dpa2) + model["descriptor"]["rcut"] = model["descriptor"]["repinit_rcut"] + model["descriptor"]["sel"] = model["descriptor"]["repinit_nsel"] + model["descriptor"]["repformer_nlayers"] = 2 + # model["descriptor"]["combine_grrg"] = cmbg2 + model["descriptor"]["repformer_update_g1_has_conv"] = conv + model["descriptor"]["repformer_update_g1_has_drrd"] = drrd + model["descriptor"]["repformer_update_g1_has_grrg"] = grrg + model["descriptor"]["repformer_update_g1_has_attn"] = attn1 + model["descriptor"]["repformer_update_g2_has_g1g1"] = g1g1 + model["descriptor"]["repformer_update_g2_has_attn"] = attn2 + model["descriptor"]["repformer_update_h2"] = h2 + model["fitting_net"]["neuron"] = [12, 12, 12] + self._test_unused(model) + + def _test_unused(self, model_params): + sampled = make_sample(model_params) + self.model = get_model(model_params, sampled).to(env.DEVICE) + natoms = 5 + cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) + cell = (cell + cell.T) + 5.0 * torch.eye(3).to(env.DEVICE) + coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) + coord = torch.matmul(coord, cell) + atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) + idx_perm = [1, 0, 4, 3, 2] + e0, f0, v0 = eval_model( + self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype + ) + ret0 = { + "energy": e0.squeeze(0), + "force": f0.squeeze(0), + "virial": v0.squeeze(0), + } - def _test_unused(self, model_params): - sampled = make_sample(model_params) - self.model = get_model(model_params, sampled).to(env.DEVICE) - natoms = 5 - cell = torch.rand([3, 3], dtype=dtype).to(env.DEVICE) - cell = (cell + cell.T) + 5. * torch.eye(3).to(env.DEVICE) - coord = torch.rand([natoms, 3], dtype=dtype).to(env.DEVICE) - coord = torch.matmul(coord, cell) - atype = torch.IntTensor([0, 0, 0, 1, 1]).to(env.DEVICE) - idx_perm = [1, 0, 4, 3, 2] - e0, f0, v0 = eval_model(self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype) - ret0 = {'energy': e0.squeeze(0), 'force': f0.squeeze(0), 'virial': v0.squeeze(0)} - - # use computation graph to find all contributing tensors - def get_contributing_params(y, top_level=True): - nf = y.grad_fn.next_functions if top_level else y.next_functions - for f, _ in nf: - try: - yield f.variable - except AttributeError: - pass # node has no tensor - if f is not None: - yield from get_contributing_params(f, top_level=False) + # use computation graph to find all contributing tensors + def get_contributing_params(y, top_level=True): + nf = y.grad_fn.next_functions if top_level else y.next_functions + for f, _ in nf: + try: + yield f.variable + except AttributeError: + pass # node has no tensor + if f is not None: + yield from get_contributing_params(f, top_level=False) - contributing_parameters = set(get_contributing_params(ret0['energy'])) - all_parameters = set(self.model.parameters()) - non_contributing = all_parameters - contributing_parameters - for ii in non_contributing: - print(ii.shape) - self.assertEqual(len(non_contributing), 0) + contributing_parameters = set(get_contributing_params(ret0["energy"])) + all_parameters = set(self.model.parameters()) + non_contributing = all_parameters - contributing_parameters + for ii in non_contributing: + print(ii.shape) + self.assertEqual(len(non_contributing), 0) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/water/input_AlCuMg.json b/tests/water/input_AlCuMg.json index 92c6639f..1b4fe307 100644 --- a/tests/water/input_AlCuMg.json +++ b/tests/water/input_AlCuMg.json @@ -1,7513 +1,7513 @@ { - "model": { - "descriptor": { - "type": "se_e2_a", - "sel": [ - 120 - ], - "rcut_smth": 2.0, - "rcut": 9.0, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 12, - "attn": 128, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "seed": 1801819940, - "_activation_function": "tanh" + "model": { + "descriptor": { + "type": "se_e2_a", + "sel": [ + 120 + ], + "rcut_smth": 2.0, + "rcut": 9.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 12, + "attn": 128, + "attn_layer": 2, + "attn_dotr": true, + "attn_mask": false, + "seed": 1801819940, + "_activation_function": "tanh" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "_coord_norm": true, + "_type_fitting_net": false, + "seed": 2375417769, + "_activation_function": "tanh" + }, + "type_map": [ + "Al", + "Cu", + "Mg" + ] }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "_coord_norm": true, - "_type_fitting_net": false, - "seed": 2375417769, - "_activation_function": "tanh" + "learning_rate": { + "type": "exp", + "start_lr": 0.001, + "decay_steps": 80000, + "stop_lr": 5e-08, + "_decay_rate": 0.95 }, - "type_map": [ - "Al", - "Cu", - "Mg" - ] - }, - "learning_rate": { - "type": "exp", - "start_lr": 0.001, - "decay_steps": 80000, - "stop_lr": 5e-08, - "_decay_rate": 0.95 - }, - "loss": { - "start_pref_e": 0.02, - "limit_pref_e": 2, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0.01, - "limit_pref_v": 1 - }, - "training": { - "training_data": { - "systems": [ - "/root/data/AlMgCu/data/init.000", - "/root/data/AlMgCu/data/init.001", - "/root/data/AlMgCu/data/init.002", - "/root/data/AlMgCu/data/init.003", - "/root/data/AlMgCu/data/init.004", - "/root/data/AlMgCu/data/init.005", - "/root/data/AlMgCu/data/init.006", - "/root/data/AlMgCu/data/init.007", - "/root/data/AlMgCu/data/init.009", - "/root/data/AlMgCu/data/init.010", - "/root/data/AlMgCu/data/init.011", - "/root/data/AlMgCu/data/init.012", - "/root/data/AlMgCu/data/init.014", - "/root/data/AlMgCu/data/init.015", - "/root/data/AlMgCu/data/init.016", - "/root/data/AlMgCu/data/init.017", - "/root/data/AlMgCu/data/init.018", - "/root/data/AlMgCu/data/init.019", - "/root/data/AlMgCu/data/init.020", - "/root/data/AlMgCu/data/init.021", - "/root/data/AlMgCu/data/init.022", - "/root/data/AlMgCu/data/init.023", - "/root/data/AlMgCu/data/init.024", - "/root/data/AlMgCu/data/init.025", - "/root/data/AlMgCu/data/init.026", - "/root/data/AlMgCu/data/init.027", - "/root/data/AlMgCu/data/init.028", - 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>tmp_log 2>&1", - "log_file": "tmp_log", - "backward_files": ["lcurve.out", "model.ckpt"], - "project_id": 11176, - "platform": "ali", - "machine_type": "c12_m92_1 * NVIDIA V100", - "job_type": "container", - "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" -} \ No newline at end of file + "job_name": "torch water", + "command": " source deactivate && conda activate gnn && PYTHONPATH=./ python3 -u deepmd_pt/main.py train tests/water/water_large.json >tmp_log 2>&1", + "log_file": "tmp_log", + "backward_files": [ + "lcurve.out", + "model.ckpt" + ], + "project_id": 11176, + "platform": "ali", + "machine_type": "c12_m92_1 * NVIDIA V100", + "job_type": "container", + "image_address": "registry.dp.tech/dptech/prod-195/stable:torch2.0_cu116" +} diff --git a/tests/water/lkf.json b/tests/water/lkf.json index fe86eb92..7d275602 100644 --- a/tests/water/lkf.json +++ b/tests/water/lkf.json @@ -1,81 +1,81 @@ { - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_e2_a", - "sel": [ - 46, - 92 - ], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 25, - 25, - 25 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [ - 100, - 100, - 100 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "data_stat_nbatch": 20, - "_comment": " that's all" + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 25, + 25 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" + "fitting_net": { + "neuron": [ + 100, + 100, + 100 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - 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"lcurve.out", - "disp_freq": 1, - "save_freq": 1, - "opt_type": "LKF", - "kf_blocksize": 1024, - "_comment": "that's all" + "validation_data": { + "systems": [ + "tests/water/data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" }, + "numb_steps": 1, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 1, + "save_freq": 1, + "opt_type": "LKF", + "kf_blocksize": 1024, "_comment": "that's all" + }, + "_comment": "that's all" } diff --git a/tests/water/se_atten.json b/tests/water/se_atten.json index 4cf280d2..f50135f4 100644 --- a/tests/water/se_atten.json +++ b/tests/water/se_atten.json @@ -1,86 +1,86 @@ { - "_comment": "that's all", - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_atten", - "sel": 40, - "rcut_smth": 0.5, - "rcut": 4.0, - "neuron": [ - 25, - 50, - 100 - ], - "axis_neuron": 16, - "attn": 64, - "attn_layer": 2, - "attn_dotr": true, - "attn_mask": false, - "post_ln": true, - "ffn": false, - "ffn_embed_dim": 512, 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{ - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "tests/water/data/data_0", + "tests/water/data/data_1", + "tests/water/data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": [ - "tests/water/data/data_0", - "tests/water/data/data_1", - "tests/water/data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "tests/water/data/data_3" - ], - "batch_size": 1, - "numb_btch": 1, - "_comment": "that's all" - }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 1000, - "_comment": "that's all" - } + "validation_data": { + "systems": [ + "tests/water/data/data_3" + ], + "batch_size": 1, + "numb_btch": 1, + "_comment": "that's all" + }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + } } diff --git a/tests/water/se_e2_a.json b/tests/water/se_e2_a.json index 1abaa6f4..1276deda 100644 --- a/tests/water/se_e2_a.json +++ b/tests/water/se_e2_a.json @@ -1,79 +1,79 @@ { - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_e2_a", - "sel": [ - 46, - 92 - ], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "data_stat_nbatch": 20, - "_comment": " that's all" + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "_comment": " that's all" + "data_stat_nbatch": 20, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "tests/water/data/data_0", + "tests/water/data/data_1", + "tests/water/data/data_2" + ], + "batch_size": 1, + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": [ - "tests/water/data/data_0", - "tests/water/data/data_1", - "tests/water/data/data_2" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "tests/water/data/data_3" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "numb_steps": 100000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 100, - "save_freq": 10000, - "_comment": "that's all" + "validation_data": { + "systems": [ + "tests/water/data/data_3" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" }, + "numb_steps": 100000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 10000, "_comment": "that's all" + }, + "_comment": "that's all" } diff --git a/tests/water/water_large.json b/tests/water/water_large.json index c5110a13..89cc002c 100644 --- a/tests/water/water_large.json +++ b/tests/water/water_large.json @@ -1,79 +1,79 @@ { - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_e2_a", - "sel": [ - 46, - 92 - ], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "data_stat_nbatch": 20, - "_comment": " that's all" + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_e2_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "_comment": " that's all" }, - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" }, - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "_comment": " that's all" + "data_stat_nbatch": 20, + "_comment": " that's all" + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "_comment": " that's all" + }, + "training": { + "training_data": { + "systems": [ + "data/water_train.hdf5/O64H128", + "data/water_train.hdf5/O96H192" + ], + "batch_size": 1, + "_comment": "that's all" }, - "training": { - "training_data": { - "systems": [ - "data/water_train.hdf5/O64H128", - "data/water_train.hdf5/O96H192" - ], - "batch_size": 1, - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "data/water_test.hdf5/O64H128", - "data/water_test.hdf5/O96H192" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "numb_steps": 1000000, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 2, - "save_freq": 10000, - "_comment": "that's all" + "validation_data": { + "systems": [ + "data/water_test.hdf5/O64H128", + "data/water_test.hdf5/O96H192" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" }, + "numb_steps": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 2, + "save_freq": 10000, "_comment": "that's all" + }, + "_comment": "that's all" } diff --git a/visualize.py b/visualize.py deleted file mode 100644 index c33738c3..00000000 --- a/visualize.py +++ /dev/null @@ -1,71 +0,0 @@ -import argparse -import os - -from collections import namedtuple -from matplotlib import pyplot -import numpy as np - -CurveRecord = namedtuple('CurveRecord', ['step', 'rmse_e', 'rmse_f']) - - -def load_lcurve_file(path): - with open(path, 'r') as fin: - lines = fin.readlines() - lcurve = [] - for item in lines: - line = item.strip() - if not line or line.startswith('#'): - continue - fields = line.split() - lcurve.append(fields) - return lcurve - -def rolling_window(a, window): - shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) - strides = a.strides + (a.strides[-1],) - return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) - -def draw_line_2d(path, step_interval, rolling=1000): - lcurve = load_lcurve_file(path) - steps = [] - rmse_e = [] - rmse_f = [] - for idx in range(0, len(lcurve), step_interval): - item = lcurve[idx] - step = item[0].rstrip(',').split('=')[-1] - e = item[1].rstrip(',').split('=')[-1] - f = item[2].rstrip(',').split('=')[-1] - #e = item[4].rstrip(',').split('=')[-1] - #f = item[6].rstrip(',').split('=')[-1] - #e = item[3].rstrip(',').split('=')[-1] - #f = item[3].rstrip(',').split('=')[-1] - steps.append(int(step)) - rmse_e.append(float(e)) - rmse_f.append(float(f)) - pyplot.yscale("log") - rmse_e = rolling_window(np.array(rmse_e), rolling).mean(axis=-1) - rmse_f = rolling_window(np.array(rmse_f), rolling).mean(axis=-1) - pyplot.plot(steps[rolling-1:], rmse_e, label='Energy') - pyplot.plot(steps[rolling-1:], rmse_f, label='Force') - import pdb - pdb.set_trace() - -def run(FLAGS): - pyplot.figure(figsize=(15,5)) - pyplot.title('RMSE over step') - pyplot.xlabel('Step') - pyplot.ylabel('RMSE') - draw_line_2d('lcurve.out', FLAGS.sample_every_steps) - pyplot.legend() - - if os.path.isfile(FLAGS.output_path): - os.remove(FLAGS.output_path) - pyplot.savefig(FLAGS.output_path) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Draw lines from a lcurve file.') - parser.add_argument('-n', '--sample_every_steps', type=int, default=1, help='Sample every N steps.') - parser.add_argument('-o', '--output_path', default='rmse_over_step.png', help='Where to write image.') - FLAGS = parser.parse_args() - run(FLAGS)