File size: 7,180 Bytes
8e4e24e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | core:
version: ${get_flowmm_version:}
tags:
- ${now:%Y-%m-%d}
- ⚡️pytorch lightning
logging:
val_check_interval: 25
wandb:
project: uniug_uni
entity: null
log_model: true
mode: cloud
experiment_name: mp20_PathRefine_wuns0.001_wc0.01_e400_e450
wandb_watch:
log: all
log_freq: 500
lr_monitor:
logging_interval: step
log_momentum: false
optim:
optimizer:
_target_: torch.optim.AdamW
lr: 0.0005
weight_decay: 0.0
lr_scheduler:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: ${data.train_max_epochs}
eta_min: 1.0e-05
interval: epoch
ema_decay: 0.999
train:
deterministic: warn
random_seed: 42
pl_trainer:
fast_dev_run: false
strategy: ddp_find_unused_parameters_true
num_nodes: 1
devices: 8
accelerator: gpu
precision: 32
max_epochs: ${data.train_max_epochs}
accumulate_grad_batches: 1
num_sanity_val_steps: 1
gradient_clip_val: 10.0
gradient_clip_algorithm: norm
profiler: simple
log_every_n_steps: 500
monitor_metric: val/loss_csp
monitor_metric_mode: min
model_checkpoints:
save_top_k: 1
verbose: false
save_last: false
every_n_epochs_checkpoint:
every_n_epochs: 25
save_top_k: -1
verbose: false
save_last: false
val:
compute_nll: false
test:
compute_nll: false
compute_loss: true
integrate:
div_mode: rademacher
method: euler
num_steps: 1000
normalize_loglik: true
inference_anneal_slope: 0.0
inference_anneal_offset: 0.0
base_distribution_from_data: false
partial_ckpt_load: true
partial_ckpt_path: /mnt/ai4sci_develop_fast/songyouli/crystal-uniug/runs/trash/uniug_uni/bz256_mp20_mptsub_tclearn_freeze_epoch500/every_n_epochs/epoch=399-step=754800.ckpt
data:
dataset_name: mp_20
dim_coords: 3
root_path: ${oc.env:DATA_DIR}/mp_20
prop: formation_energy_per_atom
num_targets: 1
niggli: true
primitive: false
graph_method: crystalnn
lattice_scale_method: scale_length
preprocess_workers: 30
readout: mean
max_atoms: 20
otf_graph: false
eval_model_name: mp20
tolerance: 0.1
use_space_group: false
use_pos_index: false
train_max_epochs: 500
early_stopping_patience: 100000
teacher_forcing_max_epoch: 500
md_dataset_name: mptsubmp20_v0
root_path_md: ${oc.env:DATA_DIR}/${data.md_dataset_name}
require_order: false
prop_md:
- energy
- forces
energy_only: true
t_mode: c_learn
t_constant: 1.0
datamodule:
_target_: uniug.datamodule_uni.CrystDataModule
task_mode: uni
datasets:
train:
_target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy train
path: ${data.root_path}/train.csv
save_path: ${data.root_path}/train_ori.pt
prop: ${data.prop}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
val:
- _target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy val
path: ${data.root_path}/val.csv
save_path: ${data.root_path}/val_ori.pt
prop: ${data.prop}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
test:
- _target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy test
path: ${data.root_path}/test.csv
save_path: ${data.root_path}/test_ori.pt
prop: ${data.prop}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
train_md:
_target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy train
path: ${data.root_path_md}/train.csv
save_path: ${data.root_path_md}/train_ori.pt
require_order: ${data.require_order}
prop: ${data.prop_md}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
val_md:
- _target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy val
path: ${data.root_path_md}/val.csv
save_path: ${data.root_path_md}/val_ori.pt
require_order: ${data.require_order}
prop: ${data.prop_md}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
num_workers:
train: 40
val: 40
test: 40
batch_size:
train: 32
val: 32
test: 32
train_md: 32
val_md: 32
pin_memory: true
persistent_workers: false
prefetch_factor: 2
model:
w_csp: 0.95
w_pflow: 0.2
w_time: 0.1
cost_coord: 400.0
cost_lattice: 1.0
cost_type: 0.0
cost_energy: 1.0
cost_forces: 1.0
cost_stress: 1.0
affine_combine_costs: true
target_distribution: conditional
self_cond: false
t_pflow_clip: false
use_tangent: false
tclearn_freeze_epoch: 250
use_uns_flow_task: true
w_uns_flow: 0.001
w_consist: 0.01
uns_flow_tscale: true
uns_flow_tlearn: true
uns_path_refine: true
uns_path_t_clip: 0.9
use_consist_flow: true
consist_freeze_epoch: 450
uns_flow_freeze_epoch: 400
manifold_getter:
atom_type_manifold: null_manifold
coord_manifold: flat_torus_01
lattice_manifold: lattice_params
length_inner_coef: 1.0
vectorfield:
_target_: uniug.arch_uni.FlowmmUniModel
force_pred_way: direct
use_pflow_head: true
hidden_dim: 512
time_dim: 256
num_layers: 6
act_fn: silu
dis_emb: sin
num_freqs: 128
edge_style: fc
max_neighbors: 20
cutoff: 7.0
ln: true
use_log_map: true
dim_atomic_rep: ${get_dim_atomic_rep:${model.manifold_getter.atom_type_manifold}}
lattice_manifold: ${model.manifold_getter.lattice_manifold}
concat_sum_pool: true
represent_num_atoms: true
represent_angle_edge_to_lattice: true
self_edges: false
self_cond: ${model.self_cond}
t_mode: ${data.t_mode}
t_mask: -1.0
use_pflow_task: false
tlearn_clip: false
learnable_time_emb: false
|