From 4c077347a7b10a72d0615944ea35fef04f925c17 Mon Sep 17 00:00:00 2001 From: Jaesun0912 Date: Wed, 14 Aug 2024 19:49:24 +0900 Subject: [PATCH 001/113] multimodal --- sevenn/_const.py | 24 ++ sevenn/_keys.py | 25 +++ sevenn/error_recorder.py | 8 +- sevenn/main/sevenn_get_model.py | 31 ++- sevenn/main/sevenn_inference.py | 16 +- sevenn/model_build.py | 61 ++++- sevenn/nn/force_output.py | 20 ++ sevenn/nn/linear.py | 29 +++ sevenn/nn/scale.py | 55 +++++ sevenn/scripts/convert_model_modality.py | 275 +++++++++++++++++++++++ sevenn/scripts/inference.py | 20 +- sevenn/scripts/processing_continue.py | 49 ++++ sevenn/scripts/processing_dataset.py | 257 ++++++++++++++++++--- sevenn/scripts/train.py | 12 +- sevenn/sevennet_calculator.py | 12 + sevenn/train/dataload.py | 115 +++++++--- sevenn/train/dataset.py | 51 ++++- sevenn/train/loss.py | 77 ++++++- sevenn/train/trainer.py | 4 +- sevenn/util.py | 35 ++- 20 files changed, 1090 insertions(+), 86 deletions(-) create mode 100644 sevenn/scripts/convert_model_modality.py diff --git a/sevenn/_const.py b/sevenn/_const.py index df752237..a473ec2c 100644 --- a/sevenn/_const.py +++ b/sevenn/_const.py @@ -106,6 +106,10 @@ def error_record_condition(x): KEY.TRAIN_SHIFT_SCALE: False, # KEY.OPTIMIZE_BY_REDUCE: True, # deprecated, always True KEY.USE_BIAS_IN_LINEAR: False, + KEY.USE_MODAL_NODE_EMBEDDING: False, + KEY.USE_MODAL_SELF_INTER_INTRO: False, + KEY.USE_MODAL_SELF_INTER_OUTRO: False, + KEY.USE_MODAL_OUTPUT_BLOCK: False, KEY.READOUT_AS_FCN: False, # Applied af readout as fcn is True KEY.READOUT_FCN_HIDDEN_NEURONS: [30, 30], @@ -138,6 +142,10 @@ def error_record_condition(x): KEY.TRAIN_SHIFT_SCALE: bool, KEY.TRAIN_DENOMINTAOR: bool, KEY.USE_BIAS_IN_LINEAR: bool, + KEY.USE_MODAL_NODE_EMBEDDING: bool, + KEY.USE_MODAL_SELF_INTER_INTRO: bool, + KEY.USE_MODAL_SELF_INTER_OUTRO: bool, + KEY.USE_MODAL_OUTPUT_BLOCK: bool, KEY.READOUT_AS_FCN: bool, KEY.READOUT_FCN_HIDDEN_NEURONS: list, KEY.READOUT_FCN_ACTIVATION: str, @@ -170,9 +178,13 @@ def model_defaults(config): KEY.BATCH_SIZE: 6, KEY.PREPROCESS_NUM_CORES: 1, # KEY.USE_SPECIES_WISE_SHIFT_SCALE: False, + KEY.USE_MODAL_WISE_SHIFT: False, + KEY.USE_MODAL_WISE_SCALE: False, KEY.SHIFT: 'per_atom_energy_mean', KEY.SCALE: 'force_rms', KEY.DATA_SHUFFLE: True, + # KEY.DATA_WEIGHT: False, + # KEY.DATA_MODALITY: False, } DATA_CONFIG_CONDITION = { @@ -188,8 +200,12 @@ def model_defaults(config): # KEY.USE_SPECIES_WISE_SHIFT_SCALE: bool, KEY.SHIFT: lambda x: type(x) in [float, list] or x in IMPLEMENTED_SHIFT, KEY.SCALE: lambda x: type(x) in [float, list] or x in IMPLEMENTED_SCALE, + KEY.USE_MODAL_WISE_SHIFT: bool, + KEY.USE_MODAL_WISE_SCALE: bool, KEY.DATA_SHUFFLE: bool, KEY.SAVE_DATASET: str, + # KEY.DATA_WEIGHT: bool, + # KEY.DATA_MODALITY: bool, } @@ -219,7 +235,9 @@ def data_defaults(config): KEY.RESET_SCHEDULER: False, KEY.RESET_EPOCH: False, KEY.USE_STATISTIC_VALUES_OF_CHECKPOINT: True, + KEY.USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY: False, }, + KEY.DEFAULT_MODAL: 'common', KEY.CSV_LOG: 'log.csv', KEY.NUM_WORKERS: 0, KEY.IS_TRACE_STRESS: False, @@ -232,6 +250,8 @@ def data_defaults(config): ['TotalLoss', 'None'], ], KEY.BEST_METRIC: 'TotalLoss', + KEY.USE_WEIGHT: False, + KEY.USE_MODALITY: False, } @@ -249,13 +269,17 @@ def data_defaults(config): KEY.RESET_SCHEDULER: bool, KEY.RESET_EPOCH: bool, KEY.USE_STATISTIC_VALUES_OF_CHECKPOINT: bool, + KEY.USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY: bool, }, + KEY.DEFAULT_MODAL: str, KEY.IS_TRACE_STRESS: bool, # Not used KEY.IS_TRAIN_STRESS: bool, KEY.TRAIN_SHUFFLE: bool, KEY.ERROR_RECORD: error_record_condition, KEY.BEST_METRIC: str, KEY.CSV_LOG: str, + KEY.USE_MODALITY: bool, + KEY.USE_WEIGHT: bool, } diff --git a/sevenn/_keys.py b/sevenn/_keys.py index 277342a9..8458f1c9 100644 --- a/sevenn/_keys.py +++ b/sevenn/_keys.py @@ -38,6 +38,10 @@ NODE_FEATURE: Final[str] = 'x' # (N, ?) PyG NODE_FEATURE_GHOST: Final[str] = 'x_ghost' NODE_ATTR: Final[str] = 'node_attr' # (N, N_species) from one_hot +MODAL_ATTR: Final[str] = ( + 'modal_attr' # (1, N_modalities) for handling multi-modal +) +MODAL_TYPE: Final[str] = 'modal_type' # (1) one-hot index of modal EDGE_ATTR: Final[str] = 'edge_attr' # (from spherical harmonics) EDGE_EMBEDDING: Final[str] = 'edge_embedding' # (from edge embedding) @@ -68,6 +72,10 @@ NUM_GHOSTS: Final[str] = 'num_ghosts' NLOCAL: Final[str] = 'nlocal' # only for lammps parallel, must be on cpu USER_LABEL: Final[str] = 'user_label' +DATA_WEIGHT: Final[str] = 'data_weight' # weight for given data +DATA_MODALITY: Final[str] = ( + 'data_modality' # modality of given data. e.g. PBE and SCAN +) BATCH: Final[str] = 'batch' # etc @@ -119,6 +127,9 @@ RESET_SCHEDULER = 'reset_scheduler' RESET_EPOCH = 'reset_epoch' USE_STATISTIC_VALUES_OF_CHECKPOINT = 'use_statistic_values_of_checkpoint' +USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY = ( + 'use_statistic_values_for_cp_modal_only' +) CSV_LOG = 'csv_log' @@ -134,6 +145,10 @@ PER_EPOCH = 'per_epoch' +USE_WEIGHT = 'use_weight' +USE_MODALITY = 'use_modality' +DEFAULT_MODAL = 'default_modal' + # ==================================================# # ~~~~~~~~ KEY for model configuration ~~~~~~~~~~~ # @@ -143,9 +158,12 @@ MODEL_TYPE = '_model_type' CUTOFF = 'cutoff' CHEMICAL_SPECIES = 'chemical_species' +MODAL_LIST = 'modal_list' CHEMICAL_SPECIES_BY_ATOMIC_NUMBER = '_chemical_species_by_atomic_number' NUM_SPECIES = '_number_of_species' +NUM_MODALITIES = '_number_of_modalities' TYPE_MAP = '_type_map' +MODAL_MAP = '_modal_map' # ~~ E3 equivariant model build configuration keys ~~ # # see model_build default_config for type @@ -175,6 +193,11 @@ USE_BIAS_IN_LINEAR = 'use_bias_in_linear' +USE_MODAL_NODE_EMBEDDING = 'use_modal_node_embedding' +USE_MODAL_SELF_INTER_INTRO = 'use_modal_self_inter_intro' +USE_MODAL_SELF_INTER_OUTRO = 'use_modal_self_inter_outro' +USE_MODAL_OUTPUT_BLOCK = 'use_modal_output_block' + READOUT_AS_FCN = 'readout_as_fcn' READOUT_FCN_HIDDEN_NEURONS = 'readout_fcn_hidden_neurons' READOUT_FCN_ACTIVATION = 'readout_fcn_activation' @@ -185,6 +208,8 @@ SCALE = 'scale' USE_SPECIES_WISE_SHIFT_SCALE = 'use_species_wise_shift_scale' +USE_MODAL_WISE_SHIFT = 'use_modal_wise_shift' +USE_MODAL_WISE_SCALE = 'use_modal_wise_scale' TRAIN_SHIFT_SCALE = 'train_shift_scale' TRAIN_DENOMINTAOR = 'train_denominator' diff --git a/sevenn/error_recorder.py b/sevenn/error_recorder.py index 7e40b349..e2172bf5 100644 --- a/sevenn/error_recorder.py +++ b/sevenn/error_recorder.py @@ -67,6 +67,7 @@ def __init__( coeff: float = 1.0, unit: str = None, per_atom: bool = False, + delete_unlabled: bool = True, **kwargs, ): self.name = name @@ -75,6 +76,7 @@ def __init__( self.ref_key = ref_key self.pred_key = pred_key self.per_atom = per_atom + self.delete_unlabled = delete_unlabled self.value = AverageNumber() def update(self, output: AtomGraphData): @@ -87,6 +89,10 @@ def _retrieve(self, output: AtomGraphData): natoms = output[KEY.NUM_ATOMS] y_ref = y_ref / natoms y_pred = y_pred / natoms + if self.delete_unlabled: + unlabelled_idx = torch.isnan(y_ref) + y_ref = y_ref[~unlabelled_idx] + y_pred = y_pred[~unlabelled_idx] return y_ref, y_pred def ddp_reduce(self, device): @@ -187,7 +193,7 @@ def __init__(self, func: Callable, **kwargs): def update(self, output: AtomGraphData): y_ref, y_pred = self._retrieve(output) - se = self.func(y_ref, y_pred) + se = self.func(y_ref, y_pred) if len(y_ref) > 0 else torch.tensor([]) self.value.update(se) diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py index 50d097b8..2ff56d7e 100644 --- a/sevenn/main/sevenn_get_model.py +++ b/sevenn/main/sevenn_get_model.py @@ -1,8 +1,12 @@ import argparse import os +import torch + import sevenn._const as _const import sevenn.util +import sevenn._keys as KEY +from sevenn.scripts.convert_model_modality import get_single_modal_model_dct from sevenn.scripts.deploy import deploy, deploy_parallel description_get_model = ( @@ -18,7 +22,7 @@ def main(args=None): - checkpoint, output_prefix, get_parallel = cmd_parse_get_model(args) + checkpoint, output_prefix, get_parallel, modal, save_cp = cmd_parse_get_model(args) get_serial = not get_parallel if output_prefix is None: @@ -35,6 +39,14 @@ def main(args=None): model, config = sevenn.util.model_from_checkpoint(checkpoint_path) stct_dct = model.state_dict() + if KEY.USE_MODALITY in config.keys() and config[KEY.USE_MODALITY]: + stct_dct = get_single_modal_model_dct(stct_dct, config, modal) + output_prefix = modal + '_' + output_prefix + if save_cp: + cp_file = torch.load(checkpoint_path, map_location='cpu') + cp_file.update({'model_state_dict': stct_dct, 'config': config}) + torch.save(cp_file, checkpoint_path.replace('.', f'_{modal}.')) + if get_serial: deploy(stct_dct, config, output_prefix) else: @@ -50,8 +62,23 @@ def cmd_parse_get_model(args=None): ag.add_argument( '-p', '--get_parallel', help=get_parallel_help, action='store_true' ) + ag.add_argument( + '-m', + '--modal', + help='Modality of multi-modal model', + default='common', + type=str, + ) + ag.add_argument( + '-s', + '--save_checkpoint', + help='Save converted checkpoint', + action='store_true', + ) args = ag.parse_args() checkpoint = args.checkpoint output_prefix = args.output_prefix get_parallel = args.get_parallel - return checkpoint, output_prefix, get_parallel + modal = args.modal + save_cp = args.save_checkpoint + return checkpoint, output_prefix, get_parallel, modal, save_cp diff --git a/sevenn/main/sevenn_inference.py b/sevenn/main/sevenn_inference.py index e7de7b41..0b2f485a 100644 --- a/sevenn/main/sevenn_inference.py +++ b/sevenn/main/sevenn_inference.py @@ -17,7 +17,8 @@ def main(args=None): - checkpoint, target, device, ncores, output, batch = cmd_parse_data(args) + checkpoint, target, device, ncores, output, batch, modal, =\ + cmd_parse_data(args) if not os.path.exists(checkpoint): print(f'{checkpoint} does not exist') sys.exit(1) @@ -26,7 +27,7 @@ def main(args=None): if device == 'auto': device = 'cuda' if torch.cuda.is_available() else 'cpu' - inference_main(checkpoint, target, output, ncores, device, batch) + inference_main(checkpoint, target, output, ncores, device, batch, modal) def cmd_parse_data(args=None): @@ -56,6 +57,14 @@ def cmd_parse_data(args=None): ) ag.add_argument('-b', '--batch', type=int, default='5', help='batch size') + ag.add_argument( + '-m', + '--modal', + type=str, + default='common', + help='modality for multi-modal inference', + ) + args = ag.parse_args() checkpoint = args.checkpoint @@ -65,4 +74,5 @@ def cmd_parse_data(args=None): ncores = args.ncores output = args.output batch = args.batch - return checkpoint, target, device, ncores, output, batch + modal = args.modal + return checkpoint, target, device, ncores, output, batch, modal diff --git a/sevenn/model_build.py b/sevenn/model_build.py index b9ec8a42..80ca8a1d 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -1,6 +1,7 @@ import warnings from collections import OrderedDict +import torch from e3nn.o3 import Irreps import sevenn._const as _const @@ -19,7 +20,7 @@ from sevenn.nn.force_output import ForceStressOutput from sevenn.nn.linear import AtomReduce, FCN_e3nn, IrrepsLinear from sevenn.nn.node_embedding import OnehotEmbedding -from sevenn.nn.scale import Rescale, SpeciesWiseRescale +from sevenn.nn.scale import Rescale, SpeciesWiseRescale, ModalWiseRescale from sevenn.nn.self_connection import ( SelfConnectionIntro, SelfConnectionLinearIntro, @@ -94,6 +95,7 @@ def build_E3_equivariant_model(config: dict, parallel=False): num_convolution_layer = config[KEY.NUM_CONVOLUTION] is_parity = config[KEY.IS_PARITY] # boolean + use_modality = config[KEY.USE_MODALITY] # boolean irreps_spherical_harm = Irreps.spherical_harmonics( lmax_edge, -1 if is_parity else 1 @@ -165,12 +167,19 @@ def build_E3_equivariant_model(config: dict, parallel=False): if irreps_manual is None else irreps_manual[0] ) + + num_modalities = ( + config[KEY.NUM_MODALITIES] + if use_modality and config[KEY.USE_MODAL_NODE_EMBEDDING] + else 1 + ) layers.update({ 'onehot_idx_to_onehot': OnehotEmbedding(num_classes=num_species), 'onehot_to_feature_x': IrrepsLinear( irreps_in=one_hot_irreps, irreps_out=irreps_x, data_key_in=KEY.NODE_FEATURE, + num_modalities=num_modalities, biases=use_bias_in_linear, ), }) @@ -188,6 +197,7 @@ def build_E3_equivariant_model(config: dict, parallel=False): irreps_in=one_hot_irreps, irreps_out=irreps_x, data_key_in=KEY.NODE_FEATURE_GHOST, + num_modalities=num_modalities, biases=use_bias_in_linear, ), }) @@ -240,10 +250,17 @@ def build_E3_equivariant_model(config: dict, parallel=False): irreps_out=irreps_for_gate_in, ) + num_modalities = ( + config[KEY.NUM_MODALITIES] + if use_modality and config[KEY.USE_MODAL_SELF_INTER_INTRO] + else 1 + ) + interaction_block[f'{i}_self_interaction_1'] = IrrepsLinear( irreps_x, irreps_x, data_key_in=KEY.NODE_FEATURE, + num_modalities=num_modalities, biases=use_bias_in_linear, ) @@ -253,6 +270,7 @@ def build_E3_equivariant_model(config: dict, parallel=False): irreps_x, irreps_x, data_key_in=KEY.NODE_FEATURE_GHOST, + num_modalities=num_modalities, biases=use_bias_in_linear, ) elif parallel and i != 0: @@ -293,11 +311,17 @@ def build_E3_equivariant_model(config: dict, parallel=False): is_parallel=parallel, ) + num_modalities = ( + config[KEY.NUM_MODALITIES] + if use_modality and config[KEY.USE_MODAL_SELF_INTER_OUTRO] + else 1 + ) # irreps of x increase to gate_irreps_in interaction_block[f'{i}_self_interaction_2'] = IrrepsLinear( tp_irreps_out, irreps_for_gate_in, data_key_in=KEY.NODE_FEATURE, + num_modalities=num_modalities, biases=use_bias_in_linear, ) @@ -319,11 +343,17 @@ def build_E3_equivariant_model(config: dict, parallel=False): else irreps_manual[-1].num_irreps ) hidden_irreps = Irreps([(mid_dim // 2, (0, 1))]) + num_modalities = ( + config[KEY.NUM_MODALITIES] + if use_modality and config[KEY.USE_MODAL_OUTPUT_BLOCK] + else 1 + ) layers.update({ 'reduce_input_to_hidden': IrrepsLinear( irreps_x, hidden_irreps, data_key_in=KEY.NODE_FEATURE, + num_modalities=num_modalities, biases=use_bias_in_linear, ), 'reduce_hidden_to_energy': IrrepsLinear( @@ -351,11 +381,32 @@ def build_E3_equivariant_model(config: dict, parallel=False): shift = config[KEY.SHIFT] scale = config[KEY.SCALE] train_shift_scale = config[KEY.TRAIN_SHIFT_SCALE] + modal_wise_shift_scale = config[KEY.USE_MODALITY] and ( + config[KEY.USE_MODAL_WISE_SHIFT] + or config[KEY.USE_MODAL_WISE_SCALE] + ) rescale_module = ( - SpeciesWiseRescale - if config[KEY.USE_SPECIES_WISE_SHIFT_SCALE] - else Rescale + ModalWiseRescale + if modal_wise_shift_scale + else ( + SpeciesWiseRescale + if config[KEY.USE_SPECIES_WISE_SHIFT_SCALE] + else Rescale + ) ) + + if not modal_wise_shift_scale: + # This is only for tensor size compatibility when deploying single modal model. + if not isinstance(shift, torch.Tensor): + shift = torch.FloatTensor(shift) + if not isinstance(scale, torch.Tensor): + scale = torch.FloatTensor(scale) + + if shift.dim() != 1: + shift = shift[0] + if scale.dim() != 1: + scale = scale[0] + layers.update({ 'rescale_atomic_energy': rescale_module( shift=shift, @@ -363,6 +414,8 @@ def build_E3_equivariant_model(config: dict, parallel=False): data_key_in=KEY.SCALED_ATOMIC_ENERGY, data_key_out=KEY.ATOMIC_ENERGY, train_shift_scale=train_shift_scale, + use_modal_wise_shift=config[KEY.USE_MODAL_WISE_SHIFT], + use_modal_wise_scale=config[KEY.USE_MODAL_WISE_SCALE], ), 'reduce_total_enegy': AtomReduce( data_key_in=KEY.ATOMIC_ENERGY, diff --git a/sevenn/nn/force_output.py b/sevenn/nn/force_output.py index 184a907d..bd18aba4 100644 --- a/sevenn/nn/force_output.py +++ b/sevenn/nn/force_output.py @@ -27,10 +27,17 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: pos_tensor = [data[self.key_pos]] energy = [(data[self.key_energy]).sum()] + # `materialize_grads` not supported in low version of pytorch + # Also can not be deployed when using it. + # But not using it makes problem in + # force/stress inference in sparse systems + # TODO: use it only in sevennet_calculator? grad = torch.autograd.grad( energy, pos_tensor, create_graph=self.training, + allow_unused=True, + # materialize_grads=True, )[0] # For torchscript @@ -62,10 +69,17 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: pos_tensor = data[self.key_pos] energy = [(data[self.key_energy]).sum()] + # `materialize_grads` not supported in low version of pytorch + # Also can not be deployed when using it. + # But not using it makes problem in + # force/stress inference in sparse systems + # TODO: use it only in sevennet_calculator? grad = torch.autograd.grad( energy, [pos_tensor, data['_strain']], create_graph=self.training, + allow_unused = True, + # materialize_grads=True, ) # make grad is not Optional[Tensor] @@ -75,6 +89,12 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: sgrad = grad[1] volume = data[self.key_cell_volume] + vlim = 1e-3 # for cell volume = 0 for non PBC structures + if self._is_batch_data: + volume[volume < vlim] = vlim + elif volume < vlim: + volume = torch.tensor(vlim) + if sgrad is not None: if self._is_batch_data: stress = sgrad / volume.view(-1, 1, 1) diff --git a/sevenn/nn/linear.py b/sevenn/nn/linear.py index 015e1475..aef33d32 100644 --- a/sevenn/nn/linear.py +++ b/sevenn/nn/linear.py @@ -22,6 +22,7 @@ def __init__( irreps_out: Irreps, data_key_in: str, data_key_out: Optional[str] = None, + num_modalities: int = 0, **e3nn_linear_params, ): super().__init__() @@ -31,9 +32,37 @@ def __init__( else: self.key_output = data_key_out + self.num_modalities = num_modalities + if num_modalities > 1: # in case of multi-modal + irreps_in = irreps_in + Irreps(f'{num_modalities}x0e') + self.linear = Linear(irreps_in, irreps_out, **e3nn_linear_params) + self._is_batch_data = True def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + if self.num_modalities > 1: + if self._is_batch_data: + batch = data[KEY.BATCH] + batch_modality_onehot = data[KEY.MODAL_ATTR].reshape( + -1, self.num_modalities + ) + batch_modality_onehot = batch_modality_onehot.type( + data[self.key_input].dtype + ) + data[self.key_input] = torch.cat( + [data[self.key_input], batch_modality_onehot[batch]], dim=1 + ) + else: + modality_onehot = data[KEY.MODAL_ATTR].expand( + len(data[self.key_input]), -1 + ) + modality_onehot = modality_onehot.type( + data[self.key_input].dtype + ) + data[self.key_input] = torch.cat( + [data[self.key_input], modality_onehot], dim=1 + ) + data[self.key_output] = self.linear(data[self.key_input]) return data diff --git a/sevenn/nn/scale.py b/sevenn/nn/scale.py index 773914e9..3b4828af 100644 --- a/sevenn/nn/scale.py +++ b/sevenn/nn/scale.py @@ -21,6 +21,7 @@ def __init__( data_key_in=KEY.SCALED_ATOMIC_ENERGY, data_key_out=KEY.ATOMIC_ENERGY, train_shift_scale: bool = False, + **kwargs ): super().__init__() self.shift = nn.Parameter( @@ -52,6 +53,7 @@ def __init__( data_key_out=KEY.ATOMIC_ENERGY, data_key_indices=KEY.ATOM_TYPE, train_shift_scale: bool = False, + **kwargs ): super().__init__() self.shift = nn.Parameter( @@ -71,3 +73,56 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: ].view(-1, 1) + self.shift[indices].view(-1, 1) return data + + +@compile_mode('script') +class ModalWiseRescale(nn.Module): + """ + Scaling and shifting energy (and automatically force and stress) + """ + + def __init__( + self, + shift: List[List[float]], + scale: List[List[float]], + data_key_in=KEY.SCALED_ATOMIC_ENERGY, + data_key_out=KEY.ATOMIC_ENERGY, + data_key_modal_indices=KEY.MODAL_TYPE, + data_key_atom_indices=KEY.ATOM_TYPE, + use_modal_wise_shift: bool = False, + use_modal_wise_scale: bool = False, + train_shift_scale: bool = False, + ): + super().__init__() + self.shift = nn.Parameter( + torch.FloatTensor(shift), requires_grad=train_shift_scale + ) + self.scale = nn.Parameter( + torch.FloatTensor(scale), requires_grad=train_shift_scale + ) + self.KEY_INPUT = data_key_in + self.KEY_OUTPUT = data_key_out + self.KEY_ATOM_INDICES = data_key_atom_indices + self.KEY_MODAL_INDICES = data_key_modal_indices + self.use_modal_wise_shift = use_modal_wise_shift + self.use_modal_wise_scale = use_modal_wise_scale + + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + batch = data[KEY.BATCH] + modal_indices = data[self.KEY_MODAL_INDICES][batch] + atom_indices = data[self.KEY_ATOM_INDICES] + shift = ( + self.shift[modal_indices, atom_indices] + if self.use_modal_wise_shift + else self.shift[atom_indices] + ) + scale = ( + self.scale[modal_indices, atom_indices] + if self.use_modal_wise_scale + else self.scale[atom_indices] + ) + data[self.KEY_OUTPUT] = data[self.KEY_INPUT] * scale.view( + -1, 1 + ) + shift.view(-1, 1) + + return data diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py new file mode 100644 index 00000000..853b973b --- /dev/null +++ b/sevenn/scripts/convert_model_modality.py @@ -0,0 +1,275 @@ +import math +from typing import List + +import torch +import torch.nn as nn +from e3nn.o3 import Irreps, Linear + +import sevenn._keys as KEY +from sevenn.model_build import build_E3_equivariant_model + + +modal_module_dict = { + KEY.USE_MODAL_NODE_EMBEDDING: 'onehot_to_feature_x', + KEY.USE_MODAL_SELF_INTER_INTRO: 'self_interaction_1', + KEY.USE_MODAL_SELF_INTER_OUTRO: 'self_interaction_2', + KEY.USE_MODAL_OUTPUT_BLOCK: 'reduce_input_to_hidden', +} + + +def _get_scalar_index(irreps: Irreps): + scalar_indices = [] + for idx, (mul, (l, p)) in enumerate(irreps): + if ( + l == 0 and p == 1 + ): # get index of parameter for scalar (0e), which is used for modality + scalar_indices.append(idx) + + return scalar_indices + + +def _reshape_weight_of_linear( + irreps_in: Irreps, irreps_out: Irreps, weight: torch.Tensor +) -> List[torch.Tensor]: + linear = Linear(irreps_in, irreps_out) + linear.weight = nn.Parameter(weight) + return list(linear.weight_views()) + + +def _erase_linear_modal_params( + model_state_dct: dict, + erase_modal_indices: List[int], + key: str, + irreps_in: Irreps, + irreps_out: Irreps, +): + orig_input_dim = irreps_in.count('0e') + new_input_dim = orig_input_dim - len(erase_modal_indices) + + orig_weight = model_state_dct[key + '.linear.weight'] + scalar_idx = _get_scalar_index(irreps_in) + linear_weight_list = _reshape_weight_of_linear( + irreps_in, irreps_out, orig_weight + ) + + new_weight_list = [] + + for idx, l_p_weight in enumerate(linear_weight_list[:-1]): + new_weight = torch.reshape(l_p_weight, (1, -1)).squeeze() + if idx in scalar_idx: + new_weight = new_weight * math.sqrt(new_input_dim / orig_input_dim) + + new_weight_list.append(new_weight) + + """ + Following works for normalization = `path`, which is not used in SEVENNet + for l_p_weight in linear_weight_list[:-1]: + new_weight_list.append(torch.reshape(l_p_weight, (1, -1)).squeeze()) + """ + + flattened_weight = torch.cat(new_weight_list) + + return flattened_weight + + +def _get_modal_weight_as_bias( + model_state_dct: dict, + key: str, + ref_index: int, + irreps_in: Irreps, + irreps_out: Irreps, +): + input_dim = irreps_in.count('0e') + output_dim = irreps_out.count('0e') + orig_weight = model_state_dct[key + '.linear.weight'] + orig_bias = model_state_dct[key + '.linear.bias'] + if len(orig_bias) == 0: + orig_bias = torch.zeros(output_dim, dtype=orig_weight.dtype) + + modal_weight = _reshape_weight_of_linear( + irreps_in, irreps_out, orig_weight + )[-1] + + new_bias = orig_bias + modal_weight[ref_index] / math.sqrt(input_dim) + + return new_bias + + +def _append_modal_weight( + model_state_dct: dict, + key: str, + irreps_in: Irreps, + irreps_out: Irreps, + append_number: int, +): + # This works for normalization = `element`, default in SEVENNet. + # (normalization = `path` is curruently deprecated in SEVENNet.) + input_dim = irreps_in.count('0e') + output_dim = irreps_out.count('0e') + new_input_dim = input_dim + append_number + orig_weight = model_state_dct[key + '.linear.weight'] + scalar_idx = _get_scalar_index(irreps_in) + linear_weight_list = _reshape_weight_of_linear( + irreps_in, irreps_out, orig_weight + ) + + new_weight_list = [] + + # TODO: combine following as function with _erase_linear_modal_params + + for idx, l_p_weight in enumerate(linear_weight_list): + new_weight = torch.reshape(l_p_weight, (1, -1)).squeeze() + if idx in scalar_idx: + new_weight = new_weight * math.sqrt(new_input_dim / input_dim) + + new_weight_list.append(new_weight) + + flattened_weight_list = [] + for l_p_weight in new_weight_list: + flattened_weight_list.append( + torch.reshape(l_p_weight, (1, -1)).squeeze() + ) + flattened_weight = torch.cat(flattened_weight_list) + + append_weight = torch.cat([ + flattened_weight, + torch.zeros(append_number * output_dim, dtype=flattened_weight.dtype), + ]) # zeros: starting from common model + + return append_weight + + +def get_single_modal_model_dct( + model_state_dct: dict, + config: dict, + ref_modal: str, + from_processing_cp: bool = False, +): + if ( + not from_processing_cp and not config[KEY.USE_MODALITY] + ): # model is already single modal + return model_state_dct + + config[KEY.USE_BIAS_IN_LINEAR] = True + + model = build_E3_equivariant_model(config) + key_add = '_cp' if from_processing_cp else '' + modal_type_dict = config[KEY.MODAL_MAP + key_add] + erase_modal_indices = range(len(modal_type_dict.keys())) # starts with 0 + + if ref_modal != 'common': + try: + ref_modal_index = modal_type_dict[ref_modal] + except: + raise KeyError( + f'{ref_modal} not in modal type. Use one of' + f' {modal_type_dict.keys()}.' + ) + + for module_key in model._modules.keys(): + for ( + use_modal_module_key, + modal_module_name, + ) in modal_module_dict.items(): + irreps_out = Irreps(model.get_irreps_in(module_key, 'irreps_out')) + # TODO: directly using "irreps_in" might not be compatible when changing `nn/linear.py` + output_dim = irreps_out.count('0e') + if ( + config[use_modal_module_key] + and modal_module_name in module_key + ): # this module is used for giving modality + + irreps_in = Irreps( + model.get_irreps_in(module_key, 'irreps_in') + ) + + new_bias = ( + torch.zeros(output_dim) + if ref_modal == 'common' + else _get_modal_weight_as_bias( + model_state_dct, + module_key, + ref_modal_index, + irreps_in, + irreps_out, + ) + ) + erased_modal_weight = _erase_linear_modal_params( + model_state_dct, + erase_modal_indices, + module_key, + irreps_in, + irreps_out, + ) + + model_state_dct[module_key + '.linear.weight'] = ( + erased_modal_weight + ) + model_state_dct[module_key + '.linear.bias'] = new_bias + elif modal_module_name in module_key: + model_state_dct[module_key + '.linear.bias'] = torch.zeros( + output_dim, + dtype=model_state_dct[module_key + '.linear.weight'].dtype, + ) + + final_block_key = 'reduce_hidden_to_energy' + model_state_dct[final_block_key + '.linear.bias'] = torch.tensor( + [0], dtype=model_state_dct[final_block_key + '.linear.weight'].dtype + ) + + if config[KEY.USE_MODAL_WISE_SHIFT] or config[KEY.USE_MODAL_WISE_SHIFT]: + rescaler_names = [] + if config[KEY.USE_MODAL_WISE_SHIFT]: + rescaler_names.append('shift') + if config[KEY.USE_MODAL_WISE_SCALE]: + rescaler_names.append('scale') + config[KEY.USE_MODAL_WISE_SHIFT] = False + config[KEY.USE_MODAL_WISE_SCALE] = False + for rescaler_name in rescaler_names: + rescaler_key = 'rescale_atomic_energy.' + rescaler_name + rescaler = model_state_dct[rescaler_key][ref_modal_index] + model_state_dct.update({rescaler_key: rescaler}) + config.update({rescaler_name: rescaler}) + + config[KEY.USE_MODALITY] = False + + return model_state_dct + + +def append_modality_to_model_dct( + model_state_dct: dict, + config: dict, + orig_num_modal: int, + append_modal_length: int, +): + config_num_modal = config[KEY.NUM_MODALITIES] + config.update({KEY.NUM_MODALITIES: orig_num_modal, KEY.USE_MODALITY: True}) + + model = build_E3_equivariant_model(config) + + for module_key in model._modules.keys(): + for ( + use_modal_module_key, + modal_module_name, + ) in modal_module_dict.items(): + if ( + config[use_modal_module_key] + and modal_module_name in module_key + ): # this module is used for giving modality + irreps_in = model.get_irreps_in( + module_key, 'irreps_in' + ) # TODO: directly using "irreps_in" might not be compatible when changing `nn/linear.py` + irreps_out = model.get_irreps_in(module_key, 'irreps_out') + irreps_in, irreps_out = Irreps(irreps_in), Irreps(irreps_out) + + append_weight = _append_modal_weight( + model_state_dct, + module_key, + irreps_in, + irreps_out, + append_modal_length, + ) + model_state_dct[module_key + '.linear.weight'] = append_weight + config[KEY.NUM_MODALITIES] = config_num_modal + + return model_state_dct diff --git a/sevenn/scripts/inference.py b/sevenn/scripts/inference.py index ebf1b125..c3aa1d84 100644 --- a/sevenn/scripts/inference.py +++ b/sevenn/scripts/inference.py @@ -176,7 +176,13 @@ def per_atom_dct_list(dct, keys): def inference_main( - checkpoint, fnames, output_path, num_cores=1, device='cpu', batch_size=5 + checkpoint, + fnames, + output_path, + num_cores=1, + device='cpu', + batch_size=5, + modal='common', ): model, config = model_from_checkpoint(checkpoint) model.to(device) @@ -200,6 +206,18 @@ def inference_main( data_list = graph_build(atoms_list, cutoff, num_cores=num_cores) inference_set = AtomGraphDataset(data_list, cutoff) + if ( + KEY.USE_MODALITY in config.keys() and config[KEY.USE_MODALITY] + ): # case of multimodal model + for data_list in inference_set.dataset.values(): + for data in data_list: + data[KEY.DATA_MODALITY] = modal + inference_set.write_modal_attr( + config[KEY.MODAL_MAP], + config[KEY.USE_MODAL_WISE_SHIFT] + or config[KEY.USE_MODAL_WISE_SCALE], + ) + inference_set.x_to_one_hot_idx(type_map) inference_set.toggle_requires_grad_of_data(KEY.POS, True) diff --git a/sevenn/scripts/processing_continue.py b/sevenn/scripts/processing_continue.py index 607b3ac9..6d17c9e9 100644 --- a/sevenn/scripts/processing_continue.py +++ b/sevenn/scripts/processing_continue.py @@ -4,6 +4,10 @@ import torch import sevenn._keys as KEY +from sevenn.scripts.convert_model_modality import ( + append_modality_to_model_dct, + get_single_modal_model_dct, +) import sevenn.util as util from sevenn.sevenn_logger import Logger @@ -113,6 +117,21 @@ def processing_continue(config): } config.update(chem_speices_related) + if ( + KEY.USE_MODALITY in config_cp.keys() and config_cp[KEY.USE_MODALITY] + ): # checkpoint model is multimodal + config.update({ + KEY.MODAL_MAP + '_cp': config_cp[KEY.MODAL_MAP], + KEY.USE_MODALITY + '_cp': True, + KEY.NUM_MODALITIES + '_cp': len(config_cp[KEY.MODAL_MAP]), + }) + else: + config.update({ + KEY.MODAL_MAP + '_cp': {}, + KEY.USE_MODALITY + '_cp': False, + KEY.NUM_MODALITIES + '_cp': 0, + }) + Logger().write(f'checkpoint previous epoch was: {from_epoch}\n') # decide start epoch @@ -144,3 +163,33 @@ def processing_continue(config): scheduler_state_dict_cp, ) return state_dicts, start_epoch, init_csv + + +def convert_modality_of_checkpoint_state_dct(config, state_dicts): + # TODO: this requires updating model state dict after seeing dataset + model_state_dict_cp, optimizer_state_dict_cp, scheduler_state_dict_cp = ( + state_dicts + ) + + if config[KEY.USE_MODALITY]: # current model is multimodal + num_modalities_cp = len(config[KEY.MODAL_MAP + '_cp']) + append_modal_length = config[KEY.NUM_MODALITIES] - num_modalities_cp + + model_state_dict_cp = append_modality_to_model_dct( + model_state_dict_cp, config, num_modalities_cp, append_modal_length + ) + + else: # current model is single modal + if config[KEY.USE_MODALITY + '_cp']: # checkpoint model is multimodal + # change model state dict to single modal, default = "common" + model_state_dict_cp = get_single_modal_model_dct( + model_state_dict_cp, config, config[KEY.DEFAULT_MODAL], True + ) + + state_dicts = ( + model_state_dict_cp, + optimizer_state_dict_cp, + scheduler_state_dict_cp, + ) + + return state_dicts diff --git a/sevenn/scripts/processing_dataset.py b/sevenn/scripts/processing_dataset.py index 91be3a85..62c0ddcf 100644 --- a/sevenn/scripts/processing_dataset.py +++ b/sevenn/scripts/processing_dataset.py @@ -2,8 +2,10 @@ import random import torch +import torch.distributed as dist import sevenn._keys as KEY +import sevenn._const as CONST from sevenn.sevenn_logger import Logger from sevenn.train.dataload import file_to_dataset, match_reader from sevenn.train.dataset import AtomGraphDataset @@ -29,13 +31,37 @@ def dataset_load(file: str, config): config[KEY.CUTOFF], config[KEY.PREPROCESS_NUM_CORES], reader=reader, + use_modality=config[KEY.USE_MODALITY], + use_weight=config[KEY.USE_WEIGHT], ) Logger().format_k_v('loaded dataset size is', dataset.len(), write=True) Logger().timer_end('loading dataset', 'data set loading time') return dataset -def handle_shift_scale(config, train_set, checkpoint_given): +def calculate_shift_or_scale_from_key(train_set: AtomGraphDataset, key_given, n_chem): + _expand = True + use_species_wise_shift_scale = False + if key_given == 'per_atom_energy_mean': + shift_or_scale = train_set.get_per_atom_energy_mean() + elif key_given == 'elemwise_reference_energies': + shift_or_scale = train_set.get_species_ref_energy_by_linear_comb(n_chem) + _expand = False + use_species_wise_shift_scale = True + + elif key_given == 'force_rms': + shift_or_scale = train_set.get_force_rms() + elif key_given == 'per_atom_energy_std': + shift_or_scale = train_set.get_statistics(KEY.PER_ATOM_ENERGY)['Total']['std'] + elif key_given == 'elemwise_force_rms': + shift_or_scale = train_set.get_species_wise_force_rms(n_chem) + _expand = False + use_species_wise_shift_scale = True + + return shift_or_scale, _expand, use_species_wise_shift_scale + + +def handle_shift_scale(config, train_set: AtomGraphDataset, checkpoint_given): """ Priority (first comes later to overwrite): 1. Float given in yaml @@ -45,6 +71,7 @@ def handle_shift_scale(config, train_set, checkpoint_given): shift, scale, conv_denominator = None, None, None type_map = config[KEY.TYPE_MAP] n_chem = len(type_map) + chem_strs = onehot_to_chem(list(range(n_chem)), type_map) Logger().writeline('\nCalculating statistic values from dataset') @@ -52,23 +79,64 @@ def handle_shift_scale(config, train_set, checkpoint_given): scale_given = config[KEY.SCALE] _expand_shift = True _expand_scale = True - use_species_wise_shift_scale = False + use_species_wise_shift = False + use_species_wise_scale = False - if shift_given == 'per_atom_energy_mean': - shift = train_set.get_per_atom_energy_mean() - elif shift_given == 'elemwise_reference_energies': - shift = train_set.get_species_ref_energy_by_linear_comb(n_chem) - _expand_shift = False - use_species_wise_shift_scale = True + use_modal_wise_shift = config[KEY.USE_MODAL_WISE_SHIFT] + use_modal_wise_scale = config[KEY.USE_MODAL_WISE_SCALE] - if scale_given == 'force_rms': - scale = train_set.get_force_rms() - elif scale_given == 'per_atom_energy_std': - scale = train_set.get_statistics(KEY.PER_ATOM_ENERGY)['Total']['std'] - elif scale_given == 'elemwise_force_rms': - scale = train_set.get_species_wise_force_rms(n_chem) - _expand_scale = False - use_species_wise_shift_scale = True + if shift_given in CONST.IMPLEMENTED_SHIFT: + shift, _expand_shift, use_species_wise_shift =\ + calculate_shift_or_scale_from_key(train_set, shift_given, n_chem) + + if scale_given in CONST.IMPLEMENTED_SCALE: + scale, _expand_scale, use_species_wise_scale =\ + calculate_shift_or_scale_from_key(train_set, scale_given, n_chem) + + if use_modal_wise_shift or use_modal_wise_scale: + atomdata_dict_sort_by_modal = train_set.get_dict_sort_by_modality() + modal_map = config[KEY.MODAL_MAP] + n_modal = len(modal_map) + cutoff = config[KEY.CUTOFF] + + if use_modal_wise_shift: + shift = torch.zeros((n_modal, n_chem)) + + if use_modal_wise_scale: + scale = torch.zeros((n_modal, n_chem)) + + for modal_key, data_list in atomdata_dict_sort_by_modal.items(): + modal_set = AtomGraphDataset( + data_list, cutoff, x_is_one_hot_idx=True + ) + + if use_modal_wise_shift: + if shift_given == 'elemwise_reference_energies': + modal_shift, _expand_shift, use_species_wise_shift =\ + calculate_shift_or_scale_from_key( + modal_set, shift_given, n_chem + ) + shift[modal_map[modal_key]] = torch.tensor( + modal_shift + ) # this is np.array + elif shift_given in CONST.IMPLEMENTED_SHIFT: + raise NotImplementedError( + 'Currently, modal-wise shift implemented for' + 'species-dependent case only.' + ) + + if use_modal_wise_scale: + if scale_given == 'elemwise_force_rms': + modal_scale, _expand_scale, use_species_wise_scale =\ + calculate_shift_or_scale_from_key( + modal_set, scale_given, n_chem + ) + scale[modal_map[modal_key]] = modal_scale + elif scale_given in CONST.IMPLEMENTED_SCALE: + raise NotImplementedError( + 'Currently, modal-wise scale implemented for' + 'species-dependent case only.' + ) avg_num_neigh = train_set.get_avg_num_neigh() Logger().format_k_v( @@ -87,18 +155,76 @@ def handle_shift_scale(config, train_set, checkpoint_given): Logger().writeline( 'Overwrite shift, scale, conv_denominator from model checkpoint' ) - # Values extracted from checkpoint in processing_continue.py - shift = config[KEY.SHIFT + '_cp'] - scale = config[KEY.SCALE + '_cp'] - # shift & scale would be both array (with same length) or scalar - assert len(list(shift)) == len(list(scale)) - if len(list(shift)) > 1: - use_species_wise_shift_scale = True - _expand_shift = _expand_scale = False - else: - shift = shift.item() - scale = scale.item() + # TODO: This needs refactoring conv_denominator = config[KEY.CONV_DENOMINATOR + '_cp'] + if not ( + use_modal_wise_shift or use_modal_wise_scale + ): + # Values extracted from checkpoint in processing_continue.py + if len(list(shift)) > 1: + use_species_wise_shift = True + use_species_wise_scale = True + _expand_shift = _expand_scale = False + else: + shift = shift.item() + scale = scale.item() + else: + # Case of modal wise shift scale + shift_cp = config[KEY.SHIFT + '_cp'] + scale_cp = config[KEY.SCALE + '_cp'] + if not use_modal_wise_shift: + shift = shift_cp + if not use_modal_wise_scale: + scale = scale_cp + modal_map = config[KEY.MODAL_MAP] + modal_map_cp = config[KEY.MODAL_MAP + '_cp'] + + # Extracting shift, scale for modal in checkpoint model. + if config[KEY.USE_MODALITY + '_cp']: # cp model is multimodal + for modal_key_cp, modal_idx_cp in modal_map_cp.items(): + modal_idx = modal_map[modal_key_cp] + if use_modal_wise_shift: + shift[modal_idx] = torch.tensor(shift_cp[modal_idx_cp]) + if use_modal_wise_scale: + scale[modal_idx] = torch.tensor(scale_cp[modal_idx_cp]) + + else: # cp model is single modal + try: + modal_idx = modal_map[config[KEY.DEFAULT_MODAL]] + except: + raise KeyError( + f'{config[KEY.DEFAULT_MODAL]} should be one of' + f' {modal_map.keys()}' + ) + if use_modal_wise_shift: + shift[modal_idx] = torch.tensor(shift_cp) + if use_modal_wise_scale: + scale[modal_idx] = torch.tensor(scale_cp) + + if not config[KEY.CONTINUE][ + KEY.USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY + ]: + # Also overwrite values of new modal to reference value + # For multimodal, set reference modal with KEY.DEFAULT_MODAL + shift_ref = shift_cp + scale_ref = scale_cp + if config[KEY.USE_MODALITY + '_cp']: + try: + modal_idx_cp = modal_map_cp[config[KEY.DEFAULT_MODAL]] + except: + raise KeyError( + f'{config[KEY.DEFAULT_MODAL]} should be one of' + f' {modal_map_cp.keys()}' + ) + shift_ref = shift_cp[modal_idx_cp] + scale_ref = scale_cp[modal_idx_cp] + + for modal_key, modal_idx in modal_map.items(): + if modal_key not in modal_map_cp.keys(): + if use_modal_wise_shift: + shift[modal_idx] = shift_ref + if use_modal_wise_scale: + scale[modal_idx] = scale_ref # overwrite shift scale anyway if defined in yaml. if type(shift_given) in [list, float]: @@ -117,15 +243,31 @@ def handle_shift_scale(config, train_set, checkpoint_given): if isinstance(conv_denominator, float): conv_denominator = [conv_denominator] * config[KEY.NUM_CONVOLUTION] + use_species_wise_shift_scale = use_species_wise_shift or use_species_wise_scale if use_species_wise_shift_scale: chem_strs = onehot_to_chem(list(range(n_chem)), type_map) if _expand_shift: - shift = [shift] * n_chem + if use_modal_wise_shift: + shift = torch.full((n_modal, n_chem), shift) + else: + shift = [shift] * n_chem if _expand_scale: - scale = [scale] * n_chem + if use_modal_wise_scale: + scale = torch.full((n_modal, n_chem), scale) + else: + scale = [scale] * n_chem + Logger().write('Use element-wise shift, scale\n') - for cstr, sh, sc in zip(chem_strs, shift, scale): - Logger().format_k_v(f'{cstr}', f'{sh:.6f}, {sc:.6f}', write=True) + if use_modal_wise_shift or use_modal_wise_scale: + for modal_key, modal_idx in modal_map.items(): + Logger().writeline(f'For modal = {modal_key}') + print_shift = shift[modal_idx] if use_modal_wise_shift else shift + print_scale = scale[modal_idx] if use_modal_wise_scale else scale + for cstr, sh, sc in zip(chem_strs, print_shift, print_scale): + Logger().format_k_v(f'{cstr}', f'{sh:.6f}, {sc:.6f}', write=True) + else: + for cstr, sh, sc in zip(chem_strs, shift, scale): + Logger().format_k_v(f'{cstr}', f'{sh:.6f}, {sc:.6f}', write=True) else: Logger().write('Use global shift, scale\n') Logger().format_k_v( @@ -182,6 +324,44 @@ def processing_dataset(config, working_dir): 'Chemical species in checkpoint is not compatible' ) + # check what modalities are used in dataset + if config[KEY.USE_MODALITY]: + modalities = dataset.get_modalities() + num_modalities = len(modalities) + if num_modalities < 2: + Logger().writeline('Only one modal is given, ignore modality') + config.uptate({KEY.USE_MODALITY: False}) + + else: + modal_map_cp = ( + config[KEY.MODAL_MAP + '_cp'] if checkpoint_given else {} + ) + modal_map = modal_map_cp.copy() + current_idx = len(modal_map_cp) + for modal_key in modalities: + if modal_key not in modal_map.keys(): + modal_map[modal_key] = current_idx + current_idx += 1 + + if config[KEY.IS_DDP]: + # Synchronize modal_map + torch.cuda.set_device(config[KEY.LOCAL_RANK]) + modal_map_bcast = [modal_map] + dist.broadcast_object_list(modal_map_bcast, src=0) + modal_map = modal_map_bcast[0] + + config.update({ + KEY.NUM_MODALITIES: len(modal_map), + KEY.MODAL_MAP: modal_map, + KEY.MODAL_LIST: list(modal_map.keys()), + }) + + dataset.write_modal_attr( + modal_map, + config[KEY.USE_MODAL_WISE_SHIFT] + or config[KEY.USE_MODAL_WISE_SCALE], + ) + # --------------- save dataset regardless of train/valid--------------# save_dataset = config[KEY.SAVE_DATASET] save_by_label = config[KEY.SAVE_BY_LABEL] @@ -237,6 +417,21 @@ def processing_dataset(config, working_dir): Logger().write('the validset loaded, load_dataset is now train_set\n') Logger().write('the ratio will be ignored\n') + + # condition: validset modalities should be subset of trainset modalities + if config[KEY.USE_MODALITY]: + config_modality = config[KEY.MODAL_LIST] + valid_modality = valid_set.get_modalities() + + if set(valid_modality).issubset(set(config_modality)) is False: + raise ValueError('validset modality is not subset of trainset') + + valid_set.write_modal_attr( + config[KEY.MODAL_MAP], + config[KEY.USE_MODAL_WISE_SHIFT] + or config[KEY.USE_MODAL_WISE_SCALE], + ) + else: train_set, valid_set, test_set = dataset.divide_dataset( config[KEY.RATIO], ignore_test=ignore_test diff --git a/sevenn/scripts/train.py b/sevenn/scripts/train.py index d3bfc693..1c7598f4 100644 --- a/sevenn/scripts/train.py +++ b/sevenn/scripts/train.py @@ -7,7 +7,10 @@ import sevenn._keys as KEY from sevenn.model_build import build_E3_equivariant_model -from sevenn.scripts.processing_continue import processing_continue +from sevenn.scripts.processing_continue import ( + convert_modality_of_checkpoint_state_dct, + processing_continue, +) from sevenn.scripts.processing_dataset import processing_dataset from sevenn.scripts.processing_epoch import processing_epoch from sevenn.sevenn_logger import Logger @@ -70,11 +73,18 @@ def train(config, working_dir: str): trainer = Trainer(model, config) if state_dicts is not None: + state_dicts = convert_modality_of_checkpoint_state_dct( + config, state_dicts + ) trainer.load_state_dicts(*state_dicts, strict=False) Logger().print_model_info(model, config) # log_model_info(model, config) + # ckd = trainer.get_checkpoint_dict() + # ckd.update({'config': config, 'epoch': 0}) + # torch.save(ckd, 'initial_model.pth') + Logger().write('Trainer initialized, ready to training\n') Logger().bar() diff --git a/sevenn/sevennet_calculator.py b/sevenn/sevennet_calculator.py index 3c9eb1fa..d08e3ee8 100644 --- a/sevenn/sevennet_calculator.py +++ b/sevenn/sevennet_calculator.py @@ -34,6 +34,7 @@ def __init__( file_type: str = 'checkpoint', device: Union[torch.device, str] = 'auto', sevennet_config=None, + modal_selection=None, **kwargs, ): """Initialize the calculator @@ -106,6 +107,8 @@ def __init__( self.model.to(self.device) self.model.eval() + self.modal_selection = modal_selection + self.implemented_properties = [ 'free_energy', 'energy', @@ -124,6 +127,15 @@ def calculate( data[KEY.NODE_FEATURE] = torch.LongTensor( [self.type_map[z.item()] for z in data[KEY.NODE_FEATURE]] ) + if self.modal_selection is not None: + modal_type_mapper = self.sevennet_config[KEY.MODAL_MAP] + num_modalities = len(modal_type_mapper) + modal_idx = modal_type_mapper[self.modal_selection] + tmp_tensor = torch.zeros(num_modalities) + tmp_tensor[modal_idx] = 1.0 + data[KEY.MODAL_ATTR] = tmp_tensor + data[KEY.MODAL_TYPE] = torch.tensor([modal_idx]) + data[KEY.BATCH] = [0] * len(atoms) data.to(self.device) if isinstance(self.model, torch_script_type): diff --git a/sevenn/train/dataload.py b/sevenn/train/dataload.py index 89a5d0d0..36f1efc2 100644 --- a/sevenn/train/dataload.py +++ b/sevenn/train/dataload.py @@ -23,6 +23,7 @@ from braceexpand import braceexpand import sevenn._keys as KEY +from sevenn._const import LossType from sevenn.atom_graph_data import AtomGraphData from sevenn.train.dataset import AtomGraphDataset @@ -103,7 +104,7 @@ def atoms_to_graph( y_stress = -1 * atoms.get_stress() y_stress = np.array([y_stress[[0, 1, 2, 5, 3, 4]]]) except RuntimeError: - y_stress = None + y_stress = np.full((1, 6), np.nan) pos = atoms.get_positions() cell = np.array(atoms.get_cell()) @@ -209,7 +210,7 @@ def pkl_atoms_reader(fname): # Reader -def structure_list_reader(filename: str, format_outputs='vasp-out'): +def structure_list_reader(filename: str, format_outputs='extxyz'): parsers = DefaultParsersContainer( PositionsAndForces, Stress, Energy, Cell ).make_parsers() @@ -269,32 +270,42 @@ def parse_fileline(line): files_expr, index_expr = file_line index = string2index(index_expr) for expanded_filename in list(braceexpand(files_expr)): - f_stream = open(expanded_filename, 'r') - # generator of all outcar ionic steps - gen_all = outcarchunks(f_stream, ocp) - try: - it_atoms = islice( - gen_all, index.start, index.stop, index.step - ) - except ValueError: - # TODO: support - # negative index - raise ValueError('Negative index is not supported yet') - - info_dct_f = { - **info_dct, - 'file': os.path.abspath(expanded_filename), - } - for idx, o in enumerate(it_atoms): + # TODO: this is crude way of handling data + # Using .sevenn_data file: use sevenn_graph_build + if 'OUTCAR' in expanded_filename: + f_stream = open(expanded_filename, 'r') + # generator of all outcar ionic steps + gen_all = outcarchunks(f_stream, ocp) try: - istep = index.start + idx * index.step - atoms = o.build() - atoms.info = {**info_dct_f, 'ionic_step': istep} - except TypeError: # it is not slice of ionic steps - atoms = o.build() - atoms.info = info_dct_f - stct_lists.append(atoms) - f_stream.close() + it_atoms = islice( + gen_all, index.start, index.stop, index.step + ) + except ValueError: + # TODO: support + # negative index + raise ValueError('Negative index is not supported yet') + + info_dct_f = { + **info_dct, + 'file': os.path.abspath(expanded_filename), + } + for idx, o in enumerate(it_atoms): + try: + istep = index.start + idx * index.step + atoms = o.build() + atoms.info = {**info_dct_f, 'ionic_step': istep} + except TypeError: # it is not slice of ionic steps + atoms = o.build() + atoms.info = info_dct_f + stct_lists.append(atoms) + f_stream.close() + else: + stct_lists += ase.io.read( + expanded_filename, + index=index_expr, + format=format_outputs, + parallel=False, + ) structures_dict[title] = stct_lists return structures_dict @@ -321,6 +332,8 @@ def file_to_dataset( reader=None, label: str = None, transfer_info: bool = True, + use_weight: bool = False, + use_modality: bool = False, ): """ Read file by reader > get list of atoms or dict of atoms @@ -347,8 +360,52 @@ def file_to_dataset( graph_list = graph_build( atoms_list, cutoff, cores, transfer_info=transfer_info ) + + label_info = label.split(':') for graph in graph_list: - graph[KEY.USER_LABEL] = label - graph_dct[label] = graph_list + graph[KEY.USER_LABEL] = label_info[0].strip() + if use_weight: + find_weight = False + for info in label_info[1:]: + if 'w=' in info.lower(): + weights = info.split('=')[1] + try: + if ',' in weights: + weight_list = list( + map(float, weights.split(',')) + ) + else: + weight_list = [float(weights)] * 3 + weight_dict = {} + for idx, loss_type in enumerate(LossType): + weight_dict[loss_type.value] = ( + weight_list[idx] + if idx < len(weight_list) + else 1 + ) + graph[KEY.DATA_WEIGHT] = weight_dict + find_weight = True + break + except: + raise ValueError( + 'Weight must be a real number, but' + f' {weights} is given for {label}' + ) + if not find_weight: + weight_dict = {} + for loss_type in LossType: + weight_dict[loss_type.value] = 1 + graph[KEY.DATA_WEIGHT] = weight_dict + if use_modality: + find_modality = False + for info in label_info[1:]: + if 'm=' in info.lower(): + graph[KEY.DATA_MODALITY] = (info.split('=')[1]).strip() + find_modality = True + break + if not find_modality: + raise ValueError(f'Modality not given for {label}') + + graph_dct[label_info[0].strip()] = graph_list db = AtomGraphDataset(graph_dct, cutoff) return db diff --git a/sevenn/train/dataset.py b/sevenn/train/dataset.py index 1f99998d..ce3ebef4 100644 --- a/sevenn/train/dataset.py +++ b/sevenn/train/dataset.py @@ -134,6 +134,44 @@ def get_species(self): species = sorted(list(species)) return species + def get_modalities(self): + modalities = set() + for data_list in self.dataset.values(): + datum = data_list[0].to_dict() + if KEY.DATA_MODALITY in datum.keys(): + modalities.add(datum[KEY.DATA_MODALITY]) + else: + return [] + return list(modalities) + + def write_modal_attr( + self, modal_type_mapper: dict, write_modal_type: bool = False + ): + num_modalities = len(modal_type_mapper) + for data_list in self.dataset.values(): + for data in data_list: + tmp_tensor = torch.zeros(num_modalities) + if data[KEY.DATA_MODALITY] != 'common': + modal_idx = modal_type_mapper[data[KEY.DATA_MODALITY]] + tmp_tensor[modal_idx] = 1.0 + if write_modal_type: + data[KEY.MODAL_TYPE] = modal_idx + data[KEY.MODAL_ATTR] = tmp_tensor + + def get_dict_sort_by_modality(self): + dict_sort_by_modality = {} + for data_list in self.dataset.values(): + try: + modal_key = data_list[0].to_dict()[KEY.DATA_MODALITY] + except: # Dataset is not modal + raise ValueError('This dataset has no modality.') + + if modal_key not in dict_sort_by_modality.keys(): + dict_sort_by_modality[modal_key] = [] + dict_sort_by_modality[modal_key].extend(data_list) + + return dict_sort_by_modality + def len(self): if ( len(self.dataset.keys()) == 1 @@ -367,12 +405,21 @@ def _get_statistic_dict(tensor_list): for tensor in tensor_list ] ) + data_list = data_list[~torch.isnan(data_list)] return { 'mean': float(torch.mean(data_list)), 'std': float(torch.std(data_list)), 'median': float(torch.median(data_list)), - 'max': float(torch.max(data_list)), - 'min': float(torch.min(data_list)), + 'max': ( + torch.nan + if data_list.numel() == 0 + else float(torch.max(data_list)) + ), + 'min': ( + torch.nan + if data_list.numel() == 0 + else float(torch.min(data_list)) + ), } res = {} diff --git a/sevenn/train/loss.py b/sevenn/train/loss.py index d5096070..8913ad55 100644 --- a/sevenn/train/loss.py +++ b/sevenn/train/loss.py @@ -10,13 +10,22 @@ class LossDefinition: """ def __init__( - self, name=None, unit=None, criterion=None, ref_key=None, pred_key=None + self, + name=None, + unit=None, + criterion=None, + ref_key=None, + pred_key=None, + use_weight=False, + delete_unlabled=True, ): self.name = name self.unit = unit self.criterion = criterion self.ref_key = ref_key self.pred_key = pred_key + self.use_weight = use_weight + self.delete_unlabled = delete_unlabled def __repr__(self): return self.name @@ -38,7 +47,34 @@ def get_loss(self, batch_data, model=None): Function that return scalar """ pred, ref = self._preprocess(batch_data, model) - return self.criterion(pred, ref) + if self.use_weight: + vdim = {'energy': 1, 'force': 3, 'stress': 6} + loss_type = self.name.lower() + weight = batch_data[KEY.DATA_WEIGHT][loss_type] + weight_tensor = ( + weight[batch_data[KEY.BATCH]] + if loss_type == "force" + else weight + ) + weight_tensor = torch.repeat_interleave(weight_tensor, vdim[loss_type]) + + if self.delete_unlabled: + # nan in `pred`` should be deleted in other process + unlabled_idx = torch.isnan(ref) + pred = pred[~unlabled_idx] + ref = ref[~unlabled_idx] + + if self.use_weight: + weight_tensor = weight_tensor[~unlabled_idx] + + if len(pred) > 0: # more than one ref value is labelled. + loss = self.criterion(pred, ref) + if self.use_weight: + loss = torch.mean(loss*weight_tensor) + else: + loss = None + + return loss class PerAtomEnergyLoss(LossDefinition): @@ -53,9 +89,16 @@ def __init__( criterion=None, ref_key=KEY.ENERGY, pred_key=KEY.PRED_TOTAL_ENERGY, + use_weight=False, + delete_unlabled=True, ): super().__init__( - name=name, criterion=criterion, ref_key=ref_key, pred_key=pred_key + name=name, + criterion=criterion, + ref_key=ref_key, + pred_key=pred_key, + use_weight = use_weight, + delete_unlabled = delete_unlabled ) def _preprocess(self, batch_data, model=None): @@ -78,9 +121,16 @@ def __init__( criterion=None, ref_key=KEY.FORCE, pred_key=KEY.PRED_FORCE, + use_weight=False, + delete_unlabled=True, ): super().__init__( - name=name, criterion=criterion, ref_key=ref_key, pred_key=pred_key + name=name, + criterion=criterion, + ref_key=ref_key, + pred_key=pred_key, + use_weight = use_weight, + delete_unlabled = delete_unlabled ) def _preprocess(self, batch_data, model=None): @@ -101,9 +151,16 @@ def __init__( criterion=None, ref_key=KEY.STRESS, pred_key=KEY.PRED_STRESS, + use_weight=False, + delete_unlabled=True, ): super().__init__( - name=name, criterion=criterion, ref_key=ref_key, pred_key=pred_key + name=name, + criterion=criterion, + ref_key=ref_key, + pred_key=pred_key, + use_weight = use_weight, + delete_unlabled = delete_unlabled ) def _preprocess(self, batch_data, model=None): @@ -123,12 +180,16 @@ def get_loss_functions_from_config(config): loss_param = config[KEY.LOSS_PARAM] except KeyError: loss_param = {} + if config[KEY.USE_WEIGHT]: + loss_param['reduction'] = 'none' criterion = loss(**loss_param) - loss_functions.append((PerAtomEnergyLoss(), 1.0)) - loss_functions.append((ForceLoss(), config[KEY.FORCE_WEIGHT])) + use_weight = config[KEY.USE_WEIGHT] + + loss_functions.append((PerAtomEnergyLoss(use_weight=use_weight), 1.0)) + loss_functions.append((ForceLoss(use_weight=use_weight), config[KEY.FORCE_WEIGHT])) if config[KEY.IS_TRAIN_STRESS]: - loss_functions.append((StressLoss(), config[KEY.STRESS_WEIGHT])) + loss_functions.append((StressLoss(use_weight=use_weight), config[KEY.STRESS_WEIGHT])) for loss_function, _ in loss_functions: if loss_function.criterion is None: diff --git a/sevenn/train/trainer.py b/sevenn/train/trainer.py index b7d22cb0..748aaba9 100644 --- a/sevenn/train/trainer.py +++ b/sevenn/train/trainer.py @@ -54,7 +54,9 @@ def run_one_epoch( if is_train: total_loss = torch.tensor([0.0], device=self.device) for loss_def, w in self.loss_functions: - total_loss += loss_def.get_loss(output, self.model) * w + indv_loss = loss_def.get_loss(output, self.model) + if indv_loss is not None: + total_loss += (indv_loss * w) total_loss.backward() self.optimizer.step() diff --git a/sevenn/util.py b/sevenn/util.py index 9a309684..974e84fe 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -105,7 +105,9 @@ def error_recorder_from_loss_functions(loss_functions): return ErrorRecorder(metrics) -def postprocess_output(output, loss_types): +def postprocess_output( + output, loss_types, use_weight=False, delete_unlabled=True +): from sevenn._const import LossType """ @@ -115,13 +117,17 @@ def postprocess_output(output, loss_types): Args: output (dict): output from model loss_types (list): list of loss types to be calculated + use_weight (bool): if True, trying to multiply weight to output. Used for training only. + delete_unlabled (bool): if True, delete unlabled (i.e. nan) data from reference. Returns: results (dict): dictionary of loss type and its corresponding """ TO_KB = 1602.1766208 # eV/A^3 to kbar results = {} + weight_tensor = None for loss_type in loss_types: + is_valid = True if loss_type is LossType.ENERGY: # dim: (num_batch) num_atoms = output[KEY.NUM_ATOMS] @@ -141,11 +147,34 @@ def postprocess_output(output, loss_types): vdim = 6 else: raise ValueError(f'Unknown loss type: {loss_type}') - results[loss_type] = (pred, ref, vdim) + + if use_weight: + weight = output[KEY.DATA_WEIGHT][loss_type.value] + weight_tensor = ( + weight[output[KEY.BATCH]] + if loss_type is LossType.FORCE + else weight + ) + weight_tensor = torch.repeat_interleave(weight_tensor, vdim) + + if delete_unlabled: + # nan in pred might not be deleted, which is natural. + # This must be done after multiplying weight. + unlabeld_idx = torch.isnan(ref) + pred = pred[~unlabeld_idx] + ref = ref[~unlabeld_idx] + + if len(pred) == 0: + is_valid = False # not a valid error, erase for loss.backward + + if use_weight: + weight_tensor = weight_tensor[~unlabeld_idx] + + results[loss_type] = (pred, ref, vdim, is_valid, weight_tensor) return results -def squared_error(pred, ref, vdim): +def squared_error(pred, ref, vdim, *args): MSE = torch.nn.MSELoss(reduction='none') return torch.reshape(MSE(pred, ref), (-1, vdim)).sum(dim=1) From 8cba28e2f4df6d6a8c3ff45d2633e325835d7815 Mon Sep 17 00:00:00 2001 From: Jaesun0912 Date: Fri, 16 Aug 2024 13:24:16 +0900 Subject: [PATCH 002/113] pre-commit done --- sevenn/main/sevenn_get_model.py | 5 +++-- sevenn/main/sevenn_graph_build.py | 2 +- sevenn/main/sevenn_preset.py | 8 ++++---- sevenn/nn/force_output.py | 2 +- sevenn/scripts/convert_model_modality.py | 8 +++++--- sevenn/scripts/inference.py | 4 ++-- sevenn/scripts/processing_continue.py | 2 +- sevenn/scripts/processing_dataset.py | 10 ++++++---- sevenn/train/loss.py | 19 ++++++++++--------- 9 files changed, 33 insertions(+), 27 deletions(-) diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py index 2ff56d7e..7495bbd6 100644 --- a/sevenn/main/sevenn_get_model.py +++ b/sevenn/main/sevenn_get_model.py @@ -4,8 +4,8 @@ import torch import sevenn._const as _const -import sevenn.util import sevenn._keys as KEY +import sevenn.util from sevenn.scripts.convert_model_modality import get_single_modal_model_dct from sevenn.scripts.deploy import deploy, deploy_parallel @@ -22,7 +22,8 @@ def main(args=None): - checkpoint, output_prefix, get_parallel, modal, save_cp = cmd_parse_get_model(args) + checkpoint, output_prefix, get_parallel, modal, save_cp =\ + cmd_parse_get_model(args) get_serial = not get_parallel if output_prefix is None: diff --git a/sevenn/main/sevenn_graph_build.py b/sevenn/main/sevenn_graph_build.py index 58a287cb..8dcf14d7 100644 --- a/sevenn/main/sevenn_graph_build.py +++ b/sevenn/main/sevenn_graph_build.py @@ -9,7 +9,7 @@ description = ( f'sevenn version={SEVENN_VERSION}, sevenn_graph_build.\n' + "Create '.sevenn_data' from ase readable or VASP OUTCARs (by" - " structure_list).\n" + ' structure_list).\n' ) source_help = 'source data to build graph' diff --git a/sevenn/main/sevenn_preset.py b/sevenn/main/sevenn_preset.py index 78a7dd3c..c27dab84 100644 --- a/sevenn/main/sevenn_preset.py +++ b/sevenn/main/sevenn_preset.py @@ -1,5 +1,5 @@ -import os import argparse +import os import sevenn._const as _const @@ -9,14 +9,14 @@ + ' ex) sevennet_preset fine_tune > my_input.yaml' ) -preset_help = "Name of preset" +preset_help = 'Name of preset' def main(args=None): preset = cmd_parse_preset(args) prefix = os.path.abspath(f'{os.path.dirname(__file__)}/../presets') - with open(f"{prefix}/{preset}.yaml", "r") as f: + with open(f'{prefix}/{preset}.yaml', 'r') as f: print(f.read()) @@ -24,7 +24,7 @@ def cmd_parse_preset(args=None): ag = argparse.ArgumentParser(description=description_preset) ag.add_argument( 'preset', choices=['fine_tune', 'sevennet-0', 'base'], - help = preset_help + help=preset_help ) args = ag.parse_args() return args.preset diff --git a/sevenn/nn/force_output.py b/sevenn/nn/force_output.py index bd18aba4..fcefda14 100644 --- a/sevenn/nn/force_output.py +++ b/sevenn/nn/force_output.py @@ -78,7 +78,7 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: energy, [pos_tensor, data['_strain']], create_graph=self.training, - allow_unused = True, + allow_unused=True, # materialize_grads=True, ) diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py index 853b973b..9025c5ef 100644 --- a/sevenn/scripts/convert_model_modality.py +++ b/sevenn/scripts/convert_model_modality.py @@ -8,7 +8,6 @@ import sevenn._keys as KEY from sevenn.model_build import build_E3_equivariant_model - modal_module_dict = { KEY.USE_MODAL_NODE_EMBEDDING: 'onehot_to_feature_x', KEY.USE_MODAL_SELF_INTER_INTRO: 'self_interaction_1', @@ -172,7 +171,8 @@ def get_single_modal_model_dct( modal_module_name, ) in modal_module_dict.items(): irreps_out = Irreps(model.get_irreps_in(module_key, 'irreps_out')) - # TODO: directly using "irreps_in" might not be compatible when changing `nn/linear.py` + # TODO: directly using "irreps_in" might not be compatible + # when changing `nn/linear.py` output_dim = irreps_out.count('0e') if ( config[use_modal_module_key] @@ -258,7 +258,9 @@ def append_modality_to_model_dct( ): # this module is used for giving modality irreps_in = model.get_irreps_in( module_key, 'irreps_in' - ) # TODO: directly using "irreps_in" might not be compatible when changing `nn/linear.py` + ) + # TODO: directly using "irreps_in" might not be compatible + # when changing `nn/linear.py` irreps_out = model.get_irreps_in(module_key, 'irreps_out') irreps_in, irreps_out = Irreps(irreps_in), Irreps(irreps_out) diff --git a/sevenn/scripts/inference.py b/sevenn/scripts/inference.py index c3aa1d84..fb9c55d4 100644 --- a/sevenn/scripts/inference.py +++ b/sevenn/scripts/inference.py @@ -38,8 +38,8 @@ def load_sevenn_data(sevenn_datas: str, cutoff, type_map): if full_dataset.x_is_one_hot_idx and full_dataset.type_map != type_map: raise ValueError( "loaded dataset's x is not atomic numbers. this is" - " deprecated. Create dataset from structure list " - " with the newest version of sevenn" + ' deprecated. Create dataset from structure list ' + ' with the newest version of sevenn' ) return full_dataset diff --git a/sevenn/scripts/processing_continue.py b/sevenn/scripts/processing_continue.py index 6d17c9e9..6ec6e093 100644 --- a/sevenn/scripts/processing_continue.py +++ b/sevenn/scripts/processing_continue.py @@ -4,11 +4,11 @@ import torch import sevenn._keys as KEY +import sevenn.util as util from sevenn.scripts.convert_model_modality import ( append_modality_to_model_dct, get_single_modal_model_dct, ) -import sevenn.util as util from sevenn.sevenn_logger import Logger diff --git a/sevenn/scripts/processing_dataset.py b/sevenn/scripts/processing_dataset.py index 62c0ddcf..fd332cff 100644 --- a/sevenn/scripts/processing_dataset.py +++ b/sevenn/scripts/processing_dataset.py @@ -4,8 +4,8 @@ import torch import torch.distributed as dist -import sevenn._keys as KEY import sevenn._const as CONST +import sevenn._keys as KEY from sevenn.sevenn_logger import Logger from sevenn.train.dataload import file_to_dataset, match_reader from sevenn.train.dataset import AtomGraphDataset @@ -52,7 +52,8 @@ def calculate_shift_or_scale_from_key(train_set: AtomGraphDataset, key_given, n_ elif key_given == 'force_rms': shift_or_scale = train_set.get_force_rms() elif key_given == 'per_atom_energy_std': - shift_or_scale = train_set.get_statistics(KEY.PER_ATOM_ENERGY)['Total']['std'] + shift_or_scale =\ + train_set.get_statistics(KEY.PER_ATOM_ENERGY)['Total']['std'] elif key_given == 'elemwise_force_rms': shift_or_scale = train_set.get_species_wise_force_rms(n_chem) _expand = False @@ -134,7 +135,7 @@ def handle_shift_scale(config, train_set: AtomGraphDataset, checkpoint_given): scale[modal_map[modal_key]] = modal_scale elif scale_given in CONST.IMPLEMENTED_SCALE: raise NotImplementedError( - 'Currently, modal-wise scale implemented for' + 'Currently, modal-wise scale implemented for' 'species-dependent case only.' ) @@ -243,7 +244,8 @@ def handle_shift_scale(config, train_set: AtomGraphDataset, checkpoint_given): if isinstance(conv_denominator, float): conv_denominator = [conv_denominator] * config[KEY.NUM_CONVOLUTION] - use_species_wise_shift_scale = use_species_wise_shift or use_species_wise_scale + use_species_wise_shift_scale =\ + use_species_wise_shift or use_species_wise_scale if use_species_wise_shift_scale: chem_strs = onehot_to_chem(list(range(n_chem)), type_map) if _expand_shift: diff --git a/sevenn/train/loss.py b/sevenn/train/loss.py index 8913ad55..0a8688a8 100644 --- a/sevenn/train/loss.py +++ b/sevenn/train/loss.py @@ -53,10 +53,11 @@ def get_loss(self, batch_data, model=None): weight = batch_data[KEY.DATA_WEIGHT][loss_type] weight_tensor = ( weight[batch_data[KEY.BATCH]] - if loss_type == "force" + if loss_type == 'force' else weight ) - weight_tensor = torch.repeat_interleave(weight_tensor, vdim[loss_type]) + weight_tensor =\ + torch.repeat_interleave(weight_tensor, vdim[loss_type]) if self.delete_unlabled: # nan in `pred`` should be deleted in other process @@ -70,7 +71,7 @@ def get_loss(self, batch_data, model=None): if len(pred) > 0: # more than one ref value is labelled. loss = self.criterion(pred, ref) if self.use_weight: - loss = torch.mean(loss*weight_tensor) + loss = torch.mean(loss * weight_tensor) else: loss = None @@ -97,8 +98,8 @@ def __init__( criterion=criterion, ref_key=ref_key, pred_key=pred_key, - use_weight = use_weight, - delete_unlabled = delete_unlabled + use_weight=use_weight, + delete_unlabled=delete_unlabled ) def _preprocess(self, batch_data, model=None): @@ -129,8 +130,8 @@ def __init__( criterion=criterion, ref_key=ref_key, pred_key=pred_key, - use_weight = use_weight, - delete_unlabled = delete_unlabled + use_weight=use_weight, + delete_unlabled=delete_unlabled ) def _preprocess(self, batch_data, model=None): @@ -159,8 +160,8 @@ def __init__( criterion=criterion, ref_key=ref_key, pred_key=pred_key, - use_weight = use_weight, - delete_unlabled = delete_unlabled + use_weight=use_weight, + delete_unlabled=delete_unlabled ) def _preprocess(self, batch_data, model=None): From cbf1c0c0b4895d62607d82f05e5230b4dc9311bf Mon Sep 17 00:00:00 2001 From: Jaesun0912 Date: Mon, 19 Aug 2024 15:31:36 +0900 Subject: [PATCH 003/113] fix: shift bug in model build --- sevenn/main/sevenn_get_model.py | 2 +- sevenn/model_build.py | 2 +- sevenn/scripts/convert_model_modality.py | 3 +++ sevenn/scripts/processing_continue.py | 5 ++++- 4 files changed, 9 insertions(+), 3 deletions(-) diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py index d4411631..129515ae 100644 --- a/sevenn/main/sevenn_get_model.py +++ b/sevenn/main/sevenn_get_model.py @@ -41,7 +41,7 @@ def main(args=None): stct_dct = model.state_dict() if KEY.USE_MODALITY in config.keys() and config[KEY.USE_MODALITY]: - stct_dct = get_single_modal_model_dct(stct_dct, config, modal) + stct_dct = get_single_modal_model_dct(stct_dct, config, modal, is_deploy=True) output_prefix = modal + '_' + output_prefix if save_cp: cp_file = torch.load(checkpoint_path, map_location='cpu') diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 80ca8a1d..59153f2e 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -395,7 +395,7 @@ def build_E3_equivariant_model(config: dict, parallel=False): ) ) - if not modal_wise_shift_scale: + if not modal_wise_shift_scale and '_deploy' in config.keys() and config['_deploy']: # This is only for tensor size compatibility when deploying single modal model. if not isinstance(shift, torch.Tensor): shift = torch.FloatTensor(shift) diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py index 9025c5ef..407307e3 100644 --- a/sevenn/scripts/convert_model_modality.py +++ b/sevenn/scripts/convert_model_modality.py @@ -143,6 +143,7 @@ def get_single_modal_model_dct( config: dict, ref_modal: str, from_processing_cp: bool = False, + is_deploy: bool = False, ): if ( not from_processing_cp and not config[KEY.USE_MODALITY] @@ -150,8 +151,10 @@ def get_single_modal_model_dct( return model_state_dct config[KEY.USE_BIAS_IN_LINEAR] = True + config['_deploy'] = is_deploy model = build_E3_equivariant_model(config) + del config['_deploy'] key_add = '_cp' if from_processing_cp else '' modal_type_dict = config[KEY.MODAL_MAP + key_add] erase_modal_indices = range(len(modal_type_dict.keys())) # starts with 0 diff --git a/sevenn/scripts/processing_continue.py b/sevenn/scripts/processing_continue.py index 6ec6e093..05ed8e60 100644 --- a/sevenn/scripts/processing_continue.py +++ b/sevenn/scripts/processing_continue.py @@ -183,7 +183,10 @@ def convert_modality_of_checkpoint_state_dct(config, state_dicts): if config[KEY.USE_MODALITY + '_cp']: # checkpoint model is multimodal # change model state dict to single modal, default = "common" model_state_dict_cp = get_single_modal_model_dct( - model_state_dict_cp, config, config[KEY.DEFAULT_MODAL], True + model_state_dict_cp, + config, + config[KEY.DEFAULT_MODAL], + from_processing_cp=True, ) state_dicts = ( From 4cf77494c0ab9bc71bc82be0b5bd22d2ce5c8554 Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Tue, 22 Oct 2024 13:18:30 +0900 Subject: [PATCH 004/113] Create CHANGELOG.md --- CHANGELOG.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..6d0557ad --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,13 @@ +### Added +- Read modality and weight of each data label from `structure_list` +- Loss weighting for energy, force and stress for corresponding data label +- Build multi-fidelity model, SevenNet-MF, based on given modality in the training set +- `AtomGraphDataset` contains information about the modality of given data. +- `sevenn_get_model` / `sevenn_inference` selects the target fidelity when deploying / inferring SevenNet-MF. +- `SevenNetCalculator` takes target fidelity for inference as input. +- Automatic fine-tuning for SevenNet-MF by converting initial weights and shift & scale from checkpoint file. + +### Changed +- Ignore unlabelled data when calculating loss. (e.g. stress data for non-pbc structure) +- Record shift and scale used for each modality in `log.sevenn`. +- Record error and loss in the `log.sevenn` with the value after weighting the loss and ignoring unlabelled data. From df953261c096eb66878d95b55c0913a560cc31d5 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 27 Oct 2024 14:05:12 +0900 Subject: [PATCH 005/113] refactor+WIP: modal patch for linear --- sevenn/model_build.py | 45 +++++++++++++++++----- sevenn/nn/linear.py | 84 +++++++++++++++++++++++++++-------------- sevenn/nn/sequential.py | 11 ++++++ 3 files changed, 102 insertions(+), 38 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 28ffe747..778ffda9 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -121,6 +121,7 @@ def init_feature_reduce(config, irreps_x): def init_shift_scale(config): + # for mm, ex, shift: modal_idx -> shifts shift_scale = [] type_map = config[KEY.TYPE_MAP] # correct typing (I really want static python) @@ -154,6 +155,34 @@ def init_shift_scale(config): ) +def _patch_modality(layers: OrderedDict, config): + cfg = config + if not cfg[KEY.USE_MODALITY]: + return + + num_modal = config[KEY.NUM_MODALITIES] + for k, module in layers.items(): + if not isinstance(module, IrrepsLinear): + continue + if ( + ( + cfg[KEY.USE_MODAL_NODE_EMBEDDING] + and k.endswith('onehot_to_feature_x') + ) + or ( + cfg[KEY.USE_MODAL_SELF_INTER_INTRO] + and k.endswith('self_interaction_1') + ) + or ( + cfg[KEY.USE_MODAL_SELF_INTER_OUTRO] + and k.endswith('self_interaction_2') + ) + or (cfg[KEY.USE_MODAL_OUTPUT_BLOCK] and k == 'reduce_input_to_hidden') + ): + module.set_num_modalities(num_modal) + return layers + + def _to_parallel_model(layers: OrderedDict, config): num_classes = layers['onehot_idx_to_onehot'].num_classes one_hot_irreps = Irreps(f'{num_classes}x0e') @@ -246,7 +275,7 @@ def build_E3_equivariant_model(config: dict, parallel=False): PRED_FORCE: (natoms, 3), PRED_STRESS: (6,), - for data w/o shell volume, pred_stress has garbage values + for data w/o cell volume, pred_stress has garbage values """ layers = OrderedDict() @@ -258,7 +287,6 @@ def build_E3_equivariant_model(config: dict, parallel=False): use_bias_in_linear = config[KEY.USE_BIAS_IN_LINEAR] # is_parity = config[KEY.IS_PARITY] # boolean - use_modality = config[KEY.USE_MODALITY] # boolean lmax_node = _ = config[KEY.LMAX] # ignore second (lmax_edge) if config[KEY.LMAX_EDGE] > 0: _ = config[KEY.LMAX_EDGE] @@ -294,11 +322,6 @@ def build_E3_equivariant_model(config: dict, parallel=False): else irreps_manual[0] ) - num_modalities = ( - config[KEY.NUM_MODALITIES] - if use_modality and config[KEY.USE_MODAL_NODE_EMBEDDING] - else 1 - ) layers.update( { 'onehot_idx_to_onehot': OnehotEmbedding(num_classes=num_species), @@ -306,7 +329,6 @@ def build_E3_equivariant_model(config: dict, parallel=False): irreps_in=one_hot_irreps, irreps_out=irreps_x, data_key_in=KEY.NODE_FEATURE, - num_modalities=num_modalities, biases=use_bias_in_linear, ), } @@ -416,6 +438,9 @@ def build_E3_equivariant_model(config: dict, parallel=False): if parallel: layers_list = _to_parallel_model(layers, config) - return [AtomGraphSequential(v, **common_args) for v in layers_list] + return [ + AtomGraphSequential(_patch_modality(layers, config), **common_args) + for layers in layers_list + ] else: - return AtomGraphSequential(layers, **common_args) + return AtomGraphSequential(_patch_modality(layers, config), **common_args) diff --git a/sevenn/nn/linear.py b/sevenn/nn/linear.py index aef33d32..180e0a7c 100644 --- a/sevenn/nn/linear.py +++ b/sevenn/nn/linear.py @@ -23,6 +23,7 @@ def __init__( data_key_in: str, data_key_out: Optional[str] = None, num_modalities: int = 0, + lazy_layer_instantiate: bool = True, **e3nn_linear_params, ): super().__init__() @@ -32,36 +33,63 @@ def __init__( else: self.key_output = data_key_out + self._irreps_in_wo_modal = irreps_in + self.irreps_in = irreps_in + self.irreps_out = irreps_out + self.e3nn_linear_params = e3nn_linear_params + + self.linear = None + self.layer_instantiated = False self.num_modalities = num_modalities + self._is_batch_data = True + if num_modalities > 1: # in case of multi-modal - irreps_in = irreps_in + Irreps(f'{num_modalities}x0e') + self.set_num_modalities(num_modalities) - self.linear = Linear(irreps_in, irreps_out, **e3nn_linear_params) - self._is_batch_data = True + if not lazy_layer_instantiate: + self.instantiate() + + def set_num_modalities(self, num_modalities): + if self.layer_instantiated: + raise ValueError('Layer already instantiated, can not change modalities') + irreps_in = self._irreps_in_wo_modal + Irreps(f'{num_modalities}x0e') + self.num_modalities = num_modalities + self.irreps_in = irreps_in + + def instantiate(self): + if self.linear is not None: + raise ValueError('Linear layer already exists') + self.linear = Linear( + self.irreps_in, self.irreps_out, **self.e3nn_linear_params + ) + self.layer_instantiated = True + + def _patch_modal_to_data(self, data: AtomGraphDataType) -> AtomGraphDataType: + if self._is_batch_data: + batch = data[KEY.BATCH] + batch_modality_onehot = data[KEY.MODAL_ATTR].reshape( + -1, self.num_modalities + ) + batch_modality_onehot = batch_modality_onehot.type( + data[self.key_input].dtype + ) + data[self.key_input] = torch.cat( + [data[self.key_input], batch_modality_onehot[batch]], dim=1 + ) + else: + modality_onehot = data[KEY.MODAL_ATTR].expand( + len(data[self.key_input]), -1 + ) + modality_onehot = modality_onehot.type(data[self.key_input].dtype) + data[self.key_input] = torch.cat( + [data[self.key_input], modality_onehot], dim=1 + ) + return data def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + assert self.linear is not None if self.num_modalities > 1: - if self._is_batch_data: - batch = data[KEY.BATCH] - batch_modality_onehot = data[KEY.MODAL_ATTR].reshape( - -1, self.num_modalities - ) - batch_modality_onehot = batch_modality_onehot.type( - data[self.key_input].dtype - ) - data[self.key_input] = torch.cat( - [data[self.key_input], batch_modality_onehot[batch]], dim=1 - ) - else: - modality_onehot = data[KEY.MODAL_ATTR].expand( - len(data[self.key_input]), -1 - ) - modality_onehot = modality_onehot.type( - data[self.key_input].dtype - ) - data[self.key_input] = torch.cat( - [data[self.key_input], modality_onehot], dim=1 - ) + data = self._patch_modal_to_data(data) data[self.key_output] = self.linear(data[self.key_input]) return data @@ -96,14 +124,14 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: src = data[self.key_input].squeeze(1) size = int(data[KEY.BATCH].max()) + 1 output = torch.zeros( - (size), dtype=src.dtype, device=src.device, + (size), + dtype=src.dtype, + device=src.device, ) output.scatter_reduce_(0, data[KEY.BATCH], src, reduce='sum') data[self.key_output] = output * self.constant else: - data[self.key_output] = ( - torch.sum(data[self.key_input]) * self.constant - ) + data[self.key_output] = torch.sum(data[self.key_input]) * self.constant return data diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index 168b1aa9..bf959315 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -10,6 +10,16 @@ from sevenn._const import AtomGraphDataType +def _instantiate_modules(modules): + # see IrrepsLinear of linear.py + for module in modules.values(): + try: + if not module.layer_instantiated: + module.instantiate() + except AttributeError: + pass + + @compile_mode('script') class AtomGraphSequential(nn.Sequential): """ @@ -57,6 +67,7 @@ def __init__( self.key_node_feature = data_key_node_feature self.key_grad = data_key_grad + _instantiate_modules(modules) super().__init__(modules) def set_is_batch_data(self, flag: bool): From 5346e7ea2a73ece6285bd8f9fc7892757c518000 Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Mon, 28 Oct 2024 13:53:44 +0900 Subject: [PATCH 006/113] Add comment to convert_model_modality.py --- sevenn/scripts/convert_model_modality.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py index 407307e3..02ade5ee 100644 --- a/sevenn/scripts/convert_model_modality.py +++ b/sevenn/scripts/convert_model_modality.py @@ -145,6 +145,16 @@ def get_single_modal_model_dct( from_processing_cp: bool = False, is_deploy: bool = False, ): + """ + Convert multimodal model state dictionary to single modal model. + Modal is selected by `ref_modal` + + `model_state_dct`: model state dictionary from multimodal checkpoint file + `config`: dictionary containing configuration of the checkpoint model + `ref_modal`: modal that are going to be converted + `from_processing_cp`: if True, use modal_map of the checkpoint file + `is_deploy`: if True, model is build with single-modal shift and scale + """ if ( not from_processing_cp and not config[KEY.USE_MODALITY] ): # model is already single modal @@ -245,6 +255,15 @@ def append_modality_to_model_dct( orig_num_modal: int, append_modal_length: int, ): + """ + Append modal-wise parameters to the original linear layers. + This enables expanding modal to single/multi modal model checkpoint. + + `model_state_dct`: model state dictionary from multimodal checkpoint file + `config`: dictionary containing configuration of the checkpoint model + `orig_num_modal`: Number of modality used in original checkpoint + `append_modal_length`: Number of modality to be appended in new checkpoint. + """ config_num_modal = config[KEY.NUM_MODALITIES] config.update({KEY.NUM_MODALITIES: orig_num_modal, KEY.USE_MODALITY: True}) From 85ea475a4ab22706c2b6c96be636ad3c9fe1fdac Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 30 Oct 2024 15:27:43 +0900 Subject: [PATCH 007/113] add: dict reader --- sevenn/train/dataload.py | 49 +++++++++++++++++++++++++++++----------- 1 file changed, 36 insertions(+), 13 deletions(-) diff --git a/sevenn/train/dataload.py b/sevenn/train/dataload.py index 0e1fdb7f..aad37624 100644 --- a/sevenn/train/dataload.py +++ b/sevenn/train/dataload.py @@ -1,7 +1,7 @@ import copy import os.path from functools import partial -from itertools import islice +from itertools import chain, islice from typing import Callable, List, Optional import ase @@ -181,12 +181,9 @@ def atoms_to_graph( info = copy.deepcopy(atoms.info) # save only metadata # TODO: is it really necessary? - if 'y_energy' in info: - del info['y_energy'] - if 'y_force' in info: - del info['y_force'] - if 'y_stress' in info: - del info['y_stress'] + info.pop('y_energy', None) + info.pop('y_force', None) + info.pop('y_stress', None) data[KEY.INFO] = info else: @@ -432,6 +429,36 @@ def parse_fileline(line): return {k: _set_atoms_y(v) for k, v in structures_dict.items()} +def dict_reader(data_dict: dict): + data_dict_cp = copy.deepcopy(data_dict) + + ret = [] + file_list = data_dict_cp.pop('file_list', None) + if file_list is None: + raise KeyError('file_list is not found') + + data_weight_default = { + 'energy': 1.0, + 'force': 1.0, + 'stress': 1.0, + } + data_weight = data_weight_default.copy() + data_weight.update(data_dict_cp.pop(KEY.DATA_WEIGHT, {})) + + for file_dct in file_list: + ftype = file_dct.pop('data_format', 'ase') + files = list(braceexpand(file_dct.pop('file'))) + if ftype == 'ase': + ret.extend(chain(*[ase_reader(f, **file_dct) for f in files])) + else: + raise ValueError(f'{ftype} yet') + + for atoms in ret: + atoms.info.update(data_dict_cp) + atoms.info.update({KEY.DATA_WEIGHT: data_weight}) + return _set_atoms_y(ret) + + def match_reader(reader_name: str, **kwargs): reader = None metadata = {} @@ -495,17 +522,13 @@ def file_to_dataset( weights = info.split('=')[1] try: if ',' in weights: - weight_list = list( - map(float, weights.split(',')) - ) + weight_list = list(map(float, weights.split(','))) else: weight_list = [float(weights)] * 3 weight_dict = {} for idx, loss_type in enumerate(LossType): weight_dict[loss_type.value] = ( - weight_list[idx] - if idx < len(weight_list) - else 1 + weight_list[idx] if idx < len(weight_list) else 1 ) graph[KEY.DATA_WEIGHT] = weight_dict find_weight = True From d41c1f84972ce7960ff8c6036c8ba76ce291a444 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 30 Oct 2024 15:53:54 +0900 Subject: [PATCH 008/113] WIP add: multimodal dataset --- sevenn/train/graph_dataset.py | 111 +++++++---- sevenn/train/modal_dataset.py | 357 ++++++++++++++++++++++++++++++++++ 2 files changed, 427 insertions(+), 41 deletions(-) create mode 100644 sevenn/train/modal_dataset.py diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index 34ef2de4..d716e055 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -1,6 +1,7 @@ import os import warnings from collections import Counter +from copy import deepcopy from datetime import datetime from typing import Any, Callable, Dict, List, Optional, Union @@ -118,6 +119,15 @@ def _elemwise_reference_energies(composition: np.ndarray, energies: np.ndarray): return full_coeff.tolist() # ex: full_coeff[1] = H_reference_energy +def _info_copy_transform(transform, info_copy_keys): + def chained_transform(graph): + graph = transform(graph) if transform is not None else graph + for key in info_copy_keys: + graph[key] = deepcopy(graph.get(KEY.INFO, {}).get(key)) + return graph + return chained_transform + + class SevenNetGraphDataset(InMemoryDataset): """ Replacement of AtomGraphDataset. (and .sevenn_data) @@ -133,14 +143,19 @@ class SevenNetGraphDataset(InMemoryDataset): Args: root: path to save/load processed PyG dataset cutoff: edge cutoff of given AtomGraphData - files: list of filenames to initialize dataset: - ASE readable (with proper extension), structure_list, .sevenn_data + files: list of filenames or dict describing how to parse the file + ASE readable (with proper extension), structure_list, .sevenn_data, + dict containing file_list (see dict_reader of train/dataload.py) process_num_cores: # of cpu cores to build graph processed_name: save as {root}/sevenn_data/{processed_name}.pt pre_transfrom: optional transform for each graph: def (graph) -> graph pre_filter: optional filtering function for each graph: def (graph) -> graph force_reload: if True, reload dataset from files even if there exist {root}/sevenn_data/{processed_name} + info_copy_keys: patch these keys from KEY.INFO to graph when accessing. + default is KEY.DATA_WEIGHT and KEY.DATA_MODALITY, which may accessed + while training. User have to ensure these keys exist in info dict. See + consistent_info_dict function. **process_kwargs: keyword arguments that will be passed into ase.io.read """ @@ -148,7 +163,7 @@ def __init__( self, cutoff: float, root: Optional[str] = None, - files: Optional[Union[str, List[str]]] = None, + files: Optional[Union[str, List[Any]]] = None, process_num_cores: int = 1, processed_name: str = 'graph.pt', transform: Optional[Callable] = None, @@ -156,6 +171,7 @@ def __init__( pre_filter: Optional[Callable] = None, log: bool = True, force_reload: bool = False, + info_copy_keys: Optional[List[str]] = None, **process_kwargs, ): self.cutoff = cutoff @@ -163,8 +179,14 @@ def __init__( files = [] elif isinstance(files, str): files = [files] # user convenience - files = [os.path.abspath(file) for file in files] - self._files = files + + _files = [] + for f in files: + if isinstance(f, str): + f = os.path.abspath(f) + _files.append(f) + self._files = _files + self._full_file_list = [] if not processed_name.endswith('.pt'): processed_name += '.pt' @@ -175,6 +197,10 @@ def __init__( self.process_num_cores = process_num_cores self.process_kwargs = process_kwargs + # chain info_copy function + info_copy_keys = info_copy_keys if info_copy_keys is not None else [] + transform = _info_copy_transform(transform, info_copy_keys) + self.tag_map = {} self.statistics = {} self.finalized = False @@ -191,7 +217,9 @@ def __init__( def load(self, path: str, data_cls=Data) -> None: super().load(path, data_cls) - self._load_meta() + if len(self.statistics) == 0: + # dataset is loaded from existing pt file. + self._load_meta() def _load_meta(self) -> None: with open(self.processed_paths[1], 'r') as f: @@ -215,8 +243,17 @@ def _load_meta(self) -> None: self._files = meta['files'] self.statistics = meta['statistics'] + def consistent_info_dict(self, strategy: str = 'largest', **default_kwargs): + info_dict_lists = [g[KEY.INFO] for g in self] + util.get_consistent_dict_list( + info_dict_lists, + in_place=True, + strategy=strategy, + key_defaults=default_kwargs, + ) + @property - def raw_file_names(self) -> List[str]: + def raw_file_names(self) -> List[Any]: return self._files @property @@ -235,12 +272,13 @@ def process(self): graph_list: list[AtomGraphData] = [] for file in self.raw_file_names: tmplist = SevenNetGraphDataset.file_to_graph_list( - filename=file, + entry=file, cutoff=self.cutoff, num_cores=self.process_num_cores, **self.process_kwargs, ) - self._full_file_list.extend([os.path.abspath(file)] * len(tmplist)) + # TODO: + # self._full_file_list.extend([os.path.abspath(file)] * len(tmplist)) graph_list.extend(tmplist) processed_graph_list = [] @@ -398,55 +436,46 @@ def _read_graph_dataset( g[KEY.STRESS] = g[KEY.STRESS].unsqueeze(0) return glist + @staticmethod + def _read_dict( + data_dict: dict, + cutoff: float, + num_cores: int = 1, + ): + atoms_list = dataload.dict_reader(data_dict) + return dataload.graph_build(atoms_list, cutoff, num_cores) + @staticmethod def file_to_graph_list( - filename: str, cutoff: float, num_cores: int = 1, **kwargs + entry: Union[str, dict], cutoff: float, num_cores: int = 1, **kwargs ) -> List[AtomGraphData]: """ kwargs: if file is ase readable, passed to ase.io.read """ - if not os.path.isfile(filename): - raise ValueError(f'No such file: {filename}') + if isinstance(entry, str) and not os.path.isfile(entry): + raise ValueError(f'No such file: {entry}') graph_list: list[AtomGraphData] - if filename.endswith('.pt'): - graph_list = SevenNetGraphDataset._read_graph_dataset(filename, cutoff) - elif filename.endswith('.sevenn_data'): - graph_list, cutoff_other = SevenNetGraphDataset._read_sevenn_data( - filename + if isinstance(entry, dict): + graph_list = SevenNetGraphDataset._read_dict( + entry, cutoff, num_cores, **kwargs ) + elif entry.endswith('.pt'): + graph_list = SevenNetGraphDataset._read_graph_dataset(entry, cutoff) + elif entry.endswith('.sevenn_data'): + graph_list, cutoff_other = SevenNetGraphDataset._read_sevenn_data(entry) if cutoff_other != cutoff: - warnings.warn(f'Given {filename} has different {cutoff_other}!') + warnings.warn(f'Given {entry} has different {cutoff_other}!') cutoff = cutoff_other - elif 'structure_list' in filename: + elif 'structure_list' in entry: graph_list = SevenNetGraphDataset._read_structure_list( - filename, cutoff, num_cores + entry, cutoff, num_cores ) else: graph_list = SevenNetGraphDataset._read_ase_readable( - filename, cutoff, num_cores, **kwargs + entry, cutoff, num_cores, **kwargs ) return graph_list - @staticmethod - def from_pt(path: str): - """ - Return SevenNetGraphDataset from path (xxx/yyy/sevenn_data/train.pt) - An yaml file with meta and statistics information must present in the - same directory. - """ - pass - meta_f = path.replace('.pt', '.yaml') - if not os.path.exists(meta_f): - raise ValueError('No saved meta information found') - - with open(meta_f, 'r') as f: - meta = yaml.safe_load(f) - - cutoff = float(meta['cutoff']) - ds_args: dict[str, Any] = dict({'cutoff': cutoff}) - ds_args.update(pt_to_args(path)) - return SevenNetGraphDataset(**ds_args) - # script, return dict of SevenNetGraphDataset def from_config( diff --git a/sevenn/train/modal_dataset.py b/sevenn/train/modal_dataset.py new file mode 100644 index 00000000..787e34e5 --- /dev/null +++ b/sevenn/train/modal_dataset.py @@ -0,0 +1,357 @@ +import os +from copy import deepcopy +from typing import Any, Dict, List, Optional, Union + +import numpy as np +from torch.utils.data import ConcatDataset, Dataset + +import sevenn._keys as KEY +import sevenn.util as util +from sevenn.sevenn_logger import Logger + + +def _arrange_paths_by_modality( + paths: List[Union[str, dict]], default_modality: str = 'common' +): + modal_dct = {} + for path in paths: + if isinstance(path, str): + modal = default_modality + elif isinstance(path, dict): + modal = path.get(KEY.DATA_MODALITY, default_modality) + else: + raise TypeError(f'{path} is not dict or str') + if modal not in modal_dct: + modal_dct[modal] = [] + modal_dct[modal].append(path) + return modal_dct + + +def combined_variance( + means: np.ndarray, stds: np.ndarray, sample_sizes: np.ndarray, ddof: int = 0 +) -> float: + """ + Calculate the combined variance for multiple datasets. + """ + assert len(means) == len(stds) and len(stds) == len(sample_sizes) + # Total number of samples + total_samples = np.sum(sample_sizes) + + # Combined mean + combined_mean = np.sum(sample_sizes * means) / total_samples + + # Combined variance calculation + variance_terms = (sample_sizes - ddof) * (stds**2) + mean_diff_terms = sample_sizes * ((means - combined_mean) ** 2) + combined_variance = (np.sum(variance_terms) + np.sum(mean_diff_terms)) / ( + total_samples - ddof + ) + + return combined_variance + + +def combined_std( + means: List[float], stds: List[float], sample_sizes: List[int] +) -> float: + """ + Calculate the combined std for multiple datasets. + """ + assert len(means) == len(stds) and len(stds) == len(sample_sizes) + means_arr = np.array(means) + stds_arr = np.array(stds) + sample_sizes_arr = np.array(sample_sizes) + + cv = combined_variance(means_arr, stds_arr, sample_sizes_arr) + return np.sqrt(cv) + + +def combined_mean(means: List[float], sample_sizes: List[int]) -> float: + """ + Calculate the combined mean for multiple datasets. + """ + assert len(means) == len(sample_sizes) + means_arr = np.array(means) + sample_sizes_arr = np.array(sample_sizes) + + return np.sum(sample_sizes_arr * means_arr) / np.sum(sample_sizes_arr) + + +def combined_rms( + means: List[float], stds: List[float], sample_sizes: List[int] +) -> float: + """ + Calculate the combined RMS for multiple datasets. + """ + assert len(means) == len(stds) and len(stds) == len(sample_sizes) + means_arr = np.array(means) + stds_arr = np.array(stds) + sample_sizes_arr = np.array(sample_sizes) + + cm = combined_mean(means, sample_sizes) + cv = combined_variance(means_arr, stds_arr, sample_sizes_arr) + + # Combined RMS calculation + return np.sqrt(cm**2 + cv) + + +class SevenNetMultiModalDataset(ConcatDataset): + def __init__( + self, + modal_dataset_dict: Dict[str, Dataset], + ): + datasets = [] + modals = [] + for modal, dataset in modal_dataset_dict.items(): + modals.append(modal) + datasets.append(dataset) + self.modals = modals + super().__init__(datasets) + + def _modal_wise_property(self, attribute_name: str): + dct = {} + for modal, dataset in zip(self.modals, self.datasets): + try: + if hasattr(dataset, attribute_name): + dct[modal] = getattr(dataset, attribute_name) + except AttributeError: + dct[modal] = None + return dct + + @property + def dataset_dict(self): + arr = {} + for idx, modality in enumerate(self.modals): + arr[modality] = self.datasets[idx] + return arr + + @property + def species(self): + dct = self._modal_wise_property('species') + tot = set() + for sp in dct.values(): + tot.update(sp) + dct['total'] = list(tot) + return dct + + @property + def natoms(self): + return self._modal_wise_property('natoms') + + @property + def per_atom_energy_mean(self): + dct = self._modal_wise_property('per_atom_energy_mean') + try: + means = [] + sample_sizes = [] + for modality, mean in dct.items(): + means.append(mean) + sample_sizes.append( + self.statistics[modality][KEY.PER_ATOM_ENERGY]['count'] + ) + cm = combined_mean(means, sample_sizes) + dct['total'] = cm + except KeyError: + pass + return dct + + @property + def elemwise_reference_energies(self): + # total is not supported (it is expensive and complex, but useless) + return self._modal_wise_property('elemwise_reference_energies') + + @property + def force_rms(self): + dct = self._modal_wise_property('force_rms') + try: + means = [] + sample_sizes = [] + stds = [] + for modality in dct: + means.append(self.statistics[modality][KEY.FORCE]['mean']) + sample_sizes.append(self.statistics[modality][KEY.FORCE]['count']) + stds.append(self.statistics[modality][KEY.FORCE]['std']) + cm = combined_rms(means, stds, sample_sizes) + dct['total'] = cm + except KeyError: + pass + return dct + + @property + def per_atom_energy_std(self): + dct = self._modal_wise_property('per_atom_energy_std') + try: + means = [] + sample_sizes = [] + stds = [] + for modality in dct: + means.append(self.statistics[modality][KEY.PER_ATOM_ENERGY]['mean']) + sample_sizes.append( + self.statistics[modality][KEY.PER_ATOM_ENERGY]['count'] + ) + stds.append(self.statistics[modality][KEY.PER_ATOM_ENERGY]['std']) + cm = combined_std(means, stds, sample_sizes) + dct['total'] = cm + except KeyError: + pass + return dct + + @property + def avg_num_neigh(self): + dct = self._modal_wise_property('avg_num_neigh') + try: + means = [] + sample_sizes = [] + for modality, mean in dct.items(): + means.append(mean) + sample_sizes.append( + self.statistics[modality]['num_neighbor']['count'] + ) + cm = combined_mean(means, sample_sizes) + dct['total'] = cm + except KeyError: + pass + return dct + + @property + def sqrt_avg_num_neigh(self): + avg_nn = self.avg_num_neigh + return {k: v**0.5 for k, v in avg_nn.items()} + + @property + def statistics(self): + return self._modal_wise_property('statistics') + + @staticmethod + def as_graph_dataset( + paths: List[Union[str, dict]], + cutoff: float, + default_modality: str, + **graph_dataset_kwargs, + ): + from .graph_dataset import SevenNetGraphDataset + + modal_paths = _arrange_paths_by_modality(paths, default_modality) + dataset_dct = {} + kwargs = deepcopy(graph_dataset_kwargs) + for modality, paths in modal_paths.items(): + processed_name = kwargs.pop('processed_name', 'graph') + processed_name = processed_name.replace('.pt', '') + processed_name = processed_name + '_' + modality + '.pt' + dataset_dct[modality] = SevenNetGraphDataset( + cutoff=cutoff, + files=paths, + processed_name=processed_name, + **kwargs, + ) + return SevenNetMultiModalDataset(dataset_dct) + + +def from_config( + config: dict[str, Any], + working_dir: str = os.getcwd(), + dataset_keys: Optional[list[str]] = None, +): + log = Logger() + if dataset_keys is None: + dataset_keys = [ + k for k in config if (k.startswith('load_') and k.endswith('_path')) + ] + + if KEY.LOAD_TRAINSET not in dataset_keys: + raise ValueError(f'{KEY.LOAD_TRAINSET} must be present in config') + + info_copy_keys = [KEY.DATA_MODALITY] # you will use modality anyway + if config[KEY.USE_WEIGHT]: + info_copy_keys.append(KEY.DATA_WEIGHT) + + dataset_args = { + 'cutoff': config[KEY.CUTOFF], + 'root': working_dir, + 'process_num_cores': config[KEY.PREPROCESS_NUM_CORES], + 'default_modality': config[KEY.DEFAULT_MODAL], # for paths w/o modality + 'info_copy_keys': info_copy_keys, + **config[KEY.DATA_FORMAT_ARGS], + } + + datasets = {} + for dk in dataset_keys: + if not (paths := config[dk]): + continue + if isinstance(paths, str): + paths = [paths] + name = dk.split('_')[1].strip() + dataset_args.update({'processed_name': name}) + datasets[name] = SevenNetMultiModalDataset.as_graph_dataset( + paths, # type: ignore + **dataset_args, + ) + + train_set = datasets['trainset'] + + modals = set() + chem_species = set() + # print statistics of each dataset + for name, dataset in datasets.items(): + for idx, modality in enumerate(dataset.modals): + log.bar() + log.writeline(f'{name} - {modality} distribution:') + log.statistic_write(dataset.statistics[modality]) + log.format_k_v( + '# structures (graph)', len(dataset.datasets[idx]), write=True + ) + modals.update([modality]) + chem_species.update(dataset.species['total']) + log.bar() + + config[KEY.NUM_MODALITIES] = len(modals) + + modals = sorted(list(modals)) + modal_map = {modal: i for i, modal in enumerate(modals)} + config[KEY.MODAL_MAP] = modal_map + + # initialize known species from dataset if 'auto' + # sorted to alphabetical order (which is same as before) + chem_keys = [KEY.CHEMICAL_SPECIES, KEY.NUM_SPECIES, KEY.TYPE_MAP] + if all([config[ck] == 'auto' for ck in chem_keys]): # see parse_input.py + log.writeline('Known species are obtained from the dataset') + config.update(util.chemical_species_preprocess(sorted(list(chem_species)))) + + # retrieve shift, scale, conv_denominaotrs from user input (keyword) + init_from_stats_candid = [KEY.SHIFT, KEY.SCALE, KEY.CONV_DENOMINATOR] + init_from_stats = [ + k for k in init_from_stats_candid if isinstance(config[k], str) + ] + + for k in init_from_stats: + input = config[k] + if not hasattr(train_set, input): + raise NotImplementedError(input) + modal_stat = getattr(train_set, input) + try: + if k == KEY.CONV_DENOMINATOR and 'total' in modal_stat: + # conv_denominator is not modal-wise + var = modal_stat['total'] + elif k == KEY.SHIFT and config[KEY.USE_MODAL_WISE_SHIFT]: + modal_stat.pop('total', None) + var = modal_stat + elif k == KEY.SHIFT and not config[KEY.USE_MODAL_WISE_SHIFT]: + var = modal_stat['total'] + elif k == KEY.SCALE and config[KEY.USE_MODAL_WISE_SCALE]: + modal_stat.pop('total', None) + var = modal_stat + elif k == KEY.SCALE and not config[KEY.USE_MODAL_WISE_SCALE]: + var = modal_stat['total'] + else: + raise NotImplementedError(f'Failed to init {k} from statistics') + except KeyError as e: + if e.args[0] == 'total': + raise NotImplementedError( + f'{k}: {input} does not support total statistics. ' + + f'Try to set modal-wise True or specify {k} manually' + ) + else: + raise e + config.update({k: var}) + log.writeline(f'{k} is obtained from statistics') + + return datasets From e7554761747bb1294617a35f2e3a386cd939c13d Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 30 Oct 2024 15:55:03 +0900 Subject: [PATCH 009/113] chore --- sevenn/_const.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/_const.py b/sevenn/_const.py index 02d1263e..a20c09bb 100644 --- a/sevenn/_const.py +++ b/sevenn/_const.py @@ -60,7 +60,7 @@ AtomGraphDataType = Dict[str, torch.Tensor] -class LossType(Enum): +class LossType(Enum): # only used for train_v1, do not use it afterwards ENERGY = 'energy' # eV or eV/atom FORCE = 'force' # eV/A STRESS = 'stress' # kB From d6404649dbf5301a97a46b1376d6d2e79413fe14 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 30 Oct 2024 15:57:54 +0900 Subject: [PATCH 010/113] WIP refactor: multimodal, modality using onehot, modal map in sequential, patch modalities all at once, modality shift scale refacotr --- sevenn/model_build.py | 108 ++++++++++++++++++----------- sevenn/nn/linear.py | 6 +- sevenn/nn/node_embedding.py | 9 ++- sevenn/nn/scale.py | 133 ++++++++++++++++++++++++++++++++---- sevenn/nn/sequential.py | 24 +++++++ 5 files changed, 221 insertions(+), 59 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 778ffda9..7ac64f85 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -2,7 +2,6 @@ import warnings from collections import OrderedDict -import torch from e3nn.o3 import Irreps import sevenn._const as _const @@ -37,6 +36,18 @@ ) +def _insert_after(module_name_after, key_module_pair, layers): + idx = -1 + for i, (key, _) in enumerate(layers): + if key == module_name_after: + idx = i + break + if idx == -1: + assert False + layers.insert(idx + 1, key_module_pair) + return layers + + def init_self_connection(config): self_connection_type = config[KEY.SELF_CONNECTION_TYPE] intro, outro = None, None @@ -123,52 +134,76 @@ def init_feature_reduce(config, irreps_x): def init_shift_scale(config): # for mm, ex, shift: modal_idx -> shifts shift_scale = [] + train_shift_scale = config[KEY.TRAIN_SHIFT_SCALE] type_map = config[KEY.TYPE_MAP] + + # in case of modal, shift or scale has more dims [][] + print(config[KEY.SHIFT]) + print(config[KEY.SCALE]) # correct typing (I really want static python) for s in (config[KEY.SHIFT], config[KEY.SCALE]): if hasattr(s, 'tolist'): # numpy or torch s = s.tolist() if isinstance(s, list) and len(s) == 1: s = s[0] - # check whether list shift scale matches the size of type_map: - if isinstance(s, list) and len(s) > len(type_map): - # assume s is indexed with atomic numbers from 0 to 120 - if len(s) != _const.NUM_UNIV_ELEMENT: - raise ValueError('given shift or scale is strange') - s = [s[z] for z in sorted(type_map, key=type_map.get)] shift_scale.append(s) rescale_module = None - if all([isinstance(s, float) for s in shift_scale]): - rescale_module = Rescale + shift, scale = shift_scale + if config[KEY.USE_MODALITY]: + rescale_module = ModalWiseRescale.from_mappers( # type: ignore + shift, + scale, + config[KEY.USE_MODAL_WISE_SHIFT], + config[KEY.USE_BIAS_IN_LINEAR], + type_map=type_map, + modal_map=config[KEY.MODAL_MAP], + train_shift_scale=train_shift_scale, + ) + elif all([isinstance(s, float) for s in shift_scale]): + rescale_module = Rescale(shift, scale, train_shift_scale=train_shift_scale) elif any([isinstance(s, list) for s in shift_scale]): - rescale_module = SpeciesWiseRescale + rescale_module = SpeciesWiseRescale.from_mappers( # type: ignore + shift, scale, type_map=type_map, train_shift_scale=train_shift_scale + ) else: raise ValueError('shift, scale should be list of float or float') - shift, scale = shift_scale - - return rescale_module( - shift=shift, - scale=scale, - train_shift_scale=config[KEY.TRAIN_SHIFT_SCALE], - ) + return rescale_module -def _patch_modality(layers: OrderedDict, config): +def patch_modality(layers: OrderedDict, config): + """ + Postprocess 7net-model to multimodal model. + 1. prepend modality one-hot embedding layer + 2. patch modalities of IrrepsLinear layers + Modality aware shift scale is handled by init_shift_scale, not here + """ cfg = config if not cfg[KEY.USE_MODALITY]: - return + return layers + + _layers = list(layers.items()) + _layers = _insert_after( + 'edge_embedding', + ( + 'one_hot_modality', + OnehotEmbedding( + num_classes=config[KEY.NUM_MODALITIES], + data_key_x=KEY.MODAL_TYPE, + data_key_out=KEY.MODAL_ATTR, + ), + ), + _layers, + ) + layers = OrderedDict(_layers) num_modal = config[KEY.NUM_MODALITIES] for k, module in layers.items(): if not isinstance(module, IrrepsLinear): continue if ( - ( - cfg[KEY.USE_MODAL_NODE_EMBEDDING] - and k.endswith('onehot_to_feature_x') - ) + (cfg[KEY.USE_MODAL_NODE_EMBEDDING] and k.endswith('onehot_to_feature_x')) or ( cfg[KEY.USE_MODAL_SELF_INTER_INTRO] and k.endswith('self_interaction_1') @@ -193,17 +228,6 @@ def _to_parallel_model(layers: OrderedDict, config): num_convolution_layer = config[KEY.NUM_CONVOLUTION] - def insert_after(module_name_after, key_module_pair, layers): - idx = -1 - for i, (key, _) in enumerate(layers): - if key == module_name_after: - idx = i - break - if idx == -1: - assert False - layers.insert(idx + 1, key_module_pair) - return layers - def slice_until_this(module_name, layers): idx = -1 for i, (key, _) in enumerate(layers): @@ -214,7 +238,7 @@ def slice_until_this(module_name, layers): remain = layers[idx + 1 :] return first_to, remain - _layers = insert_after( + _layers = _insert_after( 'onehot_to_feature_x', ( 'one_hot_ghost', @@ -227,7 +251,7 @@ def slice_until_this(module_name, layers): ), _layers, ) - _layers = insert_after( + _layers = _insert_after( 'one_hot_ghost', ( 'ghost_onehot_to_feature_x', @@ -240,7 +264,7 @@ def slice_until_this(module_name, layers): ), _layers, ) - _layers = insert_after( + _layers = _insert_after( '0_self_interaction_1', ( 'ghost_0_self_interaction_1', @@ -432,15 +456,19 @@ def build_E3_equivariant_model(config: dict, parallel=False): common_args = { 'cutoff': cutoff, 'type_map': config[KEY.TYPE_MAP], - 'eval_type_map': True if not parallel else False, + 'modal_map': config.get(KEY.MODAL_MAP, None), + 'eval_type_map': False if parallel else True, + 'eval_modal_map': False + if not config[KEY.USE_MODALITY] or parallel + else True, 'data_key_grad': grad_key, } if parallel: layers_list = _to_parallel_model(layers, config) return [ - AtomGraphSequential(_patch_modality(layers, config), **common_args) + AtomGraphSequential(patch_modality(layers, config), **common_args) for layers in layers_list ] else: - return AtomGraphSequential(_patch_modality(layers, config), **common_args) + return AtomGraphSequential(patch_modality(layers, config), **common_args) diff --git a/sevenn/nn/linear.py b/sevenn/nn/linear.py index 180e0a7c..258e46c7 100644 --- a/sevenn/nn/linear.py +++ b/sevenn/nn/linear.py @@ -22,6 +22,7 @@ def __init__( irreps_out: Irreps, data_key_in: str, data_key_out: Optional[str] = None, + data_key_modal_attr: str = KEY.MODAL_ATTR, num_modalities: int = 0, lazy_layer_instantiate: bool = True, **e3nn_linear_params, @@ -32,6 +33,7 @@ def __init__( self.key_output = data_key_in else: self.key_output = data_key_out + self.key_modal_attr = data_key_modal_attr self._irreps_in_wo_modal = irreps_in self.irreps_in = irreps_in @@ -67,7 +69,7 @@ def instantiate(self): def _patch_modal_to_data(self, data: AtomGraphDataType) -> AtomGraphDataType: if self._is_batch_data: batch = data[KEY.BATCH] - batch_modality_onehot = data[KEY.MODAL_ATTR].reshape( + batch_modality_onehot = data[self.key_modal_attr].reshape( -1, self.num_modalities ) batch_modality_onehot = batch_modality_onehot.type( @@ -77,7 +79,7 @@ def _patch_modal_to_data(self, data: AtomGraphDataType) -> AtomGraphDataType: [data[self.key_input], batch_modality_onehot[batch]], dim=1 ) else: - modality_onehot = data[KEY.MODAL_ATTR].expand( + modality_onehot = data[self.key_modal_attr].expand( len(data[self.key_input]), -1 ) modality_onehot = modality_onehot.type(data[self.key_input].dtype) diff --git a/sevenn/nn/node_embedding.py b/sevenn/nn/node_embedding.py index ab47335a..377a2a98 100644 --- a/sevenn/nn/node_embedding.py +++ b/sevenn/nn/node_embedding.py @@ -1,4 +1,4 @@ -from typing import Dict, List +from typing import Dict, List, Optional import torch import torch.nn as nn @@ -27,12 +27,17 @@ def __init__( self, num_classes: int, data_key_x: str = KEY.NODE_FEATURE, + data_key_out: Optional[str] = None, data_key_save: str = KEY.ATOM_TYPE, data_key_additional: str = KEY.NODE_ATTR, # additional output ): super().__init__() self.num_classes = num_classes self.key_x = data_key_x + if data_key_out is None: + self.key_output = data_key_x + else: + self.key_output = data_key_out self.key_save = data_key_save self.key_additional_output = data_key_additional @@ -40,7 +45,7 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: inp = data[self.key_x] embd = torch.nn.functional.one_hot(inp, self.num_classes) embd = embd.float() - data[self.key_x] = embd + data[self.key_output] = embd if self.key_additional_output is not None: data[self.key_additional_output] = embd # for self-connection if self.key_save is not None: diff --git a/sevenn/nn/scale.py b/sevenn/nn/scale.py index 94cf52a1..883d392b 100644 --- a/sevenn/nn/scale.py +++ b/sevenn/nn/scale.py @@ -1,11 +1,11 @@ -from typing import List, Union +from typing import Any, Dict, List, Union import torch import torch.nn as nn from e3nn.util.jit import compile_mode import sevenn._keys as KEY -from sevenn._const import AtomGraphDataType +from sevenn._const import NUM_UNIV_ELEMENT, AtomGraphDataType @compile_mode('script') @@ -21,7 +21,7 @@ def __init__( data_key_in: str = KEY.SCALED_ATOMIC_ENERGY, data_key_out: str = KEY.ATOMIC_ENERGY, train_shift_scale: bool = False, - **kwargs + **kwargs, ): assert isinstance(shift, float) and isinstance(scale, float) super().__init__() @@ -56,7 +56,6 @@ def __init__( data_key_out: str = KEY.ATOMIC_ENERGY, data_key_indices: str = KEY.ATOM_TYPE, train_shift_scale: bool = False, - **kwargs ): super().__init__() assert isinstance(shift, float) or isinstance(shift, list) @@ -89,6 +88,33 @@ def __init__( self.key_output = data_key_out self.key_indices = data_key_indices + @staticmethod + def from_mappers( + shift: Union[float, List[float]], + scale: Union[float, List[float]], + type_map: Dict[int, int], + **kwargs, + ): + """ + Fit dimensions or mapping raw shift scale values to that is valid under + the given type_map: (atomic_numbers -> type_indices) + """ + shift_scale = [] + n_atom_types = len(type_map) + for s in (shift, scale): + if isinstance(s, list) and len(s) > len(type_map): + if len(s) != NUM_UNIV_ELEMENT: + raise ValueError('given shift or scale is strange') + s = [s[z] for z in sorted(type_map, key=lambda x: type_map[x])] + # s = [s[z] for z in sorted(type_map, key=type_map.get)] + elif isinstance(s, float): + s = [s] * n_atom_types + elif isinstance(s, list) and len(s) == 1: + s = s * n_atom_types + shift_scale.append(s) + assert all([len(s) == n_atom_types for s in shift_scale]) + return SpeciesWiseRescale(shift, scale, **kwargs) + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: indices = data[self.key_indices] data[self.key_output] = data[self.key_input] * self.scale[indices].view( @@ -102,16 +128,19 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: class ModalWiseRescale(nn.Module): """ Scaling and shifting energy (and automatically force and stress) + Given shift or scale is either modal-wise and atom-wise or + not modal-wise but atom-wise. It is always atom-wise. + If given scalar, it tries to expand to atom-wise """ def __init__( self, shift: List[List[float]], scale: List[List[float]], - data_key_in=KEY.SCALED_ATOMIC_ENERGY, - data_key_out=KEY.ATOMIC_ENERGY, - data_key_modal_indices=KEY.MODAL_TYPE, - data_key_atom_indices=KEY.ATOM_TYPE, + data_key_in: str = KEY.SCALED_ATOMIC_ENERGY, + data_key_out: str = KEY.ATOMIC_ENERGY, + data_key_modal_indices: str = KEY.MODAL_TYPE, + data_key_atom_indices: str = KEY.ATOM_TYPE, use_modal_wise_shift: bool = False, use_modal_wise_scale: bool = False, train_shift_scale: bool = False, @@ -123,17 +152,91 @@ def __init__( self.scale = nn.Parameter( torch.FloatTensor(scale), requires_grad=train_shift_scale ) - self.KEY_INPUT = data_key_in - self.KEY_OUTPUT = data_key_out - self.KEY_ATOM_INDICES = data_key_atom_indices - self.KEY_MODAL_INDICES = data_key_modal_indices + self.key_input = data_key_in + self.key_output = data_key_out + self.key_atom_indices = data_key_atom_indices + self.key_modal_indices = data_key_modal_indices self.use_modal_wise_shift = use_modal_wise_shift self.use_modal_wise_scale = use_modal_wise_scale + @staticmethod + def from_mappers( + shift: Union[float, List[float], Dict[str, Any]], + scale: Union[float, List[float], Dict[str, Any]], + use_modal_wise_shift: bool, + use_modal_wise_scale: bool, + type_map: Dict[int, int], + modal_map: Dict[str, int], + **kwargs, + ): + """ + Fit dimensions or mapping raw shift scale values to that is valid under + the given type_map: (atomic_numbers -> type_indices) + If given List[float] and its length matches length of _const.NUM_UNIV_ELEMENT + , assume it is element-wise list + otherwise, it is modal-wise list + """ + + def solve_mapper(arr, map): + # value is attr index and never overlap, key is either 'z' or modal str + return [arr[z] for z in sorted(map, key=lambda x: map[x])] + + shift_scale = [] + n_atom_types = len(type_map) + n_modals = len(modal_map) + + for s, use_mw in ( + (shift, use_modal_wise_shift), + (scale, use_modal_wise_scale), + ): + # solve elemewise, or broadcast + shape = (n_modals, n_atom_types) if use_mw else (n_atom_types,) + if isinstance(s, float): + res = torch.full(shape, s).tolist() # TODO: with plain python + elif isinstance(s, list) and len(s) == NUM_UNIV_ELEMENT: + # element-wise list + s = solve_mapper(s, type_map) + res = [s] * n_modals if use_mw else s + elif isinstance(s, list) and len(s) == len(modal_map): + # modal-wise list, for element-wise, use same value + res = [[v] * len(type_map) for v in s] + elif isinstance(s, dict) and use_mw: + # solve modal dict + s = solve_mapper(s, modal_map) + if isinstance(s[0], list) and len(s[0]) == NUM_UNIV_ELEMENT: + res = [solve_mapper(v, type_map) for v in s] + pass + elif isinstance(s[0], float): + res = [[v] * len(type_map) for v in s] + else: + raise ValueError() + else: + raise ValueError() + shift_scale.append(res) + print('bf') + print('shift:') + print(shift) + print('scale:') + print(scale) + shift, scale = shift_scale + print('af') + print('shift:') + print(shift) + print('scale:') + print(scale) + + return ModalWiseRescale( + shift, + scale, + use_modal_wise_shift=use_modal_wise_shift, + use_modal_wise_scale=use_modal_wise_scale, + **kwargs, + ) + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: batch = data[KEY.BATCH] - modal_indices = data[self.KEY_MODAL_INDICES][batch] - atom_indices = data[self.KEY_ATOM_INDICES] + modal_indices = data[self.key_modal_indices][batch] + atom_indices = data[self.key_atom_indices] shift = ( self.shift[modal_indices, atom_indices] if self.use_modal_wise_shift @@ -144,7 +247,7 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: if self.use_modal_wise_scale else self.scale[atom_indices] ) - data[self.KEY_OUTPUT] = data[self.KEY_INPUT] * scale.view( + data[self.key_output] = data[self.key_input] * scale.view( -1, 1 ) + shift.view(-1, 1) diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index bf959315..05b5281e 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -40,7 +40,9 @@ def __init__( modules: Dict[str, nn.Module], cutoff: float = 0.0, type_map: Optional[Dict[int, int]] = None, + modal_map: Optional[Dict[str, int]] = None, eval_type_map: bool = True, + eval_modal_map: bool = False, data_key_atomic_numbers: str = KEY.ATOMIC_NUMBERS, data_key_node_feature: str = KEY.NODE_FEATURE, data_key_grad: Optional[str] = None, @@ -50,6 +52,7 @@ def __init__( self.cutoff = cutoff self.type_map = type_map self.eval_type_map = eval_type_map + self.is_batch_data = True if cutoff == 0.0: warnings.warn('cutoff is 0.0 or not given', UserWarning) @@ -63,6 +66,11 @@ def __init__( z_to_onehot_tensor[z] = onehot self.z_to_onehot_tensor = z_to_onehot_tensor + if eval_modal_map and modal_map is None: + raise ValueError('eval_modal_map is True but modal_map is None') + self.eval_modal_map = eval_modal_map + self.modal_map = modal_map + self.key_atomic_numbers = data_key_atomic_numbers self.key_node_feature = data_key_node_feature self.key_grad = data_key_grad @@ -79,6 +87,7 @@ def set_is_batch_data(self, flag: bool): module._is_batch_data = flag # type: ignore except AttributeError: pass + self.is_batch_data = flag def get_irreps_in(self, modlue_name: str, attr_key: str = 'irreps_in'): tg_module = self._modules[modlue_name] @@ -108,6 +117,18 @@ def _atomic_numbers_to_onehot(self, atomic_numbers: torch.Tensor): input=z_to_onehot_tensor, dim=0, index=atomic_numbers ) + def _init_modal_attr(self, data: AtomGraphDataType): + assert self.modal_map is not None + if not self.is_batch_data: + modal_idx = self.modal_map[data[KEY.DATA_MODALITY]] # type: ignore + else: + modal_idx = [ + self.modal_map[ii] # type: ignore + for ii in data[KEY.DATA_MODALITY] + ] + modal_idx = torch.tensor(modal_idx, dtype=torch.int64) + data[KEY.MODAL_TYPE] = modal_idx + def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: data = input if self.eval_type_map: @@ -115,6 +136,9 @@ def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: onehot = self._atomic_numbers_to_onehot(atomic_numbers) data[self.key_node_feature] = onehot + if self.eval_modal_map: + self._init_modal_attr(input) + if self.key_grad is not None: data[self.key_grad].requires_grad_(True) From dce6617a46088c2dab916872b0a0128e926e384f Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 30 Oct 2024 15:58:48 +0900 Subject: [PATCH 011/113] refactor: for dict reader --- sevenn/parse_input.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/sevenn/parse_input.py b/sevenn/parse_input.py index f847e6ac..f0406d8c 100644 --- a/sevenn/parse_input.py +++ b/sevenn/parse_input.py @@ -185,7 +185,10 @@ def init_data_config(config: Dict): extended = glob.glob(inp) elif type(inp) is list: for i in inp: - extended.extend(glob.glob(i)) + if isinstance(i, str): + extended.extend(glob.glob(i)) + elif isinstance(i, dict): + extended.append(i) if len(extended) == 0: raise ValueError( f'Cannot find {inp} for {load_data_key}' From 4bd1b7df80d18448f88f9ace7a94c1771ffed8d4 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Thu, 31 Oct 2024 23:16:31 +0900 Subject: [PATCH 012/113] chore --- sevenn/_const.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/_const.py b/sevenn/_const.py index a20c09bb..5cca4084 100644 --- a/sevenn/_const.py +++ b/sevenn/_const.py @@ -243,7 +243,7 @@ def data_defaults(config): KEY.RESET_SCHEDULER: False, KEY.RESET_EPOCH: False, KEY.USE_STATISTIC_VALUES_OF_CHECKPOINT: True, - KEY.USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY: False, + KEY.USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY: True, }, KEY.DEFAULT_MODAL: 'common', KEY.CSV_LOG: 'log.csv', From 6cc917b25e4138363208c1e6ef7f6b518a307061 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Thu, 31 Oct 2024 23:17:11 +0900 Subject: [PATCH 013/113] fix: for dict converts string, device is needed to send it --- sevenn/nn/sequential.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index 05b5281e..51913568 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -117,8 +117,9 @@ def _atomic_numbers_to_onehot(self, atomic_numbers: torch.Tensor): input=z_to_onehot_tensor, dim=0, index=atomic_numbers ) - def _init_modal_attr(self, data: AtomGraphDataType): + def _eval_modal_map(self, data: AtomGraphDataType): assert self.modal_map is not None + # modal_map: dict[str, int] if not self.is_batch_data: modal_idx = self.modal_map[data[KEY.DATA_MODALITY]] # type: ignore else: @@ -126,7 +127,11 @@ def _init_modal_attr(self, data: AtomGraphDataType): self.modal_map[ii] # type: ignore for ii in data[KEY.DATA_MODALITY] ] - modal_idx = torch.tensor(modal_idx, dtype=torch.int64) + modal_idx = torch.tensor( + modal_idx, + dtype=torch.int64, + device=data.x.device, # type: ignore + ) data[KEY.MODAL_TYPE] = modal_idx def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: @@ -137,7 +142,7 @@ def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: data[self.key_node_feature] = onehot if self.eval_modal_map: - self._init_modal_attr(input) + self._eval_modal_map(input) if self.key_grad is not None: data[self.key_grad].requires_grad_(True) From 5c3486a4dccc1a1724f228c9d2b98056084caeea Mon Sep 17 00:00:00 2001 From: YutackPark Date: Thu, 31 Oct 2024 23:19:04 +0900 Subject: [PATCH 014/113] refactor: single pt specialized routines + dataweight --- sevenn/train/graph_dataset.py | 148 ++++++++++++++++++++++++++-------- 1 file changed, 113 insertions(+), 35 deletions(-) diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index d716e055..f0a4cd66 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -119,15 +119,6 @@ def _elemwise_reference_energies(composition: np.ndarray, energies: np.ndarray): return full_coeff.tolist() # ex: full_coeff[1] = H_reference_energy -def _info_copy_transform(transform, info_copy_keys): - def chained_transform(graph): - graph = transform(graph) if transform is not None else graph - for key in info_copy_keys: - graph[key] = deepcopy(graph.get(KEY.INFO, {}).get(key)) - return graph - return chained_transform - - class SevenNetGraphDataset(InMemoryDataset): """ Replacement of AtomGraphDataset. (and .sevenn_data) @@ -152,10 +143,6 @@ class SevenNetGraphDataset(InMemoryDataset): pre_filter: optional filtering function for each graph: def (graph) -> graph force_reload: if True, reload dataset from files even if there exist {root}/sevenn_data/{processed_name} - info_copy_keys: patch these keys from KEY.INFO to graph when accessing. - default is KEY.DATA_WEIGHT and KEY.DATA_MODALITY, which may accessed - while training. User have to ensure these keys exist in info dict. See - consistent_info_dict function. **process_kwargs: keyword arguments that will be passed into ase.io.read """ @@ -169,9 +156,9 @@ def __init__( transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, pre_filter: Optional[Callable] = None, + use_data_weight: bool = False, log: bool = True, force_reload: bool = False, - info_copy_keys: Optional[List[str]] = None, **process_kwargs, ): self.cutoff = cutoff @@ -196,10 +183,7 @@ def __init__( ] self.process_num_cores = process_num_cores self.process_kwargs = process_kwargs - - # chain info_copy function - info_copy_keys = info_copy_keys if info_copy_keys is not None else [] - transform = _info_copy_transform(transform, info_copy_keys) + self.use_data_weight = use_data_weight self.tag_map = {} self.statistics = {} @@ -287,6 +271,12 @@ def process(self): continue if self.pre_transform is not None: data = self.pre_transform(data) + if self.use_data_weight: + # pop data weight from info, and assign to graph + weight = data[KEY.INFO].pop( + KEY.DATA_WEIGHT, {'energy': 1.0, 'force': 1.0, 'stress': 1.0} + ) + data[KEY.DATA_WEIGHT] = weight processed_graph_list.append(data) if len(processed_graph_list) == 0: @@ -442,8 +432,37 @@ def _read_dict( cutoff: float, num_cores: int = 1, ): + # logic same as the dataload dict_reader, but handles graphs + data_dict_cp = deepcopy(data_dict) + file_list = data_dict_cp.get('file_list', None) + if file_list is None: + raise KeyError('file_list is not found') + + data_weight_default = { + 'energy': 1.0, + 'force': 1.0, + 'stress': 1.0, + } + data_weight = data_weight_default.copy() + data_weight.update(data_dict_cp.pop(KEY.DATA_WEIGHT, {})) + + graph_list = [] + for file_dct in file_list: + ftype = file_dct.pop('data_format', 'ase') + if ftype != 'graph': + continue + graph_list.extend( + SevenNetGraphDataset._read_graph_dataset( + file_dct.get('file'), cutoff=cutoff + ) + ) + for graph in graph_list: + graph[KEY.INFO].update(data_dict_cp) + graph[KEY.INFO].update({KEY.DATA_WEIGHT: data_weight}) + atoms_list = dataload.dict_reader(data_dict) - return dataload.graph_build(atoms_list, cutoff, num_cores) + graph_list.extend(dataload.graph_build(atoms_list, cutoff, num_cores)) + return graph_list @staticmethod def file_to_graph_list( @@ -477,6 +496,70 @@ def file_to_graph_list( return graph_list +def from_single_path( + path: Union[str, List], override_data_weight: bool = True, **dataset_kwargs +) -> Union[SevenNetGraphDataset, None]: + """ + Convenient routine for loading a single .pt dataset. + If given dict and it has data_weight, apply it using transform + """ + data_weight = {'energy': 1.0, 'force': 1.0, 'stress': 1.0} + spath = _extract_single_path(path) + if spath is None: + return None + + if isinstance(spath, str): + if not spath.endswith('.pt'): + return None + dataset_kwargs.update(pt_to_args(spath)) + elif isinstance(spath, dict): + file = _extract_file_from_dict(spath) + if file is None or not file.endswith('.pt'): + return None + dataset_kwargs.update(pt_to_args(file)) + data_weight_user = spath.get(KEY.DATA_WEIGHT, None) + if data_weight_user is not None: + data_weight.update(data_weight_user) + else: + return None + + if override_data_weight: + dataset_kwargs['transform'] = _chain_data_weight_override( + dataset_kwargs.get('transform'), data_weight + ) + + return SevenNetGraphDataset(**dataset_kwargs) + + +def _extract_single_path(path: Union[str, List]) -> Union[str, dict, None]: + """Extracts a single path from the input, + ensuring it's either a single string or list with one item.""" + if isinstance(path, list): + return path[0] if len(path) == 1 else None + return path if isinstance(path, (str, dict)) else None + + +def _extract_file_from_dict(path_dict: dict) -> Union[str, None]: + """Extracts a single file path from the dictionary, ensuring it's valid.""" + file_list = path_dict.get('file_list', None) + if file_list and len(file_list) == 1: + file = file_list[0].get('file', None) + return file if isinstance(file, str) else None + return None + + +def _chain_data_weight_override(transform_func, data_weight): + """Creates a transform function that overrides the data weight.""" + + def chained_transform(graph): + graph = transform_func(graph) if transform_func is not None else graph + graph[KEY.INFO].pop(KEY.DATA_WEIGHT, None) + graph[KEY.DATA_WEIGHT] = data_weight + return graph + + return chained_transform + + # script, return dict of SevenNetGraphDataset def from_config( config: dict[str, Any], @@ -498,6 +581,7 @@ def from_config( 'cutoff': config[KEY.CUTOFF], 'root': working_dir, 'process_num_cores': config[KEY.PREPROCESS_NUM_CORES], + 'use_data_weight': config[KEY.USE_WEIGHT], **config[KEY.DATA_FORMAT_ARGS], } @@ -508,24 +592,18 @@ def from_config( if isinstance(paths, str): paths = [paths] name = dk.split('_')[1].strip() - if ( - len(paths) == 1 - and 'sevenn_data' in paths[0] - and paths[0].endswith('.pt') - ): - dataset_args.update(pt_to_args(paths[0])) + if (dataset := from_single_path(paths, **dataset_args)) is not None: + datasets[name] = dataset else: dataset_args.update({'files': paths, 'processed_name': name}) - dataset_path = os.path.join(working_dir, 'sevenn_data', f'{name}.pt') - if os.path.exists(dataset_path) and 'force_reload' not in dataset_args: - log.writeline( - f'Dataset will be loaded from {dataset_path}, without update.' - ) - log.writeline( - 'If you have changed your files to read, put force_reload=True' - + ' under the data_format_args key' - ) - datasets[name] = SevenNetGraphDataset(**dataset_args) + dataset_path = os.path.join(working_dir, 'sevenn_data', f'{name}.pt') + if os.path.exists(dataset_path) and 'force_reload' not in dataset_args: + log.writeline( + f'Dataset will be loaded from {dataset_path}, without update.' + + 'If you have changed your files to read, put force_reload=True' + + ' under the data_format_args key' + ) + datasets[name] = SevenNetGraphDataset(**dataset_args) train_set = datasets['trainset'] From 75747c74ec09a674f4423236cfe88a399aa2ef94 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Thu, 31 Oct 2024 23:20:17 +0900 Subject: [PATCH 015/113] fix,add: able to read single pt, with updating data weight --- sevenn/train/modal_dataset.py | 57 +++++++++++++++++------------------ 1 file changed, 28 insertions(+), 29 deletions(-) diff --git a/sevenn/train/modal_dataset.py b/sevenn/train/modal_dataset.py index 787e34e5..3d89629f 100644 --- a/sevenn/train/modal_dataset.py +++ b/sevenn/train/modal_dataset.py @@ -1,6 +1,7 @@ +import bisect import os from copy import deepcopy -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional import numpy as np from torch.utils.data import ConcatDataset, Dataset @@ -10,15 +11,13 @@ from sevenn.sevenn_logger import Logger -def _arrange_paths_by_modality( - paths: List[Union[str, dict]], default_modality: str = 'common' -): +def _arrange_paths_by_modality(paths: List[dict]): modal_dct = {} for path in paths: - if isinstance(path, str): - modal = default_modality - elif isinstance(path, dict): - modal = path.get(KEY.DATA_MODALITY, default_modality) + if isinstance(path, dict): + if KEY.DATA_MODALITY not in path: + raise ValueError(f'{KEY.DATA_MODALITY} is missing') + modal = path.pop(KEY.DATA_MODALITY) else: raise TypeError(f'{path} is not dict or str') if modal not in modal_dct: @@ -107,6 +106,13 @@ def __init__( self.modals = modals super().__init__(datasets) + def __getitem__(self, idx): + graph = super().__getitem__(idx) + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + modality = self.modals[dataset_idx] + graph[KEY.DATA_MODALITY] = modality + return graph + def _modal_wise_property(self, attribute_name: str): dct = {} for modal, dataset in zip(self.modals, self.datasets): @@ -223,26 +229,23 @@ def statistics(self): @staticmethod def as_graph_dataset( - paths: List[Union[str, dict]], - cutoff: float, - default_modality: str, + paths: List[dict], **graph_dataset_kwargs, ): - from .graph_dataset import SevenNetGraphDataset + import sevenn.train.graph_dataset as gd - modal_paths = _arrange_paths_by_modality(paths, default_modality) + modal_paths = _arrange_paths_by_modality(paths) dataset_dct = {} - kwargs = deepcopy(graph_dataset_kwargs) for modality, paths in modal_paths.items(): - processed_name = kwargs.pop('processed_name', 'graph') - processed_name = processed_name.replace('.pt', '') - processed_name = processed_name + '_' + modality + '.pt' - dataset_dct[modality] = SevenNetGraphDataset( - cutoff=cutoff, - files=paths, - processed_name=processed_name, - **kwargs, - ) + kwargs = deepcopy(graph_dataset_kwargs) + if (dataset := gd.from_single_path(paths, **kwargs)) is None: + pname = kwargs.pop('processed_name', 'graph').replace('.pt', '') + dataset = gd.SevenNetGraphDataset( + files=paths, + processed_name=f'{pname}_{modality}.pt', + **kwargs, + ) + dataset_dct[modality] = dataset return SevenNetMultiModalDataset(dataset_dct) @@ -260,16 +263,11 @@ def from_config( if KEY.LOAD_TRAINSET not in dataset_keys: raise ValueError(f'{KEY.LOAD_TRAINSET} must be present in config') - info_copy_keys = [KEY.DATA_MODALITY] # you will use modality anyway - if config[KEY.USE_WEIGHT]: - info_copy_keys.append(KEY.DATA_WEIGHT) - dataset_args = { 'cutoff': config[KEY.CUTOFF], 'root': working_dir, 'process_num_cores': config[KEY.PREPROCESS_NUM_CORES], - 'default_modality': config[KEY.DEFAULT_MODAL], # for paths w/o modality - 'info_copy_keys': info_copy_keys, + 'use_data_weight': config[KEY.USE_WEIGHT], **config[KEY.DATA_FORMAT_ARGS], } @@ -308,6 +306,7 @@ def from_config( modals = sorted(list(modals)) modal_map = {modal: i for i, modal in enumerate(modals)} config[KEY.MODAL_MAP] = modal_map + log.writeline(f'Modalities of this model: {modals}') # initialize known species from dataset if 'auto' # sorted to alphabetical order (which is same as before) From fd9d5af3f8a94b8806aea2158951a440a62c57a8 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Fri, 1 Nov 2024 20:55:09 +0900 Subject: [PATCH 016/113] refactor: error recorder, loss definitions, record error directly using loss_defs --- sevenn/error_recorder.py | 95 +++++++++++++++++++++----- sevenn/scripts/processing_epoch.py | 4 +- sevenn/train/loss.py | 106 ++++++++++++++++------------- 3 files changed, 138 insertions(+), 67 deletions(-) diff --git a/sevenn/error_recorder.py b/sevenn/error_recorder.py index c35fd545..c6e128f2 100644 --- a/sevenn/error_recorder.py +++ b/sevenn/error_recorder.py @@ -5,6 +5,7 @@ import torch.distributed as dist import sevenn._keys as KEY +from sevenn.train.loss import LossDefinition from .atom_graph_data import AtomGraphData from .train.optim import loss_dict @@ -59,6 +60,13 @@ def get_err_type(name: str) -> dict[str, Any]: return deepcopy(_ERROR_TYPES[name]) +def _get_loss_function_from_name(loss_functions, name): + for loss_def, w in loss_functions: + if loss_def.name.lower() == name.lower(): + return loss_def, w + return None, None + + class AverageNumber: def __init__(self): self._sum = 0.0 @@ -96,7 +104,7 @@ def __init__( coeff: float = 1.0, unit: Optional[str] = None, per_atom: bool = False, - delete_unlabled: bool = True, + delete_unlabeled: bool = True, **kwargs, ): self.name = name @@ -105,7 +113,7 @@ def __init__( self.ref_key = ref_key self.pred_key = pred_key self.per_atom = per_atom - self.delete_unlabled = delete_unlabled + self.delete_unlabeled = delete_unlabeled self.value = AverageNumber() def update(self, output: AtomGraphData): @@ -119,7 +127,7 @@ def _retrieve(self, output: AtomGraphData): natoms = output[KEY.NUM_ATOMS] y_ref = y_ref / natoms y_pred = y_pred / natoms - if self.delete_unlabled: + if self.delete_unlabeled: unlabelled_idx = torch.isnan(y_ref) y_ref = y_ref[~unlabelled_idx] y_pred = y_pred[~unlabelled_idx] @@ -227,6 +235,29 @@ def update(self, output: AtomGraphData): self.value.update(se) +class LossError(ErrorMetric): + """ + Error metric that record loss + """ + + def __init__( + self, + name: str, + loss_def: LossDefinition, + **kwargs, + ): + super().__init__( + name, + delete_unlabeld=loss_def.delete_unlabeled, + **kwargs, + ) + self.loss_def = loss_def + + def update(self, output: AtomGraphData): + loss = self.loss_def.get_loss(output) # type: ignore + self.value.update(loss) # type: ignore + + class CombinedError(ErrorMetric): """ Combine multiple error metrics with weights @@ -266,7 +297,7 @@ class ErrorRecorder: 'RMSE': RMSError, 'ComponentRMSE': ComponentRMSError, 'MAE': MAError, - 'Loss': CustomError, + 'Loss': LossError, } def __init__(self, metrics: List[ErrorMetric]): @@ -320,29 +351,47 @@ def epoch_forward(self): return pretty # for print @staticmethod - def init_total_loss_metric(config, criteria): + def init_total_loss_metric( + config, + criteria: Optional[Callable] = None, + loss_functions: Optional[List[Tuple[LossDefinition, float]]] = None, + ): + if criteria is None and loss_functions is None: + raise ValueError('both criteria and loss functions not given') + is_stress = config[KEY.IS_TRAIN_STRESS] metrics = [] - energy_metric = CustomError(criteria, **get_err_type('Energy')) - metrics.append((energy_metric, 1)) - force_metric = CustomError(criteria, **get_err_type('Force')) - metrics.append((force_metric, config[KEY.FORCE_WEIGHT])) - if is_stress: - stress_metric = CustomError(criteria, **get_err_type('Stress')) - metrics.append((stress_metric, config[KEY.STRESS_WEIGHT])) + if criteria is not None: + energy_metric = CustomError(criteria, **get_err_type('Energy')) + metrics.append((energy_metric, 1)) + force_metric = CustomError(criteria, **get_err_type('Force')) + metrics.append((force_metric, config[KEY.FORCE_WEIGHT])) + if is_stress: + stress_metric = CustomError(criteria, **get_err_type('Stress')) + metrics.append((stress_metric, config[KEY.STRESS_WEIGHT])) + else: + for efs in ['Energy', 'Force', 'Stress']: + if efs == 'stress' and not is_stress: + continue + lf, w = _get_loss_function_from_name(loss_functions, efs) + if lf is None: + raise ValueError(f'{efs} not found from loss_functions') + metric = LossError(loss_def=lf, **get_err_type(efs)) + metrics.append((metric, w)) + total_loss_metric = CombinedError( metrics, name='TotalLoss', unit=None, ref_key=None, pred_key=None ) return total_loss_metric @staticmethod - def from_config(config: dict): + def from_config(config: dict, loss_functions=None): loss_cls = loss_dict[config[KEY.LOSS].lower()] try: loss_param = config[KEY.LOSS_PARAM] except KeyError: loss_param = {} - criteria = loss_cls(**loss_param) + criteria = loss_cls(**loss_param) if loss_functions is None else None err_config = config[KEY.ERROR_RECORD] err_config_n = [] @@ -358,14 +407,24 @@ def from_config(config: dict): metric_kwargs = get_err_type(err_type) if err_type == 'TotalLoss': # special case err_metrics.append( - ErrorRecorder.init_total_loss_metric(config, criteria) + ErrorRecorder.init_total_loss_metric( + config, criteria, loss_functions + ) ) continue metric_cls = ErrorRecorder.METRIC_DICT[metric_name] assert isinstance(metric_kwargs['name'], str) - metric_kwargs['name'] += f'_{metric_name}' if metric_name == 'Loss': - metric_kwargs['func'] = criteria - metric_kwargs['unit'] = None + if loss_functions is not None: + metric_cls = LossError + metric_kwargs['loss_def'], _ = _get_loss_function_from_name( + loss_functions, metric_kwargs['name'] + ) + else: + metric_cls = CustomError + metric_kwargs['func'] = criteria + print(metric_cls) + metric_kwargs.pop('unit', None) + metric_kwargs['name'] += f'_{metric_name}' err_metrics.append(metric_cls(**metric_kwargs)) return ErrorRecorder(err_metrics) diff --git a/sevenn/scripts/processing_epoch.py b/sevenn/scripts/processing_epoch.py index aabcb270..3b6be989 100644 --- a/sevenn/scripts/processing_epoch.py +++ b/sevenn/scripts/processing_epoch.py @@ -34,7 +34,9 @@ def processing_epoch_v2( total_epoch = total_epoch or config[KEY.EPOCH] per_epoch = per_epoch or config[KEY.PER_EPOCH] best_metric = best_metric or config[KEY.BEST_METRIC] - recorder = error_recorder or ErrorRecorder.from_config(config) + recorder = error_recorder or ErrorRecorder.from_config( + config, trainer.loss_functions + ) recorders = {k: deepcopy(recorder) for k in loaders} best_val = float('inf') diff --git a/sevenn/train/loss.py b/sevenn/train/loss.py index 964fd147..7bdcecd1 100644 --- a/sevenn/train/loss.py +++ b/sevenn/train/loss.py @@ -1,4 +1,4 @@ -from typing import Any, Callable, Dict, Optional +from typing import Any, Callable, Dict, Optional, Tuple import torch @@ -19,7 +19,7 @@ def __init__( ref_key: Optional[str] = None, pred_key: Optional[str] = None, use_weight: bool = False, - delete_unlabled: bool = True, + delete_unlabeled: bool = True, ): self.name = name self.unit = unit @@ -27,7 +27,7 @@ def __init__( self.ref_key = ref_key self.pred_key = pred_key self.use_weight = use_weight - self.delete_unlabled = delete_unlabled + self.delete_unlabeled = delete_unlabeled def __repr__(self): return self.name @@ -41,12 +41,20 @@ def _preprocess( self, batch_data: Dict[str, Any], model: Optional[Callable] = None - ): + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: if self.pred_key is None or self.ref_key is None: raise NotImplementedError('LossDefinition is not implemented.') - return torch.reshape(batch_data[self.pred_key], (-1,)), torch.reshape( - batch_data[self.ref_key], (-1,) - ) + pred = torch.reshape(batch_data[self.pred_key], (-1,)) + ref = torch.reshape(batch_data[self.ref_key], (-1,)) + return pred, ref, None + + def _delete_unlabeled(self, pred, ref, data_weights=None): + unlabeled = torch.isnan(ref) + pred = pred[~unlabeled] + ref = ref[~unlabeled] + if data_weights is not None: + data_weights = data_weights[~unlabeled] + return pred, ref, data_weights def get_loss( self, @@ -58,35 +66,17 @@ def get_loss( """ if self.criterion is None: raise NotImplementedError('LossDefinition has no criterion.') - pred, ref = self._preprocess(batch_data, model) - if self.use_weight: - vdim = {'energy': 1, 'force': 3, 'stress': 6} - loss_type = self.name.lower() - weight = batch_data[KEY.DATA_WEIGHT][loss_type] - weight_tensor = ( - weight[batch_data[KEY.BATCH]] - if loss_type == 'force' - else weight - ) - weight_tensor =\ - torch.repeat_interleave(weight_tensor, vdim[loss_type]) - - if self.delete_unlabled: - # nan in `pred`` should be deleted in other process - unlabled_idx = torch.isnan(ref) - pred = pred[~unlabled_idx] - ref = ref[~unlabled_idx] - - if self.use_weight: - weight_tensor = weight_tensor[~unlabled_idx] - - if len(pred) > 0: # more than one ref value is labelled. - loss = self.criterion(pred, ref) - if self.use_weight: - loss = torch.mean(loss * weight_tensor) - else: - loss = None + pred, ref, w_tensor = self._preprocess(batch_data, model) + + if self.delete_unlabeled: + pred, ref, w_tensor = self._delete_unlabeled(pred, ref, w_tensor) + + if len(pred) == 0: + return None + loss = self.criterion(pred, ref) + if self.use_weight: + loss = torch.mean(loss * w_tensor) return loss @@ -120,10 +110,16 @@ def _preprocess( ): num_atoms = batch_data[KEY.NUM_ATOMS] assert isinstance(self.pred_key, str) and isinstance(self.ref_key, str) - return ( - batch_data[self.pred_key] / num_atoms, - batch_data[self.ref_key] / num_atoms, - ) + pred = batch_data[self.pred_key] / num_atoms + ref = batch_data[self.ref_key] / num_atoms + w_tensor = None + + if self.use_weight: + loss_type = self.name.lower() + weight = batch_data[KEY.DATA_WEIGHT][loss_type] + w_tensor = torch.repeat_interleave(weight, 1) + + return pred, ref, w_tensor class ForceLoss(LossDefinition): @@ -155,10 +151,17 @@ def _preprocess( model: Optional[Callable] = None ): assert isinstance(self.pred_key, str) and isinstance(self.ref_key, str) - return ( - torch.reshape(batch_data[self.pred_key], (-1,)), - torch.reshape(batch_data[self.ref_key], (-1,)), - ) + pred = torch.reshape(batch_data[self.pred_key], (-1,)) + ref = torch.reshape(batch_data[self.ref_key], (-1,)) + w_tensor = None + + if self.use_weight: + loss_type = self.name.lower() + weight = batch_data[KEY.DATA_WEIGHT][loss_type] + w_tensor = weight[batch_data[KEY.BATCH]] + w_tensor = torch.repeat_interleave(w_tensor, 3) + + return pred, ref, w_tensor class StressLoss(LossDefinition): @@ -191,10 +194,17 @@ def _preprocess( model: Optional[Callable] = None ): assert isinstance(self.pred_key, str) and isinstance(self.ref_key, str) - return ( - torch.reshape(batch_data[self.pred_key] * self.TO_KB, (-1,)), - torch.reshape(batch_data[self.ref_key] * self.TO_KB, (-1,)), - ) + + pred = torch.reshape(batch_data[self.pred_key] * self.TO_KB, (-1,)) + ref = torch.reshape(batch_data[self.ref_key] * self.TO_KB, (-1,)) + w_tensor = None + + if self.use_weight: + loss_type = self.name.lower() + weight = batch_data[KEY.DATA_WEIGHT][loss_type] + w_tensor = torch.repeat_interleave(weight, 6) + + return pred, ref, w_tensor def get_loss_functions_from_config(config: Dict[str, Any]): @@ -218,7 +228,7 @@ def get_loss_functions_from_config(config: Dict[str, Any]): if config[KEY.IS_TRAIN_STRESS]: loss_functions.append((StressLoss(**commons), config[KEY.STRESS_WEIGHT])) - for loss_function, _ in loss_functions: + for loss_function, _ in loss_functions: # why do these? if loss_function.criterion is None: loss_function.assign_criteria(criterion) From 22170f20458849c041df6fe99729719ff7a4260b Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:50:24 +0900 Subject: [PATCH 017/113] refactor: delete_unlabeled -> ignore_unlabeled --- sevenn/error_recorder.py | 8 ++++---- sevenn/train/loss.py | 21 ++++++++++----------- 2 files changed, 14 insertions(+), 15 deletions(-) diff --git a/sevenn/error_recorder.py b/sevenn/error_recorder.py index c6e128f2..e2684bdb 100644 --- a/sevenn/error_recorder.py +++ b/sevenn/error_recorder.py @@ -104,7 +104,7 @@ def __init__( coeff: float = 1.0, unit: Optional[str] = None, per_atom: bool = False, - delete_unlabeled: bool = True, + ignore_unlabeled: bool = True, **kwargs, ): self.name = name @@ -113,7 +113,7 @@ def __init__( self.ref_key = ref_key self.pred_key = pred_key self.per_atom = per_atom - self.delete_unlabeled = delete_unlabeled + self.ignore_unlabeled = ignore_unlabeled self.value = AverageNumber() def update(self, output: AtomGraphData): @@ -127,7 +127,7 @@ def _retrieve(self, output: AtomGraphData): natoms = output[KEY.NUM_ATOMS] y_ref = y_ref / natoms y_pred = y_pred / natoms - if self.delete_unlabeled: + if self.ignore_unlabeled: unlabelled_idx = torch.isnan(y_ref) y_ref = y_ref[~unlabelled_idx] y_pred = y_pred[~unlabelled_idx] @@ -248,7 +248,7 @@ def __init__( ): super().__init__( name, - delete_unlabeld=loss_def.delete_unlabeled, + ignore_unlabeld=loss_def.ignore_unlabeled, **kwargs, ) self.loss_def = loss_def diff --git a/sevenn/train/loss.py b/sevenn/train/loss.py index 7bdcecd1..0d3bbf7d 100644 --- a/sevenn/train/loss.py +++ b/sevenn/train/loss.py @@ -19,7 +19,7 @@ def __init__( ref_key: Optional[str] = None, pred_key: Optional[str] = None, use_weight: bool = False, - delete_unlabeled: bool = True, + ignore_unlabeled: bool = True, ): self.name = name self.unit = unit @@ -27,7 +27,7 @@ def __init__( self.ref_key = ref_key self.pred_key = pred_key self.use_weight = use_weight - self.delete_unlabeled = delete_unlabeled + self.ignore_unlabeled = ignore_unlabeled def __repr__(self): return self.name @@ -48,7 +48,7 @@ def _preprocess( ref = torch.reshape(batch_data[self.ref_key], (-1,)) return pred, ref, None - def _delete_unlabeled(self, pred, ref, data_weights=None): + def _ignore_unlabeled(self, pred, ref, data_weights=None): unlabeled = torch.isnan(ref) pred = pred[~unlabeled] ref = ref[~unlabeled] @@ -68,8 +68,8 @@ def get_loss( raise NotImplementedError('LossDefinition has no criterion.') pred, ref, w_tensor = self._preprocess(batch_data, model) - if self.delete_unlabeled: - pred, ref, w_tensor = self._delete_unlabeled(pred, ref, w_tensor) + if self.ignore_unlabeled: + pred, ref, w_tensor = self._ignore_unlabeled(pred, ref, w_tensor) if len(pred) == 0: return None @@ -213,15 +213,14 @@ def get_loss_functions_from_config(config: Dict[str, Any]): loss_functions = [] # list of tuples (loss_definition, weight) loss = loss_dict[config[KEY.LOSS].lower()] - try: - loss_param = config[KEY.LOSS_PARAM] - except KeyError: - loss_param = {} - if config[KEY.USE_WEIGHT]: + loss_param = config.get(KEY.LOSS_PARAM, {}) + + use_weight = config.get(KEY.USE_WEIGHT, False) + if use_weight: loss_param['reduction'] = 'none' criterion = loss(**loss_param) - commons = {'use_weight': config[KEY.USE_WEIGHT]} + commons = {'use_weight': use_weight} loss_functions.append((PerAtomEnergyLoss(**commons), 1.0)) loss_functions.append((ForceLoss(**commons), config[KEY.FORCE_WEIGHT])) From fd5f1d6a8603833c1bc3f5b7d00c753191c2bfc8 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:52:36 +0900 Subject: [PATCH 018/113] refactor: use_modality, get optional --- sevenn/main/sevenn_get_model.py | 15 ++++++++------- sevenn/model_build.py | 16 ++++++---------- 2 files changed, 14 insertions(+), 17 deletions(-) diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py index 129515ae..9e996186 100644 --- a/sevenn/main/sevenn_get_model.py +++ b/sevenn/main/sevenn_get_model.py @@ -22,14 +22,13 @@ def main(args=None): - checkpoint, output_prefix, get_parallel, modal, save_cp =\ - cmd_parse_get_model(args) + checkpoint, output_prefix, get_parallel, modal, save_cp = cmd_parse_get_model( + args + ) get_serial = not get_parallel if output_prefix is None: - output_prefix = ( - 'deployed_parallel' if not get_serial else 'deployed_serial' - ) + output_prefix = 'deployed_parallel' if not get_serial else 'deployed_serial' checkpoint_path = None if os.path.isfile(checkpoint): @@ -40,8 +39,10 @@ def main(args=None): model, config = sevenn.util.model_from_checkpoint(checkpoint_path) stct_dct = model.state_dict() - if KEY.USE_MODALITY in config.keys() and config[KEY.USE_MODALITY]: - stct_dct = get_single_modal_model_dct(stct_dct, config, modal, is_deploy=True) + if config.get(KEY.USE_MODALITY, False): + stct_dct = get_single_modal_model_dct( + stct_dct, config, modal, is_deploy=True + ) output_prefix = modal + '_' + output_prefix if save_cp: cp_file = torch.load(checkpoint_path, map_location='cpu') diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 7ac64f85..8fee3482 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -138,24 +138,20 @@ def init_shift_scale(config): type_map = config[KEY.TYPE_MAP] # in case of modal, shift or scale has more dims [][] - print(config[KEY.SHIFT]) - print(config[KEY.SCALE]) # correct typing (I really want static python) for s in (config[KEY.SHIFT], config[KEY.SCALE]): if hasattr(s, 'tolist'): # numpy or torch s = s.tolist() - if isinstance(s, list) and len(s) == 1: - s = s[0] shift_scale.append(s) + shift, scale = shift_scale rescale_module = None - shift, scale = shift_scale - if config[KEY.USE_MODALITY]: + if config.get(KEY.USE_MODALITY, False): rescale_module = ModalWiseRescale.from_mappers( # type: ignore shift, scale, config[KEY.USE_MODAL_WISE_SHIFT], - config[KEY.USE_BIAS_IN_LINEAR], + config[KEY.USE_MODAL_WISE_SCALE], type_map=type_map, modal_map=config[KEY.MODAL_MAP], train_shift_scale=train_shift_scale, @@ -180,7 +176,7 @@ def patch_modality(layers: OrderedDict, config): Modality aware shift scale is handled by init_shift_scale, not here """ cfg = config - if not cfg[KEY.USE_MODALITY]: + if not cfg.get(KEY.USE_MODALITY, False): return layers _layers = list(layers.items()) @@ -221,7 +217,7 @@ def patch_modality(layers: OrderedDict, config): def _to_parallel_model(layers: OrderedDict, config): num_classes = layers['onehot_idx_to_onehot'].num_classes one_hot_irreps = Irreps(f'{num_classes}x0e') - irreps_node_zero = layers['onehot_to_feature_x'].linear.irreps_out + irreps_node_zero = layers['onehot_to_feature_x'].irreps_out _layers = list(layers.items()) layers_list = [] @@ -459,7 +455,7 @@ def build_E3_equivariant_model(config: dict, parallel=False): 'modal_map': config.get(KEY.MODAL_MAP, None), 'eval_type_map': False if parallel else True, 'eval_modal_map': False - if not config[KEY.USE_MODALITY] or parallel + if not config.get(KEY.USE_MODALITY, False) or parallel else True, 'data_key_grad': grad_key, } From 5ec9b89c8e3fed587af8540e88f76bf396b83095 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:55:03 +0900 Subject: [PATCH 019/113] fix,refactor: from mapper --- sevenn/nn/scale.py | 72 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 50 insertions(+), 22 deletions(-) diff --git a/sevenn/nn/scale.py b/sevenn/nn/scale.py index 883d392b..7e7af60b 100644 --- a/sevenn/nn/scale.py +++ b/sevenn/nn/scale.py @@ -102,7 +102,7 @@ def from_mappers( shift_scale = [] n_atom_types = len(type_map) for s in (shift, scale): - if isinstance(s, list) and len(s) > len(type_map): + if isinstance(s, list) and len(s) > n_atom_types: if len(s) != NUM_UNIV_ELEMENT: raise ValueError('given shift or scale is strange') s = [s[z] for z in sorted(type_map, key=lambda x: type_map[x])] @@ -190,40 +190,68 @@ def solve_mapper(arr, map): (scale, use_modal_wise_scale), ): # solve elemewise, or broadcast - shape = (n_modals, n_atom_types) if use_mw else (n_atom_types,) if isinstance(s, float): - res = torch.full(shape, s).tolist() # TODO: with plain python + # given, modal-wise: no, elem-wise: no => broadcast + shape = (n_modals, n_atom_types) if use_mw else (n_atom_types,) + res = torch.full(shape, s).tolist() # TODO: w/o torch elif isinstance(s, list) and len(s) == NUM_UNIV_ELEMENT: - # element-wise list + # given, modal-wise: no, elem-wise: yes(univ) => solve elem map s = solve_mapper(s, type_map) res = [s] * n_modals if use_mw else s - elif isinstance(s, list) and len(s) == len(modal_map): - # modal-wise list, for element-wise, use same value - res = [[v] * len(type_map) for v in s] + elif ( # given, modal-wise: yes, elem-wise: no => broadcast to elemwise + isinstance(s, list) + and isinstance(s[0], float) + and len(s) == n_modals + and use_mw + ): + res = [[v] * n_atom_types for v in s] + elif ( # given, modal-wise: no, elem-wise: yes => as it is + isinstance(s, list) + and isinstance(s[0], float) + and len(s) == n_atom_types + and not use_mw + ): + res = s + elif ( # given, modal-wise: yes, elem-wise: yes => as it is + isinstance(s, list) + and isinstance(s[0], list) + and len(s) == n_modals + and len(s[0]) == n_atom_types + and use_mw + ): + res = s elif isinstance(s, dict) and use_mw: - # solve modal dict + # solve modal dict, modal-wise: yes s = solve_mapper(s, modal_map) if isinstance(s[0], list) and len(s[0]) == NUM_UNIV_ELEMENT: + # elem-wise: yes(univ) => solve elem map res = [solve_mapper(v, type_map) for v in s] - pass elif isinstance(s[0], float): - res = [[v] * len(type_map) for v in s] + # elem-wise: no => broadcast to elemwise + res = [[v] * n_atom_types for v in s] + elif isinstance(s[0], list) and len(s[0]) == n_atom_types: + # elem-wise: yes => not univ, already mapped => as it is + res = s else: - raise ValueError() + raise ValueError(f'Invalid shift or scale {s}') else: - raise ValueError() + raise ValueError(f'Invalid shift or scale {s}') + + if use_mw: + assert ( + isinstance(res, list) + and isinstance(res[0], list) + and len(res) == n_modals + and len(res[0]) == n_atom_types + ) + else: + assert ( + isinstance(res, list) + and isinstance(res[0], float) + and len(res) == n_atom_types + ) shift_scale.append(res) - print('bf') - print('shift:') - print(shift) - print('scale:') - print(scale) shift, scale = shift_scale - print('af') - print('shift:') - print(shift) - print('scale:') - print(scale) return ModalWiseRescale( shift, From 40bb640eaeb43560b7f8ee9d6116bc452eac87f5 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:55:34 +0900 Subject: [PATCH 020/113] fix: linear --- sevenn/nn/linear.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sevenn/nn/linear.py b/sevenn/nn/linear.py index 258e46c7..11a55381 100644 --- a/sevenn/nn/linear.py +++ b/sevenn/nn/linear.py @@ -36,7 +36,7 @@ def __init__( self.key_modal_attr = data_key_modal_attr self._irreps_in_wo_modal = irreps_in - self.irreps_in = irreps_in + self._irreps_in = irreps_in self.irreps_out = irreps_out self.e3nn_linear_params = e3nn_linear_params @@ -56,14 +56,15 @@ def set_num_modalities(self, num_modalities): raise ValueError('Layer already instantiated, can not change modalities') irreps_in = self._irreps_in_wo_modal + Irreps(f'{num_modalities}x0e') self.num_modalities = num_modalities - self.irreps_in = irreps_in + self._irreps_in = irreps_in def instantiate(self): if self.linear is not None: raise ValueError('Linear layer already exists') self.linear = Linear( - self.irreps_in, self.irreps_out, **self.e3nn_linear_params + self._irreps_in, self.irreps_out, **self.e3nn_linear_params ) + self.irreps_in = self._irreps_in self.layer_instantiated = True def _patch_modal_to_data(self, data: AtomGraphDataType) -> AtomGraphDataType: From 96286f00ad4b74084defddb062304a6b58adbf6b Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:56:52 +0900 Subject: [PATCH 021/113] fix: support modality model continue, some logging, refactor graph dataset --- sevenn/scripts/processing_continue.py | 8 ++++ sevenn/train/graph_dataset.py | 69 +++++++++++++++------------ 2 files changed, 46 insertions(+), 31 deletions(-) diff --git a/sevenn/scripts/processing_continue.py b/sevenn/scripts/processing_continue.py index fc2ac176..b487a704 100644 --- a/sevenn/scripts/processing_continue.py +++ b/sevenn/scripts/processing_continue.py @@ -64,6 +64,14 @@ def processing_continue_v2(config): # simpler + f'This model knows {config[KEY.NUM_SPECIES]} species' ) + modal_map = config_cp.get(KEY.MODAL_MAP, {}) + config.update({KEY.MODAL_MAP: modal_map}) + if len(modal_map) > 0: + modalities = list(modal_map.keys()) + log.writeline(f'Multimodal model found: {modalities}') + log.writeline(f'{KEY.USE_MODALITY}: True') + config[KEY.USE_MODALITY] = True + from_epoch = checkpoint['epoch'] log.writeline(f'Checkpoint previous epoch was: {from_epoch}') epoch = 1 if continue_dct[KEY.RESET_EPOCH] else from_epoch + 1 diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index f0a4cd66..66318f5b 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -227,15 +227,6 @@ def _load_meta(self) -> None: self._files = meta['files'] self.statistics = meta['statistics'] - def consistent_info_dict(self, strategy: str = 'largest', **default_kwargs): - info_dict_lists = [g[KEY.INFO] for g in self] - util.get_consistent_dict_list( - info_dict_lists, - in_place=True, - strategy=strategy, - key_defaults=default_kwargs, - ) - @property def raw_file_names(self) -> List[Any]: return self._files @@ -249,20 +240,22 @@ def processed_dir(self) -> str: return os.path.join(self.root, 'sevenn_data') @property - def full_file_list(self) -> List[str]: + def full_file_list(self) -> Union[List[str], None]: return self._full_file_list def process(self): graph_list: list[AtomGraphData] = [] for file in self.raw_file_names: tmplist = SevenNetGraphDataset.file_to_graph_list( - entry=file, + file=file, cutoff=self.cutoff, num_cores=self.process_num_cores, **self.process_kwargs, ) - # TODO: - # self._full_file_list.extend([os.path.abspath(file)] * len(tmplist)) + if isinstance(file, str) and self._full_file_list is not None: + self._full_file_list.extend([os.path.abspath(file)] * len(tmplist)) + else: + self._full_file_list = None graph_list.extend(tmplist) processed_graph_list = [] @@ -389,10 +382,22 @@ def _read_structure_list( @staticmethod def _read_ase_readable( - filename: str, cutoff: float, num_cores: int = 1, tag: str = '', **ase_kwargs + filename: str, + cutoff: float, + num_cores: int = 1, + tag: str = '', + transfer_info: bool = True, + allow_unlabeled: bool = False, + **ase_kwargs, ) -> list[AtomGraphData]: atoms_list = dataload.ase_reader(filename, **ase_kwargs) - graph_list = dataload.graph_build(atoms_list, cutoff, num_cores) + graph_list = dataload.graph_build( + atoms_list, + cutoff, + num_cores, + transfer_info=transfer_info, + allow_unlabeled=allow_unlabeled, + ) if tag != '': graph_list = _tag_graphs(graph_list, tag) return graph_list @@ -403,6 +408,8 @@ def _read_graph_dataset( ) -> list[AtomGraphData]: meta_f = filename.replace('.pt', '.yaml') orig_cutoff = cutoff + if not os.path.exists(filename): + raise FileNotFoundError(f'No such file: {filename}') if not os.path.exists(meta_f): warnings.warn('No meta info found, beware of cutoff...') else: @@ -466,32 +473,32 @@ def _read_dict( @staticmethod def file_to_graph_list( - entry: Union[str, dict], cutoff: float, num_cores: int = 1, **kwargs + file: Union[str, dict], cutoff: float, num_cores: int = 1, **kwargs ) -> List[AtomGraphData]: """ kwargs: if file is ase readable, passed to ase.io.read """ - if isinstance(entry, str) and not os.path.isfile(entry): - raise ValueError(f'No such file: {entry}') + if isinstance(file, str) and not os.path.isfile(file): + raise ValueError(f'No such file: {file}') graph_list: list[AtomGraphData] - if isinstance(entry, dict): + if isinstance(file, dict): graph_list = SevenNetGraphDataset._read_dict( - entry, cutoff, num_cores, **kwargs + file, cutoff, num_cores, **kwargs ) - elif entry.endswith('.pt'): - graph_list = SevenNetGraphDataset._read_graph_dataset(entry, cutoff) - elif entry.endswith('.sevenn_data'): - graph_list, cutoff_other = SevenNetGraphDataset._read_sevenn_data(entry) + elif file.endswith('.pt'): + graph_list = SevenNetGraphDataset._read_graph_dataset(file, cutoff) + elif file.endswith('.sevenn_data'): + graph_list, cutoff_other = SevenNetGraphDataset._read_sevenn_data(file) if cutoff_other != cutoff: - warnings.warn(f'Given {entry} has different {cutoff_other}!') + warnings.warn(f'Given {file} has different {cutoff_other}!') cutoff = cutoff_other - elif 'structure_list' in entry: + elif 'structure_list' in file: graph_list = SevenNetGraphDataset._read_structure_list( - entry, cutoff, num_cores + file, cutoff, num_cores ) else: graph_list = SevenNetGraphDataset._read_ase_readable( - entry, cutoff, num_cores, **kwargs + file, cutoff, num_cores, **kwargs ) return graph_list @@ -580,9 +587,9 @@ def from_config( dataset_args = { 'cutoff': config[KEY.CUTOFF], 'root': working_dir, - 'process_num_cores': config[KEY.PREPROCESS_NUM_CORES], - 'use_data_weight': config[KEY.USE_WEIGHT], - **config[KEY.DATA_FORMAT_ARGS], + 'process_num_cores': config.get(KEY.PREPROCESS_NUM_CORES, 1), + 'use_data_weight': config.get(KEY.USE_WEIGHT, False), + **config.get(KEY.DATA_FORMAT_ARGS, {}), } datasets = {} From 282ce6e02bb87e23c6aa80326f29f46ea4725fcc Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:57:36 +0900 Subject: [PATCH 022/113] fix,refactor: inference --- sevenn/main/sevenn_inference.py | 7 +++ sevenn/scripts/inference.py | 47 ++++++++-------- sevenn/train/dataload.py | 95 ++++++++++++++++++--------------- 3 files changed, 81 insertions(+), 68 deletions(-) diff --git a/sevenn/main/sevenn_inference.py b/sevenn/main/sevenn_inference.py index 58169dcb..4a5f0dad 100644 --- a/sevenn/main/sevenn_inference.py +++ b/sevenn/main/sevenn_inference.py @@ -55,6 +55,7 @@ def main(args=None): device, args.batch, args.save_graph, + args.allow_unlabeled, **fmt_kwargs, ) @@ -97,6 +98,12 @@ def cmd_parse_data(args=None): action='store_true', help='Additionally, save preprocessed graph as sevenn_data' ) + ag.add_argument( + '-au', + '--allow_unlabeled', + action='store_true', + help='Allow energy or force unlabeled data' + ) ag.add_argument( '--kwargs', nargs=argparse.REMAINDER, diff --git a/sevenn/scripts/inference.py b/sevenn/scripts/inference.py index d704682b..603436f3 100644 --- a/sevenn/scripts/inference.py +++ b/sevenn/scripts/inference.py @@ -174,6 +174,7 @@ def inference( device: str = 'cpu', batch_size: int = 4, save_graph: bool = False, + allow_unlabeled: bool = False, **data_kwargs, ) -> None: """ @@ -207,36 +208,30 @@ def inference( targets = [targets] full_file_list = [] - with tempfile.NamedTemporaryFile('w+') as tmp_file: - targets, unlabeled_file_list = _extract_unlabeled_data( - targets, tmp_file, **data_kwargs + if save_graph: + dataset = SevenNetGraphDataset( + cutoff=cutoff, + root=output_dir, + files=targets, + process_num_cores=num_workers, + processed_name='saved_graph.pt', + **data_kwargs, ) - if save_graph: - dataset = SevenNetGraphDataset( + full_file_list = dataset.full_file_list + else: + dataset = [] + for file in targets: + tmplist = SevenNetGraphDataset.file_to_graph_list( + file, cutoff=cutoff, - root=output_dir, - files=targets, - process_num_cores=num_workers, - processed_name='saved_graph.pt', + num_cores=num_workers, + allow_unlabeled=allow_unlabeled, **data_kwargs, ) - full_file_list = dataset.full_file_list - else: - dataset = [] - for file in targets: - tmplist = SevenNetGraphDataset.file_to_graph_list( - filename=file, - cutoff=cutoff, - num_cores=num_workers, - **data_kwargs, - ) - dataset.extend(tmplist) - full_file_list.extend([os.path.abspath(file)] * len(tmplist)) - if len(unlabeled_file_list) > 0: - full_file_list = full_file_list[: -len(unlabeled_file_list)] - full_file_list.extend(unlabeled_file_list) - assert len(full_file_list) == len(dataset) - _patch_data_info(dataset, full_file_list) # type: ignore + dataset.extend(tmplist) + full_file_list.extend([os.path.abspath(file)] * len(tmplist)) + if full_file_list is not None and len(full_file_list) == len(dataset): + _patch_data_info(dataset, full_file_list) # type: ignore loader = DataLoader(dataset, batch_size=batch_size, shuffle=False) model.to(device) diff --git a/sevenn/train/dataload.py b/sevenn/train/dataload.py index aad37624..22021253 100644 --- a/sevenn/train/dataload.py +++ b/sevenn/train/dataload.py @@ -86,6 +86,7 @@ def atoms_to_graph( cutoff: float, transfer_info: bool = True, y_from_calc: bool = False, + allow_unlabeled: bool = False, ): """ From ase atoms, return AtomGraphData as graph based on cutoff radius @@ -124,17 +125,14 @@ def atoms_to_graph( else: y_stress = y_stress.squeeze() else: - try: - y_energy = atoms.get_potential_energy(force_consistent=True) - except NotImplementedError: - y_energy = atoms.get_potential_energy() - y_force = atoms.get_forces(apply_constraint=False) - try: - y_stress = -1 * atoms.get_stress() # it ensures correct shape - y_stress = np.array(y_stress[[0, 1, 2, 5, 3, 4]]) - except RuntimeError: - y_stress = np.full((6,), np.nan) - assert y_stress.shape == (6,), 'If you see this, please report to the maintainer' + from_calc = _y_from_calc(atoms) + y_energy = from_calc['energy'] + y_force = from_calc['force'] + y_stress = from_calc['stress'] + assert y_stress.shape == (6,), 'If you see this, please raise a issue' + + if not allow_unlabeled and (np.isnan(y_energy) or np.isnan(y_force).any()): + raise ValueError('Unlabeled E or F found, set allow_unlabeled True') pos = atoms.get_positions() cell = np.array(atoms.get_cell()) @@ -180,12 +178,10 @@ def atoms_to_graph( if transfer_info and atoms.info is not None: info = copy.deepcopy(atoms.info) # save only metadata - # TODO: is it really necessary? info.pop('y_energy', None) info.pop('y_force', None) info.pop('y_stress', None) data[KEY.INFO] = info - else: data[KEY.INFO] = {} @@ -198,6 +194,7 @@ def graph_build( num_cores: int = 1, transfer_info: bool = True, y_from_calc: bool = False, + allow_unlabeled: bool = False, ) -> List[AtomGraphData]: """ parallel version of graph_build @@ -213,7 +210,10 @@ def graph_build( List[AtomGraphData]: list of AtomGraphData """ serial = num_cores == 1 - inputs = [(atoms, cutoff, transfer_info, y_from_calc) for atoms in atoms_list] + inputs = [ + (atoms, cutoff, transfer_info, y_from_calc, allow_unlabeled) + for atoms in atoms_list + ] if not serial: pool = mp.Pool(num_cores) @@ -234,6 +234,34 @@ def graph_build( return graph_list +def _y_from_calc(atoms: ase.Atoms): + ret = { + 'energy': np.nan, + 'force': np.full((len(atoms), 3), np.nan), + 'stress': np.full((6,), np.nan), + } + + if atoms.calc is None: + return ret + + try: + ret['energy'] = atoms.get_potential_energy(force_consistent=True) + except NotImplementedError: + ret['energy'] = atoms.get_potential_energy() + + try: + ret['force'] = atoms.get_forces(apply_constraint=False) + except NotImplementedError: + pass + + try: + y_stress = -1 * atoms.get_stress() # it ensures correct shape + ret['stress'] = np.array(y_stress[[0, 1, 2, 5, 3, 4]]) + except RuntimeError: + pass + return ret + + def _set_atoms_y( atoms_list: list[ase.Atoms], energy_key: Optional[str] = None, @@ -265,38 +293,23 @@ def _set_atoms_y( Ignore constraints like selective dynamics """ for atoms in atoms_list: - # access energy + from_calc = _y_from_calc(atoms) if energy_key is not None: - atoms.info['y_energy'] = atoms.info[energy_key] - del atoms.info[energy_key] + atoms.info['y_energy'] = atoms.info.pop(energy_key) else: - try: - atoms.info['y_energy'] = atoms.get_potential_energy( - force_consistent=True - ) - except NotImplementedError: - atoms.info['y_energy'] = atoms.get_potential_energy() - # access force + atoms.info['y_energy'] = from_calc['energy'] + if force_key is not None: - atoms.arrays['y_force'] = atoms.arrays[force_key] - del atoms.arrays[force_key] + atoms.arrays['y_force'] = atoms.arrays.pop(force_key) else: - atoms.arrays['y_force'] = atoms.get_forces(apply_constraint=False) - # access stress + atoms.arrays['y_force'] = from_calc['force'] + if stress_key is not None: - y_stress = -1 * atoms.info[stress_key] + y_stress = -1 * atoms.info.pop(stress_key) atoms.info['y_stress'] = np.array(y_stress[[0, 1, 2, 5, 3, 4]]) - del atoms.info[stress_key] else: - try: - # xx yy zz xy yz zx order - # We expect this is eV/A^3 unit - # (ASE automatically converts vasp kB to eV/A^3) - # So we restore it - y_stress = -1 * atoms.get_stress() - atoms.info['y_stress'] = np.array(y_stress[[0, 1, 2, 5, 3, 4]]) - except RuntimeError: - atoms.info['y_stress'] = np.full((6,), np.nan) + atoms.info['y_stress'] = from_calc['stress'] + return atoms_list @@ -319,9 +332,8 @@ def ase_reader( # Reader -def structure_list_reader(filename: str, format_outputs='vasp-out'): +def structure_list_reader(filename: str, format_outputs: Optional[str] = None): """ - Deprecated Read from structure_list using braceexpand and ASE Args: @@ -422,7 +434,6 @@ def parse_fileline(line): stct_lists += ase.io.read( expanded_filename, index=index_expr, - format=format_outputs, parallel=False, ) structures_dict[title] = stct_lists From 5b4008284e398035f0f9de76ba75826829bbe374 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:58:26 +0900 Subject: [PATCH 023/113] fix: modal dataset --- sevenn/train/modal_dataset.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/sevenn/train/modal_dataset.py b/sevenn/train/modal_dataset.py index 3d89629f..d7f61191 100644 --- a/sevenn/train/modal_dataset.py +++ b/sevenn/train/modal_dataset.py @@ -285,8 +285,9 @@ def from_config( ) train_set = datasets['trainset'] + modals = train_set.modals - modals = set() + _modals_all = set() chem_species = set() # print statistics of each dataset for name, dataset in datasets.items(): @@ -297,17 +298,24 @@ def from_config( log.format_k_v( '# structures (graph)', len(dataset.datasets[idx]), write=True ) - modals.update([modality]) + _modals_all.update([modality]) chem_species.update(dataset.species['total']) log.bar() - config[KEY.NUM_MODALITIES] = len(modals) - - modals = sorted(list(modals)) - modal_map = {modal: i for i, modal in enumerate(modals)} - config[KEY.MODAL_MAP] = modal_map + if (modal_map := config.get(KEY.MODAL_MAP, None)) is None: + modals = sorted(list(modals)) + modal_map = {modal: i for i, modal in enumerate(modals)} + config[KEY.MODAL_MAP] = modal_map log.writeline(f'Modalities of this model: {modals}') + if not _modals_all.issubset(modal_map): + raise ValueError( + f'Found modalities of datasets: {_modals_all} are not subset of {modals}' + + '. Use a sevenn_cp cli tool to append/assign modality' + ) + + config[KEY.NUM_MODALITIES] = len(modal_map) + # initialize known species from dataset if 'auto' # sorted to alphabetical order (which is same as before) chem_keys = [KEY.CHEMICAL_SPECIES, KEY.NUM_SPECIES, KEY.TYPE_MAP] @@ -346,7 +354,7 @@ def from_config( if e.args[0] == 'total': raise NotImplementedError( f'{k}: {input} does not support total statistics. ' - + f'Try to set modal-wise True or specify {k} manually' + + f'Set use_modal_wise_{k} True or specify numbers manually' ) else: raise e From 50c65b37b6c1b90617769646b9427e44e789c983 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:59:00 +0900 Subject: [PATCH 024/113] fix --- tests/unit_tests/test_cli.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/tests/unit_tests/test_cli.py b/tests/unit_tests/test_cli.py index c96a1091..b0df4400 100644 --- a/tests/unit_tests/test_cli.py +++ b/tests/unit_tests/test_cli.py @@ -137,7 +137,14 @@ def test_inference_unlabeled(atoms_hfo, tmp_path): ase.io.write(unlabeled, atoms_hfo) output_dir = tmp_path / 'inference_results' - cli_args = ['--output', str(output_dir), cp_0_path, labeled, unlabeled] + cli_args = [ + '--output', + str(output_dir), + '--allow_unlabeled', + cp_0_path, + labeled, + unlabeled, + ] with mock.patch('sys.argv', [f'{main}/sevenn_inference.py'] + cli_args): inference_main() From cb4635832920456d9f13dfd90cfa369d37dbeea6 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 14:59:20 +0900 Subject: [PATCH 025/113] add: modal dataset train --- sevenn/scripts/train.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/sevenn/scripts/train.py b/sevenn/scripts/train.py index d9c8c106..45f6dfb4 100644 --- a/sevenn/scripts/train.py +++ b/sevenn/scripts/train.py @@ -42,6 +42,7 @@ def train_v2(config, working_dir: str): """ import sevenn.train.atoms_dataset as atoms_dataset import sevenn.train.graph_dataset as graph_dataset + import sevenn.train.modal_dataset as modal_dataset from .processing_continue import processing_continue_v2 from .processing_epoch import processing_epoch_v2 @@ -62,7 +63,9 @@ def train_v2(config, working_dir: str): if config[KEY.CONTINUE][KEY.CHECKPOINT]: state_dicts, start_epoch = processing_continue_v2(config) - if config[KEY.DATASET_TYPE] == 'graph': + if config.get(KEY.USE_MODALITY, False): + datasets = modal_dataset.from_config(config, working_dir) + elif config[KEY.DATASET_TYPE] == 'graph': datasets = graph_dataset.from_config(config, working_dir) elif config[KEY.DATASET_TYPE] == 'atoms': datasets = atoms_dataset.from_config(config, working_dir) From 5940e3e1561d0a198a7c536ff40f1616647bf48d Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 15:20:33 +0900 Subject: [PATCH 026/113] chore --- sevenn/scripts/inference.py | 48 ------------------------------------- 1 file changed, 48 deletions(-) diff --git a/sevenn/scripts/inference.py b/sevenn/scripts/inference.py index 603436f3..4d56c51f 100644 --- a/sevenn/scripts/inference.py +++ b/sevenn/scripts/inference.py @@ -1,6 +1,5 @@ import csv import os -import tempfile from typing import IO, Iterable, List, Union import ase.io @@ -106,53 +105,6 @@ def per_atom_dct_list(dct, keys): writer.writerow(data) -def _extract_unlabeled_data(targets: List[str], tmp_file: IO, **data_kwargs): - # for only, ase readable, it may be unlabeled - # extract such files and returns graph list built with these - - def assign_dummy_y(atoms): - dummy = {'energy': np.nan, 'free_energy': np.nan} - dummy['forces'] = np.full((len(atoms), 3), np.nan) # type: ignore - dummy['stress'] = np.full((6,), np.nan) # type: ignore - calc = SinglePointCalculator(atoms, **dummy) - atoms = calc.get_atoms() - return calc.get_atoms() - - new_targets = [] - atoms_list_patched = [] - unlabeled_file_list = [] - for target in targets: - if not ( - not target.endswith('.pt') - and not target.endswith('.sevenn_data') - and 'structure_list' not in target - ): - new_targets.append(target) - continue - # it must be ase readable - try: - _ = dl.ase_reader(target, **data_kwargs) - new_targets.append(target) # No error occurred, target is labeled - except RuntimeError or KeyError: - # The data is not labeled - print( - f'{target} seems not labeled, dummy values will be used', - flush=True, - ) - atoms_list = ase.io.read(target, index=':') - for atoms in atoms_list: - atoms_patched = assign_dummy_y(atoms) - atoms_patched.info.update({'y_is_dummy': 'Yes'}) - atoms_list_patched.append(atoms_patched) - unlabeled_file_list.extend([target] * len(atoms_list)) - - if len(atoms_list_patched) > 0: - ase.io.write(tmp_file, atoms_list_patched, format='extxyz') - tmp_file.flush() - new_targets.append(tmp_file.name) - return new_targets, unlabeled_file_list - - def _patch_data_info( graph_list: Iterable[AtomGraphData], full_file_list: List[str] ) -> None: From 08f4056c7b593cf99ab708590f150f8c54b47d90 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 21:36:59 +0900 Subject: [PATCH 027/113] fix: modal + inference --- sevenn/main/sevenn_inference.py | 17 ++++++++++------- sevenn/scripts/inference.py | 20 +++++++++++++++----- 2 files changed, 25 insertions(+), 12 deletions(-) diff --git a/sevenn/main/sevenn_inference.py b/sevenn/main/sevenn_inference.py index 4a5f0dad..b30035ec 100644 --- a/sevenn/main/sevenn_inference.py +++ b/sevenn/main/sevenn_inference.py @@ -47,6 +47,9 @@ def main(args=None): k, v = kwarg.split('=') fmt_kwargs[k] = v + if args.save_graph and args.allow_unlabeled: + raise ValueError('save_graph and allow_unlabeled are mutually exclusive') + inference( cp, targets, @@ -56,6 +59,7 @@ def main(args=None): args.batch, args.save_graph, args.allow_unlabeled, + args.modal, **fmt_kwargs, ) @@ -104,19 +108,18 @@ def cmd_parse_data(args=None): action='store_true', help='Allow energy or force unlabeled data' ) - ag.add_argument( - '--kwargs', - nargs=argparse.REMAINDER, - help='will be passed to reader, or can be used to specify EFS key', - ) - ag.add_argument( '-m', '--modal', type=str, - default='common', + default=None, help='modality for multi-modal inference', ) + ag.add_argument( + '--kwargs', + nargs=argparse.REMAINDER, + help='will be passed to reader, or can be used to specify EFS key', + ) args = ag.parse_args() return args diff --git a/sevenn/scripts/inference.py b/sevenn/scripts/inference.py index 4d56c51f..0cd8f10e 100644 --- a/sevenn/scripts/inference.py +++ b/sevenn/scripts/inference.py @@ -1,18 +1,16 @@ import csv import os -from typing import IO, Iterable, List, Union +from typing import Iterable, List, Optional, Union -import ase.io import numpy as np -from ase.calculators.singlepoint import SinglePointCalculator from torch_geometric.loader import DataLoader from tqdm import tqdm import sevenn._keys as KEY -import sevenn.train.dataload as dl import sevenn.util as util from sevenn.atom_graph_data import AtomGraphData from sevenn.train.graph_dataset import SevenNetGraphDataset +from sevenn.train.modal_dataset import SevenNetMultiModalDataset def write_inference_csv(output_list, out): @@ -127,6 +125,7 @@ def inference( batch_size: int = 4, save_graph: bool = False, allow_unlabeled: bool = False, + modal: Optional[str] = None, **data_kwargs, ) -> None: """ @@ -156,6 +155,13 @@ def inference( model, _ = util.model_from_checkpoint(checkpoint) cutoff = model.cutoff + if modal: + if model.modal_map is None: + raise ValueError('Modality given, but model has no modal_map') + if modal not in model.modal_map: + _modals = list(model.modal_map.keys()) + raise ValueError(f'Unknown modal {modal} (not in {_modals})') + if isinstance(targets, str): targets = [targets] @@ -184,7 +190,11 @@ def inference( full_file_list.extend([os.path.abspath(file)] * len(tmplist)) if full_file_list is not None and len(full_file_list) == len(dataset): _patch_data_info(dataset, full_file_list) # type: ignore - loader = DataLoader(dataset, batch_size=batch_size, shuffle=False) + + if modal: + dataset = SevenNetMultiModalDataset({modal: dataset}) # type: ignore + + loader = DataLoader(dataset, batch_size, shuffle=False) # type: ignore model.to(device) model.set_is_batch_data(True) From 960c389ea4cc5a735058c154220a0d6d5f39d59e Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 21:37:31 +0900 Subject: [PATCH 028/113] refactor: no batch data for scale --- sevenn/nn/scale.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/sevenn/nn/scale.py b/sevenn/nn/scale.py index 7e7af60b..4b42de2d 100644 --- a/sevenn/nn/scale.py +++ b/sevenn/nn/scale.py @@ -158,6 +158,7 @@ def __init__( self.key_modal_indices = data_key_modal_indices self.use_modal_wise_shift = use_modal_wise_shift self.use_modal_wise_scale = use_modal_wise_scale + self._is_batch_data = True @staticmethod def from_mappers( @@ -262,8 +263,11 @@ def solve_mapper(arr, map): ) def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: - batch = data[KEY.BATCH] - modal_indices = data[self.key_modal_indices][batch] + if self._is_batch_data: + batch = data[KEY.BATCH] + modal_indices = data[self.key_modal_indices][batch] + else: + modal_indices = data[self.key_modal_indices] atom_indices = data[self.key_atom_indices] shift = ( self.shift[modal_indices, atom_indices] From da55095c1588b5ccf21ee1673ac20606d0cfe315 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 21:37:42 +0900 Subject: [PATCH 029/113] chore --- sevenn/nn/sequential.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index 51913568..00cb6705 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -142,7 +142,7 @@ def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: data[self.key_node_feature] = onehot if self.eval_modal_map: - self._eval_modal_map(input) + self._eval_modal_map(data) if self.key_grad is not None: data[self.key_grad].requires_grad_(True) From c4011f75dfb4a608e7187a36370401cc707fef81 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 21:38:55 +0900 Subject: [PATCH 030/113] chore, change: remove dead code + print model config warning only when SEVENN_DEBUG envar is set --- sevenn/util.py | 76 ++++---------------------------------------------- 1 file changed, 6 insertions(+), 70 deletions(-) diff --git a/sevenn/util.py b/sevenn/util.py index 21b8a0d0..6b98251f 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -1,6 +1,6 @@ import os import warnings -from typing import Any, Dict, List, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch @@ -73,72 +73,6 @@ def error_recorder_from_loss_functions(loss_functions): return ErrorRecorder(metrics) -def postprocess_output(output, loss_types, use_weight=False, delete_unlabeld=True): - from ._const import LossType - - """ - Postprocess output from model to be used for loss calculation - Flatten all the output & unit converting and store them as (pred, ref, vdim) - Averaging them without care of vdim results in component-wise something - Args: - output (dict): output from model - loss_types (list): list of loss types to be calculated - use_weight (bool): if True, trying to multiply weight to output. - Used for training only. - delete_unlabeld (bool): if True, delete unlabeld (i.e. nan) data - from reference. - Returns: - results (dict): dictionary of loss type and its corresponding - """ - TO_KB = 1602.1766208 # eV/A^3 to kbar - results = {} - weight_tensor = None - for loss_type in loss_types: - is_valid = True - if loss_type is LossType.ENERGY: - # dim: (num_batch) - num_atoms = output[KEY.NUM_ATOMS] - pred = output[KEY.PRED_TOTAL_ENERGY] / num_atoms - ref = output[KEY.ENERGY] / num_atoms - vdim = 1 - elif loss_type is LossType.FORCE: - # dim: (total_number_of_atoms_over_batch, 3) - pred = torch.reshape(output[KEY.PRED_FORCE], (-1,)) - ref = torch.reshape(output[KEY.FORCE], (-1,)) - vdim = 3 - elif loss_type is LossType.STRESS: - # dim: (num_batch, 6) - # calculate stress loss based on kB unit (was eV/A^3) - pred = torch.reshape(output[KEY.PRED_STRESS] * TO_KB, (-1,)) - ref = torch.reshape(output[KEY.STRESS] * TO_KB, (-1,)) - vdim = 6 - else: - raise ValueError(f'Unknown loss type: {loss_type}') - - if use_weight: - weight = output[KEY.DATA_WEIGHT][loss_type.value] - weight_tensor = ( - weight[output[KEY.BATCH]] if loss_type is LossType.FORCE else weight - ) - weight_tensor = torch.repeat_interleave(weight_tensor, vdim) - - if delete_unlabeld: - # nan in pred might not be deleted, which is natural. - # This must be done after multiplying weight. - unlabeld_idx = torch.isnan(ref) - pred = pred[~unlabeld_idx] - ref = ref[~unlabeld_idx] - - if len(pred) == 0: - is_valid = False # not a valid error, erase for loss.backward - - if use_weight: - weight_tensor = weight_tensor[~unlabeld_idx] - - results[loss_type] = (pred, ref, vdim, is_valid, weight_tensor) - return results - - def onehot_to_chem(one_hot_indices: List[int], type_map: Dict[int, int]): from ase.data import chemical_symbols @@ -217,9 +151,11 @@ def model_from_checkpoint(checkpoint) -> Tuple[torch.nn.Module, Dict]: config = _patch_old_config(config) for k, v in defaults.items(): - if k not in config: - warnings.warn(f'{k} not in config, using default value {v}', UserWarning) - config[k] = v + if k in config: + continue + if os.getenv('SEVENN_DEBUG', False): + warnings.warn(f'{k} not in config, use default value {v}', UserWarning) + config[k] = v # expect only non-tensor values in config, if exists, move to cpu # This can be happen if config has torch tensor as value (shift, scale) From 56a7796cddc008edbc093156c9dc132c30db6645 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 21:49:15 +0900 Subject: [PATCH 031/113] fix,refactor: raise error for unknown chem + modality --- sevenn/sevennet_calculator.py | 39 ++++++++++++++++++++++++----------- 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/sevenn/sevennet_calculator.py b/sevenn/sevennet_calculator.py index ddeeb197..db135552 100644 --- a/sevenn/sevennet_calculator.py +++ b/sevenn/sevennet_calculator.py @@ -37,7 +37,7 @@ def __init__( model: Union[str, pathlib.PurePath, AtomGraphSequential] = '7net-0', file_type: str = 'checkpoint', device: Union[torch.device, str] = 'auto', - modal_selection=None, + modal: Optional[str] = None, sevennet_config: Optional[Any] = None, # hold meta information **kwargs, ): @@ -77,7 +77,10 @@ def __init__( self.type_map = config[KEY.TYPE_MAP] self.cutoff = config[KEY.CUTOFF] self.sevennet_config = config + elif file_type == 'torchscript' and isinstance(model, str): + if modal: + raise NotImplementedError() extra_dict = { 'chemical_symbols_to_index': b'', 'cutoff': b'', @@ -96,6 +99,7 @@ def __init__( sym_to_num[sym]: i for i, sym in enumerate(chem_symbols.split()) } self.cutoff = float(extra_dict['cutoff'].decode('utf-8')) + elif isinstance(model, AtomGraphSequential): if model.type_map is None: raise ValueError( @@ -108,18 +112,26 @@ def __init__( model_loaded = model self.type_map = model.type_map self.cutoff = model.cutoff + else: - raise ValueError('Unexpected input combinations') + raise ValueError('Unexpected input combination') if self.sevennet_config is None and sevennet_config is not None: self.sevennet_config = sevennet_config self.model = model_loaded + if isinstance(self.model, AtomGraphSequential) and modal: + if self.model.modal_map is None: + raise ValueError('Modality given, but model has no modal_map') + if modal not in self.model.modal_map: + _modals = list(self.model.modal_map.keys()) + raise ValueError(f'Unknown modal {modal} (not in {_modals})') + self.model.to(self.device) self.model.eval() - self.modal_selection = modal_selection + self.modal = modal self.implemented_properties = [ 'free_energy', @@ -129,6 +141,16 @@ def __init__( 'energies', ] + def set_atoms(self, atoms): + # called by ase, when atoms.calc = calc + zs = tuple(set(atoms.get_atomic_numbers())) + for z in zs: + if z not in self.type_map: + sp = list(self.type_map.keys()) + raise ValueError( + f'Model do not know atomic number: {z}, (knows: {sp})' + ) + def calculate(self, atoms=None, properties=None, system_changes=all_changes): # call parent class to set necessary atom attributes Calculator.calculate(self, atoms, properties, system_changes) @@ -137,15 +159,8 @@ def calculate(self, atoms=None, properties=None, system_changes=all_changes): data = AtomGraphData.from_numpy_dict( unlabeled_atoms_to_graph(atoms, self.cutoff) ) - if self.modal_selection is not None: - modal_type_mapper = self.sevennet_config[KEY.MODAL_MAP] - num_modalities = len(modal_type_mapper) - modal_idx = modal_type_mapper[self.modal_selection] - tmp_tensor = torch.zeros(num_modalities) - tmp_tensor[modal_idx] = 1.0 - data[KEY.MODAL_ATTR] = tmp_tensor - data[KEY.MODAL_TYPE] = torch.tensor([modal_idx]) - data[KEY.BATCH] = [0] * len(atoms) + if self.modal: + data[KEY.DATA_MODALITY] = self.modal data.to(self.device) # type: ignore if isinstance(self.model, torch_script_type): From 65148ae3a2cd384f94e70b8de95b6814dedd671f Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 2 Nov 2024 22:03:43 +0900 Subject: [PATCH 032/113] add: use_weight for atoms dataset --- sevenn/train/atoms_dataset.py | 60 +++++++++++++++++++++++------------ 1 file changed, 40 insertions(+), 20 deletions(-) diff --git a/sevenn/train/atoms_dataset.py b/sevenn/train/atoms_dataset.py index cef1ba1a..63458acc 100644 --- a/sevenn/train/atoms_dataset.py +++ b/sevenn/train/atoms_dataset.py @@ -17,9 +17,25 @@ from sevenn._const import NUM_UNIV_ELEMENT from sevenn.atom_graph_data import AtomGraphData +_warn_avg_num_neigh = """SevenNetAtomsDataset does not provide correct avg_num_neigh +as it does not build graph. We will compute only random 10000 structures graph to +approximate this value. If you want more precise avg_num_neigh, +use SevenNetGraphDataset. If it is not viable due to memory limit, you +need online algorithm to do this , which is not yet implemented in the SevenNet""" + class SevenNetAtomsDataset(torch.utils.data.Dataset): - """ """ + """ + Args: + cutoff: edge cutoff of given AtomGraphData + files: list of filenames or dict describing how to parse the file + ASE readable (with proper extension), structure_list, .sevenn_data, + dict containing file_list (see dict_reader of train/dataload.py) + info_dict_copy_keys: patch these keys from KEY.INFO to graph when accessing. + default is KEY.DATA_WEIGHT and KEY.DATA_MODALITY, which may accessed + while training. + **process_kwargs: keyword arguments that will be passed into ase.io.read + """ def __init__( self, @@ -27,7 +43,8 @@ def __init__( files: Union[str, List[str]], atoms_filter: Optional[Callable] = None, atoms_transform: Optional[Callable] = None, - graph_transform: Optional[Callable] = None, + transform: Optional[Callable] = None, + use_data_weight: bool = False, **process_kwargs, ): self.cutoff = cutoff @@ -36,8 +53,9 @@ def __init__( files = [os.path.abspath(file) for file in files] self._files = files self.atoms_filter = atoms_filter - self.atoms_trasform = atoms_transform - self.graph_trasform = graph_transform + self.atoms_transform = atoms_transform + self.transform = transform + self.use_data_weight = use_data_weight self._scanned = False self._avg_num_neigh_approx = None self.statistics = {} @@ -52,14 +70,16 @@ def __init__( super().__init__() @staticmethod - def file_to_atoms_list(filename: str, **kwargs) -> List[Atoms]: - if 'structure_list' in filename: - atoms_dct = dataload.structure_list_reader(filename) + def file_to_atoms_list(file: Union[str, dict], **kwargs) -> List[Atoms]: + if isinstance(file, dict): + atoms_list = dataload.dict_reader(file) + elif 'structure_list' in file: + atoms_dct = dataload.structure_list_reader(file) atoms_list = [] for lst in atoms_dct.values(): atoms_list.extend(lst) else: - atoms_list = dataload.ase_reader(filename, **kwargs) + atoms_list = dataload.ase_reader(file, **kwargs) return atoms_list def save(self, path): @@ -76,12 +96,18 @@ def __len__(self): def __getitem__(self, index): atoms = self._atoms_list[index] - if self.atoms_trasform is not None: - atoms = self.atoms_trasform(atoms) + if self.atoms_transform is not None: + atoms = self.atoms_transform(atoms) graph = self._graph_build(atoms) - if self.graph_trasform is not None: - graph = self.graph_trasform(graph) + if self.transform is not None: + graph = self.transform(graph) + + if self.use_data_weight: + weight = graph[KEY.INFO].pop( + KEY.DATA_WEIGHT, {'energy': 1.0, 'force': 1.0, 'stress': 1.0} + ) + graph[KEY.DATA_WEIGHT] = weight return AtomGraphData.from_numpy_dict(graph) @@ -131,14 +157,7 @@ def per_atom_energy_std(self): def avg_num_neigh(self, n_sample=10000): if self._avg_num_neigh_approx is None: if len(self) > n_sample: - warnings.warn( - """SevenNetAtomsDataset does not provide correct avg_num_neigh - as it does not build graph. We will compute only random 10000 - structures graph to approximate this value. If you want more - precise avg_num_neigh, use SevenNetGraphDataset. If it is not - viable due to memory limit, you need online algorithm to do this - , which is not yet implemented in the SevenNet""" - ) + warnings.warn(_warn_avg_num_neigh) n_sample = min(len(self), n_sample) indices = random.sample(range(len(self)), n_sample) n_neigh = [] @@ -233,6 +252,7 @@ def from_config( # initialize arguments for loading dataset dataset_args = { 'cutoff': config[KEY.CUTOFF], + 'use_data_weight': config.get(KEY.USE_WEIGHT, False), **config[KEY.DATA_FORMAT_ARGS], } From d80b80d6fbde9988bba8210b7866f68adcdeacc9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 2 Nov 2024 13:19:43 +0000 Subject: [PATCH 033/113] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- sevenn/scripts/convert_model_modality.py | 4 ++-- sevenn/train/graph_dataset.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py index 02ade5ee..cb5e4308 100644 --- a/sevenn/scripts/convert_model_modality.py +++ b/sevenn/scripts/convert_model_modality.py @@ -148,7 +148,7 @@ def get_single_modal_model_dct( """ Convert multimodal model state dictionary to single modal model. Modal is selected by `ref_modal` - + `model_state_dct`: model state dictionary from multimodal checkpoint file `config`: dictionary containing configuration of the checkpoint model `ref_modal`: modal that are going to be converted @@ -258,7 +258,7 @@ def append_modality_to_model_dct( """ Append modal-wise parameters to the original linear layers. This enables expanding modal to single/multi modal model checkpoint. - + `model_state_dct`: model state dictionary from multimodal checkpoint file `config`: dictionary containing configuration of the checkpoint model `orig_num_modal`: Number of modality used in original checkpoint diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index b0f6cb56..5fce4d6b 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -203,7 +203,7 @@ def __init__( def load(self, path: str, data_cls=Data) -> None: super().load(path, data_cls) - + if len(self) == 0: warnings.warn(f'No graphs found {self.processed_paths[0]}') if len(self.statistics) == 0: From a11118c370adc961b31ba5e86c6c08f7e442de0c Mon Sep 17 00:00:00 2001 From: YutackPark Date: Tue, 5 Nov 2024 22:23:20 +0900 Subject: [PATCH 034/113] version up + dev suffix --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5c846615..700b806e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "sevenn" -version = "0.10.1" +version = "0.11.0.dev" authors = [ { name = "Yutack Park", email = "parkyutack@snu.ac.kr" }, { name = "Jaesun Kim" }, From 4287ca6619fc5cccef7945de7b8c49f904719f80 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 10:13:49 +0900 Subject: [PATCH 035/113] bugfix: replace pt, .pt --- sevenn/train/graph_dataset.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index 5fce4d6b..2c3df1bd 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -181,7 +181,7 @@ def __init__( processed_name += '.pt' self._processed_names = [ processed_name, # {root}/sevenn_data/{name}.pt - processed_name.replace('pt', 'yaml'), + processed_name.replace('.pt', '.yaml'), ] self.process_num_cores = process_num_cores self.process_kwargs = process_kwargs @@ -507,6 +507,7 @@ def file_to_graph_list( ) return graph_list + def from_single_path( path: Union[str, List], override_data_weight: bool = True, **dataset_kwargs ) -> Union[SevenNetGraphDataset, None]: @@ -570,6 +571,7 @@ def chained_transform(graph): return chained_transform + # script, return dict of SevenNetGraphDataset def from_config( config: dict[str, Any], From 467c834700771257d089bf2a38b837ad550073cb Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 14:27:04 +0900 Subject: [PATCH 036/113] refactor: better error message for graph dataset --- sevenn/train/graph_dataset.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index 2c3df1bd..fb89ba94 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -183,6 +183,14 @@ def __init__( processed_name, # {root}/sevenn_data/{name}.pt processed_name.replace('.pt', '.yaml'), ] + + _pt = self.processed_file_names[0] + if not os.path.isfile(_pt) and len(self._files) == 0: + raise ValueError(f'{_pt} not found and no files to process.') + _yam = self.processed_file_names[1] + if not os.path.isfile(_yam) and len(self._files) == 0: + raise ValueError(f'{_yam} not found and no files to process') + self.process_num_cores = process_num_cores self.process_kwargs = process_kwargs self.use_data_weight = use_data_weight From 26e9d02740f58d9f826db5025ac66ac272f02ea3 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 14:48:18 +0900 Subject: [PATCH 037/113] refactor: drop info option, fix --- sevenn/train/graph_dataset.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index fb89ba94..29c0116e 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -161,6 +161,7 @@ def __init__( use_data_weight: bool = False, log: bool = True, force_reload: bool = False, + drop_info: bool = True, **process_kwargs, ): self.cutoff = cutoff @@ -184,16 +185,19 @@ def __init__( processed_name.replace('.pt', '.yaml'), ] - _pt = self.processed_file_names[0] - if not os.path.isfile(_pt) and len(self._files) == 0: + root = root or './' + _pdir = os.path.join(root, 'sevenn_data') + _pt = os.path.join(_pdir, self._processed_names[0]) + if not os.path.exists(_pt) and len(self._files) == 0: raise ValueError(f'{_pt} not found and no files to process.') - _yam = self.processed_file_names[1] - if not os.path.isfile(_yam) and len(self._files) == 0: + _yam = os.path.join(_pdir, self._processed_names[1]) + if not os.path.exists(_yam) and len(self._files) == 0: raise ValueError(f'{_yam} not found and no files to process') self.process_num_cores = process_num_cores self.process_kwargs = process_kwargs self.use_data_weight = use_data_weight + self.drop_info = drop_info self.tag_map = {} self.statistics = {} @@ -240,6 +244,12 @@ def _load_meta(self) -> None: self._files = meta['files'] self.statistics = meta['statistics'] + def __getitem__(self, idx): + graph = super().__getitem__(idx) + if self.drop_info: + graph.pop(KEY.INFO, None) # type: ignore + return graph + @property def raw_file_names(self) -> List[Any]: return self._files From f119832ecd0a40a1d56bafd0a0b52901b0e3f5f5 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 14:50:35 +0900 Subject: [PATCH 038/113] chore --- sevenn/_const.py | 1 - sevenn/error_recorder.py | 1 - 2 files changed, 2 deletions(-) diff --git a/sevenn/_const.py b/sevenn/_const.py index 5cca4084..08788e73 100644 --- a/sevenn/_const.py +++ b/sevenn/_const.py @@ -77,7 +77,6 @@ def error_record_condition(x): if v[0] == 'TotalLoss': continue if v[1] not in SUPPORTING_METRICS: - print('w') return False return True diff --git a/sevenn/error_recorder.py b/sevenn/error_recorder.py index e2684bdb..838733a6 100644 --- a/sevenn/error_recorder.py +++ b/sevenn/error_recorder.py @@ -423,7 +423,6 @@ def from_config(config: dict, loss_functions=None): else: metric_cls = CustomError metric_kwargs['func'] = criteria - print(metric_cls) metric_kwargs.pop('unit', None) metric_kwargs['name'] += f'_{metric_name}' err_metrics.append(metric_cls(**metric_kwargs)) From 1289333b53ff2675cbc5012a1d73458454953c10 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 15:31:38 +0900 Subject: [PATCH 039/113] lint,fix --- sevenn/train/loss.py | 31 ++++++++++--------------------- 1 file changed, 10 insertions(+), 21 deletions(-) diff --git a/sevenn/train/loss.py b/sevenn/train/loss.py index 0d3bbf7d..7aae162c 100644 --- a/sevenn/train/loss.py +++ b/sevenn/train/loss.py @@ -38,9 +38,7 @@ def assign_criteria(self, criterion: Callable): self.criterion = criterion def _preprocess( - self, - batch_data: Dict[str, Any], - model: Optional[Callable] = None + self, batch_data: Dict[str, Any], model: Optional[Callable] = None ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: if self.pred_key is None or self.ref_key is None: raise NotImplementedError('LossDefinition is not implemented.') @@ -56,11 +54,7 @@ def _ignore_unlabeled(self, pred, ref, data_weights=None): data_weights = data_weights[~unlabeled] return pred, ref, data_weights - def get_loss( - self, - batch_data: Dict[str, Any], - model: Optional[Callable] = None - ): + def get_loss(self, batch_data: Dict[str, Any], model: Optional[Callable] = None): """ Function that return scalar """ @@ -72,7 +66,8 @@ def get_loss( pred, ref, w_tensor = self._ignore_unlabeled(pred, ref, w_tensor) if len(pred) == 0: - return None + assert self.ref_key is not None + return torch.zeros(1, device=batch_data[self.ref_key].device) loss = self.criterion(pred, ref) if self.use_weight: @@ -100,13 +95,11 @@ def __init__( criterion=criterion, ref_key=ref_key, pred_key=pred_key, - **kwargs + **kwargs, ) def _preprocess( - self, - batch_data: Dict[str, Any], - model: Optional[Callable] = None + self, batch_data: Dict[str, Any], model: Optional[Callable] = None ): num_atoms = batch_data[KEY.NUM_ATOMS] assert isinstance(self.pred_key, str) and isinstance(self.ref_key, str) @@ -142,13 +135,11 @@ def __init__( criterion=criterion, ref_key=ref_key, pred_key=pred_key, - **kwargs + **kwargs, ) def _preprocess( - self, - batch_data: Dict[str, Any], - model: Optional[Callable] = None + self, batch_data: Dict[str, Any], model: Optional[Callable] = None ): assert isinstance(self.pred_key, str) and isinstance(self.ref_key, str) pred = torch.reshape(batch_data[self.pred_key], (-1,)) @@ -184,14 +175,12 @@ def __init__( criterion=criterion, ref_key=ref_key, pred_key=pred_key, - **kwargs + **kwargs, ) self.TO_KB = 1602.1766208 # eV/A^3 to kbar def _preprocess( - self, - batch_data: Dict[str, Any], - model: Optional[Callable] = None + self, batch_data: Dict[str, Any], model: Optional[Callable] = None ): assert isinstance(self.pred_key, str) and isinstance(self.ref_key, str) From cbec7204ca561661771ed5f61a4063bc4b59f307 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 15:31:46 +0900 Subject: [PATCH 040/113] chore --- sevenn/train/graph_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index 29c0116e..af79fefe 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -237,7 +237,7 @@ def _load_meta(self) -> None: ( 'Loaded dataset is built with different cutoff length: ' + f'{cutoff} != {self.cutoff}, dataset cutoff will be' - + f'overwritten to {cutoff}' + + f' overwritten to {cutoff}' ) ) self.cutoff = cutoff From 54004b0c69b8c614888fe3f0275ddd2a6e5ef4fe Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 6 Nov 2024 16:11:35 +0900 Subject: [PATCH 041/113] refactor: more robust dataset naming --- sevenn/train/atoms_dataset.py | 2 +- sevenn/train/graph_dataset.py | 2 +- sevenn/train/modal_dataset.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/sevenn/train/atoms_dataset.py b/sevenn/train/atoms_dataset.py index 63458acc..81b9b4bf 100644 --- a/sevenn/train/atoms_dataset.py +++ b/sevenn/train/atoms_dataset.py @@ -262,7 +262,7 @@ def from_config( continue if isinstance(paths, str): paths = [paths] - name = dk.split('_')[1].strip() + name = '_'.join([nn.strip() for nn in dk.split('_')[1:-1]]) dataset_args.update({'files': paths}) datasets[name] = SevenNetAtomsDataset(**dataset_args) diff --git a/sevenn/train/graph_dataset.py b/sevenn/train/graph_dataset.py index af79fefe..1dd0bfaa 100644 --- a/sevenn/train/graph_dataset.py +++ b/sevenn/train/graph_dataset.py @@ -621,7 +621,7 @@ def from_config( continue if isinstance(paths, str): paths = [paths] - name = dk.split('_')[1].strip() + name = '_'.join([nn.strip() for nn in dk.split('_')[1:-1]]) if (dataset := from_single_path(paths, **dataset_args)) is not None: datasets[name] = dataset else: diff --git a/sevenn/train/modal_dataset.py b/sevenn/train/modal_dataset.py index d7f61191..b42d5ee3 100644 --- a/sevenn/train/modal_dataset.py +++ b/sevenn/train/modal_dataset.py @@ -277,7 +277,7 @@ def from_config( continue if isinstance(paths, str): paths = [paths] - name = dk.split('_')[1].strip() + name = '_'.join([nn.strip() for nn in dk.split('_')[1:-1]]) dataset_args.update({'processed_name': name}) datasets[name] = SevenNetMultiModalDataset.as_graph_dataset( paths, # type: ignore From fa7d07751aab6dc40e788d521b7f975fccda2ac7 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 23 Nov 2024 17:29:26 +0900 Subject: [PATCH 042/113] fix: save real first epoch cp --- sevenn/scripts/processing_epoch.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sevenn/scripts/processing_epoch.py b/sevenn/scripts/processing_epoch.py index 3b6be989..c04067ba 100644 --- a/sevenn/scripts/processing_epoch.py +++ b/sevenn/scripts/processing_epoch.py @@ -58,8 +58,9 @@ def processing_epoch_v2( with open(csv_path, 'w') as f: f.write(','.join(head) + '\n') - path = f'{prefix}/checkpoint_0.pth' # save first epoch - trainer.write_checkpoint(path, config=config, epoch=0) + if start_epoch == 0: + path = f'{prefix}/checkpoint_0.pth' # save first epoch + trainer.write_checkpoint(path, config=config, epoch=0) for epoch in range(start_epoch, total_epoch + 1): # one indexing log.timer_start('epoch') From 2c12601cd84a668c56f98c1381c80eb75be78a80 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 24 Nov 2024 00:30:56 +0900 Subject: [PATCH 043/113] refactor: util model_from_checkpont --- sevenn/util.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/sevenn/util.py b/sevenn/util.py index 6b98251f..3577dc36 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -166,12 +166,10 @@ def model_from_checkpoint(checkpoint) -> Tuple[torch.nn.Module, Dict]: model = build_E3_equivariant_model(config) assert isinstance(model, torch.nn.Module) - missing, _ = model.load_state_dict(model_state_dict, strict=False) - if len(missing) > 0: - updated = _map_old_model(model_state_dict) - missing, not_used = model.load_state_dict(updated, strict=False) - if len(not_used) > 0: - warnings.warn(f'Some keys are not used: {not_used}', UserWarning) + model_state_dict = _map_old_model(model_state_dict) + missing, not_used = model.load_state_dict(model_state_dict, strict=False) + if len(not_used) > 0: + warnings.warn(f'Some keys are not used: {not_used}', UserWarning) assert len(missing) == 0, f'Missing keys: {missing}' From 05e64c840dd9f5bd1ca730b5463fd6d0200e82df Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 24 Nov 2024 01:58:21 +0900 Subject: [PATCH 044/113] refactor: sort convolution instructions, and fix flipped w3j coeff of old model --- sevenn/nn/convolution.py | 6 +++- sevenn/util.py | 75 +++++++++++++++++++++++++++++++++++++++- 2 files changed, 79 insertions(+), 2 deletions(-) diff --git a/sevenn/nn/convolution.py b/sevenn/nn/convolution.py index 94bd588a..b8de94a1 100644 --- a/sevenn/nn/convolution.py +++ b/sevenn/nn/convolution.py @@ -3,7 +3,7 @@ import torch import torch.nn as nn from e3nn.nn import FullyConnectedNet -from e3nn.o3 import Irreps, TensorProduct +from e3nn.o3 import Instruction, Irreps, TensorProduct from e3nn.util.jit import compile_mode import sevenn._keys as KEY @@ -76,6 +76,10 @@ def __init__( (i_in1, i_in2, p[i_out], mode, train) for i_in1, i_in2, i_out, mode, train in instructions ] + # From v1.11.x, to compatible with cuEquivariance + self._instructions_before_sort = instructions + instructions = sorted(instructions, key=lambda x: x[2]) + self.convolution = TensorProduct( irreps_x, irreps_filter, diff --git a/sevenn/util.py b/sevenn/util.py index 3577dc36..5622939c 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -1,6 +1,7 @@ +import copy import os import warnings -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Tuple, Union import numpy as np import torch @@ -134,6 +135,77 @@ def _map_old_model(old_model_state_dict): return new_model_state_dict +def _sort_old_convolution(model_now, state_dict): + """ Reason1: we have to sort instructions of convolution to be compatible with + cuEquivariance. (therefore, sort weight) + Reason2: some of old convolution module's w3j coeff has flipped sign. This also + has to be fixed to be compatible with cuEquivarinace.""" + conv_dicts = {} + for k, v in state_dict.items(): + key_name = k.split('.')[0] + if key_name.split('_')[1] == 'convolution': + if key_name not in conv_dicts: + conv_dicts[key_name] = {} + conv_dicts[key_name].update({k: v}) + + def patch(stct): + inst_old = copy.copy(conv._instructions_before_sort) + inst_old = [(inst[0], inst[1], inst[2]) for inst in inst_old] + del conv._instructions_before_sort + inst_sorted = [ + (inst.i_in1, inst.i_in2, inst.i_out, inst.path_shape) + for inst in tp.instructions + ] + irreps_in1 = tp.irreps_in1 + irreps_in2 = tp.irreps_in2 + irreps_out = tp.irreps_out + n = len(weight_nn.hs) - 2 + ww_key = f'{conv_key}.weight_nn.layer{n}.weight' + ww = stct[ww_key] + ww_sorted = [None] * len(inst_old) + _prev_idx = 0 + for ist_src in inst_old: + for j, ist_dst in enumerate(inst_sorted): + if not all(ist_src[ii] == ist_dst[ii] for ii in range(3)): + continue + + numel = ist_dst[3][0] # path_shape + ww_src = ww[:, _prev_idx : _prev_idx + numel] + l1, l2, l3 = ( + irreps_in1[ist_src[0]].ir.l, + irreps_in2[ist_src[1]].ir.l, + irreps_out[ist_src[2]].ir.l, + ) + if l1 > 0 and l2 > 0 and l3 > 0: + w3j_key = f'_w3j_{l1}_{l2}_{l3}' + conv_w3j_key = ( + f'{conv_key}.convolution._compiled_main_left_right.{w3j_key}' + ) + w3j_old = stct[conv_w3j_key] + w3j_tp = getattr( + tp._compiled_main_left_right, w3j_key + ) + if not torch.allclose(w3j_old.to(w3j_tp.device), w3j_tp): + assert torch.allclose(w3j_old.to(w3j_tp.device), -1 * w3j_tp) + ww_src = -1 * ww_src + stct[conv_w3j_key] *= -1 # !!stct updated!! + _prev_idx += numel + ww_sorted[j] = ww_src + ww_sorted = torch.cat(ww_sorted, dim=1) # type: ignore + stct[ww_key] = ww_sorted.clone() + + new_state_dict = {} + new_state_dict.update(state_dict) + for conv_key, conv_state_dict in conv_dicts.items(): + conv = model_now._modules[conv_key] + tp = conv.convolution + weight_nn = conv.weight_nn + patch(conv_state_dict) + new_state_dict.update(conv_state_dict) + + return new_state_dict + + def model_from_checkpoint(checkpoint) -> Tuple[torch.nn.Module, Dict]: from ._const import model_defaults from .model_build import build_E3_equivariant_model @@ -167,6 +239,7 @@ def model_from_checkpoint(checkpoint) -> Tuple[torch.nn.Module, Dict]: model = build_E3_equivariant_model(config) assert isinstance(model, torch.nn.Module) model_state_dict = _map_old_model(model_state_dict) + model_state_dict = _sort_old_convolution(model, model_state_dict) missing, not_used = model.load_state_dict(model_state_dict, strict=False) if len(not_used) > 0: warnings.warn(f'Some keys are not used: {not_used}', UserWarning) From db5932a9d0ca9b2333765369e405a1911381b94f Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 24 Nov 2024 10:41:17 +0900 Subject: [PATCH 045/113] refactor: _broadcast > broadcast --- sevenn/nn/convolution.py | 6 +++--- sevenn/nn/force_output.py | 10 +++++----- sevenn/nn/util.py | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/sevenn/nn/convolution.py b/sevenn/nn/convolution.py index b8de94a1..ae6072fe 100644 --- a/sevenn/nn/convolution.py +++ b/sevenn/nn/convolution.py @@ -3,14 +3,14 @@ import torch import torch.nn as nn from e3nn.nn import FullyConnectedNet -from e3nn.o3 import Instruction, Irreps, TensorProduct +from e3nn.o3 import Irreps, TensorProduct from e3nn.util.jit import compile_mode import sevenn._keys as KEY from sevenn._const import AtomGraphDataType from .activation import ShiftedSoftPlus -from .util import _broadcast +from .util import broadcast def message_gather( @@ -18,7 +18,7 @@ def message_gather( edge_dst: torch.Tensor, message: torch.Tensor ): - index = _broadcast(edge_dst, message, 0) + index = broadcast(edge_dst, message, 0) out_shape = [len(node_features)] + list(message.shape[1:]) out = torch.zeros( out_shape, diff --git a/sevenn/nn/force_output.py b/sevenn/nn/force_output.py index fd6c61f5..f6b90e58 100644 --- a/sevenn/nn/force_output.py +++ b/sevenn/nn/force_output.py @@ -5,7 +5,7 @@ import sevenn._keys as KEY from sevenn._const import AtomGraphDataType -from .util import _broadcast +from .util import broadcast @compile_mode('script') @@ -184,8 +184,8 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: # compute force pf = torch.zeros(tot_num, 3, dtype=fij.dtype, device=fij.device) nf = torch.zeros(tot_num, 3, dtype=fij.dtype, device=fij.device) - _edge_src = _broadcast(edge_idx[0], fij, 0) - _edge_dst = _broadcast(edge_idx[1], fij, 0) + _edge_src = broadcast(edge_idx[0], fij, 0) + _edge_dst = broadcast(edge_idx[1], fij, 0) pf.scatter_reduce_(0, _edge_src, fij, reduce='sum') nf.scatter_reduce_(0, _edge_dst, fij, reduce='sum') data[self.key_force] = pf - nf @@ -204,7 +204,7 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: ], dim=-1) _s = torch.zeros(tot_num, 6, dtype=fij.dtype, device=fij.device) - _edge_dst6 = _broadcast(edge_idx[1], _virial, 0) + _edge_dst6 = broadcast(edge_idx[1], _virial, 0) _s.scatter_reduce_(0, _edge_dst6, _virial, reduce='sum') if self._is_batch_data: @@ -213,7 +213,7 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: sout = torch.zeros( (nbatch, 6), dtype=_virial.dtype, device=_virial.device ) - _batch = _broadcast(batch, _s, 0) + _batch = broadcast(batch, _s, 0) sout.scatter_reduce_(0, _batch, _s, reduce='sum') else: sout = torch.sum(_s, dim=0) diff --git a/sevenn/nn/util.py b/sevenn/nn/util.py index 8545ce56..cf29c969 100644 --- a/sevenn/nn/util.py +++ b/sevenn/nn/util.py @@ -1,7 +1,7 @@ import torch -def _broadcast( +def broadcast( src: torch.Tensor, other: torch.Tensor, dim: int From faba81a3da31982ec4f32852363c65da516f13c9 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 24 Nov 2024 16:39:37 +0900 Subject: [PATCH 046/113] chore: trained -> deployed --- sevenn/pair_e3gnn/pair_e3gnn.cpp | 2 +- sevenn/pair_e3gnn/pair_e3gnn_parallel.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/sevenn/pair_e3gnn/pair_e3gnn.cpp b/sevenn/pair_e3gnn/pair_e3gnn.cpp index 7a9c754a..fa966bc7 100644 --- a/sevenn/pair_e3gnn/pair_e3gnn.cpp +++ b/sevenn/pair_e3gnn/pair_e3gnn.cpp @@ -379,7 +379,7 @@ void PairE3GNN::coeff(int narg, char **arg) { if (lmp->logfile) { fprintf(lmp->logfile, "from sevenn version '%s' ", meta_dict["version"].c_str()); - fprintf(lmp->logfile, "%s precision model trained at %s is loaded\n", + fprintf(lmp->logfile, "%s precision model, deployed when: %s\n", meta_dict["dtype"].c_str(), meta_dict["time"].c_str()); } } diff --git a/sevenn/pair_e3gnn/pair_e3gnn_parallel.cpp b/sevenn/pair_e3gnn/pair_e3gnn_parallel.cpp index 1c802b3c..44baa199 100644 --- a/sevenn/pair_e3gnn/pair_e3gnn_parallel.cpp +++ b/sevenn/pair_e3gnn/pair_e3gnn_parallel.cpp @@ -658,7 +658,7 @@ void PairE3GNNParallel::coeff(int narg, char **arg) { if (lmp->logfile) { fprintf(lmp->logfile, "from sevenn version '%s' ", meta_dict["version"].c_str()); - fprintf(lmp->logfile, "%s precision model trained at %s is loaded\n", + fprintf(lmp->logfile, "%s precision model, deployed when: %s\n", meta_dict["dtype"].c_str(), meta_dict["time"].c_str()); } } From 597800c5b53a234fc51a7e6f8e52c1ab721198f6 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 24 Nov 2024 16:49:37 +0900 Subject: [PATCH 047/113] refactor: deploy takes checkpoint path --- sevenn/main/sevenn_get_model.py | 6 ++++-- sevenn/scripts/deploy.py | 16 ++++++++++------ tests/lammps_tests/test_lammps.py | 7 ++----- tests/unit_tests/test_calculator.py | 6 +----- 4 files changed, 17 insertions(+), 18 deletions(-) diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py index 9e996186..3700146c 100644 --- a/sevenn/main/sevenn_get_model.py +++ b/sevenn/main/sevenn_get_model.py @@ -36,6 +36,7 @@ def main(args=None): else: checkpoint_path = sevenn.util.pretrained_name_to_path(checkpoint) + """ model, config = sevenn.util.model_from_checkpoint(checkpoint_path) stct_dct = model.state_dict() @@ -48,11 +49,12 @@ def main(args=None): cp_file = torch.load(checkpoint_path, map_location='cpu') cp_file.update({'model_state_dict': stct_dct, 'config': config}) torch.save(cp_file, checkpoint_path.replace('.', f'_{modal}.')) + """ if get_serial: - deploy(stct_dct, config, output_prefix) + deploy(checkpoint_path, output_prefix) else: - deploy_parallel(stct_dct, config, output_prefix) + deploy_parallel(checkpoint_path, output_prefix) def cmd_parse_get_model(args=None): diff --git a/sevenn/scripts/deploy.py b/sevenn/scripts/deploy.py index 573f2e1b..1cecf4ec 100644 --- a/sevenn/scripts/deploy.py +++ b/sevenn/scripts/deploy.py @@ -9,10 +9,11 @@ import sevenn._keys as KEY from sevenn import __version__ from sevenn.model_build import build_E3_equivariant_model +from sevenn.util import model_from_checkpoint # TODO: this is E3_equivariant specific -def deploy(model_state_dct, config, fname): +def deploy(checkpoint, fname): """ This method is messy to avoid changes in pair_e3gnn.cpp, while refactoring python part. @@ -22,8 +23,9 @@ def deploy(model_state_dct, config, fname): from sevenn.nn.edge_embedding import EdgePreprocess from sevenn.nn.force_output import ForceStressOutput - model = build_E3_equivariant_model(config) - assert isinstance(model, torch.nn.Module) + model, config = model_from_checkpoint(checkpoint) + model_state_dct = model.state_dict() + model.prepand_module('edge_preprocess', EdgePreprocess(True)) grad_module = ForceStressOutput() model.replace_module('force_output', grad_module) @@ -64,12 +66,14 @@ def deploy(model_state_dct, config, fname): # TODO: this is E3_equivariant specific -def deploy_parallel(model_state_dct, config, fname): +def deploy_parallel(checkpoint, fname): # Additional layer for ghost atom (and copy parameters from original) GHOST_LAYERS_KEYS = ['onehot_to_feature_x', '0_self_interaction_1'] - # config[KEY.IS_TRACE_STRESS] = False - # config[KEY.IS_TRAIN_STRESS] = False #now model_build has no dependency on this + model, config = model_from_checkpoint(checkpoint) + model_state_dct = model.state_dict() + + # TODO: build model only once model_list = build_E3_equivariant_model(config, parallel=True) assert isinstance(model_list, list) dct_temp = {} diff --git a/tests/lammps_tests/test_lammps.py b/tests/lammps_tests/test_lammps.py index 6a655217..664350a6 100644 --- a/tests/lammps_tests/test_lammps.py +++ b/tests/lammps_tests/test_lammps.py @@ -12,7 +12,6 @@ from sevenn.scripts.deploy import deploy, deploy_parallel from sevenn.sevennet_calculator import SevenNetCalculator -from sevenn.util import model_from_checkpoint logger = logging.getLogger('test_lammps') @@ -29,19 +28,17 @@ @pytest.fixture(scope='module') def serial_potential_path(tmp_path_factory): - model, config = model_from_checkpoint(cp_0_path) tmp = tmp_path_factory.mktemp('serial_potential') pot_path = str(tmp / 'deployed_serial.pt') - deploy(model.state_dict(), config, pot_path) + deploy(cp_0_path, pot_path) return pot_path @pytest.fixture(scope='module') def parallel_potential_path(tmp_path_factory): - model, config = model_from_checkpoint(cp_0_path) tmp = tmp_path_factory.mktemp('paralllel_potential') pot_path = str(tmp / 'deployed_parallel') - deploy_parallel(model.state_dict(), config, pot_path) + deploy_parallel(cp_0_path, pot_path) return ' '.join(['3', pot_path]) diff --git a/tests/unit_tests/test_calculator.py b/tests/unit_tests/test_calculator.py index 3bbf4a83..0d42846c 100644 --- a/tests/unit_tests/test_calculator.py +++ b/tests/unit_tests/test_calculator.py @@ -79,12 +79,8 @@ def test_sevennet_0_cal_mol(atoms_mol, sevennet_0_cal): def test_sevennet_0_cal_deployed(tmp_path, atoms_pbc): - model, config = model_from_checkpoint( - pretrained_name_to_path('7net-0_11July2024') - ) - fname = str(tmp_path / '7net_0.pt') - deploy(model.state_dict(), config, fname) + deploy(pretrained_name_to_path('7net-0_11July2024'), fname) calc_script = SevenNetCalculator(fname, file_type='torchscript') calc_cp = SevenNetCalculator(pretrained_name_to_path('7net-0_11July2024')) From 0f3452c2f60a8d6de4f8db550b45971182ce3051 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 24 Nov 2024 16:52:23 +0900 Subject: [PATCH 048/113] docs: changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1b32be28..dc49f236 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -15,6 +15,8 @@ All notable changes to this project will be documented in this file. - Ignore unlabelled data when calculating loss. (e.g. stress data for non-pbc structure) - Record shift and scale used for each modality in `log.sevenn`. - Record error and loss in the `log.sevenn` with the value after weighting the loss and ignoring unlabelled data. +- Deploy takes checkpoint path (now deployed serial build model only once) +- Sort instructions of tensor product in convolution (+ fix flipped w3j coeff of old model) ## [0.10.1] ### Added From aa07c9b5509338710d60ed689b6117d11850d6a4 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Mon, 25 Nov 2024 16:06:30 +0900 Subject: [PATCH 049/113] add: cuEquivariance support --- pyproject.toml | 2 +- sevenn/_const.py | 2 + sevenn/_keys.py | 2 + sevenn/main/sevenn.py | 18 ++ sevenn/model_build.py | 39 ++- sevenn/nn/convolution.py | 49 +++- sevenn/nn/cue_helper.py | 117 +++++++++ sevenn/nn/interaction_blocks.py | 2 +- sevenn/nn/linear.py | 30 +-- sevenn/nn/self_connection.py | 32 ++- sevenn/nn/sequential.py | 7 +- sevenn/scripts/backward_compatibility.py | 153 ++++++++++++ sevenn/scripts/deploy.py | 6 +- sevenn/scripts/processing_epoch.py | 2 +- sevenn/sevennet_calculator.py | 13 +- sevenn/util.py | 291 ++++++++++++----------- tests/unit_tests/test_calculator.py | 47 +++- 17 files changed, 623 insertions(+), 189 deletions(-) create mode 100644 sevenn/nn/cue_helper.py create mode 100644 sevenn/scripts/backward_compatibility.py diff --git a/pyproject.toml b/pyproject.toml index 700b806e..8bd712d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "ase", "braceexpand", "pyyaml", - "e3nn", + "e3nn>=0.4.4", "tqdm", "scikit-learn", "torch_geometric>=2.5.0", diff --git a/sevenn/_const.py b/sevenn/_const.py index 08788e73..ec10bfdb 100644 --- a/sevenn/_const.py +++ b/sevenn/_const.py @@ -118,6 +118,7 @@ def error_record_condition(x): KEY.SELF_CONNECTION_TYPE: 'nequip', KEY.INTERACTION_TYPE: 'nequip', KEY._NORMALIZE_SPH: True, + KEY.CUEQUIVARIANCE_CONFIG: {}, } @@ -155,6 +156,7 @@ def error_record_condition(x): KEY.SELF_CONNECTION_TYPE: lambda x: x in IMPLEMENTED_SELF_CONNECTION_TYPE, KEY.INTERACTION_TYPE: lambda x: x in IMPLEMENTED_INTERACTION_TYPE, KEY._NORMALIZE_SPH: bool, + KEY.CUEQUIVARIANCE_CONFIG: dict, } diff --git a/sevenn/_keys.py b/sevenn/_keys.py index d8e95a7a..671f6809 100644 --- a/sevenn/_keys.py +++ b/sevenn/_keys.py @@ -222,5 +222,7 @@ INTERACTION_TYPE = 'interaction_type' TRAIN_AVG_NUM_NEIGH = 'train_avg_num_neigh' # deprecated +CUEQUIVARIANCE_CONFIG = 'cuequivariance_config' + _NORMALIZE_SPH = '_normalize_sph' OPTIMIZE_BY_REDUCE = 'optimize_by_reduce' diff --git a/sevenn/main/sevenn.py b/sevenn/main/sevenn.py index 61bb9aec..2a2ae921 100644 --- a/sevenn/main/sevenn.py +++ b/sevenn/main/sevenn.py @@ -48,6 +48,12 @@ def main(args=None): screen = args.screen distributed = args.distributed distributed_backend = args.distributed_backend + use_cue = args.enable_cueq + + if use_cue: + import sevenn.nn.cue_helper + if not sevenn.nn.cue_helper.is_cue_available(): + raise ImportError('cuEquivariance not installed.') if working_dir is None: working_dir = os.getcwd() @@ -96,6 +102,12 @@ def main(args=None): train_config[KEY.RANK] = rank train_config[KEY.WORLD_SIZE] = world_size + if use_cue: + if KEY.CUEQUIVARIANCE_CONFIG not in model_config: + model_config[KEY.CUEQUIVARIANCE_CONFIG] = {'use': True} + else: + model_config[KEY.CUEQUIVARIANCE_CONFIG].update({'use': True}) + logger.print_config(model_config, data_config, train_config) # don't have to distinguish configs inside program global_config.update(model_config) @@ -133,6 +145,12 @@ def cmd_parse_main(args=None): help=mode_help, type=str, ) + ag.add_argument( + '-cue', + '--enable_cueq', + help='use cuEquivariance for training', + action='store_true' + ) ag.add_argument( '-w', '--working_dir', diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 8fee3482..1c419d1d 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -8,6 +8,7 @@ import sevenn._keys as KEY import sevenn.util as util +from .nn.convolution import IrrepsConvolution from .nn.edge_embedding import ( BesselBasis, EdgeEmbedding, @@ -214,6 +215,40 @@ def patch_modality(layers: OrderedDict, config): return layers +def patch_cue(layers: OrderedDict, config): + import sevenn.nn.cue_helper as cue_helper + + cue_cfg = copy.deepcopy(config.get(KEY.CUEQUIVARIANCE_CONFIG, {})) + + if not cue_cfg.pop('use', False): + return layers + + group = 'O3' if config[KEY.IS_PARITY] else 'SO3' + updates = {} + for k, module in layers.items(): + if isinstance(module, (IrrepsLinear, SelfConnectionLinearIntro)): + if k == 'reduce_hidden_to_energy': # TODO: has bug with 0 shape + continue + module_patched = cue_helper.patch_linear( + module, group, layout='mul_ir', **cue_cfg + ) + updates[k] = module_patched + elif isinstance(module, IrrepsConvolution): + module_patched = cue_helper.patch_convolution( + module, group, layout='mul_ir', **cue_cfg + ) + updates[k] = module_patched + + layers.update(updates) + return layers + + +def patch_modules(layers: OrderedDict, config): + layers = patch_modality(layers, config) + layers = patch_cue(layers, config) + return layers + + def _to_parallel_model(layers: OrderedDict, config): num_classes = layers['onehot_idx_to_onehot'].num_classes one_hot_irreps = Irreps(f'{num_classes}x0e') @@ -463,8 +498,8 @@ def build_E3_equivariant_model(config: dict, parallel=False): if parallel: layers_list = _to_parallel_model(layers, config) return [ - AtomGraphSequential(patch_modality(layers, config), **common_args) + AtomGraphSequential(patch_modules(layers, config), **common_args) for layers in layers_list ] else: - return AtomGraphSequential(patch_modality(layers, config), **common_args) + return AtomGraphSequential(patch_modules(layers, config), **common_args) diff --git a/sevenn/nn/convolution.py b/sevenn/nn/convolution.py index ae6072fe..cdc615ac 100644 --- a/sevenn/nn/convolution.py +++ b/sevenn/nn/convolution.py @@ -48,6 +48,7 @@ def __init__( data_key_filter: str = KEY.EDGE_ATTR, data_key_weight_input: str = KEY.EDGE_EMBEDDING, data_key_edge_idx: str = KEY.EDGE_IDX, + lazy_layer_instantiate: bool = True, is_parallel: bool = False, ): super().__init__() @@ -62,40 +63,65 @@ def __init__( instructions = [] irreps_mid = [] + weight_numel = 0 for i, (mul_x, ir_x) in enumerate(irreps_x): for j, (_, ir_filter) in enumerate(irreps_filter): for ir_out in ir_x * ir_filter: if ir_out in irreps_out: # here we drop l > lmax k = len(irreps_mid) + weight_numel += mul_x * 1 # path shape irreps_mid.append((mul_x, ir_out)) instructions.append((i, j, k, 'uvu', True)) irreps_mid = Irreps(irreps_mid) - irreps_mid, p, _ = irreps_mid.sort() + irreps_mid, p, _ = irreps_mid.sort() # type: ignore instructions = [ (i_in1, i_in2, p[i_out], mode, train) for i_in1, i_in2, i_out, mode, train in instructions ] - # From v1.11.x, to compatible with cuEquivariance + + # From v0.11.x, to compatible with cuEquivariance self._instructions_before_sort = instructions instructions = sorted(instructions, key=lambda x: x[2]) - self.convolution = TensorProduct( - irreps_x, - irreps_filter, - irreps_mid, - instructions, + self._convolution_kwargs = dict( + irreps_in1=irreps_x, + irreps_in2=irreps_filter, + irreps_out=irreps_mid, + instructions=instructions, shared_weights=False, internal_weights=False, ) - self.weight_nn = FullyConnectedNet( - weight_layer_input_to_hidden + [self.convolution.weight_numel], - weight_layer_act, + self._weight_nn_kwargs = dict( + hs=weight_layer_input_to_hidden + [weight_numel], + act=weight_layer_act ) - self._comm_size = self.convolution.irreps_in1.dim # used in parallel + + self.convolution = None + self.weight_nn = None + self.layer_instantiated = False + self.convolution_cls = TensorProduct + self.weight_nn_cls = FullyConnectedNet + + if not lazy_layer_instantiate: + self.instantiate() + + self._comm_size = irreps_x.dim # used in parallel + + def instantiate(self): + if self.convolution is not None: + raise ValueError('Convolution layer already exists') + if self.weight_nn is not None: + raise ValueError('Weight_nn layer already exists') + + self.convolution = self.convolution_cls(**self._convolution_kwargs) + self.weight_nn = self.weight_nn_cls(**self._weight_nn_kwargs) + self.layer_instantiated = True def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + assert self.convolution is not None, 'Convolution is not instantiated' + assert self.weight_nn is not None, 'Weight_nn is not instantiated' weight = self.weight_nn(data[self.key_weight_input]) x = data[self.key_x] if self.is_parallel: @@ -110,7 +136,6 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: x = message_gather(x, edge_dst, message) x = x.div(self.denominator) if self.is_parallel: - # NLOCAL is # of atoms in system at 'CPU' x = torch.tensor_split(x, data[KEY.NLOCAL])[0] data[self.key_x] = x return data diff --git a/sevenn/nn/cue_helper.py b/sevenn/nn/cue_helper.py new file mode 100644 index 00000000..01b3ecde --- /dev/null +++ b/sevenn/nn/cue_helper.py @@ -0,0 +1,117 @@ +import warnings +from typing import Literal, Union + +try: + import cuequivariance as cue + import cuequivariance_torch as cuet + + _CUE_AVAILABLE = True +except ImportError: + _CUE_AVAILABLE = False + +import e3nn.o3 + +from .convolution import IrrepsConvolution +from .linear import IrrepsLinear +from .self_connection import SelfConnectionLinearIntro + + +def is_cue_available(): + return _CUE_AVAILABLE + + +def cue_needed(func): + def wrapper(*args, **kwargs): + if is_cue_available(): + return func(*args, **kwargs) + else: + raise ImportError('cue is not available') + + return wrapper + + +def _check_may_not_compatible(orig_kwargs, defaults): + for k, v in defaults.items(): + v_given = orig_kwargs.pop(k, v) + if v_given != v: + warnings.warn(f'{k}: {v} is ignored to use cuEquivariance') + + +@cue_needed +def as_cue_irreps(irreps: e3nn.o3.Irreps, group: Literal['SO3', 'O3']): + """Convert e3nn irreps to given group's cue irreps""" + if group == 'SO3': + assert all(irrep.ir.p == 1 for irrep in irreps) + return cue.Irreps('SO3', str(irreps).replace('e', '')) # type: ignore + elif group == 'O3': + return cue.Irreps('O3', str(irreps)) # type: ignore + else: + raise ValueError(f'Unknown group: {group}') + + +@cue_needed +def patch_linear( + module: Union[IrrepsLinear, SelfConnectionLinearIntro], + group: Literal['SO3', 'O3'], + **cue_kwargs, +): + assert not module.layer_instantiated + + module.irreps_in = as_cue_irreps(module.irreps_in, group) # type: ignore + module.irreps_out = as_cue_irreps(module.irreps_out, group) # type: ignore + + orig_kwargs = module.linear_params + + may_not_compatible_default = dict( + f_in=None, + f_out=None, + instructions=None, + biases=False, + path_normalization='element', + _optimize_einsums=None, + ) + # pop may_not_compatible_defaults + _check_may_not_compatible(orig_kwargs, may_not_compatible_default) + + module.linear_cls = cuet.Linear # type: ignore + orig_kwargs.update(**cue_kwargs) + return module + + +@cue_needed +def patch_convolution( + module: IrrepsConvolution, + group: Literal['SO3', 'O3'], + **cue_kwargs, +): + assert not module.layer_instantiated + + # conv_kwargs will be patched in place + conv_kwargs = module._convolution_kwargs + conv_kwargs.update(dict( + irreps_in1=as_cue_irreps(conv_kwargs.get('irreps_in1'), group), + irreps_in2=as_cue_irreps(conv_kwargs.get('irreps_in2'), group), + filter_irreps_out=as_cue_irreps(conv_kwargs.pop('irreps_out'), group), + )) + + inst_orig = conv_kwargs.pop('instructions') + inst_sorted = sorted(inst_orig, key=lambda x: x[2]) + assert all([a == b for a, b in zip(inst_orig, inst_sorted)]) + + may_not_compatible_default = dict( + in1_var=None, + in2_var=None, + out_var=None, + irrep_normalization=False, + path_normalization='element', + compile_left_right=True, + compile_right=False, + _specialized_code=None, + _optimize_einsums=None, + ) + # pop may_not_compatible_defaults + _check_may_not_compatible(conv_kwargs, may_not_compatible_default) + + module.convolution_cls = cuet.ChannelWiseTensorProduct # type: ignore + conv_kwargs.update(**cue_kwargs) + return module diff --git a/sevenn/nn/interaction_blocks.py b/sevenn/nn/interaction_blocks.py index 526105b4..3f93768f 100644 --- a/sevenn/nn/interaction_blocks.py +++ b/sevenn/nn/interaction_blocks.py @@ -37,7 +37,7 @@ def NequIP_interaction_block( irreps_for_gate_in = gate_layer.get_gate_irreps_in() block[f'{t}_self_connection_intro'] = sc_intro( - irreps_x=irreps_x, + irreps_x, irreps_operand=irreps_node_attr, irreps_out=irreps_for_gate_in, ) diff --git a/sevenn/nn/linear.py b/sevenn/nn/linear.py index 11a55381..be83f711 100644 --- a/sevenn/nn/linear.py +++ b/sevenn/nn/linear.py @@ -25,7 +25,7 @@ def __init__( data_key_modal_attr: str = KEY.MODAL_ATTR, num_modalities: int = 0, lazy_layer_instantiate: bool = True, - **e3nn_linear_params, + **linear_params, ): super().__init__() self.key_input = data_key_in @@ -36,37 +36,39 @@ def __init__( self.key_modal_attr = data_key_modal_attr self._irreps_in_wo_modal = irreps_in - self._irreps_in = irreps_in + self.irreps_in = irreps_in self.irreps_out = irreps_out - self.e3nn_linear_params = e3nn_linear_params + self.linear_params = linear_params self.linear = None self.layer_instantiated = False self.num_modalities = num_modalities self._is_batch_data = True + # use getter setter + self.linear_cls = Linear + if num_modalities > 1: # in case of multi-modal self.set_num_modalities(num_modalities) if not lazy_layer_instantiate: self.instantiate() - def set_num_modalities(self, num_modalities): - if self.layer_instantiated: - raise ValueError('Layer already instantiated, can not change modalities') - irreps_in = self._irreps_in_wo_modal + Irreps(f'{num_modalities}x0e') - self.num_modalities = num_modalities - self._irreps_in = irreps_in - def instantiate(self): if self.linear is not None: raise ValueError('Linear layer already exists') - self.linear = Linear( - self._irreps_in, self.irreps_out, **self.e3nn_linear_params + self.linear = self.linear_cls( + self.irreps_in, self.irreps_out, **self.linear_params ) - self.irreps_in = self._irreps_in self.layer_instantiated = True + def set_num_modalities(self, num_modalities): + if self.layer_instantiated: + raise ValueError('Layer already instantiated, can not change modalities') + irreps_in = self._irreps_in_wo_modal + Irreps(f'{num_modalities}x0e') + self.num_modalities = num_modalities + self.irreps_in = irreps_in + def _patch_modal_to_data(self, data: AtomGraphDataType) -> AtomGraphDataType: if self._is_batch_data: batch = data[KEY.BATCH] @@ -90,7 +92,7 @@ def _patch_modal_to_data(self, data: AtomGraphDataType) -> AtomGraphDataType: return data def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: - assert self.linear is not None + assert self.linear is not None, 'Layer is not instantiated' if self.num_modalities > 1: data = self._patch_modal_to_data(data) diff --git a/sevenn/nn/self_connection.py b/sevenn/nn/self_connection.py index f4f0921d..94ee620e 100644 --- a/sevenn/nn/self_connection.py +++ b/sevenn/nn/self_connection.py @@ -15,7 +15,7 @@ class SelfConnectionIntro(nn.Module): def __init__( self, - irreps_x: Irreps, + irreps_in: Irreps, irreps_operand: Irreps, irreps_out: Irreps, data_key_x: str = KEY.NODE_FEATURE, @@ -25,7 +25,7 @@ def __init__( super().__init__() self.fc_tensor_product = FullyConnectedTensorProduct( - irreps_x, irreps_operand, irreps_out + irreps_in, irreps_operand, irreps_out ) self.key_x = data_key_x self.key_operand = data_key_operand @@ -45,16 +45,38 @@ class SelfConnectionLinearIntro(nn.Module): def __init__( self, - irreps_x: Irreps, + irreps_in: Irreps, irreps_out: Irreps, data_key_x: str = KEY.NODE_FEATURE, - **kwargs, # for compatibility + lazy_layer_instantiate: bool = True, + **kwargs, ): super().__init__() - self.linear = Linear(irreps_x, irreps_out) + self.irreps_in = irreps_in + self.irreps_out = irreps_out self.key_x = data_key_x + self.linear = None + self.layer_instantiated = False + self.linear_cls = Linear + + # TODO: better to have SelfConnectionIntro super class + kwargs.pop('irreps_operand') + self.linear_params = kwargs + + if not lazy_layer_instantiate: + self.instantiate() + + def instantiate(self): + if self.linear is not None: + raise ValueError('Linear layer already exists') + self.linear = self.linear_cls( + self.irreps_in, self.irreps_out, **self.linear_params + ) + self.layer_instantiated = True + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + assert self.linear is not None, 'Layer is not instantiated' data[KEY.SELF_CONNECTION_TEMP] = self.linear(data[self.key_x]) return data diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index 00cb6705..7f55a499 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -13,11 +13,8 @@ def _instantiate_modules(modules): # see IrrepsLinear of linear.py for module in modules.values(): - try: - if not module.layer_instantiated: - module.instantiate() - except AttributeError: - pass + if not getattr(module, 'layer_instantiated', True): + module.instantiate() @compile_mode('script') diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py new file mode 100644 index 00000000..e500f37f --- /dev/null +++ b/sevenn/scripts/backward_compatibility.py @@ -0,0 +1,153 @@ +""" +Debt +keep old pre-trained checkpoints unchanged. +""" +import copy + +import torch + +import sevenn._keys as KEY + + +def patch_old_config(config): + if config[KEY.CUTOFF_FUNCTION][KEY.CUTOFF_FUNCTION_NAME] == 'XPLOR': + config[KEY.CUTOFF_FUNCTION].pop('poly_cut_p_value', None) + if KEY.TRAIN_DENOMINTAOR not in config: + config[KEY.TRAIN_DENOMINTAOR] = config.pop('train_avg_num_neigh', False) + _opt = config.pop('optimize_by_reduce', None) + if _opt is False: + raise ValueError( + 'This checkpoint(optimize_by_reduce: False) is no longer supported' + ) + if KEY.CONV_DENOMINATOR not in config: + config[KEY.CONV_DENOMINATOR] = 0.0 + if KEY._NORMALIZE_SPH not in config: + config[KEY._NORMALIZE_SPH] = False + return config + + +def map_old_model(old_model_state_dict): + """ + For compatibility with old namings (before 'correct' branch merged 2404XX) + Map old model's module names to new model's module names + """ + _old_module_name_mapping = { + 'EdgeEmbedding': 'edge_embedding', + 'reducing nn input to hidden': 'reduce_input_to_hidden', + 'reducing nn hidden to energy': 'reduce_hidden_to_energy', + 'rescale atomic energy': 'rescale_atomic_energy', + } + for i in range(10): + _old_module_name_mapping[f'{i} self connection intro'] = ( + f'{i}_self_connection_intro' + ) + _old_module_name_mapping[f'{i} convolution'] = f'{i}_convolution' + _old_module_name_mapping[f'{i} self interaction 2'] = ( + f'{i}_self_interaction_2' + ) + _old_module_name_mapping[f'{i} equivariant gate'] = f'{i}_equivariant_gate' + + new_model_state_dict = {} + for k, v in old_model_state_dict.items(): + key_name = k.split('.')[0] + follower = '.'.join(k.split('.')[1:]) + if 'denumerator' in follower: + follower = follower.replace('denumerator', 'denominator') + if key_name in _old_module_name_mapping: + new_key_name = _old_module_name_mapping[key_name] + '.' + follower + new_model_state_dict[new_key_name] = v + else: + new_model_state_dict[k] = v + return new_model_state_dict + + +def sort_old_convolution(model_now, state_dict): + from e3nn.o3 import wigner_3j + + """ + Reason1: we have to sort instructions of convolution to be compatible with + cuEquivariance. (therefore, sort weight) + Reason2: some of old convolution module's w3j coeff has flipped sign. This also + has to be fixed to be compatible with cuEquivarinace. + """ + + def patch(stct): + inst_old = copy.copy(conv._instructions_before_sort) + inst_old = [(inst[0], inst[1], inst[2]) for inst in inst_old] + del conv._instructions_before_sort + + conv_args = conv._convolution_kwargs + irreps_in1 = conv_args['irreps_in1'] + irreps_in2 = conv_args['irreps_in2'] + irreps_out = conv_args.get('irreps_out', conv_args.get('filter_irreps_out')) + + inst_sorted = sorted(inst_old, key=lambda x: x[2]) + inst_sorted = [ + # in1, in2, out, weights + (inst[0], inst[1], inst[2], irreps_in1[inst[0]].mul) + for inst in inst_sorted + ] + + n = len(weight_nn.hs) - 2 + ww_key = f'{conv_key}.weight_nn.layer{n}.weight' + ww = stct[ww_key] + ww_sorted = [None] * len(inst_old) + + _prev_idx = 0 + for ist_src in inst_old: + for j, ist_dst in enumerate(inst_sorted): + if not all(ist_src[ii] == ist_dst[ii] for ii in range(3)): + continue + + numel = ist_dst[3] # weight num + ww_src = ww[:, _prev_idx : _prev_idx + numel] + l1, l2, l3 = ( + irreps_in1[ist_src[0]].ir.l, + irreps_in2[ist_src[1]].ir.l, + irreps_out[ist_src[2]].ir.l, + ) + if l1 > 0 and l2 > 0 and l3 > 0: + w3j_key = f'_w3j_{l1}_{l2}_{l3}' + conv_w3j_key = ( + f'{conv_key}.convolution._compiled_main_left_right.{w3j_key}' + ) + w3j_old = stct[conv_w3j_key] + w3j_now = wigner_3j(l1, l2, l3) + if not torch.allclose(w3j_old.to(w3j_now.device), w3j_now): + assert torch.allclose( + w3j_old.to(w3j_now.device), -1 * w3j_now + ) + ww_src = -1 * ww_src + stct[conv_w3j_key] *= -1 # stct updated + _prev_idx += numel + ww_sorted[j] = ww_src + ww_sorted = torch.cat(ww_sorted, dim=1) # type: ignore + stct[ww_key] = ww_sorted.clone() # stct updated + + conv_dicts = {} + for k, v in state_dict.items(): + key_name = k.split('.')[0] + if key_name.split('_')[1] == 'convolution': + if key_name not in conv_dicts: + conv_dicts[key_name] = {} + conv_dicts[key_name].update({k: v}) + + new_state_dict = {} + new_state_dict.update(state_dict) + for conv_key, conv_state_dict in conv_dicts.items(): + conv = model_now._modules[conv_key] + weight_nn = conv.weight_nn + patch(conv_state_dict) + new_state_dict.update(conv_state_dict) + + return new_state_dict + + +def patch_state_dict_if_old(state_dict, config_cp, now_model): + stct_src = map_old_model(state_dict) + + cue_cfg = config_cp.get(KEY.CUEQUIVARIANCE_CONFIG, {'use': False}) + if not cue_cfg.get('use', False): + # patch only when it is e3nn model + stct_src = sort_old_convolution(now_model, stct_src) + return stct_src diff --git a/sevenn/scripts/deploy.py b/sevenn/scripts/deploy.py index 1cecf4ec..17ae7c23 100644 --- a/sevenn/scripts/deploy.py +++ b/sevenn/scripts/deploy.py @@ -9,7 +9,7 @@ import sevenn._keys as KEY from sevenn import __version__ from sevenn.model_build import build_E3_equivariant_model -from sevenn.util import model_from_checkpoint +from sevenn.util import model_from_checkpoint_with_backend # TODO: this is E3_equivariant specific @@ -23,7 +23,7 @@ def deploy(checkpoint, fname): from sevenn.nn.edge_embedding import EdgePreprocess from sevenn.nn.force_output import ForceStressOutput - model, config = model_from_checkpoint(checkpoint) + model, config = model_from_checkpoint_with_backend(checkpoint, 'e3nn') model_state_dct = model.state_dict() model.prepand_module('edge_preprocess', EdgePreprocess(True)) @@ -70,7 +70,7 @@ def deploy_parallel(checkpoint, fname): # Additional layer for ghost atom (and copy parameters from original) GHOST_LAYERS_KEYS = ['onehot_to_feature_x', '0_self_interaction_1'] - model, config = model_from_checkpoint(checkpoint) + model, config = model_from_checkpoint_with_backend(checkpoint, 'e3nn') model_state_dct = model.state_dict() # TODO: build model only once diff --git a/sevenn/scripts/processing_epoch.py b/sevenn/scripts/processing_epoch.py index b06fc43e..49c669ff 100644 --- a/sevenn/scripts/processing_epoch.py +++ b/sevenn/scripts/processing_epoch.py @@ -58,7 +58,7 @@ def processing_epoch_v2( with open(csv_path, 'w') as f: f.write(','.join(head) + '\n') - if start_epoch == 0: + if start_epoch == 1: path = f'{prefix}/checkpoint_0.pth' # save first epoch trainer.write_checkpoint(path, config=config, epoch=0) diff --git a/sevenn/sevennet_calculator.py b/sevenn/sevennet_calculator.py index db135552..eada4f4e 100644 --- a/sevenn/sevennet_calculator.py +++ b/sevenn/sevennet_calculator.py @@ -1,5 +1,6 @@ import os import pathlib +import warnings from typing import Any, Optional, Union import numpy as np @@ -38,6 +39,7 @@ def __init__( file_type: str = 'checkpoint', device: Union[torch.device, str] = 'auto', modal: Optional[str] = None, + enable_cueq: bool = False, sevennet_config: Optional[Any] = None, # hold meta information **kwargs, ): @@ -57,6 +59,12 @@ def __init__( if file_type not in ['checkpoint', 'torchscript', 'model_instance']: raise ValueError('file_type should be checkpoint or torchscript') + if enable_cueq and file_type in ['model_instance', 'torchscript']: + warnings.warn( + 'file_type should be checkpoint to enable cueq. cueq set to False' + ) + enable_cueq = False + if isinstance(device, str): # TODO: do we really need this? if device == 'auto': self.device = torch.device( @@ -72,7 +80,10 @@ def __init__( checkpoint = model else: checkpoint = util.pretrained_name_to_path(model) - model_loaded, config = util.model_from_checkpoint(checkpoint) + backend = 'e3nn' if not enable_cueq else 'cue' + model_loaded, config = util.model_from_checkpoint_with_backend( + checkpoint, backend + ) model_loaded.set_is_batch_data(False) self.type_map = config[KEY.TYPE_MAP] self.cutoff = config[KEY.CUTOFF] diff --git a/sevenn/util.py b/sevenn/util.py index 5622939c..8182ac65 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -1,7 +1,7 @@ import copy import os import warnings -from typing import Any, Dict, List, Tuple, Union +from typing import Dict, List, Tuple, Union import numpy as np import torch @@ -9,6 +9,7 @@ from e3nn.o3 import FullTensorProduct, Irreps import sevenn._keys as KEY +import sevenn.scripts.backward_compatibility as compat def to_atom_graph_list(atom_graph_batch): @@ -81,146 +82,11 @@ def onehot_to_chem(one_hot_indices: List[int], type_map: Dict[int, int]): return [chemical_symbols[type_map_rev[x]] for x in one_hot_indices] -def _patch_old_config(config: Dict[str, Any]): - # Fixing my old mistakes - if config[KEY.CUTOFF_FUNCTION][KEY.CUTOFF_FUNCTION_NAME] == 'XPLOR': - config[KEY.CUTOFF_FUNCTION].pop('poly_cut_p_value', None) - if KEY.TRAIN_DENOMINTAOR not in config: - config[KEY.TRAIN_DENOMINTAOR] = config.pop('train_avg_num_neigh', False) - _opt = config.pop('optimize_by_reduce', None) - if _opt is False: - raise ValueError( - 'This checkpoint(optimize_by_reduce: False) is no longer supported' - ) - if KEY.CONV_DENOMINATOR not in config: - config[KEY.CONV_DENOMINATOR] = 0.0 - if KEY._NORMALIZE_SPH not in config: - config[KEY._NORMALIZE_SPH] = False - # Warn this in the docs, not here for SevenNet-0 (22May2024) - return config - - -def _map_old_model(old_model_state_dict): - """ - For compatibility with old namings (before 'correct' branch merged 2404XX) - Map old model's module names to new model's module names - """ - _old_module_name_mapping = { - 'EdgeEmbedding': 'edge_embedding', - 'reducing nn input to hidden': 'reduce_input_to_hidden', - 'reducing nn hidden to energy': 'reduce_hidden_to_energy', - 'rescale atomic energy': 'rescale_atomic_energy', - } - for i in range(10): - _old_module_name_mapping[f'{i} self connection intro'] = ( - f'{i}_self_connection_intro' - ) - _old_module_name_mapping[f'{i} convolution'] = f'{i}_convolution' - _old_module_name_mapping[f'{i} self interaction 2'] = ( - f'{i}_self_interaction_2' - ) - _old_module_name_mapping[f'{i} equivariant gate'] = f'{i}_equivariant_gate' - - new_model_state_dict = {} - for k, v in old_model_state_dict.items(): - key_name = k.split('.')[0] - follower = '.'.join(k.split('.')[1:]) - if 'denumerator' in follower: - follower = follower.replace('denumerator', 'denominator') - if key_name in _old_module_name_mapping: - new_key_name = _old_module_name_mapping[key_name] + '.' + follower - new_model_state_dict[new_key_name] = v - else: - new_model_state_dict[k] = v - return new_model_state_dict - - -def _sort_old_convolution(model_now, state_dict): - """ Reason1: we have to sort instructions of convolution to be compatible with - cuEquivariance. (therefore, sort weight) - Reason2: some of old convolution module's w3j coeff has flipped sign. This also - has to be fixed to be compatible with cuEquivarinace.""" - conv_dicts = {} - for k, v in state_dict.items(): - key_name = k.split('.')[0] - if key_name.split('_')[1] == 'convolution': - if key_name not in conv_dicts: - conv_dicts[key_name] = {} - conv_dicts[key_name].update({k: v}) - - def patch(stct): - inst_old = copy.copy(conv._instructions_before_sort) - inst_old = [(inst[0], inst[1], inst[2]) for inst in inst_old] - del conv._instructions_before_sort - inst_sorted = [ - (inst.i_in1, inst.i_in2, inst.i_out, inst.path_shape) - for inst in tp.instructions - ] - irreps_in1 = tp.irreps_in1 - irreps_in2 = tp.irreps_in2 - irreps_out = tp.irreps_out - n = len(weight_nn.hs) - 2 - ww_key = f'{conv_key}.weight_nn.layer{n}.weight' - ww = stct[ww_key] - ww_sorted = [None] * len(inst_old) - _prev_idx = 0 - for ist_src in inst_old: - for j, ist_dst in enumerate(inst_sorted): - if not all(ist_src[ii] == ist_dst[ii] for ii in range(3)): - continue - - numel = ist_dst[3][0] # path_shape - ww_src = ww[:, _prev_idx : _prev_idx + numel] - l1, l2, l3 = ( - irreps_in1[ist_src[0]].ir.l, - irreps_in2[ist_src[1]].ir.l, - irreps_out[ist_src[2]].ir.l, - ) - if l1 > 0 and l2 > 0 and l3 > 0: - w3j_key = f'_w3j_{l1}_{l2}_{l3}' - conv_w3j_key = ( - f'{conv_key}.convolution._compiled_main_left_right.{w3j_key}' - ) - w3j_old = stct[conv_w3j_key] - w3j_tp = getattr( - tp._compiled_main_left_right, w3j_key - ) - if not torch.allclose(w3j_old.to(w3j_tp.device), w3j_tp): - assert torch.allclose(w3j_old.to(w3j_tp.device), -1 * w3j_tp) - ww_src = -1 * ww_src - stct[conv_w3j_key] *= -1 # !!stct updated!! - _prev_idx += numel - ww_sorted[j] = ww_src - ww_sorted = torch.cat(ww_sorted, dim=1) # type: ignore - stct[ww_key] = ww_sorted.clone() - - new_state_dict = {} - new_state_dict.update(state_dict) - for conv_key, conv_state_dict in conv_dicts.items(): - conv = model_now._modules[conv_key] - tp = conv.convolution - weight_nn = conv.weight_nn - patch(conv_state_dict) - new_state_dict.update(conv_state_dict) - - return new_state_dict - - -def model_from_checkpoint(checkpoint) -> Tuple[torch.nn.Module, Dict]: +def _config_cp_routine(config): from ._const import model_defaults - from .model_build import build_E3_equivariant_model - - if isinstance(checkpoint, str): - checkpoint = torch.load(checkpoint, map_location='cpu', weights_only=False) - elif isinstance(checkpoint, dict): - pass - else: - raise ValueError('checkpoint must be either str or dict') - model_state_dict = checkpoint['model_state_dict'] - config = checkpoint['config'] defaults = {**model_defaults(config)} - config = _patch_old_config(config) + config = compat.patch_old_config(config) # type: ignore for k, v in defaults.items(): if k in config: @@ -235,12 +101,155 @@ def model_from_checkpoint(checkpoint) -> Tuple[torch.nn.Module, Dict]: for k, v in config.items(): if isinstance(v, torch.Tensor): config[k] = v.cpu() + return config + + +def _e3nn_to_cue(stct_src, stct_dst, src_config): + """ + manually check keys and assert if something unexpected happens + """ + n_layer = src_config['num_convolution_layer'] + + linear_module_names = [ + 'onehot_to_feature_x', + 'reduce_input_to_hidden', + 'reduce_hidden_to_energy', + ] + convolution_module_names = [] + for i in range(n_layer): + linear_module_names.append(f'{i}_self_interaction_1') + linear_module_names.append(f'{i}_self_interaction_2') + linear_module_names.append(f'{i}_self_connection_intro') + convolution_module_names.append(f'{i}_convolution') + + # Rule: those keys can be safely ignored before state dict load, + # except for linear.bias. This should be aborted in advance to + # this function. Others are not parameters but constants. + cue_only_linear_followers = ['linear.f.tp.f_fx.module.c'] + e3nn_only_linear_followers = ['linear.bias', 'linear.output_mask'] + ignores_in_linear = cue_only_linear_followers + e3nn_only_linear_followers + + cue_only_conv_followers = [ + 'convolution.f.tp.f_fx.module.c' + ] + e3nn_only_conv_followers = [ + 'convolution._compiled_main_left_right._w3j', + 'convolution.weight', + 'convolution.output_mask', + ] + ignores_in_conv = cue_only_conv_followers + e3nn_only_conv_followers + + updated_keys = [] + stct_updated = stct_dst.copy() + for k, v in stct_src.items(): + module_name = k.split('.')[0] + flag = False + if module_name in linear_module_names: + for ignore in ignores_in_linear: + if '.'.join([module_name, ignore]) in k: + flag = True + break + if not flag and k == '.'.join([module_name, 'linear.weight']): + updated_keys.append(k) + stct_updated[k] = v.clone() + flag = True + assert flag, f'Unexpected key from linear: {k}' + elif module_name in convolution_module_names: + for ignore in ignores_in_conv: + if '.'.join([module_name, ignore]) in k: + flag = True + break + if not flag and ( + k.startswith(f'{module_name}.weight_nn') + or k == '.'.join([module_name, 'denominator']) + ): + updated_keys.append(k) + stct_updated[k] = v.clone() + flag = True + assert flag, f'Unexpected key from linear: {k}' + else: + # assert k in stct_updated + updated_keys.append(k) + stct_updated[k] = v.clone() + + return stct_updated + + +def model_from_checkpoint( + checkpoint: Union[str, dict], +) -> Tuple[torch.nn.Module, Dict]: + from .model_build import build_E3_equivariant_model + + if isinstance(checkpoint, str): + checkpoint = torch.load(checkpoint, map_location='cpu', weights_only=False) + assert isinstance(checkpoint, dict) + elif isinstance(checkpoint, dict): + pass + else: + raise ValueError('checkpoint must be either str or dict') + + stct_cp = checkpoint['model_state_dict'] # type: ignore + config = _config_cp_routine(checkpoint.get('config')) model = build_E3_equivariant_model(config) assert isinstance(model, torch.nn.Module) - model_state_dict = _map_old_model(model_state_dict) - model_state_dict = _sort_old_convolution(model, model_state_dict) - missing, not_used = model.load_state_dict(model_state_dict, strict=False) + + stct_cp = compat.patch_state_dict_if_old(stct_cp, config, model) + missing, not_used = model.load_state_dict(stct_cp, strict=False) + if len(not_used) > 0: + warnings.warn(f'Some keys are not used: {not_used}', UserWarning) + + assert len(missing) == 0, f'Missing keys: {missing}' + + return model, config + + +def model_from_checkpoint_with_backend( + checkpoint: Union[str, dict], + backend: str = 'e3nn', +) -> Tuple[torch.nn.Module, Dict]: + from .model_build import build_E3_equivariant_model + + use_cue = backend == 'cue' + + if isinstance(checkpoint, str): + checkpoint = torch.load(checkpoint, map_location='cpu', weights_only=False) + assert isinstance(checkpoint, dict) + elif isinstance(checkpoint, dict): + pass + else: + raise ValueError('checkpoint must be either str or dict') + config_cp = _config_cp_routine(checkpoint.get('config')) + + # early return + cue_cfg = config_cp.get(KEY.CUEQUIVARIANCE_CONFIG, {'use': False}) + cp_already_use_cue = cue_cfg.get('use', False) + if use_cue == cp_already_use_cue: + return model_from_checkpoint(checkpoint) + + print(f'model convert to {backend}') + + # build empty [e3nn | cue] model + config = copy.deepcopy(config_cp) + config[KEY.CUEQUIVARIANCE_CONFIG] = {'use': use_cue} + model = build_E3_equivariant_model(config) + assert isinstance(model, torch.nn.Module) + + # patch model checkpoint's state dict + stct_src = checkpoint['model_state_dict'].copy() # type: ignore + stct_src = compat.patch_state_dict_if_old(stct_src, config_cp, model) + + # get empty [e3nn | cue] state dicts + stct_dst = model.state_dict() + # patch state dicts + stct_new = ( + _e3nn_to_cue(stct_src, stct_dst, config_cp) + if use_cue + # TODO: for now, no special routine needed when src dst changed + else _e3nn_to_cue(stct_src, stct_dst, config_cp) + ) + + missing, not_used = model.load_state_dict(stct_new, strict=False) if len(not_used) > 0: warnings.warn(f'Some keys are not used: {not_used}', UserWarning) diff --git a/tests/unit_tests/test_calculator.py b/tests/unit_tests/test_calculator.py index 0d42846c..1170a63b 100644 --- a/tests/unit_tests/test_calculator.py +++ b/tests/unit_tests/test_calculator.py @@ -6,7 +6,11 @@ from sevenn.scripts.deploy import deploy from sevenn.sevennet_calculator import SevenNetCalculator -from sevenn.util import model_from_checkpoint, pretrained_name_to_path +from sevenn.util import ( + model_from_checkpoint, + model_from_checkpoint_with_backend, + pretrained_name_to_path, +) @pytest.fixture @@ -29,6 +33,13 @@ def sevennet_0_cal(): return SevenNetCalculator('7net-0_11July2024') +@pytest.fixture(scope='module') +def sevennet_0_cue_cal(): + cpp = pretrained_name_to_path('7net-0_11July2024') + model, _ = model_from_checkpoint_with_backend(cpp, 'cue') + return SevenNetCalculator(model) + + def test_sevennet_0_cal_pbc(atoms_pbc, sevennet_0_cal): atoms1_ref = { 'energy': -3.779199, @@ -78,7 +89,7 @@ def test_sevennet_0_cal_mol(atoms_mol, sevennet_0_cal): assert np.allclose(atoms_mol.get_potential_energies(), atoms2_ref['energies']) -def test_sevennet_0_cal_deployed(tmp_path, atoms_pbc): +def test_sevennet_0_cal_deployed_consistency(tmp_path, atoms_pbc): fname = str(tmp_path / '7net_0.pt') deploy(pretrained_name_to_path('7net-0_11July2024'), fname) @@ -97,7 +108,7 @@ def test_sevennet_0_cal_deployed(tmp_path, atoms_pbc): assert np.allclose(res_cp[k], res_script[k]) -def test_sevennet_0_cal_as_instnace(atoms_pbc): +def test_sevennet_0_cal_as_instance_consistency(atoms_pbc): model, _ = model_from_checkpoint( pretrained_name_to_path('7net-0_11July2024') ) @@ -115,3 +126,33 @@ def test_sevennet_0_cal_as_instnace(atoms_pbc): for k in res_cp: assert np.allclose(res_cp[k], res_script[k]) + + +def test_sevennet_0_cal_cue(atoms_pbc, sevennet_0_cue_cal): + atoms1_ref = { + 'energy': -3.779199, + 'energies': [-1.8493923, -1.9298072], + 'force': [ + [12.666697, 0.04726403, 0.04775861], + [-12.666697, -0.04726403, -0.04775861], + ], + 'stress': [ + [ + -0.6439122, + -0.03643947, + -0.03643981, + 0.00599139, + 0.04544507, + 0.04543639, + ] + ], + } + + atoms_pbc.calc = sevennet_0_cue_cal + assert np.allclose(atoms_pbc.get_potential_energy(), atoms1_ref['energy']) + assert np.allclose( + atoms_pbc.get_potential_energy(force_consistent=True), atoms1_ref['energy'] + ) + assert np.allclose(atoms_pbc.get_forces(), atoms1_ref['force']) + assert np.allclose(atoms_pbc.get_stress(), atoms1_ref['stress']) + assert np.allclose(atoms_pbc.get_potential_energies(), atoms1_ref['energies']) From 333190c5d1f547d172daa14af56f851741f0c3fd Mon Sep 17 00:00:00 2001 From: YutackPark Date: Tue, 26 Nov 2024 21:26:04 +0900 Subject: [PATCH 050/113] mistake --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 0eaec0ed..63c6bbca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,4 @@ +[project] name = "sevenn" version = "0.11.0.dev" authors = [ From 3f7017b6c9985511741a72fea3726ebfdcfb1312 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Tue, 26 Nov 2024 23:17:57 +0900 Subject: [PATCH 051/113] fix: bugfix, sort conv only if given version is below 0.12.0 --- pyproject.toml | 2 +- sevenn/scripts/backward_compatibility.py | 20 +++++++++++++------- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 63c6bbca..3fb90b83 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "sevenn" -version = "0.11.0.dev" +version = "0.12.0.dev" authors = [ { name = "Yutack Park", email = "parkyutack@snu.ac.kr" }, { name = "Jaesun Kim" }, diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index e500f37f..d137e972 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -82,6 +82,7 @@ def patch(stct): irreps_out = conv_args.get('irreps_out', conv_args.get('filter_irreps_out')) inst_sorted = sorted(inst_old, key=lambda x: x[2]) + inst_sorted = [ # in1, in2, out, weights (inst[0], inst[1], inst[2], irreps_in1[inst[0]].mul) @@ -144,10 +145,15 @@ def patch(stct): def patch_state_dict_if_old(state_dict, config_cp, now_model): - stct_src = map_old_model(state_dict) - - cue_cfg = config_cp.get(KEY.CUEQUIVARIANCE_CONFIG, {'use': False}) - if not cue_cfg.get('use', False): - # patch only when it is e3nn model - stct_src = sort_old_convolution(now_model, stct_src) - return stct_src + version = config_cp['version'] + major, minor, _ = version.split('.')[:3] + + if int(major) == 0 and int(minor) < 10: + state_dict = map_old_model(state_dict) + + # TODO: change version criteria before release!!! + # it causes problem if model is sorted but this function is called + # ... more robust way? idk + if int(major) == 0 and int(minor) < 12: + state_dict = sort_old_convolution(now_model, state_dict) + return state_dict From 0d6bbbe1ecccba0dcdb783fcae10969d862571ab Mon Sep 17 00:00:00 2001 From: YutackPark Date: Tue, 26 Nov 2024 23:19:06 +0900 Subject: [PATCH 052/113] chore: trivials --- sevenn/model_build.py | 15 +++++++++++++-- sevenn/util.py | 2 +- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 1c419d1d..74cb6557 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -223,19 +223,30 @@ def patch_cue(layers: OrderedDict, config): if not cue_cfg.pop('use', False): return layers + if not cue_helper.is_cue_available(): + warnings.warn( + ( + 'cuEquivariance is requested, but the package is not installed. ' + + 'Fallback to original code.' + ) + ) + return layers + group = 'O3' if config[KEY.IS_PARITY] else 'SO3' + cueq_module_params = dict(layout='mul_ir', optimize_fallback=True) + cueq_module_params.update(cue_cfg) updates = {} for k, module in layers.items(): if isinstance(module, (IrrepsLinear, SelfConnectionLinearIntro)): if k == 'reduce_hidden_to_energy': # TODO: has bug with 0 shape continue module_patched = cue_helper.patch_linear( - module, group, layout='mul_ir', **cue_cfg + module, group, **cueq_module_params ) updates[k] = module_patched elif isinstance(module, IrrepsConvolution): module_patched = cue_helper.patch_convolution( - module, group, layout='mul_ir', **cue_cfg + module, group, **cueq_module_params ) updates[k] = module_patched diff --git a/sevenn/util.py b/sevenn/util.py index 8182ac65..8c922e96 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -210,7 +210,7 @@ def model_from_checkpoint_with_backend( ) -> Tuple[torch.nn.Module, Dict]: from .model_build import build_E3_equivariant_model - use_cue = backend == 'cue' + use_cue = backend.lower() in ['cue', 'cueq', 'cuequivariance'] if isinstance(checkpoint, str): checkpoint = torch.load(checkpoint, map_location='cpu', weights_only=False) From 4e6e56d25c0bb330d51b6b620ba7382978f0ead1 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 12:30:07 +0900 Subject: [PATCH 053/113] refactor: separete preprocess --- sevenn/nn/sequential.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index 7f55a499..e69747b1 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -131,8 +131,7 @@ def _eval_modal_map(self, data: AtomGraphDataType): ) data[KEY.MODAL_TYPE] = modal_idx - def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: - data = input + def _preprocess(self, data: AtomGraphDataType) -> AtomGraphDataType: if self.eval_type_map: atomic_numbers = data[self.key_atomic_numbers] onehot = self._atomic_numbers_to_onehot(atomic_numbers) @@ -144,6 +143,10 @@ def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: if self.key_grad is not None: data[self.key_grad].requires_grad_(True) + return data + + def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: + data = self._preprocess(input) for module in self: data = module(data) return data From a0b15ca912d08cafce4fda7d2dbc2d585739cb4f Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 12:35:21 +0900 Subject: [PATCH 054/113] bugfix,add: fix parity=True for cueq (thanks to MACE), add cueq tests, add __version__ to test checkpoint --- sevenn/nn/cue_helper.py | 68 ++++++-- tests/data/checkpoints/cp_0.pth | Bin 113970 -> 115890 bytes tests/data/checkpoints/cp_0.pth_old | Bin 0 -> 113970 bytes tests/lammps_tests/test_lammps.py | 119 ++++++++++++- tests/unit_tests/test_calculator.py | 8 +- tests/unit_tests/test_cueq.py | 260 ++++++++++++++++++++++++++++ tests/unit_tests/test_model.py | 2 +- 7 files changed, 437 insertions(+), 20 deletions(-) create mode 100644 tests/data/checkpoints/cp_0.pth_old create mode 100644 tests/unit_tests/test_cueq.py diff --git a/sevenn/nn/cue_helper.py b/sevenn/nn/cue_helper.py index 01b3ecde..f456d655 100644 --- a/sevenn/nn/cue_helper.py +++ b/sevenn/nn/cue_helper.py @@ -1,19 +1,57 @@ +import itertools import warnings -from typing import Literal, Union +from typing import Iterator, Literal, Union + +import e3nn.o3 as o3 +import numpy as np + +from .convolution import IrrepsConvolution +from .linear import IrrepsLinear +from .self_connection import SelfConnectionLinearIntro try: import cuequivariance as cue import cuequivariance_torch as cuet _CUE_AVAILABLE = True -except ImportError: - _CUE_AVAILABLE = False -import e3nn.o3 + # Obatained from MACE + class O3_e3nn(cue.O3): + def __mul__( # type: ignore + rep1: 'O3_e3nn', rep2: 'O3_e3nn' + ) -> Iterator['O3_e3nn']: + return [ # type: ignore + O3_e3nn(l=ir.l, p=ir.p) + for ir in cue.O3.__mul__(rep1, rep2) + ] + + @classmethod + def clebsch_gordan( # type: ignore + cls, rep1: 'O3_e3nn', rep2: 'O3_e3nn', rep3: 'O3_e3nn' + ) -> np.ndarray: + rep1, rep2, rep3 =\ + cls._from(rep1), cls._from(rep2), cls._from(rep3) # type: ignore + + if rep1.p * rep2.p == rep3.p: + return o3.wigner_3j(rep1.l, rep2.l, rep3.l).numpy()[None] * np.sqrt( + rep3.dim + ) + return np.zeros((0, rep1.dim, rep2.dim, rep3.dim)) + + def __lt__( # type: ignore + rep1: 'O3_e3nn', rep2: 'O3_e3nn' + ) -> bool: + rep2 = rep1._from(rep2) # type: ignore + return (rep1.l, rep1.p) < (rep2.l, rep2.p) + + @classmethod + def iterator(cls) -> Iterator['O3_e3nn']: + for l in itertools.count(0): + yield O3_e3nn(l=l, p=1 * (-1) ** l) + yield O3_e3nn(l=l, p=-1 * (-1) ** l) -from .convolution import IrrepsConvolution -from .linear import IrrepsLinear -from .self_connection import SelfConnectionLinearIntro +except ImportError: + _CUE_AVAILABLE = False def is_cue_available(): @@ -38,13 +76,13 @@ def _check_may_not_compatible(orig_kwargs, defaults): @cue_needed -def as_cue_irreps(irreps: e3nn.o3.Irreps, group: Literal['SO3', 'O3']): +def as_cue_irreps(irreps: o3.Irreps, group: Literal['SO3', 'O3']): """Convert e3nn irreps to given group's cue irreps""" if group == 'SO3': assert all(irrep.ir.p == 1 for irrep in irreps) return cue.Irreps('SO3', str(irreps).replace('e', '')) # type: ignore elif group == 'O3': - return cue.Irreps('O3', str(irreps)) # type: ignore + return cue.Irreps(O3_e3nn, str(irreps)) # type: ignore else: raise ValueError(f'Unknown group: {group}') @@ -88,11 +126,13 @@ def patch_convolution( # conv_kwargs will be patched in place conv_kwargs = module._convolution_kwargs - conv_kwargs.update(dict( - irreps_in1=as_cue_irreps(conv_kwargs.get('irreps_in1'), group), - irreps_in2=as_cue_irreps(conv_kwargs.get('irreps_in2'), group), - filter_irreps_out=as_cue_irreps(conv_kwargs.pop('irreps_out'), group), - )) + conv_kwargs.update( + dict( + irreps_in1=as_cue_irreps(conv_kwargs.get('irreps_in1'), group), + irreps_in2=as_cue_irreps(conv_kwargs.get('irreps_in2'), group), + filter_irreps_out=as_cue_irreps(conv_kwargs.pop('irreps_out'), group), + ) + ) inst_orig = conv_kwargs.pop('instructions') inst_sorted = sorted(inst_orig, key=lambda x: x[2]) diff --git a/tests/data/checkpoints/cp_0.pth b/tests/data/checkpoints/cp_0.pth index 85d55dc1dcfc4fe016943287be77252cfeb0771d..d8549165c8b8b1c0de64002fb4cbac451ca1f314 100644 GIT binary patch delta 5569 zcma)=Yiv|S6vt<%w6%yR$bCPH56~?8MzTSytL3}7;iSRfz9I-zIaV$0T zKxq-gDV~U3I!0BHX8~HNA}_C~?TcS>6(tv#z>*OW<=7N*rRgh^;K447x?yOFA?1=4 zvT=b2X=Gx$b23R$lM5W+g`Y3~*huO+ZJg;krSQVF=!N0yexMj>7x!YNj5J=A+Hn^+ z!5ufky$;Bv$f|NM!ULsyR|>?9nDF$~Cn=n8AtwylWi$RAOf`NQY~O4n>#PD78h?&# zG|mpqXRaq}EC6?)G=&(ahIcE*&<%$0>due+3_X!%B>|2klZi(oZ&5`SZdW3Uv3ZpQ zqyZA`<7x@bl$dy8@_;gk&WW>A8LL!Lzy*kMBkD|5OfENgz$e#u%fdgD>g}I~gWhvTLM$eiuRtuB%e>138Hw|n^ zBhj{Us!>J1Xv|!hSgeBfD@xZES0*DoT%<|{oZz!+x{@kaFKy9`l?vxA5uL80er4fc z1&_sid#MAQD7Kixkh2&1vw+3+(s@Pu>3nT^jD=9=Zm@^@XuV<^(ZLLJp!ZWvwMpoF zVT#7ypvqlB=Uz--q{>}FU-W|cuG0q!!}Q14C3LPiS6Hfp6MUFEx@m@L1o;lJ zd<0=tdd9*I$Y>2=t=+U$k+Wy9DLQ(nYCrdY4H;nSO8dEo8^NY~sM*OLt5l#TT%h7+ zzFz85LD0z@T4Absz>74(9`@1#P_f!2JWw{@C0MwRs8H>#S~wW})2EuZS%B3-q>$dSS#Y z=&`FeX*QAzcOtnXBT|_$%v?4Z(Z0x4q;qFPvW8qkO!}x4E8c~eN?_1%b@qr%qCA(& zYPjOPBFny#WBU3K^S2yxYrn`W#I&^N0%Fe95IRcsIfysQj2e`L~~=ACph8{{gL&&)EO~ delta 4478 zcmaKwYfKzf6vyvfTG$GuE6`PW7%jCb!G(Ry0+q*hyDSt6ojzhoH6gUn^kutQXk*dh znl!QXgDyBn&=0NA52WOMn9OSG@hBc19$G-Is{ne zcmMaE^S|fZnGijh68$?loSnBEtwg&5&BaBnEgdbk!2Sb%@6%`pxgsTZ*Ej50)|8A8 z+S}gT+8PM#zx0_e2MI zy5W;85u9sHN6WNmcOLC%I}~hf3;MlFk@Rl0keQ0sEhbV_?Dtk6>FequX|6iG$6p8d zWeVH<-h2h;OCf(Wi+La}MF(8Cg!N-Dbx(^w*Cn$HhlD>Iv0Av<=2xhPAhC~Z=pSBcV) zMyWdBH-8}u%80T@3Y{;orlJ%odPprvDUEP;u5mgCIF_+cG%@#u%SlWU4)6^Uuk3#h z1ECFbEEp!u5B$nx=urJikf8(Y`rmzUfNR=5NXLfku%?$z1dOZHt78Sf%Z>&AVvWON zCMht+8t5PfPGn18_NPhzj+vz&#yVk{Y2^h!A@Oij+w7C za#Mqyivv!?ttKb|LsVdC`eF%OzgEoFz7tGUO5hGx`%W;^gyPdCTv!e%(GBAS3jtMD z;fKlK9$k$PauSLk^W#yrFI-?>!oF}(-8+0=w$x#^eO**F`tu+dx@%mpVGBjqCC}8W zaynd>+Qo9^Mm)kK;!?X<>Y7k8r0d@W#%X&UzuAD_=5kS8OV9x>tpb&k=>EBn z1-#Q{mMb=6JJ`3LDkF%eN^60@P=B*HNcq0({sp1CLp7d04tDeDA zC0jO+;(`@vsFGe#`arCbL!;Qi0o+~1pI&Y-Dg(*3aa<_XO(p=peoL z=?E?~=O96yK`xKr=S+50E_X$+HP`MgwU;_wjf3$IaXobU6@A`*6+XJVMJq|119o-vm28I7s$d_{Ko#W>DmLb9AC z@()4&yix8UjL1vm+fjHmW5%2#`qW7-rI5}V6lSzn>?OKSCOHd3I-BL^6fY88#uYB! zxjv0)@j4ud;a6c77w^4sqWhU=jcNV}2~WX3 zBLAMB+M~0)8E)BLoi+0e^^-ZzE6iw}M`u6H;C!a@X9?57br&bv&f)k`pT0Aq4FMHtD2JVA_JWDOC$>n_gEHL?(4nqKa{ z3C}O%h&f$-1=OOkX|@e(GZ&FJ4rl_C@~k$Sm7fSHuMe-lZ{<5jxv_A4LzHsaJzTZ6 l$)bUGMzH!ptWQo+KFm&Oe=9%5`lZT0Pf#sPsx{oGXgm z-QAto_?z8}zAwH+-=FXMpC3Lvdv^Am=W}Xj&hE@!KVL&5Jv~!Xy?^~#>e=dr#0R;$ zga)gEo#O{Zw{2NoulKKi$p#|@J*R+j+&v~PR2dzVpbAzggF+)iRDym;NL+NZGDH;_ z7n@+-m4+&*GPE_nVlEieQwWAm0kyfCGBjKnq>LG;3=NHp4R;8}!d`(k?(!TmBf<^ZESN~k2l>ZJ&kMc67@*s3bQ zK_OHNsPkvoxI|TaqADmRIAM@by%@TOP=lizsf3y$x>1TyOGK}&MR!yQbreF~09TIg z8kC@n4hss2i;dN`Zct>bipDvIh5Trccp4X)7^3=7IZnlRJcW9kXMjqmFY*jX5gLd* z4YfRtRD!cYa0zJqf8x@vsU>Ty5}GK4rU5noL&i&@ zq`^|>3v&CVjhhuiZXz`2$mLXmmxx?0Mer7peYD6jl|U2%1XTYEved}(Vz^C(795v1 za!V1HH*zZxx3w0xjY?>%5ZVRQ{R^&cg|sgQ-%RMh;dv`}6ybR*`-BKnBHeYL>-R6>7+5ES6}ubk=v>sG^n;^=Nd zFh{Sg5(bLswNr!;5j|9ku2czO3L*SA=-R9X#l|{E2M<+J*B@OxqBy#{5E)_CSx}-bE{g*o^xj24f zVGM5rKb0_6g!fAk#))k(UJIR~5+*2wi6z}aKcS^tXi{!gXm{2rPt0#DHb zPgM!i6heAQ_s~yZ-Oe+;IDS)M2FK^4N`{EfN0m$wf2J0HmP(kd5atxsiBd?692`s| zgJV@e;e2j%mPAZq^p859TO7KXFi)HH`9)b@AVM$HLN8JYixt9>qO6w;t(*0w#qpa9 z%Q${@m9SjIubv{T5b;-P@mHyY)e2#aG|IdEUq|_@Vnpt)!dgyrOC_umiEgC`>qVjs zTB40AVUt4GEDiJjJ&|s)vWv6130pW@ca^YJWb2+HY!li3(Xwq<2{{U3htwiPY=0W( zcNT|s7j|)ISCz0^gmz64_K48AT4=ROUyOQGa@1fJ1dHFTEz1z;etZASX33dh`M^wSbwQFa1-IOwkoa^RmD{i z_?i~@x=OgA5N=B2Sn;gt0_#@9t>WlSh1(qcm`b=Kq902U?uzL5wCML$!UKiyFo1}2 zPO*y6Mdue{;v=J#p*(Vmj17ubhN*(682LE|C3!>zxrzUTN5yHI36D8#WtH$mq%{=( zO%$H`nsG&cqVP;h|6C=!PzW#ocY1f>RdM>}!fQ_7NF}`aL2o=!cdVy;yG}m z`eK@yudj+4aP3Dvj3-heANo-GerUQCl} z%Zh71vc+r5nx7W+8*0OKZMJN=rlNLSDrf~R|JQ8s`l1y%PVsDcQhPB?%2p+=X|pAE zCR&-F7WF4uh3ndERppwBI&i6=)wulMv*k{!bDZMYYD{a0X=c77RkSA8eq@W+RxN(| zqi@ybx;9&mTvO3HTqwA7wl(^5C)nu<2z zQbC(?`FH9cu_$fE&5Khvqs_%MPF+?-y|}8S_L)Gv`H8O&k4vc!*R<3!uBj;DQbB>s zl4k#(@t(H&shryu=WR|~h-qe>v-MT9C098w*HdUKuJf6Mw&q7IX&bJoXj?87v>lfv z#RWe}#khdB=a$7NJzZ%BF-?pPXh*JzCDluzzFZXvJMp8Iurt?Gv+7;iw z6sL5jfnu6e)ZSbxDrz6Dij;l%QA^p6Ybx5GO9c($vZ$yfQ|cCV0Cy

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import bulk, surface from ase.calculators.singlepoint import SinglePointCalculator +import sevenn +from sevenn.model_build import build_E3_equivariant_model from sevenn.scripts.deploy import deploy, deploy_parallel from sevenn.sevennet_calculator import SevenNetCalculator +from sevenn.util import chemical_species_preprocess logger = logging.getLogger('test_lammps') @@ -22,7 +26,6 @@ ) data_root = (pathlib.Path(__file__).parent.parent / 'data').resolve() -hfo2_path = str(data_root / 'systems' / 'hfo2.extxyz') cp_0_path = str(data_root / 'checkpoints' / 'cp_0.pth') # knows Hf, O @@ -47,6 +50,52 @@ def ref_calculator(): return SevenNetCalculator(cp_0_path) +def get_model_config(): + config = { + 'cutoff': cutoff, + 'channel': 8, + 'lmax': 2, + 'is_parity': True, + 'num_convolution_layer': 3, + 'self_connection_type': 'linear', # not NequIp + 'interaction_type': 'nequip', + 'radial_basis': { + 'radial_basis_name': 'bessel', + }, + 'cutoff_function': {'cutoff_function_name': 'poly_cut'}, + 'weight_nn_hidden_neurons': [64, 64], + 'act_radial': 'silu', + 'act_scalar': {'e': 'silu', 'o': 'tanh'}, + 'act_gate': {'e': 'silu', 'o': 'tanh'}, + 'conv_denominator': 30.0, + 'train_denominator': False, + 'shift': -10.0, + 'scale': 10.0, + 'train_shift_scale': False, + 'irreps_manual': False, + 'lmax_edge': -1, + 'lmax_node': -1, + 'readout_as_fcn': False, + 'use_bias_in_linear': False, + '_normalize_sph': True, + } + config.update(chemical_species_preprocess(['Hf', 'O'])) + return config + + +def get_model(config_overwrite=None, use_cueq=False, cueq_config=None): + cf = get_model_config() + if config_overwrite is not None: + cf.update(config_overwrite) + + cueq_config = cueq_config or {'cuequivariance_config': {'use': use_cueq}} + cf.update(cueq_config) + + model = build_E3_equivariant_model(cf, parallel=False) + assert not isinstance(model, list) + return model + + def hfo2_bulk(replicate=(2, 2, 2), a=3.0): atoms = bulk('HfO', 'rocksalt', a, orthorhombic=True) atoms = atoms * replicate @@ -264,3 +313,71 @@ def test_parallel( ) atoms.calc = ref_calculator assert_atoms(atoms, atoms_lammps) + + +def test_cueq_serial(lammps_cmd, tmp_path): + """ + TODO: Use already saved cueq enabled checkpoint after cueq becomes stable + """ + cueq = True + model = get_model(use_cueq=cueq) + ref_calc = SevenNetCalculator(model, file_type='model_instance') + atoms = get_system('bulk') + + cfg = get_model_config() + cfg.update({'cuequivariance_config': {'use': cueq}, + 'version': sevenn.__version__}) + + cp_path = str(tmp_path / 'cp.pth') + torch.save( + {'model_state_dict': model.state_dict(), 'config': cfg}, + cp_path, + ) + + pot_path = str(tmp_path / 'deployed_from_cueq_serial.pt') + deploy(cp_path, pot_path) + + atoms_lammps = serial_lammps_run( + atoms=atoms, + potential=pot_path, + wd=tmp_path, + test_name='cueq checkpoint serial lmp run test', + lammps_cmd=lammps_cmd, + ) + atoms.calc = ref_calc + assert_atoms(atoms, atoms_lammps) + + +def test_cueq_parallel(lammps_cmd, mpirun_cmd, tmp_path): + """ + TODO: Use already saved cueq enabled checkpoint after cueq becomes stable + """ + cueq = True + model = get_model(use_cueq=cueq) + ref_calc = SevenNetCalculator(model, file_type='model_instance') + atoms = get_system('surface', replicate=(4, 4, 1)) + + cfg = get_model_config() + cfg.update({'cuequivariance_config': {'use': cueq}, + 'version': sevenn.__version__}) + + cp_path = str(tmp_path / 'cp.pth') + torch.save( + {'model_state_dict': model.state_dict(), 'config': cfg}, + cp_path, + ) + + pot_path = str(tmp_path / 'deployed_from_cueq_parallel') + deploy_parallel(cp_path, pot_path) + + atoms_lammps = parallel_lammps_run( + atoms=atoms, + potential=' '.join([str(cfg['num_convolution_layer']), pot_path]), + wd=tmp_path, + test_name='cueq checkpoint parallel lmp run test', + lammps_cmd=lammps_cmd, + mpirun_cmd=mpirun_cmd, + ncores=2 + ) + atoms.calc = ref_calc + assert_atoms(atoms, atoms_lammps) diff --git a/tests/unit_tests/test_calculator.py b/tests/unit_tests/test_calculator.py index 1170a63b..7c8edcce 100644 --- a/tests/unit_tests/test_calculator.py +++ b/tests/unit_tests/test_calculator.py @@ -34,9 +34,9 @@ def sevennet_0_cal(): @pytest.fixture(scope='module') -def sevennet_0_cue_cal(): +def sevennet_0_cueq_cal(): cpp = pretrained_name_to_path('7net-0_11July2024') - model, _ = model_from_checkpoint_with_backend(cpp, 'cue') + model, _ = model_from_checkpoint_with_backend(cpp, 'cueq') return SevenNetCalculator(model) @@ -128,7 +128,7 @@ def test_sevennet_0_cal_as_instance_consistency(atoms_pbc): assert np.allclose(res_cp[k], res_script[k]) -def test_sevennet_0_cal_cue(atoms_pbc, sevennet_0_cue_cal): +def test_sevennet_0_cal_cueq(atoms_pbc, sevennet_0_cueq_cal): atoms1_ref = { 'energy': -3.779199, 'energies': [-1.8493923, -1.9298072], @@ -148,7 +148,7 @@ def test_sevennet_0_cal_cue(atoms_pbc, sevennet_0_cue_cal): ], } - atoms_pbc.calc = sevennet_0_cue_cal + atoms_pbc.calc = sevennet_0_cueq_cal assert np.allclose(atoms_pbc.get_potential_energy(), atoms1_ref['energy']) assert np.allclose( atoms_pbc.get_potential_energy(force_consistent=True), atoms1_ref['energy'] diff --git a/tests/unit_tests/test_cueq.py b/tests/unit_tests/test_cueq.py new file mode 100644 index 00000000..28b4bd60 --- /dev/null +++ b/tests/unit_tests/test_cueq.py @@ -0,0 +1,260 @@ +# TODO: add gradient test from total loss after double precision. +# so far, it is empirically checked by seeing learning curves +import copy + +import numpy as np +import pytest +import torch +from ase.build import bulk +from torch_geometric.loader.dataloader import Collater + +import sevenn +import sevenn.train.dataload as dl +from sevenn.atom_graph_data import AtomGraphData +from sevenn.model_build import build_E3_equivariant_model +from sevenn.nn.sequential import AtomGraphSequential +from sevenn.sevennet_calculator import SevenNetCalculator +from sevenn.util import ( + chemical_species_preprocess, + model_from_checkpoint_with_backend, +) + +cutoff = 4.0 + +_atoms = bulk('NaCl', 'rocksalt', a=4.00) * (2, 2, 2) +_avg_num_neigh = 30.0 +_atoms.rattle() + +_graph = AtomGraphData.from_numpy_dict(dl.unlabeled_atoms_to_graph(_atoms, cutoff)) +print(_graph) + + +def get_graphs(batched): + # batch size 2 + cloned = [_graph.clone(), _graph.clone()] + if not batched: + return cloned + else: + return Collater(cloned)(cloned) + + +def get_model_config(): + config = { + 'cutoff': cutoff, + 'channel': 8, + 'lmax': 2, + 'is_parity': True, + 'num_convolution_layer': 3, + 'self_connection_type': 'linear', # not NequIp + 'interaction_type': 'nequip', + 'radial_basis': { + 'radial_basis_name': 'bessel', + }, + 'cutoff_function': {'cutoff_function_name': 'poly_cut'}, + 'weight_nn_hidden_neurons': [64, 64], + 'act_radial': 'silu', + 'act_scalar': {'e': 'silu', 'o': 'tanh'}, + 'act_gate': {'e': 'silu', 'o': 'tanh'}, + 'conv_denominator': _avg_num_neigh, + 'train_denominator': False, + 'shift': -10.0, + 'scale': 10.0, + 'train_shift_scale': False, + 'irreps_manual': False, + 'lmax_edge': -1, + 'lmax_node': -1, + 'readout_as_fcn': False, + 'use_bias_in_linear': False, + '_normalize_sph': True, + } + chems = set() + chems.update(_atoms.get_chemical_symbols()) + config.update(**chemical_species_preprocess(list(chems))) + return config + + +def get_model(config_overwrite=None, use_cueq=False, cueq_config=None): + cf = get_model_config() + if config_overwrite is not None: + cf.update(config_overwrite) + + cueq_config = cueq_config or {'cuequivariance_config': {'use': use_cueq}} + cf.update(cueq_config) + + model = build_E3_equivariant_model(cf, parallel=False) + assert isinstance(model, AtomGraphSequential) + return model + + +@pytest.mark.parametrize( + 'cf', + [ + ({}), + ({'is_parity': False}), + ({'channel': 7}), + ({'lmax': 3}), + ({'num_interaction_layer': 2}), + ({'num_interaction_layer': 4}), + # ({'self_connection_type': 'nequip'}), + ], +) +def test_model_output(cf): + torch.manual_seed(777) + model_e3nn = get_model(cf) + torch.manual_seed(777) + model_cueq = get_model(cf, use_cueq=True) + + model_e3nn.set_is_batch_data(True) + model_cueq.set_is_batch_data(True) + + e3nn_out = model_e3nn._preprocess(get_graphs(batched=True)) + cueq_out = model_cueq._preprocess(get_graphs(batched=True)) + + for k, e3nn_f in model_e3nn._modules.items(): + cueq_f = model_cueq._modules[k] + e3nn_out = e3nn_f(e3nn_out) # type: ignore + cueq_out = cueq_f(cueq_out) # type: ignore + assert torch.allclose( + e3nn_out.x, cueq_out.x, atol=1e-6 + ), f'{k} \n\n {e3nn_f} \n\n {cueq_f}' + + assert torch.allclose( + e3nn_out.inferred_total_energy, cueq_out.inferred_total_energy + ) + assert torch.allclose(e3nn_out.atomic_energy, cueq_out.atomic_energy) + assert torch.allclose( + e3nn_out.inferred_force, cueq_out.inferred_force, atol=1e-5 + ) + assert torch.allclose( + e3nn_out.inferred_stress, cueq_out.inferred_stress, atol=1e-5 + ) + + +@pytest.mark.parametrize( + 'start_from_cueq', + [ + (True), + (False), + ], +) +def test_checkpoint_convert(tmp_path, start_from_cueq): + torch.manual_seed(123) + model_from = get_model(use_cueq=start_from_cueq) + + cfg = get_model_config() + cfg.update( + { + 'cuequivariance_config': {'use': start_from_cueq}, + 'version': sevenn.__version__, + } + ) + torch.save( + {'model_state_dict': model_from.state_dict(), 'config': cfg}, + tmp_path / 'cp_from.pth', + ) + + backend = 'e3nn' if start_from_cueq else 'cueq' + model_to, _ = model_from_checkpoint_with_backend( + str(tmp_path / 'cp_from.pth'), backend + ) + + model_from.set_is_batch_data(True) + model_to.set_is_batch_data(True) + + from_out = model_from(get_graphs(batched=True)) + to_out = model_to(get_graphs(batched=True)) + + assert torch.allclose( + from_out.inferred_total_energy, to_out.inferred_total_energy + ) + assert torch.allclose(from_out.atomic_energy, to_out.atomic_energy) + assert torch.allclose(from_out.inferred_force, to_out.inferred_force, atol=1e-5) + assert torch.allclose( + from_out.inferred_stress, to_out.inferred_stress, atol=1e-5 + ) + + +@pytest.mark.parametrize( + 'start_from_cueq', + [ + (True), + (False), + ], +) +def test_checkpoint_convert_no_batch(tmp_path, start_from_cueq): + torch.manual_seed(123) + model_from = get_model(use_cueq=start_from_cueq) + + cfg = get_model_config() + cfg.update( + { + 'cuequivariance_config': {'use': start_from_cueq}, + 'version': sevenn.__version__, + } + ) + torch.save( + {'model_state_dict': model_from.state_dict(), 'config': cfg}, + tmp_path / 'cp_from.pth', + ) + + backend = 'e3nn' if start_from_cueq else 'cueq' + model_to, _ = model_from_checkpoint_with_backend( + str(tmp_path / 'cp_from.pth'), backend + ) + + model_from.set_is_batch_data(False) + model_to.set_is_batch_data(False) + + from_out = model_from(get_graphs(batched=False)[0]) + to_out = model_to(get_graphs(batched=False)[0]) + + assert torch.allclose( + from_out.inferred_total_energy, to_out.inferred_total_energy + ) + assert torch.allclose(from_out.atomic_energy, to_out.atomic_energy) + assert torch.allclose(from_out.inferred_force, to_out.inferred_force, atol=1e-5) + assert torch.allclose( + from_out.inferred_stress, to_out.inferred_stress, atol=1e-5 + ) + + +def assert_atoms(atoms1, atoms2, rtol=1e-5, atol=1e-6): + def acl(a, b, rtol=rtol, atol=atol): + return np.allclose(a, b, rtol=rtol, atol=atol) + + assert len(atoms1) == len(atoms2) + assert acl(atoms1.get_cell(), atoms2.get_cell()) + assert acl(atoms1.get_potential_energy(), atoms2.get_potential_energy()) + assert acl(atoms1.get_forces(), atoms2.get_forces(), rtol * 10, atol * 10) + assert acl( + atoms1.get_stress(voigt=False), + atoms2.get_stress(voigt=False), + rtol * 10, + atol * 10, + ) + # assert acl(atoms1.get_potential_energies(), atoms2.get_potential_energies()) + + +def test_calculator(tmp_path): + cueq = True + model = get_model(use_cueq=cueq) + ref_calc = SevenNetCalculator(model, file_type='model_instance') + atoms = copy.deepcopy(_atoms) + atoms.calc = ref_calc + + cfg = get_model_config() + cfg.update( + {'cuequivariance_config': {'use': cueq}, 'version': sevenn.__version__} + ) + + cp_path = str(tmp_path / 'cp.pth') + torch.save( + {'model_state_dict': model.state_dict(), 'config': cfg}, + cp_path, + ) + + calc2 = SevenNetCalculator(cp_path, enable_cueq=False) + atoms2 = copy.deepcopy(_atoms) + atoms2.calc = calc2 + + assert_atoms(atoms, atoms2) diff --git a/tests/unit_tests/test_model.py b/tests/unit_tests/test_model.py index a3f9a239..55bf9df6 100644 --- a/tests/unit_tests/test_model.py +++ b/tests/unit_tests/test_model.py @@ -66,7 +66,7 @@ def get_model_config(): 'act_radial': 'silu', 'act_scalar': {'e': 'silu', 'o': 'tanh'}, 'act_gate': {'e': 'silu', 'o': 'tanh'}, - 'conv_denominator': 1.0, + 'conv_denominator': 30.0, 'train_denominator': False, 'self_connection_type': 'nequip', 'shift': -10.0, From c03cf0c2af8cd2eeed60f5f87d270b4b29e4f753 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 12:35:41 +0900 Subject: [PATCH 055/113] chore --- sevenn/sevennet_calculator.py | 2 +- sevenn/util.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/sevenn/sevennet_calculator.py b/sevenn/sevennet_calculator.py index eada4f4e..df6322ab 100644 --- a/sevenn/sevennet_calculator.py +++ b/sevenn/sevennet_calculator.py @@ -80,7 +80,7 @@ def __init__( checkpoint = model else: checkpoint = util.pretrained_name_to_path(model) - backend = 'e3nn' if not enable_cueq else 'cue' + backend = 'e3nn' if not enable_cueq else 'cueq' model_loaded, config = util.model_from_checkpoint_with_backend( checkpoint, backend ) diff --git a/sevenn/util.py b/sevenn/util.py index 8c922e96..fb0ee5ff 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -227,7 +227,7 @@ def model_from_checkpoint_with_backend( if use_cue == cp_already_use_cue: return model_from_checkpoint(checkpoint) - print(f'model convert to {backend}') + print(f'convert the model to {backend}') # build empty [e3nn | cue] model config = copy.deepcopy(config_cp) From 6260ccb78cc287548cbca5be7bce6af6a3c82f59 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 14:24:10 +0900 Subject: [PATCH 056/113] add,fix: Fully connected tp + params -> kwargs --- sevenn/model_build.py | 8 ++++ sevenn/nn/convolution.py | 8 ++-- sevenn/nn/cue_helper.py | 48 +++++++++++++++++++++--- sevenn/nn/linear.py | 10 ++--- sevenn/nn/self_connection.py | 29 +++++++++++++- sevenn/scripts/backward_compatibility.py | 2 +- sevenn/util.py | 24 +++++++++++- 7 files changed, 111 insertions(+), 18 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 74cb6557..2cc8f544 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -232,6 +232,9 @@ def patch_cue(layers: OrderedDict, config): ) return layers + if not cue_helper.is_cue_cuda_available_model(config): + return layers + group = 'O3' if config[KEY.IS_PARITY] else 'SO3' cueq_module_params = dict(layout='mul_ir', optimize_fallback=True) cueq_module_params.update(cue_cfg) @@ -244,6 +247,11 @@ def patch_cue(layers: OrderedDict, config): module, group, **cueq_module_params ) updates[k] = module_patched + elif isinstance(module, SelfConnectionIntro): + module_patched = cue_helper.patch_fully_connected( + module, group, **cueq_module_params + ) + updates[k] = module_patched elif isinstance(module, IrrepsConvolution): module_patched = cue_helper.patch_convolution( module, group, **cueq_module_params diff --git a/sevenn/nn/convolution.py b/sevenn/nn/convolution.py index cdc615ac..d5f1cd7b 100644 --- a/sevenn/nn/convolution.py +++ b/sevenn/nn/convolution.py @@ -84,7 +84,7 @@ def __init__( self._instructions_before_sort = instructions instructions = sorted(instructions, key=lambda x: x[2]) - self._convolution_kwargs = dict( + self.convolution_kwargs = dict( irreps_in1=irreps_x, irreps_in2=irreps_filter, irreps_out=irreps_mid, @@ -93,7 +93,7 @@ def __init__( internal_weights=False, ) - self._weight_nn_kwargs = dict( + self.weight_nn_kwargs = dict( hs=weight_layer_input_to_hidden + [weight_numel], act=weight_layer_act ) @@ -115,8 +115,8 @@ def instantiate(self): if self.weight_nn is not None: raise ValueError('Weight_nn layer already exists') - self.convolution = self.convolution_cls(**self._convolution_kwargs) - self.weight_nn = self.weight_nn_cls(**self._weight_nn_kwargs) + self.convolution = self.convolution_cls(**self.convolution_kwargs) + self.weight_nn = self.weight_nn_cls(**self.weight_nn_kwargs) self.layer_instantiated = True def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: diff --git a/sevenn/nn/cue_helper.py b/sevenn/nn/cue_helper.py index f456d655..b982ca0e 100644 --- a/sevenn/nn/cue_helper.py +++ b/sevenn/nn/cue_helper.py @@ -7,7 +7,7 @@ from .convolution import IrrepsConvolution from .linear import IrrepsLinear -from .self_connection import SelfConnectionLinearIntro +from .self_connection import SelfConnectionIntro, SelfConnectionLinearIntro try: import cuequivariance as cue @@ -21,8 +21,7 @@ def __mul__( # type: ignore rep1: 'O3_e3nn', rep2: 'O3_e3nn' ) -> Iterator['O3_e3nn']: return [ # type: ignore - O3_e3nn(l=ir.l, p=ir.p) - for ir in cue.O3.__mul__(rep1, rep2) + O3_e3nn(l=ir.l, p=ir.p) for ir in cue.O3.__mul__(rep1, rep2) ] @classmethod @@ -75,6 +74,19 @@ def _check_may_not_compatible(orig_kwargs, defaults): warnings.warn(f'{k}: {v} is ignored to use cuEquivariance') +def is_cue_cuda_available_model(config): + if int(config.get('lmax')) < 3 and int(config.get('channel')) in [32, 64, 128]: + warnings.warn( + 'https://github.com/NVIDIA/cuEquivariance/issues/33, fallback to e3nn' + ) + return False + elif config.get('use_bias_in_linear', False): + warnings.warn('Bias in linear can not be used with cueq, fallback to e3nn') + return False + else: + return True + + @cue_needed def as_cue_irreps(irreps: o3.Irreps, group: Literal['SO3', 'O3']): """Convert e3nn irreps to given group's cue irreps""" @@ -98,7 +110,7 @@ def patch_linear( module.irreps_in = as_cue_irreps(module.irreps_in, group) # type: ignore module.irreps_out = as_cue_irreps(module.irreps_out, group) # type: ignore - orig_kwargs = module.linear_params + orig_kwargs = module.linear_kwargs may_not_compatible_default = dict( f_in=None, @@ -125,7 +137,7 @@ def patch_convolution( assert not module.layer_instantiated # conv_kwargs will be patched in place - conv_kwargs = module._convolution_kwargs + conv_kwargs = module.convolution_kwargs conv_kwargs.update( dict( irreps_in1=as_cue_irreps(conv_kwargs.get('irreps_in1'), group), @@ -155,3 +167,29 @@ def patch_convolution( module.convolution_cls = cuet.ChannelWiseTensorProduct # type: ignore conv_kwargs.update(**cue_kwargs) return module + + +@cue_needed +def patch_fully_connected( + module: SelfConnectionIntro, + group: Literal['SO3', 'O3'], + **cue_kwargs, +): + assert not module.layer_instantiated + + module.irreps_in1 = as_cue_irreps(module.irreps_in1, group) # type: ignore + module.irreps_in2 = as_cue_irreps(module.irreps_in2, group) # type: ignore + module.irreps_out = as_cue_irreps(module.irreps_out, group) # type: ignore + + may_not_compatible_default = dict( + irrep_normalization=None, + path_normalization=None, + ) + # pop may_not_compatible_defaults + _check_may_not_compatible( + module.fc_tensor_product_kwargs, may_not_compatible_default + ) + + module.fc_tensor_product_cls = cuet.FullyConnectedTensorProduct # type: ignore + module.fc_tensor_product_kwargs.update(**cue_kwargs) + return module diff --git a/sevenn/nn/linear.py b/sevenn/nn/linear.py index be83f711..b5b87d21 100644 --- a/sevenn/nn/linear.py +++ b/sevenn/nn/linear.py @@ -25,7 +25,7 @@ def __init__( data_key_modal_attr: str = KEY.MODAL_ATTR, num_modalities: int = 0, lazy_layer_instantiate: bool = True, - **linear_params, + **linear_kwargs, ): super().__init__() self.key_input = data_key_in @@ -38,7 +38,7 @@ def __init__( self._irreps_in_wo_modal = irreps_in self.irreps_in = irreps_in self.irreps_out = irreps_out - self.linear_params = linear_params + self.linear_kwargs = linear_kwargs self.linear = None self.layer_instantiated = False @@ -58,7 +58,7 @@ def instantiate(self): if self.linear is not None: raise ValueError('Linear layer already exists') self.linear = self.linear_cls( - self.irreps_in, self.irreps_out, **self.linear_params + self.irreps_in, self.irreps_out, **self.linear_kwargs ) self.layer_instantiated = True @@ -155,7 +155,7 @@ def __init__( activation: Callable, data_key_in: str, data_key_out: Optional[str] = None, - **e3nn_params, + **e3nn_kwargs, ): super().__init__() self.key_input = data_key_in @@ -172,7 +172,7 @@ def __init__( self.fcn = FullyConnectedNet( [inp_dim] + hidden_neurons + [dim_out], activation, - **e3nn_params, + **e3nn_kwargs, ) def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: diff --git a/sevenn/nn/self_connection.py b/sevenn/nn/self_connection.py index 94ee620e..83080f2b 100644 --- a/sevenn/nn/self_connection.py +++ b/sevenn/nn/self_connection.py @@ -20,6 +20,7 @@ def __init__( irreps_out: Irreps, data_key_x: str = KEY.NODE_FEATURE, data_key_operand: str = KEY.NODE_ATTR, + lazy_layer_instantiate: bool = True, **kwargs, # for compatibility ): super().__init__() @@ -27,10 +28,34 @@ def __init__( self.fc_tensor_product = FullyConnectedTensorProduct( irreps_in, irreps_operand, irreps_out ) + self.irreps_in1 = irreps_in + self.irreps_in2 = irreps_operand + self.irreps_out = irreps_out + self.key_x = data_key_x self.key_operand = data_key_operand + self.fc_tensor_product = None + self.layer_instantiated = False + self.fc_tensor_product_cls = FullyConnectedTensorProduct + self.fc_tensor_product_kwargs = kwargs + + if not lazy_layer_instantiate: + self.instantiate() + + def instantiate(self): + if self.fc_tensor_product is not None: + raise ValueError('fc_tensor_product layer already exists') + self.fc_tensor_product = self.fc_tensor_product_cls( + self.irreps_in1, + self.irreps_in2, + self.irreps_out, + **self.fc_tensor_product_kwargs, + ) + self.layer_instantiated = True + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + assert self.fc_tensor_product is not None, 'Layer is not instantiated' data[KEY.SELF_CONNECTION_TEMP] = self.fc_tensor_product( data[self.key_x], data[self.key_operand] ) @@ -62,7 +87,7 @@ def __init__( # TODO: better to have SelfConnectionIntro super class kwargs.pop('irreps_operand') - self.linear_params = kwargs + self.linear_kwargs = kwargs if not lazy_layer_instantiate: self.instantiate() @@ -71,7 +96,7 @@ def instantiate(self): if self.linear is not None: raise ValueError('Linear layer already exists') self.linear = self.linear_cls( - self.irreps_in, self.irreps_out, **self.linear_params + self.irreps_in, self.irreps_out, **self.linear_kwargs ) self.layer_instantiated = True diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index d137e972..d17cf6e1 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -76,7 +76,7 @@ def patch(stct): inst_old = [(inst[0], inst[1], inst[2]) for inst in inst_old] del conv._instructions_before_sort - conv_args = conv._convolution_kwargs + conv_args = conv.convolution_kwargs irreps_in1 = conv_args['irreps_in1'] irreps_in2 = conv_args['irreps_in2'] irreps_out = conv_args.get('irreps_out', conv_args.get('filter_irreps_out')) diff --git a/sevenn/util.py b/sevenn/util.py index fb0ee5ff..6f9aea34 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -116,10 +116,14 @@ def _e3nn_to_cue(stct_src, stct_dst, src_config): 'reduce_hidden_to_energy', ] convolution_module_names = [] + fc_tensor_product_module_names = [] for i in range(n_layer): linear_module_names.append(f'{i}_self_interaction_1') linear_module_names.append(f'{i}_self_interaction_2') - linear_module_names.append(f'{i}_self_connection_intro') + if src_config.get(KEY.SELF_CONNECTION_TYPE) == 'linear': + linear_module_names.append(f'{i}_self_connection_intro') + elif src_config.get(KEY.SELF_CONNECTION_TYPE) == 'nequip': + fc_tensor_product_module_names.append(f'{i}_self_connection_intro') convolution_module_names.append(f'{i}_convolution') # Rule: those keys can be safely ignored before state dict load, @@ -139,6 +143,14 @@ def _e3nn_to_cue(stct_src, stct_dst, src_config): ] ignores_in_conv = cue_only_conv_followers + e3nn_only_conv_followers + cue_only_fc_followers = [ + 'fc_tensor_product.f.tp.f_fx.module.c' + ] + e3nn_only_fc_followers = [ + 'fc_tensor_product.output_mask', + ] + ignores_in_fc = cue_only_fc_followers + e3nn_only_fc_followers + updated_keys = [] stct_updated = stct_dst.copy() for k, v in stct_src.items(): @@ -167,6 +179,16 @@ def _e3nn_to_cue(stct_src, stct_dst, src_config): stct_updated[k] = v.clone() flag = True assert flag, f'Unexpected key from linear: {k}' + elif module_name in fc_tensor_product_module_names: + for ignore in ignores_in_fc: + if '.'.join([module_name, ignore]) in k: + flag = True + break + if not flag and k == '.'.join([module_name, 'fc_tensor_product.weight']): + updated_keys.append(k) + stct_updated[k] = v.clone() + flag = True + assert flag, f'Unexpected key from fc tensor product: {k}' else: # assert k in stct_updated updated_keys.append(k) From fbd17e00693d06fe084c624604f69de4dee6aa41 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 14:24:45 +0900 Subject: [PATCH 057/113] fix tests --- tests/lammps_tests/test_lammps.py | 3 +++ tests/unit_tests/test_calculator.py | 2 ++ tests/unit_tests/test_cueq.py | 10 +++++++--- 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/tests/lammps_tests/test_lammps.py b/tests/lammps_tests/test_lammps.py index e03db655..30dccb05 100644 --- a/tests/lammps_tests/test_lammps.py +++ b/tests/lammps_tests/test_lammps.py @@ -13,6 +13,7 @@ import sevenn from sevenn.model_build import build_E3_equivariant_model +from sevenn.nn.cue_helper import is_cue_available from sevenn.scripts.deploy import deploy, deploy_parallel from sevenn.sevennet_calculator import SevenNetCalculator from sevenn.util import chemical_species_preprocess @@ -315,6 +316,7 @@ def test_parallel( assert_atoms(atoms, atoms_lammps) +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_cueq_serial(lammps_cmd, tmp_path): """ TODO: Use already saved cueq enabled checkpoint after cueq becomes stable @@ -348,6 +350,7 @@ def test_cueq_serial(lammps_cmd, tmp_path): assert_atoms(atoms, atoms_lammps) +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_cueq_parallel(lammps_cmd, mpirun_cmd, tmp_path): """ TODO: Use already saved cueq enabled checkpoint after cueq becomes stable diff --git a/tests/unit_tests/test_calculator.py b/tests/unit_tests/test_calculator.py index 7c8edcce..952bb92a 100644 --- a/tests/unit_tests/test_calculator.py +++ b/tests/unit_tests/test_calculator.py @@ -4,6 +4,7 @@ import pytest from ase.build import bulk, molecule +from sevenn.nn.cue_helper import is_cue_available from sevenn.scripts.deploy import deploy from sevenn.sevennet_calculator import SevenNetCalculator from sevenn.util import ( @@ -128,6 +129,7 @@ def test_sevennet_0_cal_as_instance_consistency(atoms_pbc): assert np.allclose(res_cp[k], res_script[k]) +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_sevennet_0_cal_cueq(atoms_pbc, sevennet_0_cueq_cal): atoms1_ref = { 'energy': -3.779199, diff --git a/tests/unit_tests/test_cueq.py b/tests/unit_tests/test_cueq.py index 28b4bd60..377a9ed0 100644 --- a/tests/unit_tests/test_cueq.py +++ b/tests/unit_tests/test_cueq.py @@ -12,6 +12,7 @@ import sevenn.train.dataload as dl from sevenn.atom_graph_data import AtomGraphData from sevenn.model_build import build_E3_equivariant_model +from sevenn.nn.cue_helper import is_cue_available from sevenn.nn.sequential import AtomGraphSequential from sevenn.sevennet_calculator import SevenNetCalculator from sevenn.util import ( @@ -26,7 +27,6 @@ _atoms.rattle() _graph = AtomGraphData.from_numpy_dict(dl.unlabeled_atoms_to_graph(_atoms, cutoff)) -print(_graph) def get_graphs(batched): @@ -45,7 +45,7 @@ def get_model_config(): 'lmax': 2, 'is_parity': True, 'num_convolution_layer': 3, - 'self_connection_type': 'linear', # not NequIp + 'self_connection_type': 'nequip', # not NequIp 'interaction_type': 'nequip', 'radial_basis': { 'radial_basis_name': 'bessel', @@ -86,16 +86,17 @@ def get_model(config_overwrite=None, use_cueq=False, cueq_config=None): return model +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') @pytest.mark.parametrize( 'cf', [ ({}), + ({'self_connection_type': 'linear'}), ({'is_parity': False}), ({'channel': 7}), ({'lmax': 3}), ({'num_interaction_layer': 2}), ({'num_interaction_layer': 4}), - # ({'self_connection_type': 'nequip'}), ], ) def test_model_output(cf): @@ -130,6 +131,7 @@ def test_model_output(cf): ) +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') @pytest.mark.parametrize( 'start_from_cueq', [ @@ -174,6 +176,7 @@ def test_checkpoint_convert(tmp_path, start_from_cueq): ) +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') @pytest.mark.parametrize( 'start_from_cueq', [ @@ -235,6 +238,7 @@ def acl(a, b, rtol=rtol, atol=atol): # assert acl(atoms1.get_potential_energies(), atoms2.get_potential_energies()) +@pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_calculator(tmp_path): cueq = True model = get_model(use_cueq=cueq) From 0caac3fe6e28affa111f283972db3fa0dd297da4 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 14:27:07 +0900 Subject: [PATCH 058/113] solve stash conflict --- sevenn/model_build.py | 3 +++ sevenn/nn/cue_helper.py | 3 +-- sevenn/nn/self_connection.py | 2 ++ 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 2cc8f544..2204572b 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -235,6 +235,9 @@ def patch_cue(layers: OrderedDict, config): if not cue_helper.is_cue_cuda_available_model(config): return layers + if not cue_helper.is_cue_cuda_available_model(config): + return layers + group = 'O3' if config[KEY.IS_PARITY] else 'SO3' cueq_module_params = dict(layout='mul_ir', optimize_fallback=True) cueq_module_params.update(cue_cfg) diff --git a/sevenn/nn/cue_helper.py b/sevenn/nn/cue_helper.py index b982ca0e..9bd51177 100644 --- a/sevenn/nn/cue_helper.py +++ b/sevenn/nn/cue_helper.py @@ -28,8 +28,7 @@ def __mul__( # type: ignore def clebsch_gordan( # type: ignore cls, rep1: 'O3_e3nn', rep2: 'O3_e3nn', rep3: 'O3_e3nn' ) -> np.ndarray: - rep1, rep2, rep3 =\ - cls._from(rep1), cls._from(rep2), cls._from(rep3) # type: ignore + rep1, rep2, rep3 = cls._from(rep1), cls._from(rep2), cls._from(rep3) if rep1.p * rep2.p == rep3.p: return o3.wigner_3j(rep1.l, rep2.l, rep3.l).numpy()[None] * np.sqrt( diff --git a/sevenn/nn/self_connection.py b/sevenn/nn/self_connection.py index 83080f2b..ce731b51 100644 --- a/sevenn/nn/self_connection.py +++ b/sevenn/nn/self_connection.py @@ -50,6 +50,8 @@ def instantiate(self): self.irreps_in1, self.irreps_in2, self.irreps_out, + shared_weights=True, + internal_weights=None, # same as True **self.fc_tensor_product_kwargs, ) self.layer_instantiated = True From 0cc05147c8c321bfd6c80fdf2bece52489f054c0 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 14:35:09 +0900 Subject: [PATCH 059/113] fall back to e3nn only when cuda is available --- sevenn/nn/cue_helper.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/sevenn/nn/cue_helper.py b/sevenn/nn/cue_helper.py index 9bd51177..bd725578 100644 --- a/sevenn/nn/cue_helper.py +++ b/sevenn/nn/cue_helper.py @@ -4,6 +4,7 @@ import e3nn.o3 as o3 import numpy as np +import torch.cuda from .convolution import IrrepsConvolution from .linear import IrrepsLinear @@ -74,7 +75,11 @@ def _check_may_not_compatible(orig_kwargs, defaults): def is_cue_cuda_available_model(config): - if int(config.get('lmax')) < 3 and int(config.get('channel')) in [32, 64, 128]: + if ( + int(config.get('lmax')) < 3 + and int(config.get('channel')) in [32, 64, 128] + and torch.cuda.is_available() + ): warnings.warn( 'https://github.com/NVIDIA/cuEquivariance/issues/33, fallback to e3nn' ) From e3e9345e8b4cbce2890e24aa86fd2dff76f22887 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 15:01:05 +0900 Subject: [PATCH 060/113] chore: cue -> cueq --- sevenn/main/sevenn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/main/sevenn.py b/sevenn/main/sevenn.py index 2a2ae921..705d6cf0 100644 --- a/sevenn/main/sevenn.py +++ b/sevenn/main/sevenn.py @@ -146,7 +146,7 @@ def cmd_parse_main(args=None): type=str, ) ag.add_argument( - '-cue', + '-cueq', '--enable_cueq', help='use cuEquivariance for training', action='store_true' From 2c4976ef8065e8fb72d0a5d015c3ea1a04a4df11 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 20:28:36 +0900 Subject: [PATCH 061/113] fix: merge err --- sevenn/model_build.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 2204572b..2cc8f544 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -235,9 +235,6 @@ def patch_cue(layers: OrderedDict, config): if not cue_helper.is_cue_cuda_available_model(config): return layers - if not cue_helper.is_cue_cuda_available_model(config): - return layers - group = 'O3' if config[KEY.IS_PARITY] else 'SO3' cueq_module_params = dict(layout='mul_ir', optimize_fallback=True) cueq_module_params.update(cue_cfg) From 5c61f9c9d7c4f543d9a38513f085b94313eddb38 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 20:37:50 +0900 Subject: [PATCH 062/113] refactor: lmax edge --- sevenn/model_build.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 2cc8f544..fdc2c2e4 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -360,10 +360,9 @@ def build_E3_equivariant_model(config: dict, parallel=False): interaction_type = config[KEY.INTERACTION_TYPE] use_bias_in_linear = config[KEY.USE_BIAS_IN_LINEAR] - # is_parity = config[KEY.IS_PARITY] # boolean - lmax_node = _ = config[KEY.LMAX] # ignore second (lmax_edge) - if config[KEY.LMAX_EDGE] > 0: - _ = config[KEY.LMAX_EDGE] + lmax_node = config[KEY.LMAX] # ignore second (lmax_edge) + # if config[KEY.LMAX_EDGE] > 0: # not yet used + # _ = config[KEY.LMAX_EDGE] if config[KEY.LMAX_NODE] > 0: lmax_node = config[KEY.LMAX_NODE] From 8d8b160bd4f52834312bf8b219296bbee51c7ee4 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 27 Nov 2024 21:24:36 +0900 Subject: [PATCH 063/113] fix: multi gpu training for edge cases --- sevenn/main/sevenn.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/sevenn/main/sevenn.py b/sevenn/main/sevenn.py index 2a2ae921..0c055539 100644 --- a/sevenn/main/sevenn.py +++ b/sevenn/main/sevenn.py @@ -102,6 +102,9 @@ def main(args=None): train_config[KEY.RANK] = rank train_config[KEY.WORLD_SIZE] = world_size + if distributed: + torch.cuda.set_device(torch.device('cuda', local_rank)) + if use_cue: if KEY.CUEQUIVARIANCE_CONFIG not in model_config: model_config[KEY.CUEQUIVARIANCE_CONFIG] = {'use': True} From 2d2a6a28b4aaaf3c5c14d37e66c99a281c6f4805 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 15:25:43 +0900 Subject: [PATCH 064/113] e3nn > 0.5.0, due to changed CG coeff convention --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3fb90b83..97d59293 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "ase", "braceexpand", "pyyaml", - "e3nn>=0.4.4", + "e3nn>=0.5.0", "tqdm", "scikit-learn", "torch_geometric>=2.5.0", From 22e43aa0ef720e7018e67bb78def06ffa217d611 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 15:36:08 +0900 Subject: [PATCH 065/113] restore version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 97d59293..30ac573e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "sevenn" -version = "0.12.0.dev" +version = "0.11.0.dev" authors = [ { name = "Yutack Park", email = "parkyutack@snu.ac.kr" }, { name = "Jaesun Kim" }, From facdb98a6aad7ccc7f4b5bc964ed4a8344c651b3 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 16:13:27 +0900 Subject: [PATCH 066/113] fix: old ver --- sevenn/scripts/backward_compatibility.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index d17cf6e1..37f74939 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -154,6 +154,6 @@ def patch_state_dict_if_old(state_dict, config_cp, now_model): # TODO: change version criteria before release!!! # it causes problem if model is sorted but this function is called # ... more robust way? idk - if int(major) == 0 and int(minor) < 12: + if int(major) == 0 and int(minor) < 11: state_dict = sort_old_convolution(now_model, state_dict) return state_dict From d14c0e287771be5f32835a436f8a8abd2cab74d8 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 16:13:45 +0900 Subject: [PATCH 067/113] remove old test cp --- tests/data/checkpoints/cp_0.pth_old | Bin 113970 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 tests/data/checkpoints/cp_0.pth_old diff --git a/tests/data/checkpoints/cp_0.pth_old b/tests/data/checkpoints/cp_0.pth_old deleted file mode 100644 index 85d55dc1dcfc4fe016943287be77252cfeb0771d..0000000000000000000000000000000000000000 GIT binary patch literal 0 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zewuP;m7M~g+3(tS+*3nCK8)o5!5qrIfwtd-hBZf5h^x$L=OqpKXwP5p>a$Free#W1ND0ohwmuaCPO+%I5?Ye8PbC3Q*nYB){hR_xQ%^ zt+%f&+N+p6@=e~U`ZKEDs3uc|AlYIq}TT8>dcZE-i%ELg|`Y){AW2WYC{du z!IH)jhIDLBZpd!D@fx8ROG;<}$_cfzcWO=!iwfA!(bf&}!IFt74C$;OThFa=8bi7$ zNJX4eG8S~qr(wzmYm;M1R|VO+O)j0WIY>b&Y?f@Y=n+Hh$p>pQh9&9A^X6nxXOk75 zBtvLZWjZ}G@5jbyQKdI=c#FCQjj7b&o%`Ep1lF<<5gC*}|F{IL4 zbz!ok@lJ+R5ns!v3dfQv;tLk^@nCKC$YyN5hxj(Iq>A`9%vxV2qB9~NtWAwwj7_EZ z6cUPC5_Bny=2bpeGA559Rm9iOX;=OMO34RHy6+H$Bj7b&o)m-#Apx&nYK893^PZ6eXWUiTdo1Xg_QYB7f z?H>%)lhOi)R1sgr#@p8$XpHi~#_4{LA(i4&cA{2t@>E;Nj!w4GPVCA`jUg7WfGd5MkC+u6qif_yaA*oDI>tV(O zFrbR~gsq0yFl8*M6rW-`g-r!mk{ij`R1u%>b^?}69M6y{;(H<3NMy;v;1#~pRRmQLBogskw5;;WB&&wHF@~} From 0e5426640e45727d1ad6e40bd5284e310cc6a1bb Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 16:30:43 +0900 Subject: [PATCH 068/113] refactor: add overload for model build to quite pyright --- sevenn/model_build.py | 20 ++++++++++++++++++-- sevenn/scripts/deploy.py | 1 - sevenn/scripts/train.py | 3 --- sevenn/util.py | 2 -- 4 files changed, 18 insertions(+), 8 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index fdc2c2e4..937cf66d 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -1,6 +1,7 @@ import copy import warnings from collections import OrderedDict +from typing import List, Literal, Union, overload from e3nn.o3 import Irreps @@ -339,8 +340,23 @@ def slice_until_this(module_name, layers): return layers_list -# TODO: it gets bigger and bigger. refactor it -def build_E3_equivariant_model(config: dict, parallel=False): +@overload +def build_E3_equivariant_model( + config: dict, parallel: Literal[False] = False +) -> AtomGraphSequential: + ... + + +@overload +def build_E3_equivariant_model( + config: dict, parallel: Literal[True] +) -> List[AtomGraphSequential]: + ... + + +def build_E3_equivariant_model( + config: dict, parallel: bool = False +) -> Union[AtomGraphSequential, List[AtomGraphSequential]]: """ output shapes (w/o batch) diff --git a/sevenn/scripts/deploy.py b/sevenn/scripts/deploy.py index 17ae7c23..fe7d1f41 100644 --- a/sevenn/scripts/deploy.py +++ b/sevenn/scripts/deploy.py @@ -75,7 +75,6 @@ def deploy_parallel(checkpoint, fname): # TODO: build model only once model_list = build_E3_equivariant_model(config, parallel=True) - assert isinstance(model_list, list) dct_temp = {} copy_counter = {gk: 0 for gk in GHOST_LAYERS_KEYS} for ghost_layer_key in GHOST_LAYERS_KEYS: diff --git a/sevenn/scripts/train.py b/sevenn/scripts/train.py index 45f6dfb4..e4716f43 100644 --- a/sevenn/scripts/train.py +++ b/sevenn/scripts/train.py @@ -1,7 +1,6 @@ from typing import Optional import torch.distributed as dist -from torch.nn import Module from torch.utils.data.distributed import DistributedSampler from torch_geometric.loader import DataLoader @@ -78,7 +77,6 @@ def train_v2(config, working_dir: str): log.write('\nModel building...\n') model = build_E3_equivariant_model(config) - assert isinstance(model, Module) log.print_model_info(model, config) trainer = Trainer.from_config(model, config) @@ -120,7 +118,6 @@ def train(config, working_dir: str): log.write('\nModel building...\n') model = build_E3_equivariant_model(config) - assert isinstance(model, Module) log.write('Model building was successful\n') diff --git a/sevenn/util.py b/sevenn/util.py index 6f9aea34..eebce910 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -214,7 +214,6 @@ def model_from_checkpoint( config = _config_cp_routine(checkpoint.get('config')) model = build_E3_equivariant_model(config) - assert isinstance(model, torch.nn.Module) stct_cp = compat.patch_state_dict_if_old(stct_cp, config, model) missing, not_used = model.load_state_dict(stct_cp, strict=False) @@ -255,7 +254,6 @@ def model_from_checkpoint_with_backend( config = copy.deepcopy(config_cp) config[KEY.CUEQUIVARIANCE_CONFIG] = {'use': use_cue} model = build_E3_equivariant_model(config) - assert isinstance(model, torch.nn.Module) # patch model checkpoint's state dict stct_src = checkpoint['model_state_dict'].copy() # type: ignore From ae896607cef72cd80d7ec660aad07c51a28273d5 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 19:58:54 +0900 Subject: [PATCH 069/113] refactor,add: checkpoint wrapper --- sevenn/checkpoint.py | 252 ++++++++++++++++++++++++++ sevenn/model_build.py | 4 +- sevenn/scripts/deploy.py | 19 +- sevenn/scripts/processing_continue.py | 13 +- sevenn/sevennet_calculator.py | 17 +- sevenn/train/trainer.py | 15 +- sevenn/util.py | 213 +++------------------- tests/data/checkpoints/_cp_0.pth_old | Bin 0 -> 113970 bytes tests/lammps_tests/test_lammps.py | 2 + tests/unit_tests/test_cueq.py | 3 + 10 files changed, 310 insertions(+), 228 deletions(-) create mode 100644 sevenn/checkpoint.py create mode 100644 tests/data/checkpoints/_cp_0.pth_old diff --git a/sevenn/checkpoint.py b/sevenn/checkpoint.py new file mode 100644 index 00000000..76bb167b --- /dev/null +++ b/sevenn/checkpoint.py @@ -0,0 +1,252 @@ +import os +import pathlib +import warnings +from copy import deepcopy +from typing import Any, Dict, Optional, Union + +from torch import Tensor +from torch import load as torch_load + +import sevenn._keys as KEY +import sevenn.scripts.backward_compatibility as compat +from sevenn._const import model_defaults +from sevenn.nn.sequential import AtomGraphSequential + + +def copy_state_dict(state_dict) -> dict: + if isinstance(state_dict, dict): + return {key: copy_state_dict(value) for key, value in state_dict.items()} + elif isinstance(state_dict, list): + return [copy_state_dict(item) for item in state_dict] # type: ignore + elif isinstance(state_dict, Tensor): + return state_dict.clone() # type: ignore + else: + # For non-tensor values (e.g., scalars, None), return as-is + return state_dict + + +def _config_cp_routine(config): + defaults = {**model_defaults(config)} + config = compat.patch_old_config(config) # type: ignore + + for k, v in defaults.items(): + if k in config: + continue + if os.getenv('SEVENN_DEBUG', False): + warnings.warn(f'{k} not in config, use default value {v}', UserWarning) + config[k] = v + + # expect only non-tensor values in config, if exists, move to cpu + # This can be happen if config has torch tensor as value (shift, scale) + # TODO: save only non-tensors at first place is better + for k, v in config.items(): + if isinstance(v, Tensor): + config[k] = v.cpu() + return config + + +def _e3nn_to_cue(stct_src, stct_dst, src_config): + """ + manually check keys and assert if something unexpected happens + """ + n_layer = src_config['num_convolution_layer'] + + linear_module_names = [ + 'onehot_to_feature_x', + 'reduce_input_to_hidden', + 'reduce_hidden_to_energy', + ] + convolution_module_names = [] + fc_tensor_product_module_names = [] + for i in range(n_layer): + linear_module_names.append(f'{i}_self_interaction_1') + linear_module_names.append(f'{i}_self_interaction_2') + if src_config.get(KEY.SELF_CONNECTION_TYPE) == 'linear': + linear_module_names.append(f'{i}_self_connection_intro') + elif src_config.get(KEY.SELF_CONNECTION_TYPE) == 'nequip': + fc_tensor_product_module_names.append(f'{i}_self_connection_intro') + convolution_module_names.append(f'{i}_convolution') + + # Rule: those keys can be safely ignored before state dict load, + # except for linear.bias. This should be aborted in advance to + # this function. Others are not parameters but constants. + cue_only_linear_followers = ['linear.f.tp.f_fx.module.c'] + e3nn_only_linear_followers = ['linear.bias', 'linear.output_mask'] + ignores_in_linear = cue_only_linear_followers + e3nn_only_linear_followers + + cue_only_conv_followers = ['convolution.f.tp.f_fx.module.c'] + e3nn_only_conv_followers = [ + 'convolution._compiled_main_left_right._w3j', + 'convolution.weight', + 'convolution.output_mask', + ] + ignores_in_conv = cue_only_conv_followers + e3nn_only_conv_followers + + cue_only_fc_followers = ['fc_tensor_product.f.tp.f_fx.module.c'] + e3nn_only_fc_followers = [ + 'fc_tensor_product.output_mask', + ] + ignores_in_fc = cue_only_fc_followers + e3nn_only_fc_followers + + updated_keys = [] + for k, v in stct_src.items(): + module_name = k.split('.')[0] + flag = False + if module_name in linear_module_names: + for ignore in ignores_in_linear: + if '.'.join([module_name, ignore]) in k: + flag = True + break + if not flag and k == '.'.join([module_name, 'linear.weight']): + updated_keys.append(k) + stct_dst[k] = v.clone() + flag = True + assert flag, f'Unexpected key from linear: {k}' + elif module_name in convolution_module_names: + for ignore in ignores_in_conv: + if '.'.join([module_name, ignore]) in k: + flag = True + break + if not flag and ( + k.startswith(f'{module_name}.weight_nn') + or k == '.'.join([module_name, 'denominator']) + ): + updated_keys.append(k) + stct_dst[k] = v.clone() + flag = True + assert flag, f'Unexpected key from linear: {k}' + elif module_name in fc_tensor_product_module_names: + for ignore in ignores_in_fc: + if '.'.join([module_name, ignore]) in k: + flag = True + break + if not flag and k == '.'.join([module_name, 'fc_tensor_product.weight']): + updated_keys.append(k) + stct_dst[k] = v.clone() + flag = True + assert flag, f'Unexpected key from fc tensor product: {k}' + else: + # assert k in stct_dst + updated_keys.append(k) + stct_dst[k] = v.clone() + + return stct_dst + + +class SevenNetCheckpoint: + """ + Tool box for checkpoint processed from SevenNet. + """ + + def __init__(self, checkpoint_path: Union[pathlib.Path, str]): + self._checkpoint_path = checkpoint_path + self._config = None + self._epoch = None + self._model_state_dict = None + self._optimizer_state_dict = None + self._scheduler_state_dict = None + + self._loaded = False + + @property + def checkpoint_path(self) -> str: + return str(self._checkpoint_path) + + @property + def config(self) -> Dict[str, Any]: + if not self._loaded: + self._load() + assert isinstance(self._config, dict) + return deepcopy(self._config) + + @property + def epoch(self) -> Optional[int]: + if not self._loaded: + self._load() + return self._epoch + + @property + def model_state_dict(self) -> Dict[str, Any]: + if not self._loaded: + self._load() + assert isinstance(self._model_state_dict, dict) + return copy_state_dict(self._model_state_dict) + + @property + def optimizer_state_dict(self) -> Dict[str, Any]: + if not self._loaded: + self._load() + assert isinstance(self._optimizer_state_dict, dict) + return copy_state_dict(self._optimizer_state_dict) + + @property + def scheduler_state_dict(self) -> Dict[str, Any]: + if not self._loaded: + self._load() + assert isinstance(self._scheduler_state_dict, dict) + return copy_state_dict(self._scheduler_state_dict) + + def _load(self) -> None: + assert not self._loaded + cp_path = self.checkpoint_path # just alias + + cp = torch_load(cp_path, weights_only=False, map_location='cpu') + self._config_original = cp.get('config', {}) + self._model_state_dict = cp.get('model_state_dict', {}) + self._optimizer_state_dict = cp.get('optimizer_state_dict', {}) + self._scheduler_state_dict = cp.get('scheduler_state_dict', {}) + self._epoch = cp.get('epoch', None) + + if len(self._config_original) == 0: + warnings.warn(f'config is not found from {cp_path}') + self._config = {} + else: + self._config = _config_cp_routine(self._config_original) + + if len(self._model_state_dict) == 0: + warnings.warn(f'model_state_dict is not found from {cp_path}') + + if len(self._optimizer_state_dict) == 0: + warnings.warn(f'optimizer_state_dict is not found from {cp_path}') + + if len(self._scheduler_state_dict) == 0: + warnings.warn(f'scheduler_state_dict is not found from {cp_path}') + + if self._epoch is None: + warnings.warn(f'epoch is not found from {cp_path}') + + self._loaded = True + + def build_model(self, backend: Optional[str] = None) -> AtomGraphSequential: + from .model_build import build_E3_equivariant_model + + use_cue = not backend or backend.lower() in ['cue', 'cueq'] + try: + cp_using_cue = self.config[KEY.CUEQUIVARIANCE_CONFIG]['use'] + except KeyError: + cp_using_cue = False + + if (not backend) or (use_cue == cp_using_cue): + # backend not given, or checkpoint backend is same as requested + model = build_E3_equivariant_model(self.config) + state_dict = compat.patch_state_dict_if_old( + self.model_state_dict, self.config, model + ) + else: + cfg_new = self.config + cfg_new[KEY.CUEQUIVARIANCE_CONFIG] = {'use': use_cue} + model = build_E3_equivariant_model(cfg_new) + stct_src = compat.patch_state_dict_if_old( + self.model_state_dict, self.config, model + ) + state_dict = _e3nn_to_cue(stct_src, model.state_dict(), self.config) + + missing, not_used = model.load_state_dict(state_dict, strict=False) + if len(not_used) > 0: + warnings.warn(f'Some keys are not used: {not_used}', UserWarning) + + assert len(missing) == 0, f'Missing keys: {missing}' + return model + + def __repr__(self) -> str: + return '' diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 937cf66d..345c167b 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -343,14 +343,14 @@ def slice_until_this(module_name, layers): @overload def build_E3_equivariant_model( config: dict, parallel: Literal[False] = False -) -> AtomGraphSequential: +) -> AtomGraphSequential: # noqa ... @overload def build_E3_equivariant_model( config: dict, parallel: Literal[True] -) -> List[AtomGraphSequential]: +) -> List[AtomGraphSequential]: # noqa ... diff --git a/sevenn/scripts/deploy.py b/sevenn/scripts/deploy.py index fe7d1f41..67731e68 100644 --- a/sevenn/scripts/deploy.py +++ b/sevenn/scripts/deploy.py @@ -9,10 +9,9 @@ import sevenn._keys as KEY from sevenn import __version__ from sevenn.model_build import build_E3_equivariant_model -from sevenn.util import model_from_checkpoint_with_backend +from sevenn.util import load_checkpoint -# TODO: this is E3_equivariant specific def deploy(checkpoint, fname): """ This method is messy to avoid changes in pair_e3gnn.cpp, while @@ -23,17 +22,14 @@ def deploy(checkpoint, fname): from sevenn.nn.edge_embedding import EdgePreprocess from sevenn.nn.force_output import ForceStressOutput - model, config = model_from_checkpoint_with_backend(checkpoint, 'e3nn') - model_state_dct = model.state_dict() + cp = load_checkpoint(checkpoint) + model, config = cp.build_model('e3nn'), cp.config model.prepand_module('edge_preprocess', EdgePreprocess(True)) grad_module = ForceStressOutput() model.replace_module('force_output', grad_module) new_grad_key = grad_module.get_grad_key() model.key_grad = new_grad_key - missing, not_used = model.load_state_dict(model_state_dct, strict=False) - assert len(missing) == 0, f'missing keys: {missing}' - assert len(not_used) == 0, f'not used keys: {not_used}' if hasattr(model, 'eval_type_map'): setattr(model, 'eval_type_map', False) @@ -65,15 +61,16 @@ def deploy(checkpoint, fname): torch.jit.save(model, fname, _extra_files=md_configs) -# TODO: this is E3_equivariant specific +# TODO: build model only once def deploy_parallel(checkpoint, fname): # Additional layer for ghost atom (and copy parameters from original) GHOST_LAYERS_KEYS = ['onehot_to_feature_x', '0_self_interaction_1'] - model, config = model_from_checkpoint_with_backend(checkpoint, 'e3nn') + cp = load_checkpoint(checkpoint) + model, config = cp.build_model('e3nn'), cp.config + config[KEY.CUEQUIVARIANCE_CONFIG] = {'use': False} model_state_dct = model.state_dict() - # TODO: build model only once model_list = build_E3_equivariant_model(config, parallel=True) dct_temp = {} copy_counter = {gk: 0 for gk in GHOST_LAYERS_KEYS} @@ -93,7 +90,7 @@ def deploy_parallel(checkpoint, fname): if hasattr(model_part, 'eval_type_map'): setattr(model_part, 'eval_type_map', False) # Ensure all values are inserted - assert len(missing) == 0 + assert len(missing) == 0, missing # prepare some extra information for MD md_configs = {} diff --git a/sevenn/scripts/processing_continue.py b/sevenn/scripts/processing_continue.py index b487a704..6148e2f2 100644 --- a/sevenn/scripts/processing_continue.py +++ b/sevenn/scripts/processing_continue.py @@ -21,19 +21,18 @@ def processing_continue_v2(config): # simpler continue_dct = config[KEY.CONTINUE] log.write('\nContinue found, loading checkpoint\n') - checkpoint = torch.load( - continue_dct[KEY.CHECKPOINT], map_location='cpu', weights_only=False - ) - model_cp, config_cp = util.model_from_checkpoint(checkpoint) + checkpoint = util.load_checkpoint(continue_dct[KEY.CHECKPOINT]) + model_cp = checkpoint.build_model() + config_cp = checkpoint.config model_state_dict_cp = model_cp.state_dict() optimizer_state_dict_cp = ( - checkpoint['optimizer_state_dict'] + checkpoint.optimizer_state_dict if not continue_dct[KEY.RESET_OPTIMIZER] else None ) scheduler_state_dict_cp = ( - checkpoint['scheduler_state_dict'] + checkpoint.scheduler_state_dict if not continue_dct[KEY.RESET_SCHEDULER] else None ) @@ -72,7 +71,7 @@ def processing_continue_v2(config): # simpler log.writeline(f'{KEY.USE_MODALITY}: True') config[KEY.USE_MODALITY] = True - from_epoch = checkpoint['epoch'] + from_epoch = checkpoint.epoch or 0 log.writeline(f'Checkpoint previous epoch was: {from_epoch}') epoch = 1 if continue_dct[KEY.RESET_EPOCH] else from_epoch + 1 log.writeline(f'epoch start from {epoch}') diff --git a/sevenn/sevennet_calculator.py b/sevenn/sevennet_calculator.py index df6322ab..eb6e5ae9 100644 --- a/sevenn/sevennet_calculator.py +++ b/sevenn/sevennet_calculator.py @@ -76,18 +76,15 @@ def __init__( self.device = device if file_type == 'checkpoint' and isinstance(model, str): - if os.path.isfile(model): - checkpoint = model - else: - checkpoint = util.pretrained_name_to_path(model) + cp = util.load_checkpoint(model) + backend = 'e3nn' if not enable_cueq else 'cueq' - model_loaded, config = util.model_from_checkpoint_with_backend( - checkpoint, backend - ) + model_loaded = cp.build_model(backend) model_loaded.set_is_batch_data(False) - self.type_map = config[KEY.TYPE_MAP] - self.cutoff = config[KEY.CUTOFF] - self.sevennet_config = config + + self.type_map = cp.config[KEY.TYPE_MAP] + self.cutoff = cp.config[KEY.CUTOFF] + self.sevennet_config = cp.config elif file_type == 'torchscript' and isinstance(model, str): if modal: diff --git a/sevenn/train/trainer.py b/sevenn/train/trainer.py index e0f3154e..b61f8a03 100644 --- a/sevenn/train/trainer.py +++ b/sevenn/train/trainer.py @@ -105,15 +105,12 @@ def args_from_checkpoint(checkpoint: str) -> Tuple[Dict, Dict, Dict]: optimizer_state_dict=optim_stct, scheduler_state_dict=scheduler_stct, """ - from sevenn.util import model_from_checkpoint, pretrained_name_to_path + from sevenn.util import load_checkpoint - if os.path.isfile(checkpoint): - checkpoint = checkpoint - else: - checkpoint = pretrained_name_to_path(checkpoint) + cp = load_checkpoint(checkpoint) - cp = torch.load(checkpoint, weights_only=False) - model, config = model_from_checkpoint(cp) + model = cp.build_model() + config = cp.config optimizer_cls = optim_dict[config[KEY.OPTIMIZER].lower()] scheduler_cls = scheduler_dict[config[KEY.SCHEDULER].lower()] loss_functions = get_loss_functions_from_config(config) @@ -127,8 +124,8 @@ def args_from_checkpoint(checkpoint: str) -> Tuple[Dict, Dict, Dict]: 'scheduler_cls': scheduler_cls, 'scheduler_args': config[KEY.SCHEDULER_PARAM], }, - cp['optimizer_state_dict'], - cp['scheduler_state_dict'], + cp.optimizer_state_dict, + cp.scheduler_state_dict, ) def run_one_epoch( diff --git a/sevenn/util.py b/sevenn/util.py index eebce910..95fa0f02 100644 --- a/sevenn/util.py +++ b/sevenn/util.py @@ -1,6 +1,5 @@ -import copy import os -import warnings +import pathlib from typing import Dict, List, Tuple, Union import numpy as np @@ -9,7 +8,7 @@ from e3nn.o3 import FullTensorProduct, Irreps import sevenn._keys as KEY -import sevenn.scripts.backward_compatibility as compat +from sevenn.checkpoint import SevenNetCheckpoint def to_atom_graph_list(atom_graph_batch): @@ -82,200 +81,23 @@ def onehot_to_chem(one_hot_indices: List[int], type_map: Dict[int, int]): return [chemical_symbols[type_map_rev[x]] for x in one_hot_indices] -def _config_cp_routine(config): - from ._const import model_defaults - - defaults = {**model_defaults(config)} - config = compat.patch_old_config(config) # type: ignore - - for k, v in defaults.items(): - if k in config: - continue - if os.getenv('SEVENN_DEBUG', False): - warnings.warn(f'{k} not in config, use default value {v}', UserWarning) - config[k] = v - - # expect only non-tensor values in config, if exists, move to cpu - # This can be happen if config has torch tensor as value (shift, scale) - # TODO: save only non-tensors at first place is better - for k, v in config.items(): - if isinstance(v, torch.Tensor): - config[k] = v.cpu() - return config - - -def _e3nn_to_cue(stct_src, stct_dst, src_config): - """ - manually check keys and assert if something unexpected happens - """ - n_layer = src_config['num_convolution_layer'] - - linear_module_names = [ - 'onehot_to_feature_x', - 'reduce_input_to_hidden', - 'reduce_hidden_to_energy', - ] - convolution_module_names = [] - fc_tensor_product_module_names = [] - for i in range(n_layer): - linear_module_names.append(f'{i}_self_interaction_1') - linear_module_names.append(f'{i}_self_interaction_2') - if src_config.get(KEY.SELF_CONNECTION_TYPE) == 'linear': - linear_module_names.append(f'{i}_self_connection_intro') - elif src_config.get(KEY.SELF_CONNECTION_TYPE) == 'nequip': - fc_tensor_product_module_names.append(f'{i}_self_connection_intro') - convolution_module_names.append(f'{i}_convolution') - - # Rule: those keys can be safely ignored before state dict load, - # except for linear.bias. This should be aborted in advance to - # this function. Others are not parameters but constants. - cue_only_linear_followers = ['linear.f.tp.f_fx.module.c'] - e3nn_only_linear_followers = ['linear.bias', 'linear.output_mask'] - ignores_in_linear = cue_only_linear_followers + e3nn_only_linear_followers - - cue_only_conv_followers = [ - 'convolution.f.tp.f_fx.module.c' - ] - e3nn_only_conv_followers = [ - 'convolution._compiled_main_left_right._w3j', - 'convolution.weight', - 'convolution.output_mask', - ] - ignores_in_conv = cue_only_conv_followers + e3nn_only_conv_followers - - cue_only_fc_followers = [ - 'fc_tensor_product.f.tp.f_fx.module.c' - ] - e3nn_only_fc_followers = [ - 'fc_tensor_product.output_mask', - ] - ignores_in_fc = cue_only_fc_followers + e3nn_only_fc_followers - - updated_keys = [] - stct_updated = stct_dst.copy() - for k, v in stct_src.items(): - module_name = k.split('.')[0] - flag = False - if module_name in linear_module_names: - for ignore in ignores_in_linear: - if '.'.join([module_name, ignore]) in k: - flag = True - break - if not flag and k == '.'.join([module_name, 'linear.weight']): - updated_keys.append(k) - stct_updated[k] = v.clone() - flag = True - assert flag, f'Unexpected key from linear: {k}' - elif module_name in convolution_module_names: - for ignore in ignores_in_conv: - if '.'.join([module_name, ignore]) in k: - flag = True - break - if not flag and ( - k.startswith(f'{module_name}.weight_nn') - or k == '.'.join([module_name, 'denominator']) - ): - updated_keys.append(k) - stct_updated[k] = v.clone() - flag = True - assert flag, f'Unexpected key from linear: {k}' - elif module_name in fc_tensor_product_module_names: - for ignore in ignores_in_fc: - if '.'.join([module_name, ignore]) in k: - flag = True - break - if not flag and k == '.'.join([module_name, 'fc_tensor_product.weight']): - updated_keys.append(k) - stct_updated[k] = v.clone() - flag = True - assert flag, f'Unexpected key from fc tensor product: {k}' - else: - # assert k in stct_updated - updated_keys.append(k) - stct_updated[k] = v.clone() - - return stct_updated - - def model_from_checkpoint( - checkpoint: Union[str, dict], + checkpoint: str, ) -> Tuple[torch.nn.Module, Dict]: - from .model_build import build_E3_equivariant_model - - if isinstance(checkpoint, str): - checkpoint = torch.load(checkpoint, map_location='cpu', weights_only=False) - assert isinstance(checkpoint, dict) - elif isinstance(checkpoint, dict): - pass - else: - raise ValueError('checkpoint must be either str or dict') + cp = load_checkpoint(checkpoint) + model = cp.build_model() - stct_cp = checkpoint['model_state_dict'] # type: ignore - config = _config_cp_routine(checkpoint.get('config')) - - model = build_E3_equivariant_model(config) - - stct_cp = compat.patch_state_dict_if_old(stct_cp, config, model) - missing, not_used = model.load_state_dict(stct_cp, strict=False) - if len(not_used) > 0: - warnings.warn(f'Some keys are not used: {not_used}', UserWarning) - - assert len(missing) == 0, f'Missing keys: {missing}' - - return model, config + return model, cp.config def model_from_checkpoint_with_backend( - checkpoint: Union[str, dict], + checkpoint: str, backend: str = 'e3nn', ) -> Tuple[torch.nn.Module, Dict]: - from .model_build import build_E3_equivariant_model + cp = load_checkpoint(checkpoint) + model = cp.build_model(backend) - use_cue = backend.lower() in ['cue', 'cueq', 'cuequivariance'] - - if isinstance(checkpoint, str): - checkpoint = torch.load(checkpoint, map_location='cpu', weights_only=False) - assert isinstance(checkpoint, dict) - elif isinstance(checkpoint, dict): - pass - else: - raise ValueError('checkpoint must be either str or dict') - config_cp = _config_cp_routine(checkpoint.get('config')) - - # early return - cue_cfg = config_cp.get(KEY.CUEQUIVARIANCE_CONFIG, {'use': False}) - cp_already_use_cue = cue_cfg.get('use', False) - if use_cue == cp_already_use_cue: - return model_from_checkpoint(checkpoint) - - print(f'convert the model to {backend}') - - # build empty [e3nn | cue] model - config = copy.deepcopy(config_cp) - config[KEY.CUEQUIVARIANCE_CONFIG] = {'use': use_cue} - model = build_E3_equivariant_model(config) - - # patch model checkpoint's state dict - stct_src = checkpoint['model_state_dict'].copy() # type: ignore - stct_src = compat.patch_state_dict_if_old(stct_src, config_cp, model) - - # get empty [e3nn | cue] state dicts - stct_dst = model.state_dict() - # patch state dicts - stct_new = ( - _e3nn_to_cue(stct_src, stct_dst, config_cp) - if use_cue - # TODO: for now, no special routine needed when src dst changed - else _e3nn_to_cue(stct_src, stct_dst, config_cp) - ) - - missing, not_used = model.load_state_dict(stct_new, strict=False) - if len(not_used) > 0: - warnings.warn(f'Some keys are not used: {not_used}', UserWarning) - - assert len(missing) == 0, f'Missing keys: {missing}' - - return model, config + return model, cp.config def unlabeled_atoms_to_input(atoms, cutoff: float, grad_key: str = KEY.EDGE_VEC): @@ -349,7 +171,7 @@ def infer_irreps_out( # (mul, (ir, p)) irreps_out = FullTensorProduct(irreps_x, irreps_operand).irreps_out.simplify() new_irreps_elem = [] - for mul, (l, p) in irreps_out: + for mul, (l, p) in irreps_out: # noqa elem = (mul, (l, p)) if drop_l is not False and l > drop_l: continue @@ -379,6 +201,19 @@ def pretrained_name_to_path(name: str) -> str: return checkpoint_path +def load_checkpoint(checkpoint: Union[pathlib.Path, str]): + if os.path.isfile(checkpoint): + checkpoint_path = checkpoint + else: + try: + checkpoint_path = pretrained_name_to_path(str(checkpoint)) + except ValueError: + raise ValueError( + f'Given {checkpoint} is not exists and not a pre-trained name' + ) + return SevenNetCheckpoint(checkpoint_path) + + def unique_filepath(filepath: str) -> str: if not os.path.isfile(filepath): return filepath diff --git a/tests/data/checkpoints/_cp_0.pth_old b/tests/data/checkpoints/_cp_0.pth_old new file mode 100644 index 0000000000000000000000000000000000000000..85d55dc1dcfc4fe016943287be77252cfeb0771d GIT binary patch literal 113970 zcmd43bzBwC_diYx2nMAXm?#!)0e8-|0|TxF79w0qYKM#1VxWS7g551P>Psx{oGXgm z-QAto_?z8}zAwH+-=FXMpC3Lvdv^Am=W}Xj&hE@!KVL&5Jv~!Xy?^~#>e=dr#0R;$ zga)gEo#O{Zw{2NoulKKi$p#|@J*R+j+&v~PR2dzVpbAzggF+)iRDym;NL+NZGDH;_ z7n@+-m4+&*GPE_nVlEieQwWAm0kyfCGBjKnq>LG;3=NHp4R;8}!d`(k?(!TmBf<^ZESN~k2l>ZJ&kMc67@*s3bQ zK_OHNsPkvoxI|TaqADmRIAM@by%@TOP=lizsf3y$x>1TyOGK}&MR!yQbreF~09TIg z8kC@n4hss2i;dN`Zct>bipDvIh5Trccp4X)7^3=7IZnlRJcW9kXMjqmFY*jX5gLd* z4YfRtRD!cYa0zJqf8x@vsU>Ty5}GK4rU5noL&i&@ zq`^|>3v&CVjhhuiZXz`2$mLXmmxx?0Mer7peYD6jl|U2%1XTYEved}(Vz^C(795v1 za!V1HH*zZxx3w0xjY?>%5ZVRQ{R^&cg|sgQ-%RMh;dv`}6ybR*`-BKnBHeYL>-R6>7+5ES6}ubk=v>sG^n;^=Nd zFh{Sg5(bLswNr!;5j|9ku2czO3L*SA=-R9X#l|{E2M<+J*B@OxqBy#{5E)_CSx}-bE{g*o^xj24f zVGM5rKb0_6g!fAk#))k(UJIR~5+*2wi6z}aKcS^tXi{!gXm{2rPt0#DHb zPgM!i6heAQ_s~yZ-Oe+;IDS)M2FK^4N`{EfN0m$wf2J0HmP(kd5atxsiBd?692`s| zgJV@e;e2j%mPAZq^p859TO7KXFi)HH`9)b@AVM$HLN8JYixt9>qO6w;t(*0w#qpa9 z%Q${@m9SjIubv{T5b;-P@mHyY)e2#aG|IdEUq|_@Vnpt)!dgyrOC_umiEgC`>qVjs zTB40AVUt4GEDiJjJ&|s)vWv6130pW@ca^YJWb2+HY!li3(Xwq<2{{U3htwiPY=0W( zcNT|s7j|)ISCz0^gmz64_K48AT4=ROUyOQGa@1fJ1dHFTEz1z;etZASX33dh`M^wSbwQFa1-IOwkoa^RmD{i z_?i~@x=OgA5N=B2Sn;gt0_#@9t>WlSh1(qcm`b=Kq902U?uzL5wCML$!UKiyFo1}2 zPO*y6Mdue{;v=J#p*(Vmj17ubhN*(682LE|C3!>zxrzUTN5yHI36D8#WtH$mq%{=( zO%$H`nsG&cqVP;h|6C=!PzW#ocY1f>RdM>}!fQ_7NF}`aL2o=!cdVy;yG}m z`eK@yudj+4aP3Dvj3-heANo-GerUQCl} z%Zh71vc+r5nx7W+8*0OKZMJN=rlNLSDrf~R|JQ8s`l1y%PVsDcQhPB?%2p+=X|pAE zCR&-F7WF4uh3ndERppwBI&i6=)wulMv*k{!bDZMYYD{a0X=c77RkSA8eq@W+RxN(| zqi@ybx;9&mTvO3HTqwA7wl(^5C)nu<2z zQbC(?`FH9cu_$fE&5Khvqs_%MPF+?-y|}8S_L)Gv`H8O&k4vc!*R<3!uBj;DQbB>s zl4k#(@t(H&shryu=WR|~h-qe>v-MT9C098w*HdUKuJf6Mw&q7IX&bJoXj?87v>lfv z#RWe}#khdB=a$7NJzZ%BF-?pPXh*JzCDluzzFZXvJMp8Iurt?Gv+7;iw z6sL5jfnu6e)ZSbxDrz6Dij;l%QA^p6Ybx5GO9c($vZ$yfQ|cCV0Cy

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+@pytest.mark.filterwarnings('ignore:.*is not found from.*') @pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_cueq_serial(lammps_cmd, tmp_path): """ @@ -350,6 +351,7 @@ def test_cueq_serial(lammps_cmd, tmp_path): assert_atoms(atoms, atoms_lammps) +@pytest.mark.filterwarnings('ignore:.*is not found from.*') @pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_cueq_parallel(lammps_cmd, mpirun_cmd, tmp_path): """ diff --git a/tests/unit_tests/test_cueq.py b/tests/unit_tests/test_cueq.py index 377a9ed0..62c62127 100644 --- a/tests/unit_tests/test_cueq.py +++ b/tests/unit_tests/test_cueq.py @@ -131,6 +131,7 @@ def test_model_output(cf): ) +@pytest.mark.filterwarnings('ignore:.*is not found from.*') @pytest.mark.skipif(not is_cue_available(), reason='cueq not available') @pytest.mark.parametrize( 'start_from_cueq', @@ -176,6 +177,7 @@ def test_checkpoint_convert(tmp_path, start_from_cueq): ) +@pytest.mark.filterwarnings('ignore:.*is not found from.*') @pytest.mark.skipif(not is_cue_available(), reason='cueq not available') @pytest.mark.parametrize( 'start_from_cueq', @@ -238,6 +240,7 @@ def acl(a, b, rtol=rtol, atol=atol): # assert acl(atoms1.get_potential_energies(), atoms2.get_potential_energies()) +@pytest.mark.filterwarnings('ignore:.*is not found from.*') @pytest.mark.skipif(not is_cue_available(), reason='cueq not available') def test_calculator(tmp_path): cueq = True From 3aa15bcc7c168e1efb55bfebbb84663b4527bf41 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 23:22:01 +0900 Subject: [PATCH 070/113] add,fix: multimodal model with lammps --- sevenn/main/sevenn_get_model.py | 24 ++---------------------- sevenn/model_build.py | 4 ++-- sevenn/nn/sequential.py | 29 +++++++++++++++++++++++++++++ sevenn/scripts/deploy.py | 21 +++++++++++++++++++-- 4 files changed, 52 insertions(+), 26 deletions(-) diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py index 3700146c..4547cff1 100644 --- a/sevenn/main/sevenn_get_model.py +++ b/sevenn/main/sevenn_get_model.py @@ -1,12 +1,8 @@ import argparse import os -import torch - -import sevenn._keys as KEY import sevenn.util from sevenn import __version__ -from sevenn.scripts.convert_model_modality import get_single_modal_model_dct from sevenn.scripts.deploy import deploy, deploy_parallel description_get_model = ( @@ -36,25 +32,10 @@ def main(args=None): else: checkpoint_path = sevenn.util.pretrained_name_to_path(checkpoint) - """ - model, config = sevenn.util.model_from_checkpoint(checkpoint_path) - stct_dct = model.state_dict() - - if config.get(KEY.USE_MODALITY, False): - stct_dct = get_single_modal_model_dct( - stct_dct, config, modal, is_deploy=True - ) - output_prefix = modal + '_' + output_prefix - if save_cp: - cp_file = torch.load(checkpoint_path, map_location='cpu') - cp_file.update({'model_state_dict': stct_dct, 'config': config}) - torch.save(cp_file, checkpoint_path.replace('.', f'_{modal}.')) - """ - if get_serial: - deploy(checkpoint_path, output_prefix) + deploy(checkpoint_path, output_prefix, modal) else: - deploy_parallel(checkpoint_path, output_prefix) + deploy_parallel(checkpoint_path, output_prefix, modal) def cmd_parse_get_model(args=None): @@ -70,7 +51,6 @@ def cmd_parse_get_model(args=None): '-m', '--modal', help='Modality of multi-modal model', - default='common', type=str, ) ag.add_argument( diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 345c167b..3ebd06d4 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -45,7 +45,7 @@ def _insert_after(module_name_after, key_module_pair, layers): idx = i break if idx == -1: - assert False + return layers # do nothing if not found layers.insert(idx + 1, key_module_pair) return layers @@ -183,7 +183,7 @@ def patch_modality(layers: OrderedDict, config): _layers = list(layers.items()) _layers = _insert_after( - 'edge_embedding', + 'onehot_idx_to_onehot', ( 'one_hot_modality', OnehotEmbedding( diff --git a/sevenn/nn/sequential.py b/sevenn/nn/sequential.py index e69747b1..2c33be95 100644 --- a/sevenn/nn/sequential.py +++ b/sevenn/nn/sequential.py @@ -17,6 +17,25 @@ def _instantiate_modules(modules): module.instantiate() +@compile_mode('script') +class _ModalInputPrepare(nn.Module): + + def __init__( + self, + modal_idx: int + ): + super().__init__() + self.modal_idx = modal_idx + + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: + data[KEY.MODAL_TYPE] = torch.tensor( + self.modal_idx, + dtype=torch.int64, + device=data['x'].device, + ) + return data + + @compile_mode('script') class AtomGraphSequential(nn.Sequential): """ @@ -106,6 +125,7 @@ def delete_module_by_key(self, key: str): if key in self._modules.keys(): del self._modules[key] + @torch.jit.unused def _atomic_numbers_to_onehot(self, atomic_numbers: torch.Tensor): assert atomic_numbers.dtype == torch.int64 device = atomic_numbers.device @@ -114,6 +134,7 @@ def _atomic_numbers_to_onehot(self, atomic_numbers: torch.Tensor): input=z_to_onehot_tensor, dim=0, index=atomic_numbers ) + @torch.jit.unused def _eval_modal_map(self, data: AtomGraphDataType): assert self.modal_map is not None # modal_map: dict[str, int] @@ -145,6 +166,14 @@ def _preprocess(self, data: AtomGraphDataType) -> AtomGraphDataType: return data + def prepare_modal_deploy(self, modal: str): + if self.modal_map is None: + return + self.eval_modal_map = False + self.set_is_batch_data(False) + modal_idx = self.modal_map[modal] # type: ignore + self.prepand_module('modal_input_prepare', _ModalInputPrepare(modal_idx)) + def forward(self, input: AtomGraphDataType) -> AtomGraphDataType: data = self._preprocess(input) for module in self: diff --git a/sevenn/scripts/deploy.py b/sevenn/scripts/deploy.py index 67731e68..451b3a9e 100644 --- a/sevenn/scripts/deploy.py +++ b/sevenn/scripts/deploy.py @@ -1,5 +1,6 @@ import os from datetime import datetime +from typing import Optional import e3nn.util.jit import torch @@ -12,7 +13,7 @@ from sevenn.util import load_checkpoint -def deploy(checkpoint, fname): +def deploy(checkpoint, fname='deployed_serial.pt', modal: Optional[str] = None): """ This method is messy to avoid changes in pair_e3gnn.cpp, while refactoring python part. @@ -33,6 +34,13 @@ def deploy(checkpoint, fname): if hasattr(model, 'eval_type_map'): setattr(model, 'eval_type_map', False) + if modal: + model.prepare_modal_deploy(modal) + elif model.modal_map is not None: + raise ValueError( + f'Modal is not given. It has: {list(model.modal_map.keys())}' + ) + model.set_is_batch_data(False) model.eval() @@ -62,7 +70,9 @@ def deploy(checkpoint, fname): # TODO: build model only once -def deploy_parallel(checkpoint, fname): +def deploy_parallel( + checkpoint, fname='deployed_parallel', modal: Optional[str] = None +): # Additional layer for ghost atom (and copy parameters from original) GHOST_LAYERS_KEYS = ['onehot_to_feature_x', '0_self_interaction_1'] @@ -92,6 +102,13 @@ def deploy_parallel(checkpoint, fname): # Ensure all values are inserted assert len(missing) == 0, missing + if modal: + model_list[0].prepare_modal_deploy(modal) + elif model_list[0].modal_map is not None: + raise ValueError( + f'Modal is not given. It has: {list(model_list[0].modal_map.keys())}' + ) + # prepare some extra information for MD md_configs = {} type_map = config[KEY.TYPE_MAP] From 79e2cf06271c82b1546612a0dbada60926bbb54a Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 23:22:57 +0900 Subject: [PATCH 071/113] chore --- sevenn/_keys.py | 2 -- sevenn/scripts/convert_model_modality.py | 15 ++++++++------- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/sevenn/_keys.py b/sevenn/_keys.py index 671f6809..1ff5a614 100644 --- a/sevenn/_keys.py +++ b/sevenn/_keys.py @@ -12,8 +12,6 @@ from typing import Final -from torch.jit import CompilationUnit - # see # https://github.com/pytorch/pytorch/issues/52312 # for FYI diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py index cb5e4308..17bff6f7 100644 --- a/sevenn/scripts/convert_model_modality.py +++ b/sevenn/scripts/convert_model_modality.py @@ -18,7 +18,7 @@ def _get_scalar_index(irreps: Irreps): scalar_indices = [] - for idx, (mul, (l, p)) in enumerate(irreps): + for idx, (_, (l, p)) in enumerate(irreps): # noqa if ( l == 0 and p == 1 ): # get index of parameter for scalar (0e), which is used for modality @@ -78,6 +78,7 @@ def _get_modal_weight_as_bias( irreps_in: Irreps, irreps_out: Irreps, ): + assert ref_index != -1 input_dim = irreps_in.count('0e') output_dim = irreps_out.count('0e') orig_weight = model_state_dct[key + '.linear.weight'] @@ -203,16 +204,16 @@ def get_single_modal_model_dct( model_state_dct, module_key, ref_modal_index, - irreps_in, - irreps_out, + irreps_in, # type: ignore + irreps_out, # type: ignore ) ) erased_modal_weight = _erase_linear_modal_params( model_state_dct, erase_modal_indices, module_key, - irreps_in, - irreps_out, + irreps_in, # type: ignore + irreps_out, # type: ignore ) model_state_dct[module_key + '.linear.weight'] = ( @@ -289,8 +290,8 @@ def append_modality_to_model_dct( append_weight = _append_modal_weight( model_state_dct, module_key, - irreps_in, - irreps_out, + irreps_in, # type: ignore + irreps_out, # type: ignore append_modal_length, ) model_state_dct[module_key + '.linear.weight'] = append_weight From cc2969bd23ee8197a80c549998f57eab773bfd87 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 30 Nov 2024 23:30:32 +0900 Subject: [PATCH 072/113] add: save time and hash key for checkpoint --- sevenn/train/trainer.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sevenn/train/trainer.py b/sevenn/train/trainer.py index b61f8a03..cb2eb91a 100644 --- a/sevenn/train/trainer.py +++ b/sevenn/train/trainer.py @@ -1,4 +1,6 @@ import os +import uuid +from datetime import datetime from typing import Any, Dict, Iterable, List, Optional, Tuple, Union import torch @@ -198,6 +200,8 @@ def get_checkpoint_dict(self) -> dict: 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler is not None else None, + 'time': datetime.now().strftime('%Y-%m-%d %H:%M'), + 'hash': uuid.uuid4().hex, } def write_checkpoint(self, path: str, **extra) -> None: From a4c72c8c97ede5ae1d16b556b0000a5275f06302 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 1 Dec 2024 18:44:34 +0900 Subject: [PATCH 073/113] chore --- sevenn/scripts/deploy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sevenn/scripts/deploy.py b/sevenn/scripts/deploy.py index 451b3a9e..c5f9a32b 100644 --- a/sevenn/scripts/deploy.py +++ b/sevenn/scripts/deploy.py @@ -120,7 +120,7 @@ def deploy_parallel( comm_size = max( [ - seg._modules[f'{t}_convolution']._comm_size + seg._modules[f'{t}_convolution']._comm_size # type: ignore for t, seg in enumerate(model_list) ] ) From 4da8a20b809491be6af4800c4dcd2d23e708dd4a Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 1 Dec 2024 18:52:08 +0900 Subject: [PATCH 074/113] refactor: some changes in _const order --- sevenn/_const.py | 13 ++++++------- sevenn/scripts/processing_dataset.py | 2 +- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/sevenn/_const.py b/sevenn/_const.py index ec10bfdb..73d12b73 100644 --- a/sevenn/_const.py +++ b/sevenn/_const.py @@ -82,12 +82,14 @@ def error_record_condition(x): DEFAULT_E3_EQUIVARIANT_MODEL_CONFIG = { - KEY.IRREPS_MANUAL: False, + KEY.CUTOFF: 4.5, KEY.NODE_FEATURE_MULTIPLICITY: 32, + KEY.IRREPS_MANUAL: False, KEY.LMAX: 1, KEY.LMAX_EDGE: -1, # -1 means lmax_edge = lmax KEY.LMAX_NODE: -1, # -1 means lmax_node = lmax KEY.IS_PARITY: True, + KEY.NUM_CONVOLUTION: 3, KEY.RADIAL_BASIS: { KEY.RADIAL_BASIS_NAME: 'bessel', }, @@ -95,11 +97,9 @@ def error_record_condition(x): KEY.CUTOFF_FUNCTION_NAME: 'poly_cut', }, KEY.ACTIVATION_RADIAL: 'silu', - KEY.CUTOFF: 4.5, - KEY.CONVOLUTION_WEIGHT_NN_HIDDEN_NEURONS: [64, 64], - KEY.NUM_CONVOLUTION: 3, KEY.ACTIVATION_SCARLAR: {'e': 'silu', 'o': 'tanh'}, KEY.ACTIVATION_GATE: {'e': 'silu', 'o': 'tanh'}, + KEY.CONVOLUTION_WEIGHT_NN_HIDDEN_NEURONS: [64, 64], # KEY.AVG_NUM_NEIGH: True, # deprecated # KEY.TRAIN_AVG_NUM_NEIGH: False, # deprecated KEY.CONV_DENOMINATOR: 'avg_num_neigh', @@ -212,7 +212,6 @@ def model_defaults(config): KEY.USE_MODAL_WISE_SCALE: bool, # KEY.DATA_SHUFFLE: bool, KEY.COMPUTE_STATISTICS: bool, - KEY.SAVE_DATASET: str, # KEY.DATA_WEIGHT: bool, # KEY.DATA_MODALITY: bool, } @@ -237,7 +236,7 @@ def data_defaults(config): KEY.FORCE_WEIGHT: 0.1, KEY.STRESS_WEIGHT: 1e-6, # SIMPLE-NN default KEY.PER_EPOCH: 5, - KEY.USE_TESTSET: False, + # KEY.USE_TESTSET: False, KEY.CONTINUE: { KEY.CHECKPOINT: False, KEY.RESET_OPTIMIZER: False, @@ -246,7 +245,7 @@ def data_defaults(config): KEY.USE_STATISTIC_VALUES_OF_CHECKPOINT: True, KEY.USE_STATISTIC_VALUES_FOR_CP_MODAL_ONLY: True, }, - KEY.DEFAULT_MODAL: 'common', + # KEY.DEFAULT_MODAL: 'common', KEY.CSV_LOG: 'log.csv', KEY.NUM_WORKERS: 0, KEY.IS_TRAIN_STRESS: True, diff --git a/sevenn/scripts/processing_dataset.py b/sevenn/scripts/processing_dataset.py index c46b34b8..2d32c446 100644 --- a/sevenn/scripts/processing_dataset.py +++ b/sevenn/scripts/processing_dataset.py @@ -377,7 +377,7 @@ def processing_dataset(config, working_dir): # --------------------------------------------------------------------# # TODO: testset is not used - ignore_test = not config[KEY.USE_TESTSET] + ignore_test = not config.get(KEY.USE_TESTSET, False) if KEY.LOAD_VALIDSET in config and config[KEY.LOAD_VALIDSET]: train_set = dataset test_set = AtomGraphDataset([], config[KEY.CUTOFF]) From 16fd0418a2d1c9ab3f1d5252167e7a4ff0c68db7 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 1 Dec 2024 18:54:06 +0900 Subject: [PATCH 075/113] add: sevenn_cp tool --- pyproject.toml | 1 + setup.cfg | 2 +- sevenn/checkpoint.py | 222 +++++++++++++++++++++++++++++++++++---- sevenn/main/sevenn_cp.py | 50 +++++++++ 4 files changed, 253 insertions(+), 22 deletions(-) create mode 100644 sevenn/main/sevenn_cp.py diff --git a/pyproject.toml b/pyproject.toml index 30ac573e..92c96c52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ sevenn_graph_build = "sevenn.main.sevenn_graph_build:main" sevenn_inference = "sevenn.main.sevenn_inference:main" sevenn_patch_lammps = "sevenn.main.sevenn_patch_lammps:main" sevenn_preset = "sevenn.main.sevenn_preset:main" +sevenn_cp = "sevenn.main.sevenn_cp:main" [project.urls] Homepage = "https://github.com/MDIL-SNU/SevenNet" diff --git a/setup.cfg b/setup.cfg index 798f2a5e..f21091ec 100644 --- a/setup.cfg +++ b/setup.cfg @@ -10,5 +10,5 @@ include_trailing_comma=True force_grid_wrap=0 use_parentheses=True line_length=80 -known_third_party=ase,braceexpand,e3nn,numpy,pytest,setuptools,sklearn,torch,torch_geometric,tqdm,yaml +known_third_party=ase,braceexpand,e3nn,numpy,pandas,pytest,setuptools,sklearn,torch,torch_geometric,tqdm,yaml known_first_party= diff --git a/sevenn/checkpoint.py b/sevenn/checkpoint.py index 76bb167b..0b234f18 100644 --- a/sevenn/checkpoint.py +++ b/sevenn/checkpoint.py @@ -2,14 +2,15 @@ import pathlib import warnings from copy import deepcopy -from typing import Any, Dict, Optional, Union +from typing import Any, Dict, List, Optional, Union +import pandas as pd from torch import Tensor from torch import load as torch_load +import sevenn._const as consts import sevenn._keys as KEY import sevenn.scripts.backward_compatibility as compat -from sevenn._const import model_defaults from sevenn.nn.sequential import AtomGraphSequential @@ -26,7 +27,7 @@ def copy_state_dict(state_dict) -> dict: def _config_cp_routine(config): - defaults = {**model_defaults(config)} + defaults = {**consts.model_defaults(config)} config = compat.patch_old_config(config) # type: ignore for k, v in defaults.items(): @@ -139,15 +140,41 @@ class SevenNetCheckpoint: """ def __init__(self, checkpoint_path: Union[pathlib.Path, str]): - self._checkpoint_path = checkpoint_path + self._checkpoint_path = os.path.abspath(checkpoint_path) self._config = None self._epoch = None self._model_state_dict = None self._optimizer_state_dict = None self._scheduler_state_dict = None + self._hash = None + self._time = None self._loaded = False + def __repr__(self) -> str: + cfg = self.config # just alias + if len(cfg) == 0: + return '' + dct = { + 'Sevennet version': cfg.get('version', 'Not found'), + 'When': self.time, + 'Hash': self.hash, + 'Cutoff': cfg.get('cutoff'), + 'Channel': cfg.get('channel'), + 'Lmax': cfg.get('lmax'), + 'Group (parity)': 'O3' if cfg.get('is_parity') else 'SO3', + 'Interaction layers': cfg.get('num_convolution_layer'), + 'Self connection type': cfg.get('self_connection_type', 'nequip'), + 'Last epoch': self.epoch, + 'Elements': len(cfg.get('chemical_species', [])), + } + if cfg.get('use_modality', False): + dct['Modality'] = ', '.join(list(cfg.get('_modal_map', {}).keys())) + + df = pd.DataFrame.from_dict([dct]).T # type: ignore + df.columns = [''] + return df.to_string() + @property def checkpoint_path(self) -> str: return str(self._checkpoint_path) @@ -159,12 +186,6 @@ def config(self) -> Dict[str, Any]: assert isinstance(self._config, dict) return deepcopy(self._config) - @property - def epoch(self) -> Optional[int]: - if not self._loaded: - self._load() - return self._epoch - @property def model_state_dict(self) -> Dict[str, Any]: if not self._loaded: @@ -186,6 +207,26 @@ def scheduler_state_dict(self) -> Dict[str, Any]: assert isinstance(self._scheduler_state_dict, dict) return copy_state_dict(self._scheduler_state_dict) + @property + def epoch(self) -> Optional[int]: + if not self._loaded: + self._load() + return self._epoch + + @property + def time(self) -> str: + if not self._loaded: + self._load() + assert isinstance(self._time, str) + return self._time + + @property + def hash(self) -> str: + if not self._loaded: + self._load() + assert isinstance(self._hash, str) + return self._hash + def _load(self) -> None: assert not self._loaded cp_path = self.checkpoint_path # just alias @@ -196,6 +237,8 @@ def _load(self) -> None: self._optimizer_state_dict = cp.get('optimizer_state_dict', {}) self._scheduler_state_dict = cp.get('scheduler_state_dict', {}) self._epoch = cp.get('epoch', None) + self._time = cp.get('time', 'Not found') + self._hash = cp.get('hash', 'Not found') if len(self._config_original) == 0: warnings.warn(f'config is not found from {cp_path}') @@ -206,15 +249,6 @@ def _load(self) -> None: if len(self._model_state_dict) == 0: warnings.warn(f'model_state_dict is not found from {cp_path}') - if len(self._optimizer_state_dict) == 0: - warnings.warn(f'optimizer_state_dict is not found from {cp_path}') - - if len(self._scheduler_state_dict) == 0: - warnings.warn(f'scheduler_state_dict is not found from {cp_path}') - - if self._epoch is None: - warnings.warn(f'epoch is not found from {cp_path}') - self._loaded = True def build_model(self, backend: Optional[str] = None) -> AtomGraphSequential: @@ -248,5 +282,151 @@ def build_model(self, backend: Optional[str] = None) -> AtomGraphSequential: assert len(missing) == 0, f'Missing keys: {missing}' return model - def __repr__(self) -> str: - return '' + def yaml_dict(self, mode: str) -> dict: + """ + Return dict for input.yaml from checkpoint config + Dataset paths and statistic values are removed intentionally + """ + if mode not in ['reproduce', 'continue', 'continue_modal']: + raise ValueError(f'Unknown mode: {mode}') + + ignore = [ + 'when', + KEY.DDP_BACKEND, + KEY.LOCAL_RANK, + KEY.IS_DDP, + KEY.DEVICE, + KEY.MODEL_TYPE, + KEY.SHIFT, + KEY.SCALE, + KEY.CONV_DENOMINATOR, + KEY.SAVE_DATASET, + KEY.SAVE_BY_LABEL, + KEY.SAVE_BY_TRAIN_VALID, + KEY.CONTINUE, + KEY.LOAD_DATASET, # old + ] + + cfg = self.config + + world_size = cfg.pop(KEY.WORLD_SIZE, 1) + cfg[KEY.BATCH_SIZE] = cfg[KEY.BATCH_SIZE] * world_size + cfg[KEY.LOAD_TRAINSET] = '**path_to_training_set**' + + major, minor, _ = cfg.pop('version', '0.0.0').split('.')[:3] + if int(major) == 0 and int(minor) <= 9: + warnings.warn('checkpoint version too old, yaml may wrong') + + ret = {'model': {}, 'train': {}, 'data': {}} + for k, v in cfg.items(): + if k.startswith('_') or k in ignore or k.endswith('set_path'): + continue + + if k in consts.DEFAULT_E3_EQUIVARIANT_MODEL_CONFIG: + ret['model'][k] = v + elif k in consts.DEFAULT_TRAINING_CONFIG: + ret['train'][k] = v + elif k in consts.DEFAULT_DATA_CONFIG: + ret['data'][k] = v + + ret['data'][KEY.LOAD_TRAINSET] = '**path_to_trainset**' + ret['data'][KEY.LOAD_VALIDSET] = '**path_to_validset**' + + if mode.startswith('continue'): + ret['train'].update( + {KEY.CONTINUE: {KEY.CHECKPOINT: self.checkpoint_path}} + ) + modal_names = None + if mode == 'continue_modal' and not cfg.get(KEY.USE_MODALITY, False): + ret['train'][KEY.USE_MODALITY] = True + + # suggest defaults + ret['model'][KEY.USE_MODAL_NODE_EMBEDDING] = False + ret['model'][KEY.USE_MODAL_SELF_INTER_INTRO] = True + ret['model'][KEY.USE_MODAL_SELF_INTER_OUTRO] = True + ret['model'][KEY.USE_MODAL_OUTPUT_BLOCK] = True + + ret['data'][KEY.USE_MODAL_WISE_SHIFT] = True + ret['data'][KEY.USE_MODAL_WISE_SCALE] = False + + modal_names = ['my_modal1', 'my_modal2'] + elif cfg.get(KEY.USE_MODALITY, False): + modal_names = list(cfg[KEY.MODAL_MAP].keys()) + + if modal_names: + ret['data'][KEY.LOAD_TRAINSET] = [ + {'data_modality': mm, 'file_list': [{'file': f'**path_to_{mm}**'}]} + for mm in modal_names + ] + + return ret + + def append_modal( + self, + new_modal_names: List[str], + use_modal_node_embedding: bool = False, + use_modal_self_inter_intro: bool = True, + use_modal_self_inter_outro: bool = True, + use_modal_output_block: bool = True, + ): + from sevenn.scripts.convert_model_modality import _append_modal_weight + + # check inputs + if len(new_modal_names) == 0: + raise ValueError('No modal names is given to append') + + orig_modal_map = self.config.get(KEY.MODAL_MAP, {}) + new_modal_map = orig_modal_map.copy() + for new_modal_name in new_modal_names: + if new_modal_name in orig_modal_map: + warnings.warn(f'{new_modal_name} already exists, skipping') + continue + new_modal_map[new_modal_name] = len(new_modal_map) + + append_num = len(new_modal_map) - len(orig_modal_map) + + orig_model = self.build_model() + orig_state_dict = orig_model.state_dict() + + new_cfg = self.config + new_cfg[KEY.NUM_MODALITIES] = len(new_modal_map) + new_cfg[KEY.MODAL_MAP] = new_modal_map + new_cfg[KEY.USE_MODAL_NODE_EMBEDDING] = use_modal_node_embedding + new_cfg[KEY.USE_MODAL_SELF_INTER_INTRO] = use_modal_self_inter_intro + new_cfg[KEY.USE_MODAL_SELF_INTER_OUTRO] = use_modal_self_inter_outro + new_cfg[KEY.USE_MODAL_OUTPUT_BLOCK] = use_modal_output_block + + new_state_dict = copy_state_dict(orig_state_dict) + for stct_key in orig_state_dict: + sp = stct_key.split('.') + k, follower = sp[0], '.'.join(sp[1:]) + if follower != 'linear.weight': + continue + if ( + ( + new_cfg[KEY.USE_MODAL_NODE_EMBEDDING] + and k.endswith('onehot_to_feature_x') + ) + or ( + new_cfg[KEY.USE_MODAL_SELF_INTER_INTRO] + and k.endswith('self_interaction_1') + ) + or ( + new_cfg[KEY.USE_MODAL_SELF_INTER_OUTRO] + and k.endswith('self_interaction_2') + ) + or ( + new_cfg[KEY.USE_MODAL_OUTPUT_BLOCK] + and k == 'reduce_input_to_hidden' + ) + ): + orig_linear = getattr(orig_model._modules[k], 'linear') + # assert normalization element + new_state_dict[stct_key] = _append_modal_weight( + orig_state_dict, + stct_key, + orig_linear.irreps_in, + orig_linear.irreps_out, + append_num, + ) + return new_state_dict diff --git a/sevenn/main/sevenn_cp.py b/sevenn/main/sevenn_cp.py new file mode 100644 index 00000000..24818ba3 --- /dev/null +++ b/sevenn/main/sevenn_cp.py @@ -0,0 +1,50 @@ +import argparse +import os.path as osp + +import yaml + +from sevenn import __version__ +from sevenn.util import load_checkpoint + +description = ( + f'sevenn version={__version__}, sevenn_cp.\n' + + 'tool box for checkpoints generated from sevennet' +) + + +def main(args=None): + args = cmd_parse_data(args) + checkpoint = load_checkpoint(args.checkpoint) + if args.get_yaml: + mode = args.get_yaml + cfg = checkpoint.yaml_dict(mode) + print(yaml.dump(cfg, indent=4, sort_keys=False, default_flow_style=False)) + elif args.append_modal: + ref_yaml = args.append_modal + if not osp.exists(ref_yaml): + raise FileNotFoundError(f'No yaml file {ref_yaml}') + with open(ref_yaml, 'r') as f: + ref_config = yaml.safe_load(f) # noqa + + +def cmd_parse_data(args=None): + ag = argparse.ArgumentParser(description=description) + + ag.add_argument('checkpoint', help='checkpoint or pretrained', type=str) + + group = ag.add_mutually_exclusive_group(required=False) + group.add_argument( + '--get_yaml', + choices=['reproduce', 'continue', 'continue_modal'], + help='create input.yaml based on the given checkpoint', + type=str, + ) + + group.add_argument( + '--append_modal', + help='append modality with given yaml. Yaml file changes in-place.', + type=str, + ) + + args = ag.parse_args() + return args From 6b6c26736552f0354d902e2da7e810d7abac3412 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 1 Dec 2024 22:54:29 +0900 Subject: [PATCH 076/113] bugfix: scale from mapper was not working, but saved my like it was also done in __init__ --- sevenn/nn/scale.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sevenn/nn/scale.py b/sevenn/nn/scale.py index 4b42de2d..3f33610d 100644 --- a/sevenn/nn/scale.py +++ b/sevenn/nn/scale.py @@ -113,6 +113,7 @@ def from_mappers( s = s * n_atom_types shift_scale.append(s) assert all([len(s) == n_atom_types for s in shift_scale]) + shift, scale = shift_scale return SpeciesWiseRescale(shift, scale, **kwargs) def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: @@ -129,8 +130,7 @@ class ModalWiseRescale(nn.Module): """ Scaling and shifting energy (and automatically force and stress) Given shift or scale is either modal-wise and atom-wise or - not modal-wise but atom-wise. It is always atom-wise. - If given scalar, it tries to expand to atom-wise + not modal-wise but atom-wise. It is always interpreted as atom-wise. """ def __init__( From a86d4a1af45482704d6b33381d30e5a7387350c6 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 1 Dec 2024 22:55:50 +0900 Subject: [PATCH 077/113] bugfix: multimodal training dumping atom_type key after changing onehot insert position --- sevenn/model_build.py | 10 +++++++++- sevenn/nn/node_embedding.py | 4 ++-- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 3ebd06d4..df25efe4 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -190,6 +190,8 @@ def patch_modality(layers: OrderedDict, config): num_classes=config[KEY.NUM_MODALITIES], data_key_x=KEY.MODAL_TYPE, data_key_out=KEY.MODAL_ATTR, + data_key_save=None, + data_key_additional=None, ), ), _layers, @@ -413,7 +415,13 @@ def build_E3_equivariant_model( layers.update( { - 'onehot_idx_to_onehot': OnehotEmbedding(num_classes=num_species), + 'onehot_idx_to_onehot': OnehotEmbedding( + num_classes=num_species, + data_key_x=KEY.NODE_FEATURE, + data_key_out=KEY.NODE_FEATURE, + data_key_save=KEY.ATOM_TYPE, # atomic numbers + data_key_additional=KEY.NODE_ATTR, # one-hot embeddings + ), 'onehot_to_feature_x': IrrepsLinear( irreps_in=one_hot_irreps, irreps_out=irreps_x, diff --git a/sevenn/nn/node_embedding.py b/sevenn/nn/node_embedding.py index 377a2a98..a5f272ae 100644 --- a/sevenn/nn/node_embedding.py +++ b/sevenn/nn/node_embedding.py @@ -28,8 +28,8 @@ def __init__( num_classes: int, data_key_x: str = KEY.NODE_FEATURE, data_key_out: Optional[str] = None, - data_key_save: str = KEY.ATOM_TYPE, - data_key_additional: str = KEY.NODE_ATTR, # additional output + data_key_save: Optional[str] = None, + data_key_additional: Optional[str] = None, # additional output ): super().__init__() self.num_classes = num_classes From ff7c4bc16f85668a8646bd190c034e8ab2df879b Mon Sep 17 00:00:00 2001 From: YutackPark Date: Tue, 3 Dec 2024 12:38:01 +0900 Subject: [PATCH 078/113] refactor: more robust irreps_tp_out --- sevenn/model_build.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 8fee3482..ff85f741 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -403,27 +403,27 @@ def build_E3_equivariant_model(config: dict, parallel=False): if t == num_convolution_layer - 1: lmax_node = 0 parity_mode = 'even' + # TODO: irreps_manual is applicable to both irreps_out_tp and irreps_out + irreps_out = ( + util.infer_irreps_out( + irreps_x, # type: ignore + irreps_filter, + lmax_node, # type: ignore + parity_mode, + fix_multiplicity=feature_multiplicity, + ) + if irreps_manual is None + else irreps_manual[t + 1] + ) irreps_out_tp = util.infer_irreps_out( irreps_x, # type: ignore irreps_filter, - lmax_node, # type: ignore + irreps_out.lmax, # type: ignore parity_mode, fix_multiplicity, ) else: raise ValueError(f'Unknown interaction type: {interaction_type}') - # TODO: irreps_manual is applicable to both irreps_out_tp and irreps_out - irreps_out = ( - util.infer_irreps_out( - irreps_x, # type: ignore - irreps_filter, - lmax_node, # type: ignore - parity_mode, - fix_multiplicity=feature_multiplicity, - ) - if irreps_manual is None - else irreps_manual[t + 1] - ) param_interaction_block.update( { 'irreps_out_tp': irreps_out_tp, From 8c1e1a296f10902fe6ab43eb5dc10e556f3a46c5 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 11:14:30 +0900 Subject: [PATCH 079/113] add: modality append --- sevenn/checkpoint.py | 180 ++++++++++++++++++----- sevenn/main/sevenn_cp.py | 51 ++++++- sevenn/model_build.py | 2 + sevenn/nn/scale.py | 127 ++++++++++++++-- sevenn/scripts/backward_compatibility.py | 33 +++-- sevenn/scripts/convert_model_modality.py | 7 +- sevenn/scripts/processing_continue.py | 3 + sevenn/train/modal_dataset.py | 21 +-- 8 files changed, 341 insertions(+), 83 deletions(-) diff --git a/sevenn/checkpoint.py b/sevenn/checkpoint.py index 0b234f18..04246f16 100644 --- a/sevenn/checkpoint.py +++ b/sevenn/checkpoint.py @@ -1,19 +1,43 @@ import os import pathlib +import uuid import warnings from copy import deepcopy -from typing import Any, Dict, List, Optional, Union +from datetime import datetime +from typing import Any, Dict, Optional, Union import pandas as pd from torch import Tensor from torch import load as torch_load +import sevenn import sevenn._const as consts import sevenn._keys as KEY import sevenn.scripts.backward_compatibility as compat +from sevenn import model_build +from sevenn.nn.scale import get_resolved_shift_scale from sevenn.nn.sequential import AtomGraphSequential +def assert_atoms(atoms1, atoms2, rtol=1e-5, atol=1e-6): + import numpy as np + + def acl(a, b, rtol=rtol, atol=atol): + return np.allclose(a, b, rtol=rtol, atol=atol) + + assert len(atoms1) == len(atoms2) + assert acl(atoms1.get_cell(), atoms2.get_cell()) + assert acl(atoms1.get_potential_energy(), atoms2.get_potential_energy()) + assert acl(atoms1.get_forces(), atoms2.get_forces(), rtol * 10, atol * 10) + assert acl( + atoms1.get_stress(voigt=False), + atoms2.get_stress(voigt=False), + rtol * 10, + atol * 10, + ) + # assert acl(atoms1.get_potential_energies(), atoms2.get_potential_energies()) + + def copy_state_dict(state_dict) -> dict: if isinstance(state_dict, dict): return {key: copy_state_dict(value) for key, value in state_dict.items()} @@ -30,6 +54,13 @@ def _config_cp_routine(config): defaults = {**consts.model_defaults(config)} config = compat.patch_old_config(config) # type: ignore + scaler = model_build.init_shift_scale(config) + shift, scale = get_resolved_shift_scale( + scaler, config.get(KEY.TYPE_MAP), config.get(KEY.MODAL_MAP, None) + ) + config['shift'] = shift + config['scale'] = scale + for k, v in defaults.items(): if k in config: continue @@ -37,9 +68,6 @@ def _config_cp_routine(config): warnings.warn(f'{k} not in config, use default value {v}', UserWarning) config[k] = v - # expect only non-tensor values in config, if exists, move to cpu - # This can be happen if config has torch tensor as value (shift, scale) - # TODO: save only non-tensors at first place is better for k, v in config.items(): if isinstance(v, Tensor): config[k] = v.cpu() @@ -308,6 +336,7 @@ def yaml_dict(self, mode: str) -> dict: ] cfg = self.config + len_atoms = len(cfg[KEY.TYPE_MAP]) world_size = cfg.pop(KEY.WORLD_SIZE, 1) cfg[KEY.BATCH_SIZE] = cfg[KEY.BATCH_SIZE] * world_size @@ -321,7 +350,6 @@ def yaml_dict(self, mode: str) -> dict: for k, v in cfg.items(): if k.startswith('_') or k in ignore or k.endswith('set_path'): continue - if k in consts.DEFAULT_E3_EQUIVARIANT_MODEL_CONFIG: ret['model'][k] = v elif k in consts.DEFAULT_TRAINING_CONFIG: @@ -329,9 +357,16 @@ def yaml_dict(self, mode: str) -> dict: elif k in consts.DEFAULT_DATA_CONFIG: ret['data'][k] = v + ret['model'][KEY.CHEMICAL_SPECIES] = ( + 'univ' if len_atoms == consts.NUM_UNIV_ELEMENT else 'auto' + ) ret['data'][KEY.LOAD_TRAINSET] = '**path_to_trainset**' ret['data'][KEY.LOAD_VALIDSET] = '**path_to_validset**' + # TODO + ret['data'][KEY.SHIFT] = '**failed to infer shift, should be set**' + ret['data'][KEY.SCALE] = '**failed to infer scale, should be set**' + if mode.startswith('continue'): ret['train'].update( {KEY.CONTINUE: {KEY.CHECKPOINT: self.checkpoint_path}} @@ -363,60 +398,115 @@ def yaml_dict(self, mode: str) -> dict: def append_modal( self, - new_modal_names: List[str], - use_modal_node_embedding: bool = False, - use_modal_self_inter_intro: bool = True, - use_modal_self_inter_outro: bool = True, - use_modal_output_block: bool = True, + dst_config, + original_modal_name: str = 'origin', + working_dir: str = os.getcwd(), ): + """ """ + import sevenn.train.modal_dataset as modal_dataset + from sevenn.model_build import init_shift_scale from sevenn.scripts.convert_model_modality import _append_modal_weight - # check inputs - if len(new_modal_names) == 0: - raise ValueError('No modal names is given to append') + src_config = self.config + src_has_no_modal = not src_config.get(KEY.USE_MODALITY, False) - orig_modal_map = self.config.get(KEY.MODAL_MAP, {}) + # inherit element things first + chem_keys = [ + KEY.TYPE_MAP, + KEY.NUM_SPECIES, + KEY.CHEMICAL_SPECIES, + KEY.CHEMICAL_SPECIES_BY_ATOMIC_NUMBER, + ] + dst_config.update({k: src_config[k] for k in chem_keys}) + + if dst_config[KEY.USE_MODAL_WISE_SHIFT] and ( + KEY.SHIFT not in dst_config or not isinstance(dst_config[KEY.SHIFT], str) + ): + raise ValueError('To use modal wise shift, keyword shift is required') + if dst_config[KEY.USE_MODAL_WISE_SCALE] and ( + KEY.SCALE not in dst_config or not isinstance(dst_config[KEY.SCALE], str) + ): + raise ValueError('To use modal wise scale, keyword scale is required') + + if src_has_no_modal and not dst_config[KEY.USE_MODAL_WISE_SHIFT]: + dst_config[KEY.SHIFT] = src_config[KEY.SHIFT] + if src_has_no_modal and not dst_config[KEY.USE_MODAL_WISE_SCALE]: + dst_config[KEY.SCALE] = src_config[KEY.SCALE] + + # get statistics of given datasets of yaml + # dst_config updated + _ = modal_dataset.from_config(dst_config, working_dir=working_dir) + dst_modal_map = dst_config[KEY.MODAL_MAP] + + found_modal_names = list(dst_modal_map.keys()) + if len(found_modal_names) == 0: + raise ValueError('No modality is found from config') + + # Check difference btw given modals and new modal map + orig_modal_map = src_config.get(KEY.MODAL_MAP, {original_modal_name: 0}) + assert isinstance(orig_modal_map, dict) new_modal_map = orig_modal_map.copy() - for new_modal_name in new_modal_names: - if new_modal_name in orig_modal_map: - warnings.warn(f'{new_modal_name} already exists, skipping') + for modal_name in found_modal_names: + if modal_name in orig_modal_map: # duplicate, skipping continue - new_modal_map[new_modal_name] = len(new_modal_map) - - append_num = len(new_modal_map) - len(orig_modal_map) + new_modal_map[modal_name] = len(new_modal_map) # assign new + print(f'New modals: {list(new_modal_map.keys())}') + if src_has_no_modal: + append_num = len(new_modal_map) + else: + append_num = len(new_modal_map) - len(orig_modal_map) + if append_num == 0: + raise ValueError('Nothing to append from checkpoint') + + dst_config[KEY.NUM_MODALITIES] = len(new_modal_map) + dst_config[KEY.MODAL_MAP] = new_modal_map + + # update dst_config's shift scales based on src_config + for ss_key, use_mw in ( + (KEY.SHIFT, dst_config[KEY.USE_MODAL_WISE_SHIFT]), + (KEY.SCALE, dst_config[KEY.USE_MODAL_WISE_SCALE]), + ): + if not use_mw: # not using mw ss, just assign + assert not isinstance(dst_config[ss_key], dict) + dst_config[ss_key] = src_config[ss_key] + elif src_has_no_modal: + assert isinstance(dst_config[ss_key], dict) + # mw ss, update by dict but use original_modal_name + dst_config[ss_key].update({original_modal_name: src_config[ss_key]}) + else: + assert isinstance(dst_config[ss_key], dict) + # mw ss, update by dict + dst_config[ss_key].update(src_config[ss_key]) + scaler = init_shift_scale(dst_config) + + # finally, prepare updated continuable state dict using above orig_model = self.build_model() orig_state_dict = orig_model.state_dict() - new_cfg = self.config - new_cfg[KEY.NUM_MODALITIES] = len(new_modal_map) - new_cfg[KEY.MODAL_MAP] = new_modal_map - new_cfg[KEY.USE_MODAL_NODE_EMBEDDING] = use_modal_node_embedding - new_cfg[KEY.USE_MODAL_SELF_INTER_INTRO] = use_modal_self_inter_intro - new_cfg[KEY.USE_MODAL_SELF_INTER_OUTRO] = use_modal_self_inter_outro - new_cfg[KEY.USE_MODAL_OUTPUT_BLOCK] = use_modal_output_block - new_state_dict = copy_state_dict(orig_state_dict) for stct_key in orig_state_dict: sp = stct_key.split('.') k, follower = sp[0], '.'.join(sp[1:]) - if follower != 'linear.weight': - continue - if ( + if k == 'rescale_atomic_energy' and follower == 'shift': + new_state_dict[stct_key] = scaler.shift.clone() + elif k == 'rescale_atomic_energy' and follower == 'scale': + new_state_dict[stct_key] = scaler.scale.clone() + elif follower == 'linear.weight' and ( # append linear layer ( - new_cfg[KEY.USE_MODAL_NODE_EMBEDDING] + dst_config[KEY.USE_MODAL_NODE_EMBEDDING] and k.endswith('onehot_to_feature_x') ) or ( - new_cfg[KEY.USE_MODAL_SELF_INTER_INTRO] + dst_config[KEY.USE_MODAL_SELF_INTER_INTRO] and k.endswith('self_interaction_1') ) or ( - new_cfg[KEY.USE_MODAL_SELF_INTER_OUTRO] + dst_config[KEY.USE_MODAL_SELF_INTER_OUTRO] and k.endswith('self_interaction_2') ) or ( - new_cfg[KEY.USE_MODAL_OUTPUT_BLOCK] + dst_config[KEY.USE_MODAL_OUTPUT_BLOCK] and k == 'reduce_input_to_hidden' ) ): @@ -424,9 +514,27 @@ def append_modal( # assert normalization element new_state_dict[stct_key] = _append_modal_weight( orig_state_dict, - stct_key, + k, orig_linear.irreps_in, orig_linear.irreps_out, append_num, ) + + dst_config['version'] = sevenn.__version__ + return new_state_dict + + def get_checkpoint_dict(self) -> dict: + """ + Return duplicate of this checkpoint with new hash and time. + Convenient for creating variant of the checkpoint + """ + return { + 'config': self.config, + 'epoch': self.epoch, + 'model_state_dict': self.model_state_dict, + 'optimizer_state_dict': self.optimizer_state_dict, + 'scheduler_state_dict': self.scheduler_state_dict, + 'time': datetime.now().strftime('%Y-%m-%d %H:%M'), + 'hash': uuid.uuid4().hex, + } diff --git a/sevenn/main/sevenn_cp.py b/sevenn/main/sevenn_cp.py index 24818ba3..93603127 100644 --- a/sevenn/main/sevenn_cp.py +++ b/sevenn/main/sevenn_cp.py @@ -1,9 +1,11 @@ import argparse import os.path as osp +import torch import yaml from sevenn import __version__ +from sevenn.parse_input import read_config_yaml from sevenn.util import load_checkpoint description = ( @@ -19,12 +21,36 @@ def main(args=None): mode = args.get_yaml cfg = checkpoint.yaml_dict(mode) print(yaml.dump(cfg, indent=4, sort_keys=False, default_flow_style=False)) - elif args.append_modal: - ref_yaml = args.append_modal - if not osp.exists(ref_yaml): - raise FileNotFoundError(f'No yaml file {ref_yaml}') - with open(ref_yaml, 'r') as f: - ref_config = yaml.safe_load(f) # noqa + elif args.append_modal_yaml: + dst_yaml = args.append_modal_yaml + if not osp.exists(dst_yaml): + raise FileNotFoundError(f'No yaml file {dst_yaml}') + + dst_config = read_config_yaml(dst_yaml, return_separately=False) + model_state_dict = checkpoint.append_modal( + dst_config, args.original_modal_name + ) + + to_save = checkpoint.get_checkpoint_dict() + to_save.update({'config': dst_config, 'model_state_dict': model_state_dict}) + + torch.save(to_save, 'checkpoint_modal_appended.pth') + print('checkpoint_modal_appended.pth is successfully saved.') + print(f'update continue of {dst_yaml} as blow (recommend) to continue') + cont_dct = { + 'continue': { + 'checkpoint': 'checkpoint_modal_appended.pth', + 'reset_epoch': True, + 'reset_optimizer': True, + 'reset_scheduler': True, + } + } + print( + yaml.dump(cont_dct, indent=4, sort_keys=False, default_flow_style=False) + ) + + else: + print(checkpoint) def cmd_parse_data(args=None): @@ -41,8 +67,17 @@ def cmd_parse_data(args=None): ) group.add_argument( - '--append_modal', - help='append modality with given yaml. Yaml file changes in-place.', + '--append_modal_yaml', + help='append modality with given yaml.', + type=str, + ) + ag.add_argument( + '--original_modal_name', + help=( + 'when the append_modal is used and checkpoint is not multi-modal, ' + + 'used to name previously trained modality. defaults to "origin"' + ), + default='origin', type=str, ) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index 0b0fc105..c40be976 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -144,6 +144,8 @@ def init_shift_scale(config): for s in (config[KEY.SHIFT], config[KEY.SCALE]): if hasattr(s, 'tolist'): # numpy or torch s = s.tolist() + if isinstance(s, dict): + s = {k: v.tolist() if hasattr(v, 'tolist') else v for k, v in s.items()} shift_scale.append(s) shift, scale = shift_scale diff --git a/sevenn/nn/scale.py b/sevenn/nn/scale.py index 3f33610d..73da8356 100644 --- a/sevenn/nn/scale.py +++ b/sevenn/nn/scale.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Union +from typing import Any, Dict, List, Optional, Union import torch import torch.nn as nn @@ -8,6 +8,16 @@ from sevenn._const import NUM_UNIV_ELEMENT, AtomGraphDataType +def _as_univ( + ss: List[float], type_map: Dict[int, int], default: float +) -> List[float]: + assert len(ss) <= NUM_UNIV_ELEMENT, 'shift scale is too long' + return [ + ss[type_map[z]] if z in type_map else default + for z in range(NUM_UNIV_ELEMENT) + ] + + @compile_mode('script') class Rescale(nn.Module): """ @@ -34,6 +44,12 @@ def __init__( self.key_input = data_key_in self.key_output = data_key_out + def get_shift(self) -> float: + return self.shift.detach().cpu().tolist()[0] + + def get_scale(self) -> float: + return self.scale.detach().cpu().tolist()[0] + def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: data[self.key_output] = data[self.key_input] * self.scale + self.shift @@ -88,6 +104,26 @@ def __init__( self.key_output = data_key_out self.key_indices = data_key_indices + def get_shift(self, type_map: Optional[Dict[int, int]] = None) -> List[float]: + """ + Return shift in list of float. If type_map is given, return type_map reversed + shift, which index equals atomic_number. 0.0 is assigned for atomis not found + """ + shift = self.shift.detach().cpu().tolist() + if type_map: + shift = _as_univ(shift, type_map, 0.0) + return shift + + def get_scale(self, type_map: Optional[Dict[int, int]] = None) -> List[float]: + """ + Return scale in list of float. If type_map is given, return type_map reversed + scale, which index equals atomic_number. 1.0 is assigned for atomis not found + """ + scale = self.scale.detach().cpu().tolist() + if type_map: + scale = _as_univ(scale, type_map, 1.0) + return scale + @staticmethod def from_mappers( shift: Union[float, List[float]], @@ -160,6 +196,45 @@ def __init__( self.use_modal_wise_scale = use_modal_wise_scale self._is_batch_data = True + def get_shift( + self, + type_map: Optional[Dict[int, int]] = None, + modal_map: Optional[Dict[str, int]] = None, + ) -> Union[List[float], Dict[str, List[float]]]: + """ + Nothing is given: return as it is + type_map is given but not modal wise shift: return univ shift + both type_map and modal_map is given and modal wise shift: return fully + resolved modalwise univ shift + """ + shift = self.shift.detach().cpu().tolist() + if type_map and not self.use_modal_wise_shift: + shift = _as_univ(shift, type_map, 0.0) + elif self.use_modal_wise_shift and modal_map and type_map: + shift = [_as_univ(s, type_map, 0.0) for s in shift] + shift = {modal: shift[idx] for modal, idx in modal_map.items()} + + return shift + + def get_scale( + self, + type_map: Optional[Dict[int, int]] = None, + modal_map: Optional[Dict[str, int]] = None, + ) -> Union[List[float], Dict[str, List[float]]]: + """ + Nothing is given: return as it is + type_map is given but not modal wise scale: return univ scale + both type_map and modal_map is given and modal wise scale: return fully + resolved modalwise univ scale + """ + scale = self.scale.detach().cpu().tolist() + if type_map and not self.use_modal_wise_scale: + scale = _as_univ(scale, type_map, 0.0) + elif self.use_modal_wise_scale and modal_map and type_map: + scale = [_as_univ(s, type_map, 0.0) for s in scale] + scale = {modal: scale[idx] for modal, idx in modal_map.items()} + return scale + @staticmethod def from_mappers( shift: Union[float, List[float], Dict[str, Any]], @@ -224,17 +299,17 @@ def solve_mapper(arr, map): elif isinstance(s, dict) and use_mw: # solve modal dict, modal-wise: yes s = solve_mapper(s, modal_map) - if isinstance(s[0], list) and len(s[0]) == NUM_UNIV_ELEMENT: - # elem-wise: yes(univ) => solve elem map - res = [solve_mapper(v, type_map) for v in s] - elif isinstance(s[0], float): - # elem-wise: no => broadcast to elemwise - res = [[v] * n_atom_types for v in s] - elif isinstance(s[0], list) and len(s[0]) == n_atom_types: - # elem-wise: yes => not univ, already mapped => as it is - res = s - else: - raise ValueError(f'Invalid shift or scale {s}') + res = [] + for v in s: + if isinstance(v, list) and len(v) == NUM_UNIV_ELEMENT: + # elem-wise: yes(univ) => solve elem map + v = solve_mapper(v, type_map) + elif isinstance(v, float): + # elem-wise: no => broadcast to elemwise + v = [v] * n_atom_types + else: + raise ValueError(f'Invalid shift or scale {s}') + res.append(v) else: raise ValueError(f'Invalid shift or scale {s}') @@ -243,8 +318,8 @@ def solve_mapper(arr, map): isinstance(res, list) and isinstance(res[0], list) and len(res) == n_modals - and len(res[0]) == n_atom_types ) + assert all([len(r) == n_atom_types for r in res]) # type: ignore else: assert ( isinstance(res, list) @@ -284,3 +359,29 @@ def forward(self, data: AtomGraphDataType) -> AtomGraphDataType: ) + shift.view(-1, 1) return data + + +def get_resolved_shift_scale( + module: Union[Rescale, SpeciesWiseRescale, ModalWiseRescale], + type_map: Optional[Dict[int, int]] = None, + modal_map: Optional[Dict[str, int]] = None, +): + """ + Return resolved shift and scale from scale modules. For element wise case, + convert to list of floats where idx is atomic number. For modal wise case, return + dictionary of shift scale where key is modal name given in modal_map + + Return: + Tuple of solved shift and scale + """ + + if isinstance(module, Rescale): + return (module.get_shift(), module.get_scale()) + elif isinstance(module, SpeciesWiseRescale): + return (module.get_shift(type_map), module.get_scale(type_map)) + elif isinstance(module, ModalWiseRescale): + return ( + module.get_shift(type_map, modal_map), + module.get_scale(type_map, modal_map), + ) + raise ValueError('Not scale module') diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index 37f74939..eac5266f 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -2,6 +2,7 @@ Debt keep old pre-trained checkpoints unchanged. """ + import copy import torch @@ -10,19 +11,25 @@ def patch_old_config(config): - if config[KEY.CUTOFF_FUNCTION][KEY.CUTOFF_FUNCTION_NAME] == 'XPLOR': - config[KEY.CUTOFF_FUNCTION].pop('poly_cut_p_value', None) - if KEY.TRAIN_DENOMINTAOR not in config: - config[KEY.TRAIN_DENOMINTAOR] = config.pop('train_avg_num_neigh', False) - _opt = config.pop('optimize_by_reduce', None) - if _opt is False: - raise ValueError( - 'This checkpoint(optimize_by_reduce: False) is no longer supported' - ) - if KEY.CONV_DENOMINATOR not in config: - config[KEY.CONV_DENOMINATOR] = 0.0 - if KEY._NORMALIZE_SPH not in config: - config[KEY._NORMALIZE_SPH] = False + version = config['version'] + major, minor, _ = version.split('.')[:3] + major, minor = int(major), int(minor) + + if major == 0 and minor <= 9: + if config[KEY.CUTOFF_FUNCTION][KEY.CUTOFF_FUNCTION_NAME] == 'XPLOR': + config[KEY.CUTOFF_FUNCTION].pop('poly_cut_p_value', None) + if KEY.TRAIN_DENOMINTAOR not in config: + config[KEY.TRAIN_DENOMINTAOR] = config.pop('train_avg_num_neigh', False) + _opt = config.pop('optimize_by_reduce', None) + if _opt is False: + raise ValueError( + 'This checkpoint(optimize_by_reduce: False) is no longer supported' + ) + if KEY.CONV_DENOMINATOR not in config: + config[KEY.CONV_DENOMINATOR] = 0.0 + if KEY._NORMALIZE_SPH not in config: + config[KEY._NORMALIZE_SPH] = False + return config diff --git a/sevenn/scripts/convert_model_modality.py b/sevenn/scripts/convert_model_modality.py index 17bff6f7..5581e196 100644 --- a/sevenn/scripts/convert_model_modality.py +++ b/sevenn/scripts/convert_model_modality.py @@ -96,9 +96,9 @@ def _get_modal_weight_as_bias( def _append_modal_weight( - model_state_dct: dict, - key: str, - irreps_in: Irreps, + model_state_dct: dict, # state dict to be targeted + key: str, # linear weight modune name + irreps_in: Irreps, # irreps_in before modality append irreps_out: Irreps, append_number: int, ): @@ -262,6 +262,7 @@ def append_modality_to_model_dct( `model_state_dct`: model state dictionary from multimodal checkpoint file `config`: dictionary containing configuration of the checkpoint model + + modality appended `orig_num_modal`: Number of modality used in original checkpoint `append_modal_length`: Number of modality to be appended in new checkpoint. """ diff --git a/sevenn/scripts/processing_continue.py b/sevenn/scripts/processing_continue.py index 6148e2f2..1029025b 100644 --- a/sevenn/scripts/processing_continue.py +++ b/sevenn/scripts/processing_continue.py @@ -63,6 +63,9 @@ def processing_continue_v2(config): # simpler + f'This model knows {config[KEY.NUM_SPECIES]} species' ) + if config_cp.get(KEY.USE_MODALITY, False) != config.get(KEY.USE_MODALITY): + raise ValueError('use_modality is not same. Check sevenn_cp') + modal_map = config_cp.get(KEY.MODAL_MAP, {}) config.update({KEY.MODAL_MAP: modal_map}) if len(modal_map) > 0: diff --git a/sevenn/train/modal_dataset.py b/sevenn/train/modal_dataset.py index b42d5ee3..d4ee4a29 100644 --- a/sevenn/train/modal_dataset.py +++ b/sevenn/train/modal_dataset.py @@ -266,8 +266,8 @@ def from_config( dataset_args = { 'cutoff': config[KEY.CUTOFF], 'root': working_dir, - 'process_num_cores': config[KEY.PREPROCESS_NUM_CORES], - 'use_data_weight': config[KEY.USE_WEIGHT], + 'process_num_cores': config.get(KEY.PREPROCESS_NUM_CORES, 1), + 'use_data_weight': config.get(KEY.USE_WEIGHT, False), **config[KEY.DATA_FORMAT_ARGS], } @@ -285,9 +285,8 @@ def from_config( ) train_set = datasets['trainset'] - modals = train_set.modals - _modals_all = set() + modals_dataset = set() chem_species = set() # print statistics of each dataset for name, dataset in datasets.items(): @@ -298,22 +297,24 @@ def from_config( log.format_k_v( '# structures (graph)', len(dataset.datasets[idx]), write=True ) - _modals_all.update([modality]) + modals_dataset.update([modality]) chem_species.update(dataset.species['total']) log.bar() if (modal_map := config.get(KEY.MODAL_MAP, None)) is None: - modals = sorted(list(modals)) + modals = sorted(list(modals_dataset)) modal_map = {modal: i for i, modal in enumerate(modals)} config[KEY.MODAL_MAP] = modal_map - log.writeline(f'Modalities of this model: {modals}') - if not _modals_all.issubset(modal_map): + modals = list(modal_map.keys()) + if not modals_dataset.issubset(modal_map): raise ValueError( - f'Found modalities of datasets: {_modals_all} are not subset of {modals}' - + '. Use a sevenn_cp cli tool to append/assign modality' + f'Found modalities in datasets: {modals_dataset} are not subset of' + + f' {modals}. Use sevenn_cp tool to append/assign modality' ) + log.writeline(f'Modalities of this model: {modals}') + config[KEY.NUM_MODALITIES] = len(modal_map) # initialize known species from dataset if 'auto' From 97372e0cfe609fe79c503d14b7c98ae9b1546d35 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 11:14:44 +0900 Subject: [PATCH 080/113] refactor --- sevenn/sevennet_calculator.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sevenn/sevennet_calculator.py b/sevenn/sevennet_calculator.py index eb6e5ae9..012a3561 100644 --- a/sevenn/sevennet_calculator.py +++ b/sevenn/sevennet_calculator.py @@ -55,9 +55,10 @@ def __init__( if isinstance(model, pathlib.PurePath): model = str(model) + allowed_file_types = ['checkpoint', 'torchscript', 'model_instance'] file_type = file_type.lower() - if file_type not in ['checkpoint', 'torchscript', 'model_instance']: - raise ValueError('file_type should be checkpoint or torchscript') + if file_type not in allowed_file_types: + raise ValueError('file_type not in {allowed_file_types}') if enable_cueq and file_type in ['model_instance', 'torchscript']: warnings.warn( From 37762582f5331193eeed20c157959ec014e961ad Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 11:15:10 +0900 Subject: [PATCH 081/113] add: modal related tests --- tests/unit_tests/test_data.py | 53 ++++++++++++- tests/unit_tests/test_modal.py | 136 +++++++++++++++++++++++++++++++++ tests/unit_tests/test_model.py | 33 +++++++- tests/unit_tests/test_train.py | 49 ++++++++++-- 4 files changed, 258 insertions(+), 13 deletions(-) create mode 100644 tests/unit_tests/test_modal.py diff --git a/tests/unit_tests/test_data.py b/tests/unit_tests/test_data.py index 6350dd36..fda1effd 100644 --- a/tests/unit_tests/test_data.py +++ b/tests/unit_tests/test_data.py @@ -1,5 +1,6 @@ import logging import os +import os.path as osp import uuid from collections import Counter from copy import deepcopy @@ -17,6 +18,7 @@ import sevenn._keys as KEY import sevenn.train.dataload as dl import sevenn.train.graph_dataset as ds +import sevenn.train.modal_dataset as modal_dataset from sevenn._const import NUM_UNIV_ELEMENT from sevenn.atom_graph_data import AtomGraphData from sevenn.util import model_from_checkpoint, pretrained_name_to_path @@ -295,10 +297,6 @@ def test_sevenn_graph_dataset_elemwise_energies(graph_dataset_tuple): assert ref_e[z] == 0 -# def test_sevenn_graph_dataset_num_neigh(graph_dataset_tuple): -# pass - - def test_sevenn_graph_dataset_statistics(graph_dataset_tuple): dataset, atoms_list = graph_dataset_tuple @@ -332,6 +330,53 @@ def test_sevenn_graph_dataset_statistics(graph_dataset_tuple): assert np.allclose(dataset.statistics[key]['min'], dct[key].min()), key +def test_sevenn_mm_dataset_statistics(tmp_path): + + files = osp.join(tmp_path, 'gd_one.extxyz') + atoms_list1 = [ + get_atoms(atype, 'calc')[0] # type: ignore + for atype in ['bulk', 'bulk', 'bulk', 'bulk'] + ] + ase.io.write(files, atoms_list1, 'extxyz') + + gd1 = ds.SevenNetGraphDataset( + cutoff=cutoff, + root=tmp_path, + files=files, + processed_name='gd_one.pt', + ) + + files = osp.join(tmp_path, 'gd_two.extxyz') + atoms_list2 = [ + get_atoms(atype, 'calc')[0] # type: ignore + for atype in ['mol', 'mol', 'bulk'] + ] + ase.io.write(files, atoms_list2, 'extxyz') + + gd2 = ds.SevenNetGraphDataset( + cutoff=cutoff, + root=tmp_path, + files=files, + processed_name='gd_two.pt', + ) + + ref = ds.SevenNetGraphDataset( + cutoff=cutoff, + root=tmp_path, + files=[gd1.processed_paths[0], gd2.processed_paths[0]], + processed_name='combined.pt', + ) + + mm = modal_dataset.SevenNetMultiModalDataset( + {'modal1': gd1, 'modal2': gd2} + ) + + assert np.allclose(ref.per_atom_energy_mean, mm.per_atom_energy_mean['total']) + assert np.allclose(ref.avg_num_neigh, mm.avg_num_neigh['total']) + assert np.allclose(ref.force_rms, mm.force_rms['total']) + assert set(ref.species) == set(mm.species['total']) + + @pytest.mark.parametrize( 'a_types,init_ys', [(['bulk', 'mol', 'isolated'], ['calc', 'calc', 'calc'])] ) diff --git a/tests/unit_tests/test_modal.py b/tests/unit_tests/test_modal.py new file mode 100644 index 00000000..b1b4f3e6 --- /dev/null +++ b/tests/unit_tests/test_modal.py @@ -0,0 +1,136 @@ +# # deploy is test on lammps +# test append modality +# from no modality model to modality yes model +# from modality model to more modality model +# different shift scale settings +# test modality options (check num param) +# calculators with modality + +import copy +# + modal checkpoint continue and test_train +# + sevenn_cp test things in test_cli +import pathlib + +import pytest +from ase.build import bulk + +import sevenn.train.graph_dataset as graph_ds +import sevenn.util as util +from sevenn.model_build import build_E3_equivariant_model +from sevenn.sevennet_calculator import SevenNetCalculator + +cutoff = 5.0 +data_root = (pathlib.Path(__file__).parent.parent / 'data').resolve() +hfo2_path = str(data_root / 'systems' / 'hfo2.extxyz') +sevennet_0_path = util.pretrained_name_to_path('7net-0_11July2024') + + +@pytest.fixture(scope='module') +def graph_dataset_path(tmp_path_factory): + gd_path = tmp_path_factory.mktemp('gd') + ds = graph_ds.SevenNetGraphDataset( + cutoff=cutoff, root=str(gd_path), files=[hfo2_path], processed_name='tmp.pt' + ) + return ds.processed_paths[0] + + +_modal_cfg = { + 'use_modal_node_embedding': False, + 'use_modal_self_inter_intro': True, + 'use_modal_self_inter_outro': True, + 'use_modal_output_block': True, + 'use_modality': True, + 'use_modal_wise_shift': True, # T/F should be tested + 'use_modal_wise_scale': False, # T/F should be tested + 'load_trainset_path': [ + { + 'data_modality': 'modal_new', + 'file_list': [{'file': hfo2_path}], + } + ], +} + + +@pytest.fixture(scope='module') +def snet_0_cp(): + return util.load_checkpoint(sevennet_0_path) + + +@pytest.fixture(scope='module') +def snet_0_calc(): + return SevenNetCalculator() + + +@pytest.fixture() +def bulk_atoms(): + atoms = bulk('Si') * 3 + atoms.rattle() + return atoms + + +def assert_atoms(atoms1, atoms2, rtol=1e-5, atol=1e-6): + import numpy as np + + def acl(a, b, rtol=rtol, atol=atol): + return np.allclose(a, b, rtol=rtol, atol=atol) + + assert len(atoms1) == len(atoms2) + assert acl(atoms1.get_cell(), atoms2.get_cell()) + assert acl(atoms1.get_potential_energy(), atoms2.get_potential_energy()) + assert acl(atoms1.get_forces(), atoms2.get_forces(), rtol * 10, atol * 10) + assert acl( + atoms1.get_stress(voigt=False), + atoms2.get_stress(voigt=False), + rtol * 10, + atol * 10, + ) + # assert acl(atoms1.get_potential_energies(), atoms2.get_potential_energies()) + + +def get_modal_cfg(overwrite=None): + modal_cfg = copy.deepcopy(_modal_cfg).copy() + if overwrite: + modal_cfg.update(overwrite) + return modal_cfg + + +@pytest.mark.parametrize( + 'cfg_overwrite', + [ + ({}), + ({'use_modal_wise_scale': True}), + ({'use_modal_wise_shift': False}), + ({'use_modal_self_inter_intro': False}), + ], +) +def test_append_modal_sevennet_0( + cfg_overwrite, + snet_0_cp, + snet_0_calc, + bulk_atoms, + graph_dataset_path, + tmp_path, +): + modal_cfg = snet_0_cp.config + modal_cfg.pop('load_dataset_path') + modal_cfg.pop('load_validset_path') + modal_cfg.update(get_modal_cfg(cfg_overwrite)) + modal_cfg['shift'] = 'elemwise_reference_energies' + modal_cfg['scale'] = 'per_atom_energy_std' + modal_cfg['load_trainset_path'][0]['file_list'] = [{'file': graph_dataset_path}] + + new_state_dict = snet_0_cp.append_modal( + modal_cfg, original_modal_name='pbe', working_dir=tmp_path + ) + sevennet_0_w_modal = build_E3_equivariant_model(modal_cfg) + sevennet_0_w_modal.load_state_dict(new_state_dict, strict=True) + + atoms1 = bulk_atoms + atoms2 = copy.deepcopy(atoms1) + + atoms1.calc = snet_0_calc + atoms2.calc = SevenNetCalculator( + model=sevennet_0_w_modal, file_type='model_instance', modal='pbe' + ) + + assert_atoms(atoms1, atoms2) diff --git a/tests/unit_tests/test_model.py b/tests/unit_tests/test_model.py index 55bf9df6..d75976f8 100644 --- a/tests/unit_tests/test_model.py +++ b/tests/unit_tests/test_model.py @@ -152,7 +152,7 @@ def test_batch(): assert torch.allclose( torch.round(f_concat, decimals=5), torch.round(output_batched['inferred_force'], decimals=5), - atol=1e-5 + atol=1e-5, ) assert torch.allclose( # TODO, hard-coded, assumes the first structure is bulk @@ -166,8 +166,7 @@ def test_batch(): ({'train_denominator': True}, 20642 + 3), ({'train_shift_scale': True}, 20642 + 2), ({'shift': [1.0] * 4}, 20642), - ({'scale': [1.0] * 4, - 'train_shift_scale': True}, 20642 + 8), + ({'scale': [1.0] * 4, 'train_shift_scale': True}, 20642 + 8), ({'num_convolution_layer': 4}, 33458), ({'lmax': 3}, 26866), ({'channel': 2}, 16883), @@ -183,4 +182,32 @@ def test_num_params(cf, ref): assert param == ref, f'ref: {ref} != given: {param}' +_n_modal_param_tests = [ + ({}, 20642), + ({'use_modal_node_embedding': True}, 20642 + 8), + ({'use_modal_self_inter_intro': True}, 20642 + 2 * 4 * 3), + ({'use_modal_self_inter_outro': True}, 20642 + 2 * (12 + 20 + 4)), + ({'use_modal_output_block': True}, 20642 + 2 * 4 / 2), +] + + +@pytest.mark.parametrize('cf,ref', _n_modal_param_tests) +def test_modal_num_params(cf, ref): + modal_cfg = { + 'use_modality': True, + '_number_of_modalities': 2, + '_modal_map': {'x1': 0, 'x2': 1}, + 'use_modal_node_embedding': False, + 'use_modal_self_inter_intro': False, + 'use_modal_self_inter_outro': False, + 'use_modal_output_block': False, + 'use_modal_wise_shift': False, + 'use_modal_wise_scale': False, + } + modal_cfg.update(cf) + model = get_model(modal_cfg) + param = sum([p.numel() for p in model.parameters() if p.requires_grad]) + assert param == ref, f'ref: {ref} != given: {param}' + + # TODO: test_irreps, test_gard, test_equivariance diff --git a/tests/unit_tests/test_train.py b/tests/unit_tests/test_train.py index 2e05608f..b822bb4e 100644 --- a/tests/unit_tests/test_train.py +++ b/tests/unit_tests/test_train.py @@ -8,11 +8,13 @@ import sevenn.train.graph_dataset as graph_ds from sevenn._const import NUM_UNIV_ELEMENT +from sevenn.error_recorder import ErrorRecorder from sevenn.scripts.processing_continue import processing_continue_v2 from sevenn.scripts.processing_epoch import processing_epoch_v2 from sevenn.sevenn_logger import Logger from sevenn.train.dataload import graph_build from sevenn.train.graph_dataset import from_config as dataset_from_config +from sevenn.train.loss import get_loss_functions_from_config from sevenn.train.trainer import Trainer from sevenn.util import ( chemical_species_preprocess, @@ -48,10 +50,11 @@ def HfO2_loader(): return DataLoader(graphs, batch_size=2) -@pytest.fixture() -def graph_dataset_path(tmp_path): +@pytest.fixture(scope='module') +def graph_dataset_path(tmp_path_factory): + gd_path = tmp_path_factory.mktemp('gd') ds = graph_ds.SevenNetGraphDataset( - cutoff=cutoff, root=tmp_path, files=[hfo2_path], processed_name='tmp.pt' + cutoff=cutoff, root=str(gd_path), files=[hfo2_path], processed_name='tmp.pt' ) return ds.processed_paths[0] @@ -115,6 +118,8 @@ def get_train_config(): ('Stress', 'RMSE'), ('TotalLoss', 'None'), ], + 'use_modality': False, + 'use_weight': False, 'device': 'cpu', 'is_ddp': False, } @@ -133,12 +138,13 @@ def get_data_config(): return config -def get_config(overwrite={}): +def get_config(overwrite=None): cf = {} cf.update(get_model_config()) cf.update(get_train_config()) cf.update(get_data_config()) - cf.update(overwrite) + if overwrite: + cf.update(overwrite) return cf @@ -159,6 +165,7 @@ def test_processing_continue_v2_7net0(tmp_path): scale_ref = np.array([1.73] * 89) conv_denominator_ref = np.array([35.989574] * 5) + print(cfg) with Logger().switch_file(str(tmp_path / 'log.sevenn')): state_dicts, epoch = processing_continue_v2(cfg) assert epoch == 601 @@ -192,7 +199,6 @@ def test_dataset_from_config(cfg_overwrite, ds_names, tmp_path): def test_dataset_from_config_as_it_is_load(graph_dataset_path, tmp_path): cfg = get_config({'load_trainset_path': graph_dataset_path}) - print(graph_dataset_path) new_wd = tmp_path / 'tmp_wd' with Logger().switch_file(str(tmp_path / 'log.sevenn')): _ = dataset_from_config(cfg, str(new_wd)) @@ -352,6 +358,37 @@ def test_processing_epoch_v2(HfO2_loader, tmp_path): assert lasts[-2] == '0.087873' # myset Force MAE +def test_data_weight(graph_dataset_path, tmp_path): + cfg = get_config( + { + 'load_trainset_path': [{ + 'file_list': [{'file': graph_dataset_path}], + 'data_weight': {'energy': 0.1, 'force': 3.0, 'stress': 1.0}, + }], + 'error_record': [ + ('Energy', 'Loss'), + ('Force', 'Loss'), + ('Stress', 'Loss'), + ('TotalLoss', 'None'), + ], + 'use_weight': True + } + ) + trainer_args, _, _ = Trainer.args_from_checkpoint(cp_0_path) + trainer_args['loss_functions'] = get_loss_functions_from_config(cfg) + trainer = Trainer(**trainer_args) + erc = ErrorRecorder.from_config(cfg, trainer.loss_functions) + + db = graph_ds.from_config(cfg, working_dir=tmp_path)['trainset'] + loader_w_weight = DataLoader(db, batch_size=len(db)) + + trainer.run_one_epoch(loader_w_weight, False, erc) + loss = erc.epoch_forward() + assert np.allclose(loss['Energy_Loss'], 839.7104492 * 0.1) + assert np.allclose(loss['Force_Loss'], 0.0152806 * 3.0) + assert np.allclose(loss['Stress_Loss'], 6017.568847 * 1.0) + + def _write_empty_checkpoint(): from sevenn.model_build import build_E3_equivariant_model From 5ed3e0951e902af233bf20757b664bb481f6af8b Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 17:22:35 +0900 Subject: [PATCH 082/113] bugfix --- sevenn/scripts/backward_compatibility.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index eac5266f..bf09091f 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -153,14 +153,19 @@ def patch(stct): def patch_state_dict_if_old(state_dict, config_cp, now_model): version = config_cp['version'] - major, minor, _ = version.split('.')[:3] - - if int(major) == 0 and int(minor) < 10: + vs = version.split('.') + vsuffix = '' + if len(vs) == 4: + vsuffix = vs[-1] + vs = vs[:3] + ver = '.'.join(vs) + + if ver < '0.10.0': state_dict = map_old_model(state_dict) # TODO: change version criteria before release!!! # it causes problem if model is sorted but this function is called # ... more robust way? idk - if int(major) == 0 and int(minor) < 11: + if ver < '0.11.0' or ver == '0.11.0' and vsuffix == 'dev0': state_dict = sort_old_convolution(now_model, state_dict) return state_dict From e2096cfe52c3c0b89077e5d8365441e26f039835 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 17:28:38 +0900 Subject: [PATCH 083/113] add: multimodal preset --- sevenn/main/sevenn_preset.py | 3 +- sevenn/presets/multi_modal.yaml | 108 ++++++++++++++++++++++++++++++++ 2 files changed, 110 insertions(+), 1 deletion(-) create mode 100644 sevenn/presets/multi_modal.yaml diff --git a/sevenn/main/sevenn_preset.py b/sevenn/main/sevenn_preset.py index 65fe23cc..60de5b51 100644 --- a/sevenn/main/sevenn_preset.py +++ b/sevenn/main/sevenn_preset.py @@ -28,7 +28,8 @@ def cmd_parse_preset(args=None): 'sevennet-0', 'base', 'fine_tune_v1', - 'base_v1' + 'base_v1', + 'multi_modal' ], help=preset_help ) diff --git a/sevenn/presets/multi_modal.yaml b/sevenn/presets/multi_modal.yaml new file mode 100644 index 00000000..fe74de4c --- /dev/null +++ b/sevenn/presets/multi_modal.yaml @@ -0,0 +1,108 @@ +model: + chemical_species: 'univ' # Ready for 119 elements + cutoff: 6.0 + channel: 128 + is_parity: False + lmax: 3 + num_convolution_layer: 3 + irreps_manual: + - "128x0e" + - "128x0e+64x1e+32x2e+16x3e" + - "128x0e+64x1e+32x2e+16x3e" + - "128x0e" + + weight_nn_hidden_neurons: [64, 64] + radial_basis: + radial_basis_name: 'bessel' + bessel_basis_num: 8 + cutoff_function: + cutoff_function_name: 'XPLOR' + cutoff_on: 5.5 + + act_gate: {'e': 'silu', 'o': 'tanh'} + act_scalar: {'e': 'silu', 'o': 'tanh'} + + conv_denominator: 'avg_num_neigh' + train_shift_scale: True + train_denominator: False + self_connection_type: 'linear' + + use_modal_node_embedding: False + use_modal_self_inter_intro: True + use_modal_self_inter_outro: True + use_modal_output_block: True +train: + train_shuffle: True + random_seed: 777 + is_train_stress : True + epoch: 200 + + loss: 'Huber' + loss_param: + delta: 0.01 + + optimizer: 'adam' + optim_param: + lr: 0.01 + scheduler: 'linearlr' + scheduler_param: + start_factor: 1.0 + total_iters: 200 + end_factor: 0.0001 + + force_loss_weight : 1.00 + stress_loss_weight: 0.01 + + error_record: + - ['Energy', 'MAE'] + - ['Force', 'MAE'] + - ['Stress', 'MAE'] + - ['Energy', 'Loss'] + - ['Force', 'Loss'] + - ['Stress', 'Loss'] + - ['TotalLoss', 'None'] + + per_epoch: 10 + use_modality: True + use_weight: True + + continue: + checkpoint: './checkpoint_190.pth' + +data: + batch_size: 16 + shift: 'elemwise_reference_energies' + scale: 'force_rms' + + use_modal_wise_shift: True + use_modal_wise_scale: False + + load_trainset_path: + + - data_modality: pbe + file_list: + - file: "path to pbe dataset" # ASE readable or .pt under sevenn_data + data_weight: + energy: 1.0 + force: 0.1 + stress: 1.0 + + - data_modality: scan + file_list: + - file: "path to scan dataset" + data_weight: + energy: 1.0 + force: 10.0 + stress: 1.0 + + load_pbe_validset_path: # any name starts with 'load' and ends with 'set_path' + - data_modality: pbe # modality must be given for mm valid set + file_list: + - file: "path to pbe validset" + data_format: graph + + load_scan_validset_path: + - data_modality: scan + file_list: + - file: "path to scan validset" + data_format: ase From 3c9e9ff70bdcdd763f4450fe62af4130c033072c Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 17:32:17 +0900 Subject: [PATCH 084/113] fix: mm preset --- sevenn/presets/multi_modal.yaml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/sevenn/presets/multi_modal.yaml b/sevenn/presets/multi_modal.yaml index fe74de4c..bf08bbef 100644 --- a/sevenn/presets/multi_modal.yaml +++ b/sevenn/presets/multi_modal.yaml @@ -66,9 +66,6 @@ train: use_modality: True use_weight: True - continue: - checkpoint: './checkpoint_190.pth' - data: batch_size: 16 shift: 'elemwise_reference_energies' @@ -81,7 +78,7 @@ data: - data_modality: pbe file_list: - - file: "path to pbe dataset" # ASE readable or .pt under sevenn_data + - file: "path to pbe dataset" # ASE readable or .pt file (graph.pt) data_weight: energy: 1.0 force: 0.1 @@ -99,10 +96,8 @@ data: - data_modality: pbe # modality must be given for mm valid set file_list: - file: "path to pbe validset" - data_format: graph load_scan_validset_path: - data_modality: scan file_list: - file: "path to scan validset" - data_format: ase From 802b280b950b1264f7c7741d25c0dee3a6cda7bc Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 17:42:30 +0900 Subject: [PATCH 085/113] changelog --- CHANGELOG.md | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0e1d61b1..9881d9a5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,20 +3,27 @@ All notable changes to this project will be documented in this file. ## WIP [0.11.0] ### Added -- Read modality and weight of each data label from `structure_list` +- Build multi-fidelity model, SevenNet-MF, based on given modality in the yaml +- Modality support for sevenn_inference, sevenn_get_modal, and SevenNetCalculator +- [cli] sevenn_cp tool for checkpoint summary, input generation, multi-modal routines +- Modality append / assign using sevenn_cp - Loss weighting for energy, force and stress for corresponding data label -- Build multi-fidelity model, SevenNet-MF, based on given modality in the training set -- `AtomGraphDataset` contains information about the modality of given data. -- `sevenn_get_model` / `sevenn_inference` selects the target fidelity when deploying / inferring SevenNet-MF. -- `SevenNetCalculator` takes target fidelity for inference as input. -- Automatic fine-tuning for SevenNet-MF by converting initial weights and shift & scale from checkpoint file. +- Ignore unlabelled data when calculating loss. (e.g. stress data for non-pbc structure) +- Dict style dataset input for multi-modal and data-weight + +### Added (code) +- sevenn.train.modal_dataset SevenNetMultiModalDataset +- sevenn.scripts.backward_compatibility.py +- sevenn.checkpoint.py ### Changed -- Ignore unlabelled data when calculating loss. (e.g. stress data for non-pbc structure) -- Record shift and scale used for each modality in `log.sevenn`. -- Record error and loss in the `log.sevenn` with the value after weighting the loss and ignoring unlabelled data. -- Deploy takes checkpoint path (now deployed serial build model only once) - Sort instructions of tensor product in convolution (+ fix flipped w3j coeff of old model) +- Lazy initialization for `IrrepsLinear` and `SelfConnection*` +- Checkpoint things using `sevenn/checkpoint.py` +- e3nn >= 0.5.0, to ensure changed CG coeff later on + +### Fixed +- More refactor for shift scale things + few bug fixes ## [0.10.2] ### Added From 8357f5afed4c76c51bb30bb0e239cfc8f0b2b1ef Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sun, 8 Dec 2024 18:01:21 +0900 Subject: [PATCH 086/113] fix: pandas dep --- CHANGELOG.md | 1 + pyproject.toml | 1 + 2 files changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9881d9a5..9488a85e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -21,6 +21,7 @@ All notable changes to this project will be documented in this file. - Lazy initialization for `IrrepsLinear` and `SelfConnection*` - Checkpoint things using `sevenn/checkpoint.py` - e3nn >= 0.5.0, to ensure changed CG coeff later on +- pandas as dependency ### Fixed - More refactor for shift scale things + few bug fixes diff --git a/pyproject.toml b/pyproject.toml index 2951edb8..aecc6760 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ "torch_geometric>=2.5.0", "numpy<2.0", "matscipy", + "pandas", ] [project.optional-dependencies] test = ["matscipy", "pytest-cov>=5"] From c35cfff7ffcc398bbea57313aa9947c99d744759 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 11 Dec 2024 16:00:13 +0900 Subject: [PATCH 087/113] fix --- sevenn/scripts/backward_compatibility.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index bf09091f..893c84f6 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -4,6 +4,7 @@ """ import copy +import warnings import torch @@ -11,7 +12,11 @@ def patch_old_config(config): - version = config['version'] + version = config.get('version', None) + if not version: + warnings.warn('No version found in config. Assume 0.9.0') + version = '0.9.0' + major, minor, _ = version.split('.')[:3] major, minor = int(major), int(minor) @@ -152,7 +157,10 @@ def patch(stct): def patch_state_dict_if_old(state_dict, config_cp, now_model): - version = config_cp['version'] + version = config_cp.get('version', None) + if not version: + warnings.warn('No version found in config. Assume 0.9.0') + version = '0.9.0' vs = version.split('.') vsuffix = '' if len(vs) == 4: From 0b7cbf41d6e41cfde4b31902e186786d0639071f Mon Sep 17 00:00:00 2001 From: YutackPark Date: Wed, 11 Dec 2024 16:42:18 +0900 Subject: [PATCH 088/113] version up + raise Error not warning if version is not found --- pyproject.toml | 2 +- sevenn/scripts/backward_compatibility.py | 9 +++------ 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3fc3fe3c..43122c79 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "sevenn" -version = "0.11.0.dev" +version = "0.11.0" authors = [ { name = "Yutack Park", email = "parkyutack@snu.ac.kr" }, { name = "Haekwan Jeon", email = "haekwan98@snu.ac.kr" }, diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index 893c84f6..6ac78f8b 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -4,7 +4,6 @@ """ import copy -import warnings import torch @@ -14,8 +13,7 @@ def patch_old_config(config): version = config.get('version', None) if not version: - warnings.warn('No version found in config. Assume 0.9.0') - version = '0.9.0' + raise ValueError('No version found in config') major, minor, _ = version.split('.')[:3] major, minor = int(major), int(minor) @@ -159,8 +157,7 @@ def patch(stct): def patch_state_dict_if_old(state_dict, config_cp, now_model): version = config_cp.get('version', None) if not version: - warnings.warn('No version found in config. Assume 0.9.0') - version = '0.9.0' + raise ValueError('No version found in config') vs = version.split('.') vsuffix = '' if len(vs) == 4: @@ -174,6 +171,6 @@ def patch_state_dict_if_old(state_dict, config_cp, now_model): # TODO: change version criteria before release!!! # it causes problem if model is sorted but this function is called # ... more robust way? idk - if ver < '0.11.0' or ver == '0.11.0' and vsuffix == 'dev0': + if ver < '0.11.0' or (ver == '0.11.0' and vsuffix == 'dev0'): state_dict = sort_old_convolution(now_model, state_dict) return state_dict From 75c8b7284c7941bc02f0ce22caa79a41dcfedd1b Mon Sep 17 00:00:00 2001 From: YutackPark Date: Thu, 12 Dec 2024 15:54:49 +0900 Subject: [PATCH 089/113] fix: backward version checkp --- sevenn/scripts/backward_compatibility.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/sevenn/scripts/backward_compatibility.py b/sevenn/scripts/backward_compatibility.py index 6ac78f8b..b8e81b1a 100644 --- a/sevenn/scripts/backward_compatibility.py +++ b/sevenn/scripts/backward_compatibility.py @@ -10,6 +10,11 @@ import sevenn._keys as KEY +def version_tuple(v1): + v1 = tuple(map(int, v1.split('.'))) + return v1 + + def patch_old_config(config): version = config.get('version', None) if not version: @@ -162,15 +167,18 @@ def patch_state_dict_if_old(state_dict, config_cp, now_model): vsuffix = '' if len(vs) == 4: vsuffix = vs[-1] - vs = vs[:3] - ver = '.'.join(vs) + vs = version_tuple('.'.join(vs[:3])) + else: + vs = version_tuple('.'.join(vs)) - if ver < '0.10.0': + if vs < version_tuple('0.10.0'): state_dict = map_old_model(state_dict) # TODO: change version criteria before release!!! # it causes problem if model is sorted but this function is called # ... more robust way? idk - if ver < '0.11.0' or (ver == '0.11.0' and vsuffix == 'dev0'): + if vs < version_tuple('0.11.0') or ( + vs == version_tuple('0.11.0') and vsuffix == 'dev0' + ): state_dict = sort_old_convolution(now_model, state_dict) return state_dict From 6c18ffcad2cda42bde67dc0117a79d6b12f805d6 Mon Sep 17 00:00:00 2001 From: YutackPark Date: Sat, 14 Dec 2024 11:40:15 +0900 Subject: [PATCH 090/113] fix: non-species wise shift scale continue --- sevenn/model_build.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sevenn/model_build.py b/sevenn/model_build.py index c40be976..725aac89 100644 --- a/sevenn/model_build.py +++ b/sevenn/model_build.py @@ -146,6 +146,8 @@ def init_shift_scale(config): s = s.tolist() if isinstance(s, dict): s = {k: v.tolist() if hasattr(v, 'tolist') else v for k, v in s.items()} + if isinstance(s, list) and len(s) == 1: + s = s[0] shift_scale.append(s) shift, scale = shift_scale From 7d1477e614c4cbc519f6fb6b00c1030bdc6e757b Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:48:12 +0900 Subject: [PATCH 091/113] Update README.md --- README.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3433c6eb..3a086c71 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,7 @@ SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural networ - Python [Atomic Simulation Environment (ASE)](https://wiki.fysik.dtu.dk/ase/) calculator support - GPU-parallelized molecular dynamics with LAMMPS - CUDA-accelerated D3 (van der Waals) dispersion + - Multi-fidelity training for combining multiple database with different calculation settings. ## Pre-trained models So far, we have released three pre-trained SevenNet models. Each model has various hyperparameters and training sets, resulting in different accuracy and speed. Please read the descriptions below carefully and choose the model that best suits your purpose. @@ -27,6 +28,18 @@ Additionally, `keywords` can be called in other parts of SevenNet, such as `seve --- +### **SevenNet-MF-0 (16Dec2024)** +> Keywords in ASE: `7net-MF-0` and `SevenNet-MF-0` + +The model is trained on PBE (+U) and r$^2$SCAN database provided in Materials Project. +It has the same architecture with **SevenNet-0 (11Jul2024)**, except this model contains additional 'fidelity-dependent' parameters utilized for multi-fidelity training. +However, overhead of calculations regarding fidelity-dependent parameters are negligible, which results in almost the same inference speed with **SevenNet-0 (11Jul2024)**. + + +* Training set MAE (r$^2$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) +* Training time: 6.11 GPU-days on A100 +--- + ### **SevenNet-l3i5 (12Dec2024)** > Keywords in ASE: `7net-l3i5` and `SevenNet-l3i5` @@ -117,7 +130,7 @@ With the `sevenn_preset` command, the input file that sets the training paramete sevenn_preset {preset keyword} > input.yaml ``` -Available preset keywords are: `base`, `fine_tune`, `sevennet-0`, and `sevennet-l3i5`. +Available preset keywords are: `base`, `fine_tune`, `multi_modal`, `sevennet-0`, and `sevennet-l3i5`. Check comments in the preset yaml files for explanations. For fine-tuning, note that most model hyperparameters cannot be modified unless explicitly indicated. To reuse a preprocessed training set, you can specify `sevenn_data/${dataset_name}.pt` to the `load_trainset_path:` in the `input.yaml`. From a93c51e59f351c52efb3e554242fce8561f2c4fc Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:49:05 +0900 Subject: [PATCH 092/113] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3a086c71..7b8c14a6 100644 --- a/README.md +++ b/README.md @@ -31,12 +31,12 @@ Additionally, `keywords` can be called in other parts of SevenNet, such as `seve ### **SevenNet-MF-0 (16Dec2024)** > Keywords in ASE: `7net-MF-0` and `SevenNet-MF-0` -The model is trained on PBE (+U) and r$^2$SCAN database provided in Materials Project. +The model is trained on PBE (+U) and $\rm{r}^2$SCAN database provided in Materials Project. It has the same architecture with **SevenNet-0 (11Jul2024)**, except this model contains additional 'fidelity-dependent' parameters utilized for multi-fidelity training. However, overhead of calculations regarding fidelity-dependent parameters are negligible, which results in almost the same inference speed with **SevenNet-0 (11Jul2024)**. -* Training set MAE (r$^2$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) +* Training set MAE ($\rm{r}^2$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) * Training time: 6.11 GPU-days on A100 --- From a9cd332f13ab7ecdde131577bd3c8d1bd39b7061 Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:49:44 +0900 Subject: [PATCH 093/113] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 7b8c14a6..27104497 100644 --- a/README.md +++ b/README.md @@ -31,12 +31,12 @@ Additionally, `keywords` can be called in other parts of SevenNet, such as `seve ### **SevenNet-MF-0 (16Dec2024)** > Keywords in ASE: `7net-MF-0` and `SevenNet-MF-0` -The model is trained on PBE (+U) and $\rm{r}^2$SCAN database provided in Materials Project. +The model is trained on PBE (+U) and $\rm{r}^{2}$SCAN database provided in Materials Project. It has the same architecture with **SevenNet-0 (11Jul2024)**, except this model contains additional 'fidelity-dependent' parameters utilized for multi-fidelity training. However, overhead of calculations regarding fidelity-dependent parameters are negligible, which results in almost the same inference speed with **SevenNet-0 (11Jul2024)**. -* Training set MAE ($\rm{r}^2$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) +* Training set MAE ($\rm{r}^{2}$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) * Training time: 6.11 GPU-days on A100 --- From 443bcf973a1148f7de81b0cea24b777661e05514 Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:50:14 +0900 Subject: [PATCH 094/113] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 27104497..e64d5fdf 100644 --- a/README.md +++ b/README.md @@ -31,12 +31,12 @@ Additionally, `keywords` can be called in other parts of SevenNet, such as `seve ### **SevenNet-MF-0 (16Dec2024)** > Keywords in ASE: `7net-MF-0` and `SevenNet-MF-0` -The model is trained on PBE (+U) and $\rm{r}^{2}$SCAN database provided in Materials Project. +The model is trained on PBE (+U) and $\mathrm{r}^{2}$SCAN database provided in Materials Project. It has the same architecture with **SevenNet-0 (11Jul2024)**, except this model contains additional 'fidelity-dependent' parameters utilized for multi-fidelity training. However, overhead of calculations regarding fidelity-dependent parameters are negligible, which results in almost the same inference speed with **SevenNet-0 (11Jul2024)**. -* Training set MAE ($\rm{r}^{2}$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) +* Training set MAE ($\mathrm{r}^{2}$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) * Training time: 6.11 GPU-days on A100 --- From ce2b8944e62fbc7e9481623b8c64e5474a48e679 Mon Sep 17 00:00:00 2001 From: Jaesun0912 <108048201+Jaesun0912@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:51:06 +0900 Subject: [PATCH 095/113] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e64d5fdf..0c5226ed 100644 --- a/README.md +++ b/README.md @@ -31,12 +31,12 @@ Additionally, `keywords` can be called in other parts of SevenNet, such as `seve ### **SevenNet-MF-0 (16Dec2024)** > Keywords in ASE: `7net-MF-0` and `SevenNet-MF-0` -The model is trained on PBE (+U) and $\mathrm{r}^{2}$SCAN database provided in Materials Project. +The model is trained on PBE (+U) and $\mathrm{r}^{2}$ SCAN database provided in Materials Project. It has the same architecture with **SevenNet-0 (11Jul2024)**, except this model contains additional 'fidelity-dependent' parameters utilized for multi-fidelity training. However, overhead of calculations regarding fidelity-dependent parameters are negligible, which results in almost the same inference speed with **SevenNet-0 (11Jul2024)**. -* Training set MAE ($\mathrm{r}^{2}$SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) +* Training set MAE ($\mathrm{r}^{2}$ SCAN): 10.8 meV/atom (energy), 0.018 eV/Ang. (force), and 0.58 kbar (stress) * Training time: 6.11 GPU-days on A100 --- From 2c1347e52a925ac3f13890ac6c4a8c5a2276d775 Mon Sep 17 00:00:00 2001 From: Jaesun0912 Date: Mon, 16 Dec 2024 16:09:59 +0900 Subject: [PATCH 096/113] (docs) Upload 7net-MF-0 and comment yaml --- sevenn/presets/multi_modal.yaml | 21 ++++++++++-------- .../checkpoint_sevennet_mf_0.pth | Bin 0 -> 10322158 bytes .../checkpoint_sevennet_mf_0_r2SCAN.pth | Bin 0 -> 10318512 bytes 3 files changed, 12 insertions(+), 9 deletions(-) create mode 100644 sevenn/pretrained_potentials/SevenNet_MF_0/checkpoint_sevennet_mf_0.pth create mode 100644 sevenn/pretrained_potentials/SevenNet_MF_0/checkpoint_sevennet_mf_0_r2SCAN.pth diff --git a/sevenn/presets/multi_modal.yaml b/sevenn/presets/multi_modal.yaml index bf08bbef..a5c48557 100644 --- a/sevenn/presets/multi_modal.yaml +++ b/sevenn/presets/multi_modal.yaml @@ -27,10 +27,13 @@ model: train_denominator: False self_connection_type: 'linear' - use_modal_node_embedding: False - use_modal_self_inter_intro: True - use_modal_self_inter_outro: True - use_modal_output_block: True + # Followings are used to specify which part of the model would utilize fidelity-dependent parameters for multi-fidelity training. + # For detailed architecture, please refer to https://arxiv.org/abs/2409.07947 + # Parts using fidelity-dependent weights are indicated as `Modified linear` layers in Figure 1. + use_modal_node_embedding: False # If true, use modified linear layer in atom-type embedding layer. + use_modal_self_inter_intro: True # If true, use modified linear layers in self-interaction block before the convolution in the interaction blocks. + use_modal_self_inter_outro: True # If true, use modified linear layers in self-interaction block after the convolution in the interaction blocks. + use_modal_output_block: True # If true, use modified linear layer in the output block. train: train_shuffle: True random_seed: 777 @@ -71,18 +74,18 @@ data: shift: 'elemwise_reference_energies' scale: 'force_rms' - use_modal_wise_shift: True - use_modal_wise_scale: False + use_modal_wise_shift: True # If true, use different atomic energy shift for each database + use_modal_wise_scale: False # If true, use different atomic energy scale for each database load_trainset_path: - - data_modality: pbe + - data_modality: pbe # Name of database file_list: - file: "path to pbe dataset" # ASE readable or .pt file (graph.pt) data_weight: energy: 1.0 - force: 0.1 - stress: 1.0 + force: 0.1 # This weight would be additionally multiplied to `force_loss_weight` for this database + stress: 1.0 # This weight would be additionally multiplied to `stress_loss_weight` for this database - data_modality: scan file_list: diff --git a/sevenn/pretrained_potentials/SevenNet_MF_0/checkpoint_sevennet_mf_0.pth b/sevenn/pretrained_potentials/SevenNet_MF_0/checkpoint_sevennet_mf_0.pth new file mode 100644 index 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