From 560ca5cae8ca80257371e02fe72efc4fd86c3089 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:04:55 -0400 Subject: [PATCH 01/29] removing needless file --- .../config_diffusion_mace.yaml | 86 ------------------- 1 file changed, 86 deletions(-) delete mode 100644 experiments/si_diffusion_1x1x1/config_diffusion_mace.yaml diff --git a/experiments/si_diffusion_1x1x1/config_diffusion_mace.yaml b/experiments/si_diffusion_1x1x1/config_diffusion_mace.yaml deleted file mode 100644 index c6b79aa1..00000000 --- a/experiments/si_diffusion_1x1x1/config_diffusion_mace.yaml +++ /dev/null @@ -1,86 +0,0 @@ -# general -exp_name: difface_ode -run_name: run1 -max_epoch: 25 -log_every_n_steps: 1 -gradient_clipping: 0.1 - -# set to null to avoid setting a seed (can speed up GPU computation, but -# results will not be reproducible) -seed: 1234 - -# data -data: - batch_size: 1024 - num_workers: 8 - max_atom: 8 - -# architecture -spatial_dimension: 3 -model: - score_network: - architecture: diffusion_mace - number_of_atoms: 8 - r_max: 5.0 - num_bessel: 8 - num_polynomial_cutoff: 5 - max_ell: 2 - interaction_cls: RealAgnosticResidualInteractionBlock - interaction_cls_first: RealAgnosticInteractionBlock - num_interactions: 2 - hidden_irreps: 64x0e + 64x1o + 64x2e - mlp_irreps: 64x0e - number_of_mlp_layers: 3 - avg_num_neighbors: 1 - correlation: 3 - gate: silu - radial_MLP: [64, 64, 64] - radial_type: bessel - noise: - total_time_steps: 100 - sigma_min: 0.001 # default value - sigma_max: 0.5 # default value' - -# optimizer and scheduler -optimizer: - name: adamw - learning_rate: 0.001 - weight_decay: 1.0e-8 - -scheduler: - name: ReduceLROnPlateau - factor: 0.1 - patience: 20 - -# early stopping -early_stopping: - metric: validation_epoch_loss - mode: min - patience: 10 - -model_checkpoint: - monitor: validation_epoch_loss - mode: min - -# Sampling from the generative model -diffusion_sampling: - noise: - total_time_steps: 100 - sigma_min: 0.001 # default value - sigma_max: 0.5 # default value - sampling: - algorithm: ode - spatial_dimension: 3 - number_of_atoms: 8 - number_of_samples: 1000 - sample_every_n_epochs: 5 - record_samples: True - cell_dimensions: [5.43, 5.43, 5.43] - -# A callback to check the loss vs. sigma -loss_monitoring: - number_of_bins: 50 - sample_every_n_epochs: 5 - -logging: - - comet \ No newline at end of file From 89bec5f2be865b7c6738e2f72856ec7498508191 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:08:38 -0400 Subject: [PATCH 02/29] Move dataset analysis code to another folder. --- {experiment_analysis => experiments}/dataset_analysis/__init__.py | 0 .../dataset_analysis/dataset_covariance.py | 0 .../dataset_analysis/energy_consistency_analysis.py | 0 .../dataset_analysis/plot_si_phonon_DOS.py | 0 .../dataset_analysis/si_diffusion_analysis.py | 0 .../dataset_analysis/si_diffusion_v1_analysis.py | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename {experiment_analysis => experiments}/dataset_analysis/__init__.py (100%) rename {experiment_analysis => experiments}/dataset_analysis/dataset_covariance.py (100%) rename {experiment_analysis => experiments}/dataset_analysis/energy_consistency_analysis.py (100%) rename {experiment_analysis => experiments}/dataset_analysis/plot_si_phonon_DOS.py (100%) rename {experiment_analysis => experiments}/dataset_analysis/si_diffusion_analysis.py (100%) rename {experiment_analysis => experiments}/dataset_analysis/si_diffusion_v1_analysis.py (100%) diff --git a/experiment_analysis/dataset_analysis/__init__.py b/experiments/dataset_analysis/__init__.py similarity index 100% rename from experiment_analysis/dataset_analysis/__init__.py rename to experiments/dataset_analysis/__init__.py diff --git a/experiment_analysis/dataset_analysis/dataset_covariance.py b/experiments/dataset_analysis/dataset_covariance.py similarity index 100% rename from experiment_analysis/dataset_analysis/dataset_covariance.py rename to experiments/dataset_analysis/dataset_covariance.py diff --git a/experiment_analysis/dataset_analysis/energy_consistency_analysis.py b/experiments/dataset_analysis/energy_consistency_analysis.py similarity index 100% rename from experiment_analysis/dataset_analysis/energy_consistency_analysis.py rename to experiments/dataset_analysis/energy_consistency_analysis.py diff --git a/experiment_analysis/dataset_analysis/plot_si_phonon_DOS.py b/experiments/dataset_analysis/plot_si_phonon_DOS.py similarity index 100% rename from experiment_analysis/dataset_analysis/plot_si_phonon_DOS.py rename to experiments/dataset_analysis/plot_si_phonon_DOS.py diff --git a/experiment_analysis/dataset_analysis/si_diffusion_analysis.py b/experiments/dataset_analysis/si_diffusion_analysis.py similarity index 100% rename from experiment_analysis/dataset_analysis/si_diffusion_analysis.py rename to experiments/dataset_analysis/si_diffusion_analysis.py diff --git a/experiment_analysis/dataset_analysis/si_diffusion_v1_analysis.py b/experiments/dataset_analysis/si_diffusion_v1_analysis.py similarity index 100% rename from experiment_analysis/dataset_analysis/si_diffusion_v1_analysis.py rename to experiments/dataset_analysis/si_diffusion_v1_analysis.py From 6e01d0f35e4bea2527630d66002d98077f7e5fb2 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:18:04 -0400 Subject: [PATCH 03/29] Remove files that are not really needed from revision. --- .../sampling_analysis/__init__.py | 0 .../diffusion_mace_ode_sampling_analysis.py | 199 ------------------ .../diffusion_mace_ode_trajctory_analysis.py | 63 ------ ...ion_mace_score_and_partial_ode_analysis.py | 178 ---------------- .../generate_partial_ode_trajectories.py | 165 --------------- .../ode_sample_positions_to_cif_files.py | 50 ----- .../plot_energy_samples_distribution.py | 56 ----- .../sampling_EGNN_sept_10/__init__.py | 0 .../generate_trajectory_visualization.py | 44 ---- .../identify_trajectories_by_energy.py | 37 ---- .../sampling_EGNN_sept_10/plot_energies.py | 156 -------------- .../sampling_si_diffusion.py | 102 --------- 12 files changed, 1050 deletions(-) delete mode 100644 experiment_analysis/sampling_analysis/__init__.py delete mode 100644 experiment_analysis/sampling_analysis/diffusion_mace_ode_sampling_analysis.py delete mode 100644 experiment_analysis/sampling_analysis/diffusion_mace_ode_trajctory_analysis.py delete mode 100644 experiment_analysis/sampling_analysis/diffusion_mace_score_and_partial_ode_analysis.py delete mode 100644 experiment_analysis/sampling_analysis/generate_partial_ode_trajectories.py delete mode 100644 experiment_analysis/sampling_analysis/ode_sample_positions_to_cif_files.py delete mode 100644 experiment_analysis/sampling_analysis/plot_energy_samples_distribution.py delete mode 100644 experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/__init__.py delete mode 100644 experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/generate_trajectory_visualization.py delete mode 100644 experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/identify_trajectories_by_energy.py delete mode 100644 experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/plot_energies.py delete mode 100644 experiment_analysis/sampling_analysis/sampling_si_diffusion.py diff --git a/experiment_analysis/sampling_analysis/__init__.py b/experiment_analysis/sampling_analysis/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/experiment_analysis/sampling_analysis/diffusion_mace_ode_sampling_analysis.py b/experiment_analysis/sampling_analysis/diffusion_mace_ode_sampling_analysis.py deleted file mode 100644 index 45709973..00000000 --- a/experiment_analysis/sampling_analysis/diffusion_mace_ode_sampling_analysis.py +++ /dev/null @@ -1,199 +0,0 @@ -import logging -from pathlib import Path - -import matplotlib.pyplot as plt -import torch -from einops import einops - -from crystal_diffusion import DATA_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.generator_sample_analysis_utils import \ - get_interatomic_distances -from crystal_diffusion.data.diffusion.data_loader import ( - LammpsForDiffusionDataModule, LammpsLoaderParameters) -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from experiment_analysis import EXPERIMENT_ANALYSIS_DIR - -logger = logging.getLogger(__name__) - -plt.style.use(PLOT_STYLE_PATH) - - -# Some hardcoded paths and parameters. Change as needed! -epoch = 30 -base_data_dir = Path("/Users/bruno/courtois/difface_ode/run7") -position_samples_dir = base_data_dir / "diffusion_position_samples" -energy_samples_dir = base_data_dir / "energy_samples" - - -dataset_name = "si_diffusion_1x1x1" -lammps_run_dir = str(DATA_DIR / dataset_name) -processed_dataset_dir = str(DATA_DIR / dataset_name / 'processed') -data_params = LammpsLoaderParameters(batch_size=64, max_atom=8) -cache_dir = str(EXPERIMENT_ANALYSIS_DIR / "cache" / dataset_name) - - -if __name__ == '__main__': - - datamodule = LammpsForDiffusionDataModule( - lammps_run_dir=lammps_run_dir, - processed_dataset_dir=processed_dataset_dir, - hyper_params=data_params, - working_cache_dir=cache_dir, - ) - - datamodule.setup() - - train_dataset = datamodule.train_dataset - batch = train_dataset[:1000] - - positions_data = torch.load(position_samples_dir / f"diffusion_position_sample_epoch={epoch}_steps=0.pt", - map_location=torch.device('cpu')) - - unit_cell = positions_data['unit_cell'] - - batch_times = positions_data['time'][0] - batch_noisy_relative_coordinates = positions_data['relative_coordinates'][0] - number_of_atoms, spatial_dimension = batch_noisy_relative_coordinates.shape[-2:] - - batch_flat_noisy_relative_coordinates = einops.rearrange(batch_noisy_relative_coordinates, - "b t n d -> b t (n d)") - - fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle(f'ODE Trajectories: Sample at Epoch {epoch}') - - ax = fig1.add_subplot(111) - ax.set_xlabel('Diffusion Time') - ax.set_ylabel('Raw Relative Coordinate') - ax.yaxis.tick_right() - ax.spines['top'].set_visible(True) - ax.spines['right'].set_visible(True) - ax.spines['bottom'].set_visible(True) - ax.spines['left'].set_visible(True) - - time = batch_times[0] # all time arrays are the same - for flat_relative_coordinates in batch_flat_noisy_relative_coordinates: - for i in range(number_of_atoms * spatial_dimension): - coordinate = flat_relative_coordinates[:, i] - ax.plot(time.cpu(), coordinate.cpu(), '-', color='b', alpha=0.05) - - ax.set_xlim([1.01, -0.01]) - ax.set_ylim([-2.0, 2.0]) - plt.show() - - training_relative_coordinates = batch['relative_coordinates'] - training_center_of_mass = training_relative_coordinates.mean(dim=1).mean(dim=0) - - raw_sample_relative_coordinates = map_relative_coordinates_to_unit_cell(batch_noisy_relative_coordinates[:, -1]) - raw_sample_centers_of_mass = raw_sample_relative_coordinates.mean(dim=1) - - zero_centered_sample_relative_coordinates = (raw_sample_relative_coordinates - - raw_sample_centers_of_mass.unsqueeze(1)) - sample_relative_coordinates = (zero_centered_sample_relative_coordinates - + training_center_of_mass.unsqueeze(0).unsqueeze(0)) - - sample_relative_coordinates = map_relative_coordinates_to_unit_cell(sample_relative_coordinates) - - fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig2.suptitle(f'ODE Marginal Distributions, Sample at Epoch {epoch}') - ax1 = fig2.add_subplot(131, aspect='equal') - ax2 = fig2.add_subplot(132, aspect='equal') - ax3 = fig2.add_subplot(133, aspect='equal') - - xs = einops.rearrange(sample_relative_coordinates, 'b n d -> (b n) d') - zs = einops.rearrange(training_relative_coordinates, 'b n d -> (b n) d') - ax1.set_title('XY Projection') - ax1.plot(xs[:, 0], xs[:, 1], 'ro', alpha=0.5, mew=0, label='ODE Solver Samples') - ax1.plot(zs[:, 0], zs[:, 1], 'go', alpha=0.05, mew=0, label='Training Data') - - ax2.set_title('XZ Projection') - ax2.plot(xs[:, 0], xs[:, 2], 'ro', alpha=0.5, mew=0, label='ODE Solver Samples') - ax2.plot(zs[:, 0], zs[:, 2], 'go', alpha=0.05, mew=0, label='Training Data') - - ax3.set_title('YZ Projection') - ax3.plot(xs[:, 1], xs[:, 2], 'ro', alpha=0.5, mew=0, label='ODE Solver Samples') - ax3.plot(zs[:, 1], zs[:, 2], 'go', alpha=0.05, mew=0, label='Training Data') - - for ax in [ax1, ax2, ax3]: - ax.set_xlim(-0.01, 1.01) - ax.set_ylim(-0.01, 1.01) - ax.vlines(x=[0, 1], ymin=0, ymax=1, color='k', lw=2) - ax.hlines(y=[0, 1], xmin=0, xmax=1, color='k', lw=2) - - ax2.legend(loc='lower center', bbox_to_anchor=(0.5, -0.5), ncol=2, fancybox=True, shadow=True) - fig2.tight_layout() - plt.show() - - fig3 = plt.figure(figsize=PLEASANT_FIG_SIZE) - ax1 = fig3.add_subplot(131) - ax2 = fig3.add_subplot(132) - ax3 = fig3.add_subplot(133) - fig3.suptitle(f"Marginal Distributions of t=0 Samples, Sample at Epoch {epoch}") - - common_params = dict(histtype='stepfilled', alpha=0.5, bins=50) - - ax1.hist(xs[:, 0], **common_params, facecolor='r', label='ODE solver') - ax2.hist(xs[:, 1], **common_params, facecolor='r', label='ODE solver') - ax3.hist(xs[:, 2], **common_params, facecolor='r', label='ODE solver') - - ax1.hist(zs[:, 0], **common_params, facecolor='g', label='Training Data') - ax2.hist(zs[:, 1], **common_params, facecolor='g', label='Training Data') - ax3.hist(zs[:, 2], **common_params, facecolor='g', label='Training Data') - - ax1.set_xlabel('X') - ax2.set_xlabel('Y') - ax3.set_xlabel('Z') - - for ax in [ax1, ax2, ax3]: - ax.set_xlim(-0.01, 1.01) - ax.set_yscale('log') - - ax1.legend(loc=0) - fig3.tight_layout() - plt.show() - - radial_cutoff = 5.4 - training_cartesian_positions = batch['cartesian_positions'] - basis_vectors = torch.diag_embed(batch['box']) - training_interatomic_distances = get_interatomic_distances(training_cartesian_positions, - basis_vectors, - radial_cutoff=radial_cutoff) - - sample_relative_coordinates = map_relative_coordinates_to_unit_cell(batch_noisy_relative_coordinates[:, -1]) - sample_cartesian_positions = torch.bmm(sample_relative_coordinates, unit_cell) - sample_interatomic_distances = get_interatomic_distances(sample_cartesian_positions, - unit_cell, - radial_cutoff=radial_cutoff) - - fig4 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig4.suptitle(f'Interatomic Distance Distribution: Sample at Epoch {epoch}') - - ax1 = fig4.add_subplot(121) - ax2 = fig4.add_subplot(122) - - ax1.set_title('Training vs. Samples') - ax2.set_title('Intermediate Diffusion') - - common_params = dict(histtype='stepfilled', alpha=0.5, bins=75) - ax1.hist(training_interatomic_distances, **common_params, facecolor='g', label='Training Data') - ax1.hist(sample_interatomic_distances, **common_params, facecolor='r', label='ODE Sample, t = 0') - - for time_idx, color in zip([0, len(time) // 2 + 1, -1], ['blue', 'yellow', 'red']): - sample_relative_coordinates = map_relative_coordinates_to_unit_cell( - batch_noisy_relative_coordinates[:, time_idx]) - sample_cartesian_positions = torch.bmm(sample_relative_coordinates, unit_cell) - sample_interatomic_distances = get_interatomic_distances(sample_cartesian_positions, - unit_cell, - radial_cutoff=radial_cutoff) - ax2.hist(sample_interatomic_distances, **common_params, facecolor=color, - label=f'Noisy Sample t = {time[time_idx]:2.1f}') - - for ax in [ax1, ax2]: - ax.set_xlabel('Distance (Angstrom)') - ax.set_ylabel('Count') - ax.set_xlim([-0.01, radial_cutoff]) - ax.legend(loc=0) - ax.set_yscale('log') - fig4.tight_layout() - plt.show() diff --git a/experiment_analysis/sampling_analysis/diffusion_mace_ode_trajctory_analysis.py b/experiment_analysis/sampling_analysis/diffusion_mace_ode_trajctory_analysis.py deleted file mode 100644 index 6237081c..00000000 --- a/experiment_analysis/sampling_analysis/diffusion_mace_ode_trajctory_analysis.py +++ /dev/null @@ -1,63 +0,0 @@ -import logging -from pathlib import Path - -import matplotlib.pyplot as plt -import torch - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH - -logger = logging.getLogger(__name__) - -plt.style.use(PLOT_STYLE_PATH) - - -# Some hardcoded paths and parameters. Change as needed! -epoch = 30 -base_data_dir = Path("/Users/bruno/courtois/difface_ode/run7") -position_samples_dir = base_data_dir / "diffusion_position_samples" -energy_samples_dir = base_data_dir / "energy_samples" -energy_data_directory = base_data_dir / "energy_samples" - - -if __name__ == '__main__': - energies = torch.load(energy_data_directory / f"energies_sample_epoch={epoch}.pt") - positions_data = torch.load(position_samples_dir / f"diffusion_position_sample_epoch={epoch}_steps=0.pt", - map_location=torch.device('cpu')) - - unit_cell = positions_data['unit_cell'] - - batch_times = positions_data['time'][0] - batch_noisy_relative_coordinates = positions_data['relative_coordinates'][0] - number_of_atoms, spatial_dimension = batch_noisy_relative_coordinates.shape[-2:] - - idx = energies.argmax() - relative_coordinates = batch_noisy_relative_coordinates[idx] - - fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle(f'ODE trajectory: Sample {idx} at Epoch {epoch} - Energy = {energies.max():4.2f}') - - ax1 = fig1.add_subplot(131) - ax2 = fig1.add_subplot(132) - ax3 = fig1.add_subplot(133) - - time = batch_times[0] # all time arrays are the same - - for atom_idx in range(number_of_atoms): - ax1.plot(time, relative_coordinates[:, atom_idx, 0], '-', alpha=0.5) - ax2.plot(time, relative_coordinates[:, atom_idx, 1], '-', alpha=0.5) - ax3.plot(time, relative_coordinates[:, atom_idx, 2], '-', alpha=0.5) - - for ax in [ax1, ax2, ax3]: - ax.set_xlabel('Diffusion Time') - ax.set_ylabel('Raw Relative Coordinate') - ax.yaxis.tick_right() - ax.spines['top'].set_visible(True) - ax.spines['right'].set_visible(True) - ax.spines['bottom'].set_visible(True) - ax.spines['left'].set_visible(True) - ax.set_xlim([1.01, -0.01]) - ax1.set_ylabel('X') - ax2.set_ylabel('Y') - ax3.set_ylabel('Z') - fig1.tight_layout() - plt.show() diff --git a/experiment_analysis/sampling_analysis/diffusion_mace_score_and_partial_ode_analysis.py b/experiment_analysis/sampling_analysis/diffusion_mace_score_and_partial_ode_analysis.py deleted file mode 100644 index ba295470..00000000 --- a/experiment_analysis/sampling_analysis/diffusion_mace_score_and_partial_ode_analysis.py +++ /dev/null @@ -1,178 +0,0 @@ -import logging -import tempfile -from pathlib import Path - -import einops -import matplotlib.patches as mpatches -import matplotlib.pyplot as plt -import numpy as np -import torch -from tqdm import tqdm - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.generator_sample_analysis_utils import \ - PartialODEPositionGenerator -from crystal_diffusion.models.position_diffusion_lightning_model import \ - PositionDiffusionLightningModel -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ - NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from crystal_diffusion.utils.tensor_utils import \ - broadcast_batch_tensor_to_all_dimensions - -logger = logging.getLogger(__name__) - -plt.style.use(PLOT_STYLE_PATH) - - -# Some hardcoded paths and parameters. Change as needed! -base_data_dir = Path("/Users/bruno/courtois/difface_ode/run1") -position_samples_dir = base_data_dir / "diffusion_position_samples" -energy_data_directory = base_data_dir / "energy_samples" -model_path = base_data_dir / "best_model" / "best_model-epoch=016-step=001666.ckpt" - -partial_samples_dir = base_data_dir / "partial_samples" -partial_samples_dir.mkdir(exist_ok=True) - - -canonical_relative_coordinates = torch.tensor([[0.00, 0.00, 0.00], - [0.00, 0.50, 0.50], - [0.50, 0.00, 0.50], - [0.00, 0.50, 0.50], - [0.25, 0.25, 0.25], - [0.25, 0.75, 0.75], - [0.75, 0.25, 0.75], - [0.75, 0.75, 0.25]]) - -reference_relative_coordinates = torch.tensor([[0.0166, 0.0026, 0.9913], - [0.9936, 0.4954, 0.5073], - [0.4921, 0.9992, 0.4994], - [0.4954, 0.5009, 0.9965], - [0.2470, 0.2540, 0.2664], - [0.2481, 0.7434, 0.7445], - [0.7475, 0.2483, 0.7489], - [0.7598, 0.7563, 0.2456]]) - -sigma_min = 0.001 -sigma_max = 0.5 -total_time_steps = 100 - -noise_parameters = NoiseParameters(total_time_steps=total_time_steps, sigma_min=sigma_min, sigma_max=sigma_max) - - -cell_dimensions = torch.tensor([5.43, 5.43, 5.43]) - -number_of_atoms = 8 -spatial_dimension = 3 -batch_size = 100 -if __name__ == '__main__': - noisy_relative_coordinates_sampler = NoisyRelativeCoordinatesSampler() - unit_cell = torch.diag(torch.Tensor(cell_dimensions)).unsqueeze(0).repeat(batch_size, 1, 1) - - x0 = einops.repeat(reference_relative_coordinates, "n d -> b n d", b=batch_size) - - x_bad = torch.rand(x0.shape) - - model = PositionDiffusionLightningModel.load_from_checkpoint(model_path) - model.eval() - - list_tf = np.linspace(0.1, 1, 10) - - atom_types = np.ones(number_of_atoms, dtype=int) - - on_manifold_dataset = [] - off_manifold_dataset = [] - with torch.no_grad(): - for tf in tqdm(list_tf, 'times'): - times = torch.ones(batch_size) * tf - sigmas = sigma_min ** (1.0 - times) * sigma_max ** times - - broadcast_sigmas = broadcast_batch_tensor_to_all_dimensions(batch_values=sigmas, final_shape=x0.shape) - xt = noisy_relative_coordinates_sampler.get_noisy_relative_coordinates_sample(x0, broadcast_sigmas) - - noise_parameters.total_time_steps = int(100 * tf) + 1 - generator = PartialODEPositionGenerator(noise_parameters, - number_of_atoms, - spatial_dimension, - model.sigma_normalized_score_network, - initial_relative_coordinates=xt, - record_samples=True, - tf=tf) - - logger.info("Generating Samples") - batch_relative_coordinates = generator.sample(number_of_samples=batch_size, - device=torch.device('cpu'), - unit_cell=unit_cell) - sample_output_path = str(partial_samples_dir / f"diffusion_position_sample_time={tf:2.1f}.pt") - # write trajectories to disk and reset to save memory - generator.sample_trajectory_recorder.write_to_pickle(sample_output_path) - logger.info("Done Generating Samples") - - batch_cartesian_positions = torch.bmm(batch_relative_coordinates, unit_cell) - - box = unit_cell[0].numpy() - - list_energy = [] - - logger.info("Compute energy from Oracle") - with tempfile.TemporaryDirectory() as tmp_work_dir: - for positions in batch_cartesian_positions.numpy(): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - atom_types, - tmp_work_dir=tmp_work_dir) - list_energy.append(energy) - - energies = torch.tensor(list_energy) - energy_output_path = str(partial_samples_dir / f"diffusion_energies_sample_time={tf:2.1f}.pt") - with open(energy_output_path, 'wb') as fd: - torch.save(energies, fd) - - batch = {NOISY_RELATIVE_COORDINATES: xt, - NOISE: sigmas.unsqueeze(-1), - TIME: times.unsqueeze(-1), - UNIT_CELL: unit_cell, - CARTESIAN_FORCES: torch.zeros_like(x0) - } - normalized_scores = model.sigma_normalized_score_network(batch) - rms_norm_scores = torch.sqrt((torch.linalg.norm(normalized_scores, dim=2) ** 2).mean(dim=1)) - on_manifold_dataset.append(rms_norm_scores.numpy()) - - bad_batch = {NOISY_RELATIVE_COORDINATES: x_bad, - NOISE: sigmas.unsqueeze(-1), - TIME: times.unsqueeze(-1), - UNIT_CELL: unit_cell, - CARTESIAN_FORCES: torch.zeros_like(x0) - } - bad_normalized_scores = model.sigma_normalized_score_network(bad_batch) - bad_rms_norm_scores = torch.sqrt((torch.linalg.norm(bad_normalized_scores, dim=2) ** 2).mean(dim=1)) - off_manifold_dataset.append(bad_rms_norm_scores.numpy()) - - labels = [] - - def add_label(violin, label): - """Add some labels to the plot.""" - color = violin["bodies"][0].get_facecolor().flatten() - labels.append((mpatches.Patch(color=color), label)) - - fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle('Mean Score Norm') - - ax1 = fig1.add_subplot(111) - - add_label(ax1.violinplot(on_manifold_dataset, positions=list_tf, widths=0.05, showmeans=True), - "ON Manifold") - add_label(ax1.violinplot(off_manifold_dataset, positions=list_tf, widths=0.05, showmeans=True), - "OFF Manifold") - - ax1.set_xlabel('Diffusion Time') - ax1.set_ylabel('RMS Normalized Score') - ax1.set_yscale('log') - - ax1.legend(*zip(*labels), loc=0) - fig1.tight_layout() - plt.show() diff --git a/experiment_analysis/sampling_analysis/generate_partial_ode_trajectories.py b/experiment_analysis/sampling_analysis/generate_partial_ode_trajectories.py deleted file mode 100644 index f6e0eeb8..00000000 --- a/experiment_analysis/sampling_analysis/generate_partial_ode_trajectories.py +++ /dev/null @@ -1,165 +0,0 @@ -import logging -import tempfile -from pathlib import Path - -import einops -import numpy as np -import torch -from pymatgen.core import Lattice, Structure -from tqdm import tqdm - -from crystal_diffusion.analysis.generator_sample_analysis_utils import \ - PartialODEPositionGenerator -from crystal_diffusion.data.diffusion.data_loader import ( - LammpsForDiffusionDataModule, LammpsLoaderParameters) -from crystal_diffusion.generators.ode_position_generator import \ - ODESamplingParameters -from crystal_diffusion.models.position_diffusion_lightning_model import \ - PositionDiffusionLightningModel -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ - NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from crystal_diffusion.utils.tensor_utils import \ - broadcast_batch_tensor_to_all_dimensions - -logger = logging.getLogger(__name__) - -setup_analysis_logger() -# Some hardcoded paths and parameters. Change as needed! - -data_directory = Path("/home/mila/r/rousseab/scratch/data/") -dataset_name = "si_diffusion_2x2x2" -lammps_run_dir = data_directory / dataset_name -processed_dataset_dir = lammps_run_dir / "processed" -cache_dir = lammps_run_dir / "cache" - -data_params = LammpsLoaderParameters(batch_size=1024, max_atom=64) - -checkpoint_path = "/network/scratch/r/rousseab/checkpoints/EGNN_Sept_10/last_model-epoch=045-step=035972.ckpt" - -partial_samples_dir = Path("/network/scratch/r/rousseab/partial_samples_EGNN_Sept_10/") -partial_samples_dir.mkdir(exist_ok=True) - -sigma_min = 0.001 -sigma_max = 0.5 -total_time_steps = 100 - -noise_parameters = NoiseParameters( - total_time_steps=total_time_steps, sigma_min=sigma_min, sigma_max=sigma_max -) - -absolute_solver_tolerance = 1.0e-3 -relative_solver_tolerance = 1.0e-2 - -spatial_dimension = 3 -batch_size = 32 -device = torch.device("cuda") - -if __name__ == "__main__": - logger.info("Extracting a validation configuration") - # Extract a configuration from the validation set - datamodule = LammpsForDiffusionDataModule( - lammps_run_dir=lammps_run_dir, - processed_dataset_dir=processed_dataset_dir, - hyper_params=data_params, - working_cache_dir=cache_dir, - ) - datamodule.setup() - - validation_example = datamodule.valid_dataset[0] - reference_relative_coordinates = validation_example["relative_coordinates"] - number_of_atoms = int(validation_example["natom"]) - cell_dimensions = validation_example["box"] - - logger.info("Writing validation configuration to cif file") - a, b, c = cell_dimensions.numpy() - lattice = Lattice.from_parameters(a=a, b=b, c=c, alpha=90, beta=90, gamma=90) - - reference_structure = Structure( - lattice=lattice, - species=number_of_atoms * ["Si"], - coords=reference_relative_coordinates.numpy(), - ) - - reference_structure.to( - str(partial_samples_dir / "reference_validation_structure.cif") - ) - - logger.info("Extracting checkpoint") - noisy_relative_coordinates_sampler = NoisyRelativeCoordinatesSampler() - - unit_cell = ( - torch.diag(torch.Tensor(cell_dimensions)).unsqueeze(0).repeat(batch_size, 1, 1) - ) - box = unit_cell[0].numpy() - - x0 = einops.repeat(reference_relative_coordinates, "n d -> b n d", b=batch_size) - - model = PositionDiffusionLightningModel.load_from_checkpoint(checkpoint_path) - model.eval() - - list_tf = np.linspace(0.1, 1, 20) - atom_types = np.ones(number_of_atoms, dtype=int) - - logger.info("Draw samples") - with torch.no_grad(): - for tf in tqdm(list_tf, "times"): - times = torch.ones(batch_size) * tf - sigmas = sigma_min ** (1.0 - times) * sigma_max**times - - broadcast_sigmas = broadcast_batch_tensor_to_all_dimensions( - batch_values=sigmas, final_shape=x0.shape - ) - xt = noisy_relative_coordinates_sampler.get_noisy_relative_coordinates_sample( - x0, broadcast_sigmas - ) - - noise_parameters.total_time_steps = int(1000 * tf) + 1 - sampling_parameters = ODESamplingParameters( - number_of_atoms=number_of_atoms, - number_of_samples=batch_size, - record_samples=True, - cell_dimensions=list(cell_dimensions.cpu().numpy()), - absolute_solver_tolerance=absolute_solver_tolerance, - relative_solver_tolerance=relative_solver_tolerance, - ) - - generator = PartialODEPositionGenerator( - noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=model.sigma_normalized_score_network, - initial_relative_coordinates=xt, - tf=tf, - ) - - logger.info("Generating Samples") - batch_relative_coordinates = generator.sample( - number_of_samples=batch_size, device=device, unit_cell=unit_cell - ).cpu() - sample_output_path = str( - partial_samples_dir / f"diffusion_position_sample_time={tf:4.3f}.pt" - ) - generator.sample_trajectory_recorder.write_to_pickle(sample_output_path) - logger.info("Done Generating Samples") - - batch_cartesian_positions = torch.bmm(batch_relative_coordinates, unit_cell) - - list_energy = [] - logger.info("Compute energy from Oracle") - with tempfile.TemporaryDirectory() as tmp_work_dir: - for positions in batch_cartesian_positions.numpy(): - energy, forces = get_energy_and_forces_from_lammps( - positions, box, atom_types, tmp_work_dir=tmp_work_dir - ) - list_energy.append(energy) - - energies = torch.tensor(list_energy) - logger.info("Done Computing energy from Oracle") - - energy_output_path = str( - partial_samples_dir / f"diffusion_energies_sample_time={tf:4.3f}.pt" - ) - with open(energy_output_path, "wb") as fd: - torch.save(energies, fd) diff --git a/experiment_analysis/sampling_analysis/ode_sample_positions_to_cif_files.py b/experiment_analysis/sampling_analysis/ode_sample_positions_to_cif_files.py deleted file mode 100644 index 2ef2f0b8..00000000 --- a/experiment_analysis/sampling_analysis/ode_sample_positions_to_cif_files.py +++ /dev/null @@ -1,50 +0,0 @@ -"""Position to cif files for the ODE sampler. - -A simple script to extract the diffusion positions from a pickle on disk and output -in cif format for visualization. -""" -from pathlib import Path - -import torch -from pymatgen.core import Lattice, Structure - -from crystal_diffusion.utils.sample_trajectory import ODESampleTrajectory - -# Hard coding some paths to local results. Modify as needed... -epoch = 15 - -base_data_dir = Path("/Users/bruno/courtois/difface_ode/run1") -trajectory_data_directory = base_data_dir / "diffusion_position_samples" -energy_data_directory = base_data_dir / "energy_samples" -output_top_dir = trajectory_data_directory.parent / "visualization" - - -if __name__ == '__main__': - energies = torch.load(energy_data_directory / f"energies_sample_epoch={epoch}.pt") - - sample_idx = energies.argmax() - output_dir = output_top_dir / f"visualise_sampling_trajectory_epoch_{epoch}_sample_{sample_idx}" - output_dir.mkdir(exist_ok=True, parents=True) - - pickle_path = trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}_steps=0.pt" - sample_trajectory = ODESampleTrajectory.read_from_pickle(pickle_path) - - basis_vectors = sample_trajectory.data['unit_cell'][sample_idx].numpy() - lattice = Lattice(matrix=basis_vectors, pbc=(True, True, True)) - - # Shape [batch, time, number of atoms, space dimension] - batch_noisy_relative_coordinates = sample_trajectory.data['relative_coordinates'][0] - - noisy_relative_coordinates = batch_noisy_relative_coordinates[sample_idx].numpy() - - for idx, coordinates in enumerate(noisy_relative_coordinates): - number_of_atoms = coordinates.shape[0] - species = number_of_atoms * ['Si'] - - structure = Structure(lattice=lattice, - species=species, - coords=coordinates, - coords_are_cartesian=False) - - file_path = str(output_dir / f"diffusion_positions_{idx}.cif") - structure.to_file(file_path) diff --git a/experiment_analysis/sampling_analysis/plot_energy_samples_distribution.py b/experiment_analysis/sampling_analysis/plot_energy_samples_distribution.py deleted file mode 100644 index fdd7e138..00000000 --- a/experiment_analysis/sampling_analysis/plot_energy_samples_distribution.py +++ /dev/null @@ -1,56 +0,0 @@ -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from experiment_analysis.analysis_utils import get_thermo_dataset - -plt.style.use(PLOT_STYLE_PATH) - -dataset_name = 'si_diffusion_2x2x2' - - -if dataset_name == 'si_diffusion_1x1x1': - dataset_name_for_data_fetching = 'si_diffusion_1x1x1' - upper_limit_factor = 0.1 -elif dataset_name == 'si_diffusion_2x2x2': - dataset_name_for_data_fetching = 'si_diffusion_small' - upper_limit_factor = 5. - -sample_pickle_path = Path(__file__).parent.joinpath(f'{dataset_name}_energy_samples.pkl') - - -if __name__ == '__main__': - sample_df = pd.read_pickle(sample_pickle_path) - - train_df, _ = get_thermo_dataset(dataset_name_for_data_fetching) - - train_energies = train_df['energy'].values - sample_energies = sample_df['energy'].values - - delta_e = train_energies.max() - train_energies.min() - emin = train_energies.min() - 0.1 * delta_e - emax = train_energies.max() + upper_limit_factor * delta_e - - number_of_samples_in_range = np.logical_and(sample_energies >= emin, sample_energies <= emax).sum() - bins = np.linspace(emin, emax, 101) - - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - - fig.suptitle(f'Comparing Training and Sampling Energy Distributions\n Dataset = {dataset_name}') - - ax1 = fig.add_subplot(111) - - common_params = dict(density=True, bins=bins, histtype="stepfilled", alpha=0.25) - ax1.hist(sample_energies, **common_params, - label=f'Samples (total count = {len(sample_df)}, in range = {number_of_samples_in_range})', color='red') - ax1.hist(train_energies, **common_params, label=f'Training Data (count = {len(train_df)})', color='green') - ax1.set_xlabel('Energy (eV)') - ax1.set_ylabel('Density') - ax1.legend(loc=0) - - fig.tight_layout() - fig.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_sample_energy_distribution.png")) diff --git a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/__init__.py b/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/generate_trajectory_visualization.py b/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/generate_trajectory_visualization.py deleted file mode 100644 index 73840d51..00000000 --- a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/generate_trajectory_visualization.py +++ /dev/null @@ -1,44 +0,0 @@ -import logging -from pathlib import Path - -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from crystal_diffusion.utils.ovito_utils import (create_cif_files, - create_ovito_session_state) - -logger = logging.getLogger(__name__) - -setup_analysis_logger() - -results_dir = Path( - "/Users/bruno/courtois/partial_trajectory_sampling_EGNN_sept_10/partial_samples_EGNN_Sept_10" -) -number_of_trajectories = 32 # the 'batch' dimension - -reference_cif_file = results_dir / "reference_validation_structure_Hydrogen.cif" - -list_sample_times = [1.0] -if __name__ == "__main__": - for sample_time in list_sample_times: - logger.info(f"Processing sample time = {sample_time}") - pickle_path = ( - results_dir / f"diffusion_position_sample_time={sample_time:4.3f}.pt" - ) - - trajectory_directory = ( - results_dir / f"trajectories_sample_time={sample_time:4.3f}" - ) - for trj_idx in range(number_of_trajectories): - logger.info( - f" - Computing Ovito trajectory session state for trajectory index {trj_idx}" - ) - create_cif_files( - visualization_artifacts_path=trajectory_directory, - trajectory_index=trj_idx, - ode_trajectory_pickle=pickle_path, - ) - - create_ovito_session_state( - visualization_artifacts_path=trajectory_directory, - trajectory_index=trj_idx, - reference_cif_file=reference_cif_file, - ) diff --git a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/identify_trajectories_by_energy.py b/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/identify_trajectories_by_energy.py deleted file mode 100644 index 8d67a18f..00000000 --- a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/identify_trajectories_by_energy.py +++ /dev/null @@ -1,37 +0,0 @@ -import glob -import logging -from pathlib import Path - -import matplotlib.pyplot as plt -import pandas as pd -import torch - -from crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.utils.logging_utils import setup_analysis_logger - -plt.style.use(PLOT_STYLE_PATH) - -logger = logging.getLogger(__name__) -setup_analysis_logger() - -results_dir = Path("/Users/bruno/courtois/partial_trajectory_sampling_EGNN_sept_10/partial_samples_EGNN_Sept_10") - - -tf = 1.0 - -if __name__ == '__main__': - - list_rows = [] - for pickle_path in glob.glob(str(results_dir / 'diffusion_energies_sample_time=*.pt')): - energies = torch.load(pickle_path).numpy() - time = float(pickle_path.split('=')[1].split('.pt')[0]) - - for idx, energy in enumerate(energies): - row = dict(tf=time, trajectory_index=idx, energy=energy) - list_rows.append(row) - - df = pd.DataFrame(list_rows).sort_values(by=['tf', 'energy']) - - groups = df.groupby('tf') - - sub_df = groups.get_group(tf) diff --git a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/plot_energies.py b/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/plot_energies.py deleted file mode 100644 index d7ee5812..00000000 --- a/experiment_analysis/sampling_analysis/sampling_EGNN_sept_10/plot_energies.py +++ /dev/null @@ -1,156 +0,0 @@ -import glob -import logging -import tempfile -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch -from pymatgen.core import Structure - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.utils.logging_utils import setup_analysis_logger - -plt.style.use(PLOT_STYLE_PATH) - -logger = logging.getLogger(__name__) -setup_analysis_logger() - -results_dir = Path( - "/Users/bruno/courtois/partial_trajectory_sampling_EGNN_sept_10/partial_samples_EGNN_Sept_10" -) -reference_cif = results_dir / "reference_validation_structure.cif" - - -if __name__ == "__main__": - times = np.linspace(0, 1, 1001) - sigma_min = 0.001 - sigma_max = 0.5 - - def sigma_function(times): - """Compute sigma.""" - return sigma_min ** (1.0 - times) * sigma_max**times - - sigmas = sigma_function(times) - - special_times = [0.479, 0.668, 0.905] - list_colors = ["green", "black", "red"] - - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle("Noise Schedule") - ax = fig.add_subplot(111) - ax.plot(times, sigmas, "b-") - - for tf, c in zip(special_times, list_colors): - sf = sigma_function(tf) - ax.vlines( - tf, 0, 0.5, colors=c, linestyles="dashed", label=r"$\sigma$ = " + f"{sf:5.4f}" - ) - - ax.set_xlabel(r"Time ($t_f$)") - ax.set_ylabel(r"Noise $\sigma$") - ax.set_xlim([0, 1]) - ax.set_ylim([0, 0.5]) - ax.legend(loc=0) - plt.show() - - reference_structure = Structure.from_file(reference_cif) - list_energy = [] - logger.info("Compute reference energy from Oracle") - with tempfile.TemporaryDirectory() as tmp_work_dir: - atom_types = np.array(len(reference_structure) * [1]) - positions = reference_structure.frac_coords @ reference_structure.lattice.matrix - reference_energy, _ = get_energy_and_forces_from_lammps( - positions, - reference_structure.lattice.matrix, - atom_types, - tmp_work_dir=tmp_work_dir, - ) - - list_times = [] - list_energies = [] - for pickle_path in glob.glob( - str(results_dir / "diffusion_energies_sample_time=*.pt") - ): - energies = torch.load(pickle_path).numpy() - time = float(pickle_path.split("=")[1].split(".pt")[0]) - - list_times.append(time) - list_energies.append(energies) - - times = np.array(list_times) - energies = np.array(list_energies) - - sorting_indices = np.argsort(times) - times = times[sorting_indices] - energies = energies[sorting_indices] - - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle("Energy Quantiles for Partial Trajectories Final Point") - ax1 = fig.add_subplot(221) - ax2 = fig.add_subplot(222) - ax3 = fig.add_subplot(223) - ax4 = fig.add_subplot(224) - list_axes = [ax1, ax2, ax3, ax4] - list_q = np.linspace(0, 1, 21) - number_of_times = len(times) - - for ax, indices in zip( - list_axes, np.split(np.arange(number_of_times), len(list_axes)) - ): - # Ad Hoc hack so we can see something - e_min = np.min([energies[indices].min(), reference_energy]) - e_95 = np.quantile(energies[indices], 0.95) - delta = (e_95 - e_min) / 10.0 - e_max = e_95 + delta - - ax.set_ylim(e_min - 0.1, e_max) - - ax.hlines( - reference_energy, - 0, - 100, - color="black", - linestyles="dashed", - label="Reference Energy", - ) - - for idx in indices: - tf = times[idx] - time_energies = energies[idx] - energy_quantiles = np.quantile(time_energies, list_q) - ax.plot(100 * list_q, energy_quantiles, "-", label=f"time = {tf:3.2f}") - - ax.legend(loc=0, fontsize=7) - ax.set_xlim([-0.1, 100.1]) - ax.set_xlabel("Quantile (%)") - ax.set_ylabel("Energy (eV)") - fig.tight_layout() - plt.show() - - fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig2.suptitle("Energy Extrema of Trajectory End Points") - ax = fig2.add_subplot(111) - ax.plot(times, energies.min(axis=1), "o-", label="Minimum Energy") - ax.plot( - times, np.quantile(energies, 0.5, axis=1), "o-", label="50% Quantile Energy" - ) - ax.plot(times, energies.max(axis=1), "o-", label="Maximum Energy") - ax.set_xlabel("Starting Diffusion Time, $t_f$") - ax.set_ylabel("Energy (eV)") - - ax.hlines( - reference_energy, - 0, - 100, - color="black", - linestyles="dashed", - label="Reference Energy", - ) - - ax.set_ylim(-280, -120) - ax.set_xlim(0.0, 1.01) - ax.legend(loc=0) - - plt.show() diff --git a/experiment_analysis/sampling_analysis/sampling_si_diffusion.py b/experiment_analysis/sampling_analysis/sampling_si_diffusion.py deleted file mode 100644 index dd7d4518..00000000 --- a/experiment_analysis/sampling_analysis/sampling_si_diffusion.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Sampling Si diffusion 1x1x1. - -This script loads a pre-trained model, creates the sampler and draws samples. -The energy of the samples are then obtained from the LAMMPS oracle. The dump -files are renamed and kept so that they can be visualized. -""" -import logging -import os -from pathlib import Path - -import numpy as np -import pandas as pd -import torch -import yaml -from yaml import load - -from crystal_diffusion import DATA_DIR, TOP_DIR -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.models.instantiate_diffusion_model import \ - load_diffusion_model -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from crystal_diffusion.utils.logging_utils import setup_analysis_logger - -logger = logging.getLogger(__name__) - -setup_analysis_logger() - -dataset_name = 'si_diffusion_2x2x2' -# dataset_name = 'si_diffusion_1x1x1' - - -# TODO: cell_size should be easily available programmatically. -if dataset_name == 'si_diffusion_1x1x1': - cell_size = 5.43 - experiment_dir = TOP_DIR.joinpath("experiments/si_diffusion_1x1x1/run1/") - config_path = str(experiment_dir.joinpath("config_diffusion.yaml")) - checkpoint_path = str(experiment_dir.joinpath("best_model/best_model-epoch=156-step=061386.ckpt")) -elif dataset_name == 'si_diffusion_2x2x2': - cell_size = 10.86 - experiment_dir = TOP_DIR.joinpath("experiments/si_diffusion_2x2x2/run1/") - config_path = str(experiment_dir.joinpath("config_diffusion.yaml")) - checkpoint_path = str(experiment_dir.joinpath("best_model/best_model-epoch=1972-step=173623.ckpt")) - - -lammps_work_directory = Path(__file__).parent.joinpath(f"lammps_work_directory/{dataset_name}") -lammps_work_directory.mkdir(exist_ok=True, parents=True) -lammps_work_directory = str(lammps_work_directory) - -number_of_corrector_steps = 3 -total_time_steps = 100 -noise_parameters = NoiseParameters(total_time_steps=total_time_steps) - -box = np.diag([cell_size, cell_size, cell_size]) - -number_of_samples = 4096 - - -if __name__ == '__main__': - logger.info("Loading checkpoint") - checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) - - with open(config_path, 'r') as stream: - hyper_params = load(stream, Loader=yaml.FullLoader) - pl_model = load_diffusion_model(hyper_params) - pl_model.load_state_dict(state_dict=checkpoint['state_dict']) - - score_network_parameters = pl_model.hyper_params.score_network_parameters - number_of_atoms = score_network_parameters.number_of_atoms - atom_types = np.ones(number_of_atoms, dtype=int) - - sigma_normalized_score_network = pl_model.sigma_normalized_score_network - - logger.info("Creating sampler") - pc_sampler = LangevinGenerator(noise_parameters=noise_parameters, - number_of_corrector_steps=number_of_corrector_steps, - number_of_atoms=number_of_atoms, - spatial_dimension=score_network_parameters.spatial_dimension, - sigma_normalized_score_network=sigma_normalized_score_network) - - logger.info("Draw samples") - samples = pc_sampler.sample(number_of_samples) - - batch_relative_positions = samples.cpu().numpy() - batch_positions = np.dot(batch_relative_positions, box) - - list_energy = [] - - logger.info("Compute energy from Oracle") - for idx, positions in enumerate(batch_positions): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - atom_types, - tmp_work_dir=lammps_work_directory, - pair_coeff_dir=DATA_DIR) - list_energy.append(energy) - src = os.path.join(lammps_work_directory, "dump.yaml") - dst = os.path.join(lammps_work_directory, f"dump_{idx}.yaml") - os.rename(src, dst) - - df = pd.DataFrame({'energy': list_energy}) - pd.to_pickle(df, f'{dataset_name}_energy_samples.pkl') From 6d7501b08f1fb577a1d2474493fea29305d31d8a Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:20:56 -0400 Subject: [PATCH 04/29] Move stuff. --- .../sampling_sota_model/plot_repaint_score_trajectories.py | 0 .../sampling_sota_model/repaint_with_sota_score.py | 0 .../sampling_sota_model/sota_score_sampling_and_plotting.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename {experiment_analysis => experiments}/sampling_sota_model/plot_repaint_score_trajectories.py (100%) rename {experiment_analysis => experiments}/sampling_sota_model/repaint_with_sota_score.py (100%) rename {experiment_analysis => experiments}/sampling_sota_model/sota_score_sampling_and_plotting.py (100%) diff --git a/experiment_analysis/sampling_sota_model/plot_repaint_score_trajectories.py b/experiments/sampling_sota_model/plot_repaint_score_trajectories.py similarity index 100% rename from experiment_analysis/sampling_sota_model/plot_repaint_score_trajectories.py rename to experiments/sampling_sota_model/plot_repaint_score_trajectories.py diff --git a/experiment_analysis/sampling_sota_model/repaint_with_sota_score.py b/experiments/sampling_sota_model/repaint_with_sota_score.py similarity index 100% rename from experiment_analysis/sampling_sota_model/repaint_with_sota_score.py rename to experiments/sampling_sota_model/repaint_with_sota_score.py diff --git a/experiment_analysis/sampling_sota_model/sota_score_sampling_and_plotting.py b/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py similarity index 100% rename from experiment_analysis/sampling_sota_model/sota_score_sampling_and_plotting.py rename to experiments/sampling_sota_model/sota_score_sampling_and_plotting.py From 06aa6e9254fc2746b7efc3b2db913b8780007db2 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:22:16 -0400 Subject: [PATCH 05/29] Clean needless stuff. --- .../score_analysis/__init__.py | 0 .../compute_scores_along_path.py | 102 ------------ .../score_analysis/plot_scores_along_path.py | 146 ------------------ 3 files changed, 248 deletions(-) delete mode 100644 experiment_analysis/score_analysis/__init__.py delete mode 100644 experiment_analysis/score_analysis/compute_scores_along_path.py delete mode 100644 experiment_analysis/score_analysis/plot_scores_along_path.py diff --git a/experiment_analysis/score_analysis/__init__.py b/experiment_analysis/score_analysis/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/experiment_analysis/score_analysis/compute_scores_along_path.py b/experiment_analysis/score_analysis/compute_scores_along_path.py deleted file mode 100644 index de979e43..00000000 --- a/experiment_analysis/score_analysis/compute_scores_along_path.py +++ /dev/null @@ -1,102 +0,0 @@ -import logging -from pathlib import Path - -import torch -from tqdm import tqdm - -from crystal_diffusion.models.position_diffusion_lightning_model import \ - PositionDiffusionLightningModel -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ - NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell - -logger = logging.getLogger(__name__) - -# Some hardcoded paths and parameters. Change as needed! -# base_data_dir = Path("/home/mila/r/rousseab/experiments/mlp_june12/run5/output/") -# model_path = base_data_dir / "best_model/best_model-epoch=135-step=013328.ckpt" -# model_name = "mlp_jun12_run5" - - -base_data_dir = Path("/home/mila/r/rousseab/experiments/diffusion_mace_ode/run7/output/") -model_path = base_data_dir / "best_model/best_model-epoch=027-step=000924.ckpt" -model_name = "diffusion_mace_ode_run7" - - -output_dir = base_data_dir / "scores_along_a_path" -output_dir.mkdir(exist_ok=True) -output_path = output_dir / f"{model_name}_path_scores.pkl" - -if torch.cuda.is_available(): - device = torch.device('cuda') -else: - device = torch.device('cpu') - -canonical_relative_coordinates = torch.tensor([[0.00, 0.00, 0.00], - [0.00, 0.50, 0.50], - [0.50, 0.00, 0.50], - [0.50, 0.50, 0.00], - [0.25, 0.25, 0.25], - [0.25, 0.75, 0.75], - [0.75, 0.25, 0.75], - [0.75, 0.75, 0.25]]).to(device) - -sigma_min = 0.001 -sigma_max = 0.5 -total_time_steps = 10 - -noise_parameters = NoiseParameters(total_time_steps=total_time_steps, sigma_min=sigma_min, sigma_max=sigma_max) -noise_sampler = ExplodingVarianceSampler(noise_parameters) - -cell_dimensions = torch.tensor([5.43, 5.43, 5.43]).to(device) - - -number_of_atoms = 8 -spatial_dimension = 3 - -number_of_steps = 101 - -if __name__ == '__main__': - - torch.manual_seed(42) - epsilon = torch.randn((number_of_atoms, spatial_dimension)).to(device) - record = dict(epsilon=epsilon) - - trajectory_sigmas = torch.linspace(sigma_min, sigma_max, number_of_steps).to(device) - record['trajectory_sigmas'] = trajectory_sigmas - - noisy_relative_coordinates_sampler = NoisyRelativeCoordinatesSampler() - unit_cell = torch.diag(torch.Tensor(cell_dimensions)).unsqueeze(0).repeat(number_of_steps, 1, 1) - - x = torch.stack([canonical_relative_coordinates + sigma * epsilon for sigma in trajectory_sigmas]).to(device) - x = map_relative_coordinates_to_unit_cell(x) - - times = noise_sampler._time_array - sigmas = noise_sampler._sigma_array - record['times'] = times - record['sigmas'] = sigmas - - model = PositionDiffusionLightningModel.load_from_checkpoint(model_path) - model.eval() - - normalized_scores = [] - for time, sigma in tqdm(zip(times, sigmas), 'Times'): - batch = {NOISY_RELATIVE_COORDINATES: x, - NOISE: sigma * torch.ones(number_of_steps, 1).to(device), - TIME: time * torch.ones(number_of_steps, 1).to(device), - UNIT_CELL: unit_cell, - CARTESIAN_FORCES: torch.zeros_like(x).to(device) - } - with torch.no_grad(): - normalized_scores.append(model.sigma_normalized_score_network(batch)) - - record['normalized_scores'] = torch.stack(normalized_scores) - - with open(str(output_path), 'wb') as fd: - torch.save(record, fd) diff --git a/experiment_analysis/score_analysis/plot_scores_along_path.py b/experiment_analysis/score_analysis/plot_scores_along_path.py deleted file mode 100644 index cc7b998f..00000000 --- a/experiment_analysis/score_analysis/plot_scores_along_path.py +++ /dev/null @@ -1,146 +0,0 @@ -import logging -from pathlib import Path - -import matplotlib.pyplot as plt -import torch -from tqdm import tqdm - -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.score.wrapped_gaussian_score import \ - get_sigma_normalized_score -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from crystal_diffusion.utils.tensor_utils import \ - broadcast_batch_tensor_to_all_dimensions - -setup_analysis_logger() - -logger = logging.getLogger(__name__) - -plt.style.use(PLOT_STYLE_PATH) - -base_path = Path("/Users/bruno/courtois/scores_along_a_path") - -model_name1 = 'diffusion_mace_ode-run7' -data_path1 = base_path / "diffusion_mace_ode_run7_path_scores.pkl" - -model_name2 = 'mlp_jun12-run5' -data_path2 = base_path / "mlp_jun12_run5_path_scores.pkl" - -output_dir = ANALYSIS_RESULTS_DIR / "scores_along_path" -output_dir.mkdir(exist_ok=True) - - -x0 = torch.tensor([[0.00, 0.00, 0.00], - [0.00, 0.50, 0.50], - [0.50, 0.00, 0.50], - [0.50, 0.50, 0.00], - [0.25, 0.25, 0.25], - [0.25, 0.75, 0.75], - [0.75, 0.25, 0.75], - [0.75, 0.75, 0.25]]) - - -lambda_data = 0.0075 # estimated from the training data. -if __name__ == '__main__': - - data1 = torch.load(data_path1, map_location=torch.device('cpu')) - data2 = torch.load(data_path2, map_location=torch.device('cpu')) - - logging.info(f"Checking that {data_path1} and {data_path2} are consistent") - # Double check that the reference data is the same for both models. - for key in ['epsilon', 'trajectory_sigmas', 'times', 'sigmas']: - values1 = data1[key] - values2 = data2[key] - torch.testing.assert_close(values1, values2) - - epsilon = data1['epsilon'] - trajectory_lambda = data1['trajectory_sigmas'] - sigmas = data1['sigmas'] - - number_of_trajectory_points = len(trajectory_lambda) - - normalized_scores1 = data1['normalized_scores'] - normalized_scores2 = data2['normalized_scores'] - - logging.info("Computing the x0 targets") - # xt is different points along a trajectory - # shape: [batch_size = number_of_trajectory_points, natoms, spatial dimension] - xt = torch.stack([x0 + lbda * epsilon for lbda in trajectory_lambda]) - # yes, it's silly to add sigma * epsilon only to remove it after, but I think it makes the script clearer. - delta_relative_coordinates = map_relative_coordinates_to_unit_cell(xt - x0) - - list_target_normalized_scores_x0 = [] - list_target_normalized_scores_x_data = [] - - for sigma in sigmas: - broadcast_sigmas = broadcast_batch_tensor_to_all_dimensions( - batch_values=sigma * torch.ones(number_of_trajectory_points), final_shape=xt.shape - ) - target_normalized_scores = get_sigma_normalized_score(delta_relative_coordinates, broadcast_sigmas, kmax=4) - list_target_normalized_scores_x0.append(target_normalized_scores) - - broadcast_sigmas = broadcast_batch_tensor_to_all_dimensions( - batch_values=(sigma + lambda_data) * torch.ones(number_of_trajectory_points), final_shape=xt.shape) - target_normalized_scores = get_sigma_normalized_score(delta_relative_coordinates, broadcast_sigmas, kmax=4) - list_target_normalized_scores_x_data.append(target_normalized_scores) - - target_normalized_scores_x0 = torch.stack(list_target_normalized_scores_x0) - target_normalized_scores_x_data = torch.stack(list_target_normalized_scores_x_data) - - logging.info("Generating plots...") - scores = [target_normalized_scores_x0, target_normalized_scores_x_data, normalized_scores1, normalized_scores2] - labels = ['${\\bf x}_0$ TARGET', '${\\bf x}_0 +\\lambda_{data} \\epsilon$ TARGET', - f'Model {model_name1}', f'Model {model_name2}'] - linestyles = ['--', '--', '-', '-'] - - for time_idx in tqdm(range(1, len(sigmas)), "time"): - reference_sigma = sigmas[time_idx] - list_x = trajectory_lambda / reference_sigma - - on_manifold_range_min = lambda_data / reference_sigma - on_manifold_range_max = lambda_data / reference_sigma + 2. - - idx_min = torch.where(list_x < on_manifold_range_min)[0][-1] - try: - idx_max = torch.where(list_x > on_manifold_range_max)[0][0] - except KeyError: - idx_max = len(list_x) - in_range_mask = torch.zeros_like(list_x, dtype=torch.bool) - in_range_mask[idx_min:idx_max + 1] = True - - for space_index, component in zip([0, 1, 2], ['X', 'Y', 'Z']): - for atom_idx in range(8): - fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle(f"Comparing the {component} component of the Score Atom {atom_idx}" - f"\n $\\sigma(t)$ = {reference_sigma:4.3f}") - - ax1 = fig1.add_subplot(121) - ax2 = fig1.add_subplot(122) - - for score, label, ls in zip(scores, labels, linestyles): - list_y = score[time_idx, :, atom_idx, space_index] - ax1.plot(list_x[in_range_mask], list_y[in_range_mask], ls=ls, label='__nolabel__') - ax2.plot(list_x, list_y, ls=ls, label=label) - - ax1.axvspan(on_manifold_range_min, on_manifold_range_max, - ymin=0, ymax=1, alpha=0.1, color='gray', label='On Manifold') - ax2.axvspan(on_manifold_range_min, on_manifold_range_max, - ymin=0, ymax=1, alpha=0.1, color='gray', label='__nolabel__') - - ax1.legend(loc=0) - ax2.legend(loc=0) - ax1.set_xlim([on_manifold_range_min - 0.1, on_manifold_range_max + 0.1]) - ax2.set_xlim(xmin=0) - - ax1.set_title('Zoom In') - ax2.set_title('Broad View') - for ax in [ax1, ax2]: - ax.set_xlabel('$\\lambda / \\sigma(t)$') - ax.set_ylabel('$\\sigma(t)\\times {\\bf s}( x(\\lambda), \\sigma(t))$') - - fig1.tight_layout() - fig1.savefig(output_dir / f"path_scores_{component}_atom_idx={atom_idx}_time_idx={time_idx}.png") - plt.close(fig1) From 55b903be7f6ae07014ee772789bdc54e7e1e687e Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:24:02 -0400 Subject: [PATCH 06/29] Move utils file to relevant location. --- {experiment_analysis => experiments}/analysis_utils.py | 0 experiments/dataset_analysis/energy_consistency_analysis.py | 2 +- experiments/dataset_analysis/si_diffusion_analysis.py | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) rename {experiment_analysis => experiments}/analysis_utils.py (100%) diff --git a/experiment_analysis/analysis_utils.py b/experiments/analysis_utils.py similarity index 100% rename from experiment_analysis/analysis_utils.py rename to experiments/analysis_utils.py diff --git a/experiments/dataset_analysis/energy_consistency_analysis.py b/experiments/dataset_analysis/energy_consistency_analysis.py index c3ed4afe..f2445fce 100644 --- a/experiments/dataset_analysis/energy_consistency_analysis.py +++ b/experiments/dataset_analysis/energy_consistency_analysis.py @@ -22,7 +22,7 @@ from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps from crystal_diffusion.utils.logging_utils import setup_analysis_logger from experiment_analysis import EXPERIMENT_ANALYSIS_DIR -from experiment_analysis.analysis_utils import get_thermo_dataset +from experiments.analysis_utils import get_thermo_dataset plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/dataset_analysis/si_diffusion_analysis.py b/experiments/dataset_analysis/si_diffusion_analysis.py index 850edc36..d0de054b 100644 --- a/experiments/dataset_analysis/si_diffusion_analysis.py +++ b/experiments/dataset_analysis/si_diffusion_analysis.py @@ -19,7 +19,7 @@ from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from experiment_analysis.analysis_utils import get_thermo_dataset +from experiments.analysis_utils import get_thermo_dataset plt.style.use(PLOT_STYLE_PATH) From b44e8184af31ddfe340878ffbb84ca1620be2924 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:25:46 -0400 Subject: [PATCH 07/29] Moving more stuff around. --- {experiment_analysis => experiments}/__init__.py | 0 experiments/analysis_utils.py | 2 +- experiments/dataset_analysis/energy_consistency_analysis.py | 2 +- .../score_stability_analysis/draw_samples_from_equilibrium.py | 0 .../score_stability_analysis/plot_hessian_eigenvalues.py | 3 +-- .../score_stability_analysis/plot_score_norm.py | 3 +-- .../score_stability_analysis/util.py | 0 7 files changed, 4 insertions(+), 6 deletions(-) rename {experiment_analysis => experiments}/__init__.py (100%) rename {experiment_analysis => experiments}/score_stability_analysis/draw_samples_from_equilibrium.py (100%) rename {experiment_analysis => experiments}/score_stability_analysis/plot_hessian_eigenvalues.py (98%) rename {experiment_analysis => experiments}/score_stability_analysis/plot_score_norm.py (97%) rename {experiment_analysis => experiments}/score_stability_analysis/util.py (100%) diff --git a/experiment_analysis/__init__.py b/experiments/__init__.py similarity index 100% rename from experiment_analysis/__init__.py rename to experiments/__init__.py diff --git a/experiments/analysis_utils.py b/experiments/analysis_utils.py index 6c9a5189..156de546 100644 --- a/experiments/analysis_utils.py +++ b/experiments/analysis_utils.py @@ -7,7 +7,7 @@ from crystal_diffusion import DATA_DIR from crystal_diffusion.data.parse_lammps_outputs import parse_lammps_thermo_log -from experiment_analysis import EXPERIMENT_ANALYSIS_DIR +from experiments import EXPERIMENT_ANALYSIS_DIR logger = logging.getLogger(__name__) diff --git a/experiments/dataset_analysis/energy_consistency_analysis.py b/experiments/dataset_analysis/energy_consistency_analysis.py index f2445fce..1c6113f0 100644 --- a/experiments/dataset_analysis/energy_consistency_analysis.py +++ b/experiments/dataset_analysis/energy_consistency_analysis.py @@ -21,7 +21,7 @@ LammpsForDiffusionDataModule, LammpsLoaderParameters) from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from experiment_analysis import EXPERIMENT_ANALYSIS_DIR +from experiments import EXPERIMENT_ANALYSIS_DIR from experiments.analysis_utils import get_thermo_dataset plt.style.use(PLOT_STYLE_PATH) diff --git a/experiment_analysis/score_stability_analysis/draw_samples_from_equilibrium.py b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py similarity index 100% rename from experiment_analysis/score_stability_analysis/draw_samples_from_equilibrium.py rename to experiments/score_stability_analysis/draw_samples_from_equilibrium.py diff --git a/experiment_analysis/score_stability_analysis/plot_hessian_eigenvalues.py b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py similarity index 98% rename from experiment_analysis/score_stability_analysis/plot_hessian_eigenvalues.py rename to experiments/score_stability_analysis/plot_hessian_eigenvalues.py index b48669be..6fd5a0a4 100644 --- a/experiment_analysis/score_stability_analysis/plot_hessian_eigenvalues.py +++ b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py @@ -16,8 +16,7 @@ from crystal_diffusion.samplers.exploding_variance import ExplodingVariance from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from experiment_analysis.score_stability_analysis.util import \ - get_normalized_score_function +from experiments import get_normalized_score_function plt.style.use(PLOT_STYLE_PATH) diff --git a/experiment_analysis/score_stability_analysis/plot_score_norm.py b/experiments/score_stability_analysis/plot_score_norm.py similarity index 97% rename from experiment_analysis/score_stability_analysis/plot_score_norm.py rename to experiments/score_stability_analysis/plot_score_norm.py index ef4ddbc7..7236d8e3 100644 --- a/experiment_analysis/score_stability_analysis/plot_score_norm.py +++ b/experiments/score_stability_analysis/plot_score_norm.py @@ -16,8 +16,7 @@ from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from experiment_analysis.score_stability_analysis.util import \ - create_fixed_time_normalized_score_function +from experiments import create_fixed_time_normalized_score_function plt.style.use(PLOT_STYLE_PATH) diff --git a/experiment_analysis/score_stability_analysis/util.py b/experiments/score_stability_analysis/util.py similarity index 100% rename from experiment_analysis/score_stability_analysis/util.py rename to experiments/score_stability_analysis/util.py From 277736bc52cf8ff16d01b1fbedaddb1e173091d8 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:27:44 -0400 Subject: [PATCH 08/29] Update the readme. --- experiment_analysis/README | 7 ------- experiments/README | 6 ++++++ 2 files changed, 6 insertions(+), 7 deletions(-) delete mode 100644 experiment_analysis/README create mode 100644 experiments/README diff --git a/experiment_analysis/README b/experiment_analysis/README deleted file mode 100644 index 8b12aad6..00000000 --- a/experiment_analysis/README +++ /dev/null @@ -1,7 +0,0 @@ -# Analysis - -This folder contains various analysis scripts. These scripts may point to -data in the data/ folder or the experiment/ folder: these artifacts will not -be under revision, it will be assumed that these data sources are present -when executing the scripts. - diff --git a/experiments/README b/experiments/README new file mode 100644 index 00000000..8be7f5c8 --- /dev/null +++ b/experiments/README @@ -0,0 +1,6 @@ +# Experiments + +This folder contains various experiments and analysis scripts. These various scripts +may have been useful at some point during the development of the code, but are not +actively maintained. They should be viewed as (perhaps outdated) examples of how +one can analyse the data generated by this code base. From 1ff53c6d33a01ff4acade815f0b2379699f9954d Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:29:26 -0400 Subject: [PATCH 09/29] ignore mac system files --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 33223421..6b691186 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ .idea +.DS_Store mlruns analysis_results/ From 1a708bb18877c60544d2b7e9b04a10084c4e3f9d Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:30:12 -0400 Subject: [PATCH 10/29] ignore mlip-3 folder --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 6b691186..2cc0b025 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,8 @@ mlruns analysis_results/ +mlip-3/ + examples/data/ examples/*/output/ examples/*/lightning_logs/ From 9384c71a3b16959f117eb0f55eff0adf3c61ded3 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:32:53 -0400 Subject: [PATCH 11/29] Remove needless stuff. --- .../__init__.py | 3 - .../fake_data_utils.py | 83 ------------------- .../generate_fake_trajectory_visualization.py | 30 ------- 3 files changed, 116 deletions(-) delete mode 100644 sanity_checks/visualizing_fake_trajectories_with_ovito/__init__.py delete mode 100644 sanity_checks/visualizing_fake_trajectories_with_ovito/fake_data_utils.py delete mode 100644 sanity_checks/visualizing_fake_trajectories_with_ovito/generate_fake_trajectory_visualization.py diff --git a/sanity_checks/visualizing_fake_trajectories_with_ovito/__init__.py b/sanity_checks/visualizing_fake_trajectories_with_ovito/__init__.py deleted file mode 100644 index b703e3ee..00000000 --- a/sanity_checks/visualizing_fake_trajectories_with_ovito/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from pathlib import Path - -VISUALIZATION_SANITY_CHECK_DIRECTORY = Path(__file__).parent diff --git a/sanity_checks/visualizing_fake_trajectories_with_ovito/fake_data_utils.py b/sanity_checks/visualizing_fake_trajectories_with_ovito/fake_data_utils.py deleted file mode 100644 index 35c6c69e..00000000 --- a/sanity_checks/visualizing_fake_trajectories_with_ovito/fake_data_utils.py +++ /dev/null @@ -1,83 +0,0 @@ -from pathlib import Path - -import einops -import torch - -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.sample_trajectory import ODESampleTrajectory - - -def generate_fake_trajectories_pickle( - acell: float, - number_of_atoms: int, - number_of_frames: int, - number_of_trajectories: int, - pickle_path: Path, -): - """Generate fake trajectories pickle. - - This function creates a torch pickle with the needed data fields to sanity check that - we can visualize paths with Ovito. - - Args: - acell : A cell dimension parameter - number_of_atoms : number of atoms in the cell - number_of_frames : number of time steps in the trajectories - number_of_trajectories : number of trajectories - pickle_path : where the pickle should be written - - Returns: - None. - """ - spatial_dimension = 3 - t0 = 0.0 - tf = 1.0 - # These parameters don't really matter for the purpose of generating fake trajectories. - sigma_min = 0.01 - sigma_max = 0.5 - - # Times have dimension [number_of_time_steps] - times = torch.linspace(tf, t0, number_of_frames) - - # evaluation_times have dimension [batch_size, number_of_time_steps] - evaluation_times = einops.repeat( - times, "t -> batch t", batch=number_of_trajectories - ) - - shifts = torch.rand(number_of_trajectories) - - a = acell + torch.cos(2 * torch.pi * shifts) - b = acell + torch.sin(2 * torch.pi * shifts) - c = acell + shifts - - # unit_cells have dimensions [number_of_samples, spatial_dimension, spatial_dimension] - unit_cells = torch.diag_embed(einops.rearrange([a, b, c], "d batch -> batch d")) - - sigmas = sigma_min ** (1.0 - evaluation_times) * sigma_max**evaluation_times - - normalized_scores = 0.1 * torch.rand( - number_of_trajectories, number_of_frames, number_of_atoms, spatial_dimension - ) - - initial_relative_coordinates = torch.rand( - [number_of_trajectories, 1, number_of_atoms, spatial_dimension] - ) - relative_coordinates = map_relative_coordinates_to_unit_cell( - initial_relative_coordinates + normalized_scores - ) - - sample_trajectory_recorder = ODESampleTrajectory() - - sample_trajectory_recorder.record_unit_cell(unit_cells) - - sample_trajectory_recorder.record_ode_solution( - times=evaluation_times, - sigmas=sigmas, - relative_coordinates=relative_coordinates, - normalized_scores=normalized_scores, - stats="not applicable", - status="not applicable", - ) - - sample_trajectory_recorder.write_to_pickle(pickle_path) diff --git a/sanity_checks/visualizing_fake_trajectories_with_ovito/generate_fake_trajectory_visualization.py b/sanity_checks/visualizing_fake_trajectories_with_ovito/generate_fake_trajectory_visualization.py deleted file mode 100644 index 121007be..00000000 --- a/sanity_checks/visualizing_fake_trajectories_with_ovito/generate_fake_trajectory_visualization.py +++ /dev/null @@ -1,30 +0,0 @@ -from crystal_diffusion.utils.ovito_utils import (create_cif_files, - create_ovito_session_state) -from sanity_checks.visualizing_fake_trajectories_with_ovito import \ - VISUALIZATION_SANITY_CHECK_DIRECTORY -from sanity_checks.visualizing_fake_trajectories_with_ovito.fake_data_utils import \ - generate_fake_trajectories_pickle - -acell = 5 -number_of_atoms = 8 -number_of_frames = 101 # the 'time' dimension -number_of_trajectories = 4 # the 'batch' dimension -if __name__ == '__main__': - - pickle_path = VISUALIZATION_SANITY_CHECK_DIRECTORY / "trajectories.pt" - - generate_fake_trajectories_pickle(acell=acell, - number_of_atoms=number_of_atoms, - number_of_frames=number_of_frames, - number_of_trajectories=number_of_trajectories, - pickle_path=pickle_path) - - trajectory_directory = VISUALIZATION_SANITY_CHECK_DIRECTORY / "trajectories" - for trj_idx in range(number_of_trajectories): - print(f"Computing Ovito trajectory session state for trajectory index {trj_idx}") - create_cif_files(visualization_artifacts_path=trajectory_directory, - trajectory_index=trj_idx, - ode_trajectory_pickle=pickle_path) - - create_ovito_session_state(visualization_artifacts_path=trajectory_directory, - trajectory_index=trj_idx) From 7b5feb02918425233b1ea27ebf9bd193174c5300 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:35:04 -0400 Subject: [PATCH 12/29] Remove more noise. --- sanity_checks/__init__.py | 3 - sanity_checks/overfit_fake_data.py | 106 ------------ sanity_checks/sanity_check_callbacks.py | 205 ------------------------ 3 files changed, 314 deletions(-) delete mode 100644 sanity_checks/__init__.py delete mode 100644 sanity_checks/overfit_fake_data.py delete mode 100644 sanity_checks/sanity_check_callbacks.py diff --git a/sanity_checks/__init__.py b/sanity_checks/__init__.py deleted file mode 100644 index e311896f..00000000 --- a/sanity_checks/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from pathlib import Path - -SANITY_CHECK_FOLDER = str(Path(__file__).parent) diff --git a/sanity_checks/overfit_fake_data.py b/sanity_checks/overfit_fake_data.py deleted file mode 100644 index 69c790e5..00000000 --- a/sanity_checks/overfit_fake_data.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Overfit fake data. - -A simple sanity check experiment to check the learning behavior of the position diffusion model. -The training data is taken to be a large batch of identical configurations composed of one atom in 1D at x0. -This highly artificial case is useful to sanity check that the code behaves as expected: - - the loss should converge towards zero - - the trained score network should reproduce the perturbation kernel, at least in the regions where it is sampled. - - the generated samples should be tightly clustered around x0. -""" -import dataclasses -import os - -import pytorch_lightning -import torch -from pytorch_lightning import Callback, Trainer -from pytorch_lightning.callbacks import LearningRateMonitor -from pytorch_lightning.loggers import TensorBoardLogger -from torch.utils.data import DataLoader - -from crystal_diffusion.models.optimizer import OptimizerParameters -from crystal_diffusion.models.position_diffusion_lightning_model import ( - PositionDiffusionLightningModel, PositionDiffusionParameters) -from crystal_diffusion.models.score_networks.mlp_score_network import \ - MLPScoreNetworkParameters -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from sanity_checks import SANITY_CHECK_FOLDER -from sanity_checks.sanity_check_callbacks import ( - TensorboardGeneratedSamplesLoggingCallback, - TensorboardHistogramLoggingCallback, TensorboardSamplesLoggingCallback, - TensorboardScoreAndErrorLoggingCallback) - - -class HPLoggingCallback(Callback): - """This callback is responsible for logging hyperparameters.""" - - def on_train_start(self, trainer, pl_module): - """Log hyperparameters when training starts.""" - assert hasattr( - pl_module, "hyper_params" - ), "The lightning module should have a hyper_params attribute for HP logging." - hp_dict = dataclasses.asdict(pl_module.hyper_params) - trainer.logger.log_hyperparams(hp_dict) - - -batch_size = 4096 -number_of_atoms = 1 -spatial_dimension = 1 -total_time_steps = 100 -number_of_corrector_steps = 1 - -x0 = 0.5 - -sigma_min = 0.005 -sigma_max = 0.5 - -lr = 0.001 -max_epochs = 2000 - -hidden_dimensions = [64, 128, 256] - - -score_network_parameters = MLPScoreNetworkParameters( - number_of_atoms=number_of_atoms, - hidden_dimensions=hidden_dimensions, - spatial_dimension=spatial_dimension, -) - -optimizer_parameters = OptimizerParameters(name="adam", learning_rate=lr) - -noise_parameters = NoiseParameters(total_time_steps=total_time_steps, sigma_min=sigma_min, sigma_max=sigma_max) - -hyper_params = PositionDiffusionParameters( - score_network_parameters=score_network_parameters, - optimizer_parameters=optimizer_parameters, - noise_parameters=noise_parameters, -) - -generated_samples_callback = ( - TensorboardGeneratedSamplesLoggingCallback(noise_parameters=noise_parameters, - number_of_corrector_steps=number_of_corrector_steps, - score_network_parameters=score_network_parameters, - number_of_samples=1024)) - -score_error_callback = TensorboardScoreAndErrorLoggingCallback(x0=x0) - -tbx_logger = TensorBoardLogger(save_dir=os.path.join(SANITY_CHECK_FOLDER, "tensorboard"), name="overfit_fake_data") - -if __name__ == '__main__': - pytorch_lightning.seed_everything(123) - all_positions = x0 * torch.ones(batch_size, number_of_atoms, spatial_dimension) - data = [dict(relative_positions=configuration) for configuration in all_positions] - train_dataloader = DataLoader(data, batch_size=batch_size) - - lightning_model = PositionDiffusionLightningModel(hyper_params) - - trainer = Trainer(accelerator='cpu', - max_epochs=max_epochs, - logger=tbx_logger, - log_every_n_steps=25, - callbacks=[HPLoggingCallback(), - generated_samples_callback, - score_error_callback, - TensorboardHistogramLoggingCallback(), - TensorboardSamplesLoggingCallback(), - LearningRateMonitor(logging_interval='step')]) - trainer.fit(lightning_model, train_dataloaders=train_dataloader) diff --git a/sanity_checks/sanity_check_callbacks.py b/sanity_checks/sanity_check_callbacks.py deleted file mode 100644 index 88e3c94e..00000000 --- a/sanity_checks/sanity_check_callbacks.py +++ /dev/null @@ -1,205 +0,0 @@ -import torch -from matplotlib import pyplot as plt -from pytorch_lightning import Callback -from pytorch_lightning.loggers import TensorBoardLogger - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.models.score_networks.mlp_score_network import \ - MLPScoreNetworkParameters -from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from crystal_diffusion.score.wrapped_gaussian_score import \ - get_sigma_normalized_score -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell - -plt.style.use(PLOT_STYLE_PATH) - - -class TensorBoardDebuggingLoggingCallback(Callback): - """Base class to log debugging information for plotting on TensorBoard.""" - - def __init__(self): - """Init method.""" - self.training_step_outputs = [] - - @staticmethod - def _get_tensorboard_logger(trainer): - if type(trainer.logger) is TensorBoardLogger: - return trainer.logger.experiment - return None - - def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): - """Action to perform at the end of a training batch.""" - if self._get_tensorboard_logger(trainer) is None: - return - self.training_step_outputs.append(outputs) - - def on_train_epoch_end(self, trainer, pl_module): - """Action to perform at the end of a training epoch.""" - tbx_logger = self._get_tensorboard_logger(trainer) - if tbx_logger is None: - return - - if pl_module.global_step % trainer.log_every_n_steps == 0: - self.log_artifact(pl_module, tbx_logger) - # free up the memory - self.training_step_outputs.clear() - - def log_artifact(self, pl_module, tbx_logger): - """This method must create logging artifacts and log to the tbx logger.""" - raise NotImplementedError( - "This method should be implemented to specific logging." - ) - - -class TensorboardGeneratedSamplesLoggingCallback(TensorBoardDebuggingLoggingCallback): - """This callback will log an image of a histogram of generated samples on tensorboard.""" - - def __init__(self, noise_parameters: NoiseParameters, - number_of_corrector_steps: int, - score_network_parameters: MLPScoreNetworkParameters, number_of_samples: int): - """Init method.""" - super().__init__() - self.noise_parameters = noise_parameters - self.number_of_corrector_steps = number_of_corrector_steps - self.score_network_parameters = score_network_parameters - self.number_of_atoms = score_network_parameters.number_of_atoms - self.spatial_dimension = score_network_parameters.spatial_dimension - self.number_of_samples = number_of_samples - - def log_artifact(self, pl_module, tbx_logger): - """Create artifact and log to tensorboard.""" - sigma_normalized_score_network = pl_module.sigma_normalized_score_network - pc_generator = LangevinGenerator(noise_parameters=self.noise_parameters, - number_of_corrector_steps=self.number_of_corrector_steps, - number_of_atoms=self.number_of_atoms, - spatial_dimension=self.spatial_dimension, - sigma_normalized_score_network=sigma_normalized_score_network) - - samples = pc_generator.sample(self.number_of_samples).flatten() - - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - ax = fig.add_subplot(111) - ax.set_title(f"Generated Samples: global step = {pl_module.global_step}") - ax.set_xlabel('$x$') - ax.hist(samples, bins=101, range=(0, 1), label=f'{self.number_of_samples} samples') - ax.set_title("Samples Count") - ax.set_xlim([-0.05, 1.05]) - ax.set_ylim([0., self.number_of_samples]) - fig.tight_layout() - tbx_logger.add_figure("train/generated_samples", fig, global_step=pl_module.global_step) - - -class TensorboardScoreAndErrorLoggingCallback(TensorBoardDebuggingLoggingCallback): - """This callback will log histograms of the labels, predictions and errors on tensorboard.""" - - def __init__(self, x0: float): - """Init method.""" - super().__init__() - self.x0 = x0 - - def log_artifact(self, pl_module, tbx_logger): - """Create artifact and log to tensorboard.""" - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle("Scores within 2 $\\sigma$ of Data") - ax1 = fig.add_subplot(121) - ax2 = fig.add_subplot(122) - ax1.set_ylabel('$\\sigma \\times S_{\\theta}(x, t)$') - ax2.set_ylabel('$\\sigma \\times S_{\\theta}(x, t) - \\sigma \\nabla \\log P(x | 0)$') - for ax in [ax1, ax2]: - ax.set_xlabel('$x$') - - list_x = torch.linspace(0, 1, 1001)[:-1] - - times = torch.tensor([0.25, 0.75, 1.]) - sigmas = pl_module.variance_sampler._create_sigma_array(pl_module.variance_sampler.noise_parameters, times) - - with torch.no_grad(): - for time, sigma in zip(times, sigmas): - times = time * torch.ones(1000).reshape(-1, 1) - - sigma_normalized_kernel = get_sigma_normalized_score(map_relative_coordinates_to_unit_cell(list_x - - self.x0), - sigma * torch.ones_like(list_x), - kmax=4) - predicted_normalized_scores = pl_module._get_predicted_normalized_score(list_x.reshape(-1, 1, 1), - times).flatten() - - error = predicted_normalized_scores - sigma_normalized_kernel - - # only plot the errors in the sampling region! These regions might be disconnected, let's make - # sure the continuous lines make sense. - mask1 = torch.abs(list_x - self.x0) < 2 * sigma - mask2 = torch.abs(1. - list_x + self.x0) < 2 * sigma - - lines = ax1.plot(list_x[mask1], predicted_normalized_scores[mask1], lw=1, label='Prediction') - color = lines[0].get_color() - ax1.plot(list_x[mask2], predicted_normalized_scores[mask2], lw=1, color=color, label='_none_') - - ax1.plot(list_x[mask1], sigma_normalized_kernel[mask1], '--', lw=2, color=color, label='Target') - ax1.plot(list_x[mask2], sigma_normalized_kernel[mask2], '--', lw=2, color=color, label='_none_') - - ax2.plot(list_x[mask1], error[mask1], '-', color=color, - label=f't = {time:4.3f}, $\\sigma$ = {sigma:4.3f}') - ax2.plot(list_x[mask2], error[mask2], '-', color=color, label='_none_') - - for ax in [ax1, ax2]: - ax.set_xlim([-0.05, 1.05]) - ax.legend(loc=3, prop={'size': 6}) - - ax1.set_ylim([-3., 3.]) - ax2.set_ylim([-1., 1.]) - - fig.tight_layout() - - tbx_logger.add_figure("train/scores", fig, global_step=pl_module.global_step) - - -class TensorboardHistogramLoggingCallback(TensorBoardDebuggingLoggingCallback): - """This callback will log histograms of the predictions on tensorboard.""" - - def log_artifact(self, pl_module, tbx_logger): - """Create artifact and log to tensorboard.""" - targets = [] - predictions = [] - for output in self.training_step_outputs: - targets.append(output["target_normalized_conditional_scores"].flatten()) - predictions.append(output["predicted_normalized_scores"].flatten()) - - targets = torch.cat(targets) - predictions = torch.cat(predictions) - - tbx_logger.add_histogram( - "train/targets", targets, global_step=pl_module.global_step - ) - tbx_logger.add_histogram( - "train/predictions", predictions, global_step=pl_module.global_step - ) - tbx_logger.add_histogram( - "train/errors", targets - predictions, global_step=pl_module.global_step - ) - - -class TensorboardSamplesLoggingCallback(TensorBoardDebuggingLoggingCallback): - """This callback will log histograms of the labels, predictions and errors on tensorboard.""" - - def log_artifact(self, pl_module, tbx_logger): - """Create artifact and log to tensorboard.""" - list_xt = [] - list_sigmas = [] - for output in self.training_step_outputs: - list_xt.append(output[NOISY_RELATIVE_COORDINATES].flatten()) - list_sigmas.append(output["sigmas"].flatten()) - list_xt = torch.cat(list_xt) - list_sigmas = torch.cat(list_sigmas) - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - ax = fig.add_subplot(111) - ax.set_title(f"Position Samples: global step = {pl_module.global_step}") - ax.set_ylabel("$\\sigma$") - ax.set_xlabel("position samples $x(t)$") - ax.plot(list_xt, list_sigmas, "bo") - ax.set_xlim([-0.05, 1.05]) - fig.tight_layout() - tbx_logger.add_figure("train/samples", fig, global_step=pl_module.global_step) From 66cb4dfe21214958e90dc34676789b44078724bc Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:36:22 -0400 Subject: [PATCH 13/29] Moved stuff. --- {sanity_checks => experiments}/generators/__init__.py | 0 .../generators/sde_generator_sanity_check.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename {sanity_checks => experiments}/generators/__init__.py (100%) rename {sanity_checks => experiments}/generators/sde_generator_sanity_check.py (98%) diff --git a/sanity_checks/generators/__init__.py b/experiments/generators/__init__.py similarity index 100% rename from sanity_checks/generators/__init__.py rename to experiments/generators/__init__.py diff --git a/sanity_checks/generators/sde_generator_sanity_check.py b/experiments/generators/sde_generator_sanity_check.py similarity index 98% rename from sanity_checks/generators/sde_generator_sanity_check.py rename to experiments/generators/sde_generator_sanity_check.py index 94643a6d..9c891d0d 100644 --- a/sanity_checks/generators/sde_generator_sanity_check.py +++ b/experiments/generators/sde_generator_sanity_check.py @@ -17,7 +17,7 @@ from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell -from sanity_checks.generators import GENERATOR_SANITY_CHECK_DIRECTORY +from experiments.generators import GENERATOR_SANITY_CHECK_DIRECTORY plt.style.use(PLOT_STYLE_PATH) From 3564331b2f4a77bcdc064b0dcae032a507f25da6 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:50:46 -0400 Subject: [PATCH 14/29] Moved stuff. --- .../analysis/analytic_score/__init__.py | 0 .../analytical_score_sampling_and_plotting.py | 9 ++++----- .../exploring_langevin_generator/__init__.py | 0 .../generate_sample_energies.py | 8 ++++---- .../exploring_langevin_generator/plot_sample_energies.py | 2 +- .../analytic_score/perfect_score_loss_analysis.py | 4 ++-- .../analysis/analytic_score}/repaint/__init__.py | 0 .../plot_repaint_analytical_score_trajectories.py | 5 +++-- .../repaint/repaint_with_analytic_score.py | 5 +++-- .../analytic_score/score_convergence_analysis.py | 5 ++--- .../analysis/analytic_score/utils.py | 0 .../ad_hoc_experiments_with_various_score_networks.py | 8 ++++---- .../overfit_diffusion_mace.py | 4 ++-- experiments/generators/sde_generator_sanity_check.py | 2 +- .../draw_samples_from_equilibrium.py | 7 +++---- .../score_stability_analysis/plot_hessian_eigenvalues.py | 3 +-- experiments/score_stability_analysis/plot_score_norm.py | 3 +-- 17 files changed, 31 insertions(+), 34 deletions(-) rename {crystal_diffusion => experiments}/analysis/analytic_score/__init__.py (100%) rename {crystal_diffusion => experiments}/analysis/analytic_score/analytical_score_sampling_and_plotting.py (98%) rename {crystal_diffusion => experiments}/analysis/analytic_score/exploring_langevin_generator/__init__.py (100%) rename {crystal_diffusion => experiments}/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py (97%) rename {crystal_diffusion => experiments}/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py (96%) rename {crystal_diffusion => experiments}/analysis/analytic_score/perfect_score_loss_analysis.py (98%) rename {crystal_diffusion/analysis => experiments/analysis/analytic_score}/repaint/__init__.py (100%) rename {crystal_diffusion/analysis => experiments/analysis/analytic_score}/repaint/plot_repaint_analytical_score_trajectories.py (96%) rename {crystal_diffusion/analysis => experiments/analysis/analytic_score}/repaint/repaint_with_analytic_score.py (96%) rename {crystal_diffusion => experiments}/analysis/analytic_score/score_convergence_analysis.py (96%) rename {crystal_diffusion => experiments}/analysis/analytic_score/utils.py (100%) diff --git a/crystal_diffusion/analysis/analytic_score/__init__.py b/experiments/analysis/analytic_score/__init__.py similarity index 100% rename from crystal_diffusion/analysis/analytic_score/__init__.py rename to experiments/analysis/analytic_score/__init__.py diff --git a/crystal_diffusion/analysis/analytic_score/analytical_score_sampling_and_plotting.py b/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py similarity index 98% rename from crystal_diffusion/analysis/analytic_score/analytical_score_sampling_and_plotting.py rename to experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py index 605bc592..0d34dae9 100644 --- a/crystal_diffusion/analysis/analytic_score/analytical_score_sampling_and_plotting.py +++ b/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py @@ -14,11 +14,6 @@ from einops import einops from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score import \ - ANALYTIC_SCORE_RESULTS_DIR -from crystal_diffusion.analysis.analytic_score.utils import ( - get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell, - get_unit_cells) from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) @@ -28,6 +23,10 @@ AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from experiments.analysis.analytic_score import ANALYTIC_SCORE_RESULTS_DIR +from experiments.analysis.analytic_score.utils import ( + get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell, + get_unit_cells) logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/__init__.py b/experiments/analysis/analytic_score/exploring_langevin_generator/__init__.py similarity index 100% rename from crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/__init__.py rename to experiments/analysis/analytic_score/exploring_langevin_generator/__init__.py diff --git a/crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py b/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py similarity index 97% rename from crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py rename to experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py index 64d5c4fc..ac991d62 100644 --- a/crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py +++ b/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py @@ -7,10 +7,6 @@ import torch from tqdm import tqdm -from crystal_diffusion.analysis.analytic_score.exploring_langevin_generator import \ - LANGEVIN_EXPLORATION_DIRECTORY -from crystal_diffusion.analysis.analytic_score.utils import ( - get_exact_samples, get_silicon_supercell) from crystal_diffusion.callbacks.sampling_visualization_callback import logger from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.predictor_corrector_position_generator import \ @@ -21,6 +17,10 @@ from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import ( get_positions_from_coordinates, map_relative_coordinates_to_unit_cell) +from experiments.analysis.analytic_score.exploring_langevin_generator import \ + LANGEVIN_EXPLORATION_DIRECTORY +from experiments.analysis.analytic_score.utils import (get_exact_samples, + get_silicon_supercell) class EnergyCalculator: diff --git a/crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py b/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py similarity index 96% rename from crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py rename to experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py index be2838fc..36def5d5 100644 --- a/crystal_diffusion/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py +++ b/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py @@ -4,7 +4,7 @@ import numpy as np from crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.exploring_langevin_generator import \ +from experiments.analysis.analytic_score.exploring_langevin_generator import \ LANGEVIN_EXPLORATION_DIRECTORY plt.style.use(PLOT_STYLE_PATH) diff --git a/crystal_diffusion/analysis/analytic_score/perfect_score_loss_analysis.py b/experiments/analysis/analytic_score/perfect_score_loss_analysis.py similarity index 98% rename from crystal_diffusion/analysis/analytic_score/perfect_score_loss_analysis.py rename to experiments/analysis/analytic_score/perfect_score_loss_analysis.py index 182ac5c4..2b3af48c 100644 --- a/crystal_diffusion/analysis/analytic_score/perfect_score_loss_analysis.py +++ b/experiments/analysis/analytic_score/perfect_score_loss_analysis.py @@ -12,8 +12,6 @@ from crystal_diffusion import ANALYSIS_RESULTS_DIR from crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import ( - get_exact_samples, get_silicon_supercell) from crystal_diffusion.callbacks.loss_monitoring_callback import \ LossMonitoringCallback from crystal_diffusion.callbacks.sampling_visualization_callback import \ @@ -38,6 +36,8 @@ ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from experiments.analysis.analytic_score.utils import (get_exact_samples, + get_silicon_supercell) logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/analysis/repaint/__init__.py b/experiments/analysis/analytic_score/repaint/__init__.py similarity index 100% rename from crystal_diffusion/analysis/repaint/__init__.py rename to experiments/analysis/analytic_score/repaint/__init__.py diff --git a/crystal_diffusion/analysis/repaint/plot_repaint_analytical_score_trajectories.py b/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py similarity index 96% rename from crystal_diffusion/analysis/repaint/plot_repaint_analytical_score_trajectories.py rename to experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py index 88ebc35a..c7d58a8b 100644 --- a/crystal_diffusion/analysis/repaint/plot_repaint_analytical_score_trajectories.py +++ b/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py @@ -9,9 +9,10 @@ from crystal_diffusion import ANALYSIS_RESULTS_DIR from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import ( - get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell) from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from experiments.analysis.analytic_score import (get_exact_samples, + get_samples_harmonic_energy, + get_silicon_supercell) logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/crystal_diffusion/analysis/repaint/repaint_with_analytic_score.py b/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py similarity index 96% rename from crystal_diffusion/analysis/repaint/repaint_with_analytic_score.py rename to experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py index 7d81dbcc..65312ea1 100644 --- a/crystal_diffusion/analysis/repaint/repaint_with_analytic_score.py +++ b/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py @@ -6,8 +6,6 @@ from crystal_diffusion import ANALYSIS_RESULTS_DIR from crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import ( - get_samples_harmonic_energy, get_silicon_supercell, get_unit_cells) from crystal_diffusion.generators.constrained_langevin_generator import ( ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) from crystal_diffusion.models.score_networks.analytical_score_network import ( @@ -15,6 +13,9 @@ from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger from crystal_diffusion.utils.structure_utils import create_structure +from experiments.analysis.analytic_score import (get_samples_harmonic_energy, + get_silicon_supercell, + get_unit_cells) logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/crystal_diffusion/analysis/analytic_score/score_convergence_analysis.py b/experiments/analysis/analytic_score/score_convergence_analysis.py similarity index 96% rename from crystal_diffusion/analysis/analytic_score/score_convergence_analysis.py rename to experiments/analysis/analytic_score/score_convergence_analysis.py index a057b037..aad84e78 100644 --- a/crystal_diffusion/analysis/analytic_score/score_convergence_analysis.py +++ b/experiments/analysis/analytic_score/score_convergence_analysis.py @@ -9,13 +9,12 @@ from einops import einops from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score import \ - ANALYTIC_SCORE_RESULTS_DIR -from crystal_diffusion.analysis.analytic_score.utils import get_unit_cells from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from experiments.analysis.analytic_score import ANALYTIC_SCORE_RESULTS_DIR +from experiments.analysis.analytic_score.utils import get_unit_cells logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/analysis/analytic_score/utils.py b/experiments/analysis/analytic_score/utils.py similarity index 100% rename from crystal_diffusion/analysis/analytic_score/utils.py rename to experiments/analysis/analytic_score/utils.py diff --git a/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py b/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py index cc54bbdb..026045d2 100644 --- a/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py +++ b/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py @@ -8,10 +8,6 @@ from torch.utils.data import DataLoader from crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import ( - get_exact_samples, get_relative_harmonic_energy) -from crystal_diffusion.callbacks.analysis_callbacks import \ - HarmonicEnergyDiffusionSamplingCallback from crystal_diffusion.callbacks.callback_loader import create_all_callbacks from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.models.optimizer import OptimizerParameters @@ -29,6 +25,10 @@ MaceEquivariantScorePredictionHeadParameters from crystal_diffusion.namespace import CARTESIAN_FORCES, RELATIVE_COORDINATES from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from experiments.analysis.analytic_score import (get_exact_samples, + get_relative_harmonic_energy) +from experiments.diffusion_mace_harmonic_data.analysis_callbacks import \ + HarmonicEnergyDiffusionSamplingCallback logger = logging.getLogger(__name__) diff --git a/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py b/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py index 13a7cb46..11879e90 100644 --- a/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py +++ b/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py @@ -11,8 +11,6 @@ from torch import optim from torch.utils.data import DataLoader -from crystal_diffusion.analysis.analytic_score.utils import (get_exact_samples, - get_unit_cells) from crystal_diffusion.callbacks.standard_callbacks import CustomProgressBar from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) @@ -29,6 +27,8 @@ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions +from experiments.analysis.analytic_score import (get_exact_samples, + get_unit_cells) torch.set_default_dtype(torch.float64) diff --git a/experiments/generators/sde_generator_sanity_check.py b/experiments/generators/sde_generator_sanity_check.py index 9c891d0d..ef4e1554 100644 --- a/experiments/generators/sde_generator_sanity_check.py +++ b/experiments/generators/sde_generator_sanity_check.py @@ -9,7 +9,6 @@ from matplotlib import pyplot as plt from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import get_exact_samples from crystal_diffusion.generators.sde_position_generator import ( ExplodingVarianceSDEPositionGenerator, SDESamplingParameters) from crystal_diffusion.models.score_networks.analytical_score_network import ( @@ -17,6 +16,7 @@ from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from experiments.analysis.analytic_score.utils import get_exact_samples from experiments.generators import GENERATOR_SANITY_CHECK_DIRECTORY plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/score_stability_analysis/draw_samples_from_equilibrium.py b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py index 700b0b4c..0dc28164 100644 --- a/experiments/score_stability_analysis/draw_samples_from_equilibrium.py +++ b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py @@ -6,10 +6,6 @@ import torch from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.exploring_langevin_generator.generate_sample_energies import \ - EnergyCalculator -from crystal_diffusion.analysis.analytic_score.utils import \ - get_silicon_supercell from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) @@ -22,6 +18,9 @@ from crystal_diffusion.models.score_networks import ScoreNetwork from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from experiments.analysis.analytic_score.exploring_langevin_generator.generate_sample_energies import \ + EnergyCalculator +from experiments.analysis.analytic_score.utils import get_silicon_supercell plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/score_stability_analysis/plot_hessian_eigenvalues.py b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py index 6fd5a0a4..dcfbe27c 100644 --- a/experiments/score_stability_analysis/plot_hessian_eigenvalues.py +++ b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py @@ -9,14 +9,13 @@ from tqdm import tqdm from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import \ - get_silicon_supercell from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.samplers.exploding_variance import ExplodingVariance from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger from experiments import get_normalized_score_function +from experiments.analysis.analytic_score.utils import get_silicon_supercell plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/score_stability_analysis/plot_score_norm.py b/experiments/score_stability_analysis/plot_score_norm.py index 7236d8e3..57f050df 100644 --- a/experiments/score_stability_analysis/plot_score_norm.py +++ b/experiments/score_stability_analysis/plot_score_norm.py @@ -7,8 +7,6 @@ from tqdm import tqdm from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import \ - get_silicon_supercell from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.samplers.exploding_variance import ExplodingVariance @@ -17,6 +15,7 @@ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.logging_utils import setup_analysis_logger from experiments import create_fixed_time_normalized_score_function +from experiments.analysis.analytic_score.utils import get_silicon_supercell plt.style.use(PLOT_STYLE_PATH) From 683daa9d2caa10ba7aa19a8191290dcfa1453c6f Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:50:55 -0400 Subject: [PATCH 15/29] Moved stuff. --- .../diffusion_mace_harmonic_data}/analysis_callbacks.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) rename {crystal_diffusion/callbacks => experiments/diffusion_mace_harmonic_data}/analysis_callbacks.py (96%) diff --git a/crystal_diffusion/callbacks/analysis_callbacks.py b/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py similarity index 96% rename from crystal_diffusion/callbacks/analysis_callbacks.py rename to experiments/diffusion_mace_harmonic_data/analysis_callbacks.py index 7785ec3d..28e417c3 100644 --- a/crystal_diffusion/callbacks/analysis_callbacks.py +++ b/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py @@ -10,12 +10,11 @@ import torch from crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.analysis.analytic_score.utils import \ - get_relative_harmonic_energy from crystal_diffusion.callbacks.sampling_visualization_callback import \ SamplingVisualizationCallback from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from experiments.analysis.analytic_score import get_relative_harmonic_energy logger = logging.getLogger(__name__) From 8882e700ada21ae810fc846b3d944cebc02a0755 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:55:02 -0400 Subject: [PATCH 16/29] Moving more ad hoc scripts out of the way. --- .../diffusion_sample_position_analysis.py | 0 .../analysis/exploding_variance_analysis.py | 0 .../analysis/perturbation_kernel_analysis.py | 0 .../analysis/plot_diffusion_poster_image.py | 44 ++++++++++++++ experiments/analysis/plot_repulsive_force.py | 60 +++++++++++++++++++ .../plotting_gaussian_vs_wrapped_gaussian.py | 0 .../analysis/target_score_analysis.py | 0 7 files changed, 104 insertions(+) rename {crystal_diffusion => experiments}/analysis/diffusion_sample_position_analysis.py (100%) rename {crystal_diffusion => experiments}/analysis/exploding_variance_analysis.py (100%) rename {crystal_diffusion => experiments}/analysis/perturbation_kernel_analysis.py (100%) create mode 100644 experiments/analysis/plot_diffusion_poster_image.py create mode 100644 experiments/analysis/plot_repulsive_force.py rename {crystal_diffusion => experiments}/analysis/plotting_gaussian_vs_wrapped_gaussian.py (100%) rename {crystal_diffusion => experiments}/analysis/target_score_analysis.py (100%) diff --git a/crystal_diffusion/analysis/diffusion_sample_position_analysis.py b/experiments/analysis/diffusion_sample_position_analysis.py similarity index 100% rename from crystal_diffusion/analysis/diffusion_sample_position_analysis.py rename to experiments/analysis/diffusion_sample_position_analysis.py diff --git a/crystal_diffusion/analysis/exploding_variance_analysis.py b/experiments/analysis/exploding_variance_analysis.py similarity index 100% rename from crystal_diffusion/analysis/exploding_variance_analysis.py rename to experiments/analysis/exploding_variance_analysis.py diff --git a/crystal_diffusion/analysis/perturbation_kernel_analysis.py b/experiments/analysis/perturbation_kernel_analysis.py similarity index 100% rename from crystal_diffusion/analysis/perturbation_kernel_analysis.py rename to experiments/analysis/perturbation_kernel_analysis.py diff --git a/experiments/analysis/plot_diffusion_poster_image.py b/experiments/analysis/plot_diffusion_poster_image.py new file mode 100644 index 00000000..061197a8 --- /dev/null +++ b/experiments/analysis/plot_diffusion_poster_image.py @@ -0,0 +1,44 @@ +import matplotlib.pyplot as plt +import numpy as np + +if __name__ == "__main__": + x_max = 1000 + t_max = 1500 + + x = np.linspace(0, 1, x_max) + t = np.linspace(0, 1, t_max) + + T, X = np.meshgrid(t, x) + + SIGMA = 0.5 * T + 0.001 + + P = np.zeros([x_max, t_max]) + for x0 in [0.25, 0.5, 0.85]: + P += np.exp(-((X - x0) ** 2) / SIGMA**2) + + noise = np.random.randn(100) + list_times = np.linspace(0, t_max - 1, 100) + + noise_scale = 0.05 * list_times / t_max + list_x = (0.5 + noise_scale * np.cumsum(noise)) * x_max + + fig1 = plt.figure(figsize=(6.5, 4)) + fig2 = plt.figure(figsize=(6.5, 4)) + + ax1 = fig1.add_subplot(111) + ax2 = fig2.add_subplot(111) + ax1.imshow(P) + ax2.imshow(P[:, ::-1]) + + ax2.plot(list_times[::-1], list_x, "r-", lw=3) + for ax in [ax1, ax2]: + ax.set_xticks([]) + ax.set_yticks([]) + + fig1.tight_layout() + fig2.tight_layout() + + fig1.savefig("forward_diffusion.png") + fig2.savefig("backward_diffusion.png") + + plt.show() diff --git a/experiments/analysis/plot_repulsive_force.py b/experiments/analysis/plot_repulsive_force.py new file mode 100644 index 00000000..f6e0a379 --- /dev/null +++ b/experiments/analysis/plot_repulsive_force.py @@ -0,0 +1,60 @@ +import torch +from matplotlib import pyplot as plt + +from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from crystal_diffusion.models.score_networks.force_field_augmented_score_network import ( + ForceFieldAugmentedScoreNetwork, ForceFieldParameters) +from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES, UNIT_CELL + +plt.style.use(PLOT_STYLE_PATH) + +natoms = 2 +spatial_dimension = 3 +batch_size = 1 + + +radial_cutoff = 1.5 +strength = 20 + +acell = 10.86 + +force_field_parameters = ForceFieldParameters( + radial_cutoff=radial_cutoff, strength=strength +) + +if __name__ == "__main__": + force_field_score_network = ForceFieldAugmentedScoreNetwork( + score_network=None, force_field_parameters=force_field_parameters + ) + + list_x = torch.linspace(0.0, 1.0, 1001) + list_f = [] + + for x in list_x: + relative_coordinates = torch.tensor( + [[[0.5 - 0.5 * x, 0.5, 0.0], [0.5 + 0.5 * x, 0.5, 0.0]]] + ) + + basis_vectors = acell * torch.diag(torch.ones(spatial_dimension)).unsqueeze(0) + batch = { + NOISY_RELATIVE_COORDINATES: relative_coordinates, + UNIT_CELL: basis_vectors, + } + forces = force_field_score_network.get_relative_coordinates_pseudo_force(batch) + f = torch.abs(forces[0, 0, 0]) + list_f.append(f) + + list_f = torch.tensor(list_f) + + fig = plt.figure(figsize=PLEASANT_FIG_SIZE) + fig.suptitle( + f"Repulsive Pseudo Force for Strength = {strength} and radial cutoff = {radial_cutoff} $\\AA$" + ) + ax = fig.add_subplot(111) + + ax.plot(acell * list_x, list_f, "b-") + ax.set_xlim(0, 4) + ax.set_ylim(ymin=-0.1) + ax.set_xlabel(r"Interatomic Distance ($\AA$)") + ax.set_ylabel("Magnitude of Pseudo Force") + plt.show() diff --git a/crystal_diffusion/analysis/plotting_gaussian_vs_wrapped_gaussian.py b/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py similarity index 100% rename from crystal_diffusion/analysis/plotting_gaussian_vs_wrapped_gaussian.py rename to experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py diff --git a/crystal_diffusion/analysis/target_score_analysis.py b/experiments/analysis/target_score_analysis.py similarity index 100% rename from crystal_diffusion/analysis/target_score_analysis.py rename to experiments/analysis/target_score_analysis.py From 20efccb3ef5f3ff9540240b40526bcd99399c668 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 16:57:31 -0400 Subject: [PATCH 17/29] Remove needless scripts. --- .../analysis/positions_to_cif_files.py | 50 ----------- .../timing_adjacency_matrix_calculations.py | 82 ------------------- 2 files changed, 132 deletions(-) delete mode 100644 crystal_diffusion/analysis/positions_to_cif_files.py delete mode 100644 crystal_diffusion/analysis/timing_adjacency_matrix_calculations.py diff --git a/crystal_diffusion/analysis/positions_to_cif_files.py b/crystal_diffusion/analysis/positions_to_cif_files.py deleted file mode 100644 index 994c7d9d..00000000 --- a/crystal_diffusion/analysis/positions_to_cif_files.py +++ /dev/null @@ -1,50 +0,0 @@ -"""Position to cif files. - -A simple script to extract the diffusion positions from a pickle on disk and output -in cif format for visualization. -""" - -from pymatgen.core import Lattice, Structure - -from crystal_diffusion import TOP_DIR -from crystal_diffusion.utils.sample_trajectory import \ - PredictorCorrectorSampleTrajectory - -# Hard coding some paths to local results. Modify as needed... -epoch = 35 -sample_idx = 0 -experiment_name = "mace_plus_prediction_head/run2" -# experiment_name = "mlp/run1" - -trajectory_top_output_directory = TOP_DIR / "experiments/diffusion_mace_harmonic_data/output/" - -trajectory_data_directory = trajectory_top_output_directory / experiment_name / "diffusion_position_samples" - -output_top_dir = trajectory_data_directory.parent / "visualization" -output_dir = output_top_dir / f"visualise_sampling_trajectory_epoch_{epoch}_sample_{sample_idx}" -output_dir.mkdir(exist_ok=True, parents=True) - -if __name__ == '__main__': - pickle_path = trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}.pt" - sample_trajectory = PredictorCorrectorSampleTrajectory.read_from_pickle(pickle_path) - - pickle_path = trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}.pt" - sample_trajectory = PredictorCorrectorSampleTrajectory.read_from_pickle(pickle_path) - - basis_vectors = sample_trajectory.data['unit_cell'][sample_idx].numpy() - lattice = Lattice(matrix=basis_vectors, pbc=(True, True, True)) - - list_predictor_coordinates = sample_trajectory.data['predictor_x_i'] - - for idx, sample_predictor_coordinates in enumerate(list_predictor_coordinates): - coordinates = sample_predictor_coordinates[sample_idx].numpy() - number_of_atoms = coordinates.shape[0] - species = number_of_atoms * ['Si'] - - structure = Structure(lattice=lattice, - species=species, - coords=coordinates, - coords_are_cartesian=False) - - file_path = str(output_dir / f"diffusion_positions_{idx}.cif") - structure.to_file(file_path) diff --git a/crystal_diffusion/analysis/timing_adjacency_matrix_calculations.py b/crystal_diffusion/analysis/timing_adjacency_matrix_calculations.py deleted file mode 100644 index 6608e588..00000000 --- a/crystal_diffusion/analysis/timing_adjacency_matrix_calculations.py +++ /dev/null @@ -1,82 +0,0 @@ -import logging -import time - -import matplotlib.pyplot as plt -import numpy as np -import torch - -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from crystal_diffusion.utils.neighbors import \ - get_periodic_adjacency_information - -plt.style.use(PLOT_STYLE_PATH) - -logger = logging.getLogger(__name__) - - -def create_basis_vectors(batch_size, min_dimension, max_dimension): - """Create random basis vectors.""" - delta = max_dimension - min_dimension - # orthogonal boxes with dimensions between min_dimension and max_dimension. - orthogonal_boxes = torch.stack([torch.diag(min_dimension + delta * torch.rand(3)) for _ in range(batch_size)]) - # add a bit of noise to make the vectors not quite orthogonal - basis_vectors = orthogonal_boxes + 0.1 * torch.randn(batch_size, 3, 3) - return basis_vectors - - -batch_size = 64 -powers = np.arange(3, 13) - -min_dimension = 5 -max_dimension = 8 - -dimension_increasing_factor = 2**(1. / 3.) # this keeps the mean volume per atom roughly constant. - -cutoff = 4. -if __name__ == '__main__': - setup_analysis_logger() - - if torch.cuda.is_available(): - device_type = 'GPU' - device = torch.device(type='cuda') - else: - device_type = 'CPU' - device = torch.device(type='cpu') - - for _ in range(2): - # A first pass for KeOps compilation - torch.manual_seed(1234) - list_natoms = [] - list_timing = [] - for it, power in enumerate(powers, 1): - natom = 2 ** power - list_natoms.append(natom) - logging.info(f"Doing {natom} atoms ...") - basis_vectors = create_basis_vectors(batch_size, min_dimension, max_dimension).to(device) - min_dimension = dimension_increasing_factor * min_dimension - max_dimension = dimension_increasing_factor * max_dimension - - relative_coordinates = torch.rand(batch_size, natom, 3).to(device) - - t1 = time.time() - _ = get_periodic_adjacency_information(relative_coordinates, basis_vectors, cutoff) - t2 = time.time() - dt = t2 - t1 - list_timing.append(t2 - t1) - logging.info(f" - It took {dt: 8.4e} seconds to generate A ({dt/batch_size: 8.4e} sec/structure)") - - list_natoms = np.array(list_natoms) - list_relative_timing = np.array(list_timing) / batch_size - - fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle(f"Adjacency Computation Time on {device_type} per Structure\n" - f"Random Coordinates, Batch Size of {batch_size}") - ax = fig.add_subplot(111) - ax.set_xlabel("Number of Atoms") - ax.set_ylabel("Time") - ax.loglog(list_natoms, list_relative_timing, 'y-o', alpha=0.5, label='A Calculation Times') - ax.legend(loc=0) - fig.tight_layout() - fig.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"timing_tests_{device_type}_adjacency_matrix.png")) From 30eb40c95b27d312cfaacd817bfdb9361edf9fc8 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 20:49:08 -0400 Subject: [PATCH 18/29] Changing structure. --- examples/drawing_samples/draw_samples.py | 5 ++--- .../analytical_score_sampling_and_plotting.py | 8 ++++---- .../generate_sample_energies.py | 6 +++--- .../plot_sample_energies.py | 2 +- .../analytic_score/perfect_score_loss_analysis.py | 14 +++++++------- .../plot_repaint_analytical_score_trajectories.py | 6 +++--- .../repaint/repaint_with_analytic_score.py | 6 +++--- .../analytic_score/score_convergence_analysis.py | 6 +++--- .../analysis/diffusion_sample_position_analysis.py | 3 +-- .../analysis/exploding_variance_analysis.py | 7 +++---- .../analysis/perturbation_kernel_analysis.py | 3 +-- experiments/analysis/plot_repulsive_force.py | 5 ++--- .../plotting_gaussian_vs_wrapped_gaussian.py | 5 ++--- experiments/analysis/target_score_analysis.py | 3 +-- experiments/analysis_utils.py | 5 +++-- experiments/dataset_analysis/dataset_covariance.py | 3 +-- .../energy_consistency_analysis.py | 6 +++--- experiments/dataset_analysis/plot_si_phonon_DOS.py | 3 +-- .../dataset_analysis/si_diffusion_analysis.py | 4 ++-- .../dataset_analysis/si_diffusion_v1_analysis.py | 5 ++--- ..._hoc_experiments_with_various_score_networks.py | 13 +++++++------ .../analysis_callbacks.py | 9 +++++---- .../overfit_diffusion_mace.py | 12 ++++++------ .../generators/sde_generator_sanity_check.py | 8 ++++---- .../plot_repaint_score_trajectories.py | 5 ++--- .../sampling_sota_model/repaint_with_sota_score.py | 5 ++--- .../sota_score_sampling_and_plotting.py | 7 +++---- .../draw_samples_from_equilibrium.py | 6 +++--- .../plot_hessian_eigenvalues.py | 10 +++++----- .../score_stability_analysis/plot_score_norm.py | 8 ++++---- experiments/score_stability_analysis/util.py | 3 +-- .../__init__.py | 0 .../activelearning_dataclasses.py | 0 .../active_learning_loop/benchmark.py | 3 +-- .../active_learning_loop/oracle.py | 1 - .../active_learning_loop/utils.py | 0 .../analysis/README_ovito.md | 0 .../analysis/__init__.py | 0 .../analysis/generator_sample_analysis_utils.py | 5 ++--- .../analysis/ovito_visualisation.py | 0 .../analysis/plot_style.txt | 0 .../callbacks/__init__.py | 0 .../callbacks/callback_loader.py | 5 ++--- .../callbacks/loss_monitoring_callback.py | 5 ++--- .../callbacks/sampling_visualization_callback.py | 5 ++--- .../callbacks/standard_callbacks.py | 0 .../data/__init__.py | 0 .../data/diffusion/__init__.py | 0 .../data/diffusion/data_loader.py | 3 +-- .../data/diffusion/data_preprocess.py | 3 +-- .../data/parse_lammps_outputs.py | 0 .../data/utils.py | 0 .../generators/__init__.py | 0 .../generators/constrained_langevin_generator.py | 5 ++--- .../generators/instantiate_generator.py | 5 +++-- .../generators/langevin_generator.py | 5 ++--- .../generators/load_sampling_parameters.py | 3 ++- .../generators/ode_position_generator.py | 9 ++++----- .../generators/position_generator.py | 0 .../predictor_corrector_position_generator.py | 7 +++---- .../generators/sde_position_generator.py | 7 +++---- .../loggers/__init__.py | 0 .../loggers/logger_loader.py | 0 .../main_utils.py | 0 .../metrics/__init__.py | 0 .../metrics/kolmogorov_smirnov_metrics.py | 0 .../metrics/sampling_metrics_parameters.py | 0 .../mlip/mtp_train.py | 3 +-- .../mlip/mtp_utils.py | 0 .../models/__init__.py | 0 .../models/diffusion_mace.py | 11 +++++------ .../models/egnn.py | 3 +-- .../models/egnn_utils.py | 1 - .../models/graph_utils.py | 1 - .../models/instantiate_diffusion_model.py | 2 +- .../models/loss.py | 1 - .../models/mace_utils.py | 5 ++--- .../models/mlip/mtp.py | 7 +++---- .../models/normalized_score_fokker_planck_error.py | 5 ++--- .../models/optimizer.py | 3 +-- .../models/position_diffusion_lightning_model.py | 7 +++---- .../models/scheduler.py | 3 +-- .../models/score_networks/__init__.py | 0 .../score_networks/analytical_score_network.py | 1 - .../score_networks/diffusion_mace_score_network.py | 7 +++---- .../models/score_networks/egnn_score_network.py | 1 - .../force_field_augmented_score_network.py | 1 - .../models/score_networks/mace_score_network.py | 9 ++++----- .../models/score_networks/mlp_score_network.py | 3 +-- .../models/score_networks/score_network.py | 1 - .../models/score_networks/score_network_factory.py | 0 .../models/score_networks/score_prediction_head.py | 5 ++--- .../namespace.py | 0 .../oracle/energies.py | 1 - .../oracle/lammps.py | 3 +-- .../sample_diffusion.py | 8 ++++---- .../samplers/__init__.py | 0 .../samplers/exploding_variance.py | 3 +-- .../samplers/noisy_relative_coordinates_sampler.py | 1 - .../samplers/variance_sampler.py | 0 .../samples/__init__.py | 0 .../samples/diffusion_sampling_parameters.py | 5 +++-- .../samples/sampling.py | 5 ++--- .../score/__init__.py | 0 .../score/wrapped_gaussian_score.py | 0 .../train_diffusion.py | 1 - .../utils/__init__.py | 0 .../utils/basis_transformations.py | 0 .../utils/configuration_parsing.py | 0 .../utils/file_utils.py | 0 .../utils/hp_utils.py | 0 .../utils/logging_utils.py | 0 .../utils/neighbors.py | 3 +-- .../utils/ovito_utils.py | 0 .../utils/reproducibility_utils.py | 0 .../utils/sample_trajectory.py | 0 .../utils/structure_utils.py | 5 ++--- .../utils/tensor_utils.py | 0 tests/active_learning_loop/test_benchmark.py | 1 - tests/analysis/test_ovito_visualisation.py | 5 ++--- tests/data/diffusion/test_data_loader.py | 2 +- tests/data/diffusion/test_data_preprocess.py | 2 +- tests/data/test_parse_lammps_output.py | 4 ++-- tests/data/test_utils.py | 1 - tests/fake_data_utils.py | 1 - tests/generators/conftest.py | 1 - .../test_constrained_langevin_generator.py | 2 +- tests/generators/test_langevin_generator.py | 6 +++--- tests/generators/test_ode_position_generator.py | 4 ++-- .../test_predictor_corrector_position_generator.py | 2 +- tests/generators/test_sde_position_generator.py | 4 ++-- .../test_force_field_augmented_score_network.py | 1 - tests/models/score_network/test_score_network.py | 1 - .../score_network/test_score_prediction_head.py | 3 +-- tests/models/test_analytical_score_network.py | 1 - tests/models/test_diffusion_mace.py | 5 ++--- tests/models/test_egnn.py | 1 - tests/models/test_egnn_utils.py | 1 - tests/models/test_loss.py | 1 - tests/models/test_mace_utils.py | 10 +++++----- tests/models/test_mtp.py | 5 ++--- tests/models/test_optimizer.py | 1 - .../test_position_diffusion_lightning_model.py | 7 +++---- tests/models/test_scheduler.py | 1 - tests/models/test_score_fokker_planck_error.py | 7 +++---- tests/oracle/test_lammps.py | 1 - tests/samplers/test_exploding_variance.py | 3 +-- .../test_noisy_relative_coordinates_sampler.py | 1 - tests/samplers/test_variance_sampler.py | 3 +-- tests/samples_and_metrics/test_sampling.py | 7 +++---- tests/score/test_wrapped_gaussian_score.py | 1 - tests/test_hp_utils.py | 1 - tests/test_sample_diffusion.py | 3 +-- tests/test_train_diffusion.py | 2 +- tests/utils/test_basis_transformations.py | 1 - tests/utils/test_configuration_parsing.py | 1 - tests/utils/test_neighbors.py | 4 ++-- tests/utils/test_sample_trajectory.py | 1 - tests/utils/test_structure_utils.py | 1 - tests/utils/test_tensor_utils.py | 1 - 160 files changed, 204 insertions(+), 284 deletions(-) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/__init__.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/active_learning_loop/activelearning_dataclasses.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/active_learning_loop/benchmark.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/active_learning_loop/oracle.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/active_learning_loop/utils.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/analysis/README_ovito.md (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/analysis/__init__.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/analysis/generator_sample_analysis_utils.py (97%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/analysis/ovito_visualisation.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/analysis/plot_style.txt (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/callbacks/__init__.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/callbacks/callback_loader.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/callbacks/loss_monitoring_callback.py (98%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/callbacks/sampling_visualization_callback.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/callbacks/standard_callbacks.py (100%) rename {crystal_diffusion => 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rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/generators/langevin_generator.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/generators/load_sampling_parameters.py (94%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/generators/ode_position_generator.py (98%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/generators/position_generator.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/generators/predictor_corrector_position_generator.py (98%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/generators/sde_position_generator.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/loggers/__init__.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/loggers/logger_loader.py (100%) rename {crystal_diffusion => 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{crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/scheduler.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/score_networks/__init__.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/score_networks/analytical_score_network.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/score_networks/diffusion_mace_score_network.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/score_networks/egnn_score_network.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/score_networks/force_field_augmented_score_network.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/models/score_networks/mace_score_network.py (99%) rename {crystal_diffusion => 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src/diffusion_for_multi_scale_molecular_dynamics}/train_diffusion.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/__init__.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/basis_transformations.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/configuration_parsing.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/file_utils.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/hp_utils.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/logging_utils.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/neighbors.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/ovito_utils.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/reproducibility_utils.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/sample_trajectory.py (100%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/structure_utils.py (99%) rename {crystal_diffusion => src/diffusion_for_multi_scale_molecular_dynamics}/utils/tensor_utils.py (100%) diff --git a/examples/drawing_samples/draw_samples.py b/examples/drawing_samples/draw_samples.py index 20e712a8..ab0bf130 100644 --- a/examples/drawing_samples/draw_samples.py +++ b/examples/drawing_samples/draw_samples.py @@ -9,7 +9,6 @@ import numpy as np import torch - from crystal_diffusion.generators.instantiate_generator import \ instantiate_generator from crystal_diffusion.generators.predictor_corrector_position_generator import \ @@ -17,9 +16,9 @@ from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.oracle.energies import compute_oracle_energies -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from crystal_diffusion.samples.sampling import create_batch_of_samples from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samples.sampling import create_batch_of_samples logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py b/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py index 0d34dae9..44f2b452 100644 --- a/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py +++ b/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py @@ -11,9 +11,6 @@ import matplotlib.pyplot as plt import numpy as np import torch -from einops import einops - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) @@ -21,8 +18,11 @@ PredictorCorrectorSamplingParameters from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from einops import einops +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + from experiments.analysis.analytic_score import ANALYTIC_SCORE_RESULTS_DIR from experiments.analysis.analytic_score.utils import ( get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell, diff --git a/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py b/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py index ac991d62..17ae45ed 100644 --- a/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py +++ b/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py @@ -5,8 +5,6 @@ import einops import numpy as np import torch -from tqdm import tqdm - from crystal_diffusion.callbacks.sampling_visualization_callback import logger from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.predictor_corrector_position_generator import \ @@ -14,9 +12,11 @@ from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import ( get_positions_from_coordinates, map_relative_coordinates_to_unit_cell) +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from tqdm import tqdm + from experiments.analysis.analytic_score.exploring_langevin_generator import \ LANGEVIN_EXPLORATION_DIRECTORY from experiments.analysis.analytic_score.utils import (get_exact_samples, diff --git a/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py b/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py index 36def5d5..4d897836 100644 --- a/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py +++ b/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py @@ -2,8 +2,8 @@ import matplotlib.pyplot as plt import numpy as np +from src.crystal_diffusion.analysis import PLOT_STYLE_PATH -from crystal_diffusion.analysis import PLOT_STYLE_PATH from experiments.analysis.analytic_score.exploring_langevin_generator import \ LANGEVIN_EXPLORATION_DIRECTORY diff --git a/experiments/analysis/analytic_score/perfect_score_loss_analysis.py b/experiments/analysis/analytic_score/perfect_score_loss_analysis.py index 2b3af48c..d9cdf106 100644 --- a/experiments/analysis/analytic_score/perfect_score_loss_analysis.py +++ b/experiments/analysis/analytic_score/perfect_score_loss_analysis.py @@ -6,12 +6,7 @@ import numpy as np import pytorch_lightning as pl import torch -from pytorch_lightning.loggers import CSVLogger -from torch.utils.data import DataLoader -from tqdm import tqdm - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLOT_STYLE_PATH from crystal_diffusion.callbacks.loss_monitoring_callback import \ LossMonitoringCallback from crystal_diffusion.callbacks.sampling_visualization_callback import \ @@ -32,10 +27,15 @@ from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from pytorch_lightning.loggers import CSVLogger +from src.crystal_diffusion.analysis import PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) +from torch.utils.data import DataLoader +from tqdm import tqdm + from experiments.analysis.analytic_score.utils import (get_exact_samples, get_silicon_supercell) diff --git a/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py b/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py index c7d58a8b..aa23103e 100644 --- a/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py +++ b/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py @@ -5,11 +5,11 @@ import matplotlib.pyplot as plt import numpy as np import torch -from einops import einops - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from einops import einops +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + from experiments.analysis.analytic_score import (get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell) diff --git a/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py b/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py index 65312ea1..c4667eb4 100644 --- a/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py +++ b/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py @@ -3,16 +3,16 @@ import matplotlib.pyplot as plt import numpy as np import torch - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLOT_STYLE_PATH from crystal_diffusion.generators.constrained_langevin_generator import ( ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger from crystal_diffusion.utils.structure_utils import create_structure +from src.crystal_diffusion.analysis import PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + from experiments.analysis.analytic_score import (get_samples_harmonic_energy, get_silicon_supercell, get_unit_cells) diff --git a/experiments/analysis/analytic_score/score_convergence_analysis.py b/experiments/analysis/analytic_score/score_convergence_analysis.py index aad84e78..1e61ff45 100644 --- a/experiments/analysis/analytic_score/score_convergence_analysis.py +++ b/experiments/analysis/analytic_score/score_convergence_analysis.py @@ -6,13 +6,13 @@ import matplotlib.pyplot as plt import torch -from einops import einops - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from einops import einops +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + from experiments.analysis.analytic_score import ANALYTIC_SCORE_RESULTS_DIR from experiments.analysis.analytic_score.utils import get_unit_cells diff --git a/experiments/analysis/diffusion_sample_position_analysis.py b/experiments/analysis/diffusion_sample_position_analysis.py index 0afacbc6..1986ed48 100644 --- a/experiments/analysis/diffusion_sample_position_analysis.py +++ b/experiments/analysis/diffusion_sample_position_analysis.py @@ -6,11 +6,10 @@ import matplotlib.pyplot as plt import numpy as np import torch - from crystal_diffusion import TOP_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.utils.sample_trajectory import \ PredictorCorrectorSampleTrajectory +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis/exploding_variance_analysis.py b/experiments/analysis/exploding_variance_analysis.py index c7c02560..0520683b 100644 --- a/experiments/analysis/exploding_variance_analysis.py +++ b/experiments/analysis/exploding_variance_analysis.py @@ -5,13 +5,12 @@ """ import matplotlib.pyplot as plt import torch - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.score.wrapped_gaussian_score import \ get_sigma_normalized_score +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis/perturbation_kernel_analysis.py b/experiments/analysis/perturbation_kernel_analysis.py index ce453bb3..25d2de3a 100644 --- a/experiments/analysis/perturbation_kernel_analysis.py +++ b/experiments/analysis/perturbation_kernel_analysis.py @@ -5,11 +5,10 @@ """ import matplotlib.pyplot as plt import torch - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.score.wrapped_gaussian_score import \ get_sigma_normalized_score +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis/plot_repulsive_force.py b/experiments/analysis/plot_repulsive_force.py index f6e0a379..d5e50b97 100644 --- a/experiments/analysis/plot_repulsive_force.py +++ b/experiments/analysis/plot_repulsive_force.py @@ -1,10 +1,9 @@ import torch -from matplotlib import pyplot as plt - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.models.score_networks.force_field_augmented_score_network import ( ForceFieldAugmentedScoreNetwork, ForceFieldParameters) from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES, UNIT_CELL +from matplotlib import pyplot as plt +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py b/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py index 0d073ccc..7b13971a 100644 --- a/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py +++ b/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py @@ -5,10 +5,9 @@ """ import matplotlib.pyplot as plt import numpy as np -from scipy.stats import norm - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from scipy.stats import norm +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis/target_score_analysis.py b/experiments/analysis/target_score_analysis.py index b4cfc30f..0bca3889 100644 --- a/experiments/analysis/target_score_analysis.py +++ b/experiments/analysis/target_score_analysis.py @@ -6,12 +6,11 @@ import matplotlib.pyplot as plt import numpy as np import torch - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.score.wrapped_gaussian_score import ( SIGMA_THRESHOLD, get_sigma_normalized_score, get_sigma_normalized_score_brute_force) +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis_utils.py b/experiments/analysis_utils.py index 156de546..c3452bee 100644 --- a/experiments/analysis_utils.py +++ b/experiments/analysis_utils.py @@ -4,9 +4,10 @@ from typing import Tuple import pandas as pd - from crystal_diffusion import DATA_DIR -from crystal_diffusion.data.parse_lammps_outputs import parse_lammps_thermo_log +from src.crystal_diffusion.data.parse_lammps_outputs import \ + parse_lammps_thermo_log + from experiments import EXPERIMENT_ANALYSIS_DIR logger = logging.getLogger(__name__) diff --git a/experiments/dataset_analysis/dataset_covariance.py b/experiments/dataset_analysis/dataset_covariance.py index eed61e7e..83b95dc6 100644 --- a/experiments/dataset_analysis/dataset_covariance.py +++ b/experiments/dataset_analysis/dataset_covariance.py @@ -8,14 +8,13 @@ import einops import torch -from tqdm import tqdm - from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR from crystal_diffusion.data.diffusion.data_loader import ( LammpsForDiffusionDataModule, LammpsLoaderParameters) from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from tqdm import tqdm logger = logging.getLogger(__name__) dataset_name = 'si_diffusion_2x2x2' diff --git a/experiments/dataset_analysis/energy_consistency_analysis.py b/experiments/dataset_analysis/energy_consistency_analysis.py index 1c6113f0..4c32c45d 100644 --- a/experiments/dataset_analysis/energy_consistency_analysis.py +++ b/experiments/dataset_analysis/energy_consistency_analysis.py @@ -11,16 +11,16 @@ import matplotlib.pyplot as plt import numpy as np -from tqdm import tqdm - from crystal_diffusion import DATA_DIR -from crystal_diffusion.analysis import PLOT_STYLE_PATH from crystal_diffusion.callbacks.sampling_visualization_callback import ( LOGGER_FIGSIZE, SamplingVisualizationCallback) from crystal_diffusion.data.diffusion.data_loader import ( LammpsForDiffusionDataModule, LammpsLoaderParameters) from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.analysis import PLOT_STYLE_PATH +from tqdm import tqdm + from experiments import EXPERIMENT_ANALYSIS_DIR from experiments.analysis_utils import get_thermo_dataset diff --git a/experiments/dataset_analysis/plot_si_phonon_DOS.py b/experiments/dataset_analysis/plot_si_phonon_DOS.py index 3d719b22..efda2862 100644 --- a/experiments/dataset_analysis/plot_si_phonon_DOS.py +++ b/experiments/dataset_analysis/plot_si_phonon_DOS.py @@ -6,9 +6,8 @@ """ import matplotlib.pyplot as plt import torch - from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/dataset_analysis/si_diffusion_analysis.py b/experiments/dataset_analysis/si_diffusion_analysis.py index d0de054b..3bb92a53 100644 --- a/experiments/dataset_analysis/si_diffusion_analysis.py +++ b/experiments/dataset_analysis/si_diffusion_analysis.py @@ -15,10 +15,10 @@ import numpy as np import pandas as pd import scipy - from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + from experiments.analysis_utils import get_thermo_dataset plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/dataset_analysis/si_diffusion_v1_analysis.py b/experiments/dataset_analysis/si_diffusion_v1_analysis.py index ddce7b67..81c1e1f4 100644 --- a/experiments/dataset_analysis/si_diffusion_v1_analysis.py +++ b/experiments/dataset_analysis/si_diffusion_v1_analysis.py @@ -8,10 +8,9 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd - from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.data.parse_lammps_outputs import parse_lammps_output +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.data.parse_lammps_outputs import parse_lammps_output DATASET_NAME = 'si_diffusion_v1' diff --git a/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py b/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py index 026045d2..209cd31e 100644 --- a/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py +++ b/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py @@ -4,12 +4,7 @@ import matplotlib.pyplot as plt import pytorch_lightning as pl import torch -from pytorch_lightning.loggers import TensorBoardLogger -from torch.utils.data import DataLoader - -from crystal_diffusion.analysis import PLOT_STYLE_PATH from crystal_diffusion.callbacks.callback_loader import create_all_callbacks -from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.models.optimizer import OptimizerParameters from crystal_diffusion.models.position_diffusion_lightning_model import ( PositionDiffusionLightningModel, PositionDiffusionParameters) @@ -24,7 +19,13 @@ from crystal_diffusion.models.score_networks.score_prediction_head import \ MaceEquivariantScorePredictionHeadParameters from crystal_diffusion.namespace import CARTESIAN_FORCES, RELATIVE_COORDINATES -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from pytorch_lightning.loggers import TensorBoardLogger +from src.crystal_diffusion.analysis import PLOT_STYLE_PATH +from src.crystal_diffusion.generators.position_generator import \ + SamplingParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from torch.utils.data import DataLoader + from experiments.analysis.analytic_score import (get_exact_samples, get_relative_harmonic_energy) from experiments.diffusion_mace_harmonic_data.analysis_callbacks import \ diff --git a/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py b/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py index 28e417c3..4bdb5e24 100644 --- a/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py +++ b/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py @@ -8,12 +8,13 @@ import matplotlib.pyplot as plt import numpy as np import torch - -from crystal_diffusion.analysis import PLOT_STYLE_PATH from crystal_diffusion.callbacks.sampling_visualization_callback import \ SamplingVisualizationCallback -from crystal_diffusion.generators.position_generator import SamplingParameters -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.analysis import PLOT_STYLE_PATH +from src.crystal_diffusion.generators.position_generator import \ + SamplingParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + from experiments.analysis.analytic_score import get_relative_harmonic_energy logger = logging.getLogger(__name__) diff --git a/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py b/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py index 11879e90..b9d5cbc4 100644 --- a/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py +++ b/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py @@ -7,10 +7,6 @@ import pytorch_lightning as pl import torch -from pytorch_lightning.loggers import TensorBoardLogger -from torch import optim -from torch.utils.data import DataLoader - from crystal_diffusion.callbacks.standard_callbacks import CustomProgressBar from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) @@ -21,12 +17,16 @@ UNIT_CELL) from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions +from pytorch_lightning.loggers import TensorBoardLogger +from src.crystal_diffusion.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) +from torch import optim +from torch.utils.data import DataLoader + from experiments.analysis.analytic_score import (get_exact_samples, get_unit_cells) diff --git a/experiments/generators/sde_generator_sanity_check.py b/experiments/generators/sde_generator_sanity_check.py index ef4e1554..075f441b 100644 --- a/experiments/generators/sde_generator_sanity_check.py +++ b/experiments/generators/sde_generator_sanity_check.py @@ -6,16 +6,16 @@ import einops import numpy as np import torch -from matplotlib import pyplot as plt - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.generators.sde_position_generator import ( ExplodingVarianceSDEPositionGenerator, SDESamplingParameters) from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from matplotlib import pyplot as plt +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + from experiments.analysis.analytic_score.utils import get_exact_samples from experiments.generators import GENERATOR_SANITY_CHECK_DIRECTORY diff --git a/experiments/sampling_sota_model/plot_repaint_score_trajectories.py b/experiments/sampling_sota_model/plot_repaint_score_trajectories.py index c9071386..c9f6252a 100644 --- a/experiments/sampling_sota_model/plot_repaint_score_trajectories.py +++ b/experiments/sampling_sota_model/plot_repaint_score_trajectories.py @@ -6,10 +6,9 @@ import matplotlib.pyplot as plt import numpy as np import torch -from einops import einops - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from einops import einops +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/experiments/sampling_sota_model/repaint_with_sota_score.py b/experiments/sampling_sota_model/repaint_with_sota_score.py index 3eb0c564..9a96d80d 100644 --- a/experiments/sampling_sota_model/repaint_with_sota_score.py +++ b/experiments/sampling_sota_model/repaint_with_sota_score.py @@ -6,16 +6,15 @@ import numpy as np import torch import yaml - from crystal_diffusion import DATA_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.generators.constrained_langevin_generator import ( ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) from crystal_diffusion.models.instantiate_diffusion_model import \ load_diffusion_model from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py b/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py index 0039ece7..2867eb28 100644 --- a/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py +++ b/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py @@ -8,10 +8,7 @@ import numpy as np import torch import yaml -from einops import einops - from crystal_diffusion import DATA_DIR -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) @@ -20,8 +17,10 @@ from crystal_diffusion.models.instantiate_diffusion_model import \ load_diffusion_model from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from einops import einops +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters logger = logging.getLogger(__name__) setup_analysis_logger() diff --git a/experiments/score_stability_analysis/draw_samples_from_equilibrium.py b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py index 0dc28164..303f92fb 100644 --- a/experiments/score_stability_analysis/draw_samples_from_equilibrium.py +++ b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py @@ -4,8 +4,6 @@ import matplotlib.pyplot as plt import numpy as np import torch - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) @@ -16,8 +14,10 @@ from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + from experiments.analysis.analytic_score.exploring_langevin_generator.generate_sample_energies import \ EnergyCalculator from experiments.analysis.analytic_score.utils import get_silicon_supercell diff --git a/experiments/score_stability_analysis/plot_hessian_eigenvalues.py b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py index dcfbe27c..b9508d33 100644 --- a/experiments/score_stability_analysis/plot_hessian_eigenvalues.py +++ b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py @@ -5,15 +5,15 @@ import matplotlib.pyplot as plt import numpy as np import torch -from torch.func import jacrev -from tqdm import tqdm - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from torch.func import jacrev +from tqdm import tqdm + from experiments import get_normalized_score_function from experiments.analysis.analytic_score.utils import get_silicon_supercell diff --git a/experiments/score_stability_analysis/plot_score_norm.py b/experiments/score_stability_analysis/plot_score_norm.py index 57f050df..1cfd8399 100644 --- a/experiments/score_stability_analysis/plot_score_norm.py +++ b/experiments/score_stability_analysis/plot_score_norm.py @@ -4,16 +4,16 @@ import matplotlib.pyplot as plt import numpy as np import torch -from tqdm import tqdm - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from tqdm import tqdm + from experiments import create_fixed_time_normalized_score_function from experiments.analysis.analytic_score.utils import get_silicon_supercell diff --git a/experiments/score_stability_analysis/util.py b/experiments/score_stability_analysis/util.py index 6e272894..842107de 100644 --- a/experiments/score_stability_analysis/util.py +++ b/experiments/score_stability_analysis/util.py @@ -3,13 +3,12 @@ import einops import torch - from crystal_diffusion.models.score_networks import ScoreNetwork from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters def get_normalized_score_function( diff --git a/crystal_diffusion/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/__init__.py similarity index 100% rename from crystal_diffusion/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/__init__.py diff --git a/crystal_diffusion/active_learning_loop/activelearning_dataclasses.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/activelearning_dataclasses.py similarity index 100% rename from crystal_diffusion/active_learning_loop/activelearning_dataclasses.py rename to src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/activelearning_dataclasses.py diff --git a/crystal_diffusion/active_learning_loop/benchmark.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py similarity index 99% rename from crystal_diffusion/active_learning_loop/benchmark.py rename to src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py index 7b231d3a..de3773af 100644 --- a/crystal_diffusion/active_learning_loop/benchmark.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py @@ -5,11 +5,10 @@ import numpy as np import pandas as pd import yaml -from hydra.utils import instantiate - from crystal_diffusion.active_learning_loop.utils import ( extract_target_region, get_structures_for_retraining) from crystal_diffusion.models.mlip.mtp import MTPWithMLIP3 +from hydra.utils import instantiate class ActiveLearningLoop: diff --git a/crystal_diffusion/active_learning_loop/oracle.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py similarity index 99% rename from crystal_diffusion/active_learning_loop/oracle.py rename to src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py index a1893f5c..f54f04e9 100644 --- a/crystal_diffusion/active_learning_loop/oracle.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py @@ -2,7 +2,6 @@ from typing import Dict, Tuple import numpy as np - from crystal_diffusion import DATA_DIR from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps diff --git a/crystal_diffusion/active_learning_loop/utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/utils.py similarity index 100% rename from crystal_diffusion/active_learning_loop/utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/utils.py diff --git a/crystal_diffusion/analysis/README_ovito.md b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/README_ovito.md similarity index 100% rename from crystal_diffusion/analysis/README_ovito.md rename to src/diffusion_for_multi_scale_molecular_dynamics/analysis/README_ovito.md diff --git a/crystal_diffusion/analysis/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/__init__.py similarity index 100% rename from crystal_diffusion/analysis/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/analysis/__init__.py diff --git a/crystal_diffusion/analysis/generator_sample_analysis_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py similarity index 97% rename from crystal_diffusion/analysis/generator_sample_analysis_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py index 76d1da3a..4543c330 100644 --- a/crystal_diffusion/analysis/generator_sample_analysis_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py @@ -1,11 +1,10 @@ import torch -from einops import einops - from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) from crystal_diffusion.models.mace_utils import get_adj_matrix from crystal_diffusion.models.score_networks.score_network import ScoreNetwork -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from einops import einops +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters class PartialODEPositionGenerator(ExplodingVarianceODEPositionGenerator): diff --git a/crystal_diffusion/analysis/ovito_visualisation.py b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/ovito_visualisation.py similarity index 100% rename from crystal_diffusion/analysis/ovito_visualisation.py rename to src/diffusion_for_multi_scale_molecular_dynamics/analysis/ovito_visualisation.py diff --git a/crystal_diffusion/analysis/plot_style.txt b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/plot_style.txt similarity index 100% rename from crystal_diffusion/analysis/plot_style.txt rename to src/diffusion_for_multi_scale_molecular_dynamics/analysis/plot_style.txt diff --git a/crystal_diffusion/callbacks/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/__init__.py similarity index 100% rename from crystal_diffusion/callbacks/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/callbacks/__init__.py diff --git a/crystal_diffusion/callbacks/callback_loader.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py similarity index 99% rename from crystal_diffusion/callbacks/callback_loader.py rename to src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py index 977e26b5..62c77b2c 100644 --- a/crystal_diffusion/callbacks/callback_loader.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py @@ -1,8 +1,5 @@ from typing import Any, AnyStr, Dict -from pytorch_lightning import Callback -from pytorch_lightning.callbacks import LearningRateMonitor - from crystal_diffusion.callbacks.loss_monitoring_callback import \ instantiate_loss_monitoring_callback from crystal_diffusion.callbacks.sampling_visualization_callback import \ @@ -10,6 +7,8 @@ from crystal_diffusion.callbacks.standard_callbacks import ( CustomProgressBar, instantiate_early_stopping_callback, instantiate_model_checkpoint_callbacks) +from pytorch_lightning import Callback +from pytorch_lightning.callbacks import LearningRateMonitor OPTIONAL_CALLBACK_DICTIONARY = dict(early_stopping=instantiate_early_stopping_callback, model_checkpoint=instantiate_model_checkpoint_callbacks, diff --git a/crystal_diffusion/callbacks/loss_monitoring_callback.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py similarity index 98% rename from crystal_diffusion/callbacks/loss_monitoring_callback.py rename to src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py index 8640e151..5e32892d 100644 --- a/crystal_diffusion/callbacks/loss_monitoring_callback.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py @@ -2,11 +2,10 @@ import numpy as np import torch +from crystal_diffusion.loggers.logger_loader import log_figure from matplotlib import pyplot as plt from pytorch_lightning import Callback - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.loggers.logger_loader import log_figure +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) diff --git a/crystal_diffusion/callbacks/sampling_visualization_callback.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py similarity index 99% rename from crystal_diffusion/callbacks/sampling_visualization_callback.py rename to src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py index ed7a0472..025a10bd 100644 --- a/crystal_diffusion/callbacks/sampling_visualization_callback.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py @@ -6,11 +6,10 @@ import numpy as np import torch +from crystal_diffusion.loggers.logger_loader import log_figure from matplotlib import pyplot as plt from pytorch_lightning import Callback, LightningModule, Trainer - -from crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from crystal_diffusion.loggers.logger_loader import log_figure +from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/callbacks/standard_callbacks.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/standard_callbacks.py similarity index 100% rename from crystal_diffusion/callbacks/standard_callbacks.py rename to src/diffusion_for_multi_scale_molecular_dynamics/callbacks/standard_callbacks.py diff --git a/crystal_diffusion/data/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/__init__.py similarity index 100% rename from crystal_diffusion/data/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/data/__init__.py diff --git a/crystal_diffusion/data/diffusion/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/__init__.py similarity index 100% rename from crystal_diffusion/data/diffusion/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/__init__.py diff --git a/crystal_diffusion/data/diffusion/data_loader.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py similarity index 99% rename from crystal_diffusion/data/diffusion/data_loader.py rename to src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py index 9cda99b3..0f81b5bb 100644 --- a/crystal_diffusion/data/diffusion/data_loader.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py @@ -9,12 +9,11 @@ import pytorch_lightning as pl import torch import torch.nn.functional as F -from torch.utils.data import DataLoader - from crystal_diffusion.data.diffusion.data_preprocess import \ LammpsProcessorForDiffusion from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) +from torch.utils.data import DataLoader logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/data/diffusion/data_preprocess.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py similarity index 99% rename from crystal_diffusion/data/diffusion/data_preprocess.py rename to src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py index ce7f87cb..efa00ed8 100644 --- a/crystal_diffusion/data/diffusion/data_preprocess.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py @@ -7,10 +7,9 @@ from typing import List, Optional, Tuple, Union import pandas as pd - -from crystal_diffusion.data.parse_lammps_outputs import parse_lammps_output from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) +from src.crystal_diffusion.data.parse_lammps_outputs import parse_lammps_output logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/data/parse_lammps_outputs.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/parse_lammps_outputs.py similarity index 100% rename from crystal_diffusion/data/parse_lammps_outputs.py rename to src/diffusion_for_multi_scale_molecular_dynamics/data/parse_lammps_outputs.py diff --git a/crystal_diffusion/data/utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/utils.py similarity index 100% rename from crystal_diffusion/data/utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/data/utils.py diff --git a/crystal_diffusion/generators/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/__init__.py similarity index 100% rename from crystal_diffusion/generators/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/__init__.py diff --git a/crystal_diffusion/generators/constrained_langevin_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py similarity index 98% rename from crystal_diffusion/generators/constrained_langevin_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py index 96cda59c..9cbed410 100644 --- a/crystal_diffusion/generators/constrained_langevin_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py @@ -2,17 +2,16 @@ import numpy as np import torch -from tqdm import tqdm - from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters from crystal_diffusion.models.score_networks.score_network import ScoreNetwork from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from tqdm import tqdm @dataclass(kw_only=True) diff --git a/crystal_diffusion/generators/instantiate_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py similarity index 91% rename from crystal_diffusion/generators/instantiate_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py index ac6277bb..d7d15501 100644 --- a/crystal_diffusion/generators/instantiate_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py @@ -1,11 +1,12 @@ from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.ode_position_generator import \ ExplodingVarianceODEPositionGenerator -from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.generators.sde_position_generator import \ ExplodingVarianceSDEPositionGenerator from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.generators.position_generator import \ + SamplingParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters def instantiate_generator(sampling_parameters: SamplingParameters, diff --git a/crystal_diffusion/generators/langevin_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py similarity index 99% rename from crystal_diffusion/generators/langevin_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py index e85db9ab..ed7d9888 100644 --- a/crystal_diffusion/generators/langevin_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py @@ -1,15 +1,14 @@ import torch - from crystal_diffusion.generators.predictor_corrector_position_generator import ( PredictorCorrectorPositionGenerator, PredictorCorrectorSamplingParameters) from crystal_diffusion.models.score_networks.score_network import ScoreNetwork from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.utils.sample_trajectory import ( NoOpPredictorCorrectorSampleTrajectory, PredictorCorrectorSampleTrajectory) +from src.crystal_diffusion.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) class LangevinGenerator(PredictorCorrectorPositionGenerator): diff --git a/crystal_diffusion/generators/load_sampling_parameters.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py similarity index 94% rename from crystal_diffusion/generators/load_sampling_parameters.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py index 21ce3c21..ef633bf8 100644 --- a/crystal_diffusion/generators/load_sampling_parameters.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py @@ -2,11 +2,12 @@ from crystal_diffusion.generators.ode_position_generator import \ ODESamplingParameters -from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters from crystal_diffusion.generators.sde_position_generator import \ SDESamplingParameters +from src.crystal_diffusion.generators.position_generator import \ + SamplingParameters def load_sampling_parameters(sampling_parameter_dictionary: Dict[AnyStr, Any]) -> SamplingParameters: diff --git a/crystal_diffusion/generators/ode_position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py similarity index 98% rename from crystal_diffusion/generators/ode_position_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py index ca61962f..49f53ebf 100644 --- a/crystal_diffusion/generators/ode_position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py @@ -5,19 +5,18 @@ import einops import torch import torchode as to -from torchode import Solution - -from crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) from crystal_diffusion.models.score_networks.score_network import ScoreNetwork from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.sample_trajectory import (NoOpODESampleTrajectory, ODESampleTrajectory) +from src.crystal_diffusion.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from torchode import Solution logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/generators/position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/position_generator.py similarity index 100% rename from crystal_diffusion/generators/position_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/position_generator.py diff --git a/crystal_diffusion/generators/predictor_corrector_position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py similarity index 98% rename from crystal_diffusion/generators/predictor_corrector_position_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py index 459246ba..ba7178cc 100644 --- a/crystal_diffusion/generators/predictor_corrector_position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py @@ -3,12 +3,11 @@ from dataclasses import dataclass import torch -from tqdm import tqdm - -from crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from src.crystal_diffusion.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from tqdm import tqdm logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/generators/sde_position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py similarity index 99% rename from crystal_diffusion/generators/sde_position_generator.py rename to src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py index 3a233850..f2432942 100644 --- a/crystal_diffusion/generators/sde_position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py @@ -4,17 +4,16 @@ import einops import torch import torchsde - -from crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) from crystal_diffusion.models.score_networks import ScoreNetwork from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell from crystal_diffusion.utils.sample_trajectory import SDESampleTrajectory +from src.crystal_diffusion.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/loggers/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/loggers/__init__.py similarity index 100% rename from crystal_diffusion/loggers/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/loggers/__init__.py diff --git a/crystal_diffusion/loggers/logger_loader.py b/src/diffusion_for_multi_scale_molecular_dynamics/loggers/logger_loader.py similarity index 100% rename from crystal_diffusion/loggers/logger_loader.py rename to src/diffusion_for_multi_scale_molecular_dynamics/loggers/logger_loader.py diff --git a/crystal_diffusion/main_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/main_utils.py similarity index 100% rename from crystal_diffusion/main_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/main_utils.py diff --git a/crystal_diffusion/metrics/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/__init__.py similarity index 100% rename from crystal_diffusion/metrics/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/metrics/__init__.py diff --git a/crystal_diffusion/metrics/kolmogorov_smirnov_metrics.py b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/kolmogorov_smirnov_metrics.py similarity index 100% rename from crystal_diffusion/metrics/kolmogorov_smirnov_metrics.py rename to src/diffusion_for_multi_scale_molecular_dynamics/metrics/kolmogorov_smirnov_metrics.py diff --git a/crystal_diffusion/metrics/sampling_metrics_parameters.py b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/sampling_metrics_parameters.py similarity index 100% rename from crystal_diffusion/metrics/sampling_metrics_parameters.py rename to src/diffusion_for_multi_scale_molecular_dynamics/metrics/sampling_metrics_parameters.py diff --git a/crystal_diffusion/mlip/mtp_train.py b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py similarity index 99% rename from crystal_diffusion/mlip/mtp_train.py rename to src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py index bcf527ed..93312518 100644 --- a/crystal_diffusion/mlip/mtp_train.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py @@ -6,12 +6,11 @@ from typing import Dict, Tuple import pandas as pd -from sklearn.metrics import mean_absolute_error - from crystal_diffusion.mlip.mtp_utils import (MTPInputs, crawl_lammps_directory, prepare_mtp_inputs_from_lammps) from crystal_diffusion.models.mlip.mtp import MTPWithMLIP3 +from sklearn.metrics import mean_absolute_error atom_dict = {1: 'Si'} diff --git a/crystal_diffusion/mlip/mtp_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_utils.py similarity index 100% rename from crystal_diffusion/mlip/mtp_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_utils.py diff --git a/crystal_diffusion/models/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/__init__.py similarity index 100% rename from crystal_diffusion/models/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/__init__.py diff --git a/crystal_diffusion/models/diffusion_mace.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py similarity index 99% rename from crystal_diffusion/models/diffusion_mace.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py index 6b1f732e..b4919e12 100644 --- a/crystal_diffusion/models/diffusion_mace.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py @@ -1,6 +1,11 @@ from typing import AnyStr, Callable, Dict, List, Optional, Type, Union import torch +from crystal_diffusion.models.mace_utils import (get_adj_matrix, + reshape_from_e3nn_to_mace, + reshape_from_mace_to_e3nn) +from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, + NOISY_CARTESIAN_POSITIONS, UNIT_CELL) from e3nn import o3 from e3nn.nn import Activation, BatchNorm, NormActivation from mace.modules import (EquivariantProductBasisBlock, InteractionBlock, @@ -8,12 +13,6 @@ from mace.modules.utils import get_edge_vectors_and_lengths from torch_geometric.data import Data -from crystal_diffusion.models.mace_utils import (get_adj_matrix, - reshape_from_e3nn_to_mace, - reshape_from_mace_to_e3nn) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_CARTESIAN_POSITIONS, UNIT_CELL) - class LinearVectorReadoutBlock(torch.nn.Module): """Linear readout block for vector representation.""" diff --git a/crystal_diffusion/models/egnn.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py similarity index 99% rename from crystal_diffusion/models/egnn.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py index 5170d2d8..6aa34c40 100644 --- a/crystal_diffusion/models/egnn.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py @@ -10,10 +10,9 @@ from typing import Callable, Tuple import torch -from torch import nn - from crystal_diffusion.models.egnn_utils import (unsorted_segment_mean, unsorted_segment_sum) +from torch import nn class E_GCL(nn.Module): diff --git a/crystal_diffusion/models/egnn_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py similarity index 99% rename from crystal_diffusion/models/egnn_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py index d7ad60a5..d1b03359 100644 --- a/crystal_diffusion/models/egnn_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py @@ -1,7 +1,6 @@ from typing import List import torch - from crystal_diffusion.models.mace_utils import get_adj_matrix from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates diff --git a/crystal_diffusion/models/graph_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py similarity index 99% rename from crystal_diffusion/models/graph_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py index 602e09ad..5750c4e0 100644 --- a/crystal_diffusion/models/graph_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py @@ -1,7 +1,6 @@ from typing import Tuple import torch - from crystal_diffusion.utils.neighbors import ( get_periodic_adjacency_information, shift_adjacency_matrix_indices_for_graph_batching) diff --git a/crystal_diffusion/models/instantiate_diffusion_model.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py similarity index 96% rename from crystal_diffusion/models/instantiate_diffusion_model.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py index 511fce7f..3856e095 100644 --- a/crystal_diffusion/models/instantiate_diffusion_model.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py @@ -9,9 +9,9 @@ from crystal_diffusion.models.scheduler import create_scheduler_parameters from crystal_diffusion.models.score_networks.score_network_factory import \ create_score_network_parameters -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.samples.diffusion_sampling_parameters import \ load_diffusion_sampling_parameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/models/loss.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py similarity index 99% rename from crystal_diffusion/models/loss.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py index f1b8599f..8af9aa47 100644 --- a/crystal_diffusion/models/loss.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py @@ -2,7 +2,6 @@ from typing import Any, Dict import torch - from crystal_diffusion.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary diff --git a/crystal_diffusion/models/mace_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py similarity index 99% rename from crystal_diffusion/models/mace_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py index 3b45ef4d..fde3cf5b 100644 --- a/crystal_diffusion/models/mace_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py @@ -3,11 +3,10 @@ from typing import AnyStr, Dict, Tuple import torch -from e3nn import o3 -from torch_geometric.data import Data - from crystal_diffusion.models.graph_utils import get_adj_matrix from crystal_diffusion.namespace import NOISY_CARTESIAN_POSITIONS, UNIT_CELL +from e3nn import o3 +from torch_geometric.data import Data def input_to_mace(x: Dict[AnyStr, torch.Tensor], radial_cutoff: float) -> Data: diff --git a/crystal_diffusion/models/mlip/mtp.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py similarity index 99% rename from crystal_diffusion/models/mlip/mtp.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py index bc822cf6..b6bc20ea 100644 --- a/crystal_diffusion/models/mlip/mtp.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py @@ -15,16 +15,15 @@ import numpy as np import pandas as pd +from crystal_diffusion.mlip.mtp_utils import (MTPInputs, concat_mtp_inputs, + crawl_lammps_directory, + prepare_mtp_inputs_from_lammps) from maml.apps.pes import MTPotential from maml.utils import check_structures_forces_stresses, pool_from from monty.io import zopen from monty.tempfile import ScratchDir from pymatgen.core import Structure -from crystal_diffusion.mlip.mtp_utils import (MTPInputs, concat_mtp_inputs, - crawl_lammps_directory, - prepare_mtp_inputs_from_lammps) - @dataclass(kw_only=True) class MTPArguments: diff --git a/crystal_diffusion/models/normalized_score_fokker_planck_error.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py similarity index 99% rename from crystal_diffusion/models/normalized_score_fokker_planck_error.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py index b0e939ec..2d57d9e1 100644 --- a/crystal_diffusion/models/normalized_score_fokker_planck_error.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py @@ -2,14 +2,13 @@ import einops import torch -from torch.func import jacrev - from crystal_diffusion.models.score_networks import ScoreNetwork from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from torch.func import jacrev class NormalizedScoreFokkerPlanckError(torch.nn.Module): diff --git a/crystal_diffusion/models/optimizer.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py similarity index 99% rename from crystal_diffusion/models/optimizer.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py index 1f462724..97e8cc7b 100644 --- a/crystal_diffusion/models/optimizer.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py @@ -3,10 +3,9 @@ from typing import Any, Dict import torch -from torch import optim - from crystal_diffusion.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary +from torch import optim logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/models/position_diffusion_lightning_model.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py similarity index 99% rename from crystal_diffusion/models/position_diffusion_lightning_model.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py index 2fc5e6e1..9d7bf3b7 100644 --- a/crystal_diffusion/models/position_diffusion_lightning_model.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py @@ -4,7 +4,6 @@ import pytorch_lightning as pl import torch - from crystal_diffusion.generators.instantiate_generator import \ instantiate_generator from crystal_diffusion.metrics.kolmogorov_smirnov_metrics import \ @@ -25,11 +24,8 @@ from crystal_diffusion.oracle.energies import compute_oracle_energies from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.samples.diffusion_sampling_parameters import \ DiffusionSamplingParameters -from crystal_diffusion.samples.sampling import create_batch_of_samples from crystal_diffusion.score.wrapped_gaussian_score import \ get_sigma_normalized_score from crystal_diffusion.utils.basis_transformations import ( @@ -37,6 +33,9 @@ from crystal_diffusion.utils.structure_utils import compute_distances_in_batch from crystal_diffusion.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions +from src.crystal_diffusion.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) +from src.crystal_diffusion.samples.sampling import create_batch_of_samples logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/models/scheduler.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py similarity index 99% rename from crystal_diffusion/models/scheduler.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py index b07642eb..1adc4558 100644 --- a/crystal_diffusion/models/scheduler.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py @@ -1,10 +1,9 @@ from dataclasses import asdict, dataclass from typing import Any, AnyStr, Dict, Union -from torch import optim - from crystal_diffusion.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary +from torch import optim @dataclass(kw_only=True) diff --git a/crystal_diffusion/models/score_networks/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/__init__.py similarity index 100% rename from crystal_diffusion/models/score_networks/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/__init__.py diff --git a/crystal_diffusion/models/score_networks/analytical_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/analytical_score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py index d7bebd34..f1cbf3b0 100644 --- a/crystal_diffusion/models/score_networks/analytical_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py @@ -16,7 +16,6 @@ import einops import torch - from crystal_diffusion.models.score_networks.score_network import ( ScoreNetwork, ScoreNetworkParameters) from crystal_diffusion.namespace import NOISE, NOISY_RELATIVE_COORDINATES diff --git a/crystal_diffusion/models/score_networks/diffusion_mace_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/diffusion_mace_score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py index defcbacd..31269025 100644 --- a/crystal_diffusion/models/score_networks/diffusion_mace_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py @@ -2,10 +2,6 @@ from typing import AnyStr, Dict, List import torch -from e3nn import o3 -from mace.modules import gate_dict, interaction_classes -from mace.tools.torch_geometric.dataloader import Collater - from crystal_diffusion.models.diffusion_mace import (DiffusionMACE, input_to_diffusion_mace) from crystal_diffusion.models.score_networks.score_network import ( @@ -14,6 +10,9 @@ NOISY_RELATIVE_COORDINATES, UNIT_CELL) from crystal_diffusion.utils.basis_transformations import ( get_positions_from_coordinates, get_reciprocal_basis_vectors) +from e3nn import o3 +from mace.modules import gate_dict, interaction_classes +from mace.tools.torch_geometric.dataloader import Collater @dataclass(kw_only=True) diff --git a/crystal_diffusion/models/score_networks/egnn_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/egnn_score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py index 17f6d39e..d90987a2 100644 --- a/crystal_diffusion/models/score_networks/egnn_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py @@ -3,7 +3,6 @@ import einops import torch - from crystal_diffusion.models.egnn import EGNN from crystal_diffusion.models.egnn_utils import (get_edges_batch, get_edges_with_radial_cutoff) diff --git a/crystal_diffusion/models/score_networks/force_field_augmented_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/force_field_augmented_score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py index 5532f595..59ef4f5c 100644 --- a/crystal_diffusion/models/score_networks/force_field_augmented_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py @@ -3,7 +3,6 @@ import einops import torch - from crystal_diffusion.models.score_networks import ScoreNetwork from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES, UNIT_CELL from crystal_diffusion.utils.basis_transformations import ( diff --git a/crystal_diffusion/models/score_networks/mace_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/mace_score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py index f1c4dbbf..b0c5b838 100644 --- a/crystal_diffusion/models/score_networks/mace_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py @@ -3,11 +3,6 @@ import numpy as np import torch -from e3nn import o3 -from mace.modules import MACE, gate_dict, interaction_classes -from mace.tools import get_atomic_number_table_from_zs -from mace.tools.torch_geometric.dataloader import Collater - from crystal_diffusion.models.mace_utils import ( build_mace_output_nodes_irreducible_representation, get_pretrained_mace, get_pretrained_mace_output_node_features_irreps, input_to_mace) @@ -18,6 +13,10 @@ from crystal_diffusion.namespace import (NOISY_CARTESIAN_POSITIONS, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from e3nn import o3 +from mace.modules import MACE, gate_dict, interaction_classes +from mace.tools import get_atomic_number_table_from_zs +from mace.tools.torch_geometric.dataloader import Collater @dataclass(kw_only=True) diff --git a/crystal_diffusion/models/score_networks/mlp_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/mlp_score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py index a99f5c0d..c5e989cc 100644 --- a/crystal_diffusion/models/score_networks/mlp_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py @@ -2,12 +2,11 @@ from typing import AnyStr, Dict import torch -from torch import nn - from crystal_diffusion.models.score_networks.score_network import ( ScoreNetwork, ScoreNetworkParameters) from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES) +from torch import nn @dataclass(kw_only=True) diff --git a/crystal_diffusion/models/score_networks/score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py similarity index 99% rename from crystal_diffusion/models/score_networks/score_network.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py index 39d0e38a..2701e654 100644 --- a/crystal_diffusion/models/score_networks/score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py @@ -9,7 +9,6 @@ from typing import AnyStr, Dict, Optional import torch - from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) diff --git a/crystal_diffusion/models/score_networks/score_network_factory.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network_factory.py similarity index 100% rename from crystal_diffusion/models/score_networks/score_network_factory.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network_factory.py diff --git a/crystal_diffusion/models/score_networks/score_prediction_head.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py similarity index 99% rename from crystal_diffusion/models/score_networks/score_prediction_head.py rename to src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py index 4136dc08..47c680cf 100644 --- a/crystal_diffusion/models/score_networks/score_prediction_head.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py @@ -2,14 +2,13 @@ import e3nn import torch +from crystal_diffusion.models.mace_utils import \ + get_normalized_irreps_permutation_indices from e3nn import o3 from e3nn.nn import Activation from mace.modules import LinearNodeEmbeddingBlock, gate_dict from torch import nn -from crystal_diffusion.models.mace_utils import \ - get_normalized_irreps_permutation_indices - @dataclass(kw_only=True) class MaceScorePredictionHeadParameters: diff --git a/crystal_diffusion/namespace.py b/src/diffusion_for_multi_scale_molecular_dynamics/namespace.py similarity index 100% rename from crystal_diffusion/namespace.py rename to src/diffusion_for_multi_scale_molecular_dynamics/namespace.py diff --git a/crystal_diffusion/oracle/energies.py b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py similarity index 99% rename from crystal_diffusion/oracle/energies.py rename to src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py index 29eccc7d..ccc69659 100644 --- a/crystal_diffusion/oracle/energies.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py @@ -4,7 +4,6 @@ import numpy as np import torch - from crystal_diffusion.namespace import CARTESIAN_POSITIONS, UNIT_CELL from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps diff --git a/crystal_diffusion/oracle/lammps.py b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py similarity index 99% rename from crystal_diffusion/oracle/lammps.py rename to src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py index 0107aa3f..cd0bd43c 100644 --- a/crystal_diffusion/oracle/lammps.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py @@ -7,9 +7,8 @@ import numpy as np import pandas as pd import yaml -from pymatgen.core import Element - from crystal_diffusion import DATA_DIR +from pymatgen.core import Element def get_energy_and_forces_from_lammps(cartesian_positions: np.ndarray, diff --git a/crystal_diffusion/sample_diffusion.py b/src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py similarity index 96% rename from crystal_diffusion/sample_diffusion.py rename to src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py index ea598c63..08fd58c7 100644 --- a/crystal_diffusion/sample_diffusion.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py @@ -10,21 +10,21 @@ from typing import Any, AnyStr, Dict, Optional, Union import torch - from crystal_diffusion.generators.instantiate_generator import \ instantiate_generator from crystal_diffusion.generators.load_sampling_parameters import \ load_sampling_parameters -from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.main_utils import load_and_backup_hyperparameters from crystal_diffusion.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel from crystal_diffusion.models.score_networks import ScoreNetwork from crystal_diffusion.oracle.energies import compute_oracle_energies -from crystal_diffusion.samplers.variance_sampler import NoiseParameters -from crystal_diffusion.samples.sampling import create_batch_of_samples from crystal_diffusion.utils.logging_utils import (get_git_hash, setup_console_logger) +from src.crystal_diffusion.generators.position_generator import \ + SamplingParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samples.sampling import create_batch_of_samples logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/samplers/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/__init__.py similarity index 100% rename from crystal_diffusion/samplers/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samplers/__init__.py diff --git a/crystal_diffusion/samplers/exploding_variance.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py similarity index 96% rename from crystal_diffusion/samplers/exploding_variance.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py index 0f84e26d..45dddb80 100644 --- a/crystal_diffusion/samplers/exploding_variance.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py @@ -1,6 +1,5 @@ import torch - -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters class ExplodingVariance(torch.nn.Module): diff --git a/crystal_diffusion/samplers/noisy_relative_coordinates_sampler.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py similarity index 99% rename from crystal_diffusion/samplers/noisy_relative_coordinates_sampler.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py index 7b19f7c7..f1bd093b 100644 --- a/crystal_diffusion/samplers/noisy_relative_coordinates_sampler.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py @@ -5,7 +5,6 @@ from typing import Tuple import torch - from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell diff --git a/crystal_diffusion/samplers/variance_sampler.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/variance_sampler.py similarity index 100% rename from crystal_diffusion/samplers/variance_sampler.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samplers/variance_sampler.py diff --git a/crystal_diffusion/samples/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/samples/__init__.py similarity index 100% rename from crystal_diffusion/samples/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samples/__init__.py diff --git a/crystal_diffusion/samples/diffusion_sampling_parameters.py b/src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py similarity index 92% rename from crystal_diffusion/samples/diffusion_sampling_parameters.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py index 50aec3b2..2d4682a2 100644 --- a/crystal_diffusion/samples/diffusion_sampling_parameters.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py @@ -3,10 +3,11 @@ from crystal_diffusion.generators.load_sampling_parameters import \ load_sampling_parameters -from crystal_diffusion.generators.position_generator import SamplingParameters from crystal_diffusion.metrics.sampling_metrics_parameters import \ SamplingMetricsParameters -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.generators.position_generator import \ + SamplingParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters @dataclass(kw_only=True) diff --git a/crystal_diffusion/samples/sampling.py b/src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py similarity index 97% rename from crystal_diffusion/samples/sampling.py rename to src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py index 1c3e357b..dc3545b7 100644 --- a/crystal_diffusion/samples/sampling.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py @@ -1,15 +1,14 @@ import logging import torch - -from crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) from crystal_diffusion.namespace import (CARTESIAN_POSITIONS, RELATIVE_COORDINATES, UNIT_CELL) from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates from crystal_diffusion.utils.structure_utils import \ get_orthogonal_basis_vectors +from src.crystal_diffusion.generators.position_generator import ( + PositionGenerator, SamplingParameters) logger = logging.getLogger(__name__) diff --git a/crystal_diffusion/score/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/score/__init__.py similarity index 100% rename from crystal_diffusion/score/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/score/__init__.py diff --git a/crystal_diffusion/score/wrapped_gaussian_score.py b/src/diffusion_for_multi_scale_molecular_dynamics/score/wrapped_gaussian_score.py similarity index 100% rename from crystal_diffusion/score/wrapped_gaussian_score.py rename to src/diffusion_for_multi_scale_molecular_dynamics/score/wrapped_gaussian_score.py diff --git a/crystal_diffusion/train_diffusion.py b/src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py similarity index 99% rename from crystal_diffusion/train_diffusion.py rename to src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py index bd66163d..cc301218 100644 --- a/crystal_diffusion/train_diffusion.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py @@ -8,7 +8,6 @@ import pytorch_lightning import pytorch_lightning as pl import yaml - from crystal_diffusion.callbacks.callback_loader import create_all_callbacks from crystal_diffusion.data.diffusion.data_loader import ( LammpsForDiffusionDataModule, LammpsLoaderParameters) diff --git a/crystal_diffusion/utils/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/__init__.py similarity index 100% rename from crystal_diffusion/utils/__init__.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/__init__.py diff --git a/crystal_diffusion/utils/basis_transformations.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/basis_transformations.py similarity index 100% rename from crystal_diffusion/utils/basis_transformations.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/basis_transformations.py diff --git a/crystal_diffusion/utils/configuration_parsing.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/configuration_parsing.py similarity index 100% rename from crystal_diffusion/utils/configuration_parsing.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/configuration_parsing.py diff --git a/crystal_diffusion/utils/file_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/file_utils.py similarity index 100% rename from crystal_diffusion/utils/file_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/file_utils.py diff --git a/crystal_diffusion/utils/hp_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/hp_utils.py similarity index 100% rename from crystal_diffusion/utils/hp_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/hp_utils.py diff --git a/crystal_diffusion/utils/logging_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/logging_utils.py similarity index 100% rename from crystal_diffusion/utils/logging_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/logging_utils.py diff --git a/crystal_diffusion/utils/neighbors.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py similarity index 99% rename from crystal_diffusion/utils/neighbors.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py index 0bf28f0c..7fdd16bc 100644 --- a/crystal_diffusion/utils/neighbors.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py @@ -9,10 +9,9 @@ import numpy as np import torch -from pykeops.torch import LazyTensor - from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates +from pykeops.torch import LazyTensor INDEX_PADDING_VALUE = -1 POSITION_PADDING_VALUE = np.NaN diff --git a/crystal_diffusion/utils/ovito_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/ovito_utils.py similarity index 100% rename from crystal_diffusion/utils/ovito_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/ovito_utils.py diff --git a/crystal_diffusion/utils/reproducibility_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/reproducibility_utils.py similarity index 100% rename from crystal_diffusion/utils/reproducibility_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/reproducibility_utils.py diff --git a/crystal_diffusion/utils/sample_trajectory.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/sample_trajectory.py similarity index 100% rename from crystal_diffusion/utils/sample_trajectory.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/sample_trajectory.py diff --git a/crystal_diffusion/utils/structure_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py similarity index 99% rename from crystal_diffusion/utils/structure_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py index 125d6f9c..7426bff3 100644 --- a/crystal_diffusion/utils/structure_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py @@ -2,12 +2,11 @@ import numpy as np import torch -from pykeops.torch import LazyTensor -from pymatgen.core import Lattice, Structure - from crystal_diffusion.utils.neighbors import ( _get_relative_coordinates_lattice_vectors, _get_shifted_positions, get_periodic_adjacency_information, get_positions_from_coordinates) +from pykeops.torch import LazyTensor +from pymatgen.core import Lattice, Structure def create_structure(basis_vectors: np.ndarray, relative_coordinates: np.ndarray, species: List[str]) -> Structure: diff --git a/crystal_diffusion/utils/tensor_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/tensor_utils.py similarity index 100% rename from crystal_diffusion/utils/tensor_utils.py rename to src/diffusion_for_multi_scale_molecular_dynamics/utils/tensor_utils.py diff --git a/tests/active_learning_loop/test_benchmark.py b/tests/active_learning_loop/test_benchmark.py index ac712970..b23579cc 100644 --- a/tests/active_learning_loop/test_benchmark.py +++ b/tests/active_learning_loop/test_benchmark.py @@ -3,7 +3,6 @@ import pandas as pd import pytest - from crystal_diffusion.active_learning_loop.benchmark import ActiveLearningLoop diff --git a/tests/analysis/test_ovito_visualisation.py b/tests/analysis/test_ovito_visualisation.py index 20377056..5be2d2de 100644 --- a/tests/analysis/test_ovito_visualisation.py +++ b/tests/analysis/test_ovito_visualisation.py @@ -4,9 +4,8 @@ import numpy as np import pandas as pd import pytest - -from crystal_diffusion.analysis.ovito_visualisation import ( - get_lattice_from_lammps, mtp_predictions_to_ovito) +from src.crystal_diffusion.analysis import (get_lattice_from_lammps, + mtp_predictions_to_ovito) class TestMTP2Ovito: diff --git a/tests/data/diffusion/test_data_loader.py b/tests/data/diffusion/test_data_loader.py index 893cf1a8..0297d3a2 100644 --- a/tests/data/diffusion/test_data_loader.py +++ b/tests/data/diffusion/test_data_loader.py @@ -3,11 +3,11 @@ import pytest import torch - from crystal_diffusion.data.diffusion.data_loader import ( LammpsForDiffusionDataModule, LammpsLoaderParameters) from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) + from tests.conftest import TestDiffusionDataBase from tests.fake_data_utils import Configuration, find_aligning_permutation diff --git a/tests/data/diffusion/test_data_preprocess.py b/tests/data/diffusion/test_data_preprocess.py index 0ca65cb0..b11f8830 100644 --- a/tests/data/diffusion/test_data_preprocess.py +++ b/tests/data/diffusion/test_data_preprocess.py @@ -3,11 +3,11 @@ import numpy as np import pandas as pd import pytest - from crystal_diffusion.data.diffusion.data_preprocess import \ LammpsProcessorForDiffusion from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) + from tests.conftest import TestDiffusionDataBase from tests.fake_data_utils import generate_parquet_dataframe diff --git a/tests/data/test_parse_lammps_output.py b/tests/data/test_parse_lammps_output.py index 5cabbaa0..605ba613 100644 --- a/tests/data/test_parse_lammps_output.py +++ b/tests/data/test_parse_lammps_output.py @@ -4,9 +4,9 @@ import pandas as pd import pytest import yaml - -from crystal_diffusion.data.parse_lammps_outputs import ( +from src.crystal_diffusion.data.parse_lammps_outputs import ( parse_lammps_dump, parse_lammps_output, parse_lammps_thermo_log) + from tests.fake_data_utils import (create_dump_yaml_documents, generate_fake_configuration, generate_parse_dump_output_dataframe, diff --git a/tests/data/test_utils.py b/tests/data/test_utils.py index c7fdf7d7..03668987 100644 --- a/tests/data/test_utils.py +++ b/tests/data/test_utils.py @@ -1,6 +1,5 @@ import pytest import yaml - from crystal_diffusion.data.utils import crop_lammps_yaml # Sample data for dump and thermo YAML files diff --git a/tests/fake_data_utils.py b/tests/fake_data_utils.py index 0a560f3e..390d6a96 100644 --- a/tests/fake_data_utils.py +++ b/tests/fake_data_utils.py @@ -5,7 +5,6 @@ import pandas as pd import torch import yaml - from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) diff --git a/tests/generators/conftest.py b/tests/generators/conftest.py index 051894be..bed0b402 100644 --- a/tests/generators/conftest.py +++ b/tests/generators/conftest.py @@ -2,7 +2,6 @@ import pytest import torch - from crystal_diffusion.models.score_networks import (ScoreNetwork, ScoreNetworkParameters) from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES diff --git a/tests/generators/test_constrained_langevin_generator.py b/tests/generators/test_constrained_langevin_generator.py index 725dd09b..035634f4 100644 --- a/tests/generators/test_constrained_langevin_generator.py +++ b/tests/generators/test_constrained_langevin_generator.py @@ -1,9 +1,9 @@ import einops import pytest import torch - from crystal_diffusion.generators.constrained_langevin_generator import ( ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) + from tests.generators.test_langevin_generator import TestLangevinGenerator diff --git a/tests/generators/test_langevin_generator.py b/tests/generators/test_langevin_generator.py index c5b5245c..18e2190f 100644 --- a/tests/generators/test_langevin_generator.py +++ b/tests/generators/test_langevin_generator.py @@ -1,13 +1,13 @@ import pytest import torch - from crystal_diffusion.generators.langevin_generator import LangevinGenerator from crystal_diffusion.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell +from src.crystal_diffusion.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) + from tests.generators.conftest import BaseTestGenerator diff --git a/tests/generators/test_ode_position_generator.py b/tests/generators/test_ode_position_generator.py index c9a13f4e..1564aba7 100644 --- a/tests/generators/test_ode_position_generator.py +++ b/tests/generators/test_ode_position_generator.py @@ -1,10 +1,10 @@ import pytest import torch - from crystal_diffusion.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) -from crystal_diffusion.samplers.variance_sampler import ( +from src.crystal_diffusion.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) + from tests.generators.conftest import BaseTestGenerator diff --git a/tests/generators/test_predictor_corrector_position_generator.py b/tests/generators/test_predictor_corrector_position_generator.py index 17eb789b..b8ada474 100644 --- a/tests/generators/test_predictor_corrector_position_generator.py +++ b/tests/generators/test_predictor_corrector_position_generator.py @@ -1,10 +1,10 @@ import pytest import torch - from crystal_diffusion.generators.predictor_corrector_position_generator import \ PredictorCorrectorPositionGenerator from crystal_diffusion.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell + from tests.generators.conftest import BaseTestGenerator diff --git a/tests/generators/test_sde_position_generator.py b/tests/generators/test_sde_position_generator.py index 03b78142..7950e90d 100644 --- a/tests/generators/test_sde_position_generator.py +++ b/tests/generators/test_sde_position_generator.py @@ -1,10 +1,10 @@ import pytest import torch - from crystal_diffusion.generators.sde_position_generator import ( SDE, ExplodingVarianceSDEPositionGenerator, SDESamplingParameters) -from crystal_diffusion.samplers.variance_sampler import ( +from src.crystal_diffusion.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) + from tests.generators.conftest import BaseTestGenerator diff --git a/tests/models/score_network/test_force_field_augmented_score_network.py b/tests/models/score_network/test_force_field_augmented_score_network.py index da973530..498b039b 100644 --- a/tests/models/score_network/test_force_field_augmented_score_network.py +++ b/tests/models/score_network/test_force_field_augmented_score_network.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.models.score_networks.force_field_augmented_score_network import ( ForceFieldAugmentedScoreNetwork, ForceFieldParameters) from crystal_diffusion.models.score_networks.mlp_score_network import ( diff --git a/tests/models/score_network/test_score_network.py b/tests/models/score_network/test_score_network.py index fcb54d0c..d9a60e94 100644 --- a/tests/models/score_network/test_score_network.py +++ b/tests/models/score_network/test_score_network.py @@ -6,7 +6,6 @@ import numpy as np import pytest import torch - from crystal_diffusion.models.score_networks.diffusion_mace_score_network import ( DiffusionMACEScoreNetwork, DiffusionMACEScoreNetworkParameters) from crystal_diffusion.models.score_networks.egnn_score_network import ( diff --git a/tests/models/score_network/test_score_prediction_head.py b/tests/models/score_network/test_score_prediction_head.py index 6ad54083..447cf336 100644 --- a/tests/models/score_network/test_score_prediction_head.py +++ b/tests/models/score_network/test_score_prediction_head.py @@ -1,12 +1,11 @@ import pytest import torch -from e3nn import o3 - from crystal_diffusion.models.mace_utils import \ build_mace_output_nodes_irreducible_representation from crystal_diffusion.models.score_networks.score_prediction_head import ( MaceEquivariantScorePredictionHead, MaceEquivariantScorePredictionHeadParameters) +from e3nn import o3 class TestMaceEquivariantScorePredictionHead: diff --git a/tests/models/test_analytical_score_network.py b/tests/models/test_analytical_score_network.py index 7a5cdf45..7a14d3e4 100644 --- a/tests/models/test_analytical_score_network.py +++ b/tests/models/test_analytical_score_network.py @@ -2,7 +2,6 @@ import pytest import torch - from crystal_diffusion.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) diff --git a/tests/models/test_diffusion_mace.py b/tests/models/test_diffusion_mace.py index b57d8394..db03e61b 100644 --- a/tests/models/test_diffusion_mace.py +++ b/tests/models/test_diffusion_mace.py @@ -1,8 +1,5 @@ import pytest import torch -from e3nn import o3 -from mace.modules import gate_dict, interaction_classes - from crystal_diffusion.models.diffusion_mace import (DiffusionMACE, LinearVectorReadoutBlock, input_to_diffusion_mace) @@ -14,6 +11,8 @@ get_positions_from_coordinates, get_reciprocal_basis_vectors, get_relative_coordinates_from_cartesian_positions, map_relative_coordinates_to_unit_cell) +from e3nn import o3 +from mace.modules import gate_dict, interaction_classes def test_linear_vector_readout_block(): diff --git a/tests/models/test_egnn.py b/tests/models/test_egnn.py index 98d615ea..c7e11a67 100644 --- a/tests/models/test_egnn.py +++ b/tests/models/test_egnn.py @@ -3,7 +3,6 @@ import pytest import torch - from crystal_diffusion.models.egnn import E_GCL, EGNN diff --git a/tests/models/test_egnn_utils.py b/tests/models/test_egnn_utils.py index 81f58776..8dc211ff 100644 --- a/tests/models/test_egnn_utils.py +++ b/tests/models/test_egnn_utils.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.models.egnn_utils import (unsorted_segment_mean, unsorted_segment_sum) diff --git a/tests/models/test_loss.py b/tests/models/test_loss.py index 42ea6063..674ee48e 100644 --- a/tests/models/test_loss.py +++ b/tests/models/test_loss.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.models.loss import (MSELossParameters, WeightedMSELossParameters, create_loss_calculator) diff --git a/tests/models/test_mace_utils.py b/tests/models/test_mace_utils.py index ecf5613a..71c50751 100644 --- a/tests/models/test_mace_utils.py +++ b/tests/models/test_mace_utils.py @@ -4,17 +4,17 @@ import numpy as np import pytest import torch -from e3nn import o3 -from mace.data import AtomicData, Configuration -from mace.tools import get_atomic_number_table_from_zs -from mace.tools.torch_geometric.dataloader import Collater - from crystal_diffusion.models.mace_utils import ( get_normalized_irreps_permutation_indices, get_pretrained_mace, input_to_mace, reshape_from_e3nn_to_mace, reshape_from_mace_to_e3nn) from crystal_diffusion.namespace import NOISY_CARTESIAN_POSITIONS, UNIT_CELL from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates +from e3nn import o3 +from mace.data import AtomicData, Configuration +from mace.tools import get_atomic_number_table_from_zs +from mace.tools.torch_geometric.dataloader import Collater + from tests.fake_data_utils import find_aligning_permutation diff --git a/tests/models/test_mtp.py b/tests/models/test_mtp.py index 13a661ae..0b31c655 100644 --- a/tests/models/test_mtp.py +++ b/tests/models/test_mtp.py @@ -5,14 +5,13 @@ import pandas as pd import pytest import yaml -from pymatgen.core import Structure -from sklearn.metrics import mean_absolute_error - from crystal_diffusion.mlip.mtp_utils import ( MTPInputs, extract_energy_from_thermo_log, extract_structure_and_forces_from_file, get_metrics_from_pred, prepare_mtp_inputs_from_lammps) from crystal_diffusion.models.mlip.mtp import MTPArguments, MTPWithMLIP3 +from pymatgen.core import Structure +from sklearn.metrics import mean_absolute_error class FakeStructure: diff --git a/tests/models/test_optimizer.py b/tests/models/test_optimizer.py index 3f3ec34c..11e3e98b 100644 --- a/tests/models/test_optimizer.py +++ b/tests/models/test_optimizer.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.models.optimizer import (OptimizerParameters, load_optimizer) diff --git a/tests/models/test_position_diffusion_lightning_model.py b/tests/models/test_position_diffusion_lightning_model.py index 2700ee34..6d3bd49c 100644 --- a/tests/models/test_position_diffusion_lightning_model.py +++ b/tests/models/test_position_diffusion_lightning_model.py @@ -1,8 +1,5 @@ import pytest import torch -from pytorch_lightning import LightningDataModule, Trainer -from torch.utils.data import DataLoader, random_split - from crystal_diffusion.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters from crystal_diffusion.metrics.sampling_metrics_parameters import \ @@ -16,13 +13,15 @@ from crystal_diffusion.models.score_networks.mlp_score_network import \ MLPScoreNetworkParameters from crystal_diffusion.namespace import CARTESIAN_FORCES, RELATIVE_COORDINATES -from crystal_diffusion.samplers.variance_sampler import NoiseParameters from crystal_diffusion.samples.diffusion_sampling_parameters import \ DiffusionSamplingParameters from crystal_diffusion.score.wrapped_gaussian_score import \ get_sigma_normalized_score_brute_force from crystal_diffusion.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions +from pytorch_lightning import LightningDataModule, Trainer +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from torch.utils.data import DataLoader, random_split class FakePositionsDataModule(LightningDataModule): diff --git a/tests/models/test_scheduler.py b/tests/models/test_scheduler.py index 791ec753..57b141eb 100644 --- a/tests/models/test_scheduler.py +++ b/tests/models/test_scheduler.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.models.optimizer import (OptimizerParameters, load_optimizer) from crystal_diffusion.models.scheduler import ( diff --git a/tests/models/test_score_fokker_planck_error.py b/tests/models/test_score_fokker_planck_error.py index cca17b8e..672d5a90 100644 --- a/tests/models/test_score_fokker_planck_error.py +++ b/tests/models/test_score_fokker_planck_error.py @@ -3,9 +3,6 @@ import einops import pytest import torch - -from crystal_diffusion.models.normalized_score_fokker_planck_error import \ - NormalizedScoreFokkerPlanckError from crystal_diffusion.models.score_networks.egnn_score_network import \ EGNNScoreNetworkParameters from crystal_diffusion.models.score_networks.score_network_factory import \ @@ -13,7 +10,9 @@ from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.models.normalized_score_fokker_planck_error import \ + NormalizedScoreFokkerPlanckError +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters def get_finite_difference_time_derivative( diff --git a/tests/oracle/test_lammps.py b/tests/oracle/test_lammps.py index fb29bebe..cb342d90 100644 --- a/tests/oracle/test_lammps.py +++ b/tests/oracle/test_lammps.py @@ -1,6 +1,5 @@ import numpy as np import pytest - from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps diff --git a/tests/samplers/test_exploding_variance.py b/tests/samplers/test_exploding_variance.py index 3b317304..551471fb 100644 --- a/tests/samplers/test_exploding_variance.py +++ b/tests/samplers/test_exploding_variance.py @@ -1,8 +1,7 @@ import pytest import torch - from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters class TestExplodingVariance: diff --git a/tests/samplers/test_noisy_relative_coordinates_sampler.py b/tests/samplers/test_noisy_relative_coordinates_sampler.py index 48e9f7c0..a087b8c6 100644 --- a/tests/samplers/test_noisy_relative_coordinates_sampler.py +++ b/tests/samplers/test_noisy_relative_coordinates_sampler.py @@ -1,7 +1,6 @@ import numpy as np import pytest import torch - from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler diff --git a/tests/samplers/test_variance_sampler.py b/tests/samplers/test_variance_sampler.py index 4aaa048f..50779272 100644 --- a/tests/samplers/test_variance_sampler.py +++ b/tests/samplers/test_variance_sampler.py @@ -1,7 +1,6 @@ import pytest import torch - -from crystal_diffusion.samplers.variance_sampler import ( +from src.crystal_diffusion.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) diff --git a/tests/samples_and_metrics/test_sampling.py b/tests/samples_and_metrics/test_sampling.py index 02260924..fb939520 100644 --- a/tests/samples_and_metrics/test_sampling.py +++ b/tests/samples_and_metrics/test_sampling.py @@ -1,14 +1,13 @@ import einops import pytest import torch - -from crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) from crystal_diffusion.namespace import (CARTESIAN_POSITIONS, RELATIVE_COORDINATES, UNIT_CELL) -from crystal_diffusion.samples.sampling import create_batch_of_samples from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates +from src.crystal_diffusion.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from src.crystal_diffusion.samples.sampling import create_batch_of_samples class DummyGenerator(PositionGenerator): diff --git a/tests/score/test_wrapped_gaussian_score.py b/tests/score/test_wrapped_gaussian_score.py index e2a19dc5..0787bede 100644 --- a/tests/score/test_wrapped_gaussian_score.py +++ b/tests/score/test_wrapped_gaussian_score.py @@ -1,7 +1,6 @@ import numpy as np import pytest import torch - from crystal_diffusion.score.wrapped_gaussian_score import ( SIGMA_THRESHOLD, _get_large_sigma_mask, _get_s1a_exponential, _get_s1b_exponential, _get_sigma_normalized_s2, diff --git a/tests/test_hp_utils.py b/tests/test_hp_utils.py index e964d3e0..d35c8b71 100644 --- a/tests/test_hp_utils.py +++ b/tests/test_hp_utils.py @@ -1,5 +1,4 @@ import pytest - from crystal_diffusion.utils.hp_utils import check_hp diff --git a/tests/test_sample_diffusion.py b/tests/test_sample_diffusion.py index aa652e40..56c6e07a 100644 --- a/tests/test_sample_diffusion.py +++ b/tests/test_sample_diffusion.py @@ -3,7 +3,6 @@ import pytest import torch import yaml - from crystal_diffusion import sample_diffusion from crystal_diffusion.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters @@ -14,7 +13,7 @@ from crystal_diffusion.models.score_networks.mlp_score_network import \ MLPScoreNetworkParameters from crystal_diffusion.namespace import RELATIVE_COORDINATES -from crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters @pytest.fixture() diff --git a/tests/test_train_diffusion.py b/tests/test_train_diffusion.py index 0df62750..e7866b6f 100644 --- a/tests/test_train_diffusion.py +++ b/tests/test_train_diffusion.py @@ -12,10 +12,10 @@ import numpy as np import pytest import yaml - from crystal_diffusion import train_diffusion from crystal_diffusion.callbacks.standard_callbacks import (BEST_MODEL_NAME, LAST_MODEL_NAME) + from tests.conftest import TestDiffusionDataBase best_model_regex = re.compile(r"best_model-epoch=(?P(\d+)).*.ckpt") diff --git a/tests/utils/test_basis_transformations.py b/tests/utils/test_basis_transformations.py index 3b84199c..62541103 100644 --- a/tests/utils/test_basis_transformations.py +++ b/tests/utils/test_basis_transformations.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.utils.basis_transformations import ( get_positions_from_coordinates, get_reciprocal_basis_vectors, get_relative_coordinates_from_cartesian_positions, diff --git a/tests/utils/test_configuration_parsing.py b/tests/utils/test_configuration_parsing.py index aea8fc71..c72fc92c 100644 --- a/tests/utils/test_configuration_parsing.py +++ b/tests/utils/test_configuration_parsing.py @@ -1,7 +1,6 @@ from dataclasses import asdict, dataclass import pytest - from crystal_diffusion.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary diff --git a/tests/utils/test_neighbors.py b/tests/utils/test_neighbors.py index 10529c72..aee6dc0e 100644 --- a/tests/utils/test_neighbors.py +++ b/tests/utils/test_neighbors.py @@ -3,8 +3,6 @@ import numpy as np import pytest import torch -from pymatgen.core import Lattice, Structure - from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates from crystal_diffusion.utils.neighbors import ( @@ -12,6 +10,8 @@ _get_shifted_positions, _get_shortest_distance_that_crosses_unit_cell, _get_vectors_from_multiple_indices, get_periodic_adjacency_information, shift_adjacency_matrix_indices_for_graph_batching) +from pymatgen.core import Lattice, Structure + from tests.fake_data_utils import find_aligning_permutation Neighbors = namedtuple("Neighbors", ["source_index", "destination_index", "displacement", "shift"]) diff --git a/tests/utils/test_sample_trajectory.py b/tests/utils/test_sample_trajectory.py index ab6e7a97..bef3e266 100644 --- a/tests/utils/test_sample_trajectory.py +++ b/tests/utils/test_sample_trajectory.py @@ -3,7 +3,6 @@ import einops import pytest import torch - from crystal_diffusion.utils.sample_trajectory import \ PredictorCorrectorSampleTrajectory diff --git a/tests/utils/test_structure_utils.py b/tests/utils/test_structure_utils.py index 1f366968..d73cab76 100644 --- a/tests/utils/test_structure_utils.py +++ b/tests/utils/test_structure_utils.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.utils.basis_transformations import \ get_positions_from_coordinates from crystal_diffusion.utils.structure_utils import ( diff --git a/tests/utils/test_tensor_utils.py b/tests/utils/test_tensor_utils.py index 5a07b452..dee8184c 100644 --- a/tests/utils/test_tensor_utils.py +++ b/tests/utils/test_tensor_utils.py @@ -1,6 +1,5 @@ import pytest import torch - from crystal_diffusion.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions From 852ed01a8b789ce93a82e40de5bc75a846e5f8d4 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Mon, 21 Oct 2024 21:43:41 -0400 Subject: [PATCH 19/29] Cleaned up imports --- experiments/analysis_utils.py | 6 +- .../__init__.py | 2 +- .../activelearning_dataclasses.py | 7 +- .../active_learning_loop/benchmark.py | 182 ++++++---- .../active_learning_loop/oracle.py | 39 ++- .../active_learning_loop/utils.py | 56 ++-- .../generator_sample_analysis_utils.py | 14 +- .../analysis/ovito_visualisation.py | 53 ++- .../callbacks/callback_loader.py | 31 +- .../callbacks/loss_monitoring_callback.py | 116 ++++--- .../sampling_visualization_callback.py | 8 +- .../callbacks/standard_callbacks.py | 32 +- .../data/diffusion/data_loader.py | 131 +++++--- .../data/diffusion/data_preprocess.py | 109 ++++-- .../data/parse_lammps_outputs.py | 64 ++-- .../data/utils.py | 24 +- .../constrained_langevin_generator.py | 108 +++--- .../generators/instantiate_generator.py | 56 ++-- .../generators/langevin_generator.py | 135 +++++--- .../generators/load_sampling_parameters.py | 37 ++- .../generators/ode_position_generator.py | 190 +++++++---- .../generators/position_generator.py | 17 +- .../predictor_corrector_position_generator.py | 72 ++-- .../generators/sde_position_generator.py | 248 +++++++++----- .../loggers/logger_loader.py | 84 +++-- .../main_utils.py | 92 +++-- .../metrics/kolmogorov_smirnov_metrics.py | 9 +- .../metrics/sampling_metrics_parameters.py | 5 +- .../mlip/mtp_train.py | 80 +++-- .../mlip/mtp_utils.py | 127 ++++--- .../models/diffusion_mace.py | 204 ++++++++---- .../models/egnn.py | 84 +++-- .../models/egnn_utils.py | 46 ++- .../models/graph_utils.py | 19 +- .../models/instantiate_diffusion_model.py | 43 ++- .../models/loss.py | 88 +++-- .../models/mace_utils.py | 106 ++++-- .../models/mlip/mtp.py | 163 +++++---- .../normalized_score_fokker_planck_error.py | 21 +- .../models/optimizer.py | 32 +- .../position_diffusion_lightning_model.py | 50 +-- .../models/scheduler.py | 5 +- .../models/score_networks/__init__.py | 2 +- .../analytical_score_network.py | 156 ++++++--- .../diffusion_mace_score_network.py | 74 +++-- .../score_networks/egnn_score_network.py | 84 +++-- .../force_field_augmented_score_network.py | 16 +- .../score_networks/mace_score_network.py | 96 ++++-- .../score_networks/mlp_score_network.py | 55 ++- .../models/score_networks/score_network.py | 95 ++++-- .../score_networks/score_network_factory.py | 99 +++--- .../score_networks/score_prediction_head.py | 97 ++++-- .../namespace.py | 12 +- .../oracle/__init__.py | 0 .../oracle/energies.py | 24 +- .../oracle/lammps.py | 64 ++-- .../sample_diffusion.py | 31 +- .../samplers/exploding_variance.py | 22 +- .../noisy_relative_coordinates_sampler.py | 23 +- .../samplers/variance_sampler.py | 71 ++-- .../samples/diffusion_sampling_parameters.py | 58 ++-- .../samples/sampling.py | 47 ++- .../score/wrapped_gaussian_score.py | 57 +++- .../train_diffusion.py | 150 +++++---- .../utils/basis_transformations.py | 15 +- .../utils/configuration_parsing.py | 16 +- .../utils/file_utils.py | 2 +- .../utils/hp_utils.py | 8 +- .../utils/logging_utils.py | 32 +- .../utils/neighbors.py | 158 ++++++--- .../utils/ovito_utils.py | 1 + .../utils/sample_trajectory.py | 199 +++++++---- .../utils/structure_utils.py | 84 +++-- .../utils/tensor_utils.py | 12 +- tests/active_learning_loop/test_benchmark.py | 57 ++-- tests/analysis/test_ovito_visualisation.py | 42 +-- tests/conftest.py | 47 ++- tests/data/diffusion/test_data_loader.py | 176 ++++++---- tests/data/diffusion/test_data_preprocess.py | 114 +++++-- tests/data/test_parse_lammps_output.py | 80 +++-- tests/data/test_utils.py | 6 +- tests/fake_data_utils.py | 197 +++++++---- tests/generators/conftest.py | 22 +- .../test_constrained_langevin_generator.py | 45 ++- tests/generators/test_langevin_generator.py | 115 +++++-- .../generators/test_ode_position_generator.py | 77 +++-- ..._predictor_corrector_position_generator.py | 57 +++- .../generators/test_sde_position_generator.py | 133 +++++--- ...est_force_field_augmented_score_network.py | 13 +- .../score_network/test_score_network.py | 313 ++++++++++++------ .../test_score_prediction_head.py | 34 +- tests/models/test_analytical_score_network.py | 114 +++++-- tests/models/test_diffusion_mace.py | 291 ++++++++++------ tests/models/test_egnn.py | 191 +++++++---- tests/models/test_egnn_utils.py | 17 +- tests/models/test_loss.py | 61 ++-- tests/models/test_mace_utils.py | 212 ++++++++---- tests/models/test_mtp.py | 191 +++++++---- tests/models/test_optimizer.py | 12 +- ...test_position_diffusion_lightning_model.py | 133 +++++--- tests/models/test_scheduler.py | 30 +- .../models/test_score_fokker_planck_error.py | 17 +- tests/oracle/test_lammps.py | 22 +- tests/samplers/test_exploding_variance.py | 19 +- ...test_noisy_relative_coordinates_sampler.py | 9 +- tests/samplers/test_variance_sampler.py | 44 ++- tests/samples_and_metrics/test_sampling.py | 16 +- tests/score/test_wrapped_gaussian_score.py | 23 +- tests/test_hp_utils.py | 20 +- tests/test_sample_diffusion.py | 23 +- tests/test_train_diffusion.py | 291 +++++++++------- tests/utils/test_basis_transformations.py | 52 ++- tests/utils/test_configuration_parsing.py | 28 +- tests/utils/test_neighbors.py | 204 ++++++++---- tests/utils/test_sample_trajectory.py | 235 ++++++++----- tests/utils/test_structure_utils.py | 5 +- tests/utils/test_tensor_utils.py | 11 +- 117 files changed, 5772 insertions(+), 2882 deletions(-) create mode 100644 src/diffusion_for_multi_scale_molecular_dynamics/oracle/__init__.py diff --git a/experiments/analysis_utils.py b/experiments/analysis_utils.py index c3452bee..3f92569f 100644 --- a/experiments/analysis_utils.py +++ b/experiments/analysis_utils.py @@ -4,10 +4,10 @@ from typing import Tuple import pandas as pd -from crystal_diffusion import DATA_DIR -from src.crystal_diffusion.data.parse_lammps_outputs import \ - parse_lammps_thermo_log +from diffusion_for_multi_scale_molecular_dynamics import DATA_DIR +from diffusion_for_multi_scale_molecular_dynamics.data.parse_lammps_outputs import \ + parse_lammps_thermo_log from experiments import EXPERIMENT_ANALYSIS_DIR logger = logging.getLogger(__name__) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/__init__.py index 7c38fcbb..babbfa2c 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/__init__.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/__init__.py @@ -1,6 +1,6 @@ from pathlib import Path -ROOT_DIR = Path(__file__).parent +ROOT_DIR = Path(__file__).parent.parent TOP_DIR = ROOT_DIR.parent ANALYSIS_RESULTS_DIR = TOP_DIR.joinpath("analysis_results/") ANALYSIS_RESULTS_DIR.mkdir(exist_ok=True) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/activelearning_dataclasses.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/activelearning_dataclasses.py index c5333d3c..cdb4e34b 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/activelearning_dataclasses.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/activelearning_dataclasses.py @@ -4,6 +4,7 @@ @dataclass(kw_only=True) class ActiveLearningDataArguments: """Paths to the training, validaition datasets and output directory.""" + training_data_dir: str # training data directory evaluation_data_dir: str # evaluation data directory output_dir: str # directory where to save the results @@ -12,7 +13,8 @@ class ActiveLearningDataArguments: @dataclass(kw_only=True) class StructureEvaluationArguments: """Parameters related to the MLIP evaluation.""" - evaluation_criteria: str = 'nbh_grades' + + evaluation_criteria: str = "nbh_grades" criteria_threshold: float = 10 number_of_structures: int = None extraction_radius: float = 3 @@ -21,4 +23,5 @@ class StructureEvaluationArguments: @dataclass(kw_only=True) class RepaintingArguments: """Parameters related to the structure generation model.""" - model: str = 'dev_dummy' + + model: str = "dev_dummy" diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py index de3773af..1ae2ee03 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/benchmark.py @@ -5,17 +5,21 @@ import numpy as np import pandas as pd import yaml -from crystal_diffusion.active_learning_loop.utils import ( - extract_target_region, get_structures_for_retraining) -from crystal_diffusion.models.mlip.mtp import MTPWithMLIP3 from hydra.utils import instantiate +from diffusion_for_multi_scale_molecular_dynamics.active_learning_loop.utils import ( + extract_target_region, get_structures_for_retraining) +from diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp import \ + MTPWithMLIP3 + class ActiveLearningLoop: """Method to train, evaluate and fine-tune a MLIP.""" - def __init__(self, - meta_config: str, - ): + + def __init__( + self, + meta_config: str, + ): """Active learning benchmark. Includes methods to train & evaluate a MLIP, isolate bad sub-structures, repaint new structures and retrain @@ -24,14 +28,23 @@ def __init__(self, Args: meta_config: path to a yaml configuration with the parameters for the modules in the class """ - assert os.path.exists(meta_config), "configuration file for active learning loop does not exist." + assert os.path.exists( + meta_config + ), "configuration file for active learning loop does not exist." # define the modules in the __init__ function - self.data_paths, self.mlip_model, self.eval_config, self.structure_generation = None, None, None, None + ( + self.data_paths, + self.mlip_model, + self.eval_config, + self.structure_generation, + ) = (None, None, None, None) self.oracle = None # use hydra to convert the yaml into modules and other data classes self.parse_config(meta_config) self.atom_dict = {1: "Si"} # TODO this should be define somewhere smart - self.trained_mlips = [] # history of trained MLIPs (optional - not sure if we should keep this) + self.trained_mlips = ( + [] + ) # history of trained MLIPs (optional - not sure if we should keep this) self.training_sets = [] # history of training sets def parse_config(self, meta_config: str): @@ -45,18 +58,18 @@ def parse_config(self, meta_config: str): Args: meta_config: path to configuration yaml file """ - with open(meta_config, 'r') as stream: + with open(meta_config, "r") as stream: meta_config = yaml.load(stream, Loader=yaml.FullLoader) # paths to the training & evaluation datasets - self.data_paths = instantiate(meta_config['active_learning_data']) + self.data_paths = instantiate(meta_config["active_learning_data"]) # MLIP model - for example MTP - self.mlip_model = instantiate(meta_config['mlip']) + self.mlip_model = instantiate(meta_config["mlip"]) # parameters to find and isolate the problematic regions in the evaluation dataset - self.eval_config = instantiate(meta_config['structure_evaluation']) + self.eval_config = instantiate(meta_config["structure_evaluation"]) # structure generation module - self.structure_generation = instantiate(meta_config['repainting_model']) + self.structure_generation = instantiate(meta_config["repainting_model"]) # force labeling module - self.oracle = instantiate(meta_config['oracle']) + self.oracle = instantiate(meta_config["oracle"]) def train_mlip(self, round: int = 1, training_set: Optional[Any] = None) -> str: """Train a MLIP using the parameters specified in the configuration file. @@ -72,19 +85,29 @@ def train_mlip(self, round: int = 1, training_set: Optional[Any] = None) -> str: """ if training_set is None: if len(self.training_sets) == 0: - self.training_sets = [self.mlip_model.prepare_dataset_from_lammps( - root_data_dir=self.data_paths.training_data_dir, - atom_dict=self.atom_dict, - mode="train" - )] + self.training_sets = [ + self.mlip_model.prepare_dataset_from_lammps( + root_data_dir=self.data_paths.training_data_dir, + atom_dict=self.atom_dict, + mode="train", + ) + ] training_set = self.mlip_model.merge_inputs(self.training_sets) - trained_mtp = self.mlip_model.train(training_set, mlip_name=f'mlip_round_{round}') - self.trained_mlips.append(trained_mtp) # history of trained MLIPs ... not sure if useful + trained_mtp = self.mlip_model.train( + training_set, mlip_name=f"mlip_round_{round}" + ) + self.trained_mlips.append( + trained_mtp + ) # history of trained MLIPs ... not sure if useful return trained_mtp - def evaluate_mlip(self, round: int = 1, mlip_name: Optional[str] = None, forces_available: bool = True - ) -> pd.DataFrame: + def evaluate_mlip( + self, + round: int = 1, + mlip_name: Optional[str] = None, + forces_available: bool = True, + ) -> pd.DataFrame: """Evaluate a MLIP using the parameters specified in the configuration file. Args: @@ -99,13 +122,17 @@ def evaluate_mlip(self, round: int = 1, mlip_name: Optional[str] = None, forces_ root_data_dir=self.data_paths.evaluation_data_dir, atom_dict=self.atom_dict, mode="evaluation", - get_forces=forces_available + get_forces=forces_available, ) # first returned element is the ground truth DF # TODO make sure this works even if the GT is not available... if mlip_name is None: - mlip_name = os.path.join(self.mlip_model.savedir, f'mlip_round_{round}.almtp') - _, prediction_df = self.mlip_model.evaluate(evaluation_dataset, mlip_name=mlip_name) + mlip_name = os.path.join( + self.mlip_model.savedir, f"mlip_round_{round}.almtp" + ) + _, prediction_df = self.mlip_model.evaluate( + evaluation_dataset, mlip_name=mlip_name + ) return prediction_df @@ -120,13 +147,17 @@ def get_bad_structures(self, prediction_df: pd.DataFrame) -> List[pd.DataFrame]: list of structures with a high uncertainty criteria. """ num_structures = self.eval_config.number_of_structures - structures_to_retrain = get_structures_for_retraining(prediction_df, - criteria_threshold=self.eval_config.criteria_threshold, - number_of_structures=num_structures, - evaluation_criteria=self.eval_config.evaluation_criteria) + structures_to_retrain = get_structures_for_retraining( + prediction_df, + criteria_threshold=self.eval_config.criteria_threshold, + number_of_structures=num_structures, + evaluation_criteria=self.eval_config.evaluation_criteria, + ) return structures_to_retrain - def excise_worst_atom(self, structures_to_retrain: List[pd.DataFrame]) -> List[pd.DataFrame]: + def excise_worst_atom( + self, structures_to_retrain: List[pd.DataFrame] + ) -> List[pd.DataFrame]: """For a given structure, isolate the atom with the highest uncertainty criteria. Args: @@ -138,16 +169,19 @@ def excise_worst_atom(self, structures_to_retrain: List[pd.DataFrame]) -> List[p # we assume the extraction region to be a sphere of radius extraction_radius around the worst atoms # if more than 1 atom are bad in a structure, we only extract the worst # TODO implement other extraction methods - bad_regions = [extract_target_region(s, - extraction_radius=self.eval_config.extraction_radius, - evaluation_criteria=self.eval_config.evaluation_criteria) - for s in structures_to_retrain] + bad_regions = [ + extract_target_region( + s, + extraction_radius=self.eval_config.extraction_radius, + evaluation_criteria=self.eval_config.evaluation_criteria, + ) + for s in structures_to_retrain + ] return bad_regions - def get_structure_candidate_from_generative_model(self, - fixed_atoms: pd.DataFrame, - number_of_candidates: int = 1 - ) -> pd.DataFrame: + def get_structure_candidate_from_generative_model( + self, fixed_atoms: pd.DataFrame, number_of_candidates: int = 1 + ) -> pd.DataFrame: """Generate new structures around the specified fixed atoms. Args: @@ -159,11 +193,13 @@ def get_structure_candidate_from_generative_model(self, """ # TODO: call the diffusion model and get number_of_candidates samples with repaint using the fixed_atoms - if self.structure_generation.model == 'dev_dummy': # replace with a wrapper around the diffusion model + if ( + self.structure_generation.model == "dev_dummy" + ): # replace with a wrapper around the diffusion model # and hydra instantiate return fixed_atoms else: - raise NotImplementedError('Only dev_dummy is supported at the moment.') + raise NotImplementedError("Only dev_dummy is supported at the moment.") def new_structure_to_csv(self, new_structures: List[pd.DataFrame], round: int = 1): """Save the generated structures in a csv format in the output dir. @@ -172,10 +208,14 @@ def new_structure_to_csv(self, new_structures: List[pd.DataFrame], round: int = new_structures: structures proposed by the generative model round: current round of training. Defaults to 1. """ - root_data_dir = os.path.join(self.data_paths.output_dir, f'new_structures_round_{round}') + root_data_dir = os.path.join( + self.data_paths.output_dir, f"new_structures_round_{round}" + ) os.makedirs(root_data_dir, exist_ok=True) for i, new_struc in enumerate(new_structures): - new_struc.to_csv(os.path.join(root_data_dir, f'structure_{i}.csv'), index=False) + new_struc.to_csv( + os.path.join(root_data_dir, f"structure_{i}.csv"), index=False + ) def get_labels_from_oracle(self, round: int = 1) -> Any: """Compute energy and forces from an oracle such as LAMMPS for the new candidates generated in a round of AL. @@ -187,11 +227,19 @@ def get_labels_from_oracle(self, round: int = 1) -> Any: mlip data input (for example, MTPInputs) """ new_labeled_samples = [] - for file in os.listdir(os.path.join(self.data_paths.output_dir, f'new_structures_round_{round}')): - if file.endswith('.csv'): - new_labeled_samples.append(self.call_oracle( - os.path.join(self.data_paths.output_dir, f'new_structures_round_{round}', file) - )) + for file in os.listdir( + os.path.join(self.data_paths.output_dir, f"new_structures_round_{round}") + ): + if file.endswith(".csv"): + new_labeled_samples.append( + self.call_oracle( + os.path.join( + self.data_paths.output_dir, + f"new_structures_round_{round}", + file, + ) + ) + ) new_labeled_samples = self.mlip_model.merge_inputs(new_labeled_samples) return new_labeled_samples @@ -205,9 +253,11 @@ def call_oracle(self, path_to_file: str) -> Any: mlip data inputs (for example, MTPInputs) """ data = pd.read_csv(path_to_file) - cartesian_positions = data[['x', 'y', 'z']].to_numpy() + cartesian_positions = data[["x", "y", "z"]].to_numpy() box = np.eye(3, 3) * 5.43 # TODO this is bad - fix this - atom_type = np.ones(cartesian_positions.shape[0], dtype=np.integer) # TODO also bad + atom_type = np.ones( + cartesian_positions.shape[0], dtype=np.integer + ) # TODO also bad energy, forces = self.oracle(cartesian_positions, box, atom_type) labels_as_mtp = self.mlip_model.prepare_dataset_from_numpy( cartesian_positions, @@ -218,8 +268,9 @@ def call_oracle(self, path_to_file: str) -> Any: ) return labels_as_mtp - def round_of_active_learning_loop(self, trained_mlip: Optional[MTPWithMLIP3] = None - ) -> Tuple[pd.DataFrame, pd.DataFrame]: + def round_of_active_learning_loop( + self, trained_mlip: Optional[MTPWithMLIP3] = None + ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Do a full loop of activate learning. The following steps are done in sequence: @@ -248,16 +299,22 @@ def round_of_active_learning_loop(self, trained_mlip: Optional[MTPWithMLIP3] = N pred_df = self.evaluate_mlip(mlip_name=trained_mlip) bad_structures = self.get_bad_structures(pred_df) bad_regions = self.excise_worst_atom(bad_structures) - new_candidates = [self.get_structure_candidate_from_generative_model(x) for x in bad_regions] + new_candidates = [ + self.get_structure_candidate_from_generative_model(x) for x in bad_regions + ] self.new_structure_to_csv(new_candidates) new_labeled_candidates = self.get_labels_from_oracle() - new_training_set = self.mlip_model.merge_inputs([self.training_sets[-1], new_labeled_candidates]) + new_training_set = self.mlip_model.merge_inputs( + [self.training_sets[-1], new_labeled_candidates] + ) self.training_sets.append(new_training_set) new_mtp = self.train_mlip() new_pred_df = self.evaluate_mlip(mlip_name=new_mtp) return pred_df, new_pred_df - def evaluate_mtp_update(self, original_predictions: pd.DataFrame, updated_predictions) -> Tuple[float, float]: + def evaluate_mtp_update( + self, original_predictions: pd.DataFrame, updated_predictions + ) -> Tuple[float, float]: """Find the evaluation criteria in the original predictions and the corresponding value after retraining. Args: @@ -272,10 +329,13 @@ def evaluate_mtp_update(self, original_predictions: pd.DataFrame, updated_predic # TODO we assume a max - but it could be a min i criteria = self.eval_config.evaluation_criteria atom_index, structure_index, original_value = original_predictions.iloc[ - original_predictions[criteria].argmax()][['atom_index', 'structure_index', criteria]] + original_predictions[criteria].argmax() + ][["atom_index", "structure_index", criteria]] updated_value = updated_predictions.loc[ - (updated_predictions['atom_index'] == atom_index) - & (updated_predictions['structure_index'] == structure_index), criteria].values.item() + (updated_predictions["atom_index"] == atom_index) + & (updated_predictions["structure_index"] == structure_index), + criteria, + ].values.item() return original_value, updated_value @@ -286,7 +346,7 @@ def get_arguments() -> argparse.Namespace: args: arguments """ parser = argparse.ArgumentParser() - parser.add_argument('--config', help='path to data directory', required=True) + parser.add_argument("--config", help="path to data directory", required=True) args = parser.parse_args() return args @@ -301,5 +361,5 @@ def main(): al_loop.evaluate_mtp_update(initial_df, new_df) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py index f54f04e9..eec72e20 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/oracle.py @@ -2,23 +2,28 @@ from typing import Dict, Tuple import numpy as np -from crystal_diffusion import DATA_DIR -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps + +from diffusion_for_multi_scale_molecular_dynamics import DATA_DIR +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps class LAMMPS_for_active_learning: """Oracle using LAMMPS to get the energy and forces on atoms.""" + def __init__(self): """Initialize the class.""" pass - def __call__(self, - cartesian_positions: np.ndarray, - box: np.ndarray, - atom_types: np.ndarray, - atom_type_map: Dict[int, str] = {1: 'Si'}, - tmp_work_dir: str = './', - pair_coeff_dir: Path = DATA_DIR) -> Tuple[float, np.ndarray]: + def __call__( + self, + cartesian_positions: np.ndarray, + box: np.ndarray, + atom_types: np.ndarray, + atom_type_map: Dict[int, str] = {1: "Si"}, + tmp_work_dir: str = "./", + pair_coeff_dir: Path = DATA_DIR, + ) -> Tuple[float, np.ndarray]: """Call LAMMPS to get energy and forces for a given set of atoms. Args: @@ -33,11 +38,19 @@ def __call__(self, energy and forces on each atom (n_atom x 3) """ shifted_positions = self.shift_positions(cartesian_positions, box) - energy, forces = get_energy_and_forces_from_lammps(shifted_positions, box, atom_types, atom_type_map, - tmp_work_dir, pair_coeff_dir) - return energy, forces[['fx', 'fy', 'fz']].to_numpy() + energy, forces = get_energy_and_forces_from_lammps( + shifted_positions, + box, + atom_types, + atom_type_map, + tmp_work_dir, + pair_coeff_dir, + ) + return energy, forces[["fx", "fy", "fz"]].to_numpy() - def shift_positions(self, cartesian_positions: np.ndarray, box: np.ndarray) -> np.ndarray: + def shift_positions( + self, cartesian_positions: np.ndarray, box: np.ndarray + ) -> np.ndarray: """Shift the positions of the atoms so all coordinates are positives. This is because LAMMPS will ignore atoms with coordinates outside the [0, a] range (a = size of the unit cell). diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/utils.py index 02833f89..bf5a466e 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/utils.py @@ -3,12 +3,13 @@ import pandas as pd -def get_structures_for_retraining(prediction_df: pd.DataFrame, - criteria_threshold: Optional[float] = None, - number_of_structures: Optional[int] = None, - evaluation_criteria: str = 'nbh_grades', - structure_index: str = 'structure_index' - ) -> List[pd.DataFrame]: +def get_structures_for_retraining( + prediction_df: pd.DataFrame, + criteria_threshold: Optional[float] = None, + number_of_structures: Optional[int] = None, + evaluation_criteria: str = "nbh_grades", + structure_index: str = "structure_index", +) -> List[pd.DataFrame]: """Find the structures with the worst value of the evaluation criteria. Args: @@ -27,26 +28,39 @@ def get_structures_for_retraining(prediction_df: pd.DataFrame, list of the structures with a bad evaluation criteria. Length of the list depends on criteria_threhold and number_of_structures. """ - assert criteria_threshold is not None or number_of_structures is not None, \ - "criteria_threshold or number_of_structures should be set." + assert ( + criteria_threshold is not None or number_of_structures is not None + ), "criteria_threshold or number_of_structures should be set." # get the highest evaluation_criteria for each structure i.e. only the worst atom counts for structure selection - criteria_by_structure = prediction_df[[evaluation_criteria, structure_index]].groupby(structure_index).max() + criteria_by_structure = ( + prediction_df[[evaluation_criteria, structure_index]] + .groupby(structure_index) + .max() + ) # find the top number_of_structures - structures_indices = criteria_by_structure.sort_values(by=evaluation_criteria, ascending=False) + structures_indices = criteria_by_structure.sort_values( + by=evaluation_criteria, ascending=False + ) if criteria_threshold is not None: - structures_indices = structures_indices[structures_indices[evaluation_criteria] >= criteria_threshold] + structures_indices = structures_indices[ + structures_indices[evaluation_criteria] >= criteria_threshold + ] structures_indices = structures_indices.index.to_list() if number_of_structures is not None: structures_indices = structures_indices[:number_of_structures] structures_to_retrain = [] for idx in structures_indices: - structures_to_retrain.append(prediction_df[prediction_df[structure_index] == idx]) + structures_to_retrain.append( + prediction_df[prediction_df[structure_index] == idx] + ) return structures_to_retrain -def extract_target_region(structure_df: pd.DataFrame, - extraction_radius: float, - evaluation_criteria: str = 'nbh_grades') -> pd.DataFrame: +def extract_target_region( + structure_df: pd.DataFrame, + extraction_radius: float, + evaluation_criteria: str = "nbh_grades", +) -> pd.DataFrame: """Extract the atom with the worst evaluation criteria and all the atoms within a distance extraction_radious. Args: @@ -60,9 +74,13 @@ def extract_target_region(structure_df: pd.DataFrame, # extract the worst ato and a region around of radius extraction_radius # TODO better method to determine radius: number of atoms ? target_atom = structure_df[evaluation_criteria].idxmax() - target_position = structure_df.loc[target_atom][['x', 'y', 'z']] + target_position = structure_df.loc[target_atom][["x", "y", "z"]] # TODO periodicity... - structure_df.loc[:, 'distance_squared'] = structure_df.apply( - lambda x: sum([(x[i] - target_position[i]) ** 2 for i in ['x', 'y', 'z']]), axis=1) - atom_positions = structure_df.loc[structure_df['distance_squared'] <= extraction_radius ** 2, ['x', 'y', 'z']] + structure_df.loc[:, "distance_squared"] = structure_df.apply( + lambda x: sum([(x[i] - target_position[i]) ** 2 for i in ["x", "y", "z"]]), + axis=1, + ) + atom_positions = structure_df.loc[ + structure_df["distance_squared"] <= extraction_radius**2, ["x", "y", "z"] + ] return atom_positions diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py index 4543c330..14c98513 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/generator_sample_analysis_utils.py @@ -1,10 +1,14 @@ import torch -from crystal_diffusion.generators.ode_position_generator import ( - ExplodingVarianceODEPositionGenerator, ODESamplingParameters) -from crystal_diffusion.models.mace_utils import get_adj_matrix -from crystal_diffusion.models.score_networks.score_network import ScoreNetwork from einops import einops -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import ( + ExplodingVarianceODEPositionGenerator, ODESamplingParameters) +from diffusion_for_multi_scale_molecular_dynamics.models.graph_utils import \ + get_adj_matrix +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters class PartialODEPositionGenerator(ExplodingVarianceODEPositionGenerator): diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/analysis/ovito_visualisation.py b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/ovito_visualisation.py index c88460bb..8401051b 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/analysis/ovito_visualisation.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/analysis/ovito_visualisation.py @@ -3,6 +3,7 @@ Draw the MD simulation with the MaxVol values. Some tweaking is required in the OVITO UI. See README_OVITO.md for more information. """ + import argparse import os @@ -14,16 +15,28 @@ def main(): """Read MTP output files and convert to xyz format readable by OVITO.""" parser = argparse.ArgumentParser() - parser.add_argument("--prediction_file", help="MTP prediction files. Should contain position and MaxVol gamma.") - parser.add_argument("--lammps_output", help="LAMMPS output file. Should contain the bounding box information.") - parser.add_argument("--output_name", help="Name of the output file that can be loaded by OVITO. ") + parser.add_argument( + "--prediction_file", + help="MTP prediction files. Should contain position and MaxVol gamma.", + ) + parser.add_argument( + "--lammps_output", + help="LAMMPS output file. Should contain the bounding box information.", + ) + parser.add_argument( + "--output_name", help="Name of the output file that can be loaded by OVITO. " + ) args = parser.parse_args() - assert os.path.exists(args.lammps_output), f"LAMMPS out file {args.lammps_output} does not exist." + assert os.path.exists( + args.lammps_output + ), f"LAMMPS out file {args.lammps_output} does not exist." lattice = get_lattice_from_lammps(args.lammps_output) - assert os.path.exists(args.prediction_file), f"Provided prediction file {args.prediction_file} does not exist." + assert os.path.exists( + args.prediction_file + ), f"Provided prediction file {args.prediction_file} does not exist." mtp_predictions_to_ovito(args.prediction_file, lattice, args.output_name) @@ -37,13 +50,17 @@ def get_lattice_from_lammps(lammps_output_file: str) -> np.ndarray: Returns: lattice: 3x3 array with lattice coordinates """ - with (open(lammps_output_file, 'r') as f): + with open(lammps_output_file, "r") as f: l_yaml = yaml.safe_load_all(f) - for d in l_yaml: # loop over LAMMPS outputs to get the MD box - we only need the first step + for ( + d + ) in ( + l_yaml + ): # loop over LAMMPS outputs to get the MD box - we only need the first step # lattice in yaml is 3 x 2 [0, x_lim] # we assume a rectangular lattice for now with the 2nd coordinates as the lattice vectors lattice = np.zeros((3, 3)) - for i, x in enumerate(d['box']): + for i, x in enumerate(d["box"]): lattice[i, i] = x[1] break return lattice @@ -57,16 +74,20 @@ def mtp_predictions_to_ovito(pred_file: str, lattice: np.ndarray, output_name: s lattice: lattice parameters in a 3x3 numpy array output_name: name of resulting file. An .xyz extension is added if not already in the name. """ - lattice = list(map(str, lattice.flatten())) # flatten and convert to string for formatting - lattice_str = 'Lattice=\"' + " ".join(lattice) + '\" Origin=\"0 0 0\" pbc=\"T T T\"' + lattice = list( + map(str, lattice.flatten()) + ) # flatten and convert to string for formatting + lattice_str = 'Lattice="' + " ".join(lattice) + '" Origin="0 0 0" pbc="T T T"' df = pd.read_csv(pred_file) # read the predictions xyz_file = "" # output will be written in file - for struct in sorted(df['structure_index'].unique()): # iterate over LAMMPS steps - xyz_values = df.loc[df['structure_index'] == struct, ['x', 'y', 'z']].to_numpy() - gamma_values = df.loc[df['structure_index'] == struct, 'nbh_grades'].to_numpy() # nbh_grade + for struct in sorted(df["structure_index"].unique()): # iterate over LAMMPS steps + xyz_values = df.loc[df["structure_index"] == struct, ["x", "y", "z"]].to_numpy() + gamma_values = df.loc[ + df["structure_index"] == struct, "nbh_grades" + ].to_numpy() # nbh_grade n_atom = xyz_values.shape[0] frame_txt = f"{n_atom}\n" - frame_txt += (lattice_str + ' Properties=pos:R:3:MaxVolGamma:R:1\n') + frame_txt += lattice_str + " Properties=pos:R:3:MaxVolGamma:R:1\n" # here, we can add properties to filter out atoms or identify some of them for i in range(n_atom): frame_txt += f"{' '.join(map(str, xyz_values[i, :]))} {gamma_values[i]}\n" @@ -75,9 +96,9 @@ def mtp_predictions_to_ovito(pred_file: str, lattice: np.ndarray, output_name: s if not output_name.endswith(".xyz"): output_name += ".xyz" - with open(output_name, 'w') as f: + with open(output_name, "w") as f: f.write(xyz_file) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py index 62c77b2c..934b3fd7 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/callback_loader.py @@ -1,22 +1,27 @@ from typing import Any, AnyStr, Dict -from crystal_diffusion.callbacks.loss_monitoring_callback import \ +from pytorch_lightning import Callback +from pytorch_lightning.callbacks import LearningRateMonitor + +from diffusion_for_multi_scale_molecular_dynamics.callbacks.loss_monitoring_callback import \ instantiate_loss_monitoring_callback -from crystal_diffusion.callbacks.sampling_visualization_callback import \ +from diffusion_for_multi_scale_molecular_dynamics.callbacks.sampling_visualization_callback import \ instantiate_sampling_visualization_callback -from crystal_diffusion.callbacks.standard_callbacks import ( +from diffusion_for_multi_scale_molecular_dynamics.callbacks.standard_callbacks import ( CustomProgressBar, instantiate_early_stopping_callback, instantiate_model_checkpoint_callbacks) -from pytorch_lightning import Callback -from pytorch_lightning.callbacks import LearningRateMonitor -OPTIONAL_CALLBACK_DICTIONARY = dict(early_stopping=instantiate_early_stopping_callback, - model_checkpoint=instantiate_model_checkpoint_callbacks, - sampling_visualization=instantiate_sampling_visualization_callback, - loss_monitoring=instantiate_loss_monitoring_callback) +OPTIONAL_CALLBACK_DICTIONARY = dict( + early_stopping=instantiate_early_stopping_callback, + model_checkpoint=instantiate_model_checkpoint_callbacks, + sampling_visualization=instantiate_sampling_visualization_callback, + loss_monitoring=instantiate_loss_monitoring_callback, +) -def create_all_callbacks(hyper_params: Dict[AnyStr, Any], output_directory: str, verbose: bool) -> Dict[str, Callback]: +def create_all_callbacks( + hyper_params: Dict[AnyStr, Any], output_directory: str, verbose: bool +) -> Dict[str, Callback]: """Create all callbacks. This method leverages the global dictionary OPTIONAL_CALLBACK_DICTIONARY which should be used to @@ -33,8 +38,10 @@ def create_all_callbacks(hyper_params: Dict[AnyStr, Any], output_directory: str, all_callbacks_dict: a dictionary of instantiated callbacks with relevant names as keys. """ # We always need a progress bar. We always want to know the learning rate. - all_callbacks_dict = dict(progress_bar=CustomProgressBar(), - learning_rate=LearningRateMonitor(logging_interval='epoch')) + all_callbacks_dict = dict( + progress_bar=CustomProgressBar(), + learning_rate=LearningRateMonitor(logging_interval="epoch"), + ) for callback_name, instantiate_callback in OPTIONAL_CALLBACK_DICTIONARY.items(): if callback_name not in hyper_params: diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py index 5e32892d..897b2fa2 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/loss_monitoring_callback.py @@ -2,30 +2,41 @@ import numpy as np import torch -from crystal_diffusion.loggers.logger_loader import log_figure from matplotlib import pyplot as plt from pytorch_lightning import Callback -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.loggers.logger_loader import \ + log_figure plt.style.use(PLOT_STYLE_PATH) -def instantiate_loss_monitoring_callback(callback_params: Dict[AnyStr, Any], - output_directory: str, verbose: bool) -> Dict[str, Callback]: +def instantiate_loss_monitoring_callback( + callback_params: Dict[AnyStr, Any], output_directory: str, verbose: bool +) -> Dict[str, Callback]: """Instantiate the Loss monitoring callback.""" - number_of_bins = callback_params['number_of_bins'] - sample_every_n_epochs = callback_params['sample_every_n_epochs'] - spatial_dimension = callback_params.get('spatial_dimension', 3) - loss_monitoring_callback = LossMonitoringCallback(number_of_bins=number_of_bins, - sample_every_n_epochs=sample_every_n_epochs, - spatial_dimension=spatial_dimension) + number_of_bins = callback_params["number_of_bins"] + sample_every_n_epochs = callback_params["sample_every_n_epochs"] + spatial_dimension = callback_params.get("spatial_dimension", 3) + loss_monitoring_callback = LossMonitoringCallback( + number_of_bins=number_of_bins, + sample_every_n_epochs=sample_every_n_epochs, + spatial_dimension=spatial_dimension, + ) return dict(loss_monitoring_callback=loss_monitoring_callback) class LossMonitoringCallback(Callback): """Callback class to monitor the loss vs. time (or sigma) relationship.""" - def __init__(self, number_of_bins: int, sample_every_n_epochs: int, spatial_dimension: int = 3): + def __init__( + self, + number_of_bins: int, + sample_every_n_epochs: int, + spatial_dimension: int = 3, + ): """Init method.""" self.number_of_bins = number_of_bins self.sample_every_n_epochs = sample_every_n_epochs @@ -41,21 +52,32 @@ def _compute_results_at_this_epoch(self, current_epoch: int) -> bool: else: return False - def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0): + def on_validation_batch_end( + self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0 + ): """Action to perform at the end of a validation batch.""" if not self._compute_results_at_this_epoch(trainer.current_epoch): return - batch_sigmas = outputs['sigmas'][:, :, 0] # the sigmas are the same for all atoms and space directions + batch_sigmas = outputs["sigmas"][ + :, :, 0 + ] # the sigmas are the same for all atoms and space directions self.all_sigmas.append(batch_sigmas.flatten()) # Compute the square errors per atoms - batched_squared_errors = ((outputs['predicted_normalized_scores'] - - outputs['target_normalized_conditional_scores'])**2).sum(dim=-1) + batched_squared_errors = ( + ( + outputs["predicted_normalized_scores"] + - outputs["target_normalized_conditional_scores"] + ) + ** 2 + ).sum(dim=-1) self.all_squared_errors.append(batched_squared_errors.flatten()) # Average over space dimensions, where the sigmas are the same. - self.all_weighted_losses.append(outputs["unreduced_loss"].mean(dim=-1).flatten()) + self.all_weighted_losses.append( + outputs["unreduced_loss"].mean(dim=-1).flatten() + ) def on_validation_epoch_end(self, trainer, pl_module): """Action to perform at the end of a training epoch.""" @@ -66,23 +88,29 @@ def on_validation_epoch_end(self, trainer, pl_module): squared_errors = torch.cat(self.all_squared_errors).detach().cpu().numpy() weighted_losses = torch.cat(self.all_weighted_losses).detach().cpu().numpy() - fig_squared_errors = self._plot_loss_scatter(sigmas, squared_errors, - trainer.current_epoch, 'Squared Error') - fig_weighted_loss = self._plot_loss_scatter(sigmas, weighted_losses, - trainer.current_epoch, 'Weighted Loss') + fig_squared_errors = self._plot_loss_scatter( + sigmas, squared_errors, trainer.current_epoch, "Squared Error" + ) + fig_weighted_loss = self._plot_loss_scatter( + sigmas, weighted_losses, trainer.current_epoch, "Weighted Loss" + ) for pl_logger in trainer.loggers: - log_figure(figure=fig_squared_errors, - global_step=trainer.global_step, - pl_logger=pl_logger, - dataset="validation", - name="squared_error") - - log_figure(figure=fig_weighted_loss, - global_step=trainer.global_step, - pl_logger=pl_logger, - dataset="validation", - name="weighted_loss") + log_figure( + figure=fig_squared_errors, + global_step=trainer.global_step, + pl_logger=pl_logger, + dataset="validation", + name="squared_error", + ) + + log_figure( + figure=fig_weighted_loss, + global_step=trainer.global_step, + pl_logger=pl_logger, + dataset="validation", + name="weighted_loss", + ) plt.close(fig_squared_errors) plt.close(fig_weighted_loss) @@ -91,13 +119,15 @@ def on_validation_epoch_end(self, trainer, pl_module): self.all_squared_errors.clear() self.all_weighted_losses.clear() - def _plot_loss_scatter(self, sigmas: np.ndarray, values: np.array, epoch: int, ylabel: str) -> plt.figure: + def _plot_loss_scatter( + self, sigmas: np.ndarray, values: np.array, epoch: int, ylabel: str + ) -> plt.figure: """Generate a scatter plot vs. the values of noise.""" total = np.mean(values) fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle(f'{ylabel} vs. Sigma\nEpoch {epoch}, Total = {total:5.3e}') + fig.suptitle(f"{ylabel} vs. Sigma\nEpoch {epoch}, Total = {total:5.3e}") - bins = np.linspace(0., np.max(sigmas), self.number_of_bins) + bins = np.linspace(0.0, np.max(sigmas), self.number_of_bins) bin_indices = np.digitize(sigmas, bins) list_mean_sigmas = [] @@ -121,15 +151,19 @@ def _plot_loss_scatter(self, sigmas: np.ndarray, values: np.array, epoch: int, y ax1 = fig.add_subplot(111) - ax1.semilogy(list_mean_sigmas, list_mean_values, 'g-o', label='Binned Mean') - ax1.fill_between(list_mean_sigmas, - list_min_values, - list_max_values, - color='g', alpha=0.25, label="5% to 95% Quantile") + ax1.semilogy(list_mean_sigmas, list_mean_values, "g-o", label="Binned Mean") + ax1.fill_between( + list_mean_sigmas, + list_min_values, + list_max_values, + color="g", + alpha=0.25, + label="5% to 95% Quantile", + ) ax1.set_xlim([-0.01, np.max(sigmas) + 0.01]) - ax1.legend(loc='best') - ax1.set_xlabel('$\\sigma$') + ax1.legend(loc="best") + ax1.set_xlabel("$\\sigma$") ax1.set_ylabel(ylabel) fig.tight_layout() return fig diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py index 025a10bd..49cb5cda 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/sampling_visualization_callback.py @@ -6,10 +6,13 @@ import numpy as np import torch -from crystal_diffusion.loggers.logger_loader import log_figure from matplotlib import pyplot as plt from pytorch_lightning import Callback, LightningModule, Trainer -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.loggers.logger_loader import \ + log_figure logger = logging.getLogger(__name__) @@ -19,6 +22,7 @@ @dataclass(kw_only=True) class SamplingVisualizationParameters: """Parameters to decide what to plot and write to disk.""" + record_every_n_epochs: int = 1 first_record_epoch: int = 1 record_trajectories: bool = True diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/standard_callbacks.py b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/standard_callbacks.py index 937f1640..511f4604 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/standard_callbacks.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/callbacks/standard_callbacks.py @@ -8,24 +8,26 @@ logger = logging.getLogger(__name__) -BEST_MODEL_NAME = 'best_model' -LAST_MODEL_NAME = 'last_model' +BEST_MODEL_NAME = "best_model" +LAST_MODEL_NAME = "last_model" -def instantiate_early_stopping_callback(callback_params: Dict[AnyStr, Any], - output_directory: str, - verbose: bool) -> Dict[str, Callback]: +def instantiate_early_stopping_callback( + callback_params: Dict[AnyStr, Any], output_directory: str, verbose: bool +) -> Dict[str, Callback]: """Instantiate early stopping callback.""" - early_stopping = EarlyStopping(callback_params['metric'], - mode=callback_params['mode'], - patience=callback_params['patience'], - verbose=verbose) + early_stopping = EarlyStopping( + callback_params["metric"], + mode=callback_params["mode"], + patience=callback_params["patience"], + verbose=verbose, + ) return dict(early_stopping=early_stopping) -def instantiate_model_checkpoint_callbacks(callback_params: Dict[AnyStr, Any], - output_directory: str, - verbose: bool) -> Dict[str, Callback]: +def instantiate_model_checkpoint_callbacks( + callback_params: Dict[AnyStr, Any], output_directory: str, verbose: bool +) -> Dict[str, Callback]: """Instantiate best and last checkpoint callbacks.""" best_model_path = os.path.join(output_directory, BEST_MODEL_NAME) best_checkpoint_callback = ModelCheckpoint( @@ -45,11 +47,15 @@ def instantiate_model_checkpoint_callbacks(callback_params: Dict[AnyStr, Any], verbose=verbose, every_n_epochs=1, ) - return dict(best_checkpoint=best_checkpoint_callback, last_checkpoint=last_checkpoint_callback) + return dict( + best_checkpoint=best_checkpoint_callback, + last_checkpoint=last_checkpoint_callback, + ) class CustomProgressBar(RichProgressBar): """A custom progress bar based on Rich that doesn't log the v_num stuff.""" + def get_metrics(self, *args, **kwargs): """Get metrics.""" # don't show the version number diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py index 0f81b5bb..df6784c4 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_loader.py @@ -1,4 +1,5 @@ """DataLoader from LAMMPS outputs for a diffusion model.""" + import logging import typing from dataclasses import dataclass @@ -9,18 +10,20 @@ import pytorch_lightning as pl import torch import torch.nn.functional as F -from crystal_diffusion.data.diffusion.data_preprocess import \ - LammpsProcessorForDiffusion -from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, - RELATIVE_COORDINATES) from torch.utils.data import DataLoader +from diffusion_for_multi_scale_molecular_dynamics.data.diffusion.data_preprocess import \ + LammpsProcessorForDiffusion +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) + logger = logging.getLogger(__name__) @dataclass(kw_only=True) class LammpsLoaderParameters: """Base Hyper-parameters for score networks.""" + # Either batch_size XOR train_batch_size and valid_batch_size should be specified. batch_size: Optional[int] = None train_batch_size: Optional[int] = None @@ -34,11 +37,11 @@ class LammpsForDiffusionDataModule(pl.LightningDataModule): """Data module class that prepares dataset parsers and instantiates data loaders.""" def __init__( - self, - lammps_run_dir: str, - processed_dataset_dir: str, - hyper_params: LammpsLoaderParameters, - working_cache_dir: Optional[str] = None + self, + lammps_run_dir: str, + processed_dataset_dir: str, + hyper_params: LammpsLoaderParameters, + working_cache_dir: Optional[str] = None, ): """Initialize a dataset of LAMMPS structures for training a diffusion model. @@ -61,24 +64,30 @@ def __init__( self.spatial_dim = hyper_params.spatial_dimension if hyper_params.batch_size is None: - assert hyper_params.valid_batch_size is not None, \ - "If batch_size is None, valid_batch_size must be specified." - assert hyper_params.train_batch_size is not None, \ - "If batch_size is None, train_batch_size must be specified." + assert ( + hyper_params.valid_batch_size is not None + ), "If batch_size is None, valid_batch_size must be specified." + assert ( + hyper_params.train_batch_size is not None + ), "If batch_size is None, train_batch_size must be specified." self.train_batch_size = hyper_params.train_batch_size self.valid_batch_size = hyper_params.valid_batch_size else: - assert hyper_params.valid_batch_size is None, \ - "If batch_size is specified, valid_batch_size must be None." - assert hyper_params.train_batch_size is None, \ - "If batch_size is specified, train_batch_size must be None." + assert ( + hyper_params.valid_batch_size is None + ), "If batch_size is specified, valid_batch_size must be None." + assert ( + hyper_params.train_batch_size is None + ), "If batch_size is specified, train_batch_size must be None." self.train_batch_size = hyper_params.batch_size self.valid_batch_size = hyper_params.batch_size @staticmethod - def dataset_transform(x: Dict[typing.AnyStr, typing.Any], spatial_dim: int = 3) -> Dict[str, torch.Tensor]: + def dataset_transform( + x: Dict[typing.AnyStr, typing.Any], spatial_dim: int = 3 + ) -> Dict[str, torch.Tensor]: """Format the tensors for the Datasets library. This function is applied right after returning the objects in __getitem__ in a torch DataLoader. Everything is @@ -93,18 +102,28 @@ def dataset_transform(x: Dict[typing.AnyStr, typing.Any], spatial_dim: int = 3) transformed_x: formatted values as tensors """ transformed_x = {} - transformed_x['natom'] = torch.as_tensor(x['natom']).long() # resulting tensor size: (batchsize, ) - bsize = transformed_x['natom'].size(0) - transformed_x['box'] = torch.as_tensor(x['box']) # size: (batchsize, spatial dimension) + transformed_x["natom"] = torch.as_tensor( + x["natom"] + ).long() # resulting tensor size: (batchsize, ) + bsize = transformed_x["natom"].size(0) + transformed_x["box"] = torch.as_tensor( + x["box"] + ) # size: (batchsize, spatial dimension) for pos in [CARTESIAN_POSITIONS, RELATIVE_COORDINATES, CARTESIAN_FORCES]: transformed_x[pos] = torch.as_tensor(x[pos]).view(bsize, -1, spatial_dim) - transformed_x['type'] = torch.as_tensor(x['type']).long() # size: (batchsize, max atom) - transformed_x['potential_energy'] = torch.as_tensor(x['potential_energy']) # size: (batchsize, ) + transformed_x["type"] = torch.as_tensor( + x["type"] + ).long() # size: (batchsize, max atom) + transformed_x["potential_energy"] = torch.as_tensor( + x["potential_energy"] + ) # size: (batchsize, ) return transformed_x @staticmethod - def pad_samples(x: Dict[typing.AnyStr, typing.Any], max_atom: int, spatial_dim: int = 3) -> Dict[str, torch.Tensor]: + def pad_samples( + x: Dict[typing.AnyStr, typing.Any], max_atom: int, spatial_dim: int = 3 + ) -> Dict[str, torch.Tensor]: """Pad a sample for batching. Args: @@ -115,25 +134,37 @@ def pad_samples(x: Dict[typing.AnyStr, typing.Any], max_atom: int, spatial_dim: Returns: x: sample with padded type and position """ - natom = x['natom'] + natom = x["natom"] if natom > max_atom: - raise ValueError(f"Hyper-parameter max_atom is smaller than an example in the dataset with {natom} atoms.") - x['type'] = F.pad(torch.as_tensor(x['type']).long(), (0, max_atom - natom), 'constant', -1) + raise ValueError( + f"Hyper-parameter max_atom is smaller than an example in the dataset with {natom} atoms." + ) + x["type"] = F.pad( + torch.as_tensor(x["type"]).long(), (0, max_atom - natom), "constant", -1 + ) for pos in [CARTESIAN_POSITIONS, RELATIVE_COORDINATES, CARTESIAN_FORCES]: - x[pos] = F.pad(torch.as_tensor(x[pos]).float(), (0, spatial_dim * (max_atom - natom)), 'constant', - torch.nan) + x[pos] = F.pad( + torch.as_tensor(x[pos]).float(), + (0, spatial_dim * (max_atom - natom)), + "constant", + torch.nan, + ) return x def setup(self, stage: Optional[str] = None): """Parse and split all samples across the train/valid/test parsers.""" # here, we will actually assign train/val datasets for use in dataloaders - processed_data = LammpsProcessorForDiffusion(self.lammps_run_dir, self.processed_dataset_dir) + processed_data = LammpsProcessorForDiffusion( + self.lammps_run_dir, self.processed_dataset_dir + ) if stage == "fit" or stage is None: - self.train_dataset = datasets.Dataset.from_parquet(processed_data.train_files, - cache_dir=self.working_cache_dir) - self.valid_dataset = datasets.Dataset.from_parquet(processed_data.valid_files, - cache_dir=self.working_cache_dir) + self.train_dataset = datasets.Dataset.from_parquet( + processed_data.train_files, cache_dir=self.working_cache_dir + ) + self.valid_dataset = datasets.Dataset.from_parquet( + processed_data.valid_files, cache_dir=self.working_cache_dir + ) # TODO QoL valid dataset is labelled as train split by Datasets. Find a way to rename. else: raise NotImplementedError("Test mode needs to be implemented.") @@ -142,19 +173,33 @@ def setup(self, stage: Optional[str] = None): # or a .select(list[int]) with a list of indices to keep a subset. This is much faster than .filter # padding needs to be done here OR in the preprocessor # check if the max number of atoms matches at least the max in the training set - if max(self.train_dataset['natom']) > self.max_atom: - raise ValueError(f"Hyper-parameter max_atom {self.max_atom} is smaller than the largest structure in the" - + f"dataset which has {max(self.train_dataset['natom'])} atoms.") + if max(self.train_dataset["natom"]) > self.max_atom: + raise ValueError( + f"Hyper-parameter max_atom {self.max_atom} is smaller than the largest structure in the" + + f"dataset which has {max(self.train_dataset['natom'])} atoms." + ) # map() are applied once, not in-place. # The keyword argument "batched" can accelerate by working with batches, not useful for padding - self.train_dataset = self.train_dataset.map(partial(self.pad_samples, max_atom=self.max_atom, - spatial_dim=self.spatial_dim), batched=False) - self.valid_dataset = self.valid_dataset.map(partial(self.pad_samples, max_atom=self.max_atom, - spatial_dim=self.spatial_dim), batched=False) + self.train_dataset = self.train_dataset.map( + partial( + self.pad_samples, max_atom=self.max_atom, spatial_dim=self.spatial_dim + ), + batched=False, + ) + self.valid_dataset = self.valid_dataset.map( + partial( + self.pad_samples, max_atom=self.max_atom, spatial_dim=self.spatial_dim + ), + batched=False, + ) # set_transform is applied on-the-fly and is less costly upfront. Works with batches, so we can't use it for # padding - self.train_dataset.set_transform(partial(self.dataset_transform, spatial_dim=self.spatial_dim)) - self.valid_dataset.set_transform(partial(self.dataset_transform, spatial_dim=self.spatial_dim)) + self.train_dataset.set_transform( + partial(self.dataset_transform, spatial_dim=self.spatial_dim) + ) + self.valid_dataset.set_transform( + partial(self.dataset_transform, spatial_dim=self.spatial_dim) + ) def train_dataloader(self) -> DataLoader: """Create the training dataloader using the training data parser.""" diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py index efa00ed8..6db8195a 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/data/diffusion/data_preprocess.py @@ -1,4 +1,5 @@ """Convert results of LAMMPS simulation into dataloader friendly format.""" + import itertools import logging import os @@ -7,9 +8,11 @@ from typing import List, Optional, Tuple, Union import pandas as pd -from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, - RELATIVE_COORDINATES) -from src.crystal_diffusion.data.parse_lammps_outputs import parse_lammps_output + +from diffusion_for_multi_scale_molecular_dynamics.data.parse_lammps_outputs import \ + parse_lammps_output +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) logger = logging.getLogger(__name__) @@ -28,10 +31,10 @@ def __init__(self, raw_data_dir: str, processed_data_dir: str): os.makedirs(processed_data_dir) # create the dir if it doesn't exist self.data_dir = processed_data_dir # TODO revisit data splits - self.train_files = self.prepare_data(raw_data_dir, mode='train') - self.valid_files = self.prepare_data(raw_data_dir, mode='valid') + self.train_files = self.prepare_data(raw_data_dir, mode="train") + self.valid_files = self.prepare_data(raw_data_dir, mode="valid") - def prepare_data(self, raw_data_dir: str, mode: str = 'train') -> List[str]: + def prepare_data(self, raw_data_dir: str, mode: str = "train") -> List[str]: """Read data in raw_data_dir and write to a parquet file for Datasets. Args: @@ -44,18 +47,32 @@ def prepare_data(self, raw_data_dir: str, mode: str = 'train') -> List[str]: # TODO split is assumed from data generation. We might revisit this. # we assume that raw_data_dir contains subdirectories named train_run_N for N>=1 # get the list of runs to parse - assert mode in ['train', 'valid', 'test'], f"Mode should be train, valid or test. Got {mode}." - list_runs = [d for d in os.listdir(raw_data_dir) if os.path.isdir(os.path.join(raw_data_dir, d)) - and d.startswith(f"{mode}_run")] + assert mode in [ + "train", + "valid", + "test", + ], f"Mode should be train, valid or test. Got {mode}." + list_runs = [ + d + for d in os.listdir(raw_data_dir) + if os.path.isdir(os.path.join(raw_data_dir, d)) + and d.startswith(f"{mode}_run") + ] list_files = [] for count, d in enumerate(list_runs, 1): - logging.info(f"Processing run directory {d} ({count} of {len(list_runs)})...") + logging.info( + f"Processing run directory {d} ({count} of {len(list_runs)})..." + ) if f"{d}.parquet" not in os.listdir(self.data_dir): logging.info(" * parquet file is absent. Generating...") df = self.parse_lammps_run(os.path.join(raw_data_dir, d)) if df is not None: logging.info(" * writing parquet file to disk...") - df.to_parquet(os.path.join(self.data_dir, f"{d}.parquet"), engine='pyarrow', index=False) + df.to_parquet( + os.path.join(self.data_dir, f"{d}.parquet"), + engine="pyarrow", + index=False, + ) if f"{d}.parquet" in os.listdir(self.data_dir): list_files.append(os.path.join(self.data_dir, f"{d}.parquet")) return list_files @@ -70,10 +87,17 @@ def _convert_coords_to_relative(row: pd.Series) -> List[float]: Returns: x, y and z in relative (reduced) coordinates """ - x_lim, y_lim, z_lim = row['box'] + x_lim, y_lim, z_lim = row["box"] # Cast the coordinates to float in case they are read in as strings - coord_red = [coord for triple in zip(row['x'], row['y'], row['z']) for coord in - ((float(triple[0]) / x_lim) % 1, (float(triple[1]) / y_lim) % 1, (float(triple[2]) / z_lim) % 1)] + coord_red = [ + coord + for triple in zip(row["x"], row["y"], row["z"]) + for coord in ( + (float(triple[0]) / x_lim) % 1, + (float(triple[1]) / y_lim) % 1, + (float(triple[2]) / z_lim) % 1, + ) + ] return coord_red def get_x_relative(self, df: pd.DataFrame) -> pd.DataFrame: @@ -85,11 +109,15 @@ def get_x_relative(self, df: pd.DataFrame) -> pd.DataFrame: Returns: dataframe with added column of relative positions [x1, y1, z1, x2, y2, ...] """ - df[RELATIVE_COORDINATES] = df.apply(lambda x: self._convert_coords_to_relative(x), axis=1) + df[RELATIVE_COORDINATES] = df.apply( + lambda x: self._convert_coords_to_relative(x), axis=1 + ) return df @staticmethod - def get_dump_and_thermo_files(run_dir: str) -> Tuple[Union[str, None], Union[str, None]]: + def get_dump_and_thermo_files( + run_dir: str, + ) -> Tuple[Union[str, None], Union[str, None]]: """Get dump and thermo files. Args: @@ -100,20 +128,26 @@ def get_dump_and_thermo_files(run_dir: str) -> Tuple[Union[str, None], Union[str one data file for each of (dump, thermo). """ # find the LAMMPS dump file and thermo file - dump_file = [d for d in os.listdir(run_dir) if 'dump' in d] + dump_file = [d for d in os.listdir(run_dir) if "dump" in d] if len(dump_file) == 1: dump_file_path = os.path.join(run_dir, dump_file[0]) else: - warnings.warn(f"Found {len(dump_file)} files with dump in the name in {run_dir}. " - f"Expected exactly one.", UserWarning) + warnings.warn( + f"Found {len(dump_file)} files with dump in the name in {run_dir}. " + f"Expected exactly one.", + UserWarning, + ) dump_file_path = None - thermo_file = [d for d in os.listdir(run_dir) if 'thermo' in d] + thermo_file = [d for d in os.listdir(run_dir) if "thermo" in d] if len(thermo_file) == 1: thermo_file_path = os.path.join(run_dir, thermo_file[0]) else: - warnings.warn(f"Found {len(thermo_file)} files with thermo in the name in {run_dir}. " - f"Expected exactly one.", UserWarning) + warnings.warn( + f"Found {len(thermo_file)} files with thermo in the name in {run_dir}. " + f"Expected exactly one.", + UserWarning, + ) thermo_file_path = None return dump_file_path, thermo_file_path @@ -155,19 +189,32 @@ def parse_lammps_run(self, run_dir: str) -> Optional[pd.DataFrame]: # Each row is a different MD step / usable example for diffusion model # TODO consider filtering out samples with large forces and MD steps that are too similar # TODO large force and similar are to be defined - df = df[['type', 'x', 'y', 'z', 'box', 'potential_energy', 'fx', 'fy', 'fz']] + df = df[["type", "x", "y", "z", "box", "potential_energy", "fx", "fy", "fz"]] df = self.get_x_relative(df) # add relative coordinates - df['natom'] = df['type'].apply(lambda x: len(x)) # count number of atoms in a structure + df["natom"] = df["type"].apply( + lambda x: len(x) + ) # count number of atoms in a structure # Parquet cannot handle a list of list; flattening positions. df[CARTESIAN_POSITIONS] = df.apply(self._flatten_positions_in_row, axis=1) # position is natom * 3 array - df[CARTESIAN_FORCES] = df.apply(partial(self._flatten_positions_in_row, keys=['fx', 'fy', 'fz']), axis=1) - return df[['natom', 'box', 'type', 'potential_energy', CARTESIAN_POSITIONS, RELATIVE_COORDINATES, - CARTESIAN_FORCES]] + df[CARTESIAN_FORCES] = df.apply( + partial(self._flatten_positions_in_row, keys=["fx", "fy", "fz"]), axis=1 + ) + return df[ + [ + "natom", + "box", + "type", + "potential_energy", + CARTESIAN_POSITIONS, + RELATIVE_COORDINATES, + CARTESIAN_FORCES, + ] + ] @staticmethod - def _flatten_positions_in_row(row: pd.Series, keys=['x', 'y', 'z']) -> List[float]: + def _flatten_positions_in_row(row: pd.Series, keys=["x", "y", "z"]) -> List[float]: """Function to flatten the positions in a dataframe row. Args: @@ -180,6 +227,10 @@ def _flatten_positions_in_row(row: pd.Series, keys=['x', 'y', 'z']) -> List[floa list_y = row[keys[1]] list_z = row[keys[2]] - flat_positions = list(itertools.chain.from_iterable([[x, y, z] for x, y, z in zip(list_x, list_y, list_z)])) + flat_positions = list( + itertools.chain.from_iterable( + [[x, y, z] for x, y, z in zip(list_x, list_y, list_z)] + ) + ) return flat_positions diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/data/parse_lammps_outputs.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/parse_lammps_outputs.py index 4783a1a3..a7f44196 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/data/parse_lammps_outputs.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/data/parse_lammps_outputs.py @@ -9,7 +9,9 @@ from yaml import CLoader -def parse_lammps_output(lammps_dump: str, lammps_thermo_log: str, output_name: Optional[str] = None) -> pd.DataFrame: +def parse_lammps_output( + lammps_dump: str, lammps_thermo_log: str, output_name: Optional[str] = None +) -> pd.DataFrame: """Parse a LAMMPS output file and save in a .csv format. Args: @@ -22,10 +24,14 @@ def parse_lammps_output(lammps_dump: str, lammps_thermo_log: str, output_name: O data in a dataframe """ if not os.path.exists(lammps_dump): - raise ValueError(f'{lammps_dump} does not exist. Please provide a valid LAMMPS dump file as yaml.') + raise ValueError( + f"{lammps_dump} does not exist. Please provide a valid LAMMPS dump file as yaml." + ) if not os.path.exists(lammps_thermo_log): - raise ValueError(f'{lammps_thermo_log} does not exist. Please provide a valid LAMMPS thermo log file as yaml.') + raise ValueError( + f"{lammps_thermo_log} does not exist. Please provide a valid LAMMPS thermo log file as yaml." + ) # get the atom information (positions and forces) from the LAMMPS 'dump' file pd_data = parse_lammps_dump(lammps_dump) @@ -34,13 +40,13 @@ def parse_lammps_output(lammps_dump: str, lammps_thermo_log: str, output_name: O thermo_log_data_dictionary = parse_lammps_thermo_log(lammps_thermo_log) pd_data.update(thermo_log_data_dictionary) - if output_name is not None and not output_name.endswith('.parquet'): - output_name += '.parquet' + if output_name is not None and not output_name.endswith(".parquet"): + output_name += ".parquet" df = pd.DataFrame(pd_data) if output_name is not None: - df.to_parquet(output_name, engine='pyarrow', index=False) + df.to_parquet(output_name, engine="pyarrow", index=False) return df @@ -56,18 +62,20 @@ def parse_lammps_dump(lammps_dump: str) -> Dict[str, Any]: Returns: data: a dictionary with all the relevant data. """ - expected_keywords = ['id', 'type', 'x', 'y', 'z', 'fx', 'fy', 'fz'] + expected_keywords = ["id", "type", "x", "y", "z", "fx", "fy", "fz"] datatypes = 2 * [np.int64] + 6 * [np.float64] pd_data = defaultdict(list) - with open(lammps_dump, 'r') as stream: + with open(lammps_dump, "r") as stream: dump_yaml = yaml.load_all(stream, Loader=CLoader) for doc in dump_yaml: # loop over MD steps - pd_data['box'].append(np.array(doc['box'])[:, 1]) + pd_data["box"].append(np.array(doc["box"])[:, 1]) - assert doc['keywords'] == expected_keywords - data = np.array(doc['data']).transpose() # convert to numpy so that we can easily slice + assert doc["keywords"] == expected_keywords + data = np.array( + doc["data"] + ).transpose() # convert to numpy so that we can easily slice for keyword, datatype, data_row in zip(expected_keywords, datatypes, data): pd_data[keyword].append(data_row.astype(datatype)) return pd_data @@ -83,26 +91,26 @@ def parse_lammps_thermo_log(lammps_thermo_log: str) -> Dict[str, List[float]]: parsed_data: the data from the log, parsed in a dictionary. """ data_dict = defaultdict(list) - optional_keywords = {'Press': 'pressure', 'Temp': 'temperature'} + optional_keywords = {"Press": "pressure", "Temp": "temperature"} optional_indices = dict() - with open(lammps_thermo_log, 'r') as f: + with open(lammps_thermo_log, "r") as f: log_yaml = yaml.safe_load(f) - kin_idx = log_yaml['keywords'].index('KinEng') - pot_idx = log_yaml['keywords'].index('PotEng') + kin_idx = log_yaml["keywords"].index("KinEng") + pot_idx = log_yaml["keywords"].index("PotEng") # For optional keys, impose better names than the LAMMPS keys. for yaml_key, long_name in optional_keywords.items(): - if yaml_key in log_yaml['keywords']: - idx = log_yaml['keywords'].index(yaml_key) + if yaml_key in log_yaml["keywords"]: + idx = log_yaml["keywords"].index(yaml_key) optional_indices[long_name] = idx - for record in log_yaml['data']: + for record in log_yaml["data"]: potential_energy = record[pot_idx] kinetic_energy = record[kin_idx] - data_dict['potential_energy'].append(potential_energy) - data_dict['kinetic_energy'].append(kinetic_energy) - data_dict['energy'].append(potential_energy + kinetic_energy) + data_dict["potential_energy"].append(potential_energy) + data_dict["kinetic_energy"].append(kinetic_energy) + data_dict["energy"].append(potential_energy + kinetic_energy) for long_name, idx in optional_indices.items(): data_dict[long_name].append(record[idx]) @@ -112,14 +120,20 @@ def parse_lammps_thermo_log(lammps_thermo_log: str) -> Dict[str, List[float]]: def main(): """Parse LAMMPS files and output a single parquet file.""" - parser = argparse.ArgumentParser(description="Convert LAMMPS outputs in parquet file compatible with a dataloader.") - parser.add_argument("--dump_file", type=str, help="LAMMPS dump file in yaml format.") - parser.add_argument("--thermo_file", type=str, help="LAMMPS thermo output file in yaml format.") + parser = argparse.ArgumentParser( + description="Convert LAMMPS outputs in parquet file compatible with a dataloader." + ) + parser.add_argument( + "--dump_file", type=str, help="LAMMPS dump file in yaml format." + ) + parser.add_argument( + "--thermo_file", type=str, help="LAMMPS thermo output file in yaml format." + ) parser.add_argument("--output_name", type=str, help="Output name") args = parser.parse_args() parse_lammps_output(args.dump_file, args.thermo_file, args.output_name) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/data/utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/data/utils.py index a147f898..86dfede2 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/data/utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/data/utils.py @@ -1,4 +1,5 @@ """Utility functions for data processing.""" + import logging import os from typing import Any, AnyStr, Dict, List, Tuple @@ -9,8 +10,9 @@ logger = logging.getLogger(__name__) -def crop_lammps_yaml(lammps_dump: str, lammps_thermo: str, crop_step: int, inplace: bool = False) \ - -> Tuple[List[Dict[AnyStr, Any]], Dict[AnyStr, Any]]: +def crop_lammps_yaml( + lammps_dump: str, lammps_thermo: str, crop_step: int, inplace: bool = False +) -> Tuple[List[Dict[AnyStr, Any]], Dict[AnyStr, Any]]: """Remove the first steps of a LAMMPS run to remove structures near the starting point. Args: @@ -25,13 +27,17 @@ def crop_lammps_yaml(lammps_dump: str, lammps_thermo: str, crop_step: int, inpla cropped LAMMPS thermodynamic output file """ if not os.path.exists(lammps_dump): - raise ValueError(f'{lammps_dump} does not exist. Please provide a valid LAMMPS dump file as yaml.') + raise ValueError( + f"{lammps_dump} does not exist. Please provide a valid LAMMPS dump file as yaml." + ) if not os.path.exists(lammps_thermo): - raise ValueError(f'{lammps_thermo} does not exist. Please provide a valid LAMMPS thermo log file as yaml.') + raise ValueError( + f"{lammps_thermo} does not exist. Please provide a valid LAMMPS thermo log file as yaml." + ) # get the atom information (positions and forces) from the LAMMPS 'dump' file - with open(lammps_dump, 'r') as f: + with open(lammps_dump, "r") as f: logger.info("loading dump file....") dump_yaml = yaml.load_all(f, Loader=CLoader) logger.info("creating list of documents...") @@ -39,16 +45,18 @@ def crop_lammps_yaml(lammps_dump: str, lammps_thermo: str, crop_step: int, inpla # every MD iteration is saved as a separate document in the yaml file # prepare a dataframe to get all the data if crop_step >= len(dump_yaml): - raise ValueError(f"Trying to remove {crop_step} steps in a run of {len(dump_yaml)} steps.") + raise ValueError( + f"Trying to remove {crop_step} steps in a run of {len(dump_yaml)} steps." + ) logger.info("cropping documents...") dump_yaml = dump_yaml[crop_step:] # get the total energy from the LAMMPS thermodynamic output - with open(lammps_thermo, 'r') as f: + with open(lammps_thermo, "r") as f: logger.info("loading thermo file....") thermo_yaml = yaml.load(f, Loader=CLoader) logger.info("cropping thermo file....") - thermo_yaml['data'] = thermo_yaml['data'][crop_step:] + thermo_yaml["data"] = thermo_yaml["data"][crop_step:] if inplace: with open("test_yaml.yaml", "w") as f: diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py index 9cbed410..31ad891a 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/constrained_langevin_generator.py @@ -2,23 +2,30 @@ import numpy as np import torch -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from tqdm import tqdm + +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.models.score_networks.score_network import ScoreNetwork -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters -from tqdm import tqdm @dataclass(kw_only=True) class ConstrainedLangevinGeneratorParameters(PredictorCorrectorSamplingParameters): """Hyper-parameters for diffusion sampling with the predictor-corrector algorithm.""" - algorithm: str = 'constrained_langevin' - constrained_relative_coordinates: np.ndarray # the positions that must be satisfied at the end of sampling. + + algorithm: str = "constrained_langevin" + constrained_relative_coordinates: ( + np.ndarray + ) # the positions that must be satisfied at the end of sampling. class ConstrainedLangevinGenerator(LangevinGenerator): @@ -29,24 +36,34 @@ class ConstrainedLangevinGenerator(LangevinGenerator): "RePaint: Inpainting using Denoising Diffusion Probabilistic Models". """ - def __init__(self, - noise_parameters: NoiseParameters, - sampling_parameters: ConstrainedLangevinGeneratorParameters, - sigma_normalized_score_network: ScoreNetwork, - ): + def __init__( + self, + noise_parameters: NoiseParameters, + sampling_parameters: ConstrainedLangevinGeneratorParameters, + sigma_normalized_score_network: ScoreNetwork, + ): """Init method.""" - super().__init__(noise_parameters, sampling_parameters, sigma_normalized_score_network) - - self.constraint_relative_coordinates = torch.from_numpy(sampling_parameters.constrained_relative_coordinates) - - assert len(self.constraint_relative_coordinates.shape) == 2, \ - "The constrained relative coordinates have the wrong shape" - - number_of_constraints, spatial_dimensions = self.constraint_relative_coordinates.shape - assert number_of_constraints <= self.number_of_atoms, \ - "There are more constrained positions than atoms!" - assert spatial_dimensions <= self.spatial_dimension, \ - "The spatial dimension of the constrained positions is inconsistent" + super().__init__( + noise_parameters, sampling_parameters, sigma_normalized_score_network + ) + + self.constraint_relative_coordinates = torch.from_numpy( + sampling_parameters.constrained_relative_coordinates + ) + + assert ( + len(self.constraint_relative_coordinates.shape) == 2 + ), "The constrained relative coordinates have the wrong shape" + + number_of_constraints, spatial_dimensions = ( + self.constraint_relative_coordinates.shape + ) + assert ( + number_of_constraints <= self.number_of_atoms + ), "There are more constrained positions than atoms!" + assert ( + spatial_dimensions <= self.spatial_dimension + ), "The spatial dimension of the constrained positions is inconsistent" # Without loss of generality, we impose that the first positions are constrained. # This should have no consequence for a permutation equivariant model. @@ -59,7 +76,9 @@ def _apply_constraint(self, x: torch.Tensor, device: torch.device) -> None: """This method applies the coordinate constraint in place on the input configuration.""" x[:, self.constraint_mask] = self.constraint_relative_coordinates.to(device) - def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor) -> torch.Tensor: + def sample( + self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor + ) -> torch.Tensor: """Sample. This method draws samples, imposing the satisfaction of positional constraints. @@ -73,35 +92,50 @@ def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch. Returns: samples: relative coordinates samples. """ - assert unit_cell.size() == (number_of_samples, self.spatial_dimension, self.spatial_dimension), \ - "Unit cell passed to sample should be of size (number of sample, spatial dimension, spatial dimension" \ + assert unit_cell.size() == ( + number_of_samples, + self.spatial_dimension, + self.spatial_dimension, + ), ( + "Unit cell passed to sample should be of size (number of sample, spatial dimension, spatial dimension" + f"Got {unit_cell.size()}" + ) # Initialize a configuration that satisfy the constraint, but is otherwise random. # Since the noising process is 'atom-per-atom', the non-constrained position should have no impact. - x0_known = map_relative_coordinates_to_unit_cell(self.initialize(number_of_samples)).to(device) + x0_known = map_relative_coordinates_to_unit_cell( + self.initialize(number_of_samples) + ).to(device) self._apply_constraint(x0_known, device) - x_ip1 = map_relative_coordinates_to_unit_cell(self.initialize(number_of_samples)).to(device) + x_ip1 = map_relative_coordinates_to_unit_cell( + self.initialize(number_of_samples) + ).to(device) forces = torch.zeros_like(x_ip1) - broadcasting = torch.ones(number_of_samples, self.number_of_atoms, self.spatial_dimension).to(device) + broadcasting = torch.ones( + number_of_samples, self.number_of_atoms, self.spatial_dimension + ).to(device) for i in tqdm(range(self.number_of_discretization_steps - 1, -1, -1)): sigma_i = self.noise.sigma[i] broadcast_sigmas_i = sigma_i * broadcasting # Noise an example satisfying the constraints from t_0 to t_i - x_i_known = ( - self.noisy_relative_coordinates_sampler.get_noisy_relative_coordinates_sample(x0_known, - broadcast_sigmas_i)) + x_i_known = self.noisy_relative_coordinates_sampler.get_noisy_relative_coordinates_sample( + x0_known, broadcast_sigmas_i + ) # Denoise from t_{i+1} to t_i - x_i = map_relative_coordinates_to_unit_cell(self.predictor_step(x_ip1, i + 1, unit_cell, forces)) + x_i = map_relative_coordinates_to_unit_cell( + self.predictor_step(x_ip1, i + 1, unit_cell, forces) + ) # Combine the known and unknown x_i[:, self.constraint_mask] = x_i_known[:, self.constraint_mask] for _ in range(self.number_of_corrector_steps): - x_i = map_relative_coordinates_to_unit_cell(self.corrector_step(x_i, i, unit_cell, forces)) + x_i = map_relative_coordinates_to_unit_cell( + self.corrector_step(x_i, i, unit_cell, forces) + ) x_ip1 = x_i # apply the constraint one last time diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py index d7d15501..dfdaf083 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/instantiate_generator.py @@ -1,37 +1,51 @@ -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.ode_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import \ ExplodingVarianceODEPositionGenerator -from crystal_diffusion.generators.sde_position_generator import \ - ExplodingVarianceSDEPositionGenerator -from crystal_diffusion.models.score_networks import ScoreNetwork -from src.crystal_diffusion.generators.position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import \ SamplingParameters -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.generators.sde_position_generator import \ + ExplodingVarianceSDEPositionGenerator +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters -def instantiate_generator(sampling_parameters: SamplingParameters, - noise_parameters: NoiseParameters, - sigma_normalized_score_network: ScoreNetwork): +def instantiate_generator( + sampling_parameters: SamplingParameters, + noise_parameters: NoiseParameters, + sigma_normalized_score_network: ScoreNetwork, +): """Instantiate generator.""" - assert sampling_parameters.algorithm in ['ode', 'sde', 'predictor_corrector'], \ - "Unknown algorithm. Possible choices are 'ode', 'sde' and 'predictor_corrector'" + assert sampling_parameters.algorithm in [ + "ode", + "sde", + "predictor_corrector", + ], "Unknown algorithm. Possible choices are 'ode', 'sde' and 'predictor_corrector'" match sampling_parameters.algorithm: - case 'predictor_corrector': - generator = LangevinGenerator(sampling_parameters=sampling_parameters, - noise_parameters=noise_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) - case 'ode': + case "predictor_corrector": + generator = LangevinGenerator( + sampling_parameters=sampling_parameters, + noise_parameters=noise_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) + case "ode": generator = ExplodingVarianceODEPositionGenerator( sampling_parameters=sampling_parameters, noise_parameters=noise_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) - case 'sde': + sigma_normalized_score_network=sigma_normalized_score_network, + ) + case "sde": generator = ExplodingVarianceSDEPositionGenerator( sampling_parameters=sampling_parameters, noise_parameters=noise_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + sigma_normalized_score_network=sigma_normalized_score_network, + ) case _: - raise NotImplementedError(f"algorithm '{sampling_parameters.algorithm}' is not implemented") + raise NotImplementedError( + f"algorithm '{sampling_parameters.algorithm}' is not implemented" + ) return generator diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py index ed7d9888..e68dd2e2 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/langevin_generator.py @@ -1,14 +1,15 @@ import torch -from crystal_diffusion.generators.predictor_corrector_position_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import ( PredictorCorrectorPositionGenerator, PredictorCorrectorSamplingParameters) -from crystal_diffusion.models.score_networks.score_network import ScoreNetwork -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.utils.sample_trajectory import ( - NoOpPredictorCorrectorSampleTrajectory, PredictorCorrectorSampleTrajectory) -from src.crystal_diffusion.samplers.variance_sampler import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) +from diffusion_for_multi_scale_molecular_dynamics.utils.sample_trajectory import ( + NoOpPredictorCorrectorSampleTrajectory, PredictorCorrectorSampleTrajectory) class LangevinGenerator(PredictorCorrectorPositionGenerator): @@ -19,15 +20,18 @@ class LangevinGenerator(PredictorCorrectorPositionGenerator): "Generative Modeling by Estimating Gradients of the Data Distribution" """ - def __init__(self, - noise_parameters: NoiseParameters, - sampling_parameters: PredictorCorrectorSamplingParameters, - sigma_normalized_score_network: ScoreNetwork, - ): + def __init__( + self, + noise_parameters: NoiseParameters, + sampling_parameters: PredictorCorrectorSamplingParameters, + sigma_normalized_score_network: ScoreNetwork, + ): """Init method.""" - super().__init__(number_of_discretization_steps=noise_parameters.total_time_steps, - number_of_corrector_steps=sampling_parameters.number_of_corrector_steps, - spatial_dimension=sampling_parameters.spatial_dimension) + super().__init__( + number_of_discretization_steps=noise_parameters.total_time_steps, + number_of_corrector_steps=sampling_parameters.number_of_corrector_steps, + spatial_dimension=sampling_parameters.spatial_dimension, + ) self.noise_parameters = noise_parameters sampler = ExplodingVarianceSampler(noise_parameters) @@ -42,15 +46,24 @@ def __init__(self, def initialize(self, number_of_samples: int): """This method must initialize the samples from the fully noised distribution.""" - relative_coordinates = torch.rand(number_of_samples, self.number_of_atoms, self.spatial_dimension) + relative_coordinates = torch.rand( + number_of_samples, self.number_of_atoms, self.spatial_dimension + ) return relative_coordinates def _draw_gaussian_sample(self, number_of_samples): - return torch.randn(number_of_samples, self.number_of_atoms, self.spatial_dimension) - - def _get_sigma_normalized_scores(self, x: torch.Tensor, time: float, - noise: float, unit_cell: torch.Tensor, cartesian_forces: torch.Tensor - ) -> torch.Tensor: + return torch.randn( + number_of_samples, self.number_of_atoms, self.spatial_dimension + ) + + def _get_sigma_normalized_scores( + self, + x: torch.Tensor, + time: float, + noise: float, + unit_cell: torch.Tensor, + cartesian_forces: torch.Tensor, + ) -> torch.Tensor: """Get sigma normalized scores. Args: @@ -69,15 +82,27 @@ def _get_sigma_normalized_scores(self, x: torch.Tensor, time: float, time_tensor = time * torch.ones(number_of_samples, 1).to(x) noise_tensor = noise * torch.ones(number_of_samples, 1).to(x) - augmented_batch = {NOISY_RELATIVE_COORDINATES: x, TIME: time_tensor, NOISE: noise_tensor, UNIT_CELL: unit_cell, - CARTESIAN_FORCES: cartesian_forces} + augmented_batch = { + NOISY_RELATIVE_COORDINATES: x, + TIME: time_tensor, + NOISE: noise_tensor, + UNIT_CELL: unit_cell, + CARTESIAN_FORCES: cartesian_forces, + } # TODO do not hard-code conditional to False - need to be able to condition sampling - predicted_normalized_scores = self.sigma_normalized_score_network(augmented_batch, conditional=False) + predicted_normalized_scores = self.sigma_normalized_score_network( + augmented_batch, conditional=False + ) return predicted_normalized_scores - def predictor_step(self, x_i: torch.Tensor, index_i: int, unit_cell: torch.Tensor, cartesian_forces: torch.Tensor - ) -> torch.Tensor: + def predictor_step( + self, + x_i: torch.Tensor, + index_i: int, + unit_cell: torch.Tensor, + cartesian_forces: torch.Tensor, + ) -> torch.Tensor: """Predictor step. Args: @@ -89,8 +114,9 @@ def predictor_step(self, x_i: torch.Tensor, index_i: int, unit_cell: torch.Tenso Returns: x_im1 : sampled relative coordinates, at time step i - 1. """ - assert 1 <= index_i <= self.number_of_discretization_steps, \ - "The predictor step can only be invoked for index_i between 1 and the total number of discretization steps." + assert ( + 1 <= index_i <= self.number_of_discretization_steps + ), "The predictor step can only be invoked for index_i between 1 and the total number of discretization steps." number_of_samples = x_i.shape[0] z = self._draw_gaussian_sample(number_of_samples).to(x_i) @@ -100,17 +126,30 @@ def predictor_step(self, x_i: torch.Tensor, index_i: int, unit_cell: torch.Tenso g_i = self.noise.g[idx].to(x_i) g2_i = self.noise.g_squared[idx].to(x_i) sigma_i = self.noise.sigma[idx].to(x_i) - sigma_score_i = self._get_sigma_normalized_scores(x_i, t_i, sigma_i, unit_cell, cartesian_forces) + sigma_score_i = self._get_sigma_normalized_scores( + x_i, t_i, sigma_i, unit_cell, cartesian_forces + ) x_im1 = x_i + g2_i / sigma_i * sigma_score_i + g_i * z self.sample_trajectory_recorder.record_unit_cell(unit_cell=unit_cell) - self.sample_trajectory_recorder.record_predictor_step(i_index=index_i, time=t_i, sigma=sigma_i, - x_i=x_i, x_im1=x_im1, scores=sigma_score_i) + self.sample_trajectory_recorder.record_predictor_step( + i_index=index_i, + time=t_i, + sigma=sigma_i, + x_i=x_i, + x_im1=x_im1, + scores=sigma_score_i, + ) return x_im1 - def corrector_step(self, x_i: torch.Tensor, index_i: int, unit_cell: torch.Tensor, cartesian_forces: torch.Tensor - ) -> torch.Tensor: + def corrector_step( + self, + x_i: torch.Tensor, + index_i: int, + unit_cell: torch.Tensor, + cartesian_forces: torch.Tensor, + ) -> torch.Tensor: """Corrector Step. Args: @@ -122,9 +161,10 @@ def corrector_step(self, x_i: torch.Tensor, index_i: int, unit_cell: torch.Tenso Returns: corrected x_i : sampled relative coordinates, after corrector step. """ - assert 0 <= index_i <= self.number_of_discretization_steps - 1, \ - ("The corrector step can only be invoked for index_i between 0 and " - "the total number of discretization steps minus 1.") + assert 0 <= index_i <= self.number_of_discretization_steps - 1, ( + "The corrector step can only be invoked for index_i between 0 and " + "the total number of discretization steps minus 1." + ) number_of_samples = x_i.shape[0] z = self._draw_gaussian_sample(number_of_samples).to(x_i) @@ -135,19 +175,28 @@ def corrector_step(self, x_i: torch.Tensor, index_i: int, unit_cell: torch.Tenso if index_i == 0: # TODO: we are extrapolating here; the score network will never have seen this time step... - sigma_i = self.noise_parameters.sigma_min # no need to change device, this is a float - t_i = 0. # same for device - this is a float + sigma_i = ( + self.noise_parameters.sigma_min + ) # no need to change device, this is a float + t_i = 0.0 # same for device - this is a float else: idx = index_i - 1 # python starts indices at zero sigma_i = self.noise.sigma[idx].to(x_i) t_i = self.noise.time[idx].to(x_i) - sigma_score_i = self._get_sigma_normalized_scores(x_i, t_i, sigma_i, unit_cell, cartesian_forces) + sigma_score_i = self._get_sigma_normalized_scores( + x_i, t_i, sigma_i, unit_cell, cartesian_forces + ) corrected_x_i = x_i + eps_i / sigma_i * sigma_score_i + sqrt_2eps_i * z - self.sample_trajectory_recorder.record_corrector_step(i_index=index_i, time=t_i, - sigma=sigma_i, x_i=x_i, corrected_x_i=corrected_x_i, - scores=sigma_score_i) + self.sample_trajectory_recorder.record_corrector_step( + i_index=index_i, + time=t_i, + sigma=sigma_i, + x_i=x_i, + corrected_x_i=corrected_x_i, + scores=sigma_score_i, + ) return corrected_x_i diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py index ef633bf8..57d841e8 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/load_sampling_parameters.py @@ -1,16 +1,18 @@ from typing import Any, AnyStr, Dict -from crystal_diffusion.generators.ode_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import \ ODESamplingParameters -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import \ + SamplingParameters +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.generators.sde_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.sde_position_generator import \ SDESamplingParameters -from src.crystal_diffusion.generators.position_generator import \ - SamplingParameters -def load_sampling_parameters(sampling_parameter_dictionary: Dict[AnyStr, Any]) -> SamplingParameters: +def load_sampling_parameters( + sampling_parameter_dictionary: Dict[AnyStr, Any] +) -> SamplingParameters: """Load sampling parameters. Extract the needed information from the configuration dictionary. @@ -21,18 +23,25 @@ def load_sampling_parameters(sampling_parameter_dictionary: Dict[AnyStr, Any]) - Returns: sampling_parameters: the relevant configuration object. """ - assert 'algorithm' in sampling_parameter_dictionary, "The sampling parameters must select an algorithm." - algorithm = sampling_parameter_dictionary['algorithm'] + assert ( + "algorithm" in sampling_parameter_dictionary + ), "The sampling parameters must select an algorithm." + algorithm = sampling_parameter_dictionary["algorithm"] - assert algorithm in ['ode', 'sde', 'predictor_corrector'], \ - "Unknown algorithm. Possible choices are 'ode', 'sde' and 'predictor_corrector'" + assert algorithm in [ + "ode", + "sde", + "predictor_corrector", + ], "Unknown algorithm. Possible choices are 'ode', 'sde' and 'predictor_corrector'" match algorithm: - case 'predictor_corrector': - sampling_parameters = PredictorCorrectorSamplingParameters(**sampling_parameter_dictionary) - case 'ode': + case "predictor_corrector": + sampling_parameters = PredictorCorrectorSamplingParameters( + **sampling_parameter_dictionary + ) + case "ode": sampling_parameters = ODESamplingParameters(**sampling_parameter_dictionary) - case 'sde': + case "sde": sampling_parameters = SDESamplingParameters(**sampling_parameter_dictionary) case _: raise NotImplementedError(f"algorithm '{algorithm}' is not implemented") diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py index 49f53ebf..d8f1bc2c 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/ode_position_generator.py @@ -5,28 +5,35 @@ import einops import torch import torchode as to -from crystal_diffusion.models.score_networks.score_network import ScoreNetwork -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.sample_trajectory import (NoOpODESampleTrajectory, - ODESampleTrajectory) -from src.crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters from torchode import Solution +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + map_relative_coordinates_to_unit_cell +from diffusion_for_multi_scale_molecular_dynamics.utils.sample_trajectory import ( + NoOpODESampleTrajectory, ODESampleTrajectory) + logger = logging.getLogger(__name__) @dataclass(kw_only=True) class ODESamplingParameters(SamplingParameters): """Hyper-parameters for diffusion sampling with the ode algorithm.""" - algorithm: str = 'ode' - absolute_solver_tolerance: float = 1.0e-3 # the absolute error tolerance passed to the ODE solver. - relative_solver_tolerance: float = 1.0e-2 # the relative error tolerance passed to the ODE solver. + + algorithm: str = "ode" + absolute_solver_tolerance: float = ( + 1.0e-3 # the absolute error tolerance passed to the ODE solver. + ) + relative_solver_tolerance: float = ( + 1.0e-2 # the relative error tolerance passed to the ODE solver. + ) class ExplodingVarianceODEPositionGenerator(PositionGenerator): @@ -36,11 +43,12 @@ class ExplodingVarianceODEPositionGenerator(PositionGenerator): It assumes that the diffusion noise is parameterized in the 'Exploding Variance' scheme. """ - def __init__(self, - noise_parameters: NoiseParameters, - sampling_parameters: ODESamplingParameters, - sigma_normalized_score_network: ScoreNetwork, - ): + def __init__( + self, + noise_parameters: NoiseParameters, + sampling_parameters: ODESamplingParameters, + sigma_normalized_score_network: ScoreNetwork, + ): """Init method. Args: @@ -54,8 +62,9 @@ def __init__(self, self.noise_parameters = noise_parameters self.sigma_normalized_score_network = sigma_normalized_score_network - assert self.noise_parameters.total_time_steps >= 2, \ - "There must at least be two time steps in the noise parameters to define the limits t0 and tf." + assert ( + self.noise_parameters.total_time_steps >= 2 + ), "There must at least be two time steps in the noise parameters to define the limits t0 and tf." self.number_of_atoms = sampling_parameters.number_of_atoms self.spatial_dimension = sampling_parameters.spatial_dimension self.absolute_solver_tolerance = sampling_parameters.absolute_solver_tolerance @@ -80,7 +89,10 @@ def _get_exploding_variance_sigma(self, times): Returns: sigmas: value of the noise parameter. """ - sigmas = self.noise_parameters.sigma_min ** (1.0 - times) * self.noise_parameters.sigma_max ** times + sigmas = ( + self.noise_parameters.sigma_min ** (1.0 - times) + * self.noise_parameters.sigma_max**times + ) return sigmas def _get_ode_prefactor(self, sigmas): @@ -107,14 +119,20 @@ def _get_ode_prefactor(self, sigmas): Returns: ode prefactor: the prefactor in the ODE. """ - log_ratio = torch.log(torch.tensor(self.noise_parameters.sigma_max / self.noise_parameters.sigma_min)) + log_ratio = torch.log( + torch.tensor( + self.noise_parameters.sigma_max / self.noise_parameters.sigma_min + ) + ) ode_prefactor = log_ratio * sigmas return ode_prefactor def generate_ode_term(self, unit_cell: torch.Tensor) -> Callable: """Generate the ode_term needed to compute the ODE solution.""" - def ode_term(times: torch.Tensor, flat_relative_coordinates: torch.Tensor) -> torch.Tensor: + def ode_term( + times: torch.Tensor, flat_relative_coordinates: torch.Tensor + ) -> torch.Tensor: """ODE term. This function is in the format required by the ODE solver. @@ -131,28 +149,38 @@ def ode_term(times: torch.Tensor, flat_relative_coordinates: torch.Tensor) -> to sigmas = self._get_exploding_variance_sigma(times) ode_prefactor = self._get_ode_prefactor(sigmas) - relative_coordinates = einops.rearrange(flat_relative_coordinates, - "batch (natom space) -> batch natom space", - natom=self.number_of_atoms, - space=self.spatial_dimension) - - batch = {NOISY_RELATIVE_COORDINATES: map_relative_coordinates_to_unit_cell(relative_coordinates), - NOISE: sigmas.unsqueeze(-1), - TIME: times.unsqueeze(-1), - UNIT_CELL: unit_cell, - CARTESIAN_FORCES: torch.zeros_like(relative_coordinates) # TODO: handle forces correctly. - } + relative_coordinates = einops.rearrange( + flat_relative_coordinates, + "batch (natom space) -> batch natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) + + batch = { + NOISY_RELATIVE_COORDINATES: map_relative_coordinates_to_unit_cell( + relative_coordinates + ), + NOISE: sigmas.unsqueeze(-1), + TIME: times.unsqueeze(-1), + UNIT_CELL: unit_cell, + CARTESIAN_FORCES: torch.zeros_like( + relative_coordinates + ), # TODO: handle forces correctly. + } # Shape [batch_size, number of atoms, spatial dimension] sigma_normalized_scores = self.sigma_normalized_score_network(batch) - flat_sigma_normalized_scores = einops.rearrange(sigma_normalized_scores, - "batch natom space -> batch (natom space)") + flat_sigma_normalized_scores = einops.rearrange( + sigma_normalized_scores, "batch natom space -> batch (natom space)" + ) return -ode_prefactor.unsqueeze(-1) * flat_sigma_normalized_scores return ode_term - def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor) -> torch.Tensor: + def sample( + self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor + ) -> torch.Tensor: """Sample. This method draws a position sample. @@ -168,21 +196,30 @@ def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch. """ ode_term = self.generate_ode_term(unit_cell) - initial_relative_coordinates = ( - map_relative_coordinates_to_unit_cell(self.initialize(number_of_samples)).to(device)) + initial_relative_coordinates = map_relative_coordinates_to_unit_cell( + self.initialize(number_of_samples) + ).to(device) - y0 = einops.rearrange(initial_relative_coordinates, 'batch natom space -> batch (natom space)') + y0 = einops.rearrange( + initial_relative_coordinates, "batch natom space -> batch (natom space)" + ) - evaluation_times = torch.linspace(self.tf, self.t0, self.noise_parameters.total_time_steps).to(device) + evaluation_times = torch.linspace( + self.tf, self.t0, self.noise_parameters.total_time_steps + ).to(device) - t_eval = einops.repeat(evaluation_times, 't -> batch t', batch=number_of_samples) + t_eval = einops.repeat( + evaluation_times, "t -> batch t", batch=number_of_samples + ) term = to.ODETerm(ode_term) step_method = to.Dopri5(term=term) - step_size_controller = to.IntegralController(atol=self.absolute_solver_tolerance, - rtol=self.relative_solver_tolerance, - term=term) + step_size_controller = to.IntegralController( + atol=self.absolute_solver_tolerance, + rtol=self.relative_solver_tolerance, + term=term, + ) solver = to.AutoDiffAdjoint(step_method, step_size_controller) # jit_solver = torch.compile(solver) # Compilation is not necessary, and breaks on the cluster... jit_solver = solver @@ -198,14 +235,22 @@ def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch. # only the final time (ie, t0) is the real sample. flat_relative_coordinates = sol.ys[:, -1, :] - relative_coordinates = einops.rearrange(flat_relative_coordinates, - 'batch (natom space) -> batch natom space', - natom=self.number_of_atoms, - space=self.spatial_dimension) + relative_coordinates = einops.rearrange( + flat_relative_coordinates, + "batch (natom space) -> batch natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) return map_relative_coordinates_to_unit_cell(relative_coordinates) - def record_sample(self, ode_term: Callable, sol: Solution, evaluation_times: torch.Tensor, unit_cell: torch.Tensor): + def record_sample( + self, + ode_term: Callable, + sol: Solution, + evaluation_times: torch.Tensor, + unit_cell: torch.Tensor, + ): """Record sample. This method takes care of recomputing the normalized score on the solution trajectory and record it to the @@ -222,30 +267,41 @@ def record_sample(self, ode_term: Callable, sol: Solution, evaluation_times: tor number_of_samples = sol.ys.shape[0] self.sample_trajectory_recorder.record_unit_cell(unit_cell) - record_relative_coordinates = einops.rearrange(sol.ys, - 'batch times (natom space) -> batch times natom space', - natom=self.number_of_atoms, - space=self.spatial_dimension) + record_relative_coordinates = einops.rearrange( + sol.ys, + "batch times (natom space) -> batch times natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) sigmas = self._get_exploding_variance_sigma(evaluation_times) ode_prefactor = self._get_ode_prefactor(sigmas) list_flat_normalized_scores = [] for time_idx, (time, gamma) in enumerate(zip(evaluation_times, ode_prefactor)): times = time * torch.ones(number_of_samples).to(sol.ys) # The score network must be called again to get scores at intermediate times - flat_normalized_score = -ode_term(times=times, - flat_relative_coordinates=sol.ys[:, time_idx]) / gamma + flat_normalized_score = ( + -ode_term(times=times, flat_relative_coordinates=sol.ys[:, time_idx]) + / gamma + ) list_flat_normalized_scores.append(flat_normalized_score) - record_normalized_scores = einops.rearrange(torch.stack(list_flat_normalized_scores), - "time batch (natom space) -> batch time natom space", - natom=self.number_of_atoms, space=self.spatial_dimension) - self.sample_trajectory_recorder.record_ode_solution(times=evaluation_times, - sigmas=sigmas, - relative_coordinates=record_relative_coordinates, - normalized_scores=record_normalized_scores, - stats=sol.stats, - status=sol.status) + record_normalized_scores = einops.rearrange( + torch.stack(list_flat_normalized_scores), + "time batch (natom space) -> batch time natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) + self.sample_trajectory_recorder.record_ode_solution( + times=evaluation_times, + sigmas=sigmas, + relative_coordinates=record_relative_coordinates, + normalized_scores=record_normalized_scores, + stats=sol.stats, + status=sol.status, + ) def initialize(self, number_of_samples: int): """This method must initialize the samples from the fully noised distribution.""" - relative_coordinates = torch.rand(number_of_samples, self.number_of_atoms, self.spatial_dimension) + relative_coordinates = torch.rand( + number_of_samples, self.number_of_atoms, self.spatial_dimension + ) return relative_coordinates diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/position_generator.py index 6ae2dd39..b102227a 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/position_generator.py @@ -8,22 +8,31 @@ @dataclass(kw_only=True) class SamplingParameters: """Hyper-parameters for diffusion sampling.""" + algorithm: str spatial_dimension: int = 3 # the dimension of Euclidean space where atoms live. - number_of_atoms: int # the number of atoms that must be generated in a sampled configuration. + number_of_atoms: ( + int # the number of atoms that must be generated in a sampled configuration. + ) number_of_samples: int # iterate up to number_of_samples with batches of this size # if None, use number_of_samples as batchsize sample_batchsize: Optional[int] = None - cell_dimensions: List[float] # unit cell dimensions; the unit cell is assumed to be an orthogonal box. - record_samples: bool = False # should the predictor and corrector steps be recorded to a file + cell_dimensions: List[ + float + ] # unit cell dimensions; the unit cell is assumed to be an orthogonal box. + record_samples: bool = ( + False # should the predictor and corrector steps be recorded to a file + ) class PositionGenerator(ABC): """This defines the interface for position generators.""" @abstractmethod - def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor) -> torch.Tensor: + def sample( + self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor + ) -> torch.Tensor: """Sample. This method draws a position sample. diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py index ba7178cc..f8c8a582 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/predictor_corrector_position_generator.py @@ -3,36 +3,49 @@ from dataclasses import dataclass import torch -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from src.crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) from tqdm import tqdm +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + map_relative_coordinates_to_unit_cell + logger = logging.getLogger(__name__) @dataclass(kw_only=True) class PredictorCorrectorSamplingParameters(SamplingParameters): """Hyper-parameters for diffusion sampling with the predictor-corrector algorithm.""" - algorithm: str = 'predictor_corrector' + + algorithm: str = "predictor_corrector" number_of_corrector_steps: int = 1 class PredictorCorrectorPositionGenerator(PositionGenerator): """This defines the interface for predictor-corrector position generators.""" - def __init__(self, number_of_discretization_steps: int, number_of_corrector_steps: int, spatial_dimension: int, - **kwargs): + def __init__( + self, + number_of_discretization_steps: int, + number_of_corrector_steps: int, + spatial_dimension: int, + **kwargs, + ): """Init method.""" - assert number_of_discretization_steps > 0, "The number of discretization steps should be larger than zero" - assert number_of_corrector_steps >= 0, "The number of corrector steps should be non-negative" + assert ( + number_of_discretization_steps > 0 + ), "The number of discretization steps should be larger than zero" + assert ( + number_of_corrector_steps >= 0 + ), "The number of corrector steps should be non-negative" self.number_of_discretization_steps = number_of_discretization_steps self.number_of_corrector_steps = number_of_corrector_steps self.spatial_dimension = spatial_dimension - def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor) -> torch.Tensor: + def sample( + self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor + ) -> torch.Tensor: """Sample. This method draws a sample using the PC sampler algorithm. @@ -46,23 +59,39 @@ def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch. Returns: samples: relative coordinates samples. """ - assert unit_cell.size() == (number_of_samples, self.spatial_dimension, self.spatial_dimension), \ - "Unit cell passed to sample should be of size (number of sample, spatial dimension, spatial dimension" \ + assert unit_cell.size() == ( + number_of_samples, + self.spatial_dimension, + self.spatial_dimension, + ), ( + "Unit cell passed to sample should be of size (number of sample, spatial dimension, spatial dimension" + f"Got {unit_cell.size()}" + ) - x_ip1 = map_relative_coordinates_to_unit_cell(self.initialize(number_of_samples)).to(device) + x_ip1 = map_relative_coordinates_to_unit_cell( + self.initialize(number_of_samples) + ).to(device) forces = torch.zeros_like(x_ip1) for i in tqdm(range(self.number_of_discretization_steps - 1, -1, -1)): - x_i = map_relative_coordinates_to_unit_cell(self.predictor_step(x_ip1, i + 1, unit_cell, forces)) + x_i = map_relative_coordinates_to_unit_cell( + self.predictor_step(x_ip1, i + 1, unit_cell, forces) + ) for _ in range(self.number_of_corrector_steps): - x_i = map_relative_coordinates_to_unit_cell(self.corrector_step(x_i, i, unit_cell, forces)) + x_i = map_relative_coordinates_to_unit_cell( + self.corrector_step(x_i, i, unit_cell, forces) + ) x_ip1 = x_i return x_i @abstractmethod - def predictor_step(self, x_ip1: torch.Tensor, ip1: int, unit_cell: torch.Tensor, cartesian_forces: torch.Tensor - ) -> torch.Tensor: + def predictor_step( + self, + x_ip1: torch.Tensor, + ip1: int, + unit_cell: torch.Tensor, + cartesian_forces: torch.Tensor, + ) -> torch.Tensor: """Predictor step. It is assumed that there are N predictor steps, with index "i" running from N-1 to 0. @@ -79,8 +108,13 @@ def predictor_step(self, x_ip1: torch.Tensor, ip1: int, unit_cell: torch.Tensor, pass @abstractmethod - def corrector_step(self, x_i: torch.Tensor, i: int, unit_cell: torch.Tensor, cartesian_forces: torch.Tensor - ) -> torch.Tensor: + def corrector_step( + self, + x_i: torch.Tensor, + i: int, + unit_cell: torch.Tensor, + cartesian_forces: torch.Tensor, + ) -> torch.Tensor: """Corrector step. It is assumed that there are N predictor steps, with index "i" running from N-1 to 0. diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py b/src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py index f2432942..4b9dcab7 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/generators/sde_position_generator.py @@ -4,16 +4,19 @@ import einops import torch import torchsde -from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.sample_trajectory import SDESampleTrajectory -from src.crystal_diffusion.generators.position_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import ( PositionGenerator, SamplingParameters) -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + map_relative_coordinates_to_unit_cell +from diffusion_for_multi_scale_molecular_dynamics.utils.sample_trajectory import \ + SDESampleTrajectory logger = logging.getLogger(__name__) @@ -21,12 +24,17 @@ @dataclass(kw_only=True) class SDESamplingParameters(SamplingParameters): """Hyper-parameters for diffusion sampling with the sde algorithm.""" - algorithm: str = 'sde' - sde_type: str = 'ito' - method: str = 'euler' + + algorithm: str = "sde" + sde_type: str = "ito" + method: str = "euler" adaptative: bool = False - absolute_solver_tolerance: float = 1.0e-7 # the absolute error tolerance passed to the SDE solver. - relative_solver_tolerance: float = 1.0e-5 # the relative error tolerance passed to the SDE solver. + absolute_solver_tolerance: float = ( + 1.0e-7 # the absolute error tolerance passed to the SDE solver. + ) + relative_solver_tolerance: float = ( + 1.0e-5 # the relative error tolerance passed to the SDE solver. + ) class SDE(torch.nn.Module): @@ -35,16 +43,19 @@ class SDE(torch.nn.Module): This class computes the drift and the diffusion coefficients in order to be consistent with the expectations of the torchsde library. """ - noise_type = 'diagonal' # we assume that there is a distinct Wiener process for each component. - sde_type = 'ito' - - def __init__(self, - noise_parameters: NoiseParameters, - sampling_parameters: SDESamplingParameters, - sigma_normalized_score_network: ScoreNetwork, - unit_cells: torch.Tensor, - initial_diffusion_time: torch.Tensor, - final_diffusion_time: torch.Tensor): + + noise_type = "diagonal" # we assume that there is a distinct Wiener process for each component. + sde_type = "ito" + + def __init__( + self, + noise_parameters: NoiseParameters, + sampling_parameters: SDESamplingParameters, + sigma_normalized_score_network: ScoreNetwork, + unit_cells: torch.Tensor, + initial_diffusion_time: torch.Tensor, + final_diffusion_time: torch.Tensor, + ): """Init method. This class will provide drift and diffusion for the torchsde solver. The SDE will be solved @@ -69,7 +80,9 @@ def __init__(self, self.initial_diffusion_time = initial_diffusion_time self.final_diffusion_time = final_diffusion_time - def _get_exploding_variance_sigma(self, diffusion_time: torch.Tensor) -> torch.Tensor: + def _get_exploding_variance_sigma( + self, diffusion_time: torch.Tensor + ) -> torch.Tensor: """Get Exploding Variance Sigma. In the 'exploding variance' scheme, the noise is defined by @@ -82,11 +95,15 @@ def _get_exploding_variance_sigma(self, diffusion_time: torch.Tensor) -> torch.T Returns: sigma: value of the noise parameter. """ - sigma = (self.noise_parameters.sigma_min ** (1.0 - diffusion_time) - * self.noise_parameters.sigma_max ** diffusion_time) + sigma = ( + self.noise_parameters.sigma_min ** (1.0 - diffusion_time) + * self.noise_parameters.sigma_max**diffusion_time + ) return sigma - def _get_diffusion_coefficient_g_squared(self, diffusion_time: torch.Tensor) -> torch.Tensor: + def _get_diffusion_coefficient_g_squared( + self, diffusion_time: torch.Tensor + ) -> torch.Tensor: """Get diffusion coefficient g(t)^2. The noise is given by sigma(t) = sigma_{min} (sigma_{max} / sigma_{min})^t @@ -99,9 +116,11 @@ def _get_diffusion_coefficient_g_squared(self, diffusion_time: torch.Tensor) -> coefficient_g : the coefficient g(t) """ s_min = torch.tensor(self.noise_parameters.sigma_min) - ratio = torch.tensor(self.noise_parameters.sigma_max / self.noise_parameters.sigma_min) + ratio = torch.tensor( + self.noise_parameters.sigma_max / self.noise_parameters.sigma_min + ) - g_squared = 2.0 * (s_min * ratio ** diffusion_time) ** 2 * torch.log(ratio) + g_squared = 2.0 * (s_min * ratio**diffusion_time) ** 2 * torch.log(ratio) return g_squared def _get_diffusion_time(self, sde_time: torch.Tensor) -> torch.Tensor: @@ -115,7 +134,9 @@ def _get_diffusion_time(self, sde_time: torch.Tensor) -> torch.Tensor: """ return self.final_diffusion_time - sde_time - def f(self, sde_time: torch.Tensor, flat_relative_coordinates: torch.Tensor) -> torch.Tensor: + def f( + self, sde_time: torch.Tensor, flat_relative_coordinates: torch.Tensor + ) -> torch.Tensor: """Drift function. Args: @@ -128,9 +149,12 @@ def f(self, sde_time: torch.Tensor, flat_relative_coordinates: torch.Tensor) -> """ diffusion_time = self._get_diffusion_time(sde_time) - sigma_normalized_scores = self.get_sigma_normalized_score(diffusion_time, flat_relative_coordinates) - flat_sigma_normalized_scores = einops.rearrange(sigma_normalized_scores, - "batch natom space -> batch (natom space)") + sigma_normalized_scores = self.get_sigma_normalized_score( + diffusion_time, flat_relative_coordinates + ) + flat_sigma_normalized_scores = einops.rearrange( + sigma_normalized_scores, "batch natom space -> batch (natom space)" + ) g_squared = self._get_diffusion_coefficient_g_squared(diffusion_time) sigma = self._get_exploding_variance_sigma(diffusion_time) @@ -141,8 +165,9 @@ def f(self, sde_time: torch.Tensor, flat_relative_coordinates: torch.Tensor) -> return prefactor * flat_sigma_normalized_scores - def get_sigma_normalized_score(self, diffusion_time: torch.Tensor, - flat_relative_coordinates: torch.Tensor) -> torch.Tensor: + def get_sigma_normalized_score( + self, diffusion_time: torch.Tensor, flat_relative_coordinates: torch.Tensor + ) -> torch.Tensor: """Get sigma normalized score. This is a utility method to wrap around the computation of the sigma normalized score in this context, @@ -160,18 +185,27 @@ def get_sigma_normalized_score(self, diffusion_time: torch.Tensor, batch_size = flat_relative_coordinates.shape[0] sigma = self._get_exploding_variance_sigma(diffusion_time) sigmas = einops.repeat(sigma.unsqueeze(0), "1 -> batch 1", batch=batch_size) - times = einops.repeat(diffusion_time.unsqueeze(0), "1 -> batch 1", batch=batch_size) - - relative_coordinates = einops.rearrange(flat_relative_coordinates, - "batch (natom space) -> batch natom space", - natom=self.number_of_atoms, - space=self.spatial_dimension) - batch = {NOISY_RELATIVE_COORDINATES: map_relative_coordinates_to_unit_cell(relative_coordinates), - NOISE: sigmas, - TIME: times, - UNIT_CELL: self.unit_cells, - CARTESIAN_FORCES: torch.zeros_like(relative_coordinates) # TODO: handle forces correctly. - } + times = einops.repeat( + diffusion_time.unsqueeze(0), "1 -> batch 1", batch=batch_size + ) + + relative_coordinates = einops.rearrange( + flat_relative_coordinates, + "batch (natom space) -> batch natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) + batch = { + NOISY_RELATIVE_COORDINATES: map_relative_coordinates_to_unit_cell( + relative_coordinates + ), + NOISE: sigmas, + TIME: times, + UNIT_CELL: self.unit_cells, + CARTESIAN_FORCES: torch.zeros_like( + relative_coordinates + ), # TODO: handle forces correctly. + } # Shape [batch_size, number of atoms, spatial dimension] sigma_normalized_scores = self.sigma_normalized_score_network(batch) return sigma_normalized_scores @@ -191,11 +225,13 @@ class ExplodingVarianceSDEPositionGenerator(PositionGenerator): This class generates position samples by solving a stochastic differential equation (SDE). It assumes that the diffusion noise is parameterized in the 'Exploding Variance' scheme. """ - def __init__(self, - noise_parameters: NoiseParameters, - sampling_parameters: SDESamplingParameters, - sigma_normalized_score_network: ScoreNetwork, - ): + + def __init__( + self, + noise_parameters: NoiseParameters, + sampling_parameters: SDESamplingParameters, + sigma_normalized_score_network: ScoreNetwork, + ): """Init method. Args: @@ -220,19 +256,25 @@ def __init__(self, def get_sde(self, unit_cells: torch.Tensor) -> SDE: """Get SDE.""" - return SDE(noise_parameters=self.noise_parameters, - sampling_parameters=self.sampling_parameters, - sigma_normalized_score_network=self.sigma_normalized_score_network, - unit_cells=unit_cells, - initial_diffusion_time=self.initial_diffusion_time, - final_diffusion_time=self.final_diffusion_time) + return SDE( + noise_parameters=self.noise_parameters, + sampling_parameters=self.sampling_parameters, + sigma_normalized_score_network=self.sigma_normalized_score_network, + unit_cells=unit_cells, + initial_diffusion_time=self.initial_diffusion_time, + final_diffusion_time=self.final_diffusion_time, + ) def initialize(self, number_of_samples: int): """This method must initialize the samples from the fully noised distribution.""" - relative_coordinates = torch.rand(number_of_samples, self.number_of_atoms, self.spatial_dimension) + relative_coordinates = torch.rand( + number_of_samples, self.number_of_atoms, self.spatial_dimension + ) return relative_coordinates - def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor) -> torch.Tensor: + def sample( + self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor + ) -> torch.Tensor: """Sample. This method draws a position sample. @@ -249,24 +291,36 @@ def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch. sde = self.get_sde(unit_cell) sde.to(device) - initial_relative_coordinates = ( - map_relative_coordinates_to_unit_cell(self.initialize(number_of_samples)).to(device)) - y0 = einops.rearrange(initial_relative_coordinates, 'batch natom space -> batch (natom space)') + initial_relative_coordinates = map_relative_coordinates_to_unit_cell( + self.initialize(number_of_samples) + ).to(device) + y0 = einops.rearrange( + initial_relative_coordinates, "batch natom space -> batch (natom space)" + ) - sde_times = torch.linspace(self.initial_diffusion_time, self.final_diffusion_time, - self.noise_parameters.total_time_steps).to(device) + sde_times = torch.linspace( + self.initial_diffusion_time, + self.final_diffusion_time, + self.noise_parameters.total_time_steps, + ).to(device) - dt = (self.final_diffusion_time - self.initial_diffusion_time) / (self.noise_parameters.total_time_steps - 1) + dt = (self.final_diffusion_time - self.initial_diffusion_time) / ( + self.noise_parameters.total_time_steps - 1 + ) with torch.no_grad(): # Dimensions [number of time steps, number of samples, natom x spatial_dimension] logger.info("Starting SDE solver...") - ys = torchsde.sdeint(sde, y0, sde_times, - method=self.sampling_parameters.method, - dt=dt, - adaptive=self.sampling_parameters.adaptative, - atol=self.sampling_parameters.absolute_solver_tolerance, - rtol=self.sampling_parameters.relative_solver_tolerance) + ys = torchsde.sdeint( + sde, + y0, + sde_times, + method=self.sampling_parameters.method, + dt=dt, + adaptive=self.sampling_parameters.adaptative, + atol=self.sampling_parameters.absolute_solver_tolerance, + rtol=self.sampling_parameters.relative_solver_tolerance, + ) logger.info("SDE solver Finished.") if self.record_samples: @@ -275,10 +329,12 @@ def sample(self, number_of_samples: int, device: torch.device, unit_cell: torch. # only the final sde time (ie, diffusion time t0) is the real sample. flat_relative_coordinates = ys[-1, :, :] - relative_coordinates = einops.rearrange(flat_relative_coordinates, - 'batch (natom space) -> batch natom space', - natom=self.number_of_atoms, - space=self.spatial_dimension) + relative_coordinates = einops.rearrange( + flat_relative_coordinates, + "batch (natom space) -> batch natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) return map_relative_coordinates_to_unit_cell(relative_coordinates) @@ -302,28 +358,38 @@ def record_sample(self, sde: SDE, ys: torch.Tensor, sde_times: torch.Tensor): sigmas = [] evaluation_times = [] # Reverse the times since the SDE times are inverted compared to the diffusion times. - for sde_time, flat_relative_coordinates in zip(sde_times.flip(dims=(0,)), ys.flip(dims=(0,))): + for sde_time, flat_relative_coordinates in zip( + sde_times.flip(dims=(0,)), ys.flip(dims=(0,)) + ): diffusion_time = sde._get_diffusion_time(sde_time) sigma = sde._get_exploding_variance_sigma(diffusion_time) sigmas.append(sigma) evaluation_times.append(diffusion_time) with torch.no_grad(): - normalized_scores = sde.get_sigma_normalized_score(diffusion_time, flat_relative_coordinates) + normalized_scores = sde.get_sigma_normalized_score( + diffusion_time, flat_relative_coordinates + ) list_normalized_scores.append(normalized_scores) sigmas = torch.tensor(sigmas) evaluation_times = torch.tensor(evaluation_times) - record_normalized_scores = einops.rearrange(torch.stack(list_normalized_scores), - "time batch natom space -> batch time natom space") - - record_relative_coordinates = einops.rearrange(ys.flip(dims=(0,)), - 'times batch (natom space) -> batch times natom space', - natom=self.number_of_atoms, - space=self.spatial_dimension) - - self.sample_trajectory_recorder.record_sde_solution(times=evaluation_times, - sigmas=sigmas, - relative_coordinates=record_relative_coordinates, - normalized_scores=record_normalized_scores) + record_normalized_scores = einops.rearrange( + torch.stack(list_normalized_scores), + "time batch natom space -> batch time natom space", + ) + + record_relative_coordinates = einops.rearrange( + ys.flip(dims=(0,)), + "times batch (natom space) -> batch times natom space", + natom=self.number_of_atoms, + space=self.spatial_dimension, + ) + + self.sample_trajectory_recorder.record_sde_solution( + times=evaluation_times, + sigmas=sigmas, + relative_coordinates=record_relative_coordinates, + normalized_scores=record_normalized_scores, + ) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/loggers/logger_loader.py b/src/diffusion_for_multi_scale_molecular_dynamics/loggers/logger_loader.py index a54ed3d0..c5b69f9e 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/loggers/logger_loader.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/loggers/logger_loader.py @@ -29,7 +29,9 @@ def get_run_name(hyper_params: Dict[AnyStr, Any]) -> str: return run_name -def create_all_loggers(hyper_params: Dict[AnyStr, Any], output_directory: str) -> List[Logger]: +def create_all_loggers( + hyper_params: Dict[AnyStr, Any], output_directory: str +) -> List[Logger]: """Create all loggers. This method instantiates all machine learning loggers defined in `hyper_params`. @@ -49,18 +51,18 @@ def create_all_loggers(hyper_params: Dict[AnyStr, Any], output_directory: str) - full_run_name = f"{experiment_name}/{run_name}" - for logger_name in hyper_params.get('logging', []): + for logger_name in hyper_params.get("logging", []): match logger_name: case "csv": - logger = CSVLogger(save_dir=output_directory, - name="csv_logs") + logger = CSVLogger(save_dir=output_directory, name="csv_logs") case "tensorboard": - logger = TensorBoardLogger(save_dir=output_directory, - default_hp_metric=False, - name="tensorboard_logs", - version=0, # Necessary to resume tensorboard logging - ) + logger = TensorBoardLogger( + save_dir=output_directory, + default_hp_metric=False, + name="tensorboard_logs", + version=0, # Necessary to resume tensorboard logging + ) case "comet": # The comet logger will read the API key from ~/.comet.logger. @@ -73,20 +75,25 @@ def create_all_loggers(hyper_params: Dict[AnyStr, Any], output_directory: str) - # - when resuming an experiment, read the experiment key from the yaml file. # --> The presence or absence of this yaml file will serve as a signal of whether are starting # from scratch or resuming. - experiment_key = read_and_validate_comet_experiment_key(full_run_name=full_run_name, - output_directory=output_directory) + experiment_key = read_and_validate_comet_experiment_key( + full_run_name=full_run_name, output_directory=output_directory + ) # if 'experiment_key' is None, a new Comet Experiment will be created. If it is not None, # an ExistingExperiment will be created in order to resume logging. - logger = CometLogger(project_name='institut-courtois', # hardcoding the project. - save_dir=output_directory, - experiment_key=experiment_key, - experiment_name=full_run_name) + logger = CometLogger( + project_name="institut-courtois", # hardcoding the project. + save_dir=output_directory, + experiment_key=experiment_key, + experiment_name=full_run_name, + ) if experiment_key is None: experiment_key = logger.version - write_comet_experiment_key(experiment_key=experiment_key, - full_run_name=full_run_name, - output_directory=output_directory) + write_comet_experiment_key( + experiment_key=experiment_key, + full_run_name=full_run_name, + output_directory=output_directory, + ) case other: raise ValueError(f"Logger {other} is not implemented. Review input") @@ -96,7 +103,9 @@ def create_all_loggers(hyper_params: Dict[AnyStr, Any], output_directory: str) - return all_loggers -def write_comet_experiment_key(experiment_key: str, full_run_name: str, output_directory: str): +def write_comet_experiment_key( + experiment_key: str, full_run_name: str, output_directory: str +): """Write comet experiment key. Args: @@ -108,11 +117,13 @@ def write_comet_experiment_key(experiment_key: str, full_run_name: str, output_d No returns. """ data = {full_run_name: experiment_key} - with open(os.path.join(output_directory, "comet_experiment_key.yaml"), 'w') as fd: + with open(os.path.join(output_directory, "comet_experiment_key.yaml"), "w") as fd: yaml.dump(data, fd) -def read_and_validate_comet_experiment_key(full_run_name: str, output_directory: str) -> Union[str, None]: +def read_and_validate_comet_experiment_key( + full_run_name: str, output_directory: str +) -> Union[str, None]: """Read and validate Comet's experiment key. Args: @@ -127,16 +138,23 @@ def read_and_validate_comet_experiment_key(full_run_name: str, output_directory: if os.path.isfile(key_file): with open(key_file) as fd: data = yaml.load(fd, Loader=yaml.FullLoader) - assert full_run_name in data, (f"The experiment full name is {full_run_name}; this is not the full name " - f"that was used to initially generate this experiment. " - f"Something is inconsistent and requires a manual fix.") + assert full_run_name in data, ( + f"The experiment full name is {full_run_name}; this is not the full name " + f"that was used to initially generate this experiment. " + f"Something is inconsistent and requires a manual fix." + ) experiment_key = data[full_run_name] return experiment_key -def log_figure(figure: plt.figure, global_step: int, pl_logger: Logger, - dataset: str = "train", name: str = "samples") -> None: +def log_figure( + figure: plt.figure, + global_step: int, + pl_logger: Logger, + dataset: str = "train", + name: str = "samples", +) -> None: """Log figure. Args: @@ -150,14 +168,20 @@ def log_figure(figure: plt.figure, global_step: int, pl_logger: Logger, No return """ if type(pl_logger) is CometLogger: - pl_logger.experiment.log_figure(figure_name=f"{dataset}/{name}", figure=figure, step=global_step) + pl_logger.experiment.log_figure( + figure_name=f"{dataset}/{name}", figure=figure, step=global_step + ) elif type(pl_logger) is TensorBoardLogger: - pl_logger.experiment.add_figure(f"{dataset}/{name}", figure, global_step=global_step) + pl_logger.experiment.add_figure( + f"{dataset}/{name}", figure, global_step=global_step + ) elif type(pl_logger) is CSVLogger: # perform a deepcopy to prevent blanking the figure after writing to disk fig = deepcopy(figure) - figure_directory = os.path.join(pl_logger.root_dir, 'figures') + figure_directory = os.path.join(pl_logger.root_dir, "figures") os.makedirs(figure_directory, exist_ok=True) - figure_path = os.path.join(figure_directory, f"{dataset}_{name}_step={global_step}.png") + figure_path = os.path.join( + figure_directory, f"{dataset}_{name}_step={global_step}.png" + ) fig.savefig(figure_path) del fig diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/main_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/main_utils.py index 688c6159..8b2f53fa 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/main_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/main_utils.py @@ -14,13 +14,18 @@ @dataclass(kw_only=True) class MetricResult: """Metric result class that is self documenting.""" + report: bool = False # is there something to report - metric_name: Union[str, None] = None # default to None, if there is nothing to report + metric_name: Union[str, None] = ( + None # default to None, if there is nothing to report + ) mode: Union[str, None] # default to None, if there is nothing to report metric_value: float = np.NaN # default to NaN, if there is nothing to report -def get_optimized_metric_name_and_mode(hyper_params: Dict[AnyStr, Any]) -> Tuple[Union[str, None], Union[str, None]]: +def get_optimized_metric_name_and_mode( + hyper_params: Dict[AnyStr, Any] +) -> Tuple[Union[str, None], Union[str, None]]: """Get optimized metric name and mode. Args: @@ -30,9 +35,9 @@ def get_optimized_metric_name_and_mode(hyper_params: Dict[AnyStr, Any]) -> Tuple metric_name, metric_mode: the name and mode (min or max) for the metric to be optimized. """ # By convention, it is assumed that the metric to be reported is the early stopping metric. - if 'early_stopping' in hyper_params: - early_stopping_params = hyper_params['early_stopping'] - return early_stopping_params['metric'], early_stopping_params['mode'] + if "early_stopping" in hyper_params: + early_stopping_params = hyper_params["early_stopping"] + return early_stopping_params["metric"], early_stopping_params["mode"] else: return None, None @@ -52,12 +57,18 @@ def get_crash_metric_result(hyper_params: Dict[AnyStr, Any]) -> MetricResult: metric_name, mode = get_optimized_metric_name_and_mode(hyper_params) if metric_name is None: - return MetricResult(report=False, metric_name=None, mode=None, metric_value=np.NaN) + return MetricResult( + report=False, metric_name=None, mode=None, metric_value=np.NaN + ) else: - return MetricResult(report=True, metric_name=metric_name, mode=mode, metric_value=np.NaN) + return MetricResult( + report=True, metric_name=metric_name, mode=mode, metric_value=np.NaN + ) -def get_name_and_sign_of_orion_optimization_objective(metric_name: str, mode: str) -> Tuple[str, int]: +def get_name_and_sign_of_orion_optimization_objective( + metric_name: str, mode: str +) -> Tuple[str, int]: """Names and signs. The Orion optimizer seeks to minimize an objective. Some metrics must be maximized, @@ -84,7 +95,9 @@ def get_name_and_sign_of_orion_optimization_objective(metric_name: str, mode: st return optimization_objective_name, optimization_sign -def report_to_orion_if_on(metric_result: MetricResult, run_time_error: Union[None, RuntimeError]): +def report_to_orion_if_on( + metric_result: MetricResult, run_time_error: Union[None, RuntimeError] +): """Report to Orion if on. This function manages how to report the metric to Orion. If Orion is not turned on, or if there @@ -103,26 +116,35 @@ def report_to_orion_if_on(metric_result: MetricResult, run_time_error: Union[Non if orion.client.cli.IS_ORION_ON: optimization_objective_name, optimization_sign = ( - get_name_and_sign_of_orion_optimization_objective(metric_result.metric_name, metric_result.mode)) + get_name_and_sign_of_orion_optimization_objective( + metric_result.metric_name, metric_result.mode + ) + ) report_value = optimization_sign * metric_result.metric_value if run_time_error is None: logger.info("Reporting Results to ORION...") - results = dict(name=optimization_objective_name, type="objective", value=report_value) + results = dict( + name=optimization_objective_name, type="objective", value=report_value + ) orion.client.report_results([results]) logger.info(" Done reporting Results to ORION.") - elif 'CUDA out of memory' in str(run_time_error): - logger.error('model was out of memory - reporting a bad trial so ' - 'that Orion avoids models that are too large') + elif "CUDA out of memory" in str(run_time_error): + logger.error( + "model was out of memory - reporting a bad trial so " + "that Orion avoids models that are too large" + ) orion.client.report_bad_trial(name=optimization_objective_name) else: logger.error(f"Run time error : {run_time_error}- interrupting Orion trial") orion.client.interrupt_trial() -def load_and_backup_hyperparameters(config_file_path: Union[str, None], output_directory: str) -> Dict[str, Any]: +def load_and_backup_hyperparameters( + config_file_path: Union[str, None], output_directory: str +) -> Dict[str, Any]: """Load and process hyperparameters. If a configuration file is provided, this method reads in the hyperparameters. It either makes a copy of the @@ -142,7 +164,9 @@ def load_and_backup_hyperparameters(config_file_path: Union[str, None], output_d hyper_params = _get_hyperparameters(config_file_path) if orion.client.cli.IS_ORION_ON: - logging.info("The Orion client is ON: Orion will manage configuration file copies.") + logging.info( + "The Orion client is ON: Orion will manage configuration file copies." + ) else: config_backup_path = os.path.join(output_directory, "config_backup.yaml") _create_or_validate_backup_configuration(config_backup_path, hyper_params) @@ -150,33 +174,45 @@ def load_and_backup_hyperparameters(config_file_path: Union[str, None], output_d return hyper_params -def _create_or_validate_backup_configuration(config_backup_path: str, hyper_params: Dict[str, Any]) -> None: +def _create_or_validate_backup_configuration( + config_backup_path: str, hyper_params: Dict[str, Any] +) -> None: """Create or validate a backup of the hyperparameters.""" if os.path.exists(config_backup_path): - logging.info(f"The backup configuration file {config_backup_path} already exists. " - f"Validating hyperparameters are identical...") - - with open(config_backup_path, 'r') as stream: - logging.info(f"Reading backup hyperparameters from file {config_backup_path}") + logging.info( + f"The backup configuration file {config_backup_path} already exists. " + f"Validating hyperparameters are identical..." + ) + + with open(config_backup_path, "r") as stream: + logging.info( + f"Reading backup hyperparameters from file {config_backup_path}" + ) backup_hyper_params = yaml.load(stream, Loader=yaml.FullLoader) hp_differences = deepdiff.DeepDiff(backup_hyper_params, hyper_params) - assert hp_differences == {}, (f"Incompatible backup configuration file already present in output directory! " - f"The configuration difference is {hp_differences}. Manual clean up is needed.") + assert hp_differences == {}, ( + f"Incompatible backup configuration file already present in output directory! " + f"The configuration difference is {hp_differences}. Manual clean up is needed." + ) else: - logging.info(f"Writing a copy of the configuration file to backup configuration file {config_backup_path}.") - with open(config_backup_path, 'w') as steam: + logging.info( + f"Writing a copy of the configuration file to backup configuration file {config_backup_path}." + ) + with open(config_backup_path, "w") as steam: yaml.dump(hyper_params, steam) def _get_hyperparameters(config_file_path: Union[str, None]) -> Dict[str, Any]: """Get the hyperparameters.""" if config_file_path is None: - logging.info("No configuration file was provided. The hyperparameters are set to an empty dictionary.") + logging.info( + "No configuration file was provided. The hyperparameters are set to an empty dictionary." + ) hyper_params = dict() else: logging.info(f"Reading in hyperparameters from file {config_file_path}") - with open(config_file_path, 'r') as stream: + with open(config_file_path, "r") as stream: hyper_params = yaml.load(stream, Loader=yaml.FullLoader) return hyper_params diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/metrics/kolmogorov_smirnov_metrics.py b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/kolmogorov_smirnov_metrics.py index 7af4e714..ac758b49 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/metrics/kolmogorov_smirnov_metrics.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/kolmogorov_smirnov_metrics.py @@ -56,9 +56,12 @@ def compute_kolmogorov_smirnov_distance_and_pvalue(self) -> Tuple[float, float]: reference_samples = self.reference_samples_metric.compute() predicted_samples = self.predicted_samples_metric.compute() - test_result = ss.ks_2samp(predicted_samples.detach().cpu().numpy(), - reference_samples.detach().cpu().numpy(), - alternative='two-sided', method='auto') + test_result = ss.ks_2samp( + predicted_samples.detach().cpu().numpy(), + reference_samples.detach().cpu().numpy(), + alternative="two-sided", + method="auto", + ) # The "test statistic" of the two-sided KS test is the largest vertical distance between # the empirical CDFs of the two samples. The larger this is, the less likely the two diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/metrics/sampling_metrics_parameters.py b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/sampling_metrics_parameters.py index 86ad5642..0e6fae66 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/metrics/sampling_metrics_parameters.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/metrics/sampling_metrics_parameters.py @@ -8,6 +8,9 @@ class SamplingMetricsParameters: This dataclass configures what metrics should be computed given that samples have been generated. """ + compute_energies: bool = False # should the energies be computed - compute_structure_factor: bool = False # should the structure factor (distances distribution) be recorded + compute_structure_factor: bool = ( + False # should the structure factor (distances distribution) be recorded + ) structure_factor_max_distance: float = 10.0 # cutoff for the structure factor diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py index 93312518..100b78c4 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_train.py @@ -2,20 +2,24 @@ Running the main() runs a debugging example. Entry points are train_mtp. """ + import argparse from typing import Dict, Tuple import pandas as pd -from crystal_diffusion.mlip.mtp_utils import (MTPInputs, - crawl_lammps_directory, - prepare_mtp_inputs_from_lammps) -from crystal_diffusion.models.mlip.mtp import MTPWithMLIP3 from sklearn.metrics import mean_absolute_error -atom_dict = {1: 'Si'} +from diffusion_for_multi_scale_molecular_dynamics.mlip.mtp_utils import ( + MTPInputs, crawl_lammps_directory, prepare_mtp_inputs_from_lammps) +from diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp import \ + MTPWithMLIP3 + +atom_dict = {1: "Si"} -def prepare_dataset(root_data_dir: str, atom_dict: Dict[int, str], mode: str = "train") -> MTPInputs: +def prepare_dataset( + root_data_dir: str, atom_dict: Dict[int, str], mode: str = "train" +) -> MTPInputs: """Prepare the dataset in a given directory into a MTP format. Args: @@ -28,11 +32,15 @@ def prepare_dataset(root_data_dir: str, atom_dict: Dict[int, str], mode: str = " data in the MTPInputs dataclass """ lammps_outputs, thermo_outputs = crawl_lammps_directory(root_data_dir, mode) - mtp_dataset = prepare_mtp_inputs_from_lammps(lammps_outputs, thermo_outputs, atom_dict) + mtp_dataset = prepare_mtp_inputs_from_lammps( + lammps_outputs, thermo_outputs, atom_dict + ) return mtp_dataset -def train_mtp(train_inputs: MTPInputs, mlip_folder_path: str, save_dir: str) -> MTPWithMLIP3: +def train_mtp( + train_inputs: MTPInputs, mlip_folder_path: str, save_dir: str +) -> MTPWithMLIP3: """Create and train an MTP potential. Args: @@ -60,7 +68,9 @@ def train_mtp(train_inputs: MTPInputs, mlip_folder_path: str, save_dir: str) -> return mtp -def evaluate_mtp(eval_inputs: MTPInputs, mtp: MTPWithMLIP3) -> Tuple[pd.DataFrame, pd.DataFrame]: +def evaluate_mtp( + eval_inputs: MTPInputs, mtp: MTPWithMLIP3 +) -> Tuple[pd.DataFrame, pd.DataFrame]: """Evaluate a trained MTP potential. Args: @@ -80,7 +90,9 @@ def evaluate_mtp(eval_inputs: MTPInputs, mtp: MTPWithMLIP3) -> Tuple[pd.DataFram return df_orig, df_predict -def get_metrics_from_pred(df_orig: pd.DataFrame, df_predict: pd.DataFrame) -> Tuple[float, float]: +def get_metrics_from_pred( + df_orig: pd.DataFrame, df_predict: pd.DataFrame +) -> Tuple[float, float]: """Get mean absolute error on energy and forces from the outputs of MTP. Args: @@ -92,33 +104,53 @@ def get_metrics_from_pred(df_orig: pd.DataFrame, df_predict: pd.DataFrame) -> Tu """ # from demo in maml # get a single predicted energy per structure - predicted_energy = df_predict.groupby('structure_index').agg({'energy': 'mean', 'atom_index': 'count'}) + predicted_energy = df_predict.groupby("structure_index").agg( + {"energy": "mean", "atom_index": "count"} + ) # normalize by number of atoms - predicted_energy = (predicted_energy['energy'] / predicted_energy['atom_index']).to_numpy() + predicted_energy = ( + predicted_energy["energy"] / predicted_energy["atom_index"] + ).to_numpy() # same for ground truth - gt_energy = df_orig.groupby('structure_index').agg({'energy': 'mean', 'atom_index': 'count'}) - gt_energy = (gt_energy['energy'] / gt_energy['atom_index']).to_numpy() + gt_energy = df_orig.groupby("structure_index").agg( + {"energy": "mean", "atom_index": "count"} + ) + gt_energy = (gt_energy["energy"] / gt_energy["atom_index"]).to_numpy() - predicted_forces = (df_predict[['fx', 'fy', 'fz']].to_numpy().flatten()) - gt_forces = (df_orig[['fx', 'fy', 'fz']].to_numpy().flatten()) + predicted_forces = df_predict[["fx", "fy", "fz"]].to_numpy().flatten() + gt_forces = df_orig[["fx", "fy", "fz"]].to_numpy().flatten() - return mean_absolute_error(predicted_energy, gt_energy), mean_absolute_error(predicted_forces, gt_forces) + return mean_absolute_error(predicted_energy, gt_energy), mean_absolute_error( + predicted_forces, gt_forces + ) def main(): """Train and evaluate an example for MTP.""" parser = argparse.ArgumentParser() - parser.add_argument('--lammps_yaml', help='path to LAMMPS yaml file', required=True, nargs='+') - parser.add_argument('--lammps_thermo', help='path to LAMMPS thermo output', required=True, nargs='+') - parser.add_argument('--mlip_dir', help='directory to MLIP compilation folder', required=True) - parser.add_argument('--output_dir', help='path to folder where outputs will be saved', required=True) + parser.add_argument( + "--lammps_yaml", help="path to LAMMPS yaml file", required=True, nargs="+" + ) + parser.add_argument( + "--lammps_thermo", help="path to LAMMPS thermo output", required=True, nargs="+" + ) + parser.add_argument( + "--mlip_dir", help="directory to MLIP compilation folder", required=True + ) + parser.add_argument( + "--output_dir", help="path to folder where outputs will be saved", required=True + ) args = parser.parse_args() lammps_yaml = args.lammps_yaml lammps_thermo_yaml = args.lammps_thermo - assert len(lammps_yaml) == len(lammps_thermo_yaml), "LAMMPS outputs yaml should match thermodynamics output." + assert len(lammps_yaml) == len( + lammps_thermo_yaml + ), "LAMMPS outputs yaml should match thermodynamics output." - mtp_inputs = prepare_mtp_inputs_from_lammps(lammps_yaml, lammps_thermo_yaml, atom_dict) + mtp_inputs = prepare_mtp_inputs_from_lammps( + lammps_yaml, lammps_thermo_yaml, atom_dict + ) mtp = train_mtp(mtp_inputs, args.mlip_dir, args.output_dir) print("Training is done") df_orig, df_predict = evaluate_mtp(mtp_inputs, mtp) @@ -127,5 +159,5 @@ def main(): print(f"MAE of training force prediction is {force_mae} eV/Å") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_utils.py index d2cbf298..498a8756 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/mtp_utils.py @@ -13,13 +13,15 @@ @dataclass(kw_only=True) class MTPInputs: """Create a dataclass to train or evaluate a MTP model.""" + structure: List[Structure] forces: List[List[List[float]]] # num samples x num atoms x spatial dimension energy: List[float] -def extract_structure_and_forces_from_file(filename: str, atom_dict: Dict[int, Any], forces_avail: bool = True) -> \ - Tuple[List[Structure], Optional[List[List[float]]]]: +def extract_structure_and_forces_from_file( + filename: str, atom_dict: Dict[int, Any], forces_avail: bool = True +) -> Tuple[List[Structure], Optional[List[List[float]]]]: """Convert LAMMPS yaml output in a format compatible with MTP training and evaluation methods. Args: @@ -33,26 +35,34 @@ def extract_structure_and_forces_from_file(filename: str, atom_dict: Dict[int, A """ structures = [] forces = [] if forces_avail else None - with (open(filename, 'r') as f): + with open(filename, "r") as f: l_yaml = yaml.safe_load_all(f) - for d in l_yaml: # loop over LAMMPS outputs and convert in pymatgen Structure objects + for ( + d + ) in ( + l_yaml + ): # loop over LAMMPS outputs and convert in pymatgen Structure objects # lattice in yaml is 3 x 2 [0, x_lim] # we assume a rectangular lattice for now with the 2nd coordinates as the lattice vectors lattice = np.zeros((3, 3)) - for i, x in enumerate(d['box']): + for i, x in enumerate(d["box"]): lattice[i, i] = x[1] - type_idx = d['keywords'].index('type') - species = [atom_dict[x[type_idx]] for x in d['data']] # convert to atom type - coords_idx = [d['keywords'].index(x) for x in ['x', 'y', 'z']] - coords = [[x[i] for i in coords_idx] for x in d['data']] - pm_structure = Structure(lattice=lattice, - species=species, - coords=coords, - coords_are_cartesian=True) + type_idx = d["keywords"].index("type") + species = [ + atom_dict[x[type_idx]] for x in d["data"] + ] # convert to atom type + coords_idx = [d["keywords"].index(x) for x in ["x", "y", "z"]] + coords = [[x[i] for i in coords_idx] for x in d["data"]] + pm_structure = Structure( + lattice=lattice, + species=species, + coords=coords, + coords_are_cartesian=True, + ) structures.append(pm_structure) if forces_avail: - force_idx = [d['keywords'].index(x) for x in ['fx', 'fy', 'fz']] - structure_forces = [[x[i] for i in force_idx] for x in d['data']] + force_idx = [d["keywords"].index(x) for x in ["fx", "fy", "fz"]] + structure_forces = [[x[i] for i in force_idx] for x in d["data"]] forces.append(structure_forces) return structures, forces @@ -66,19 +76,20 @@ def extract_energy_from_thermo_log(filename: str) -> List[float]: Returns: list of energies (1 value per configuration) """ - with open(filename, 'r') as f: + with open(filename, "r") as f: log_yaml = yaml.safe_load(f) - kin_idx = log_yaml['keywords'].index('KinEng') - pot_idx = log_yaml['keywords'].index('PotEng') - energies = [x[kin_idx] + x[pot_idx] for x in log_yaml['data']] + kin_idx = log_yaml["keywords"].index("KinEng") + pot_idx = log_yaml["keywords"].index("PotEng") + energies = [x[kin_idx] + x[pot_idx] for x in log_yaml["data"]] return energies -def prepare_mtp_inputs_from_lammps(output_yaml: List[str], - thermo_yaml: List[str], - atom_dict: Dict[int, Any], - get_forces: bool = True, - ) -> MTPInputs: +def prepare_mtp_inputs_from_lammps( + output_yaml: List[str], + thermo_yaml: List[str], + atom_dict: Dict[int, Any], + get_forces: bool = True, +) -> MTPInputs: """Convert a list of LAMMPS output files and thermodynamic output files to MTP input format. Args: @@ -90,24 +101,26 @@ def prepare_mtp_inputs_from_lammps(output_yaml: List[str], Returns: dataclass used as inputs to train and evaluation a MTP model """ - mtp_inputs = { - 'structure': [], - 'energy': [], - 'forces': [] - } + mtp_inputs = {"structure": [], "energy": [], "forces": []} for filename in output_yaml: - structures, forces = extract_structure_and_forces_from_file(filename, atom_dict, get_forces) - mtp_inputs['structure'] += structures - mtp_inputs['forces'] += forces # will be None if get_forces is False + structures, forces = extract_structure_and_forces_from_file( + filename, atom_dict, get_forces + ) + mtp_inputs["structure"] += structures + mtp_inputs["forces"] += forces # will be None if get_forces is False for filename in thermo_yaml: - mtp_inputs['energy'] += extract_energy_from_thermo_log(filename) - mtp_inputs = MTPInputs(structure=mtp_inputs['structure'], - energy=mtp_inputs['energy'], - forces=mtp_inputs['forces']) + mtp_inputs["energy"] += extract_energy_from_thermo_log(filename) + mtp_inputs = MTPInputs( + structure=mtp_inputs["structure"], + energy=mtp_inputs["energy"], + forces=mtp_inputs["forces"], + ) return mtp_inputs -def crawl_lammps_directory(folder_name: str, folder_name_pattern: str = "train") -> Tuple[List[str], List[str]]: +def crawl_lammps_directory( + folder_name: str, folder_name_pattern: str = "train" +) -> Tuple[List[str], List[str]]: """Crawl through a folder and find the LAMMPS output files in folders containing a specified pattern in their name. LAMMPS outputs should end with dump.yaml and Thermondynamics variables files should end with thermo.yaml @@ -124,8 +137,16 @@ def crawl_lammps_directory(folder_name: str, folder_name_pattern: str = "train") lammps_output_files, thermo_output_files = [], [] for dirpath, _, filenames in os.walk(folder_name): if re.search(folder_name_pattern, dirpath): - lammps_output_files.extend([os.path.join(dirpath, f) for f in filenames if f.endswith("dump.yaml")]) - thermo_output_files.extend([os.path.join(dirpath, f) for f in filenames if f.endswith("thermo.yaml")]) + lammps_output_files.extend( + [os.path.join(dirpath, f) for f in filenames if f.endswith("dump.yaml")] + ) + thermo_output_files.extend( + [ + os.path.join(dirpath, f) + for f in filenames + if f.endswith("thermo.yaml") + ] + ) return lammps_output_files, thermo_output_files @@ -142,12 +163,14 @@ def concat_mtp_inputs(input1: MTPInputs, input2: MTPInputs) -> MTPInputs: concat_inputs = MTPInputs( structure=input1.structure + input2.structure, forces=input1.forces + input2.forces, - energy=input1.energy + input2.energy + energy=input1.energy + input2.energy, ) return concat_inputs -def get_metrics_from_pred(df_orig: pd.DataFrame, df_predict: pd.DataFrame) -> Tuple[float, float]: +def get_metrics_from_pred( + df_orig: pd.DataFrame, df_predict: pd.DataFrame +) -> Tuple[float, float]: """Get mean absolute error on energy and forces from the outputs of MTP. Args: @@ -159,14 +182,22 @@ def get_metrics_from_pred(df_orig: pd.DataFrame, df_predict: pd.DataFrame) -> Tu """ # from demo in maml # get a single predicted energy per structure - predicted_energy = df_predict.groupby('structure_index').agg({'energy': 'mean', 'atom_index': 'count'}) + predicted_energy = df_predict.groupby("structure_index").agg( + {"energy": "mean", "atom_index": "count"} + ) # normalize by number of atoms - predicted_energy = (predicted_energy['energy'] / predicted_energy['atom_index']).to_numpy() + predicted_energy = ( + predicted_energy["energy"] / predicted_energy["atom_index"] + ).to_numpy() # same for ground truth - gt_energy = df_orig.groupby('structure_index').agg({'energy': 'mean', 'atom_index': 'count'}) - gt_energy = (gt_energy['energy'] / gt_energy['atom_index']).to_numpy() + gt_energy = df_orig.groupby("structure_index").agg( + {"energy": "mean", "atom_index": "count"} + ) + gt_energy = (gt_energy["energy"] / gt_energy["atom_index"]).to_numpy() - predicted_forces = (df_predict[['fx', 'fy', 'fz']].to_numpy().flatten()) - gt_forces = (df_orig[['fx', 'fy', 'fz']].to_numpy().flatten()) + predicted_forces = df_predict[["fx", "fy", "fz"]].to_numpy().flatten() + gt_forces = df_orig[["fx", "fy", "fz"]].to_numpy().flatten() - return mean_absolute_error(predicted_energy, gt_energy), mean_absolute_error(predicted_forces, gt_forces) + return mean_absolute_error(predicted_energy, gt_energy), mean_absolute_error( + predicted_forces, gt_forces + ) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py index b4919e12..2af13346 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/diffusion_mace.py @@ -1,11 +1,6 @@ from typing import AnyStr, Callable, Dict, List, Optional, Type, Union import torch -from crystal_diffusion.models.mace_utils import (get_adj_matrix, - reshape_from_e3nn_to_mace, - reshape_from_mace_to_e3nn) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_CARTESIAN_POSITIONS, UNIT_CELL) from e3nn import o3 from e3nn.nn import Activation, BatchNorm, NormActivation from mace.modules import (EquivariantProductBasisBlock, InteractionBlock, @@ -13,9 +8,15 @@ from mace.modules.utils import get_edge_vectors_and_lengths from torch_geometric.data import Data +from diffusion_for_multi_scale_molecular_dynamics.models.mace_utils import ( + get_adj_matrix, reshape_from_e3nn_to_mace, reshape_from_mace_to_e3nn) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_CARTESIAN_POSITIONS, UNIT_CELL) + class LinearVectorReadoutBlock(torch.nn.Module): """Linear readout block for vector representation.""" + def __init__(self, irreps_in: o3.Irreps): """Init method.""" super().__init__() @@ -26,7 +27,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return self.linear(x) -def input_to_diffusion_mace(batch: Dict[AnyStr, torch.Tensor], radial_cutoff: float) -> Data: +def input_to_diffusion_mace( + batch: Dict[AnyStr, torch.Tensor], radial_cutoff: float +) -> Data: """Convert score network input to Diffusion MACE input. Args: @@ -42,44 +45,61 @@ def input_to_diffusion_mace(batch: Dict[AnyStr, torch.Tensor], radial_cutoff: fl basis_vectors = batch[UNIT_CELL] # batch, spatial_dimension, spatial_dimension - adj_matrix, shift_matrix, batch_tensor, num_edges = get_adj_matrix(positions=cartesian_positions, - basis_vectors=basis_vectors, - radial_cutoff=radial_cutoff) + adj_matrix, shift_matrix, batch_tensor, num_edges = get_adj_matrix( + positions=cartesian_positions, + basis_vectors=basis_vectors, + radial_cutoff=radial_cutoff, + ) # node features are int corresponding to atom type # TODO handle different atom types atom_types = torch.zeros(batch_size * n_atom_per_graph) - node_attrs = torch.nn.functional.one_hot(atom_types.long(), num_classes=1).to(atom_types) + node_attrs = torch.nn.functional.one_hot(atom_types.long(), num_classes=1).to( + atom_types + ) # The node diffusion scalars will be the diffusion noise sigma, which is constant for each structure in the batch. # We broadcast to each node to avoid complex broadcasting logic within the model itself. # TODO: it might be better to define the noise as a 'global' graph attribute, and find 'the right way' of # mixing it with bona fide node features within the model. - noises = batch[NOISE] + 1 # [batch_size, 1] - add 1 to avoid getting a zero at sigma=0 (initialization issues) - node_diffusion_scalars = noises.repeat_interleave(n_atom_per_graph, dim=0) # [flat_batch_size, 1] - edge_diffusion_scalars = noises.repeat_interleave(num_edges.long().to(device=noises.device), dim=0) + noises = ( + batch[NOISE] + 1 + ) # [batch_size, 1] - add 1 to avoid getting a zero at sigma=0 (initialization issues) + node_diffusion_scalars = noises.repeat_interleave( + n_atom_per_graph, dim=0 + ) # [flat_batch_size, 1] + edge_diffusion_scalars = noises.repeat_interleave( + num_edges.long().to(device=noises.device), dim=0 + ) # [batchsize * natoms, spatial dimension] flat_cartesian_positions = cartesian_positions.view(-1, spatial_dimension) # pointer tensor that yields the first node index for each batch - this is a fixed tensor in our case - ptr = torch.arange(0, n_atom_per_graph * batch_size + 1, step=n_atom_per_graph) # 0, natoms, 2 * natoms, ... + ptr = torch.arange( + 0, n_atom_per_graph * batch_size + 1, step=n_atom_per_graph + ) # 0, natoms, 2 * natoms, ... - flat_basis_vectors = basis_vectors.view(-1, spatial_dimension) # batch * spatial_dimension, spatial_dimension + flat_basis_vectors = basis_vectors.view( + -1, spatial_dimension + ) # batch * spatial_dimension, spatial_dimension # create the pytorch-geometric graph - forces = batch[CARTESIAN_FORCES].view(-1, spatial_dimension) # batch * n_atom_per_graph, spatial dimension - - graph_data = Data(edge_index=adj_matrix, - node_attrs=node_attrs.to(device), - node_diffusion_scalars=node_diffusion_scalars.to(device), - edge_diffusion_scalars=edge_diffusion_scalars.to(device), - positions=flat_cartesian_positions, - ptr=ptr.to(device), - batch=batch_tensor.to(device), - shifts=shift_matrix, - cell=flat_basis_vectors, - forces=forces, - ) + forces = batch[CARTESIAN_FORCES].view( + -1, spatial_dimension + ) # batch * n_atom_per_graph, spatial dimension + + graph_data = Data( + edge_index=adj_matrix, + node_attrs=node_attrs.to(device), + node_diffusion_scalars=node_diffusion_scalars.to(device), + edge_diffusion_scalars=edge_diffusion_scalars.to(device), + positions=flat_cartesian_positions, + ptr=ptr.to(device), + batch=batch_tensor.to(device), + shifts=shift_matrix, + cell=flat_basis_vectors, + forces=forces, + ) return graph_data @@ -90,6 +110,7 @@ class DiffusionMACE(torch.nn.Module): "MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields" will be referenced in the comments as the PAPER. """ + def __init__( self, r_max: float, @@ -116,9 +137,12 @@ def __init__( tanh_after_interaction: bool = True, ): """Init method.""" - assert num_elements == 1, "only a single element can be used at this time. Set 'num_elements' to 1." - assert len(atomic_numbers) == 1, \ - "only a single element can be used at this time. Set 'atomic_numbers' to length 1." + assert ( + num_elements == 1 + ), "only a single element can be used at this time. Set 'num_elements' to 1." + assert ( + len(atomic_numbers) == 1 + ), "only a single element can be used at this time. Set 'atomic_numbers' to length 1." super().__init__() self.register_buffer( "atomic_numbers", torch.tensor(atomic_numbers, dtype=torch.int64) @@ -146,35 +170,47 @@ def __init__( number_of_node_scalar_dimensions = 1 number_of_hidden_diffusion_scalar_dimensions = mlp_irreps.count(scalar_irrep) - diffusion_scalar_irreps_in = o3.Irreps([(number_of_node_scalar_dimensions, scalar_irrep)]) - diffusion_scalar_irreps_out = o3.Irreps([(number_of_hidden_diffusion_scalar_dimensions, scalar_irrep)]) + diffusion_scalar_irreps_in = o3.Irreps( + [(number_of_node_scalar_dimensions, scalar_irrep)] + ) + diffusion_scalar_irreps_out = o3.Irreps( + [(number_of_hidden_diffusion_scalar_dimensions, scalar_irrep)] + ) self.diffusion_scalar_embedding = torch.nn.Sequential() - linear = o3.Linear(irreps_in=diffusion_scalar_irreps_in, - irreps_out=diffusion_scalar_irreps_out, - biases=True) + linear = o3.Linear( + irreps_in=diffusion_scalar_irreps_in, + irreps_out=diffusion_scalar_irreps_out, + biases=True, + ) self.diffusion_scalar_embedding.append(linear) non_linearity = Activation(irreps_in=diffusion_scalar_irreps_out, acts=[gate]) for _ in range(number_of_mlp_layers): self.diffusion_scalar_embedding.append(non_linearity) - linear = o3.Linear(irreps_in=diffusion_scalar_irreps_out, - irreps_out=diffusion_scalar_irreps_out, - biases=True) + linear = o3.Linear( + irreps_in=diffusion_scalar_irreps_out, + irreps_out=diffusion_scalar_irreps_out, + biases=True, + ) self.diffusion_scalar_embedding.append(linear) # The node_attr is the one-hot version of the atom types. node_attr_irreps = o3.Irreps([(num_elements, scalar_irrep)]) # Perform a tensor product to mix the diffusion scalar and node attributes - self.attribute_mixing = o3.FullyConnectedTensorProduct(irreps_in1=diffusion_scalar_irreps_out, - irreps_in2=node_attr_irreps, - irreps_out=node_attr_irreps, - irrep_normalization='norm') + self.attribute_mixing = o3.FullyConnectedTensorProduct( + irreps_in1=diffusion_scalar_irreps_out, + irreps_in2=node_attr_irreps, + irreps_out=node_attr_irreps, + irrep_normalization="norm", + ) number_of_hidden_scalar_dimensions = hidden_irreps.count(scalar_irrep) - node_feats_irreps = o3.Irreps([(number_of_hidden_scalar_dimensions, scalar_irrep)]) + node_feats_irreps = o3.Irreps( + [(number_of_hidden_scalar_dimensions, scalar_irrep)] + ) # The "node embedding" corresponds to W^{(1)} in the definition of A^{(1)}, eq. 9 of the PAPER. self.node_embedding = LinearNodeEmbeddingBlock( irreps_in=node_attr_irreps, irreps_out=node_feats_irreps @@ -190,18 +226,22 @@ def __init__( edge_feats_irreps = o3.Irreps([(self.radial_embedding.out_dim, scalar_irrep)]) if num_edge_hidden_layers > 0: - self.edge_attribute_mixing = o3.FullyConnectedTensorProduct(irreps_in1=diffusion_scalar_irreps_out, - irreps_in2=edge_feats_irreps, - irreps_out=edge_hidden_irreps, - irrep_normalization='norm') + self.edge_attribute_mixing = o3.FullyConnectedTensorProduct( + irreps_in1=diffusion_scalar_irreps_out, + irreps_in2=edge_feats_irreps, + irreps_out=edge_hidden_irreps, + irrep_normalization="norm", + ) self.edge_hidden_layers = torch.nn.Sequential() edge_non_linearity = Activation(irreps_in=edge_hidden_irreps, acts=[gate]) for i in range(num_edge_hidden_layers): if i != 0: self.edge_hidden_layers.append(edge_non_linearity) - edge_hidden_layer = o3.Linear(irreps_in=edge_hidden_irreps, - irreps_out=edge_hidden_irreps, - biases=False) + edge_hidden_layer = o3.Linear( + irreps_in=edge_hidden_irreps, + irreps_out=edge_hidden_irreps, + biases=False, + ) self.edge_hidden_layers.append(edge_hidden_layer) else: self.edge_attribute_mixing, self.edge_hidden_layers = None, None @@ -209,7 +249,9 @@ def __init__( # The "spherical harmonics" correspond to Y_{lm} in the definition of A^{(1)}, eq. 9 of the PAPER. sh_irreps = o3.Irreps.spherical_harmonics(max_ell) - interaction_irreps = (sh_irreps * number_of_hidden_scalar_dimensions).sort()[0].simplify() + interaction_irreps = ( + (sh_irreps * number_of_hidden_scalar_dimensions).sort()[0].simplify() + ) self.spherical_harmonics = o3.SphericalHarmonics( sh_irreps, normalize=True, normalization="component" ) @@ -233,7 +275,9 @@ def __init__( self.interactions = torch.nn.ModuleList([inter]) if tanh_after_interaction: - self.interactions_tanh = torch.nn.ModuleList([NormActivation(inter.target_irreps, torch.tanh)]) + self.interactions_tanh = torch.nn.ModuleList( + [NormActivation(inter.target_irreps, torch.tanh)] + ) else: self.interactions_tanh = None @@ -261,9 +305,7 @@ def __init__( self.use_batchnorm = use_batchnorm if self.use_batchnorm: - self.batch_norms = torch.nn.ModuleList([ - BatchNorm(node_feats_irreps) - ]) + self.batch_norms = torch.nn.ModuleList([BatchNorm(node_feats_irreps)]) hidden_irreps_out = hidden_irreps @@ -282,7 +324,9 @@ def __init__( self.interactions.append(inter) if self.interactions_tanh is not None: - self.interactions_tanh.append(NormActivation(interaction_irreps, torch.tanh)) + self.interactions_tanh.append( + NormActivation(interaction_irreps, torch.tanh) + ) # prod compute h^{(t+1)} from A^{(t)} and h^{(t)}, computing B and the messages internally. prod = EquivariantProductBasisBlock( @@ -304,9 +348,9 @@ def __init__( # Apply a MLP with a bias on the forces as a conditional feature. This would be a 1o irrep forces_irreps_in = o3.Irreps("1x1o") forces_irreps_embedding = o3.Irreps(f"{condition_embedding_size}x1o") - self.condition_embedding_layer = o3.Linear(irreps_in=forces_irreps_in, - irreps_out=forces_irreps_embedding, - biases=False) # can't have biases with 1o irreps + self.condition_embedding_layer = o3.Linear( + irreps_in=forces_irreps_in, irreps_out=forces_irreps_embedding, biases=False + ) # can't have biases with 1o irreps # conditional layers for the forces as a conditional feature to guide the diffusion self.conditional_layers = torch.nn.ModuleList([]) @@ -314,18 +358,24 @@ def __init__( cond_layer = o3.Linear( irreps_in=forces_irreps_embedding, irreps_out=hidden_irreps_out, - biases=False + biases=False, ) self.conditional_layers.append(cond_layer) - def forward(self, data: Dict[str, torch.Tensor], conditional: bool = False) -> torch.Tensor: + def forward( + self, data: Dict[str, torch.Tensor], conditional: bool = False + ) -> torch.Tensor: """Forward method.""" # Setup # Augment the node attributes with information from the diffusion scalar. - diffusion_scalar_embeddings = self.diffusion_scalar_embedding(data["node_diffusion_scalars"]) + diffusion_scalar_embeddings = self.diffusion_scalar_embedding( + data["node_diffusion_scalars"] + ) raw_node_attributes = data["node_attrs"] - augmented_node_attributes = self.attribute_mixing(diffusion_scalar_embeddings, raw_node_attributes) + augmented_node_attributes = self.attribute_mixing( + diffusion_scalar_embeddings, raw_node_attributes + ) # Embeddings node_feats = self.node_embedding(augmented_node_attributes) @@ -337,14 +387,20 @@ def forward(self, data: Dict[str, torch.Tensor], conditional: bool = False) -> t edge_attrs = self.spherical_harmonics(vectors) edge_feats = self.radial_embedding(lengths) if self.edge_attribute_mixing is not None: - edge_diffusion_scalar_embeddings = self.diffusion_scalar_embedding(data["edge_diffusion_scalars"]) - augmented_edge_attributes = self.edge_attribute_mixing(edge_diffusion_scalar_embeddings, edge_feats) + edge_diffusion_scalar_embeddings = self.diffusion_scalar_embedding( + data["edge_diffusion_scalars"] + ) + augmented_edge_attributes = self.edge_attribute_mixing( + edge_diffusion_scalar_embeddings, edge_feats + ) edge_feats = self.edge_hidden_layers(augmented_edge_attributes) - forces_embedding = self.condition_embedding_layer(data["forces"]) # 0e + 1o embedding + forces_embedding = self.condition_embedding_layer( + data["forces"] + ) # 0e + 1o embedding for i, (interaction, product, cond_layer) in enumerate( - zip(self.interactions, self.products, self.conditional_layers) + zip(self.interactions, self.products, self.conditional_layers) ): if self.use_batchnorm: node_feats = self.batch_norms[i](node_feats) @@ -359,18 +415,24 @@ def forward(self, data: Dict[str, torch.Tensor], conditional: bool = False) -> t if self.interactions_tanh is not None: # reshape from (node, channels, (l_max + 1)**2) to a (node, -1) tensor compatible with e3nn - node_feats = reshape_from_mace_to_e3nn(node_feats, self.interactions_tanh[i].irreps_in) + node_feats = reshape_from_mace_to_e3nn( + node_feats, self.interactions_tanh[i].irreps_in + ) # apply non-linearity node_feats = self.interactions_tanh[i](node_feats) # reshape from e3nn shape to mace format (node, channels, (l_max+1)**2) - node_feats = reshape_from_e3nn_to_mace(node_feats, self.interactions_tanh[i].irreps_out) + node_feats = reshape_from_e3nn_to_mace( + node_feats, self.interactions_tanh[i].irreps_out + ) node_feats = product( node_feats=node_feats, sc=sc, node_attrs=augmented_node_attributes, ) - if conditional: # modify the node features to account for the conditional features i.e. forces + if ( + conditional + ): # modify the node features to account for the conditional features i.e. forces force_embed = cond_layer(forces_embedding) node_feats += force_embed diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py index 6aa34c40..86befaa9 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn.py @@ -7,13 +7,15 @@ The file is modified from the original download to fit our own linting style and add additional controls. """ + from typing import Callable, Tuple import torch -from crystal_diffusion.models.egnn_utils import (unsorted_segment_mean, - unsorted_segment_sum) from torch import nn +from diffusion_for_multi_scale_molecular_dynamics.models.egnn_utils import ( + unsorted_segment_mean, unsorted_segment_sum) + class E_GCL(nn.Module): """E(n) Equivariant Convolutional Layer.""" @@ -65,19 +67,26 @@ def __init__( if coords_agg not in ["mean", "sum"]: raise ValueError(f"coords_agg should be mean or sum. Got {coords_agg}") - self.coords_agg_fn = unsorted_segment_sum if coords_agg == "sum" else unsorted_segment_mean - self.msg_agg_fn = unsorted_segment_sum if message_agg == "sum" else unsorted_segment_mean + self.coords_agg_fn = ( + unsorted_segment_sum if coords_agg == "sum" else unsorted_segment_mean + ) + self.msg_agg_fn = ( + unsorted_segment_sum if message_agg == "sum" else unsorted_segment_mean + ) # message update MLP i.e. message m_{ij} used in the graph neural network. # \phi_e is eq. (3) in https://arxiv.org/pdf/2102.09844 # Input is a concatenation of the two node features and distance message_input_size = input_size * 2 + 1 self.message_mlp = nn.Sequential( - nn.Linear(message_input_size, message_hidden_dimensions_size), - act_fn + nn.Linear(message_input_size, message_hidden_dimensions_size), act_fn ) for _ in range(message_n_hidden_dimensions): - self.message_mlp.append(nn.Linear(message_hidden_dimensions_size, message_hidden_dimensions_size)) + self.message_mlp.append( + nn.Linear( + message_hidden_dimensions_size, message_hidden_dimensions_size + ) + ) self.message_mlp.append(act_fn) # node update mlp. Input is the node feature (size input_size) and the aggregated messages from neighbors @@ -85,11 +94,12 @@ def __init__( # \phi_h in eq. (6) in https://arxiv.org/pdf/2102.09844 node_input_size = input_size + message_hidden_dimensions_size self.node_mlp = nn.Sequential( - nn.Linear(node_input_size, node_hidden_dimensions_size), - act_fn + nn.Linear(node_input_size, node_hidden_dimensions_size), act_fn ) for _ in range(node_n_hidden_dimensions): - self.node_mlp.append(nn.Linear(node_hidden_dimensions_size, node_hidden_dimensions_size)) + self.node_mlp.append( + nn.Linear(node_hidden_dimensions_size, node_hidden_dimensions_size) + ) self.node_mlp.append(act_fn) self.node_mlp.append(nn.Linear(node_hidden_dimensions_size, output_size)) @@ -97,12 +107,20 @@ def __init__( # \phi_x in eq.(4) in https://arxiv.org/pdf/2102.09844 coordinate_input_size = message_hidden_dimensions_size - self.coord_mlp = nn.Sequential(nn.Linear(coordinate_input_size, coordinate_hidden_dimensions_size)) + self.coord_mlp = nn.Sequential( + nn.Linear(coordinate_input_size, coordinate_hidden_dimensions_size) + ) self.coord_mlp.append(act_fn) for _ in range(coordinate_n_hidden_dimensions): - self.coord_mlp.append(nn.Linear(coordinate_hidden_dimensions_size, coordinate_hidden_dimensions_size)) + self.coord_mlp.append( + nn.Linear( + coordinate_hidden_dimensions_size, coordinate_hidden_dimensions_size + ) + ) self.coord_mlp.append(act_fn) - final_coordinate_layer = nn.Linear(coordinate_hidden_dimensions_size, 1, bias=False) + final_coordinate_layer = nn.Linear( + coordinate_hidden_dimensions_size, 1, bias=False + ) # based on the original implementation - multiply the random initialization by 0.001 (default is 1) # torch.nn.init.xavier_uniform_(final_coordinate_layer.weight, gain=0.001) self.coord_mlp.append(final_coordinate_layer) # initialized with a different @@ -110,9 +128,13 @@ def __init__( self.coord_mlp.append(nn.Tanh()) if self.attention: - self.att_mlp = nn.Sequential(nn.Linear(message_hidden_dimensions_size, 1), nn.Sigmoid()) + self.att_mlp = nn.Sequential( + nn.Linear(message_hidden_dimensions_size, 1), nn.Sigmoid() + ) - def message_model(self, source: torch.Tensor, target: torch.Tensor, radial: torch.Tensor) -> torch.Tensor: + def message_model( + self, source: torch.Tensor, target: torch.Tensor, radial: torch.Tensor + ) -> torch.Tensor: r"""Constructs the message m_{ij} from source (j) to target (i). .. math:: @@ -136,8 +158,9 @@ def message_model(self, source: torch.Tensor, target: torch.Tensor, radial: torc out = out * att_val return out - def node_model(self, x: torch.Tensor, edge_index: torch.Tensor, messages: torch.Tensor - ) -> torch.Tensor: + def node_model( + self, x: torch.Tensor, edge_index: torch.Tensor, messages: torch.Tensor + ) -> torch.Tensor: r"""Update the node features. .. math:: @@ -153,15 +176,22 @@ def node_model(self, x: torch.Tensor, edge_index: torch.Tensor, messages: torch. updated node features. size: number of nodes, output_size """ row = edge_index[:, 0] - agg = self.msg_agg_fn(messages, row, num_segments=x.size(0)) # sum messages m_i = \sum_j m_{ij} + agg = self.msg_agg_fn( + messages, row, num_segments=x.size(0) + ) # sum messages m_i = \sum_j m_{ij} agg = torch.cat([x, agg], dim=1) # concat h_i and m_i out = self.node_mlp(agg) if self.residual: # optional skip connection out = x + out return out - def coord_model(self, coord: torch.Tensor, edge_index: torch.Tensor, coord_diff: torch.Tensor, - messages: torch.Tensor) -> torch.Tensor: + def coord_model( + self, + coord: torch.Tensor, + edge_index: torch.Tensor, + coord_diff: torch.Tensor, + messages: torch.Tensor, + ) -> torch.Tensor: r"""Update the coordinates. .. math:: @@ -183,7 +213,9 @@ def coord_model(self, coord: torch.Tensor, edge_index: torch.Tensor, coord_diff: coord += agg return coord - def coord2radial(self, edge_index: torch.Tensor, coord: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + def coord2radial( + self, edge_index: torch.Tensor, coord: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: """Compute distances between linked nodes. Args: @@ -206,8 +238,9 @@ def coord2radial(self, edge_index: torch.Tensor, coord: torch.Tensor) -> Tuple[t return radial, coord_diff - def forward(self, h: torch.Tensor, edge_index: torch.Tensor, coord: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: + def forward( + self, h: torch.Tensor, edge_index: torch.Tensor, coord: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: """Compute node embeddings and coordinates. Args: @@ -232,6 +265,7 @@ def forward(self, h: torch.Tensor, edge_index: torch.Tensor, coord: torch.Tensor class EGNN(nn.Module): """EGNN model.""" + def __init__( self, input_size: int, @@ -295,7 +329,9 @@ def __init__( ) ) - def forward(self, h: torch.Tensor, edges: torch.Tensor, x: torch.Tensor) -> torch.Tensor: + def forward( + self, h: torch.Tensor, edges: torch.Tensor, x: torch.Tensor + ) -> torch.Tensor: """Forward instructions for the model. Args: diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py index d1b03359..fa2417fd 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/egnn_utils.py @@ -1,12 +1,16 @@ from typing import List import torch -from crystal_diffusion.models.mace_utils import get_adj_matrix -from crystal_diffusion.utils.basis_transformations import \ + +from diffusion_for_multi_scale_molecular_dynamics.models.mace_utils import \ + get_adj_matrix +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ get_positions_from_coordinates -def unsorted_segment_sum(data: torch.Tensor, segment_ids: torch.Tensor, num_segments: int) -> torch.Tensor: +def unsorted_segment_sum( + data: torch.Tensor, segment_ids: torch.Tensor, num_segments: int +) -> torch.Tensor: """Sum all the elements in data by their ids. For example, data could be messages from atoms j to i. We want to sum all messages going to i, i.e. sum all elements @@ -22,7 +26,9 @@ def unsorted_segment_sum(data: torch.Tensor, segment_ids: torch.Tensor, num_segm tensor with the sum of data elements over ids. size: (num_segments, number of features) """ result_shape = (num_segments, data.size(1)) # output size - result = torch.zeros(result_shape).to(data) # results starting as zeros - same dtype and device as data + result = torch.zeros(result_shape).to( + data + ) # results starting as zeros - same dtype and device as data # tensor size manipulation to use a scatter_add operation # from (number of elements) to (number of elements, number of features) i.e. same size as data segment_ids = segment_ids.unsqueeze(-1).expand(-1, data.size(1)) @@ -32,7 +38,9 @@ def unsorted_segment_sum(data: torch.Tensor, segment_ids: torch.Tensor, num_segm return result -def unsorted_segment_mean(data: torch.Tensor, segment_ids: torch.Tensor, num_segments: int) -> torch.Tensor: +def unsorted_segment_mean( + data: torch.Tensor, segment_ids: torch.Tensor, num_segments: int +) -> torch.Tensor: """Average all the elements in data by their ids. For example, data could be messages from atoms j to i. We want to average all messages going to i @@ -48,12 +56,18 @@ def unsorted_segment_mean(data: torch.Tensor, segment_ids: torch.Tensor, num_seg tensor with the average of data elements over ids. size: (num_segments, number of features) """ result_shape = (num_segments, data.size(1)) # output size - segment_ids = segment_ids.unsqueeze(-1).expand(-1, data.size(1)) # tensor size manipulation for the backward pass + segment_ids = segment_ids.unsqueeze(-1).expand( + -1, data.size(1) + ) # tensor size manipulation for the backward pass result = torch.zeros(result_shape).to(data) # sum the component - count = torch.zeros(result_shape).to(data) # count the number of data elements for each id to take the average + count = torch.zeros(result_shape).to( + data + ) # count the number of data elements for each id to take the average result.scatter_add_(0, segment_ids, data) # sum the data elements count.scatter_add_(0, segment_ids, torch.ones_like(data)) - return result / count.clamp(min=1) # avoid dividing by zeros by clamping the counts to be at least 1 + return result / count.clamp( + min=1 + ) # avoid dividing by zeros by clamping the counts to be at least 1 def get_edges(n_nodes: int) -> List[List[int]]: @@ -90,8 +104,12 @@ def get_edges_batch(n_nodes: int, batch_size: int) -> torch.Tensor: return edges -def get_edges_with_radial_cutoff(relative_coordinates: torch.Tensor, unit_cell: torch.Tensor, - radial_cutoff: float = 4.0, drop_duplicate_edges: bool = True) -> torch.Tensor: +def get_edges_with_radial_cutoff( + relative_coordinates: torch.Tensor, + unit_cell: torch.Tensor, + radial_cutoff: float = 4.0, + drop_duplicate_edges: bool = True, +) -> torch.Tensor: """Get edges for a batch with a cutoff based on distance. Args: @@ -105,8 +123,12 @@ def get_edges_with_radial_cutoff(relative_coordinates: torch.Tensor, unit_cell: long tensor of size [number of edges, 2] with edge indices """ # get cartesian coordinates from relative coordinates - cartesian_coordinates = get_positions_from_coordinates(relative_coordinates, unit_cell) - adj_matrix, _, _, _ = get_adj_matrix(cartesian_coordinates, unit_cell, radial_cutoff) + cartesian_coordinates = get_positions_from_coordinates( + relative_coordinates, unit_cell + ) + adj_matrix, _, _, _ = get_adj_matrix( + cartesian_coordinates, unit_cell, radial_cutoff + ) # adj_matrix is a n_edges x 2 tensor with duplicates with different shifts. # the uplifting in 2 x spatial_dimension manages the shifts in a natural way. This means we can ignore the shifts # and possibly ignore the multiplicities i.e. no need to sum twice the contribution of a neighbor that we see diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py index 5750c4e0..3f3defc1 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/graph_utils.py @@ -1,14 +1,15 @@ from typing import Tuple import torch -from crystal_diffusion.utils.neighbors import ( + +from diffusion_for_multi_scale_molecular_dynamics.utils.neighbors import ( get_periodic_adjacency_information, shift_adjacency_matrix_indices_for_graph_batching) -def get_adj_matrix(positions: torch.Tensor, - basis_vectors: torch.Tensor, - radial_cutoff: float = 4.0) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: +def get_adj_matrix( + positions: torch.Tensor, basis_vectors: torch.Tensor, radial_cutoff: float = 4.0 +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Create the adjacency and shift matrices. Args: @@ -30,15 +31,17 @@ def get_adj_matrix(positions: torch.Tensor, """ batch_size, number_of_atoms, spatial_dimensions = positions.shape - adjacency_info = get_periodic_adjacency_information(positions, basis_vectors, radial_cutoff) + adjacency_info = get_periodic_adjacency_information( + positions, basis_vectors, radial_cutoff + ) # The indices in the adjacency matrix must be shifted to account for the batching # of multiple distinct structures into a single disconnected graph. adjacency_matrix = adjacency_info.adjacency_matrix number_of_edges = adjacency_info.number_of_edges - shifted_adjacency_matrix = shift_adjacency_matrix_indices_for_graph_batching(adjacency_matrix, - number_of_edges, - number_of_atoms) + shifted_adjacency_matrix = shift_adjacency_matrix_indices_for_graph_batching( + adjacency_matrix, number_of_edges, number_of_atoms + ) shifts = adjacency_info.shifts batch_indices = adjacency_info.node_batch_indices diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py index 3856e095..d6ff405f 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/instantiate_diffusion_model.py @@ -1,22 +1,29 @@ """Functions to instantiate a model based on the provided hyperparameters.""" + import logging from typing import Any, AnyStr, Dict -from crystal_diffusion.models.loss import create_loss_parameters -from crystal_diffusion.models.optimizer import create_optimizer_parameters -from crystal_diffusion.models.position_diffusion_lightning_model import ( +from diffusion_for_multi_scale_molecular_dynamics.models.loss import \ + create_loss_parameters +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import \ + create_optimizer_parameters +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import ( PositionDiffusionLightningModel, PositionDiffusionParameters) -from crystal_diffusion.models.scheduler import create_scheduler_parameters -from crystal_diffusion.models.score_networks.score_network_factory import \ +from diffusion_for_multi_scale_molecular_dynamics.models.scheduler import \ + create_scheduler_parameters +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network_factory import \ create_score_network_parameters -from crystal_diffusion.samples.diffusion_sampling_parameters import \ +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.samples.diffusion_sampling_parameters import \ load_diffusion_sampling_parameters -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters logger = logging.getLogger(__name__) -def load_diffusion_model(hyper_params: Dict[AnyStr, Any]) -> PositionDiffusionLightningModel: +def load_diffusion_model( + hyper_params: Dict[AnyStr, Any] +) -> PositionDiffusionLightningModel: """Load a position diffusion model from the hyperparameters. Args: @@ -25,21 +32,25 @@ def load_diffusion_model(hyper_params: Dict[AnyStr, Any]) -> PositionDiffusionLi Returns: Diffusion model randomly initialized """ - globals_dict = dict(max_atom=hyper_params['data']['max_atom'], - spatial_dimension=hyper_params.get('spatial_dimension', 3)) + globals_dict = dict( + max_atom=hyper_params["data"]["max_atom"], + spatial_dimension=hyper_params.get("spatial_dimension", 3), + ) - score_network_dict = hyper_params['model']['score_network'] - score_network_parameters = create_score_network_parameters(score_network_dict, globals_dict) + score_network_dict = hyper_params["model"]["score_network"] + score_network_parameters = create_score_network_parameters( + score_network_dict, globals_dict + ) - optimizer_configuration_dict = hyper_params['optimizer'] + optimizer_configuration_dict = hyper_params["optimizer"] optimizer_parameters = create_optimizer_parameters(optimizer_configuration_dict) scheduler_parameters = create_scheduler_parameters(hyper_params) - model_dict = hyper_params['model'] + model_dict = hyper_params["model"] loss_parameters = create_loss_parameters(model_dict) - noise_dict = model_dict['noise'] + noise_dict = model_dict["noise"] noise_parameters = NoiseParameters(**noise_dict) diffusion_sampling_parameters = load_diffusion_sampling_parameters(hyper_params) @@ -54,6 +65,6 @@ def load_diffusion_model(hyper_params: Dict[AnyStr, Any]) -> PositionDiffusionLi ) model = PositionDiffusionLightningModel(diffusion_params) - logger.info('model info:\n' + str(model) + '\n') + logger.info("model info:\n" + str(model) + "\n") return model diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py index 8af9aa47..90daaf11 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/loss.py @@ -2,26 +2,30 @@ from typing import Any, Dict import torch -from crystal_diffusion.utils.configuration_parsing import \ + +from diffusion_for_multi_scale_molecular_dynamics.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary @dataclass(kw_only=True) class LossParameters: """Specific Hyper-parameters for the loss function.""" + algorithm: str @dataclass(kw_only=True) class MSELossParameters(LossParameters): """Specific Hyper-parameters for the MSE loss function.""" - algorithm: str = 'mse' + + algorithm: str = "mse" @dataclass(kw_only=True) class WeightedMSELossParameters(LossParameters): """Specific Hyper-parameters for the weighted MSE loss function.""" - algorithm: str = 'weighted_mse' + + algorithm: str = "weighted_mse" # The default values are chosen to lead to a flat loss curve vs. sigma, based on preliminary experiments. # These parameters have no effect if the algorithm is 'mse'. # The default parameters are chosen such that weights(sigma=0.5) \sim 10^3 @@ -31,14 +35,18 @@ class WeightedMSELossParameters(LossParameters): class LossCalculator(torch.nn.Module): """Class to calculate the loss.""" + def __init__(self, loss_parameters: LossParameters): """Init method.""" super().__init__() self.loss_parameters = loss_parameters - def calculate_unreduced_loss(self, predicted_normalized_scores: torch.tensor, - target_normalized_conditional_scores: torch.tensor, - sigmas: torch.Tensor) -> torch.tensor: + def calculate_unreduced_loss( + self, + predicted_normalized_scores: torch.tensor, + target_normalized_conditional_scores: torch.tensor, + sigmas: torch.Tensor, + ) -> torch.tensor: """Calculate unreduced Loss. All inputs are assumed to be tensors of dimension [batch_size, number_of_atoms, spatial_dimension]. In @@ -57,14 +65,18 @@ def calculate_unreduced_loss(self, predicted_normalized_scores: torch.tensor, class MSELossCalculator(LossCalculator): """Class to calculate the MSE loss.""" + def __init__(self, loss_parameters: MSELossParameters): """Init method.""" super().__init__(loss_parameters) - self.mse_loss = torch.nn.MSELoss(reduction='none') - - def calculate_unreduced_loss(self, predicted_normalized_scores: torch.tensor, - target_normalized_conditional_scores: torch.tensor, - sigmas: torch.Tensor) -> torch.tensor: + self.mse_loss = torch.nn.MSELoss(reduction="none") + + def calculate_unreduced_loss( + self, + predicted_normalized_scores: torch.tensor, + target_normalized_conditional_scores: torch.tensor, + sigmas: torch.Tensor, + ) -> torch.tensor: """Calculate unreduced Loss. All inputs are assumed to be tensors of dimension [batch_size, number_of_atoms, spatial_dimension]. In @@ -78,14 +90,20 @@ def calculate_unreduced_loss(self, predicted_normalized_scores: torch.tensor, Returns: unreduced_loss: a tensor of shape [batch_size, number_of_atoms, spatial_dimension]. Its mean is the loss. """ - assert predicted_normalized_scores.shape == target_normalized_conditional_scores.shape == sigmas.shape, \ - "Inconsistent shapes" - unreduced_loss = self.mse_loss(predicted_normalized_scores, target_normalized_conditional_scores) + assert ( + predicted_normalized_scores.shape + == target_normalized_conditional_scores.shape + == sigmas.shape + ), "Inconsistent shapes" + unreduced_loss = self.mse_loss( + predicted_normalized_scores, target_normalized_conditional_scores + ) return unreduced_loss class WeightedMSELossCalculator(MSELossCalculator): """Class to calculate the loss.""" + def __init__(self, loss_parameters: WeightedMSELossParameters): """Init method.""" super().__init__(loss_parameters) @@ -97,9 +115,12 @@ def _exponential_weights(self, sigmas): weights = torch.exp(self.exponent * (sigmas - self.sigma0)) + 1.0 return weights - def calculate_unreduced_loss(self, predicted_normalized_scores: torch.tensor, - target_normalized_conditional_scores: torch.tensor, - sigmas: torch.Tensor) -> torch.tensor: + def calculate_unreduced_loss( + self, + predicted_normalized_scores: torch.tensor, + target_normalized_conditional_scores: torch.tensor, + sigmas: torch.Tensor, + ) -> torch.tensor: """Calculate unreduced Loss. All inputs are assumed to be tensors of dimension [batch_size, number_of_atoms, spatial_dimension]. In @@ -113,17 +134,24 @@ def calculate_unreduced_loss(self, predicted_normalized_scores: torch.tensor, Returns: unreduced_loss: a tensor of shape [batch_size, number_of_atoms, spatial_dimension]. It's mean is the loss. """ - assert predicted_normalized_scores.shape == target_normalized_conditional_scores.shape == sigmas.shape, \ - "Inconsistent shapes" - - unreduced_mse_loss = self.mse_loss(predicted_normalized_scores, target_normalized_conditional_scores) + assert ( + predicted_normalized_scores.shape + == target_normalized_conditional_scores.shape + == sigmas.shape + ), "Inconsistent shapes" + + unreduced_mse_loss = self.mse_loss( + predicted_normalized_scores, target_normalized_conditional_scores + ) weights = self._exponential_weights(sigmas) unreduced_loss = unreduced_mse_loss * weights return unreduced_loss -LOSS_PARAMETERS_BY_ALGO = dict(mse=MSELossParameters, weighted_mse=WeightedMSELossParameters) +LOSS_PARAMETERS_BY_ALGO = dict( + mse=MSELossParameters, weighted_mse=WeightedMSELossParameters +) LOSS_BY_ALGO = dict(mse=MSELossCalculator, weighted_mse=WeightedMSELossCalculator) @@ -138,13 +166,14 @@ def create_loss_parameters(model_dictionary: Dict[str, Any]) -> LossParameters: Returns: loss_parameters: the loss parameters. """ - default_dict = dict(algorithm='mse') + default_dict = dict(algorithm="mse") loss_config_dictionary = model_dictionary.get("loss", default_dict) - loss_parameters = ( - create_parameters_from_configuration_dictionary(configuration=loss_config_dictionary, - identifier="algorithm", - options=LOSS_PARAMETERS_BY_ALGO)) + loss_parameters = create_parameters_from_configuration_dictionary( + configuration=loss_config_dictionary, + identifier="algorithm", + options=LOSS_PARAMETERS_BY_ALGO, + ) return loss_parameters @@ -160,7 +189,8 @@ def create_loss_calculator(loss_parameters: LossParameters) -> LossCalculator: loss_calculator : the loss calculator. """ algorithm = loss_parameters.algorithm - assert algorithm in LOSS_BY_ALGO.keys(), \ - f"Algorithm {algorithm} is not implemented. Possible choices are {LOSS_BY_ALGO.keys()}" + assert ( + algorithm in LOSS_BY_ALGO.keys() + ), f"Algorithm {algorithm} is not implemented. Possible choices are {LOSS_BY_ALGO.keys()}" return LOSS_BY_ALGO[algorithm](loss_parameters) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py index fde3cf5b..1a7e1595 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/mace_utils.py @@ -3,11 +3,14 @@ from typing import AnyStr, Dict, Tuple import torch -from crystal_diffusion.models.graph_utils import get_adj_matrix -from crystal_diffusion.namespace import NOISY_CARTESIAN_POSITIONS, UNIT_CELL from e3nn import o3 from torch_geometric.data import Data +from diffusion_for_multi_scale_molecular_dynamics.models.graph_utils import \ + get_adj_matrix +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISY_CARTESIAN_POSITIONS, UNIT_CELL) + def input_to_mace(x: Dict[AnyStr, torch.Tensor], radial_cutoff: float) -> Data: """Convert score network input to MACE input. @@ -24,31 +27,41 @@ def input_to_mace(x: Dict[AnyStr, torch.Tensor], radial_cutoff: float) -> Data: batch_size, n_atom_per_graph, spatial_dimension = noisy_cartesian_positions.shape device = noisy_cartesian_positions.device - adj_matrix, shift_matrix, batch_tensor, _ = get_adj_matrix(positions=noisy_cartesian_positions, - basis_vectors=cell, - radial_cutoff=radial_cutoff) + adj_matrix, shift_matrix, batch_tensor, _ = get_adj_matrix( + positions=noisy_cartesian_positions, + basis_vectors=cell, + radial_cutoff=radial_cutoff, + ) # node features are int corresponding to atom type # TODO handle different atom types - node_attrs = torch.nn.functional.one_hot((torch.ones(batch_size * n_atom_per_graph) * 14).long(), - num_classes=89).float() - flat_positions = noisy_cartesian_positions.view(-1, spatial_dimension) # [batchsize * natoms, spatial dimension] + node_attrs = torch.nn.functional.one_hot( + (torch.ones(batch_size * n_atom_per_graph) * 14).long(), num_classes=89 + ).float() + flat_positions = noisy_cartesian_positions.view( + -1, spatial_dimension + ) # [batchsize * natoms, spatial dimension] # pointer tensor that yields the first node index for each batch - this is a fixed tensor in our case - ptr = torch.arange(0, n_atom_per_graph * batch_size + 1, step=n_atom_per_graph) # 0, natoms, 2 * natoms, ... + ptr = torch.arange( + 0, n_atom_per_graph * batch_size + 1, step=n_atom_per_graph + ) # 0, natoms, 2 * natoms, ... cell = cell.view(-1, cell.size(-1)) # batch * spatial_dimension, spatial_dimension # create the pytorch-geometric graph - graph_data = Data(edge_index=adj_matrix, - node_attrs=node_attrs.to(device), - positions=flat_positions, - ptr=ptr.to(device), - batch=batch_tensor.to(device), - shifts=shift_matrix, - cell=cell - ) + graph_data = Data( + edge_index=adj_matrix, + node_attrs=node_attrs.to(device), + positions=flat_positions, + ptr=ptr.to(device), + batch=batch_tensor.to(device), + shifts=shift_matrix, + cell=cell, + ) return graph_data -def build_mace_output_nodes_irreducible_representation(hidden_irreps_string: str, num_interactions: int) -> o3.Irreps: +def build_mace_output_nodes_irreducible_representation( + hidden_irreps_string: str, num_interactions: int +) -> o3.Irreps: """Build the mace output node irreps. Args: @@ -66,7 +79,9 @@ def build_mace_output_nodes_irreducible_representation(hidden_irreps_string: str # E3NN irreps gymnastics is a bit fragile. We have to build the scalar representation explicitly scalar_hidden_irreps = o3.Irreps(f"{hidden_irreps[0].mul}x{hidden_irreps[0].ir}") - total_irreps = o3.Irreps('') # An empty irrep to start the "sum", which is really a concatenation + total_irreps = o3.Irreps( + "" + ) # An empty irrep to start the "sum", which is really a concatenation for _ in range(num_interactions - 1): total_irreps += hidden_irreps @@ -86,21 +101,28 @@ def get_pretrained_mace_output_node_features_irreps(model_name: str) -> o3.Irrep """ match model_name: case "small": - irreps = build_mace_output_nodes_irreducible_representation(hidden_irreps_string="128x0e", - num_interactions=2) + irreps = build_mace_output_nodes_irreducible_representation( + hidden_irreps_string="128x0e", num_interactions=2 + ) case "medium": - irreps = build_mace_output_nodes_irreducible_representation(hidden_irreps_string="128x0e + 128x1o", - num_interactions=2) + irreps = build_mace_output_nodes_irreducible_representation( + hidden_irreps_string="128x0e + 128x1o", num_interactions=2 + ) case "large": - irreps = build_mace_output_nodes_irreducible_representation(hidden_irreps_string="128x0e + 128x1o + 128x2e", - num_interactions=2) + irreps = build_mace_output_nodes_irreducible_representation( + hidden_irreps_string="128x0e + 128x1o + 128x2e", num_interactions=2 + ) case _: - raise ValueError(f"Model name should be small, medium or large. Got {model_name}") + raise ValueError( + f"Model name should be small, medium or large. Got {model_name}" + ) return irreps -def get_pretrained_mace(model_name: str, model_savedir_path: str) -> Tuple[torch.nn.Module, int]: +def get_pretrained_mace( + model_name: str, model_savedir_path: str +) -> Tuple[torch.nn.Module, int]: """Download and load a pre-trained MACE network. Based on the mace-torch library. @@ -114,15 +136,25 @@ def get_pretrained_mace(model_name: str, model_savedir_path: str) -> Tuple[torch model: the pre-trained MACE model as a torch.nn.Module node_feats_output_size: size of the node features embedding in the model's output """ - assert model_name in ["small", "medium", "large"], f"Model name should be small, medium or large. Got {model_name}" + assert model_name in [ + "small", + "medium", + "large", + ], f"Model name should be small, medium or large. Got {model_name}" # from mace library code urls = dict( - small=("https://tinyurl.com/46jrkm3v", 256), # 2023-12-10-mace-128-L0_energy_epoch-249.model - medium=("https://tinyurl.com/5yyxdm76", 640), # 2023-12-03-mace-128-L1_epoch-199.model + small=( + "https://tinyurl.com/46jrkm3v", + 256, + ), # 2023-12-10-mace-128-L0_energy_epoch-249.model + medium=( + "https://tinyurl.com/5yyxdm76", + 640, + ), # 2023-12-03-mace-128-L1_epoch-199.model large=("https://tinyurl.com/5f5yavf3", 1280), # MACE_MPtrj_2022.9.model ) - checkpoint_url, node_feats_output_size = (urls.get(model_name, urls["medium"])) + checkpoint_url, node_feats_output_size = urls.get(model_name, urls["medium"]) checkpoint_url_name = "".join( c for c in os.path.basename(checkpoint_url) if c.isalnum() or c in "_" @@ -134,14 +166,18 @@ def get_pretrained_mace(model_name: str, model_savedir_path: str) -> Tuple[torch # download and save to disk _, http_msg = urllib.request.urlretrieve(checkpoint_url, cached_model_path) if "Content-Type: text/html" in http_msg: - raise RuntimeError(f"Model download failed, please check the URL {checkpoint_url}") + raise RuntimeError( + f"Model download failed, please check the URL {checkpoint_url}" + ) model = torch.load(f=cached_model_path).float() return model, node_feats_output_size -def get_normalized_irreps_permutation_indices(irreps: o3.Irreps) -> Tuple[o3.Irreps, torch.Tensor]: +def get_normalized_irreps_permutation_indices( + irreps: o3.Irreps, +) -> Tuple[o3.Irreps, torch.Tensor]: """Get normalized irreps and permutation indices. Args: @@ -192,7 +228,9 @@ def reshape_from_mace_to_e3nn(x: torch.Tensor, irreps: o3.Irreps) -> torch.Tenso x_ = [] for ell in range(irreps.lmax + 1): # for example, for l=1, take indices 1, 2, 3 (in the last index) and flatten as a channel * 3 tensor - x_l = x[:, :, (ell ** 2):(ell + 1)**2].reshape(node, -1) # node, channel * (2l + 1) + x_l = x[:, :, (ell**2): (ell + 1) ** 2].reshape( + node, -1 + ) # node, channel * (2l + 1) x_.append(x_l) # stack the flatten irrep tensors together return torch.cat(x_, dim=-1) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py index b6bc20ea..f4195616 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/mtp.py @@ -4,6 +4,7 @@ However, it cannot be called as a standard lightning module as it relies on the MLIP-3 library for the model implementation. """ + import itertools import os import re @@ -15,34 +16,50 @@ import numpy as np import pandas as pd -from crystal_diffusion.mlip.mtp_utils import (MTPInputs, concat_mtp_inputs, - crawl_lammps_directory, - prepare_mtp_inputs_from_lammps) from maml.apps.pes import MTPotential from maml.utils import check_structures_forces_stresses, pool_from from monty.io import zopen from monty.tempfile import ScratchDir from pymatgen.core import Structure +from diffusion_for_multi_scale_molecular_dynamics.mlip.mtp_utils import ( + MTPInputs, concat_mtp_inputs, crawl_lammps_directory, + prepare_mtp_inputs_from_lammps) + @dataclass(kw_only=True) class MTPArguments: """Arguments to train an MTP with the MLIP3 library.""" + mlip_path: str # path to MLIP3 library name: Optional[str] = None # MTP param: Optional[Dict[Any, Any]] = None unfitted_mtp: str = "08.almtp" # Define the initial mtp file. Default to 08g.amltp - fitted_mtp_savedir: str = '../' # save directory for the fitted MTP. Defaults to '../' (current wd) + fitted_mtp_savedir: str = ( + "../" # save directory for the fitted MTP. Defaults to '../' (current wd) + ) max_dist: float = 5 # The actual radial cutoff. Defaults to 5. - radial_basis_size: int = 8 # Relevant to number of radial basis function. Defaults to 8. + radial_basis_size: int = ( + 8 # Relevant to number of radial basis function. Defaults to 8. + ) max_iter: int = 1000 # The number of maximum iteration. Defaults to 1000. energy_weight: float = 1 # The weight of energy. Defaults to 1 force_weight: float = 1e-2 # The weight of forces. Defaults to 1e-2 - stress_weight: float = 1e-3 # The weight of stresses. Zero-weight can be assigned. Defaults to 1e-3. - init_params: str = "same" # how to initialize parameters if a potential was not pre-fitted: "same" or "random". - scale_by_force: float = 0 # If > 0 then configurations near equilibrium get more weight. Defaults to 0. - bfgs_conv_tol: float = 1e-3 # Stop training if error dropped by a factor smaller than this over 50 BFGS iterations. - weighting: str = "vibration" # How to weight configuration with different sizes relative to each other. + stress_weight: float = ( + 1e-3 # The weight of stresses. Zero-weight can be assigned. Defaults to 1e-3. + ) + init_params: str = ( + "same" # how to initialize parameters if a potential was not pre-fitted: "same" or "random". + ) + scale_by_force: float = ( + 0 # If > 0 then configurations near equilibrium get more weight. Defaults to 0. + ) + bfgs_conv_tol: float = ( + 1e-3 # Stop training if error dropped by a factor smaller than this over 50 BFGS iterations. + ) + weighting: str = ( + "vibration" # How to weight configuration with different sizes relative to each other. + ) # Choose from "vibrations", "molecules" and "structures". Defaults to "vibration". @@ -57,9 +74,13 @@ def __init__(self, mtp_args: MTPArguments): """ super().__init__(mtp_args.name, mtp_args.param) self.mlp_command = os.path.join(mtp_args.mlip_path, "build", "mlp") - assert os.path.exists(self.mlp_command), "mlp command not found in mlip-3 build folder" + assert os.path.exists( + self.mlp_command + ), "mlp command not found in mlip-3 build folder" self.mlp_templates = os.path.join(mtp_args.mlip_path, "MTP_templates") - assert os.path.exists(self.mlp_templates), "MTP templates not found in mlip-3 folder" + assert os.path.exists( + self.mlp_templates + ), "MTP templates not found in mlip-3 folder" self.fitted_mtp = None self.elements = None self.mtp_args = mtp_args @@ -80,10 +101,9 @@ def to_lammps_format(self): # ) pass - def evaluate(self, - dataset: MTPInputs, - mlip_name: str = 'mtp_fitted.almtp' - ) -> Tuple[pd.DataFrame, pd.DataFrame]: + def evaluate( + self, dataset: MTPInputs, mlip_name: str = "mtp_fitted.almtp" + ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Evaluate energies, forces, stresses and MaxVol gamma factor of structures with trained MTP. Args: @@ -98,8 +118,8 @@ def evaluate(self, dataframe with ground truth energies, forces dataframe with predicted energies, forces, MaxVol gamma (nbh grades) """ - if not mlip_name.endswith('.almtp'): - mlip_name += '.almtp' + if not mlip_name.endswith(".almtp"): + mlip_name += ".almtp" assert os.path.exists(mlip_name), f"Trained MTP does not exists: {mlip_name}" original_file = "original.cfgs" @@ -112,17 +132,27 @@ def evaluate(self, predict_pool = pool_from(test_structures, dataset.energy, test_forces) local_mtp_name = "mtp.almtp" - with ScratchDir("."): # mlip needs a tmp_work_dir - we will manually copy relevant outputs elsewhere + with ScratchDir( + "." + ): # mlip needs a tmp_work_dir - we will manually copy relevant outputs elsewhere # write the structures to evaluate in a mlp compatible format original_file = self.write_cfg(original_file, cfg_pool=predict_pool) # TODO how to handle when GT is not available - df_orig = self.read_cfgs(original_file, nbh_grade=False) # read original values as a DataFrame + df_orig = self.read_cfgs( + original_file, nbh_grade=False + ) # read original values as a DataFrame # copy the trained mtp in the scratchdir shutil.copyfile(mlip_name, os.path.join(os.getcwd(), local_mtp_name)) # calculate_grade is the method to get the forces, energy & maxvol values - cmd = [self.mlp_command, "calculate_grade", local_mtp_name, original_file, predict_file] - predict_file += '.0' # added by mlp... + cmd = [ + self.mlp_command, + "calculate_grade", + local_mtp_name, + original_file, + predict_file, + ] + predict_file += ".0" # added by mlp... stdout, rc = self._call_mlip(cmd) # check that MTP was called properly @@ -130,7 +160,9 @@ def evaluate(self, error_msg = f"mlp exited with return code {rc}" msg = stdout.decode("utf-8").split("\n")[:-1] try: - error_line = next(i for i, m in enumerate(msg) if m.startswith("ERROR")) + error_line = next( + i for i, m in enumerate(msg) if m.startswith("ERROR") + ) error_msg += ", ".join(msg[error_line:]) except Exception: error_msg += msg[-1] @@ -150,6 +182,7 @@ def read_cfgs(self, filename: str, nbh_grade: bool = False) -> pd.DataFrame: Returns: dataframe with energies, forces, optional nbh grades (MaxVol gamma) """ + def formatify(string: str) -> List[float]: """Convert string to a list of float.""" return [float(s) for s in string.split()] @@ -216,22 +249,28 @@ def convert_to_dataframe(docs: List[Dict[str, Any]]) -> pd.DataFrame: """ df = defaultdict(list) for s_idx, d in enumerate(docs): - n_atom = d['num_atoms'] + n_atom = d["num_atoms"] outputs = d["outputs"] pos_arr = np.array(outputs["position"]) - assert n_atom == pos_arr.shape[0], "Number of positions do not match number of atoms" + assert ( + n_atom == pos_arr.shape[0] + ), "Number of positions do not match number of atoms" force_arr = np.array(outputs["forces"]) - assert n_atom == force_arr.shape[0], "Number of forces do not match number of atoms" - for i, x in enumerate(['x', 'y', 'z']): + assert ( + n_atom == force_arr.shape[0] + ), "Number of forces do not match number of atoms" + for i, x in enumerate(["x", "y", "z"]): df[x] += pos_arr[:, i].tolist() - df[f'f{x}'] += force_arr[:, i].tolist() - df['energy'] += [outputs['energy']] * n_atom # copy the value to all atoms + df[f"f{x}"] += force_arr[:, i].tolist() + df["energy"] += [outputs["energy"]] * n_atom # copy the value to all atoms if "nbh_grades" in outputs.keys(): nbh_grades = outputs["nbh_grades"] - assert n_atom == len(nbh_grades), "Number of gamma values do not match number of atoms" - df['nbh_grades'] += nbh_grades - df['atom_index'] += list(range(n_atom)) - df['structure_index'] += [s_idx] * n_atom + assert n_atom == len( + nbh_grades + ), "Number of gamma values do not match number of atoms" + df["nbh_grades"] += nbh_grades + df["atom_index"] += list(range(n_atom)) + df["structure_index"] += [s_idx] * n_atom df = pd.DataFrame(df) return df @@ -265,10 +304,10 @@ def _call_cmd_to_stdout(cmd: List[str], output_file: TextIO): @staticmethod def prepare_dataset_from_lammps( - root_data_dir: str, - atom_dict: Dict[int, str], - mode: str = "train", - get_forces: bool = True, + root_data_dir: str, + atom_dict: Dict[int, str], + mode: str = "train", + get_forces: bool = True, ) -> MTPInputs: """Get the LAMMPS in a folder and organize them as inputs for a MTP. @@ -282,17 +321,19 @@ def prepare_dataset_from_lammps( inputs for MTP in the MTPInputs dataclass """ lammps_outputs, thermo_outputs = crawl_lammps_directory(root_data_dir, mode) - mtp_dataset = prepare_mtp_inputs_from_lammps(lammps_outputs, thermo_outputs, atom_dict, get_forces=get_forces) + mtp_dataset = prepare_mtp_inputs_from_lammps( + lammps_outputs, thermo_outputs, atom_dict, get_forces=get_forces + ) return mtp_dataset @staticmethod def prepare_dataset_from_numpy( - cartesian_positions: np.ndarray, - box: np.ndarray, - forces: np.ndarray, - energy: float, - atom_type: np.ndarray, - atom_dict: Dict[int, str] = {1: 'Si'} + cartesian_positions: np.ndarray, + box: np.ndarray, + forces: np.ndarray, + energy: float, + atom_type: np.ndarray, + atom_dict: Dict[int, str] = {1: "Si"}, ) -> MTPInputs: """Convert numpy array variables to a format compatible with MTP. @@ -311,9 +352,11 @@ def prepare_dataset_from_numpy( lattice=box, species=[atom_dict[x] for x in atom_type], coords=cartesian_positions, - coords_are_cartesian=True + coords_are_cartesian=True, ) - forces = forces.tolist() # from Nx3 np array to a list of length N where each element is a list of 3 forces + forces = ( + forces.tolist() + ) # from Nx3 np array to a list of length N where each element is a list of 3 forces return MTPInputs(structure=[structure], forces=[forces], energy=[energy]) @staticmethod @@ -331,7 +374,7 @@ def merge_inputs(mtp_inputs: List[MTPInputs]) -> MTPInputs: merged_inputs = concat_mtp_inputs(merged_inputs, x) return merged_inputs - def train(self, dataset: MTPInputs, mlip_name: str = 'mtp_fitted.almtp') -> str: + def train(self, dataset: MTPInputs, mlip_name: str = "mtp_fitted.almtp") -> str: """Training data with moment tensor method using MLIP-3. Override the base class method. @@ -347,24 +390,30 @@ def train(self, dataset: MTPInputs, mlip_name: str = 'mtp_fitted.almtp') -> str: Returns: fitted_mtp: path to the fitted MTP """ - train_structures, train_forces, train_stresses = check_structures_forces_stresses( - dataset.structure, dataset.forces, None + train_structures, train_forces, train_stresses = ( + check_structures_forces_stresses(dataset.structure, dataset.forces, None) ) # last argument is for stresses - not used currently train_pool = pool_from(train_structures, dataset.energy, train_forces) - elements = sorted(set(itertools.chain(*[struct.species for struct in train_structures]))) + elements = sorted( + set(itertools.chain(*[struct.species for struct in train_structures])) + ) self.elements = [str(element) for element in elements] # TODO move to __init__ atoms_filename = "train.cfgs" - with ((ScratchDir("."))): # create a tmpdir - deleted afterwards - atoms_filename = self.write_cfg(filename=atoms_filename, cfg_pool=train_pool) + with ScratchDir("."): # create a tmpdir - deleted afterwards + atoms_filename = self.write_cfg( + filename=atoms_filename, cfg_pool=train_pool + ) if not self.mtp_args.unfitted_mtp: raise RuntimeError("No specific parameter file provided.") mtp_file_path = os.path.join(self.mlp_templates, self.mtp_args.unfitted_mtp) - shutil.copyfile(mtp_file_path, os.path.join(os.getcwd(), self.mtp_args.unfitted_mtp)) + shutil.copyfile( + mtp_file_path, os.path.join(os.getcwd(), self.mtp_args.unfitted_mtp) + ) commands = [self.mlp_command, "mindist", atoms_filename] with open("min_dist", "w") as f: self._call_cmd_to_stdout(commands, f) @@ -376,8 +425,8 @@ def train(self, dataset: MTPInputs, mlip_name: str = 'mtp_fitted.almtp') -> str: # min_dist = float(lines[-1].split(split_symbol)[1]) save_fitted_mtp = mlip_name - if not save_fitted_mtp.endswith('.almtp'): - save_fitted_mtp += '.almtp' + if not save_fitted_mtp.endswith(".almtp"): + save_fitted_mtp += ".almtp" cmds_list = [ self.mlp_command, @@ -401,7 +450,9 @@ def train(self, dataset: MTPInputs, mlip_name: str = 'mtp_fitted.almtp') -> str: error_msg = f"MLP exited with return code {rc}" msg = stdout.decode("utf-8").split("\n")[:-1] try: - error_line = next(i for i, m in enumerate(msg) if m.startswith("ERROR")) + error_line = next( + i for i, m in enumerate(msg) if m.startswith("ERROR") + ) error_msg += ", ".join(msg[error_line:]) except Exception: error_msg += msg[-1] diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py index 2d57d9e1..a3f2949b 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/normalized_score_fokker_planck_error.py @@ -2,14 +2,17 @@ import einops import torch -from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters from torch.func import jacrev +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.exploding_variance import \ + ExplodingVariance +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters + class NormalizedScoreFokkerPlanckError(torch.nn.Module): """Class to calculate the Normalized Score Fokker Planck Error. @@ -253,9 +256,9 @@ def get_normalized_score_fokker_planck_error_by_iterating_over_batch( list_errors = [] for x, t, c in zip(relative_coordinates, times, unit_cells): # Iterate over the elements of the batch. In effect, compute over "batch_size = 1" tensors. - errors = self.get_normalized_score_fokker_planck_error(x.unsqueeze(0), - t.unsqueeze(0), - c.unsqueeze(0)).squeeze(0) + errors = self.get_normalized_score_fokker_planck_error( + x.unsqueeze(0), t.unsqueeze(0), c.unsqueeze(0) + ).squeeze(0) list_errors.append(errors) return torch.stack(list_errors) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py index 97e8cc7b..1607f7f0 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/optimizer.py @@ -3,16 +3,18 @@ from typing import Any, Dict import torch -from crystal_diffusion.utils.configuration_parsing import \ - create_parameters_from_configuration_dictionary from torch import optim +from diffusion_for_multi_scale_molecular_dynamics.utils.configuration_parsing import \ + create_parameters_from_configuration_dictionary + logger = logging.getLogger(__name__) @dataclass(kw_only=True) class OptimizerParameters: """Parameters for the optimizer.""" + name: str learning_rate: float weight_decay: float = 0.0 @@ -22,7 +24,9 @@ class OptimizerParameters: OPTIMIZER_PARAMETERS_BY_NAME = dict(adam=OptimizerParameters, adamw=OptimizerParameters) -def create_optimizer_parameters(optimizer_configuration_dictionary: Dict[str, Any]) -> OptimizerParameters: +def create_optimizer_parameters( + optimizer_configuration_dictionary: Dict[str, Any] +) -> OptimizerParameters: """Create Optimizer Parameters. Args: @@ -31,14 +35,17 @@ def create_optimizer_parameters(optimizer_configuration_dictionary: Dict[str, An Returns: optimizer_parameters: a Dataclass with the optimizer parameters. """ - optimizer_parameters = ( - create_parameters_from_configuration_dictionary(configuration=optimizer_configuration_dictionary, - identifier="name", - options=OPTIMIZER_PARAMETERS_BY_NAME)) + optimizer_parameters = create_parameters_from_configuration_dictionary( + configuration=optimizer_configuration_dictionary, + identifier="name", + options=OPTIMIZER_PARAMETERS_BY_NAME, + ) return optimizer_parameters -def load_optimizer(optimizer_parameters: OptimizerParameters, model: torch.nn.Module) -> optim.Optimizer: +def load_optimizer( + optimizer_parameters: OptimizerParameters, model: torch.nn.Module +) -> optim.Optimizer: """Instantiate the optimizer. Args: @@ -48,13 +55,14 @@ def load_optimizer(optimizer_parameters: OptimizerParameters, model: torch.nn.Mo Returns: optimizer : The optimizer for the given model """ - assert optimizer_parameters.name in OPTIMIZERS_BY_NAME.keys(), \ - f"Optimizer '{optimizer_parameters.name}' is not defined." + assert ( + optimizer_parameters.name in OPTIMIZERS_BY_NAME.keys() + ), f"Optimizer '{optimizer_parameters.name}' is not defined." optimizer_class = OPTIMIZERS_BY_NAME[optimizer_parameters.name] # Adapt the input configurations to the optimizer constructor expectations. parameters_dict = asdict(optimizer_parameters) - parameters_dict.pop('name') - parameters_dict['lr'] = parameters_dict.pop('learning_rate') + parameters_dict.pop("name") + parameters_dict["lr"] = parameters_dict.pop("learning_rate") return optimizer_class(model.parameters(), **parameters_dict) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py index 9d7bf3b7..143b2999 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/position_diffusion_lightning_model.py @@ -4,38 +4,42 @@ import pytorch_lightning as pl import torch -from crystal_diffusion.generators.instantiate_generator import \ + +from diffusion_for_multi_scale_molecular_dynamics.generators.instantiate_generator import \ instantiate_generator -from crystal_diffusion.metrics.kolmogorov_smirnov_metrics import \ +from diffusion_for_multi_scale_molecular_dynamics.metrics.kolmogorov_smirnov_metrics import \ KolmogorovSmirnovMetrics -from crystal_diffusion.models.loss import (LossParameters, - create_loss_calculator) -from crystal_diffusion.models.optimizer import (OptimizerParameters, - load_optimizer) -from crystal_diffusion.models.scheduler import (SchedulerParameters, - load_scheduler_dictionary) -from crystal_diffusion.models.score_networks.score_network import \ +from diffusion_for_multi_scale_molecular_dynamics.models.loss import ( + LossParameters, create_loss_calculator) +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import ( + OptimizerParameters, load_optimizer) +from diffusion_for_multi_scale_molecular_dynamics.models.scheduler import ( + SchedulerParameters, load_scheduler_dictionary) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ ScoreNetworkParameters -from crystal_diffusion.models.score_networks.score_network_factory import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network_factory import \ create_score_network -from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, - NOISE, NOISY_RELATIVE_COORDINATES, - RELATIVE_COORDINATES, TIME, UNIT_CELL) -from crystal_diffusion.oracle.energies import compute_oracle_energies -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, CARTESIAN_POSITIONS, NOISE, NOISY_RELATIVE_COORDINATES, + RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.oracle.energies import \ + compute_oracle_energies +from diffusion_for_multi_scale_molecular_dynamics.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.samples.diffusion_sampling_parameters import \ +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) +from diffusion_for_multi_scale_molecular_dynamics.samples.diffusion_sampling_parameters import \ DiffusionSamplingParameters -from crystal_diffusion.score.wrapped_gaussian_score import \ +from diffusion_for_multi_scale_molecular_dynamics.samples.sampling import \ + create_batch_of_samples +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import \ get_sigma_normalized_score -from crystal_diffusion.utils.basis_transformations import ( +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import ( get_positions_from_coordinates, map_relative_coordinates_to_unit_cell) -from crystal_diffusion.utils.structure_utils import compute_distances_in_batch -from crystal_diffusion.utils.tensor_utils import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.structure_utils import \ + compute_distances_in_batch +from diffusion_for_multi_scale_molecular_dynamics.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions -from src.crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) -from src.crystal_diffusion.samples.sampling import create_batch_of_samples logger = logging.getLogger(__name__) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py index 1adc4558..44ffca8e 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/scheduler.py @@ -1,10 +1,11 @@ from dataclasses import asdict, dataclass from typing import Any, AnyStr, Dict, Union -from crystal_diffusion.utils.configuration_parsing import \ - create_parameters_from_configuration_dictionary from torch import optim +from diffusion_for_multi_scale_molecular_dynamics.utils.configuration_parsing import \ + create_parameters_from_configuration_dictionary + @dataclass(kw_only=True) class SchedulerParameters: diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/__init__.py index a6f3a01a..e48fdcf9 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/__init__.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/__init__.py @@ -1,4 +1,4 @@ # flake8: noqa # Import here to avoid circular imports elsewhere. -from crystal_diffusion.models.score_networks.score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import ( ScoreNetwork, ScoreNetworkParameters) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py index f1cbf3b0..fa7540c6 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/analytical_score_network.py @@ -10,35 +10,43 @@ This goal of this module is to investigate and understand the properties of diffusion. It is not meant to generate 'production' results. """ + import itertools from dataclasses import dataclass from typing import Any, AnyStr, Dict import einops import torch -from crystal_diffusion.models.score_networks.score_network import ( + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import ( ScoreNetwork, ScoreNetworkParameters) -from crystal_diffusion.namespace import NOISE, NOISY_RELATIVE_COORDINATES -from crystal_diffusion.score.wrapped_gaussian_score import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISE, NOISY_RELATIVE_COORDINATES) +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import \ get_sigma_normalized_score -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell @dataclass(kw_only=True) class AnalyticalScoreNetworkParameters(ScoreNetworkParameters): """Specific Hyper-parameters for analytical score networks.""" - architecture: str = 'analytical' - number_of_atoms: int # the number of atoms in a configuration. + + architecture: str = "analytical" + number_of_atoms: int # the number of atoms in a configuration. kmax: int # the maximum lattice translation along any dimension. Translations will be [-kmax,..,kmax]. - equilibrium_relative_coordinates: torch.Tensor # Should have shape [number_of_atoms, spatial_dimensions] + equilibrium_relative_coordinates: ( + torch.Tensor + ) # Should have shape [number_of_atoms, spatial_dimensions] # Harmonic energy is defined as U = 1/2 u^T . Phi . u for "u" # the relative coordinate displacements. The 'inverse covariance' is beta Phi # and should be unitless. This is assumed to be proportional to the identity, such that # (beta Phi)^{-1} = sigma_d^2 Id, where the shape of Id is # [number_of_atoms, spatial_dimensions,number_of_atoms, spatial_dimensions] variance_parameter: float # sigma_d^2 - use_permutation_invariance: bool = False # should the analytical score consider every coordinate permutations. + use_permutation_invariance: bool = ( + False # should the analytical score consider every coordinate permutations. + ) class AnalyticalScoreNetwork(ScoreNetwork): @@ -60,11 +68,15 @@ def __init__(self, hyper_params: AnalyticalScoreNetworkParameters): self.nd = self.natoms * self.spatial_dimension self.kmax = hyper_params.kmax - assert hyper_params.variance_parameter >= 0., "the variance parameter should be non negative." + assert ( + hyper_params.variance_parameter >= 0.0 + ), "the variance parameter should be non negative." self.sigma_d_square = hyper_params.variance_parameter - assert hyper_params.equilibrium_relative_coordinates.shape == (self.natoms, self.spatial_dimension), \ - "equilibrium relative coordinates have the wrong shape" + assert hyper_params.equilibrium_relative_coordinates.shape == ( + self.natoms, + self.spatial_dimension, + ), "equilibrium relative coordinates have the wrong shape" self.use_permutation_invariance = hyper_params.use_permutation_invariance self.device = hyper_params.equilibrium_relative_coordinates.device @@ -73,30 +85,45 @@ def __init__(self, hyper_params: AnalyticalScoreNetworkParameters): self.translations_k = self._get_all_translations(self.kmax).to(self.device) self.number_of_translations = len(self.translations_k) - self.equilibrium_relative_coordinates = hyper_params.equilibrium_relative_coordinates + self.equilibrium_relative_coordinates = ( + hyper_params.equilibrium_relative_coordinates + ) if self.use_permutation_invariance: # Shape : [natom!, natoms, spatial dimension] - self.all_x0 = self._get_all_equilibrium_permutations(self.equilibrium_relative_coordinates) + self.all_x0 = self._get_all_equilibrium_permutations( + self.equilibrium_relative_coordinates + ) else: # Shape : [1, natoms, spatial dimension] - self.all_x0 = einops.rearrange(hyper_params.equilibrium_relative_coordinates, 'natom d -> 1 natom d') + self.all_x0 = einops.rearrange( + hyper_params.equilibrium_relative_coordinates, "natom d -> 1 natom d" + ) @staticmethod def _get_all_translations(kmax: int) -> torch.Tensor: return torch.arange(-kmax, kmax + 1) @staticmethod - def _get_all_equilibrium_permutations(relative_coordinates: torch.Tensor) -> torch.Tensor: + def _get_all_equilibrium_permutations( + relative_coordinates: torch.Tensor, + ) -> torch.Tensor: number_of_atoms = relative_coordinates.shape[0] # Shape : [number of permutations, number of atoms] - perm_indices = torch.stack([torch.tensor(perm) for perm in itertools.permutations(range(number_of_atoms))]) + perm_indices = torch.stack( + [ + torch.tensor(perm) + for perm in itertools.permutations(range(number_of_atoms)) + ] + ) equilibrium_permutations = relative_coordinates[perm_indices] return equilibrium_permutations - def _forward_unchecked(self, batch: Dict[AnyStr, Any], conditional: bool = False) -> torch.Tensor: + def _forward_unchecked( + self, batch: Dict[AnyStr, Any], conditional: bool = False + ) -> torch.Tensor: """Forward unchecked. This method assumes that the input data has already been checked with respect to expectations @@ -115,7 +142,9 @@ def _forward_unchecked(self, batch: Dict[AnyStr, Any], conditional: bool = False list_unnormalized_log_prob = [] for x0 in self.all_x0: - unnormalized_log_prob = self._compute_unnormalized_log_probability(sigmas, xt, x0) + unnormalized_log_prob = self._compute_unnormalized_log_probability( + sigmas, xt, x0 + ) list_unnormalized_log_prob.append(unnormalized_log_prob) list_unnormalized_log_prob = torch.stack(list_unnormalized_log_prob) @@ -123,38 +152,60 @@ def _forward_unchecked(self, batch: Dict[AnyStr, Any], conditional: bool = False grad_outputs = [torch.ones_like(log_probs)] - scores = torch.autograd.grad(outputs=[log_probs], - inputs=[xt], - grad_outputs=grad_outputs)[0] + scores = torch.autograd.grad( + outputs=[log_probs], inputs=[xt], grad_outputs=grad_outputs + )[0] # We actually want sigma x score. - broadcast_sigmas = einops.repeat(sigmas, 'batch 1 -> batch n d', n=self.natoms, d=self.spatial_dimension) + broadcast_sigmas = einops.repeat( + sigmas, "batch 1 -> batch n d", n=self.natoms, d=self.spatial_dimension + ) sigma_normalized_scores = broadcast_sigmas * scores return sigma_normalized_scores - def _compute_unnormalized_log_probability(self, sigmas: torch.Tensor, - xt: torch.Tensor, - x_eq: torch.Tensor) -> torch.Tensor: + def _compute_unnormalized_log_probability( + self, sigmas: torch.Tensor, xt: torch.Tensor, x_eq: torch.Tensor + ) -> torch.Tensor: batch_size = sigmas.shape[0] # Recast various spatial arrays to the correct dimensions to combine them, # in dimensions [batch, nd, number_of_translations] - effective_variance = einops.repeat(sigmas ** 2 + self.sigma_d_square, 'batch 1 -> batch nd t', - t=self.number_of_translations, nd=self.nd) - - sampling_coordinates = einops.repeat(xt, 'batch natom d -> batch (natom d) t', - batch=batch_size, t=self.number_of_translations) - - equilibrium_coordinates = einops.repeat(x_eq, 'natom d -> batch (natom d) t', - batch=batch_size, t=self.number_of_translations) - - translations = einops.repeat(self.translations_k, 't -> batch nd t', batch=batch_size, nd=self.nd) - - exponent = -0.5 * (sampling_coordinates - equilibrium_coordinates - translations) ** 2 / effective_variance + effective_variance = einops.repeat( + sigmas**2 + self.sigma_d_square, + "batch 1 -> batch nd t", + t=self.number_of_translations, + nd=self.nd, + ) + + sampling_coordinates = einops.repeat( + xt, + "batch natom d -> batch (natom d) t", + batch=batch_size, + t=self.number_of_translations, + ) + + equilibrium_coordinates = einops.repeat( + x_eq, + "natom d -> batch (natom d) t", + batch=batch_size, + t=self.number_of_translations, + ) + + translations = einops.repeat( + self.translations_k, "t -> batch nd t", batch=batch_size, nd=self.nd + ) + + exponent = ( + -0.5 + * (sampling_coordinates - equilibrium_coordinates - translations) ** 2 + / effective_variance + ) # logsumexp on lattice translation vectors, then sum on spatial indices - unnormalized_log_prob = torch.logsumexp(exponent, dim=2, keepdim=False).sum(dim=1) + unnormalized_log_prob = torch.logsumexp(exponent, dim=2, keepdim=False).sum( + dim=1 + ) return unnormalized_log_prob @@ -174,11 +225,14 @@ def __init__(self, hyper_params: AnalyticalScoreNetworkParameters): hyper_params : hyper parameters from the config file. """ super(TargetScoreBasedAnalyticalScoreNetwork, self).__init__(hyper_params) - assert not hyper_params.use_permutation_invariance, \ - "This implementation is only valid in the absence of permutation equivariance." + assert ( + not hyper_params.use_permutation_invariance + ), "This implementation is only valid in the absence of permutation equivariance." self.x0 = self.all_x0[0] - def _forward_unchecked(self, batch: Dict[AnyStr, Any], conditional: bool = False) -> torch.Tensor: + def _forward_unchecked( + self, batch: Dict[AnyStr, Any], conditional: bool = False + ) -> torch.Tensor: """Forward unchecked. This method assumes that the input data has already been checked with respect to expectations @@ -194,18 +248,22 @@ def _forward_unchecked(self, batch: Dict[AnyStr, Any], conditional: bool = False sigmas = batch[NOISE] # dimension: [batch_size, 1] xt = batch[NOISY_RELATIVE_COORDINATES] - broadcast_sigmas = einops.repeat(sigmas, - "batch 1 -> batch natoms spatial_dimension", - natoms=self.natoms, - spatial_dimension=self.spatial_dimension) + broadcast_sigmas = einops.repeat( + sigmas, + "batch 1 -> batch natoms spatial_dimension", + natoms=self.natoms, + spatial_dimension=self.spatial_dimension, + ) broadcast_effective_sigmas = (broadcast_sigmas**2 + self.sigma_d_square).sqrt() delta_relative_coordinates = map_relative_coordinates_to_unit_cell(xt - self.x0) - misnormalized_scores = get_sigma_normalized_score(delta_relative_coordinates, - broadcast_effective_sigmas, - kmax=self.kmax) + misnormalized_scores = get_sigma_normalized_score( + delta_relative_coordinates, broadcast_effective_sigmas, kmax=self.kmax + ) - sigma_normalized_scores = broadcast_sigmas / broadcast_effective_sigmas * misnormalized_scores + sigma_normalized_scores = ( + broadcast_sigmas / broadcast_effective_sigmas * misnormalized_scores + ) return sigma_normalized_scores diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py index 31269025..3120e58a 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/diffusion_mace_score_network.py @@ -2,45 +2,63 @@ from typing import AnyStr, Dict, List import torch -from crystal_diffusion.models.diffusion_mace import (DiffusionMACE, - input_to_diffusion_mace) -from crystal_diffusion.models.score_networks.score_network import ( - ScoreNetwork, ScoreNetworkParameters) -from crystal_diffusion.namespace import (NOISY_CARTESIAN_POSITIONS, - NOISY_RELATIVE_COORDINATES, UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import ( - get_positions_from_coordinates, get_reciprocal_basis_vectors) from e3nn import o3 from mace.modules import gate_dict, interaction_classes from mace.tools.torch_geometric.dataloader import Collater +from diffusion_for_multi_scale_molecular_dynamics.models.diffusion_mace import ( + DiffusionMACE, input_to_diffusion_mace) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import ( + ScoreNetwork, ScoreNetworkParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISY_CARTESIAN_POSITIONS, NOISY_RELATIVE_COORDINATES, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import ( + get_positions_from_coordinates, get_reciprocal_basis_vectors) + @dataclass(kw_only=True) class DiffusionMACEScoreNetworkParameters(ScoreNetworkParameters): """Specific Hyper-parameters for Diffusion MACE score networks.""" - architecture: str = 'diffusion_mace' + + architecture: str = "diffusion_mace" number_of_atoms: int # the number of atoms in a configuration. number_of_elements: int = 1 # The number of distinct elements present r_max: float = 5.0 num_bessel: int = 8 num_polynomial_cutoff: int = 5 - num_edge_hidden_layers: int = 0 # layers mixing sigma in edge features. Set to 0 to not add sigma in edges + num_edge_hidden_layers: int = ( + 0 # layers mixing sigma in edge features. Set to 0 to not add sigma in edges + ) edge_hidden_irreps: str = "16x0e" max_ell: int = 2 interaction_cls: str = "RealAgnosticResidualInteractionBlock" interaction_cls_first: str = "RealAgnosticInteractionBlock" num_interactions: int = 2 hidden_irreps: str = "128x0e + 128x1o" # irreps for hidden node states - mlp_irreps: str = "16x0e" # irreps for the embedding of the diffusion scalar features - number_of_mlp_layers: int = 3 # number of MLP layers for the embedding of the diffusion scalar features + mlp_irreps: str = ( + "16x0e" # irreps for the embedding of the diffusion scalar features + ) + number_of_mlp_layers: int = ( + 3 # number of MLP layers for the embedding of the diffusion scalar features + ) avg_num_neighbors: int = 1 # normalization factor for the message correlation: int = 3 - gate: str = "silu" # non linearity for last readout - choices: ["silu", "tanh", "abs", "None"] - radial_MLP: List[int] = field(default_factory=lambda: [64, 64, 64]) # "width of the radial MLP" - radial_type: str = "bessel" # type of radial basis functions - choices=["bessel", "gaussian", "chebyshev"] - condition_embedding_size: int = 64 # dimension of the conditional variable embedding - assumed to be l=1 (odd) + gate: str = ( + "silu" # non linearity for last readout - choices: ["silu", "tanh", "abs", "None"] + ) + radial_MLP: List[int] = field( + default_factory=lambda: [64, 64, 64] + ) # "width of the radial MLP" + radial_type: str = ( + "bessel" # type of radial basis functions - choices=["bessel", "gaussian", "chebyshev"] + ) + condition_embedding_size: int = ( + 64 # dimension of the conditional variable embedding - assumed to be l=1 (odd) + ) use_batchnorm: bool = False - tanh_after_interaction: bool = True # use a tanh non-linearity (based on irreps norm) in the message-passing + tanh_after_interaction: bool = ( + True # use a tanh non-linearity (based on irreps norm) in the message-passing + ) class DiffusionMACEScoreNetwork(ScoreNetwork): @@ -66,7 +84,9 @@ def __init__(self, hyper_params: DiffusionMACEScoreNetworkParameters): edge_hidden_irreps=o3.Irreps(hyper_params.edge_hidden_irreps), max_ell=hyper_params.max_ell, interaction_cls=interaction_classes[hyper_params.interaction_cls], - interaction_cls_first=interaction_classes[hyper_params.interaction_cls_first], + interaction_cls_first=interaction_classes[ + hyper_params.interaction_cls_first + ], num_interactions=hyper_params.num_interactions, num_elements=hyper_params.number_of_elements, hidden_irreps=o3.Irreps(hyper_params.hidden_irreps), @@ -80,7 +100,7 @@ def __init__(self, hyper_params: DiffusionMACEScoreNetworkParameters): radial_type=hyper_params.radial_type, condition_embedding_size=hyper_params.condition_embedding_size, use_batchnorm=hyper_params.use_batchnorm, - tanh_after_interaction=hyper_params.tanh_after_interaction + tanh_after_interaction=hyper_params.tanh_after_interaction, ) self._natoms = hyper_params.number_of_atoms @@ -95,7 +115,9 @@ def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): number_of_atoms == self._natoms ), "The dimension corresponding to the number of atoms is not consistent with the configuration." - def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False) -> torch.Tensor: + def _forward_unchecked( + self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False + ) -> torch.Tensor: """Forward unchecked. This method assumes that the input data has already been checked with respect to expectations @@ -113,13 +135,19 @@ def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: boo batch_size, number_of_atoms, spatial_dimension = relative_coordinates.shape basis_vectors = batch[UNIT_CELL] - batch[NOISY_CARTESIAN_POSITIONS] = get_positions_from_coordinates(relative_coordinates, basis_vectors) + batch[NOISY_CARTESIAN_POSITIONS] = get_positions_from_coordinates( + relative_coordinates, basis_vectors + ) graph_input = input_to_diffusion_mace(batch, radial_cutoff=self.r_max) flat_cartesian_scores = self.diffusion_mace_network(graph_input, conditional) - cartesian_scores = flat_cartesian_scores.reshape(batch_size, number_of_atoms, spatial_dimension) + cartesian_scores = flat_cartesian_scores.reshape( + batch_size, number_of_atoms, spatial_dimension + ) - reciprocal_basis_vectors_as_columns = get_reciprocal_basis_vectors(basis_vectors) + reciprocal_basis_vectors_as_columns = get_reciprocal_basis_vectors( + basis_vectors + ) scores = torch.bmm(cartesian_scores, reciprocal_basis_vectors_as_columns) return scores diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py index d90987a2..dd49c531 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/egnn_score_network.py @@ -3,18 +3,22 @@ import einops import torch -from crystal_diffusion.models.egnn import EGNN -from crystal_diffusion.models.egnn_utils import (get_edges_batch, - get_edges_with_radial_cutoff) -from crystal_diffusion.models.score_networks import ScoreNetworkParameters -from crystal_diffusion.models.score_networks.score_network import ScoreNetwork -from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, - UNIT_CELL) + +from diffusion_for_multi_scale_molecular_dynamics.models.egnn import EGNN +from diffusion_for_multi_scale_molecular_dynamics.models.egnn_utils import ( + get_edges_batch, get_edges_with_radial_cutoff) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetworkParameters +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISE, NOISY_RELATIVE_COORDINATES, UNIT_CELL) @dataclass(kw_only=True) class EGNNScoreNetworkParameters(ScoreNetworkParameters): """Specific Hyper-parameters for ENN score networks.""" + architecture: str = "egnn" message_n_hidden_dimensions: int = 1 message_hidden_dimensions_size: int = 16 @@ -29,7 +33,7 @@ class EGNNScoreNetworkParameters(ScoreNetworkParameters): coords_agg: str = "mean" message_agg: str = "mean" n_layers: int = 4 - edges: str = 'fully_connected' + edges: str = "fully_connected" radial_cutoff: Union[float, None] = None drop_duplicate_edges: bool = True @@ -52,21 +56,30 @@ def __init__(self, hyper_params: EGNNScoreNetworkParameters): self.number_of_features_per_node = 1 self.spatial_dimension = hyper_params.spatial_dimension - projection_matrices = self._create_block_diagonal_projection_matrices(self.spatial_dimension) - self.register_parameter('projection_matrices', - torch.nn.Parameter(projection_matrices, requires_grad=False)) + projection_matrices = self._create_block_diagonal_projection_matrices( + self.spatial_dimension + ) + self.register_parameter( + "projection_matrices", + torch.nn.Parameter(projection_matrices, requires_grad=False), + ) self.edges = hyper_params.edges - assert self.edges in ["fully_connected", "radial_cutoff"], \ - f'Edges type should be fully_connected or radial_cutoff. Got {self.edges}' + assert self.edges in [ + "fully_connected", + "radial_cutoff", + ], f"Edges type should be fully_connected or radial_cutoff. Got {self.edges}" self.radial_cutoff = hyper_params.radial_cutoff if self.edges == "fully_connected": - assert self.radial_cutoff is None, "Specifying a radial cutoff is inconsistent with edges=fully_connected." + assert ( + self.radial_cutoff is None + ), "Specifying a radial cutoff is inconsistent with edges=fully_connected." else: - assert type(self.radial_cutoff) is float, \ - "A floating point value for the radial cutoff is needed for edges=radial_cutoff." + assert ( + type(self.radial_cutoff) is float + ), "A floating point value for the radial cutoff is needed for edges=radial_cutoff." self.drop_duplicate_edges = hyper_params.drop_duplicate_edges @@ -88,7 +101,9 @@ def __init__(self, hyper_params: EGNNScoreNetworkParameters): ) @staticmethod - def _create_block_diagonal_projection_matrices(spatial_dimension: int) -> torch.Tensor: + def _create_block_diagonal_projection_matrices( + spatial_dimension: int, + ) -> torch.Tensor: """Create block diagonal projection matrices. This method creates the "Gamma" matrices that are needed to project the higher dimensional @@ -108,11 +123,15 @@ def _create_block_diagonal_projection_matrices(spatial_dimension: int) -> torch. dimension [spatial_dimension, 2 x spatial_dimension, 2 x spatial_dimension]. """ zeros = torch.zeros(2, 2) - dimensional_projector = torch.tensor([[0., -1.], [1., 0.]]) + dimensional_projector = torch.tensor([[0.0, -1.0], [1.0, 0.0]]) projection_matrices = [] for space_idx in range(spatial_dimension): - blocks = space_idx * [zeros] + [dimensional_projector] + (spatial_dimension - space_idx - 1) * [zeros] + blocks = ( + space_idx * [zeros] + + [dimensional_projector] + + (spatial_dimension - space_idx - 1) * [zeros] + ) projection_matrices.append(torch.block_diag(*blocks)) return torch.stack(projection_matrices) @@ -138,7 +157,9 @@ def _get_node_attributes(batch: Dict[AnyStr, torch.Tensor]) -> torch.Tensor: return repeated_sigmas @staticmethod - def _get_euclidean_positions(flat_relative_coordinates: torch.Tensor) -> torch.Tensor: + def _get_euclidean_positions( + flat_relative_coordinates: torch.Tensor, + ) -> torch.Tensor: """Get Euclidean positions. Get the positions that take points on the torus into a higher dimensional @@ -168,19 +189,20 @@ def _forward_unchecked( batch_size, number_of_atoms, spatial_dimension = relative_coordinates.shape if self.edges == "fully_connected": - edges = get_edges_batch( - n_nodes=number_of_atoms, batch_size=batch_size - ) + edges = get_edges_batch(n_nodes=number_of_atoms, batch_size=batch_size) else: edges = get_edges_with_radial_cutoff( - relative_coordinates, batch[UNIT_CELL], self.radial_cutoff, - drop_duplicate_edges=self.drop_duplicate_edges + relative_coordinates, + batch[UNIT_CELL], + self.radial_cutoff, + drop_duplicate_edges=self.drop_duplicate_edges, ) edges = edges.to(relative_coordinates.device) flat_relative_coordinates = einops.rearrange( - relative_coordinates, "batch natom spatial_dimension -> (batch natom) spatial_dimension" + relative_coordinates, + "batch natom spatial_dimension -> (batch natom) spatial_dimension", ) # Uplift the relative coordinates to the embedding Euclidean space. @@ -201,13 +223,17 @@ def _forward_unchecked( # - z is the uplifted "positions" in the 2 x spatial_dimension Euclidean space # - hat_z is the output of the EGNN model, also in 2 x spatial_dimension # - Gamma^alpha are the projection matrices - flat_normalized_scores = einops.einsum(euclidean_positions, self.projection_matrices, raw_normalized_score, - "nodes i, alpha i j, nodes j-> nodes alpha") + flat_normalized_scores = einops.einsum( + euclidean_positions, + self.projection_matrices, + raw_normalized_score, + "nodes i, alpha i j, nodes j-> nodes alpha", + ) normalized_scores = einops.rearrange( flat_normalized_scores, "(batch natoms) spatial_dimension -> batch natoms spatial_dimension", batch=batch_size, - natoms=number_of_atoms + natoms=number_of_atoms, ) return normalized_scores diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py index 59ef4f5c..4008e4d9 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/force_field_augmented_score_network.py @@ -3,12 +3,15 @@ import einops import torch -from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES, UNIT_CELL -from crystal_diffusion.utils.basis_transformations import ( + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISY_RELATIVE_COORDINATES, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import ( get_positions_from_coordinates, get_reciprocal_basis_vectors, get_relative_coordinates_from_cartesian_positions) -from crystal_diffusion.utils.neighbors import ( +from diffusion_for_multi_scale_molecular_dynamics.utils.neighbors import ( AdjacencyInfo, get_periodic_adjacency_information) @@ -37,6 +40,7 @@ class ForceFieldAugmentedScoreNetwork(torch.nn.Module): to such proximity: a repulsive force field will encourage atoms to separate during diffusion. """ + def __init__( self, score_network: ScoreNetwork, force_field_parameters: ForceFieldParameters ): @@ -163,7 +167,9 @@ def _get_cartesian_pseudo_forces( list_pseudo_force_components = [] for space_idx in range(spatial_dimension): - pseudo_force_component = torch.zeros(natoms * batch_size).to(cartesian_pseudo_force_contributions) + pseudo_force_component = torch.zeros(natoms * batch_size).to( + cartesian_pseudo_force_contributions + ) pseudo_force_component.scatter_add_( dim=0, index=node_idx, diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py index b0c5b838..998e428e 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mace_score_network.py @@ -3,30 +3,32 @@ import numpy as np import torch -from crystal_diffusion.models.mace_utils import ( - build_mace_output_nodes_irreducible_representation, get_pretrained_mace, - get_pretrained_mace_output_node_features_irreps, input_to_mace) -from crystal_diffusion.models.score_networks.score_network import ( - ScoreNetwork, ScoreNetworkParameters) -from crystal_diffusion.models.score_networks.score_prediction_head import ( - MaceScorePredictionHeadParameters, instantiate_mace_prediction_head) -from crystal_diffusion.namespace import (NOISY_CARTESIAN_POSITIONS, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) from e3nn import o3 from mace.modules import MACE, gate_dict, interaction_classes from mace.tools import get_atomic_number_table_from_zs from mace.tools.torch_geometric.dataloader import Collater +from diffusion_for_multi_scale_molecular_dynamics.models.mace_utils import ( + build_mace_output_nodes_irreducible_representation, get_pretrained_mace, + get_pretrained_mace_output_node_features_irreps, input_to_mace) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import ( + ScoreNetwork, ScoreNetworkParameters) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_prediction_head import ( + MaceScorePredictionHeadParameters, instantiate_mace_prediction_head) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISY_CARTESIAN_POSITIONS, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) + @dataclass(kw_only=True) class MACEScoreNetworkParameters(ScoreNetworkParameters): """Specific Hyper-parameters for MACE score networks.""" - architecture: str = 'mace' + architecture: str = "mace" number_of_atoms: int # the number of atoms in a configuration. - use_pretrained: Optional[str] = None # if None, do not use pre-trained ; else, should be in small, medium or large - pretrained_weights_path: str = '../' # path to pre-trained model weights + use_pretrained: Optional[str] = ( + None # if None, do not use pre-trained ; else, should be in small, medium or large + ) + pretrained_weights_path: str = "../" # path to pre-trained model weights r_max: float = 5.0 num_bessel: int = 8 num_polynomial_cutoff: int = 5 @@ -39,9 +41,15 @@ class MACEScoreNetworkParameters(ScoreNetworkParameters): atomic_energies: np.ndarray = field(default_factory=lambda: np.zeros((89,))) avg_num_neighbors: int = 1 # normalization factor for the message correlation: int = 3 - gate: str = "silu" # non linearity for last readout - choices: ["silu", "tanh", "abs", "None"] - radial_MLP: List[int] = field(default_factory=lambda: [64, 64, 64]) # "width of the radial MLP" - radial_type: str = "bessel" # type of radial basis functions - choices=["bessel", "gaussian", "chebyshev"] + gate: str = ( + "silu" # non linearity for last readout - choices: ["silu", "tanh", "abs", "None"] + ) + radial_MLP: List[int] = field( + default_factory=lambda: [64, 64, 64] + ) # "width of the radial MLP" + radial_type: str = ( + "bessel" # type of radial basis functions - choices=["bessel", "gaussian", "chebyshev"] + ) prediction_head_parameters: MaceScorePredictionHeadParameters @@ -59,7 +67,9 @@ def __init__(self, hyper_params: MACEScoreNetworkParameters): """ super(MACEScoreNetwork, self).__init__(hyper_params) - self.z_table = get_atomic_number_table_from_zs(list(range(89))) # need 89 for pre-trained model + self.z_table = get_atomic_number_table_from_zs( + list(range(89)) + ) # need 89 for pre-trained model # dataloader self.r_max = hyper_params.r_max self.collate_fn = Collater(follow_batch=[None], exclude_keys=[None]) @@ -70,7 +80,9 @@ def __init__(self, hyper_params: MACEScoreNetworkParameters): num_polynomial_cutoff=hyper_params.num_polynomial_cutoff, max_ell=hyper_params.max_ell, interaction_cls=interaction_classes[hyper_params.interaction_cls], - interaction_cls_first=interaction_classes[hyper_params.interaction_cls_first], + interaction_cls_first=interaction_classes[ + hyper_params.interaction_cls_first + ], num_interactions=hyper_params.num_interactions, num_elements=len(self.z_table), hidden_irreps=o3.Irreps(hyper_params.hidden_irreps), @@ -81,25 +93,35 @@ def __init__(self, hyper_params: MACEScoreNetworkParameters): correlation=hyper_params.correlation, gate=gate_dict[hyper_params.gate], radial_MLP=hyper_params.radial_MLP, - radial_type=hyper_params.radial_type + radial_type=hyper_params.radial_type, ) self._natoms = hyper_params.number_of_atoms - if hyper_params.use_pretrained is None or hyper_params.use_pretrained == 'None': + if hyper_params.use_pretrained is None or hyper_params.use_pretrained == "None": self.mace_network = MACE(**mace_config) output_node_features_irreps = ( - build_mace_output_nodes_irreducible_representation(hyper_params.hidden_irreps, - hyper_params.num_interactions)) + build_mace_output_nodes_irreducible_representation( + hyper_params.hidden_irreps, hyper_params.num_interactions + ) + ) else: - output_node_features_irreps = get_pretrained_mace_output_node_features_irreps(hyper_params.use_pretrained) - self.mace_network, mace_output_size = get_pretrained_mace(hyper_params.use_pretrained, - hyper_params.pretrained_weights_path) - assert output_node_features_irreps.dim == mace_output_size, "Something is wrong with pretrained dimensions." + output_node_features_irreps = ( + get_pretrained_mace_output_node_features_irreps( + hyper_params.use_pretrained + ) + ) + self.mace_network, mace_output_size = get_pretrained_mace( + hyper_params.use_pretrained, hyper_params.pretrained_weights_path + ) + assert ( + output_node_features_irreps.dim == mace_output_size + ), "Something is wrong with pretrained dimensions." self.mace_output_size = output_node_features_irreps.dim - self.prediction_head = instantiate_mace_prediction_head(output_node_features_irreps, - hyper_params.prediction_head_parameters) + self.prediction_head = instantiate_mace_prediction_head( + output_node_features_irreps, hyper_params.prediction_head_parameters + ) def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): super(MACEScoreNetwork, self)._check_batch(batch) @@ -108,7 +130,9 @@ def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): number_of_atoms == self._natoms ), "The dimension corresponding to the number of atoms is not consistent with the configuration." - def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False) -> torch.Tensor: + def _forward_unchecked( + self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False + ) -> torch.Tensor: """Forward unchecked. This method assumes that the input data has already been checked with respect to expectations @@ -124,19 +148,25 @@ def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: boo """ del conditional # TODO implement conditional relative_coordinates = batch[NOISY_RELATIVE_COORDINATES] - batch[NOISY_CARTESIAN_POSITIONS] = torch.bmm(relative_coordinates, batch[UNIT_CELL]) # positions in Angstrom + batch[NOISY_CARTESIAN_POSITIONS] = torch.bmm( + relative_coordinates, batch[UNIT_CELL] + ) # positions in Angstrom graph_input = input_to_mace(batch, radial_cutoff=self.r_max) - mace_output = self.mace_network(graph_input, compute_force=False, training=self.training) + mace_output = self.mace_network( + graph_input, compute_force=False, training=self.training + ) # The node features are organized as (batchsize * natoms, output_size) in the mace output because # torch_geometric puts all the graphs in a batch in a single large graph. - flat_node_features = mace_output['node_feats'] + flat_node_features = mace_output["node_feats"] # The times have a single value per batch element; we repeat the array so that there is one value per atom, # with this value the same for all atoms belonging to the same graph. times = batch[TIME].to(relative_coordinates.device) # shape [batch_size, 1] flat_times = times[graph_input.batch] # shape [batch_size * natoms, 1] - flat_scores = self.prediction_head(flat_node_features, flat_times) # shape [batch_size * natoms, spatial_dim] + flat_scores = self.prediction_head( + flat_node_features, flat_times + ) # shape [batch_size * natoms, spatial_dim] # Reshape the scores to have an explicit batch dimension scores = flat_scores.reshape(-1, self._natoms, self.spatial_dimension) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py index c5e989cc..5606b04f 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/mlp_score_network.py @@ -2,23 +2,28 @@ from typing import AnyStr, Dict import torch -from crystal_diffusion.models.score_networks.score_network import ( - ScoreNetwork, ScoreNetworkParameters) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES) from torch import nn +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import ( + ScoreNetwork, ScoreNetworkParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES) + @dataclass(kw_only=True) class MLPScoreNetworkParameters(ScoreNetworkParameters): """Specific Hyper-parameters for MLP score networks.""" - architecture: str = 'mlp' + architecture: str = "mlp" number_of_atoms: int # the number of atoms in a configuration. n_hidden_dimensions: int # the number of hidden layers. hidden_dimensions_size: int # the dimensions of the hidden layers. - embedding_dimensions_size: int # the dimension of the embedding of the noise parameter. - condition_embedding_size: int = 64 # dimension of the conditional variable embedding + embedding_dimensions_size: ( + int # the dimension of the embedding of the noise parameter. + ) + condition_embedding_size: int = ( + 64 # dimension of the conditional variable embedding + ) class MLPScoreNetwork(ScoreNetwork): @@ -34,15 +39,21 @@ def __init__(self, hyper_params: MLPScoreNetworkParameters): hyper_params : hyper parameters from the config file. """ super(MLPScoreNetwork, self).__init__(hyper_params) - hidden_dimensions = [hyper_params.hidden_dimensions_size] * hyper_params.n_hidden_dimensions + hidden_dimensions = [ + hyper_params.hidden_dimensions_size + ] * hyper_params.n_hidden_dimensions self._natoms = hyper_params.number_of_atoms output_dimension = self.spatial_dimension * self._natoms input_dimension = output_dimension + hyper_params.embedding_dimensions_size - self.noise_embedding_layer = nn.Linear(1, hyper_params.embedding_dimensions_size) + self.noise_embedding_layer = nn.Linear( + 1, hyper_params.embedding_dimensions_size + ) - self.condition_embedding_layer = nn.Linear(output_dimension, hyper_params.condition_embedding_size) + self.condition_embedding_layer = nn.Linear( + output_dimension, hyper_params.condition_embedding_size + ) self.flatten = nn.Flatten() self.mlp_layers = nn.ModuleList() @@ -50,9 +61,13 @@ def __init__(self, hyper_params: MLPScoreNetworkParameters): input_dimensions = [input_dimension] + hidden_dimensions output_dimensions = hidden_dimensions + [output_dimension] - for input_dimension, output_dimension in zip(input_dimensions, output_dimensions): + for input_dimension, output_dimension in zip( + input_dimensions, output_dimensions + ): self.mlp_layers.append(nn.Linear(input_dimension, output_dimension)) - self.conditional_layers.append(nn.Linear(hyper_params.condition_embedding_size, output_dimension)) + self.conditional_layers.append( + nn.Linear(hyper_params.condition_embedding_size, output_dimension) + ) self.non_linearity = nn.ReLU() def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): @@ -62,7 +77,9 @@ def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): number_of_atoms == self._natoms ), "The dimension corresponding to the number of atoms is not consistent with the configuration." - def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False) -> torch.Tensor: + def _forward_unchecked( + self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False + ) -> torch.Tensor: """Forward unchecked. This method assumes that the input data has already been checked with respect to expectations @@ -80,14 +97,20 @@ def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: boo # shape [batch_size, number_of_atoms, spatial_dimension] sigmas = batch[NOISE].to(relative_coordinates.device) # shape [batch_size, 1] - noise_embedding = self.noise_embedding_layer(sigmas) # shape [batch_size, embedding_dimension] + noise_embedding = self.noise_embedding_layer( + sigmas + ) # shape [batch_size, embedding_dimension] input = torch.cat([self.flatten(relative_coordinates), noise_embedding], dim=1) - forces_input = self.condition_embedding_layer(self.flatten(batch[CARTESIAN_FORCES])) + forces_input = self.condition_embedding_layer( + self.flatten(batch[CARTESIAN_FORCES]) + ) output = input - for i, (layer, condition_layer) in enumerate(zip(self.mlp_layers, self.conditional_layers)): + for i, (layer, condition_layer) in enumerate( + zip(self.mlp_layers, self.conditional_layers) + ): if i != 0: output = self.non_linearity(output) output = layer(output) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py index 2701e654..ad9b6722 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network.py @@ -4,29 +4,35 @@ Relative coordinates are with respect to lattice vectors which define the periodic unit cell. """ + import os from dataclasses import dataclass from typing import AnyStr, Dict, Optional import torch -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) + +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) # mac fun time # for mace, conflict with mac # https://stackoverflow.com/questions/53014306/error-15-initializing-libiomp5-dylib-but-found-libiomp5-dylib-already- \ # initial -os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' +os.environ["KMP_DUPLICATE_LIB_OK"] = "True" @dataclass(kw_only=True) class ScoreNetworkParameters: """Base Hyper-parameters for score networks.""" + architecture: str spatial_dimension: int = 3 # the dimension of Euclidean space where atoms live. - conditional_prob: float = 0. # probability of making a conditional forward - else, do a unconditional forward - conditional_gamma: float = 2. # conditional score weighting - see eq. B45 in MatterGen + conditional_prob: float = ( + 0.0 # probability of making a conditional forward - else, do a unconditional forward + ) + conditional_gamma: float = ( + 2.0 # conditional score weighting - see eq. B45 in MatterGen + ) # p_\gamma(x|c) = p(c|x)^\gamma p(x) @@ -70,22 +76,28 @@ def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): Returns: None. """ - assert NOISY_RELATIVE_COORDINATES in batch, \ - (f"The relative coordinates should be present in " - f"the batch dictionary with key '{NOISY_RELATIVE_COORDINATES}'") + assert NOISY_RELATIVE_COORDINATES in batch, ( + f"The relative coordinates should be present in " + f"the batch dictionary with key '{NOISY_RELATIVE_COORDINATES}'" + ) relative_coordinates = batch[NOISY_RELATIVE_COORDINATES] relative_coordinates_shape = relative_coordinates.shape batch_size = relative_coordinates_shape[0] assert ( - len(relative_coordinates_shape) == 3 and relative_coordinates_shape[2] == self.spatial_dimension + len(relative_coordinates_shape) == 3 + and relative_coordinates_shape[2] == self.spatial_dimension ), "The relative coordinates are expected to be in a tensor of shape [batch_size, number_of_atoms, 3]" assert torch.logical_and( relative_coordinates >= 0.0, relative_coordinates < 1.0 - ).all(), "All components of the relative coordinates are expected to be in [0,1)." + ).all(), ( + "All components of the relative coordinates are expected to be in [0,1)." + ) - assert TIME in batch, f"The time step should be present in the batch dictionary with key '{TIME}'" + assert ( + TIME in batch + ), f"The time step should be present in the batch dictionary with key '{TIME}'" times = batch[TIME] time_shape = times.shape @@ -100,10 +112,16 @@ def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): times >= 0.0, times <= 1.0 ).all(), "The times are expected to be normalized between 0 and 1." - assert NOISE in batch, "There should be a 'noise' parameter in the batch dictionary." - assert batch[NOISE].shape == times.shape, "the 'noise' parameter should have the same shape as the 'time'." + assert ( + NOISE in batch + ), "There should be a 'noise' parameter in the batch dictionary." + assert ( + batch[NOISE].shape == times.shape + ), "the 'noise' parameter should have the same shape as the 'time'." - assert UNIT_CELL in batch, f"The unit cell should be present in the batch dictionary with key '{UNIT_CELL}'" + assert ( + UNIT_CELL in batch + ), f"The unit cell should be present in the batch dictionary with key '{UNIT_CELL}'" unit_cell = batch[UNIT_CELL] unit_cell_shape = unit_cell.shape @@ -111,23 +129,30 @@ def _check_batch(self, batch: Dict[AnyStr, torch.Tensor]): unit_cell_shape[0] == batch_size ), "the batch size dimension is inconsistent between positions and unit cell." assert ( - len(unit_cell_shape) == 3 and unit_cell_shape[1] == self.spatial_dimension + len(unit_cell_shape) == 3 + and unit_cell_shape[1] == self.spatial_dimension and unit_cell_shape[2] == self.spatial_dimension ), "The unit cell is expected to be in a tensor of shape [batch_size, spatial_dimension, spatial_dimension]." if self.conditional_prob > 0: - assert CARTESIAN_FORCES in batch, \ - (f"The cartesian forces should be present in " - f"the batch dictionary with key '{CARTESIAN_FORCES}'") + assert CARTESIAN_FORCES in batch, ( + f"The cartesian forces should be present in " + f"the batch dictionary with key '{CARTESIAN_FORCES}'" + ) cartesian_forces = batch[CARTESIAN_FORCES] cartesian_forces_shape = cartesian_forces.shape assert ( - len(cartesian_forces_shape) == 3 and cartesian_forces_shape[2] == self.spatial_dimension - ), ("The cartesian forces are expected to be in a tensor of shape [batch_size, number_of_atoms," - f"{self.spatial_dimension}]") - - def forward(self, batch: Dict[AnyStr, torch.Tensor], conditional: Optional[bool] = None) -> torch.Tensor: + len(cartesian_forces_shape) == 3 + and cartesian_forces_shape[2] == self.spatial_dimension + ), ( + "The cartesian forces are expected to be in a tensor of shape [batch_size, number_of_atoms," + f"{self.spatial_dimension}]" + ) + + def forward( + self, batch: Dict[AnyStr, torch.Tensor], conditional: Optional[bool] = None + ) -> torch.Tensor: """Model forward. Args: @@ -140,14 +165,26 @@ def forward(self, batch: Dict[AnyStr, torch.Tensor], conditional: Optional[bool] """ self._check_batch(batch) if conditional is None: - conditional = torch.rand(1,) < self.conditional_prob + conditional = ( + torch.rand( + 1, + ) + < self.conditional_prob + ) if not conditional: return self._forward_unchecked(batch, conditional=False) else: - return (self._forward_unchecked(batch, conditional=True) * self.conditional_gamma - + self._forward_unchecked(batch, conditional=False) * (1 - self.conditional_gamma)) - - def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False) -> torch.Tensor: + return self._forward_unchecked( + batch, conditional=True + ) * self.conditional_gamma + self._forward_unchecked( + batch, conditional=False + ) * ( + 1 - self.conditional_gamma + ) + + def _forward_unchecked( + self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False + ) -> torch.Tensor: """Forward unchecked. This method assumes that the input data has already been checked with respect to expectations diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network_factory.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network_factory.py index b6f2dc38..f161236b 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network_factory.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_network_factory.py @@ -1,32 +1,38 @@ import dataclasses from typing import Any, AnyStr, Dict -from crystal_diffusion.models.score_networks import (ScoreNetwork, - ScoreNetworkParameters) -from crystal_diffusion.models.score_networks.diffusion_mace_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import ( + ScoreNetwork, ScoreNetworkParameters) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.diffusion_mace_score_network import ( DiffusionMACEScoreNetwork, DiffusionMACEScoreNetworkParameters) -from crystal_diffusion.models.score_networks.egnn_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.egnn_score_network import ( EGNNScoreNetwork, EGNNScoreNetworkParameters) -from crystal_diffusion.models.score_networks.mace_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mace_score_network import ( MACEScoreNetwork, MACEScoreNetworkParameters) -from crystal_diffusion.models.score_networks.mlp_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mlp_score_network import ( MLPScoreNetwork, MLPScoreNetworkParameters) -from crystal_diffusion.models.score_networks.score_prediction_head import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_prediction_head import ( MaceEquivariantScorePredictionHeadParameters, MaceMLPScorePredictionHeadParameters) -from crystal_diffusion.utils.configuration_parsing import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary -SCORE_NETWORKS_BY_ARCH = dict(mlp=MLPScoreNetwork, - mace=MACEScoreNetwork, - diffusion_mace=DiffusionMACEScoreNetwork, - egnn=EGNNScoreNetwork) -SCORE_NETWORK_PARAMETERS_BY_ARCH = dict(mlp=MLPScoreNetworkParameters, - mace=MACEScoreNetworkParameters, - diffusion_mace=DiffusionMACEScoreNetworkParameters, - egnn=EGNNScoreNetworkParameters) -MACE_PREDICTION_HEAD_BY_NAME = dict(mlp=MaceMLPScorePredictionHeadParameters, - equivariant=MaceEquivariantScorePredictionHeadParameters) +SCORE_NETWORKS_BY_ARCH = dict( + mlp=MLPScoreNetwork, + mace=MACEScoreNetwork, + diffusion_mace=DiffusionMACEScoreNetwork, + egnn=EGNNScoreNetwork, +) +SCORE_NETWORK_PARAMETERS_BY_ARCH = dict( + mlp=MLPScoreNetworkParameters, + mace=MACEScoreNetworkParameters, + diffusion_mace=DiffusionMACEScoreNetworkParameters, + egnn=EGNNScoreNetworkParameters, +) +MACE_PREDICTION_HEAD_BY_NAME = dict( + mlp=MaceMLPScorePredictionHeadParameters, + equivariant=MaceEquivariantScorePredictionHeadParameters, +) def create_score_network(score_network_parameters: ScoreNetworkParameters): @@ -35,16 +41,21 @@ def create_score_network(score_network_parameters: ScoreNetworkParameters): This is a factory method responsible for instantiating the score network. """ architecture = score_network_parameters.architecture - assert architecture in SCORE_NETWORKS_BY_ARCH.keys(), \ - f"Architecture {architecture} is not implemented. Possible choices are {SCORE_NETWORKS_BY_ARCH.keys()}" + assert ( + architecture in SCORE_NETWORKS_BY_ARCH.keys() + ), f"Architecture {architecture} is not implemented. Possible choices are {SCORE_NETWORKS_BY_ARCH.keys()}" - instantiated_score_network: ScoreNetwork = SCORE_NETWORKS_BY_ARCH[architecture](score_network_parameters) + instantiated_score_network: ScoreNetwork = SCORE_NETWORKS_BY_ARCH[architecture]( + score_network_parameters + ) return instantiated_score_network -def create_score_network_parameters(score_network_dictionary: Dict[AnyStr, Any], - global_parameters_dictionary: Dict[AnyStr, Any]) -> ScoreNetworkParameters: +def create_score_network_parameters( + score_network_dictionary: Dict[AnyStr, Any], + global_parameters_dictionary: Dict[AnyStr, Any], +) -> ScoreNetworkParameters: """Create the score network parameters. Args: @@ -54,30 +65,40 @@ def create_score_network_parameters(score_network_dictionary: Dict[AnyStr, Any], Returns: score_network_parameters: the dataclass configuration object describing the score network. """ - assert 'architecture' in score_network_dictionary, "The architecture of the score network must be specified." - score_network_architecture = score_network_dictionary['architecture'] + assert ( + "architecture" in score_network_dictionary + ), "The architecture of the score network must be specified." + score_network_architecture = score_network_dictionary["architecture"] - assert score_network_architecture in SCORE_NETWORK_PARAMETERS_BY_ARCH.keys(), \ - (f"Architecture {score_network_architecture} is not implemented. " - f"Possible choices are {SCORE_NETWORK_PARAMETERS_BY_ARCH.keys()}") + assert score_network_architecture in SCORE_NETWORK_PARAMETERS_BY_ARCH.keys(), ( + f"Architecture {score_network_architecture} is not implemented. " + f"Possible choices are {SCORE_NETWORK_PARAMETERS_BY_ARCH.keys()}" + ) - score_network_dataclass = SCORE_NETWORK_PARAMETERS_BY_ARCH[score_network_architecture] + score_network_dataclass = SCORE_NETWORK_PARAMETERS_BY_ARCH[ + score_network_architecture + ] # Augment the configuration dictionary with a head (if present) and relevant global parameters. augmented_score_network_dictionary = dict(score_network_dictionary) - if 'prediction_head_parameters' in score_network_dictionary: - head_config = score_network_dictionary['prediction_head_parameters'] - prediction_head_parameters = ( - create_parameters_from_configuration_dictionary(configuration=head_config, - identifier="name", - options=MACE_PREDICTION_HEAD_BY_NAME)) - augmented_score_network_dictionary['prediction_head_parameters'] = prediction_head_parameters + if "prediction_head_parameters" in score_network_dictionary: + head_config = score_network_dictionary["prediction_head_parameters"] + prediction_head_parameters = create_parameters_from_configuration_dictionary( + configuration=head_config, + identifier="name", + options=MACE_PREDICTION_HEAD_BY_NAME, + ) + augmented_score_network_dictionary["prediction_head_parameters"] = ( + prediction_head_parameters + ) # Validate that there are no contradictions between the score config and global parameters for key, value in augmented_score_network_dictionary.items(): if key in global_parameters_dictionary: - assert global_parameters_dictionary[key] == value, f"inconsistent configuration values for {key}" + assert ( + global_parameters_dictionary[key] == value + ), f"inconsistent configuration values for {key}" # Complete the score config with global values all_fields = [field.name for field in dataclasses.fields(score_network_dataclass)] @@ -85,6 +106,8 @@ def create_score_network_parameters(score_network_dictionary: Dict[AnyStr, Any], if key in all_fields: augmented_score_network_dictionary[key] = value - score_network_parameters = score_network_dataclass(**augmented_score_network_dictionary) + score_network_parameters = score_network_dataclass( + **augmented_score_network_dictionary + ) return score_network_parameters diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py index 47c680cf..ab9c4e0b 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/models/score_networks/score_prediction_head.py @@ -2,30 +2,39 @@ import e3nn import torch -from crystal_diffusion.models.mace_utils import \ - get_normalized_irreps_permutation_indices from e3nn import o3 from e3nn.nn import Activation from mace.modules import LinearNodeEmbeddingBlock, gate_dict from torch import nn +from diffusion_for_multi_scale_molecular_dynamics.models.mace_utils import \ + get_normalized_irreps_permutation_indices + @dataclass(kw_only=True) class MaceScorePredictionHeadParameters: """Base Hyper-parameters for score networks.""" + name: str # this must be overloaded to identify the type of prediction head spatial_dimension: int = 3 # the dimension of Euclidean space where atoms live. class MaceScorePredictionHead(nn.Module): """A Base class for the head that predicts the scores given node features from MACE.""" - def __init__(self, output_node_features_irreps: o3.Irreps, hyper_params: MaceScorePredictionHeadParameters): + + def __init__( + self, + output_node_features_irreps: o3.Irreps, + hyper_params: MaceScorePredictionHeadParameters, + ): """Init method.""" super().__init__() self.output_node_features_irreps = output_node_features_irreps self.hyper_params = hyper_params - def forward(self, flat_node_features: torch.Tensor, flat_times: torch.Tensor) -> torch.Tensor: + def forward( + self, flat_node_features: torch.Tensor, flat_times: torch.Tensor + ) -> torch.Tensor: """Forward method. Here, 'flat' means that the batch dimension and the number_of_atoms dimensions are combined (flattened). @@ -43,30 +52,44 @@ def forward(self, flat_node_features: torch.Tensor, flat_times: torch.Tensor) -> @dataclass(kw_only=True) class MaceMLPScorePredictionHeadParameters(MaceScorePredictionHeadParameters): """Parameters for a MLP prediction head.""" - name: str = 'mlp' - hidden_dimensions_size: int # dimension of a linear layer + + name: str = "mlp" + hidden_dimensions_size: int # dimension of a linear layer n_hidden_dimensions: int # number of linear layers in the MLP class MaceMLPScorePredictionHead(MaceScorePredictionHead): """A MLP head to predict scores given node features from MACE.""" - def __init__(self, output_node_features_irreps: o3.Irreps, hyper_params: MaceMLPScorePredictionHeadParameters): + + def __init__( + self, + output_node_features_irreps: o3.Irreps, + hyper_params: MaceMLPScorePredictionHeadParameters, + ): """Init method.""" super().__init__(output_node_features_irreps, hyper_params) - hidden_dimensions = [hyper_params.hidden_dimensions_size] * hyper_params.n_hidden_dimensions + hidden_dimensions = [ + hyper_params.hidden_dimensions_size + ] * hyper_params.n_hidden_dimensions self.mlp_layers = torch.nn.Sequential() # TODO we could add a linear layer to the times before concat with mace_output - input_dimensions = [output_node_features_irreps.dim + 1] + hidden_dimensions # add 1 for the times + input_dimensions = [ + output_node_features_irreps.dim + 1 + ] + hidden_dimensions # add 1 for the times output_dimensions = hidden_dimensions + [hyper_params.spatial_dimension] add_relus = len(input_dimensions) * [True] add_relus[-1] = False - for input_dimension, output_dimension, add_relu in zip(input_dimensions, output_dimensions, add_relus): + for input_dimension, output_dimension, add_relu in zip( + input_dimensions, output_dimensions, add_relus + ): self.mlp_layers.append(nn.Linear(input_dimension, output_dimension)) if add_relu: self.mlp_layers.append(nn.ReLU()) - def forward(self, flat_node_features: torch.Tensor, flat_times: torch.Tensor) -> torch.Tensor: + def forward( + self, flat_node_features: torch.Tensor, flat_times: torch.Tensor + ) -> torch.Tensor: """Forward method.""" mlp_input = torch.cat([flat_node_features, flat_times], dim=-1) # pass through the final MLP layers @@ -77,16 +100,23 @@ def forward(self, flat_node_features: torch.Tensor, flat_times: torch.Tensor) -> @dataclass(kw_only=True) class MaceEquivariantScorePredictionHeadParameters(MaceScorePredictionHeadParameters): """Parameters for an equivariant prediction head.""" - name: str = 'equivariant' + + name: str = "equivariant" time_embedding_irreps: str = "16x0e" - gate: str = "silu" # non linearity for last readout - choices: ["silu", "tanh", "abs", "None"] + gate: str = ( + "silu" # non linearity for last readout - choices: ["silu", "tanh", "abs", "None"] + ) number_of_layers: int class MaceEquivariantScorePredictionHead(MaceScorePredictionHead): """An Equivariant head to predict scores given node features from MACE.""" - def __init__(self, output_node_features_irreps: o3.Irreps, - hyper_params: MaceEquivariantScorePredictionHeadParameters): + + def __init__( + self, + output_node_features_irreps: o3.Irreps, + hyper_params: MaceEquivariantScorePredictionHeadParameters, + ): """Init method.""" super().__init__(output_node_features_irreps, hyper_params) @@ -94,8 +124,9 @@ def __init__(self, output_node_features_irreps: o3.Irreps, time_irreps_in = o3.Irreps("1x0e") # time is a scalar time_irreps_out = o3.Irreps(hyper_params.time_embedding_irreps) - self.time_embedding_linear_layer = LinearNodeEmbeddingBlock(irreps_in=time_irreps_in, - irreps_out=time_irreps_out) + self.time_embedding_linear_layer = LinearNodeEmbeddingBlock( + irreps_in=time_irreps_in, irreps_out=time_irreps_out + ) # The concatenated node features have representation 'output_node_features_irreps', which # is potentially out of order. We will pre-concatenate the time embedding to this data. @@ -103,8 +134,7 @@ def __init__(self, output_node_features_irreps: o3.Irreps, # by the various subsequent layers. head_input_irreps = time_irreps_out + output_node_features_irreps - sorted_irreps, _ = ( - get_normalized_irreps_permutation_indices(head_input_irreps)) + sorted_irreps, _ = get_normalized_irreps_permutation_indices(head_input_irreps) # mix the time embedding 0e irrep with the different components of the MACE output and avoid a dimensionality # explosion with FullyConnectedTensorProduct that only computes the specified channels in the output and applies @@ -112,7 +142,7 @@ def __init__(self, output_node_features_irreps: o3.Irreps, self.time_mixing_layer = o3.FullyConnectedTensorProduct( irreps_in1=time_irreps_out, irreps_in2=output_node_features_irreps, - irreps_out=sorted_irreps + irreps_out=sorted_irreps, ) self.head = torch.nn.Sequential() @@ -133,10 +163,14 @@ def __init__(self, output_node_features_irreps: o3.Irreps, self.head.append(non_linearity) # the output is a single vector. - linear_readout = o3.Linear(irreps_in=sorted_irreps, irreps_out=o3.Irreps("1x1o")) + linear_readout = o3.Linear( + irreps_in=sorted_irreps, irreps_out=o3.Irreps("1x1o") + ) self.head.append(linear_readout) - def forward(self, flat_node_features: torch.Tensor, flat_times: torch.Tensor) -> torch.Tensor: + def forward( + self, flat_node_features: torch.Tensor, flat_times: torch.Tensor + ) -> torch.Tensor: """Forward method. Here, 'flat' means that the batch dimension and the number_of_atoms dimensions are combined (flattened). @@ -156,12 +190,15 @@ def forward(self, flat_node_features: torch.Tensor, flat_times: torch.Tensor) -> # Register the possible MACE prediction heads as key: model class -MACE_PREDICTION_HEADS = dict(mlp=MaceMLPScorePredictionHead, equivariant=MaceEquivariantScorePredictionHead) +MACE_PREDICTION_HEADS = dict( + mlp=MaceMLPScorePredictionHead, equivariant=MaceEquivariantScorePredictionHead +) -def instantiate_mace_prediction_head(output_node_features_irreps: o3.Irreps, - prediction_head_parameters: MaceScorePredictionHeadParameters) \ - -> MaceScorePredictionHead: +def instantiate_mace_prediction_head( + output_node_features_irreps: o3.Irreps, + prediction_head_parameters: MaceScorePredictionHeadParameters, +) -> MaceScorePredictionHead: """Instantiate MACE prediction head. Args: @@ -172,8 +209,12 @@ def instantiate_mace_prediction_head(output_node_features_irreps: o3.Irreps, prediction_head: torch module to predict the scores from the output of MACE. """ head_name = prediction_head_parameters.name - assert head_name in MACE_PREDICTION_HEADS, f"MACE prediction head '{head_name}' is not implemented" + assert ( + head_name in MACE_PREDICTION_HEADS + ), f"MACE prediction head '{head_name}' is not implemented" head_class = MACE_PREDICTION_HEADS[head_name] - prediction_head = head_class(output_node_features_irreps, prediction_head_parameters) + prediction_head = head_class( + output_node_features_irreps, prediction_head_parameters + ) return prediction_head diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/namespace.py b/src/diffusion_for_multi_scale_molecular_dynamics/namespace.py index 21c1851c..2fb6ebeb 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/namespace.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/namespace.py @@ -10,12 +10,16 @@ # # r = \sum_{i} x_i a_i, where { a_i } are the basis vectors defining the lattice. -CARTESIAN_POSITIONS = "cartesian_positions" # position in real cartesian space -RELATIVE_COORDINATES = "relative_coordinates" # coordinates in the unit cell basis +CARTESIAN_POSITIONS = "cartesian_positions" # position in real cartesian space +RELATIVE_COORDINATES = "relative_coordinates" # coordinates in the unit cell basis CARTESIAN_FORCES = "cartesian_forces" -NOISY_RELATIVE_COORDINATES = "noisy_relative_coordinates" # relative coordinates perturbed by diffusion noise -NOISY_CARTESIAN_POSITIONS = "noisy_cartesian_positions" # cartesian positions perturbed by diffusion noise +NOISY_RELATIVE_COORDINATES = ( + "noisy_relative_coordinates" # relative coordinates perturbed by diffusion noise +) +NOISY_CARTESIAN_POSITIONS = ( + "noisy_cartesian_positions" # cartesian positions perturbed by diffusion noise +) TIME = "time" # diffusion time NOISE = "noise_parameter" # the exploding variance sigma parameter UNIT_CELL = "unit_cell" # unit cell definition diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/oracle/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py index ccc69659..b49a25c5 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/energies.py @@ -4,8 +4,11 @@ import numpy as np import torch -from crystal_diffusion.namespace import CARTESIAN_POSITIONS, UNIT_CELL -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps + +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_POSITIONS, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps logger = logging.getLogger(__name__) @@ -23,11 +26,13 @@ def compute_oracle_energies(samples: Dict[AnyStr, torch.Tensor]) -> torch.Tensor Returns: energies: a numpy array with the computed energies. """ - assert CARTESIAN_POSITIONS in samples, \ - f"the field '{CARTESIAN_POSITIONS}' must be present in the sample dictionary" + assert ( + CARTESIAN_POSITIONS in samples + ), f"the field '{CARTESIAN_POSITIONS}' must be present in the sample dictionary" - assert UNIT_CELL in samples, \ - f"the field '{UNIT_CELL}' must be present in the sample dictionary" + assert ( + UNIT_CELL in samples + ), f"the field '{UNIT_CELL}' must be present in the sample dictionary" # Dimension [batch_size, space_dimension, space_dimension] basis_vectors = samples[UNIT_CELL].detach().cpu().numpy() @@ -43,10 +48,9 @@ def compute_oracle_energies(samples: Dict[AnyStr, torch.Tensor]) -> torch.Tensor list_energy = [] with tempfile.TemporaryDirectory() as tmp_work_dir: for positions, box in zip(cartesian_positions, basis_vectors): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - atom_types, - tmp_work_dir=tmp_work_dir) + energy, forces = get_energy_and_forces_from_lammps( + positions, box, atom_types, tmp_work_dir=tmp_work_dir + ) list_energy.append(energy) logger.info("Done computing energies from Oracle") return torch.tensor(list_energy) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py index cd0bd43c..6cd96fbf 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/oracle/lammps.py @@ -1,4 +1,5 @@ """Call LAMMPS to get the forces and energy in a given configuration.""" + import os from pathlib import Path from typing import Dict, Tuple @@ -7,16 +8,19 @@ import numpy as np import pandas as pd import yaml -from crystal_diffusion import DATA_DIR from pymatgen.core import Element +from diffusion_for_multi_scale_molecular_dynamics import DATA_DIR + -def get_energy_and_forces_from_lammps(cartesian_positions: np.ndarray, - box: np.ndarray, - atom_types: np.ndarray, - atom_type_map: Dict[int, str] = {1: 'Si'}, - tmp_work_dir: str = './', - pair_coeff_dir: Path = DATA_DIR) -> Tuple[float, pd.DataFrame]: +def get_energy_and_forces_from_lammps( + cartesian_positions: np.ndarray, + box: np.ndarray, + atom_types: np.ndarray, + atom_type_map: Dict[int, str] = {1: "Si"}, + tmp_work_dir: str = "./", + pair_coeff_dir: Path = DATA_DIR, +) -> Tuple[float, pd.DataFrame]: """Call LAMMPS to compute the forces on all atoms in a configuration. Args: @@ -33,40 +37,62 @@ def get_energy_and_forces_from_lammps(cartesian_positions: np.ndarray, dataframe with x, y, z coordinates and fx, fy, fz information in a dataframe. """ n_atom = cartesian_positions.shape[0] - assert atom_types.shape == (n_atom, ), f"Atom types should match the number of atoms. Got {atom_types.shape}." + assert atom_types.shape == ( + n_atom, + ), f"Atom types should match the number of atoms. Got {atom_types.shape}." # create a lammps run, turning off logging lmp = lammps.lammps(cmdargs=["-log", "none", "-echo", "none", "-screen", "none"]) - assert np.allclose(box, np.diag(np.diag(box))), "only orthogonal LAMMPS box are valid" + assert np.allclose( + box, np.diag(np.diag(box)) + ), "only orthogonal LAMMPS box are valid" lmp.command("units metal") lmp.command("atom_style atomic") - lmp.command(f"region simbox block 0 {box[0, 0]} 0 {box[1, 1]} 0 {box[2, 2]}") # TODO what if box is not orthogonal + lmp.command( + f"region simbox block 0 {box[0, 0]} 0 {box[1, 1]} 0 {box[2, 2]}" + ) # TODO what if box is not orthogonal lmp.command("create_box 1 simbox") lmp.command("pair_style sw") for k, v in atom_type_map.items(): elem = Element(v) - lmp.command(f"mass {k} {elem.atomic_mass.real}") # the .real is to get the value without the unit + lmp.command( + f"mass {k} {elem.atomic_mass.real}" + ) # the .real is to get the value without the unit lmp.command(f"group {v} type {k}") - lmp.command(f"pair_coeff * * {os.path.join(pair_coeff_dir, f'{v.lower()}.sw')} {v}") + lmp.command( + f"pair_coeff * * {os.path.join(pair_coeff_dir, f'{v.lower()}.sw')} {v}" + ) for i in range(n_atom): - lmp.command(f"create_atoms {atom_types[i]} single {' '.join(map(str, cartesian_positions[i, :]))}") - lmp.command("fix 1 all nvt temp 300 300 0.01") # selections here do not matter because we only do 1 step + lmp.command( + f"create_atoms {atom_types[i]} single {' '.join(map(str, cartesian_positions[i, :]))}" + ) + lmp.command( + "fix 1 all nvt temp 300 300 0.01" + ) # selections here do not matter because we only do 1 step # TODO not good in 2D - lmp.command(f"dump 1 all yaml 1 {os.path.join(tmp_work_dir, 'dump.yaml')} id type x y z fx fy fz") - lmp.command("run 0") # 0 is the last step index - so run 0 means no MD update - just get the initial forces + lmp.command( + f"dump 1 all yaml 1 {os.path.join(tmp_work_dir, 'dump.yaml')} id type x y z fx fy fz" + ) + lmp.command( + "run 0" + ) # 0 is the last step index - so run 0 means no MD update - just get the initial forces # read information from lammps output with open(os.path.join(tmp_work_dir, "dump.yaml"), "r") as f: dump_yaml = yaml.safe_load_all(f) doc = next(iter(dump_yaml)) - forces = pd.DataFrame(doc['data'], columns=doc['keywords']).sort_values("id") # organize in a dataframe + forces = pd.DataFrame(doc["data"], columns=doc["keywords"]).sort_values( + "id" + ) # organize in a dataframe # get the energy - ke = lmp.get_thermo('ke') # kinetic energy - should be 0 as atoms are created with 0 velocity - pe = lmp.get_thermo('pe') # potential energy + ke = lmp.get_thermo( + "ke" + ) # kinetic energy - should be 0 as atoms are created with 0 velocity + pe = lmp.get_thermo("pe") # potential energy energy = ke + pe return energy, forces diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py b/src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py index 08fd58c7..d6fe7fea 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/sample_diffusion.py @@ -2,6 +2,7 @@ This script is the entry point to draw samples from a pre-trained model checkpoint. """ + import argparse import logging import os @@ -10,21 +11,27 @@ from typing import Any, AnyStr, Dict, Optional, Union import torch -from crystal_diffusion.generators.instantiate_generator import \ + +from diffusion_for_multi_scale_molecular_dynamics.generators.instantiate_generator import \ instantiate_generator -from crystal_diffusion.generators.load_sampling_parameters import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.load_sampling_parameters import \ load_sampling_parameters -from crystal_diffusion.main_utils import load_and_backup_hyperparameters -from crystal_diffusion.models.position_diffusion_lightning_model import \ - PositionDiffusionLightningModel -from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.oracle.energies import compute_oracle_energies -from crystal_diffusion.utils.logging_utils import (get_git_hash, - setup_console_logger) -from src.crystal_diffusion.generators.position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import \ SamplingParameters -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters -from src.crystal_diffusion.samples.sampling import create_batch_of_samples +from diffusion_for_multi_scale_molecular_dynamics.main_utils import \ + load_and_backup_hyperparameters +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import \ + PositionDiffusionLightningModel +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.oracle.energies import \ + compute_oracle_energies +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.samples.sampling import \ + create_batch_of_samples +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import ( + get_git_hash, setup_console_logger) logger = logging.getLogger(__name__) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py index 45dddb80..d37a5ae0 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/exploding_variance.py @@ -1,5 +1,7 @@ import torch -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters class ExplodingVariance(torch.nn.Module): @@ -17,11 +19,19 @@ def __init__(self, noise_parameters: NoiseParameters): """ super().__init__() - self.sigma_min = torch.nn.Parameter(torch.tensor(noise_parameters.sigma_min), requires_grad=False) - self.sigma_max = torch.nn.Parameter(torch.tensor(noise_parameters.sigma_max), requires_grad=False) + self.sigma_min = torch.nn.Parameter( + torch.tensor(noise_parameters.sigma_min), requires_grad=False + ) + self.sigma_max = torch.nn.Parameter( + torch.tensor(noise_parameters.sigma_max), requires_grad=False + ) - self.ratio = torch.nn.Parameter(self.sigma_max / self.sigma_min, requires_grad=False) - self.log_ratio = torch.nn.Parameter(torch.log(self.sigma_max / self.sigma_min), requires_grad=False) + self.ratio = torch.nn.Parameter( + self.sigma_max / self.sigma_min, requires_grad=False + ) + self.log_ratio = torch.nn.Parameter( + torch.log(self.sigma_max / self.sigma_min), requires_grad=False + ) def get_sigma(self, times: torch.Tensor) -> torch.Tensor: """Get sigma. @@ -34,7 +44,7 @@ def get_sigma(self, times: torch.Tensor) -> torch.Tensor: Returns: sigmas: the standard deviation in the exploding variance scheme. """ - return self.sigma_min * self.ratio ** times + return self.sigma_min * self.ratio**times def get_sigma_time_derivative(self, times: torch.Tensor) -> torch.Tensor: """Get sigma time derivative. diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py index f1bd093b..c57e4fb5 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/noisy_relative_coordinates_sampler.py @@ -2,10 +2,12 @@ This module is responsible for sampling relative positions from the perturbation kernel. """ + from typing import Tuple import torch -from crystal_diffusion.utils.basis_transformations import \ + +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell @@ -17,6 +19,7 @@ class NoisyRelativeCoordinatesSampler: The random samples are produced by a separate method to make this code easy to test. """ + @staticmethod def _get_gaussian_noise(shape: Tuple[int]) -> torch.Tensor: """Get Gaussian noise. @@ -32,8 +35,9 @@ def _get_gaussian_noise(shape: Tuple[int]) -> torch.Tensor: return torch.randn(shape) @staticmethod - def get_noisy_relative_coordinates_sample(real_relative_coordinates: torch.Tensor, - sigmas: torch.Tensor) -> torch.Tensor: + def get_noisy_relative_coordinates_sample( + real_relative_coordinates: torch.Tensor, sigmas: torch.Tensor + ) -> torch.Tensor: """Get noisy relative coordinates sample. This method draws a sample from the perturbation kernel centered on the real_relative_coordinates @@ -54,10 +58,15 @@ def get_noisy_relative_coordinates_sample(real_relative_coordinates: torch.Tenso noisy_relative_coordinates: a sample of noised relative coordinates, of the same shape as real_relative_coordinates. """ - assert real_relative_coordinates.shape == sigmas.shape, \ - "sigmas array is expected to be of the same shape as the real_relative_coordinates array" + assert ( + real_relative_coordinates.shape == sigmas.shape + ), "sigmas array is expected to be of the same shape as the real_relative_coordinates array" - z_scores = NoisyRelativeCoordinatesSampler._get_gaussian_noise(real_relative_coordinates.shape).to(sigmas) + z_scores = NoisyRelativeCoordinatesSampler._get_gaussian_noise( + real_relative_coordinates.shape + ).to(sigmas) noise = (sigmas * z_scores).to(real_relative_coordinates) - noisy_relative_coordinates = map_relative_coordinates_to_unit_cell(real_relative_coordinates + noise) + noisy_relative_coordinates = map_relative_coordinates_to_unit_cell( + real_relative_coordinates + noise + ) return noisy_relative_coordinates diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/samplers/variance_sampler.py b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/variance_sampler.py index 1081073e..a5ed687e 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/samplers/variance_sampler.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samplers/variance_sampler.py @@ -11,6 +11,7 @@ @dataclass class NoiseParameters: """Noise schedule parameters.""" + total_time_steps: int time_delta: float = 1e-5 # the time schedule will cover the range [time_delta, 1] # As discussed in Appendix C of "SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS", @@ -78,27 +79,46 @@ def __init__(self, noise_parameters: NoiseParameters): super().__init__() self.noise_parameters = noise_parameters - self._time_array = torch.nn.Parameter(self._get_time_array(noise_parameters), requires_grad=False) + self._time_array = torch.nn.Parameter( + self._get_time_array(noise_parameters), requires_grad=False + ) - self._sigma_array = torch.nn.Parameter(self._create_sigma_array(noise_parameters, self._time_array), - requires_grad=False) - self._sigma_squared_array = torch.nn.Parameter(self._sigma_array**2, requires_grad=False) + self._sigma_array = torch.nn.Parameter( + self._create_sigma_array(noise_parameters, self._time_array), + requires_grad=False, + ) + self._sigma_squared_array = torch.nn.Parameter( + self._sigma_array**2, requires_grad=False + ) self._g_squared_array = torch.nn.Parameter( - self._create_g_squared_array(noise_parameters, self._sigma_squared_array), requires_grad=False) - self._g_array = torch.nn.Parameter(torch.sqrt(self._g_squared_array), requires_grad=False) + self._create_g_squared_array(noise_parameters, self._sigma_squared_array), + requires_grad=False, + ) + self._g_array = torch.nn.Parameter( + torch.sqrt(self._g_squared_array), requires_grad=False + ) self._epsilon_array = torch.nn.Parameter( - self._create_epsilon_array(noise_parameters, self._sigma_squared_array), requires_grad=False) - self._sqrt_two_epsilon_array = torch.nn.Parameter(torch.sqrt(2. * self._epsilon_array), requires_grad=False) + self._create_epsilon_array(noise_parameters, self._sigma_squared_array), + requires_grad=False, + ) + self._sqrt_two_epsilon_array = torch.nn.Parameter( + torch.sqrt(2.0 * self._epsilon_array), requires_grad=False + ) - self._maximum_random_index = torch.nn.Parameter(torch.tensor(noise_parameters.total_time_steps - 1), - requires_grad=False) - self._minimum_random_index = torch.nn.Parameter(torch.tensor(0), requires_grad=False) + self._maximum_random_index = torch.nn.Parameter( + torch.tensor(noise_parameters.total_time_steps - 1), requires_grad=False + ) + self._minimum_random_index = torch.nn.Parameter( + torch.tensor(0), requires_grad=False + ) @staticmethod def _get_time_array(noise_parameters: NoiseParameters) -> torch.Tensor: - return torch.linspace(noise_parameters.time_delta, 1., noise_parameters.total_time_steps) + return torch.linspace( + noise_parameters.time_delta, 1.0, noise_parameters.total_time_steps + ) @staticmethod def _create_sigma_array( @@ -111,7 +131,9 @@ def _create_sigma_array( return sigma @staticmethod - def _create_g_squared_array(noise_parameters: NoiseParameters, sigma_squared_array: torch.Tensor) -> torch.Tensor: + def _create_g_squared_array( + noise_parameters: NoiseParameters, sigma_squared_array: torch.Tensor + ) -> torch.Tensor: # g^2_{i} = sigma^2_{i} - sigma^2_{i-1}. For the first element (i=1), we set sigma_{0} = sigma_min. sigma_min = noise_parameters.sigma_min zeroth_value_tensor = torch.tensor([sigma_squared_array[0] - sigma_min**2]) @@ -120,15 +142,22 @@ def _create_g_squared_array(noise_parameters: NoiseParameters, sigma_squared_arr ) @staticmethod - def _create_epsilon_array(noise_parameters: NoiseParameters, sigma_squared_array: torch.Tensor) -> torch.Tensor: + def _create_epsilon_array( + noise_parameters: NoiseParameters, sigma_squared_array: torch.Tensor + ) -> torch.Tensor: sigma_squared_0 = noise_parameters.sigma_min**2 sigma_squared_1 = sigma_squared_array[0] eps = noise_parameters.corrector_step_epsilon - zeroth_value_tensor = torch.tensor([0.5 * eps * sigma_squared_0 / sigma_squared_1]) + zeroth_value_tensor = torch.tensor( + [0.5 * eps * sigma_squared_0 / sigma_squared_1] + ) return torch.cat( - [zeroth_value_tensor, 0.5 * eps * sigma_squared_array[:-1] / sigma_squared_1] + [ + zeroth_value_tensor, + 0.5 * eps * sigma_squared_array[:-1] / sigma_squared_1, + ] ) def _get_random_time_step_indices(self, shape: Tuple[int]) -> torch.Tensor: @@ -147,7 +176,7 @@ def _get_random_time_step_indices(self, shape: Tuple[int]) -> torch.Tensor: self._maximum_random_index + 1, # +1 because the maximum value is not sampled size=shape, - device=self._minimum_random_index.device + device=self._minimum_random_index.device, ) return random_indices @@ -198,8 +227,10 @@ def get_all_sampling_parameters(self) -> Tuple[Noise, LangevinDynamics]: sigma=self._sigma_array, sigma_squared=self._sigma_squared_array, g=self._g_array, - g_squared=self._g_squared_array) - langevin_dynamics = LangevinDynamics(epsilon=self._epsilon_array, - sqrt_2_epsilon=self._sqrt_two_epsilon_array) + g_squared=self._g_squared_array, + ) + langevin_dynamics = LangevinDynamics( + epsilon=self._epsilon_array, sqrt_2_epsilon=self._sqrt_two_epsilon_array + ) return noise, langevin_dynamics diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py b/src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py index 2d4682a2..5a4f5fdb 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samples/diffusion_sampling_parameters.py @@ -1,13 +1,14 @@ from dataclasses import dataclass from typing import Any, AnyStr, Dict, Union -from crystal_diffusion.generators.load_sampling_parameters import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.load_sampling_parameters import \ load_sampling_parameters -from crystal_diffusion.metrics.sampling_metrics_parameters import \ - SamplingMetricsParameters -from src.crystal_diffusion.generators.position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import \ SamplingParameters -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.metrics.sampling_metrics_parameters import \ + SamplingMetricsParameters +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters @dataclass(kw_only=True) @@ -17,12 +18,21 @@ class DiffusionSamplingParameters: This dataclass holds various configuration objects that define how samples should be generated and evaluated (ie, metrics) during training. """ - sampling_parameters: SamplingParameters # Define the algorithm and parameters to draw samples. - noise_parameters: NoiseParameters # Noise for sampling, which can be different from training! - metrics_parameters: SamplingMetricsParameters # what should be done with the generated samples? + + sampling_parameters: ( + SamplingParameters # Define the algorithm and parameters to draw samples. + ) + noise_parameters: ( + NoiseParameters # Noise for sampling, which can be different from training! + ) + metrics_parameters: ( + SamplingMetricsParameters # what should be done with the generated samples? + ) -def load_diffusion_sampling_parameters(hyper_params: Dict[AnyStr, Any]) -> Union[DiffusionSamplingParameters, None]: +def load_diffusion_sampling_parameters( + hyper_params: Dict[AnyStr, Any] +) -> Union[DiffusionSamplingParameters, None]: """Load diffusion sampling parameters. Extract the needed information from the configuration dictionary. @@ -33,20 +43,28 @@ def load_diffusion_sampling_parameters(hyper_params: Dict[AnyStr, Any]) -> Union Returns: diffusion_sampling_parameters: the relevant configuration object. """ - if 'diffusion_sampling' not in hyper_params: + if "diffusion_sampling" not in hyper_params: return None - diffusion_sampling_dict = hyper_params['diffusion_sampling'] + diffusion_sampling_dict = hyper_params["diffusion_sampling"] - assert 'sampling' in diffusion_sampling_dict, "The sampling parameters must be defined to draw samples." - sampling_parameters = load_sampling_parameters(diffusion_sampling_dict['sampling']) + assert ( + "sampling" in diffusion_sampling_dict + ), "The sampling parameters must be defined to draw samples." + sampling_parameters = load_sampling_parameters(diffusion_sampling_dict["sampling"]) - assert 'noise' in diffusion_sampling_dict, "The noise parameters must be defined to draw samples." - noise_parameters = NoiseParameters(**diffusion_sampling_dict['noise']) + assert ( + "noise" in diffusion_sampling_dict + ), "The noise parameters must be defined to draw samples." + noise_parameters = NoiseParameters(**diffusion_sampling_dict["noise"]) - assert 'metrics' in diffusion_sampling_dict, "The metrics parameters must be defined to draw samples." - metrics_parameters = SamplingMetricsParameters(**diffusion_sampling_dict['metrics']) + assert ( + "metrics" in diffusion_sampling_dict + ), "The metrics parameters must be defined to draw samples." + metrics_parameters = SamplingMetricsParameters(**diffusion_sampling_dict["metrics"]) - return DiffusionSamplingParameters(sampling_parameters=sampling_parameters, - noise_parameters=noise_parameters, - metrics_parameters=metrics_parameters) + return DiffusionSamplingParameters( + sampling_parameters=sampling_parameters, + noise_parameters=noise_parameters, + metrics_parameters=metrics_parameters, + ) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py b/src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py index dc3545b7..34d2b3db 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/samples/sampling.py @@ -1,21 +1,24 @@ import logging import torch -from crystal_diffusion.namespace import (CARTESIAN_POSITIONS, - RELATIVE_COORDINATES, UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import \ + +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import ( + PositionGenerator, SamplingParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_POSITIONS, RELATIVE_COORDINATES, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ get_positions_from_coordinates -from crystal_diffusion.utils.structure_utils import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.structure_utils import \ get_orthogonal_basis_vectors -from src.crystal_diffusion.generators.position_generator import ( - PositionGenerator, SamplingParameters) logger = logging.getLogger(__name__) -def create_batch_of_samples(generator: PositionGenerator, - sampling_parameters: SamplingParameters, - device: torch.device): +def create_batch_of_samples( + generator: PositionGenerator, + sampling_parameters: SamplingParameters, + device: torch.device, +): """Create batch of samples. Utility function to drive the generation of samples. @@ -31,7 +34,9 @@ def create_batch_of_samples(generator: PositionGenerator, logger.info("Creating a batch of samples") number_of_samples = sampling_parameters.number_of_samples cell_dimensions = sampling_parameters.cell_dimensions - basis_vectors = get_orthogonal_basis_vectors(number_of_samples, cell_dimensions).to(device) + basis_vectors = get_orthogonal_basis_vectors(number_of_samples, cell_dimensions).to( + device + ) if sampling_parameters.sample_batchsize is None: sample_batch_size = number_of_samples @@ -39,18 +44,24 @@ def create_batch_of_samples(generator: PositionGenerator, sample_batch_size = sampling_parameters.sample_batchsize list_sampled_relative_coordinates = [] - for sampling_batch_indices in torch.split(torch.arange(number_of_samples), sample_batch_size): + for sampling_batch_indices in torch.split( + torch.arange(number_of_samples), sample_batch_size + ): basis_vectors_ = basis_vectors[sampling_batch_indices] - sampled_relative_coordinates = generator.sample(len(sampling_batch_indices), - unit_cell=basis_vectors_, - device=device) + sampled_relative_coordinates = generator.sample( + len(sampling_batch_indices), unit_cell=basis_vectors_, device=device + ) list_sampled_relative_coordinates.append(sampled_relative_coordinates) relative_coordinates = torch.concat(list_sampled_relative_coordinates) - cartesian_positions = get_positions_from_coordinates(relative_coordinates, basis_vectors) + cartesian_positions = get_positions_from_coordinates( + relative_coordinates, basis_vectors + ) - batch = {CARTESIAN_POSITIONS: cartesian_positions, - RELATIVE_COORDINATES: relative_coordinates, - UNIT_CELL: basis_vectors} + batch = { + CARTESIAN_POSITIONS: cartesian_positions, + RELATIVE_COORDINATES: relative_coordinates, + UNIT_CELL: basis_vectors, + } return batch diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/score/wrapped_gaussian_score.py b/src/diffusion_for_multi_scale_molecular_dynamics/score/wrapped_gaussian_score.py index 9a19fc4c..c7062291 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/score/wrapped_gaussian_score.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/score/wrapped_gaussian_score.py @@ -27,6 +27,7 @@ "Generative Modeling by Estimating Gradients of the Data Distribution", Song & Ermon """ + from typing import Optional import numpy as np @@ -36,7 +37,9 @@ U_THRESHOLD = torch.Tensor([0.5]) -def get_sigma_normalized_score_brute_force(u: float, sigma: float, kmax: Optional[int] = None) -> float: +def get_sigma_normalized_score_brute_force( + u: float, sigma: float, kmax: Optional[int] = None +) -> float: """Brute force implementation. A brute force implementation of the sigma normalized score to check that the main code is correct. @@ -93,11 +96,14 @@ def get_sigma_normalized_score( assert torch.logical_and( relative_coordinates >= 0, relative_coordinates < 1 ).all(), "the relative coordinates should all be in [0, 1)" - assert sigmas.shape == relative_coordinates.shape, \ - "The relative_coordinates and sigmas inputs should have the same shape" + assert ( + sigmas.shape == relative_coordinates.shape + ), "The relative_coordinates and sigmas inputs should have the same shape" device = relative_coordinates.device - assert sigmas.device == device, "relative_coordinates and sigmas should be on the same device." + assert ( + sigmas.device == device + ), "relative_coordinates and sigmas should be on the same device." total_number_of_elements = relative_coordinates.nelement() list_u = relative_coordinates.view(total_number_of_elements) @@ -131,7 +137,9 @@ def get_sigma_normalized_score( return sigma_normalized_scores -def _get_small_sigma_small_u_mask(list_u: torch.Tensor, list_sigma: torch.Tensor) -> torch.Tensor: +def _get_small_sigma_small_u_mask( + list_u: torch.Tensor, list_sigma: torch.Tensor +) -> torch.Tensor: """Get the boolean mask for small sigma and small u. Args: @@ -142,10 +150,15 @@ def _get_small_sigma_small_u_mask(list_u: torch.Tensor, list_sigma: torch.Tensor mask_1a : an array of booleans of shape [Nu] """ device = list_u.device - return torch.logical_and(list_sigma.to(device) <= SIGMA_THRESHOLD.to(device), list_u < U_THRESHOLD.to(device)) + return torch.logical_and( + list_sigma.to(device) <= SIGMA_THRESHOLD.to(device), + list_u < U_THRESHOLD.to(device), + ) -def _get_small_sigma_large_u_mask(list_u: torch.Tensor, list_sigma: torch.Tensor) -> torch.Tensor: +def _get_small_sigma_large_u_mask( + list_u: torch.Tensor, list_sigma: torch.Tensor +) -> torch.Tensor: """Get the boolean mask for small sigma and large u. Args: @@ -156,10 +169,15 @@ def _get_small_sigma_large_u_mask(list_u: torch.Tensor, list_sigma: torch.Tensor mask_1b : an array of booleans of shape [Nu] """ device = list_u.device - return torch.logical_and(list_sigma.to(device) <= SIGMA_THRESHOLD.to(device), list_u >= U_THRESHOLD.to(device)) + return torch.logical_and( + list_sigma.to(device) <= SIGMA_THRESHOLD.to(device), + list_u >= U_THRESHOLD.to(device), + ) -def _get_large_sigma_mask(list_u: torch.Tensor, list_sigma: torch.Tensor) -> torch.Tensor: +def _get_large_sigma_mask( + list_u: torch.Tensor, list_sigma: torch.Tensor +) -> torch.Tensor: """Get the boolean mask for large sigma. Args: @@ -214,9 +232,7 @@ def _get_s1b_exponential( column_sigma = list_sigma.view(list_u.nelement(), 1) exponential = torch.exp( - -0.5 - * ((list_k**2 - 1.0) + 2.0 * column_u * (list_k + 1.0)) - / column_sigma**2 + -0.5 * ((list_k**2 - 1.0) + 2.0 * column_u * (list_k + 1.0)) / column_sigma**2 ) return exponential @@ -259,7 +275,9 @@ def _get_sigma_normalized_score_1a( list_sigma_normalized_score : the sigma x s1a scores, with shape [Nu]. """ exponential = _get_s1a_exponential(list_u, list_sigma, list_k) - list_sigma_square_times_score = _get_sigma_square_times_score_1_from_exponential(exponential, list_u, list_k) + list_sigma_square_times_score = _get_sigma_square_times_score_1_from_exponential( + exponential, list_u, list_k + ) list_normalized_score = list_sigma_square_times_score / list_sigma return list_normalized_score @@ -280,7 +298,9 @@ def _get_sigma_normalized_score_1b( list_sigma_normalized_score : the sigma x s1b scores, with shape [Nu]. """ exponential = _get_s1b_exponential(list_u, list_sigma, list_k) - list_sigma_square_times_score = _get_sigma_square_times_score_1_from_exponential(exponential, list_u, list_k) + list_sigma_square_times_score = _get_sigma_square_times_score_1_from_exponential( + exponential, list_u, list_k + ) list_normalized_score = list_sigma_square_times_score / list_sigma return list_normalized_score @@ -325,7 +345,14 @@ def _get_sigma_normalized_s2( # The sum is over Nk, leaving arrays of dimensions [Nu] z2 = exp_upk.sum(dim=1) + (g_exponential_combination * cos).sum(dim=1) - deriv_z2 = -2.0 * pi * ((upk * exp_upk).sum(dim=1) + (g * g_exponential_combination * sin).sum(dim=1)) + deriv_z2 = ( + -2.0 + * pi + * ( + (upk * exp_upk).sum(dim=1) + + (g * g_exponential_combination * sin).sum(dim=1) + ) + ) list_sigma_normalized_scores_s2 = list_sigma * deriv_z2 / z2 return list_sigma_normalized_scores_s2 diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py b/src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py index cc301218..fb5c34b3 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/train_diffusion.py @@ -1,4 +1,5 @@ """Entry point to train a diffusion model.""" + import argparse import logging import os @@ -8,20 +9,22 @@ import pytorch_lightning import pytorch_lightning as pl import yaml -from crystal_diffusion.callbacks.callback_loader import create_all_callbacks -from crystal_diffusion.data.diffusion.data_loader import ( + +from diffusion_for_multi_scale_molecular_dynamics.callbacks.callback_loader import \ + create_all_callbacks +from diffusion_for_multi_scale_molecular_dynamics.data.diffusion.data_loader import ( LammpsForDiffusionDataModule, LammpsLoaderParameters) -from crystal_diffusion.loggers.logger_loader import create_all_loggers -from crystal_diffusion.main_utils import (MetricResult, - get_crash_metric_result, - get_optimized_metric_name_and_mode, - load_and_backup_hyperparameters, - report_to_orion_if_on) -from crystal_diffusion.models.instantiate_diffusion_model import \ +from diffusion_for_multi_scale_molecular_dynamics.loggers.logger_loader import \ + create_all_loggers +from diffusion_for_multi_scale_molecular_dynamics.main_utils import ( + MetricResult, get_crash_metric_result, get_optimized_metric_name_and_mode, + load_and_backup_hyperparameters, report_to_orion_if_on) +from diffusion_for_multi_scale_molecular_dynamics.models.instantiate_diffusion_model import \ load_diffusion_model -from crystal_diffusion.utils.hp_utils import check_and_log_hp -from crystal_diffusion.utils.logging_utils import (log_exp_details, - setup_console_logger) +from diffusion_for_multi_scale_molecular_dynamics.utils.hp_utils import \ + check_and_log_hp +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import ( + log_exp_details, setup_console_logger) logger = logging.getLogger(__name__) @@ -35,20 +38,43 @@ def main(args: typing.Optional[typing.Any] = None): """ parser = argparse.ArgumentParser() # __TODO__ check you need all the following CLI parameters - parser.add_argument('--config', - help='config file with generic hyper-parameters, such as optimizer, ' - 'batch_size, ... - in yaml format') - parser.add_argument('--data', help='path to a LAMMPS data set', required=True) - parser.add_argument('--processed_datadir', help='path to the processed data directory', required=True) - parser.add_argument('--dataset_working_dir', help='path to the Datasets working directory. Defaults to None', - default=None) - parser.add_argument('--output', help='path to outputs - will store files here', required=True) - parser.add_argument('--disable-progressbar', action='store_true', - help='will disable the progressbar while going over the mini-batch') - parser.add_argument('--start-from-scratch', action='store_true', - help='will not load any existing saved model - even if present') - parser.add_argument('--accelerator', help='PL trainer accelerator. Defaults to auto.', default='auto') - parser.add_argument('--devices', default=1, help='pytorch-lightning devices kwarg. Defaults to 1.') + parser.add_argument( + "--config", + help="config file with generic hyper-parameters, such as optimizer, " + "batch_size, ... - in yaml format", + ) + parser.add_argument("--data", help="path to a LAMMPS data set", required=True) + parser.add_argument( + "--processed_datadir", + help="path to the processed data directory", + required=True, + ) + parser.add_argument( + "--dataset_working_dir", + help="path to the Datasets working directory. Defaults to None", + default=None, + ) + parser.add_argument( + "--output", help="path to outputs - will store files here", required=True + ) + parser.add_argument( + "--disable-progressbar", + action="store_true", + help="will disable the progressbar while going over the mini-batch", + ) + parser.add_argument( + "--start-from-scratch", + action="store_true", + help="will not load any existing saved model - even if present", + ) + parser.add_argument( + "--accelerator", + help="PL trainer accelerator. Defaults to auto.", + default="auto", + ) + parser.add_argument( + "--devices", default=1, help="pytorch-lightning devices kwarg. Defaults to 1." + ) args = parser.parse_args(args) if os.path.exists(args.output) and args.start_from_scratch: @@ -68,9 +94,14 @@ def main(args: typing.Optional[typing.Any] = None): output_dir = args.output - hyper_params = load_and_backup_hyperparameters(config_file_path=args.config, output_directory=output_dir) + hyper_params = load_and_backup_hyperparameters( + config_file_path=args.config, output_directory=output_dir + ) - logger.info("Input hyper-parameters:\n" + yaml.dump(hyper_params, allow_unicode=True, default_flow_style=False)) + logger.info( + "Input hyper-parameters:\n" + + yaml.dump(hyper_params, allow_unicode=True, default_flow_style=False) + ) run(args, output_dir, hyper_params) @@ -88,7 +119,7 @@ def run(args, output_dir, hyper_params): if hyper_params["seed"] is not None: pytorch_lightning.seed_everything(hyper_params["seed"]) - data_params = LammpsLoaderParameters(**hyper_params['data']) + data_params = LammpsLoaderParameters(**hyper_params["data"]) datamodule = LammpsForDiffusionDataModule( lammps_run_dir=args.data, @@ -100,13 +131,15 @@ def run(args, output_dir, hyper_params): model = load_diffusion_model(hyper_params) try: - metric_result = train(model=model, - datamodule=datamodule, - output=output_dir, - hyper_params=hyper_params, - use_progress_bar=not args.disable_progressbar, - accelerator=args.accelerator, - devices=args.devices) + metric_result = train( + model=model, + datamodule=datamodule, + output=output_dir, + hyper_params=hyper_params, + use_progress_bar=not args.disable_progressbar, + accelerator=args.accelerator, + devices=args.devices, + ) run_time_error = None except RuntimeError as err: run_time_error = err @@ -119,14 +152,15 @@ def run(args, output_dir, hyper_params): report_to_orion_if_on(metric_result, run_time_error) -def train(model, - datamodule, - output: str, - hyper_params: typing.Dict[typing.AnyStr, typing.Any], - use_progress_bar: bool, - accelerator=None, - devices=None - ): +def train( + model, + datamodule, + output: str, + hyper_params: typing.Dict[typing.AnyStr, typing.Any], + use_progress_bar: bool, + accelerator=None, + devices=None, +): """Train a model: main training loop implementation. Args: @@ -138,46 +172,50 @@ def train(model, accelerator: PL trainer accelerator devices: PL devices to use """ - check_and_log_hp(['max_epoch'], hyper_params) + check_and_log_hp(["max_epoch"], hyper_params) - callbacks_dict = create_all_callbacks(hyper_params, output, verbose=use_progress_bar) + callbacks_dict = create_all_callbacks( + hyper_params, output, verbose=use_progress_bar + ) pl_loggers = create_all_loggers(hyper_params, output) for pl_logger in pl_loggers: pl_logger.log_hyperparams(hyper_params) trainer = pl.Trainer( callbacks=list(callbacks_dict.values()), - max_epochs=hyper_params['max_epoch'], - log_every_n_steps=hyper_params.get('log_every_n_steps', None), - fast_dev_run=hyper_params.get('fast_dev_run', False), + max_epochs=hyper_params["max_epoch"], + log_every_n_steps=hyper_params.get("log_every_n_steps", None), + fast_dev_run=hyper_params.get("fast_dev_run", False), accelerator=accelerator, devices=devices, logger=pl_loggers, - gradient_clip_val=hyper_params.get('gradient_clipping', 0), - accumulate_grad_batches=hyper_params.get('accumulate_grad_batches', 1), + gradient_clip_val=hyper_params.get("gradient_clipping", 0), + accumulate_grad_batches=hyper_params.get("accumulate_grad_batches", 1), ) # Using the keyword ckpt_path="last" tells the trainer to resume from the last # checkpoint, or to start from scratch if none exists. - trainer.fit(model, datamodule=datamodule, ckpt_path='last') + trainer.fit(model, datamodule=datamodule, ckpt_path="last") # By convention, it is assumed that the metric to be reported is the early stopping metric. - if 'early_stopping' in callbacks_dict: - early_stopping = callbacks_dict['early_stopping'] + if "early_stopping" in callbacks_dict: + early_stopping = callbacks_dict["early_stopping"] best_value = float(early_stopping.best_score.cpu().numpy()) metric_name, mode = get_optimized_metric_name_and_mode(hyper_params) for pl_logger in pl_loggers: pl_logger.log_metrics({f"best_{metric_name}": best_value}) - metric_result = MetricResult(report=True, metric_name=metric_name, mode=mode, metric_value=best_value) + metric_result = MetricResult( + report=True, metric_name=metric_name, mode=mode, metric_value=best_value + ) else: metric_result = MetricResult(report=False) return metric_result -if __name__ == '__main__': +if __name__ == "__main__": # Uncomment the following in order to use Pycharm's Remote Debugging server, which allows to # launch python commands through a bash script (and through Orion!). VERY useful for debugging. # This requires a professional edition of Pycharm and installing the pydevd_pycharm package with pip. diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/basis_transformations.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/basis_transformations.py index 45d5f704..3eaeabf3 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/basis_transformations.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/basis_transformations.py @@ -26,7 +26,9 @@ def get_reciprocal_basis_vectors(basis_vectors: torch.Tensor) -> torch.Tensor: return reciprocal_basis_vectors -def get_positions_from_coordinates(relative_coordinates: torch.Tensor, basis_vectors: torch.Tensor) -> torch.Tensor: +def get_positions_from_coordinates( + relative_coordinates: torch.Tensor, basis_vectors: torch.Tensor +) -> torch.Tensor: """Get cartesian positions from relative coordinates. This method computes the positions in Euclidean space given the unitless coordinates and the basis vectors @@ -50,8 +52,9 @@ def get_positions_from_coordinates(relative_coordinates: torch.Tensor, basis_vec return cartesian_positions -def get_relative_coordinates_from_cartesian_positions(cartesian_positions: torch.Tensor, - reciprocal_basis_vectors: torch.Tensor) -> torch.Tensor: +def get_relative_coordinates_from_cartesian_positions( + cartesian_positions: torch.Tensor, reciprocal_basis_vectors: torch.Tensor +) -> torch.Tensor: """Get relative coordinates from cartesian positions. This method computes the relative coordinates from the positions in Euclidean space and the reciprocal @@ -84,7 +87,9 @@ def get_relative_coordinates_from_cartesian_positions(cartesian_positions: torch return relative_coordinates -def map_relative_coordinates_to_unit_cell(relative_coordinates: torch.Tensor) -> torch.Tensor: +def map_relative_coordinates_to_unit_cell( + relative_coordinates: torch.Tensor, +) -> torch.Tensor: """Map relative coordinates back to unit cell. The function torch.remainder does not always bring back the relative coordinates in the range [0, 1). @@ -105,5 +110,5 @@ def map_relative_coordinates_to_unit_cell(relative_coordinates: torch.Tensor) -> normalized_relative_coordinates: relative coordinates in the unit cell, ie, in the range [0, 1). """ normalized_relative_coordinates = torch.remainder(relative_coordinates, 1.0) - normalized_relative_coordinates[normalized_relative_coordinates == 1.] = 0. + normalized_relative_coordinates[normalized_relative_coordinates == 1.0] = 0.0 return normalized_relative_coordinates diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/configuration_parsing.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/configuration_parsing.py index 5acccff0..23ab4e2a 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/configuration_parsing.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/configuration_parsing.py @@ -2,9 +2,9 @@ from typing import Any, Dict -def create_parameters_from_configuration_dictionary(configuration: Dict[str, Any], - identifier: str, - options: Dict[str, dataclass]) -> dataclass: +def create_parameters_from_configuration_dictionary( + configuration: Dict[str, Any], identifier: str, options: Dict[str, dataclass] +) -> dataclass: """Create Parameters from Configuration Dictionary. This method will instantiate a dataclass container describing configuration parameters @@ -21,13 +21,15 @@ def create_parameters_from_configuration_dictionary(configuration: Dict[str, Any parameters: a dataclass object of the appropriate type, instantiated with the content of the input configuration """ - assert identifier in configuration.keys(), \ - f"The identifier field '{identifier}' is missing from the configuration dictionary." + assert ( + identifier in configuration.keys() + ), f"The identifier field '{identifier}' is missing from the configuration dictionary." option_id = configuration[identifier] - assert option_id in options.keys(), \ - f"The option field '{option_id}' is missing from the options dictionary." + assert ( + option_id in options.keys() + ), f"The option field '{option_id}' is missing from the options dictionary." parameters = options[option_id](**configuration) return parameters diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/file_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/file_utils.py index ead16538..07f95357 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/file_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/file_utils.py @@ -14,5 +14,5 @@ def rsync_folder(source, target): # pragma: no cover if not os.path.exists(target): os.makedirs(target) - logger.info('rsyincing {} to {}'.format(source, target)) + logger.info("rsyincing {} to {}".format(source, target)) subprocess.check_call(["rsync", "-avzq", source, target]) diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/hp_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/hp_utils.py index ed2433c3..b3c11862 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/hp_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/hp_utils.py @@ -31,11 +31,11 @@ def check_hp(names, hps, allow_extra=True): msgs = [] if len(missing) > 0: - msgs.append(f'please add the missing hyper-parameters: {missing}') + msgs.append(f"please add the missing hyper-parameters: {missing}") if len(extra) > 0 and not allow_extra: - msgs.append(f'please remove the extra hyper-parameters: {extra}') + msgs.append(f"please remove the extra hyper-parameters: {extra}") if len(msgs) > 0: - raise ValueError('\n'.join(msgs)) + raise ValueError("\n".join(msgs)) def log_hp(names, hps): # pragma: no cover @@ -47,4 +47,4 @@ def log_hp(names, hps): # pragma: no cover """ for name in sorted(names): logger.info('\thp "{}" => "{}"'.format(name, hps[name])) - logger.info('\n') + logger.info("\n") diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/logging_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/logging_utils.py index de6b3ceb..b1a5ecfe 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/logging_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/logging_utils.py @@ -26,7 +26,9 @@ def setup_console_logger(experiment_dir: str): """ logging.captureWarnings(capture=True) - logging_format = "%(asctime)s - %(filename)s:%(lineno)s - %(funcName)20s() - %(message)s" + logging_format = ( + "%(asctime)s - %(filename)s:%(lineno)s - %(funcName)20s() - %(message)s" + ) root = logging.getLogger() root.setLevel(logging.INFO) @@ -54,7 +56,9 @@ def setup_analysis_logger(): root = logging.getLogger() root.setLevel(logging.INFO) - analysis_logging_format = "%(asctime)s - %(filename)s:%(lineno)s - %(funcName)20s() - %(message)s" + analysis_logging_format = ( + "%(asctime)s - %(filename)s:%(lineno)s - %(funcName)20s() - %(message)s" + ) formatter = logging.Formatter(analysis_logging_format) stream_handler = logging.StreamHandler(stream=sys.stdout) @@ -84,7 +88,7 @@ def write(self, message): Args: message: (str) message to print. """ - if message != '\n': + if message != "\n": self.printer(message) def flush(self): @@ -98,14 +102,14 @@ def get_git_hash(script_location): :param script_location: (str) path to the script inside the git repos we want to find. :return: (str) the git hash for the repository of the provided script. """ - if not script_location.endswith('.py'): - raise ValueError('script_location should point to a python script') + if not script_location.endswith(".py"): + raise ValueError("script_location should point to a python script") repo_folder = os.path.dirname(script_location) try: repo = Repo(repo_folder, search_parent_directories=True) commit_hash = repo.head.commit except (InvalidGitRepositoryError, ValueError): - commit_hash = 'git repository not found' + commit_hash = "git repository not found" return commit_hash @@ -118,8 +122,14 @@ def log_exp_details(script_location, args): git_hash = get_git_hash(script_location) hostname = socket.gethostname() dependencies = freeze.freeze() - details = "\nhostname: {}\ngit code hash: {}\ndata folder: {}\ndata folder (abs): {}\n\n" \ - "dependencies:\n{}".format( - hostname, git_hash, args.data, os.path.abspath(args.data), - '\n'.join(dependencies)) - logger.info('Experiment info:' + details + '\n') + details = ( + "\nhostname: {}\ngit code hash: {}\ndata folder: {}\ndata folder (abs): {}\n\n" + "dependencies:\n{}".format( + hostname, + git_hash, + args.data, + os.path.abspath(args.data), + "\n".join(dependencies), + ) + ) + logger.info("Experiment info:" + details + "\n") diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py index 7fdd16bc..5b652d3d 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/neighbors.py @@ -4,31 +4,36 @@ for positions in the unit cell of a periodic structure. The aim is to do this efficiently on the GPU without CPU-GPU communications. """ + import itertools from collections import namedtuple import numpy as np import torch -from crystal_diffusion.utils.basis_transformations import \ - get_positions_from_coordinates from pykeops.torch import LazyTensor +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + get_positions_from_coordinates + INDEX_PADDING_VALUE = -1 POSITION_PADDING_VALUE = np.NaN -AdjacencyInfo = namedtuple(typename="AdjacencyInfo", - field_names=["adjacency_matrix", # Adjacency matrix, in COO format - "shifts", # lattice vector shifts when computing neighbor distances - "edge_batch_indices", # original batch index of each edge. - "node_batch_indices", # original batch index of each node. - "number_of_edges", # number of edges in each batch element. - ]) +AdjacencyInfo = namedtuple( + typename="AdjacencyInfo", + field_names=[ + "adjacency_matrix", # Adjacency matrix, in COO format + "shifts", # lattice vector shifts when computing neighbor distances + "edge_batch_indices", # original batch index of each edge. + "node_batch_indices", # original batch index of each node. + "number_of_edges", # number of edges in each batch element. + ], +) -def get_periodic_adjacency_information(cartesian_positions: torch.Tensor, - basis_vectors: torch.Tensor, - radial_cutoff: float) -> AdjacencyInfo: +def get_periodic_adjacency_information( + cartesian_positions: torch.Tensor, basis_vectors: torch.Tensor, radial_cutoff: float +) -> AdjacencyInfo: """Get periodic adjacency information. This method computes all the neighbors within the radial cutoff, accounting for periodicity, and returns @@ -73,15 +78,23 @@ def get_periodic_adjacency_information(cartesian_positions: torch.Tensor, * the batch indices for each node * the number of edges for each structure in the batch. """ - assert len(cartesian_positions.shape) == 3, "Wrong number of dimensions for relative_coordinates" + assert ( + len(cartesian_positions.shape) == 3 + ), "Wrong number of dimensions for relative_coordinates" assert len(basis_vectors.shape) == 3, "Wrong number of dimensions for basis_vectors" spatial_dimension = 3 # We define this to avoid "magic numbers" in the code below. batch_size, max_natom, spatial_dimension_ = cartesian_positions.shape - assert spatial_dimension_ == spatial_dimension, "Wrong spatial dimension for relative_coordinates " - assert basis_vectors.shape == (batch_size, spatial_dimension, spatial_dimension), "Wrong shape for basis vectors" + assert ( + spatial_dimension_ == spatial_dimension + ), "Wrong spatial dimension for relative_coordinates " + assert basis_vectors.shape == ( + batch_size, + spatial_dimension, + spatial_dimension, + ), "Wrong shape for basis vectors" - assert radial_cutoff > 0., "The radial cutoff should be greater than zero" + assert radial_cutoff > 0.0, "The radial cutoff should be greater than zero" device = cartesian_positions.device @@ -89,19 +102,26 @@ def get_periodic_adjacency_information(cartesian_positions: torch.Tensor, zero = torch.tensor(0.0).to(device) # Check that the radial cutoff does not lead to possible neighbors beyond the first shell. - shortest_cell_crossing_distances = _get_shortest_distance_that_crosses_unit_cell(basis_vectors) - assert torch.all(shortest_cell_crossing_distances > radial_cutoff), \ - ("The radial cutoff is so large that neighbors could be located " - "beyond the first shell of periodic unit cell images.") + shortest_cell_crossing_distances = _get_shortest_distance_that_crosses_unit_cell( + basis_vectors + ) + assert torch.all(shortest_cell_crossing_distances > radial_cutoff), ( + "The radial cutoff is so large that neighbors could be located " + "beyond the first shell of periodic unit cell images." + ) # The relative coordinates lattice vectors have dimensions [number of lattice vectors, spatial_dimension] - relative_lattice_vectors = _get_relative_coordinates_lattice_vectors(number_of_shells=1).to(device) + relative_lattice_vectors = _get_relative_coordinates_lattice_vectors( + number_of_shells=1 + ).to(device) number_of_relative_lattice_vectors = len(relative_lattice_vectors) # Repeat the relative lattice vectors along the batch dimension; the basis vectors could potentially be # different for every batch element. batched_relative_lattice_vectors = relative_lattice_vectors.repeat(batch_size, 1, 1) - lattice_vectors = get_positions_from_coordinates(batched_relative_lattice_vectors, basis_vectors) + lattice_vectors = get_positions_from_coordinates( + batched_relative_lattice_vectors, basis_vectors + ) # The shifted_positions are composed of the positions, which are located within the unit cell, shifted by # the various lattice vectors. @@ -130,9 +150,18 @@ def get_periodic_adjacency_information(cartesian_positions: torch.Tensor, # # From the point of view of KeOps, the first 2 dimensions are "batch dimensions". The KeOps 'virtual' dimensions # are dim 2 and 3, which both corresponds to 'max_natom'. - x_i = LazyTensor(cartesian_positions.view(batch_size, 1, max_natom, 1, spatial_dimension)) - x_j = LazyTensor(shifted_positions.view(batch_size, number_of_relative_lattice_vectors, 1, - max_natom, spatial_dimension)) + x_i = LazyTensor( + cartesian_positions.view(batch_size, 1, max_natom, 1, spatial_dimension) + ) + x_j = LazyTensor( + shifted_positions.view( + batch_size, + number_of_relative_lattice_vectors, + 1, + max_natom, + spatial_dimension, + ) + ) # Symbolic matrix of squared distances # Dimensions: [batch_size, number_of_relative_lattice_vectors, max_natom, max_natom] @@ -141,20 +170,26 @@ def get_periodic_adjacency_information(cartesian_positions: torch.Tensor, # Identify the number of neighbors within the cutoff distance for every atom. # This triggers a real computation, which involves a 'compilation' the first time around. # This compilation time is only paid once per code execution. - max_k_array = (d_ij <= radial_cutoff**2).sum_reduction(dim=3) # sum on "j", the second 'virtual' dimension. + max_k_array = (d_ij <= radial_cutoff**2).sum_reduction( + dim=3 + ) # sum on "j", the second 'virtual' dimension. # This is the maximum number of neighbors for any atom in any structure in the batch. # Going forward, there is no need to look beyond this number of neighbors. max_number_of_neighbors = int(max_k_array.max()) # Use KeOps KNN functionalities to find neighbors and their indices. - squared_distances, dst_indices = d_ij.Kmin_argKmin(K=max_number_of_neighbors, dim=3) # find neighbors along "j" + squared_distances, dst_indices = d_ij.Kmin_argKmin( + K=max_number_of_neighbors, dim=3 + ) # find neighbors along "j" # Dimensions: [batch_size, number_of_relative_lattice_vectors, max_natom, max_number_of_neighbors] # The 'dst_indices' array corresponds to KeOps first 'virtual' dimension (the "i" dimension). This goes from # 0 to max_atom - 1 and correspond to atom indices (specifically, destination indices!). # Identify neighbors within the radial_cutoff, but avoiding self. - valid_neighbor_mask = torch.logical_and(zero < squared_distances, squared_distances <= radial_cutoff**2) + valid_neighbor_mask = torch.logical_and( + zero < squared_distances, squared_distances <= radial_cutoff**2 + ) # Dimensions: [batch_size, number_of_relative_lattice_vectors, max_natom, max_number_of_neighbors] # Combine all the non-batch dimensions to obtain the maximum number of edges per batch element @@ -174,19 +209,22 @@ def get_periodic_adjacency_information(cartesian_positions: torch.Tensor, adjacency_matrix_coo_format = torch.stack([source_indices, destination_indices]) - lattice_vector_shifts = _get_vectors_from_multiple_indices(lattice_vectors, - edge_batch_indices, - lattice_vector_indices) + lattice_vector_shifts = _get_vectors_from_multiple_indices( + lattice_vectors, edge_batch_indices, lattice_vector_indices + ) - return AdjacencyInfo(adjacency_matrix=adjacency_matrix_coo_format, - shifts=lattice_vector_shifts, - edge_batch_indices=edge_batch_indices, - node_batch_indices=torch.repeat_interleave(torch.arange(batch_size), max_natom), - number_of_edges=number_of_edges) + return AdjacencyInfo( + adjacency_matrix=adjacency_matrix_coo_format, + shifts=lattice_vector_shifts, + edge_batch_indices=edge_batch_indices, + node_batch_indices=torch.repeat_interleave(torch.arange(batch_size), max_natom), + number_of_edges=number_of_edges, + ) -def _get_relative_coordinates_lattice_vectors(number_of_shells: int = 1, - spatial_dimension: int = 3) -> torch.Tensor: +def _get_relative_coordinates_lattice_vectors( + number_of_shells: int = 1, spatial_dimension: int = 3 +) -> torch.Tensor: """Get relative coordinates lattice vectors. Get all the lattice vectors in relative coordinates from -number_of_shells to +number_of_shells, @@ -199,12 +237,16 @@ def _get_relative_coordinates_lattice_vectors(number_of_shells: int = 1, list_relative_lattice_vectors : all the lattice vectors in relative coordinates (ie, integers). """ shifts = range(-number_of_shells, number_of_shells + 1) - list_relative_lattice_vectors = 1.0 * torch.tensor(list(itertools.product(shifts, repeat=spatial_dimension))) + list_relative_lattice_vectors = 1.0 * torch.tensor( + list(itertools.product(shifts, repeat=spatial_dimension)) + ) return list_relative_lattice_vectors -def _get_shifted_positions(cartesian_positions: torch.Tensor, lattice_vectors: torch.Tensor) -> torch.Tensor: +def _get_shifted_positions( + cartesian_positions: torch.Tensor, lattice_vectors: torch.Tensor +) -> torch.Tensor: """Get shifted positions. Args: @@ -217,11 +259,15 @@ def _get_shifted_positions(cartesian_positions: torch.Tensor, lattice_vectors: t shifted_positions: the positions within the unit cell, shifted by the lattice vectors. Dimension [batch_size, number of relative lattice vectors, max_number_of_atoms, 3]. """ - shifted_positions = cartesian_positions[:, None, :, :] + lattice_vectors[:, :, None, :] + shifted_positions = ( + cartesian_positions[:, None, :, :] + lattice_vectors[:, :, None, :] + ) return shifted_positions -def _get_shortest_distance_that_crosses_unit_cell(basis_vectors: torch.Tensor) -> torch.Tensor: +def _get_shortest_distance_that_crosses_unit_cell( + basis_vectors: torch.Tensor, +) -> torch.Tensor: """Get the shortest distance that crosses unit cell. This method computes the shortest distance that crosses the unit cell. @@ -250,8 +296,12 @@ def _get_shortest_distance_that_crosses_unit_cell(basis_vectors: torch.Tensor) - # This idea must be repeated for the three pairs of planes bounding the unit cell. spatial_dimension = 3 assert len(basis_vectors.shape) == 3, "basis_vectors has wrong shape." - assert basis_vectors.shape[1] == spatial_dimension, "Basis vectors in wrong spatial dimension." - assert basis_vectors.shape[2] == spatial_dimension, "Basis vectors in wrong spatial dimension." + assert ( + basis_vectors.shape[1] == spatial_dimension + ), "Basis vectors in wrong spatial dimension." + assert ( + basis_vectors.shape[2] == spatial_dimension + ), "Basis vectors in wrong spatial dimension." a1 = basis_vectors[:, 0, :] a2 = basis_vectors[:, 1, :] a3 = basis_vectors[:, 2, :] @@ -266,14 +316,16 @@ def _get_shortest_distance_that_crosses_unit_cell(basis_vectors: torch.Tensor) - distances_13 = cell_volume / torch.linalg.norm(cross_product_13, dim=1) distances_23 = cell_volume / torch.linalg.norm(cross_product_23, dim=1) - distances = torch.stack([distances_12, distances_13, distances_23], dim=1).min(dim=1).values + distances = ( + torch.stack([distances_12, distances_13, distances_23], dim=1).min(dim=1).values + ) return distances -def _get_vectors_from_multiple_indices(vectors: torch.Tensor, - batch_indices: torch.Tensor, - vector_indices: torch.Tensor) -> torch.Tensor: +def _get_vectors_from_multiple_indices( + vectors: torch.Tensor, batch_indices: torch.Tensor, vector_indices: torch.Tensor +) -> torch.Tensor: """Get vectors from multiple indices. Extract vectors in higher dimensional tensor from multiple indices array. @@ -298,11 +350,13 @@ def _get_vectors_from_multiple_indices(vectors: torch.Tensor, return vectors[batch_indices, vector_indices] -def shift_adjacency_matrix_indices_for_graph_batching(adjacency_matrix: torch.Tensor, - num_edges: torch.Tensor, - number_of_atoms: int) -> torch.Tensor: +def shift_adjacency_matrix_indices_for_graph_batching( + adjacency_matrix: torch.Tensor, num_edges: torch.Tensor, number_of_atoms: int +) -> torch.Tensor: """Shift the node indices in the adjacency matrix for graph batching.""" - index_shifts = torch.arange(len(num_edges)).to(adjacency_matrix.device) * number_of_atoms + index_shifts = ( + torch.arange(len(num_edges)).to(adjacency_matrix.device) * number_of_atoms + ) adj_shifts = torch.repeat_interleave(index_shifts, num_edges).repeat(2, 1) return adjacency_matrix + adj_shifts diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/ovito_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/ovito_utils.py index 16dc76b5..df72a18e 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/ovito_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/ovito_utils.py @@ -5,6 +5,7 @@ session state file will already be prepopulated with some common pipeline elements. """ + from pathlib import Path import numpy as np diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/sample_trajectory.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/sample_trajectory.py index c642a235..82407554 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/sample_trajectory.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/sample_trajectory.py @@ -22,7 +22,7 @@ def reset(self): def record_unit_cell(self, unit_cell: torch.Tensor): """Record unit cell.""" - self.data['unit_cell'] = unit_cell.detach().cpu() + self.data["unit_cell"] = unit_cell.detach().cpu() def standardize_data(self, data: Dict[AnyStr, Any]) -> Dict[AnyStr, Any]: """Method to transform the recorded data to a standard form.""" @@ -31,7 +31,7 @@ def standardize_data(self, data: Dict[AnyStr, Any]) -> Dict[AnyStr, Any]: def write_to_pickle(self, path_to_pickle: str): """Write standardized data to pickle file.""" standard_data = self.standardize_data(self.data) - with open(path_to_pickle, 'wb') as fd: + with open(path_to_pickle, "wb") as fd: torch.save(standard_data, fd) @@ -42,26 +42,34 @@ class ODESampleTrajectory(SampleTrajectory): an artifact that can then be analyzed off-line. """ - def record_ode_solution(self, times: torch.Tensor, sigmas: torch.Tensor, - relative_coordinates: torch.Tensor, normalized_scores: torch.Tensor, - stats: Dict, status: torch.Tensor): + def record_ode_solution( + self, + times: torch.Tensor, + sigmas: torch.Tensor, + relative_coordinates: torch.Tensor, + normalized_scores: torch.Tensor, + stats: Dict, + status: torch.Tensor, + ): """Record ODE solution information.""" - self.data['time'].append(times) - self.data['sigma'].append(sigmas) - self.data['relative_coordinates'].append(relative_coordinates) - self.data['normalized_scores'].append(normalized_scores) - self.data['stats'].append(stats) - self.data['status'].append(status) + self.data["time"].append(times) + self.data["sigma"].append(sigmas) + self.data["relative_coordinates"].append(relative_coordinates) + self.data["normalized_scores"].append(normalized_scores) + self.data["stats"].append(stats) + self.data["status"].append(status) def standardize_data(self, data: Dict[AnyStr, Any]) -> Dict[AnyStr, Any]: """Method to transform the recorded data to a standard form.""" - extra_fields = ['stats', 'status'] - standardized_data = dict(unit_cell=data['unit_cell'], - time=data['time'][0], - sigma=data['sigma'][0], - relative_coordinates=data['relative_coordinates'][0], - normalized_scores=data['normalized_scores'][0], - extra={key: data[key][0] for key in extra_fields}) + extra_fields = ["stats", "status"] + standardized_data = dict( + unit_cell=data["unit_cell"], + time=data["time"][0], + sigma=data["sigma"][0], + relative_coordinates=data["relative_coordinates"][0], + normalized_scores=data["normalized_scores"][0], + extra={key: data[key][0] for key in extra_fields}, + ) return standardized_data @@ -72,22 +80,28 @@ class SDESampleTrajectory(SampleTrajectory): an artifact that can then be analyzed off-line. """ - def record_sde_solution(self, times: torch.Tensor, sigmas: torch.Tensor, - relative_coordinates: torch.Tensor, normalized_scores: torch.Tensor): + def record_sde_solution( + self, + times: torch.Tensor, + sigmas: torch.Tensor, + relative_coordinates: torch.Tensor, + normalized_scores: torch.Tensor, + ): """Record ODE solution information.""" - self.data['time'].append(times) - self.data['sigma'].append(sigmas) - self.data['relative_coordinates'].append(relative_coordinates) - self.data['normalized_scores'].append(normalized_scores) + self.data["time"].append(times) + self.data["sigma"].append(sigmas) + self.data["relative_coordinates"].append(relative_coordinates) + self.data["normalized_scores"].append(normalized_scores) def standardize_data(self, data: Dict[AnyStr, Any]) -> Dict[AnyStr, Any]: """Method to transform the recorded data to a standard form.""" - standardized_data = dict(unit_cell=data['unit_cell'], - time=data['time'][0], - sigma=data['sigma'][0], - relative_coordinates=data['relative_coordinates'][0], - normalized_scores=data['normalized_scores'][0], - ) + standardized_data = dict( + unit_cell=data["unit_cell"], + time=data["time"][0], + sigma=data["sigma"][0], + relative_coordinates=data["relative_coordinates"][0], + normalized_scores=data["normalized_scores"][0], + ) return standardized_data @@ -98,9 +112,15 @@ def record_unit_cell(self, unit_cell: torch.Tensor): """No Op.""" return - def record_ode_solution(self, times: torch.Tensor, sigmas: torch.Tensor, - relative_coordinates: torch.Tensor, normalized_scores: torch.Tensor, - stats: Dict, status: torch.Tensor): + def record_ode_solution( + self, + times: torch.Tensor, + sigmas: torch.Tensor, + relative_coordinates: torch.Tensor, + normalized_scores: torch.Tensor, + stats: Dict, + status: torch.Tensor, + ): """No Op.""" return @@ -115,43 +135,70 @@ class PredictorCorrectorSampleTrajectory(SampleTrajectory): This class aims to record all details of the predictor-corrector diffusion sampling process. The goal is to produce an artifact that can then be analyzed off-line. """ - def record_predictor_step(self, i_index: int, time: float, sigma: float, - x_i: torch.Tensor, x_im1: torch.Tensor, scores: torch.Tensor): + + def record_predictor_step( + self, + i_index: int, + time: float, + sigma: float, + x_i: torch.Tensor, + x_im1: torch.Tensor, + scores: torch.Tensor, + ): """Record predictor step.""" - self.data['predictor_i_index'].append(i_index) - self.data['predictor_time'].append(time) - self.data['predictor_sigma'].append(sigma) - self.data['predictor_x_i'].append(x_i.detach().cpu()) - self.data['predictor_x_im1'].append(x_im1.detach().cpu()) - self.data['predictor_scores'].append(scores.detach().cpu()) - - def record_corrector_step(self, i_index: int, time: float, sigma: float, - x_i: torch.Tensor, corrected_x_i: torch.Tensor, scores: torch.Tensor): + self.data["predictor_i_index"].append(i_index) + self.data["predictor_time"].append(time) + self.data["predictor_sigma"].append(sigma) + self.data["predictor_x_i"].append(x_i.detach().cpu()) + self.data["predictor_x_im1"].append(x_im1.detach().cpu()) + self.data["predictor_scores"].append(scores.detach().cpu()) + + def record_corrector_step( + self, + i_index: int, + time: float, + sigma: float, + x_i: torch.Tensor, + corrected_x_i: torch.Tensor, + scores: torch.Tensor, + ): """Record corrector step.""" - self.data['corrector_i_index'].append(i_index) - self.data['corrector_time'].append(time) - self.data['corrector_sigma'].append(sigma) - self.data['corrector_x_i'].append(x_i.detach().cpu()) - self.data['corrector_corrected_x_i'].append(corrected_x_i.detach().cpu()) - self.data['corrector_scores'].append(scores.detach().cpu()) + self.data["corrector_i_index"].append(i_index) + self.data["corrector_time"].append(time) + self.data["corrector_sigma"].append(sigma) + self.data["corrector_x_i"].append(x_i.detach().cpu()) + self.data["corrector_corrected_x_i"].append(corrected_x_i.detach().cpu()) + self.data["corrector_scores"].append(scores.detach().cpu()) def standardize_data(self, data: Dict[AnyStr, Any]) -> Dict[AnyStr, Any]: """Method to transform the recorded data to a standard form.""" - predictor_relative_coordinates = einops.rearrange(torch.stack(data['predictor_x_i']), - "t b n d -> b t n d") - predictor_normalized_scores = einops.rearrange(torch.stack(data['predictor_scores']), - "t b n d -> b t n d") - - extra_fields = ['predictor_i_index', 'predictor_x_i', 'predictor_x_im1', - 'corrector_i_index', 'corrector_time', 'corrector_sigma', - 'corrector_x_i', 'corrector_corrected_x_i', 'corrector_scores'] - - standardized_data = dict(unit_cell=data['unit_cell'], - time=torch.tensor(data['predictor_time']), - sigma=torch.tensor(data['predictor_sigma']), - relative_coordinates=predictor_relative_coordinates, - normalized_scores=predictor_normalized_scores, - extra={key: data[key] for key in extra_fields}) + predictor_relative_coordinates = einops.rearrange( + torch.stack(data["predictor_x_i"]), "t b n d -> b t n d" + ) + predictor_normalized_scores = einops.rearrange( + torch.stack(data["predictor_scores"]), "t b n d -> b t n d" + ) + + extra_fields = [ + "predictor_i_index", + "predictor_x_i", + "predictor_x_im1", + "corrector_i_index", + "corrector_time", + "corrector_sigma", + "corrector_x_i", + "corrector_corrected_x_i", + "corrector_scores", + ] + + standardized_data = dict( + unit_cell=data["unit_cell"], + time=torch.tensor(data["predictor_time"]), + sigma=torch.tensor(data["predictor_sigma"]), + relative_coordinates=predictor_relative_coordinates, + normalized_scores=predictor_normalized_scores, + extra={key: data[key] for key in extra_fields}, + ) return standardized_data @@ -162,13 +209,27 @@ def record_unit_cell(self, unit_cell: torch.Tensor): """No Op.""" return - def record_predictor_step(self, i_index: int, time: float, sigma: float, - x_i: torch.Tensor, x_im1: torch.Tensor, scores: torch.Tensor): + def record_predictor_step( + self, + i_index: int, + time: float, + sigma: float, + x_i: torch.Tensor, + x_im1: torch.Tensor, + scores: torch.Tensor, + ): """No Op.""" return - def record_corrector_step(self, i_index: int, time: float, sigma: float, - x_i: torch.Tensor, corrected_x_i: torch.Tensor, scores: torch.Tensor): + def record_corrector_step( + self, + i_index: int, + time: float, + sigma: float, + x_i: torch.Tensor, + corrected_x_i: torch.Tensor, + scores: torch.Tensor, + ): """No Op.""" return diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py index 7426bff3..0f74f229 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/structure_utils.py @@ -2,14 +2,17 @@ import numpy as np import torch -from crystal_diffusion.utils.neighbors import ( - _get_relative_coordinates_lattice_vectors, _get_shifted_positions, - get_periodic_adjacency_information, get_positions_from_coordinates) from pykeops.torch import LazyTensor from pymatgen.core import Lattice, Structure +from diffusion_for_multi_scale_molecular_dynamics.utils.neighbors import ( + _get_relative_coordinates_lattice_vectors, _get_shifted_positions, + get_periodic_adjacency_information, get_positions_from_coordinates) + -def create_structure(basis_vectors: np.ndarray, relative_coordinates: np.ndarray, species: List[str]) -> Structure: +def create_structure( + basis_vectors: np.ndarray, relative_coordinates: np.ndarray, species: List[str] +) -> Structure: """Create structure. A utility method to convert various arrays in to a pymatgen structure. @@ -24,15 +27,20 @@ def create_structure(basis_vectors: np.ndarray, relative_coordinates: np.ndarray """ lattice = Lattice(matrix=basis_vectors, pbc=(True, True, True)) - structure = Structure(lattice=lattice, - species=species, - coords=relative_coordinates, - coords_are_cartesian=False) + structure = Structure( + lattice=lattice, + species=species, + coords=relative_coordinates, + coords_are_cartesian=False, + ) return structure -def compute_distances_in_batch(cartesian_positions: torch.Tensor, unit_cell: torch.Tensor, max_distance: float, - ) -> torch.Tensor: +def compute_distances_in_batch( + cartesian_positions: torch.Tensor, + unit_cell: torch.Tensor, + max_distance: float, +) -> torch.Tensor: """Compute distances between atoms in a batch up to a cutoff distance. Args: @@ -52,23 +60,35 @@ def compute_distances_in_batch(cartesian_positions: torch.Tensor, unit_cell: tor zero = torch.tensor(0.0).to(device) # The relative coordinates lattice vectors have dimensions [number of lattice vectors, spatial_dimension] - relative_lattice_vectors = _get_relative_coordinates_lattice_vectors(number_of_shells=1, - spatial_dimension=spatial_dimension).to(device) + relative_lattice_vectors = _get_relative_coordinates_lattice_vectors( + number_of_shells=1, spatial_dimension=spatial_dimension + ).to(device) number_of_relative_lattice_vectors = len(relative_lattice_vectors) # Repeat the relative lattice vectors along the batch dimension; the basis vectors could potentially be # different for every batch element. batched_relative_lattice_vectors = relative_lattice_vectors.repeat(batch_size, 1, 1) - lattice_vectors = get_positions_from_coordinates(batched_relative_lattice_vectors, unit_cell) + lattice_vectors = get_positions_from_coordinates( + batched_relative_lattice_vectors, unit_cell + ) # The shifted_positions are composed of the positions, which are located within the unit cell, shifted by # the various lattice vectors. # Dimension [batch_size, number of relative lattice vectors, max_number_of_atoms, spatial_dimension]. shifted_positions = _get_shifted_positions(cartesian_positions, lattice_vectors) # KeOps will be used to compute the distance matrix, |p_i - p_j |^2, without overflowing memory. - x_i = LazyTensor(cartesian_positions.view(batch_size, 1, max_natom, 1, spatial_dimension)) - x_j = LazyTensor(shifted_positions.view(batch_size, number_of_relative_lattice_vectors, 1, - max_natom, spatial_dimension)) + x_i = LazyTensor( + cartesian_positions.view(batch_size, 1, max_natom, 1, spatial_dimension) + ) + x_j = LazyTensor( + shifted_positions.view( + batch_size, + number_of_relative_lattice_vectors, + 1, + max_natom, + spatial_dimension, + ) + ) # Symbolic matrix of squared distances d_ij = ((x_i - x_j) ** 2).sum(dim=4) # sum on the spatial_dimension variable. @@ -76,24 +96,32 @@ def compute_distances_in_batch(cartesian_positions: torch.Tensor, unit_cell: tor # Identify the number of neighbors within the cutoff distance for every atom. # This triggers a real computation, which involves a 'compilation' the first time around. # This compilation time is only paid once per code execution. - max_k_array = (d_ij <= radial_cutoff ** 2).sum_reduction(dim=3) # sum on "j", the second 'virtual' dimension. + max_k_array = (d_ij <= radial_cutoff**2).sum_reduction( + dim=3 + ) # sum on "j", the second 'virtual' dimension. # This is the maximum number of neighbors for any atom in any structure in the batch. # Going forward, there is no need to look beyond this number of neighbors. max_number_of_neighbors = int(max_k_array.max()) # Use KeOps KNN functionalities to find neighbors and their indices. - squared_distances, dst_indices = d_ij.Kmin_argKmin(K=max_number_of_neighbors, dim=3) # find neighbors along "j" + squared_distances, dst_indices = d_ij.Kmin_argKmin( + K=max_number_of_neighbors, dim=3 + ) # find neighbors along "j" # Dimensions: [batch_size, number_of_relative_lattice_vectors, max_natom, max_number_of_neighbors] # The 'dst_indices' array corresponds to KeOps first 'virtual' dimension (the "i" dimension). This goes from # 0 to max_atom - 1 and correspond to atom indices (specifically, destination indices!). distances = torch.sqrt(squared_distances.flatten()) # Identify neighbors within the radial_cutoff, but avoiding self. - valid_neighbor_mask = torch.logical_and(zero < distances, distances <= radial_cutoff) + valid_neighbor_mask = torch.logical_and( + zero < distances, distances <= radial_cutoff + ) return distances[valid_neighbor_mask] -def get_orthogonal_basis_vectors(batch_size: int, cell_dimensions: List[float]) -> torch.Tensor: +def get_orthogonal_basis_vectors( + batch_size: int, cell_dimensions: List[float] +) -> torch.Tensor: """Get orthogonal basis vectors. Args: @@ -103,13 +131,19 @@ def get_orthogonal_basis_vectors(batch_size: int, cell_dimensions: List[float]) Returns: basis_vectors: a diagonal matrix with the dimensions along the diagonal. """ - basis_vectors = torch.diag(torch.Tensor(cell_dimensions)).unsqueeze(0).repeat(batch_size, 1, 1) + basis_vectors = ( + torch.diag(torch.Tensor(cell_dimensions)).unsqueeze(0).repeat(batch_size, 1, 1) + ) return basis_vectors -def compute_distances(cartesian_positions: torch.Tensor, basis_vectors: torch.Tensor, max_distance: float): +def compute_distances( + cartesian_positions: torch.Tensor, basis_vectors: torch.Tensor, max_distance: float +): """Compute distances.""" - adj_info = get_periodic_adjacency_information(cartesian_positions, basis_vectors, radial_cutoff=max_distance) + adj_info = get_periodic_adjacency_information( + cartesian_positions, basis_vectors, radial_cutoff=max_distance + ) # The following are 1D arrays of length equal to the total number of neighbors for all batch elements # and all atoms. @@ -119,7 +153,9 @@ def compute_distances(cartesian_positions: torch.Tensor, basis_vectors: torch.Te bch = adj_info.edge_batch_indices src, dst = adj_info.adjacency_matrix - cartesian_displacements = cartesian_positions[bch, dst] - cartesian_positions[bch, src] + adj_info.shifts + cartesian_displacements = ( + cartesian_positions[bch, dst] - cartesian_positions[bch, src] + adj_info.shifts + ) distances = torch.linalg.norm(cartesian_displacements, dim=-1) # Identify neighbors within the radial_cutoff, but avoiding self. return distances[distances > 0.0] diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/utils/tensor_utils.py b/src/diffusion_for_multi_scale_molecular_dynamics/utils/tensor_utils.py index 813478c1..92b89e67 100644 --- a/src/diffusion_for_multi_scale_molecular_dynamics/utils/tensor_utils.py +++ b/src/diffusion_for_multi_scale_molecular_dynamics/utils/tensor_utils.py @@ -3,7 +3,9 @@ import torch -def broadcast_batch_tensor_to_all_dimensions(batch_values: torch.Tensor, final_shape: Tuple[int, ...]) -> torch.Tensor: +def broadcast_batch_tensor_to_all_dimensions( + batch_values: torch.Tensor, final_shape: Tuple[int, ...] +) -> torch.Tensor: """Broadcast batch tensor to all dimensions. A data batch is typically a tensor of shape [batch_size, n1, n2, ...] where n1, n2, etc constitute @@ -21,10 +23,14 @@ def broadcast_batch_tensor_to_all_dimensions(batch_values: torch.Tensor, final_s broadcast_values : tensor of shape [batch_size, n1, n2, ...], where all entries are identical along non-batch dimensions. """ - assert len(batch_values.shape) == 1, "The batch values should be a one-dimensional tensor." + assert ( + len(batch_values.shape) == 1 + ), "The batch values should be a one-dimensional tensor." batch_size = len(batch_values) - assert final_shape[0] == batch_size, "The final shape should have the batch_size as its first dimension." + assert ( + final_shape[0] == batch_size + ), "The final shape should have the batch_size as its first dimension." # reshape the batch_values array to have the same dimension as final_shape, with all values identical # for a given batch index. diff --git a/tests/active_learning_loop/test_benchmark.py b/tests/active_learning_loop/test_benchmark.py index b23579cc..e617661b 100644 --- a/tests/active_learning_loop/test_benchmark.py +++ b/tests/active_learning_loop/test_benchmark.py @@ -3,7 +3,9 @@ import pandas as pd import pytest -from crystal_diffusion.active_learning_loop.benchmark import ActiveLearningLoop + +from diffusion_for_multi_scale_molecular_dynamics.active_learning_loop.benchmark import \ + ActiveLearningLoop class TestActiveLearningLoop: @@ -33,8 +35,12 @@ def mock_al_loop(self, mocker, mock_yaml_config, meta_config): # Mock os.path.exists to always return True mocker.patch("os.path.exists", return_value=True) # Mock the instantiate function from hydra.utils - mock_instantiate = mocker.patch("crystal_diffusion.active_learning_loop.benchmark.instantiate") - mock_instantiate.side_effect = lambda x: x # Return the config itself for simplicity + mock_instantiate = mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.active_learning_loop.benchmark.instantiate" + ) + mock_instantiate.side_effect = ( + lambda x: x + ) # Return the config itself for simplicity # Create an instance of ActiveLearningLoop loop = ActiveLearningLoop(meta_config) @@ -43,14 +49,14 @@ def mock_al_loop(self, mocker, mock_yaml_config, meta_config): def test_parse_config(self, mock_al_loop, mock_yaml_config, meta_config): # Assertions to verify that the attributes were correctly set - assert mock_al_loop.data_paths == {'key1': 'value1'} - assert mock_al_loop.mlip_model == {'key2': 'value2'} - assert mock_al_loop.eval_config == {'key3': 'value3'} - assert mock_al_loop.structure_generation == {'key4': 'value4'} - assert mock_al_loop.oracle == {'key5': 'value5'} + assert mock_al_loop.data_paths == {"key1": "value1"} + assert mock_al_loop.mlip_model == {"key2": "value2"} + assert mock_al_loop.eval_config == {"key3": "value3"} + assert mock_al_loop.structure_generation == {"key4": "value4"} + assert mock_al_loop.oracle == {"key5": "value5"} # Verify that the file was opened and the path was checked - open.assert_called_once_with(meta_config, 'r') + open.assert_called_once_with(meta_config, "r") os.path.exists.assert_called_once_with(meta_config) def test_train_mlip(self, mocker, mock_yaml_config, mock_al_loop): @@ -71,10 +77,12 @@ def test_train_mlip(self, mocker, mock_yaml_config, mock_al_loop): mock_mlip_model.prepare_dataset_from_lammps.assert_called_once_with( root_data_dir="mock_training_data_dir", atom_dict=mock_al_loop.atom_dict, - mode="train" + mode="train", ) - mock_mlip_model.train.assert_called_once_with("mock_training_set", mlip_name="mlip_round_1") + mock_mlip_model.train.assert_called_once_with( + "mock_training_set", mlip_name="mlip_round_1" + ) # Verify the trained model path is correctly returned assert result == "mock_trained_mlip_model" @@ -88,10 +96,15 @@ def test_train_mlip(self, mocker, mock_yaml_config, mock_al_loop): # The prepare_dataset_from_lammps should not be called since we provided a training_set mock_mlip_model.prepare_dataset_from_lammps.assert_called_once() # No new call - mock_mlip_model.train.assert_called_with(custom_training_set, mlip_name="mlip_round_2") + mock_mlip_model.train.assert_called_with( + custom_training_set, mlip_name="mlip_round_2" + ) assert result == "mock_trained_mlip_model" - assert mock_al_loop.trained_mlips == ["mock_trained_mlip_model", "mock_trained_mlip_model"] + assert mock_al_loop.trained_mlips == [ + "mock_trained_mlip_model", + "mock_trained_mlip_model", + ] def test_evaluate_mlip(self, mock_al_loop, tmpdir): # Mocking the mlip_model's methods @@ -100,7 +113,9 @@ def test_evaluate_mlip(self, mock_al_loop, tmpdir): mock_prediction_df = pd.DataFrame({"atom_index": [0, 1], "force": [1.0, 2.0]}) # Mocking return values for the prepare_dataset_from_lammps and evaluate methods - mock_mlip_model.prepare_dataset_from_lammps.return_value = mock_evaluation_dataset + mock_mlip_model.prepare_dataset_from_lammps.return_value = ( + mock_evaluation_dataset + ) mock_mlip_model.evaluate.return_value = (None, mock_prediction_df) loop = mock_al_loop @@ -117,11 +132,13 @@ def test_evaluate_mlip(self, mock_al_loop, tmpdir): root_data_dir="mock_evaluation_data_dir", atom_dict=loop.atom_dict, mode="evaluation", - get_forces=True + get_forces=True, ) # Verify the evaluate method was called with the correct parameters - expected_mlip_name = os.path.join(mock_mlip_model.savedir, 'mlip_round_1.almtp') - mock_mlip_model.evaluate.assert_called_once_with(mock_evaluation_dataset, mlip_name=expected_mlip_name) + expected_mlip_name = os.path.join(mock_mlip_model.savedir, "mlip_round_1.almtp") + mock_mlip_model.evaluate.assert_called_once_with( + mock_evaluation_dataset, mlip_name=expected_mlip_name + ) # Verify the method returns the correct dataframe pd.testing.assert_frame_equal(result_df, mock_prediction_df) @@ -131,7 +148,9 @@ def test_evaluate_mlip(self, mock_al_loop, tmpdir): result_df = loop.evaluate_mlip(round=2, mlip_name=custom_mlip_name) # The evaluate method should be called with the custom mlip_name - mock_mlip_model.evaluate.assert_called_with(mock_evaluation_dataset, mlip_name=custom_mlip_name) + mock_mlip_model.evaluate.assert_called_with( + mock_evaluation_dataset, mlip_name=custom_mlip_name + ) pd.testing.assert_frame_equal(result_df, mock_prediction_df) @@ -142,5 +161,5 @@ def test_evaluate_mlip(self, mock_al_loop, tmpdir): root_data_dir="mock_evaluation_data_dir", atom_dict=loop.atom_dict, mode="evaluation", - get_forces=False + get_forces=False, ) diff --git a/tests/analysis/test_ovito_visualisation.py b/tests/analysis/test_ovito_visualisation.py index 5be2d2de..d2e5a2f8 100644 --- a/tests/analysis/test_ovito_visualisation.py +++ b/tests/analysis/test_ovito_visualisation.py @@ -1,11 +1,13 @@ """Unit tests for outputs to ovito.""" + import os import numpy as np import pandas as pd import pytest -from src.crystal_diffusion.analysis import (get_lattice_from_lammps, - mtp_predictions_to_ovito) + +from diffusion_for_multi_scale_molecular_dynamics.analysis.ovito_visualisation import ( + get_lattice_from_lammps, mtp_predictions_to_ovito) class TestMTP2Ovito: @@ -32,9 +34,7 @@ def test_get_lattice_from_lammps(self, lammps_output): # Get the lattice array from the function using the test file lattice = get_lattice_from_lammps(lammps_output) - expected_lattice = np.array([[4.0, 0.0, 0.0], - [0.0, 5.0, 0.0], - [0.0, 0.0, 6.0]]) + expected_lattice = np.array([[4.0, 0.0, 0.0], [0.0, 5.0, 0.0], [0.0, 0.0, 6.0]]) # Assert that the returned lattice is as expected np.testing.assert_array_equal(lattice, expected_lattice) @@ -42,13 +42,15 @@ def test_get_lattice_from_lammps(self, lammps_output): @pytest.fixture def fake_prediction_csv(self, tmpdir): # Create a DataFrame with mock prediction data - df = pd.DataFrame({ - 'structure_index': [1, 1, 2, 2], - 'x': [0.0, 1.0, 2.0, 3.0], - 'y': [0.1, 1.1, 2.1, 3.1], - 'z': [0.2, 1.2, 2.2, 3.2], - 'nbh_grades': [0.3, 1.3, 2.3, 3.3] - }) + df = pd.DataFrame( + { + "structure_index": [1, 1, 2, 2], + "x": [0.0, 1.0, 2.0, 3.0], + "y": [0.1, 1.1, 2.1, 3.1], + "z": [0.2, 1.2, 2.2, 3.2], + "nbh_grades": [0.3, 1.3, 2.3, 3.3], + } + ) file_path = os.path.join(tmpdir, "mock_predictions.csv") df.to_csv(file_path, index=False) @@ -56,11 +58,7 @@ def fake_prediction_csv(self, tmpdir): def test_mtp_predictions_to_ovito(self, fake_prediction_csv, tmpdir): # Define the lattice - lattice = np.array([ - [10.0, 0.0, 0.0], - [0.0, 10.0, 0.0], - [0.0, 0.0, 10.0] - ]) + lattice = np.array([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 10.0]]) # output file name output_name = os.path.join(tmpdir, "test_output") # Run the conversion function @@ -78,10 +76,14 @@ def test_mtp_predictions_to_ovito(self, fake_prediction_csv, tmpdir): assert int(lines[0]) == int(lines[len(lines) // 2]) == 2 - expected_lattice_line = 'Lattice=\"10.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 10.0\" Origin=\"0 0 0\" ' - expected_lattice_line += 'pbc=\"T T T\" Properties=pos:R:3:MaxVolGamma:R:1\n' + expected_lattice_line = ( + 'Lattice="10.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 10.0" Origin="0 0 0" ' + ) + expected_lattice_line += 'pbc="T T T" Properties=pos:R:3:MaxVolGamma:R:1\n' assert lines[1] == lines[len(lines) // 2 + 1] == expected_lattice_line # check the atom values for x, y in enumerate([2, 3, 6, 7]): - assert np.array_equal(list(map(float, lines[y].split())), [x, x + 0.1, x + 0.2, x + 0.3]) + assert np.array_equal( + list(map(float, lines[y].split())), [x, x + 0.1, x + 0.2, x + 0.3] + ) diff --git a/tests/conftest.py b/tests/conftest.py index ac633de9..5fb60e8b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -35,9 +35,9 @@ def pytest_collection_modifyitems(config, items): item.add_marker(skip) -_available_devices = [torch.device('cpu')] +_available_devices = [torch.device("cpu")] if torch.cuda.is_available(): - _available_devices.append(torch.device('cuda')) + _available_devices.append(torch.device("cuda")) @pytest.fixture(params=_available_devices) @@ -47,10 +47,10 @@ def device(request): @pytest.fixture() def accelerator(device): - if str(device) == 'cpu': - return 'cpu' - elif str(device) == 'cuda': - return 'gpu' + if str(device) == "cpu": + return "cpu" + elif str(device) == "cuda": + return "gpu" else: raise ValueError("Wrong device") @@ -58,7 +58,9 @@ def accelerator(device): @pytest.fixture def basis_vectors(batch_size): # orthogonal boxes with dimensions between 5 and 10. - orthogonal_boxes = torch.stack([torch.diag(5. + 5. * torch.rand(3)) for _ in range(batch_size)]) + orthogonal_boxes = torch.stack( + [torch.diag(5.0 + 5.0 * torch.rand(3)) for _ in range(batch_size)] + ) # add a bit of noise to make the vectors not quite orthogonal basis_vectors = orthogonal_boxes + 0.1 * torch.randn(batch_size, 3, 3) return basis_vectors @@ -66,6 +68,7 @@ def basis_vectors(batch_size): class TestDiffusionDataBase: """This base class creates fake data and writes it to disk for testing.""" + @pytest.fixture(scope="class", autouse=True) def set_seed(self): """Set the random seed.""" @@ -92,9 +95,13 @@ def spatial_dimension(self): return 3 @pytest.fixture - def train_configuration_runs(self, number_of_train_runs, spatial_dimension, number_of_atoms): + def train_configuration_runs( + self, number_of_train_runs, spatial_dimension, number_of_atoms + ): """Generate multiple fake 'data' runs and return their configurations.""" - return get_configuration_runs(number_of_train_runs, spatial_dimension, number_of_atoms) + return get_configuration_runs( + number_of_train_runs, spatial_dimension, number_of_atoms + ) @pytest.fixture def all_train_configurations(self, train_configuration_runs): @@ -105,9 +112,13 @@ def all_train_configurations(self, train_configuration_runs): return all_configurations @pytest.fixture - def valid_configuration_runs(self, number_of_valid_runs, spatial_dimension, number_of_atoms): + def valid_configuration_runs( + self, number_of_valid_runs, spatial_dimension, number_of_atoms + ): """Generate multiple fake 'data' runs and return their configurations.""" - return get_configuration_runs(number_of_valid_runs, spatial_dimension, number_of_atoms) + return get_configuration_runs( + number_of_valid_runs, spatial_dimension, number_of_atoms + ) @pytest.fixture def all_valid_configurations(self, valid_configuration_runs): @@ -128,17 +139,21 @@ def paths(self, tmp_path, train_configuration_runs, valid_configuration_runs): raw_data_dir = tmp_path / "raw_data" raw_data_dir.mkdir() - for mode, list_configurations in zip(['train', 'valid'], [train_configuration_runs, valid_configuration_runs]): + for mode, list_configurations in zip( + ["train", "valid"], [train_configuration_runs, valid_configuration_runs] + ): for i, configurations in enumerate(list_configurations, 1): - run_directory = raw_data_dir / f'{mode}_run_{i}' + run_directory = raw_data_dir / f"{mode}_run_{i}" run_directory.mkdir() dump_docs = create_dump_yaml_documents(configurations) thermo_docs = create_thermo_yaml_documents(configurations) - write_to_yaml(dump_docs, str(run_directory / f'dump_{mode}.yaml')) - write_to_yaml(thermo_docs, str(run_directory / 'thermo_logs.yaml')) + write_to_yaml(dump_docs, str(run_directory / f"dump_{mode}.yaml")) + write_to_yaml(thermo_docs, str(run_directory / "thermo_logs.yaml")) processed_data_dir = tmp_path / "processed_data" processed_data_dir.mkdir() - return dict(raw_data_dir=str(raw_data_dir), processed_data_dir=str(processed_data_dir)) + return dict( + raw_data_dir=str(raw_data_dir), processed_data_dir=str(processed_data_dir) + ) diff --git a/tests/data/diffusion/test_data_loader.py b/tests/data/diffusion/test_data_loader.py index 0297d3a2..7b01a3c9 100644 --- a/tests/data/diffusion/test_data_loader.py +++ b/tests/data/diffusion/test_data_loader.py @@ -3,28 +3,30 @@ import pytest import torch -from crystal_diffusion.data.diffusion.data_loader import ( - LammpsForDiffusionDataModule, LammpsLoaderParameters) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, - RELATIVE_COORDINATES) +from diffusion_for_multi_scale_molecular_dynamics.data.diffusion.data_loader import ( + LammpsForDiffusionDataModule, LammpsLoaderParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) from tests.conftest import TestDiffusionDataBase from tests.fake_data_utils import Configuration, find_aligning_permutation -def convert_configurations_to_dataset(configurations: List[Configuration]) -> Dict[str, torch.Tensor]: +def convert_configurations_to_dataset( + configurations: List[Configuration], +) -> Dict[str, torch.Tensor]: """Convert the input configuration into a dict of torch tensors comparable to a pytorch dataset.""" # The expected dataset keys are {'natom', 'box', 'cartesian_positions', 'relative_positions', 'type', # 'cartesian_forces', 'potential_energy'} data = defaultdict(list) for configuration in configurations: - data['natom'].append(len(configuration.ids)) - data['box'].append(configuration.cell_dimensions) + data["natom"].append(len(configuration.ids)) + data["box"].append(configuration.cell_dimensions) data[CARTESIAN_FORCES].append(configuration.cartesian_forces) data[CARTESIAN_POSITIONS].append(configuration.cartesian_positions) data[RELATIVE_COORDINATES].append(configuration.relative_coordinates) - data['type'].append(configuration.types) - data['potential_energy'].append(configuration.potential_energy) + data["type"].append(configuration.types) + data["potential_energy"].append(configuration.potential_energy) configuration_dataset = dict() for key, array in data.items(): @@ -37,103 +39,155 @@ class TestDiffusionDataLoader(TestDiffusionDataBase): @pytest.fixture def input_data_to_transform(self): return { - 'natom': [2], # batch size of 1 - 'box': [[1.0, 1.0, 1.0]], - CARTESIAN_POSITIONS: [[1., 2., 3, 4., 5, 6]], # for one batch, two atoms, 3D positions - CARTESIAN_FORCES: [[11., 12., 13, 14., 15, 16]], # for one batch, two atoms, 3D forces - RELATIVE_COORDINATES: [[1., 2., 3, 4., 5, 6]], - 'type': [[1, 2]], - 'potential_energy': [23.233], + "natom": [2], # batch size of 1 + "box": [[1.0, 1.0, 1.0]], + CARTESIAN_POSITIONS: [ + [1.0, 2.0, 3, 4.0, 5, 6] + ], # for one batch, two atoms, 3D positions + CARTESIAN_FORCES: [ + [11.0, 12.0, 13, 14.0, 15, 16] + ], # for one batch, two atoms, 3D forces + RELATIVE_COORDINATES: [[1.0, 2.0, 3, 4.0, 5, 6]], + "type": [[1, 2]], + "potential_energy": [23.233], } def test_dataset_transform(self, input_data_to_transform): result = LammpsForDiffusionDataModule.dataset_transform(input_data_to_transform) # Check keys in result - assert set(result.keys()) == {'natom', CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES, - 'box', 'type', 'potential_energy'} + assert set(result.keys()) == { + "natom", + CARTESIAN_FORCES, + CARTESIAN_POSITIONS, + RELATIVE_COORDINATES, + "box", + "type", + "potential_energy", + } # Check tensor types and shapes - assert torch.equal(result['natom'], torch.tensor(input_data_to_transform['natom']).long()) - assert result[CARTESIAN_POSITIONS].shape == (1, 2, 3) # (batchsize, natom, 3 [since it's 3D]) - assert result['box'].shape == (1, 3) - assert torch.equal(result['type'], torch.tensor(input_data_to_transform['type']).long()) - assert torch.equal(result['potential_energy'], torch.tensor(input_data_to_transform['potential_energy'])) + assert torch.equal( + result["natom"], torch.tensor(input_data_to_transform["natom"]).long() + ) + assert result[CARTESIAN_POSITIONS].shape == ( + 1, + 2, + 3, + ) # (batchsize, natom, 3 [since it's 3D]) + assert result["box"].shape == (1, 3) + assert torch.equal( + result["type"], torch.tensor(input_data_to_transform["type"]).long() + ) + assert torch.equal( + result["potential_energy"], + torch.tensor(input_data_to_transform["potential_energy"]), + ) # Check tensor types explicitly - assert result['natom'].dtype == torch.long - assert result[CARTESIAN_POSITIONS].dtype == torch.float32 # default dtype for torch.as_tensor with float inputs - assert result['box'].dtype == torch.float32 - assert result['type'].dtype == torch.long - assert result['potential_energy'].dtype == torch.float32 + assert result["natom"].dtype == torch.long + assert ( + result[CARTESIAN_POSITIONS].dtype == torch.float32 + ) # default dtype for torch.as_tensor with float inputs + assert result["box"].dtype == torch.float32 + assert result["type"].dtype == torch.long + assert result["potential_energy"].dtype == torch.float32 @pytest.fixture def input_data_to_pad(self): return { - 'natom': 2, # batch size of 1 - 'box': [1.0, 1.0, 1.0], - CARTESIAN_POSITIONS: [1., 2., 3, 4., 5, 6], # for one batch, two atoms, 3D positions - CARTESIAN_FORCES: [11., 12., 13, 14., 15, 16], - RELATIVE_COORDINATES: [1., 2., 3, 4., 5, 6], - 'type': [1, 2], - 'potential_energy': 23.233, + "natom": 2, # batch size of 1 + "box": [1.0, 1.0, 1.0], + CARTESIAN_POSITIONS: [ + 1.0, + 2.0, + 3, + 4.0, + 5, + 6, + ], # for one batch, two atoms, 3D positions + CARTESIAN_FORCES: [11.0, 12.0, 13, 14.0, 15, 16], + RELATIVE_COORDINATES: [1.0, 2.0, 3, 4.0, 5, 6], + "type": [1, 2], + "potential_energy": 23.233, } def test_pad_dataset(self, input_data_to_pad): max_atom = 5 # Assume we want to pad to a max of 5 atoms - padded_sample = LammpsForDiffusionDataModule.pad_samples(input_data_to_pad, max_atom) + padded_sample = LammpsForDiffusionDataModule.pad_samples( + input_data_to_pad, max_atom + ) # Check if the type and position have been padded correctly - assert len(padded_sample['type']) == max_atom + assert len(padded_sample["type"]) == max_atom assert padded_sample[CARTESIAN_POSITIONS].shape == torch.Size([max_atom * 3]) # Check that the padding uses -1 for type # 2 atoms in the input_data - last 3 atoms should be type -1 for k in range(max_atom - 2): - assert padded_sample['type'].tolist()[-(k + 1)] == -1 + assert padded_sample["type"].tolist()[-(k + 1)] == -1 # Check that the padding uses nan for position - assert torch.isnan(padded_sample[CARTESIAN_POSITIONS][-(max_atom - 2) * 3:]).all() + assert torch.isnan( + padded_sample[CARTESIAN_POSITIONS][-(max_atom - 2) * 3:] + ).all() @pytest.fixture def data_module_hyperparameters(self, number_of_atoms, spatial_dimension): - return LammpsLoaderParameters(batch_size=2, - num_workers=0, - max_atom=number_of_atoms, - spatial_dimension=spatial_dimension) + return LammpsLoaderParameters( + batch_size=2, + num_workers=0, + max_atom=number_of_atoms, + spatial_dimension=spatial_dimension, + ) @pytest.fixture() def data_module(self, paths, data_module_hyperparameters, tmpdir): - data_module = LammpsForDiffusionDataModule(lammps_run_dir=paths['raw_data_dir'], - processed_dataset_dir=paths['processed_data_dir'], - hyper_params=data_module_hyperparameters, - working_cache_dir=tmpdir) + data_module = LammpsForDiffusionDataModule( + lammps_run_dir=paths["raw_data_dir"], + processed_dataset_dir=paths["processed_data_dir"], + hyper_params=data_module_hyperparameters, + working_cache_dir=tmpdir, + ) data_module.setup() return data_module @pytest.fixture() - def real_and_test_datasets(self, mode, data_module, all_train_configurations, all_valid_configurations): + def real_and_test_datasets( + self, mode, data_module, all_train_configurations, all_valid_configurations + ): match mode: - case 'train': + case "train": data_module_dataset = data_module.train_dataset[:] - configuration_dataset = convert_configurations_to_dataset(all_train_configurations) - case 'valid': + configuration_dataset = convert_configurations_to_dataset( + all_train_configurations + ) + case "valid": data_module_dataset = data_module.valid_dataset[:] - configuration_dataset = convert_configurations_to_dataset(all_valid_configurations) + configuration_dataset = convert_configurations_to_dataset( + all_valid_configurations + ) case _: raise ValueError(f"Unknown mode {mode}") return data_module_dataset, configuration_dataset def test_dataset_feature_names(self, data_module): - expected_feature_names = {'natom', 'box', 'type', 'potential_energy', CARTESIAN_FORCES, CARTESIAN_POSITIONS, - RELATIVE_COORDINATES} + expected_feature_names = { + "natom", + "box", + "type", + "potential_energy", + CARTESIAN_FORCES, + CARTESIAN_POSITIONS, + RELATIVE_COORDINATES, + } assert set(data_module.train_dataset.features.keys()) == expected_feature_names assert set(data_module.valid_dataset.features.keys()) == expected_feature_names - @pytest.mark.parametrize('mode', ['train', 'valid']) + @pytest.mark.parametrize("mode", ["train", "valid"]) def test_dataset(self, real_and_test_datasets): data_module_dataset, configuration_dataset = real_and_test_datasets @@ -141,13 +195,17 @@ def test_dataset(self, real_and_test_datasets): assert set(data_module_dataset.keys()) == set(configuration_dataset.keys()) # the configurations and the data module dataset might not be in the same order. Try to build a mapping. - dataset_boxes = data_module_dataset['box'] - configuration_boxes = configuration_dataset['box'] + dataset_boxes = data_module_dataset["box"] + configuration_boxes = configuration_dataset["box"] - permutation_indices = find_aligning_permutation(dataset_boxes, configuration_boxes) + permutation_indices = find_aligning_permutation( + dataset_boxes, configuration_boxes + ) for field_name in data_module_dataset.keys(): computed_values = data_module_dataset[field_name] expected_values = configuration_dataset[field_name][permutation_indices] - torch.testing.assert_close(computed_values, expected_values, check_dtype=False) + torch.testing.assert_close( + computed_values, expected_values, check_dtype=False + ) diff --git a/tests/data/diffusion/test_data_preprocess.py b/tests/data/diffusion/test_data_preprocess.py index b11f8830..3d3e262f 100644 --- a/tests/data/diffusion/test_data_preprocess.py +++ b/tests/data/diffusion/test_data_preprocess.py @@ -3,11 +3,11 @@ import numpy as np import pandas as pd import pytest -from crystal_diffusion.data.diffusion.data_preprocess import \ - LammpsProcessorForDiffusion -from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, - RELATIVE_COORDINATES) +from diffusion_for_multi_scale_molecular_dynamics.data.diffusion.data_preprocess import \ + LammpsProcessorForDiffusion +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) from tests.conftest import TestDiffusionDataBase from tests.fake_data_utils import generate_parquet_dataframe @@ -18,38 +18,59 @@ class TestDataProcess(TestDiffusionDataBase): def processor(self, paths): return LammpsProcessorForDiffusion(**paths) - def test_prepare_train_data(self, processor, paths, train_configuration_runs, number_of_train_runs): - list_files = processor.prepare_data(paths['raw_data_dir'], mode='train') + def test_prepare_train_data( + self, processor, paths, train_configuration_runs, number_of_train_runs + ): + list_files = processor.prepare_data(paths["raw_data_dir"], mode="train") assert len(list_files) == number_of_train_runs for run_number, configurations in enumerate(train_configuration_runs, 1): - expected_parquet_file = os.path.join(processor.data_dir, f"train_run_{run_number}.parquet") + expected_parquet_file = os.path.join( + processor.data_dir, f"train_run_{run_number}.parquet" + ) assert expected_parquet_file in list_files computed_df = pd.read_parquet(expected_parquet_file) expected_df = generate_parquet_dataframe(configurations) pd.testing.assert_frame_equal(computed_df, expected_df) - def test_prepare_valid_data(self, processor, paths, valid_configuration_runs, number_of_valid_runs): - list_files = processor.prepare_data(paths['raw_data_dir'], mode='valid') + def test_prepare_valid_data( + self, processor, paths, valid_configuration_runs, number_of_valid_runs + ): + list_files = processor.prepare_data(paths["raw_data_dir"], mode="valid") assert len(list_files) == number_of_valid_runs for run_number, configurations in enumerate(valid_configuration_runs, 1): - expected_parquet_file = os.path.join(processor.data_dir, f"valid_run_{run_number}.parquet") + expected_parquet_file = os.path.join( + processor.data_dir, f"valid_run_{run_number}.parquet" + ) assert expected_parquet_file in list_files computed_df = pd.read_parquet(expected_parquet_file) expected_df = generate_parquet_dataframe(configurations) pd.testing.assert_frame_equal(computed_df, expected_df) - def test_parse_lammps_run(self, processor, paths, train_configuration_runs, valid_configuration_runs): - expected_columns = ['natom', 'box', 'type', CARTESIAN_POSITIONS, CARTESIAN_FORCES, RELATIVE_COORDINATES, - 'potential_energy'] - - for mode, configuration_runs in zip(['train', 'valid'], [train_configuration_runs, valid_configuration_runs]): + def test_parse_lammps_run( + self, processor, paths, train_configuration_runs, valid_configuration_runs + ): + expected_columns = [ + "natom", + "box", + "type", + CARTESIAN_POSITIONS, + CARTESIAN_FORCES, + RELATIVE_COORDINATES, + "potential_energy", + ] + + for mode, configuration_runs in zip( + ["train", "valid"], [train_configuration_runs, valid_configuration_runs] + ): for run_number, configurations in enumerate(configuration_runs, 1): - run_dir = os.path.join(paths['raw_data_dir'], f"{mode}_run_{run_number}") + run_dir = os.path.join( + paths["raw_data_dir"], f"{mode}_run_{run_number}" + ) computed_df = processor.parse_lammps_run(run_dir) assert computed_df is not None assert not computed_df.empty @@ -66,20 +87,30 @@ def box_coordinates(self): @pytest.fixture def sample_coordinates(self, box_coordinates): # Sample data frame - return pd.DataFrame({ - 'box': [box_coordinates], - 'x': [[0.6, 0.06, 0.006, 0.00006]], - 'y': [[1.2, 0.12, 0.0012, 0.00012]], - 'z': [[1.8, 0.18, 0.018, 0.0018]] - }) + return pd.DataFrame( + { + "box": [box_coordinates], + "x": [[0.6, 0.06, 0.006, 0.00006]], + "y": [[1.2, 0.12, 0.0012, 0.00012]], + "z": [[1.8, 0.18, 0.018, 0.0018]], + } + ) def test_convert_coords_to_relative(self, sample_coordinates, box_coordinates): # Expected output: Each coordinate divided by 1, 2, 3 (the box limits) for index, row in sample_coordinates.iterrows(): - relative_coords = LammpsProcessorForDiffusion._convert_coords_to_relative(row) + relative_coords = LammpsProcessorForDiffusion._convert_coords_to_relative( + row + ) expected_coords = [] - for x, y, z in zip(row['x'], row['y'], row['z']): - expected_coords.extend([x / box_coordinates[0], y / box_coordinates[1], z / box_coordinates[2]]) + for x, y, z in zip(row["x"], row["y"], row["z"]): + expected_coords.extend( + [ + x / box_coordinates[0], + y / box_coordinates[1], + z / box_coordinates[2], + ] + ) assert relative_coords == expected_coords def test_convert_coords_to_relative2(self, processor, all_configurations): @@ -91,9 +122,18 @@ def test_convert_coords_to_relative2(self, processor, all_configurations): positions = configuration.cartesian_positions box = configuration.cell_dimensions - position_series = pd.Series({'x': positions[:, 0], 'y': positions[:, 1], 'z': positions[:, 2], 'box': box}) - - computed_coordinates = np.array(processor._convert_coords_to_relative(position_series)).reshape(natom, 3) + position_series = pd.Series( + { + "x": positions[:, 0], + "y": positions[:, 1], + "z": positions[:, 2], + "box": box, + } + ) + + computed_coordinates = np.array( + processor._convert_coords_to_relative(position_series) + ).reshape(natom, 3) np.testing.assert_almost_equal(computed_coordinates, expected_coordinates) def test_get_x_relative(self, processor, sample_coordinates): @@ -107,9 +147,19 @@ def test_flatten_positions_in_row(self): number_of_atoms = 12 spatial_dimensions = 3 position_data = np.random.rand(number_of_atoms, spatial_dimensions) - row = pd.Series(dict(x=list(position_data[:, 0]), y=list(position_data[:, 1]), z=list(position_data[:, 2]))) - - computed_flattened_positions = LammpsProcessorForDiffusion._flatten_positions_in_row(row) + row = pd.Series( + dict( + x=list(position_data[:, 0]), + y=list(position_data[:, 1]), + z=list(position_data[:, 2]), + ) + ) + + computed_flattened_positions = ( + LammpsProcessorForDiffusion._flatten_positions_in_row(row) + ) expected_flattened_positions = position_data.flatten() - np.testing.assert_almost_equal(expected_flattened_positions, computed_flattened_positions) + np.testing.assert_almost_equal( + expected_flattened_positions, computed_flattened_positions + ) diff --git a/tests/data/test_parse_lammps_output.py b/tests/data/test_parse_lammps_output.py index 605ba613..8e337a4f 100644 --- a/tests/data/test_parse_lammps_output.py +++ b/tests/data/test_parse_lammps_output.py @@ -4,9 +4,9 @@ import pandas as pd import pytest import yaml -from src.crystal_diffusion.data.parse_lammps_outputs import ( - parse_lammps_dump, parse_lammps_output, parse_lammps_thermo_log) +from src.diffusion_for_multi_scale_molecular_dynamics.data.parse_lammps_outputs import ( + parse_lammps_dump, parse_lammps_output, parse_lammps_thermo_log) from tests.fake_data_utils import (create_dump_yaml_documents, generate_fake_configuration, generate_parse_dump_output_dataframe, @@ -15,7 +15,7 @@ def generate_fake_yaml(filename, documents, multiple_docs=True): # Write the YAML content - with open(filename, 'w') as yaml_file: + with open(filename, "w") as yaml_file: if multiple_docs: yaml.dump_all(documents, yaml_file) else: @@ -27,7 +27,7 @@ def fake_yaml_content(): # fake LAMMPS output file with 4 MD steps in 1D for 3 atoms np.random.seed(23423) box = [[0, 0.6], [0, 1.6], [0, 2.6]] - keywords = ['id', 'type', 'x', 'y', 'z', 'fx', 'fy', 'fz'] + keywords = ["id", "type", "x", "y", "z", "fx", "fy", "fz"] number_of_documents = 4 list_atom_types = [1, 2, 1] @@ -47,7 +47,7 @@ def fake_yaml_content(): @pytest.fixture def fake_lammps_yaml(tmpdir, fake_yaml_content): - file = os.path.join(tmpdir, 'fake_lammps_dump.yaml') + file = os.path.join(tmpdir, "fake_lammps_dump.yaml") generate_fake_yaml(file, fake_yaml_content) return file @@ -56,24 +56,32 @@ def fake_lammps_yaml(tmpdir, fake_yaml_content): def fake_thermo_dataframe(pressure: bool, temperature: bool): np.random.seed(12231) number_of_rows = 4 - keywords = ['KinEng', 'PotEng'] + keywords = ["KinEng", "PotEng"] if pressure: - keywords.append('Press') + keywords.append("Press") if temperature: - keywords.append('Temp') + keywords.append("Temp") - fake_data_df = pd.DataFrame(np.random.rand(number_of_rows, len(keywords)), columns=keywords) + fake_data_df = pd.DataFrame( + np.random.rand(number_of_rows, len(keywords)), columns=keywords + ) return fake_data_df @pytest.fixture def expected_processed_thermo_dataframe(fake_thermo_dataframe: pd.DataFrame): - processed_thermo_dataframe = pd.DataFrame(fake_thermo_dataframe).rename(columns={'KinEng': 'kinetic_energy', - 'PotEng': 'potential_energy', - 'Press': 'pressure', - 'Temp': 'temperature'}) - processed_thermo_dataframe['energy'] = (processed_thermo_dataframe['kinetic_energy'] - + processed_thermo_dataframe['potential_energy']) + processed_thermo_dataframe = pd.DataFrame(fake_thermo_dataframe).rename( + columns={ + "KinEng": "kinetic_energy", + "PotEng": "potential_energy", + "Press": "pressure", + "Temp": "temperature", + } + ) + processed_thermo_dataframe["energy"] = ( + processed_thermo_dataframe["kinetic_energy"] + + processed_thermo_dataframe["potential_energy"] + ) return processed_thermo_dataframe @@ -82,16 +90,18 @@ def expected_processed_thermo_dataframe(fake_thermo_dataframe: pd.DataFrame): def fake_thermo_yaml(tmpdir, fake_thermo_dataframe): keywords = list(fake_thermo_dataframe.columns) data = fake_thermo_dataframe.values.tolist() - yaml_content = {'keywords': keywords, 'data': data} - file = os.path.join(tmpdir, 'fake_lammps_thermo.yaml') + yaml_content = {"keywords": keywords, "data": data} + file = os.path.join(tmpdir, "fake_lammps_thermo.yaml") generate_fake_yaml(file, yaml_content, multiple_docs=False) return file @pytest.mark.parametrize("pressure", [True, False]) @pytest.mark.parametrize("temperature", [True, False]) -def test_parse_lammps_outputs(fake_lammps_yaml, fake_thermo_yaml, expected_processed_thermo_dataframe, tmpdir): - output_name = os.path.join(tmpdir, 'test.parquet') +def test_parse_lammps_outputs( + fake_lammps_yaml, fake_thermo_yaml, expected_processed_thermo_dataframe, tmpdir +): + output_name = os.path.join(tmpdir, "test.parquet") parse_lammps_output(fake_lammps_yaml, fake_thermo_yaml, output_name) # check that a file exists assert os.path.exists(output_name) @@ -101,7 +111,10 @@ def test_parse_lammps_outputs(fake_lammps_yaml, fake_thermo_yaml, expected_proce assert len(df) == 4 - pd.testing.assert_frame_equal(expected_processed_thermo_dataframe, df[expected_processed_thermo_dataframe.columns]) + pd.testing.assert_frame_equal( + expected_processed_thermo_dataframe, + df[expected_processed_thermo_dataframe.columns], + ) @pytest.mark.parametrize("pressure", [True, False]) @@ -134,9 +147,12 @@ def number_of_configurations(): def configurations(number_of_configurations, spatial_dimension, number_of_atoms): """Generate multiple fake configurations.""" np.random.seed(23423423) - configurations = [generate_fake_configuration(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms) - for _ in range(number_of_configurations)] + configurations = [ + generate_fake_configuration( + spatial_dimension=spatial_dimension, number_of_atoms=number_of_atoms + ) + for _ in range(number_of_configurations) + ] return configurations @@ -159,9 +175,21 @@ def test_parse_lammps_dump(lammps_dump_path, expected_lammps_dump_dataframe): computed_lammps_dump_dataframe = pd.DataFrame(data_dict) - assert set(computed_lammps_dump_dataframe.columns) == set(expected_lammps_dump_dataframe) + assert set(computed_lammps_dump_dataframe.columns) == set( + expected_lammps_dump_dataframe + ) for colname in computed_lammps_dump_dataframe.columns: - computed_values = np.array([list_values for list_values in computed_lammps_dump_dataframe[colname].values]) - expected_values = np.array([list_values for list_values in expected_lammps_dump_dataframe[colname].values]) + computed_values = np.array( + [ + list_values + for list_values in computed_lammps_dump_dataframe[colname].values + ] + ) + expected_values = np.array( + [ + list_values + for list_values in expected_lammps_dump_dataframe[colname].values + ] + ) np.testing.assert_array_equal(computed_values, expected_values) diff --git a/tests/data/test_utils.py b/tests/data/test_utils.py index 03668987..12a4fbe3 100644 --- a/tests/data/test_utils.py +++ b/tests/data/test_utils.py @@ -1,6 +1,8 @@ import pytest import yaml -from crystal_diffusion.data.utils import crop_lammps_yaml + +from diffusion_for_multi_scale_molecular_dynamics.data.utils import \ + crop_lammps_yaml # Sample data for dump and thermo YAML files DUMP_YAML_CONTENT = """ @@ -49,7 +51,7 @@ def test_crop_lammps_yaml(dump_file, thermo_file): # Verify the function output assert len(cropped_dump) == len(dump_yaml_content) - crop_step - assert len(cropped_thermo['data']) == len(thermo_yaml_content['data']) - crop_step + assert len(cropped_thermo["data"]) == len(thermo_yaml_content["data"]) - crop_step # Testing exception for too large crop_step with pytest.raises(ValueError): diff --git a/tests/fake_data_utils.py b/tests/fake_data_utils.py index 390d6a96..442fa5f1 100644 --- a/tests/fake_data_utils.py +++ b/tests/fake_data_utils.py @@ -5,13 +5,25 @@ import pandas as pd import torch import yaml -from crystal_diffusion.namespace import (CARTESIAN_FORCES, CARTESIAN_POSITIONS, - RELATIVE_COORDINATES) -Configuration = namedtuple("Configuration", - ["spatial_dimension", CARTESIAN_POSITIONS, CARTESIAN_FORCES, RELATIVE_COORDINATES, - "types", "ids", "cell_dimensions", - "potential_energy", "kinetic_energy", "energy"]) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, CARTESIAN_POSITIONS, RELATIVE_COORDINATES) + +Configuration = namedtuple( + "Configuration", + [ + "spatial_dimension", + CARTESIAN_POSITIONS, + CARTESIAN_FORCES, + RELATIVE_COORDINATES, + "types", + "ids", + "cell_dimensions", + "potential_energy", + "kinetic_energy", + "energy", + ], +) def generate_fake_configuration(spatial_dimension: int, number_of_atoms: int): @@ -27,23 +39,27 @@ def generate_fake_configuration(spatial_dimension: int, number_of_atoms: int): relative_coordinates = np.random.rand(number_of_atoms, spatial_dimension) - cell_dimensions = 5. + 5. * np.random.rand(spatial_dimension) # make sure the cell is big. + cell_dimensions = 5.0 + 5.0 * np.random.rand( + spatial_dimension + ) # make sure the cell is big. unit_cell_vectors = np.diag(cell_dimensions) positions = np.dot(relative_coordinates, unit_cell_vectors) potential_energy = np.random.rand() kinetic_energy = np.random.rand() energy = potential_energy + kinetic_energy - return Configuration(spatial_dimension=spatial_dimension, - relative_coordinates=relative_coordinates, - cartesian_positions=positions, - cartesian_forces=np.random.rand(number_of_atoms, spatial_dimension), - types=np.random.randint(1, 10, number_of_atoms), - ids=np.arange(1, number_of_atoms + 1), - cell_dimensions=cell_dimensions, - potential_energy=potential_energy, - kinetic_energy=kinetic_energy, - energy=energy) + return Configuration( + spatial_dimension=spatial_dimension, + relative_coordinates=relative_coordinates, + cartesian_positions=positions, + cartesian_forces=np.random.rand(number_of_atoms, spatial_dimension), + types=np.random.randint(1, 10, number_of_atoms), + ids=np.arange(1, number_of_atoms + 1), + cell_dimensions=cell_dimensions, + potential_energy=potential_energy, + kinetic_energy=kinetic_energy, + energy=energy, + ) def get_configuration_runs(number_of_runs, spatial_dimension, number_of_atoms): @@ -51,15 +67,20 @@ def get_configuration_runs(number_of_runs, spatial_dimension, number_of_atoms): list_configurations = [] for _ in range(number_of_runs): number_of_configs = np.random.randint(1, 16) - configurations = [generate_fake_configuration(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms) - for _ in range(number_of_configs)] + configurations = [ + generate_fake_configuration( + spatial_dimension=spatial_dimension, number_of_atoms=number_of_atoms + ) + for _ in range(number_of_configs) + ] list_configurations.append(configurations) return list_configurations -def generate_parse_dump_output_dataframe(configurations: List[Configuration]) -> pd.DataFrame: +def generate_parse_dump_output_dataframe( + configurations: List[Configuration], +) -> pd.DataFrame: """Generate parse lammps run Args: @@ -70,52 +91,73 @@ def generate_parse_dump_output_dataframe(configurations: List[Configuration]) -> """ rows = [] for configuration in configurations: - row = dict(box=configuration.cell_dimensions, id=list(configuration.ids), type=list(configuration.types)) - for coordinates, name in zip(configuration.cartesian_positions.transpose(), ['x', 'y', 'z']): + row = dict( + box=configuration.cell_dimensions, + id=list(configuration.ids), + type=list(configuration.types), + ) + for coordinates, name in zip( + configuration.cartesian_positions.transpose(), ["x", "y", "z"] + ): row[name] = list(coordinates) - for coordinate_forces, name in zip(configuration.cartesian_forces.transpose(), ['fx', 'fy', 'fz']): + for coordinate_forces, name in zip( + configuration.cartesian_forces.transpose(), ["fx", "fy", "fz"] + ): row[name] = list(coordinate_forces) rows.append(row) return pd.DataFrame(rows) -def create_dump_single_record(configuration: Configuration, timestep: int) -> Dict[str, Any]: +def create_dump_single_record( + configuration: Configuration, timestep: int +) -> Dict[str, Any]: spatial_dimension = configuration.spatial_dimension box = [[0, float(dimension)] for dimension in configuration.cell_dimensions] # keywords should be of the form : [id, type, x, y, z, fx, fy, fz, ] - keywords = ['id', 'type'] + keywords = ["id", "type"] - for direction, _ in zip(['x', 'y', 'z'], range(spatial_dimension)): + for direction, _ in zip(["x", "y", "z"], range(spatial_dimension)): keywords.append(direction) - for force_direction, _ in zip(['fx', 'fy', 'fz'], range(spatial_dimension)): + for force_direction, _ in zip(["fx", "fy", "fz"], range(spatial_dimension)): keywords.append(force_direction) # Each row of data should be a list in the same order as the keywords data = [] - for id, type, position, force in ( - zip(configuration.ids, configuration.types, configuration.cartesian_positions, - configuration.cartesian_forces)): - row = [int(id), int(type)] + [float(p) for p in position] + [float(f) for f in force] + for id, type, position, force in zip( + configuration.ids, + configuration.types, + configuration.cartesian_positions, + configuration.cartesian_forces, + ): + row = ( + [int(id), int(type)] + + [float(p) for p in position] + + [float(f) for f in force] + ) data.append(row) - document = dict(creator='fake LAMMPS for tests', - timestep=timestep, - natoms=len(configuration.ids), - boundary=2 * spatial_dimension * ['p'], - box=box, - keywords=keywords, - data=data) + document = dict( + creator="fake LAMMPS for tests", + timestep=timestep, + natoms=len(configuration.ids), + boundary=2 * spatial_dimension * ["p"], + box=box, + keywords=keywords, + data=data, + ) return document -def create_dump_yaml_documents(configurations: List[Configuration]) -> List[Dict[str, Any]]: +def create_dump_yaml_documents( + configurations: List[Configuration], +) -> List[Dict[str, Any]]: docs = [] for timestep, configuration in enumerate(configurations): docs.append(create_dump_single_record(configuration, timestep)) @@ -123,19 +165,38 @@ def create_dump_yaml_documents(configurations: List[Configuration]) -> List[Dict return docs -def create_thermo_yaml_documents(configurations: List[Configuration]) -> List[Dict[str, Any]]: - - keywords = ['Step', 'Temp', 'KinEng', 'PotEng', 'E_bond', 'E_angle', - 'E_dihed', 'E_impro', 'E_vdwl', 'E_coul', 'E_long', 'Press'] +def create_thermo_yaml_documents( + configurations: List[Configuration], +) -> List[Dict[str, Any]]: + + keywords = [ + "Step", + "Temp", + "KinEng", + "PotEng", + "E_bond", + "E_angle", + "E_dihed", + "E_impro", + "E_vdwl", + "E_coul", + "E_long", + "Press", + ] number_of_keywords = len(keywords) data = [] for timestep, configuration in enumerate(configurations): - row = ([timestep] - + [float(np.random.rand())] - + [float(configuration.kinetic_energy), float(configuration.potential_energy)] - + [float(np.random.rand()) for _ in range(number_of_keywords - 4)]) + row = ( + [timestep] + + [float(np.random.rand())] + + [ + float(configuration.kinetic_energy), + float(configuration.potential_energy), + ] + + [float(np.random.rand()) for _ in range(number_of_keywords - 4)] + ) data.append(row) document = dict(keywords=keywords, data=data) @@ -146,7 +207,9 @@ def write_to_yaml(documents: List[Dict[str, Any]], output_file_path: str): """Write to yaml.""" with open(output_file_path, "w") as fd: # This output format is not perfectly identical to what LAMMPS output, but very similar. - yaml.dump_all(documents, fd, sort_keys=False, default_flow_style=None, width=1000) + yaml.dump_all( + documents, fd, sort_keys=False, default_flow_style=None, width=1000 + ) def generate_parquet_dataframe(configurations: List[Configuration]) -> pd.DataFrame: @@ -156,25 +219,28 @@ def generate_parquet_dataframe(configurations: List[Configuration]) -> pd.DataFr # A = [ v1, v2] --> A.flatten(order=c) = [v1, v2, v3, v4], A.flatten(order=f) = [v1, v3, v2, v4]. # [ v3, v4] # C-style flattening is the correct convention to interoperate with pytorch reshaping operations. - relative_positions = configuration.relative_coordinates.flatten(order='c') - positions = configuration.cartesian_positions.flatten(order='c') - forces = configuration.cartesian_forces.flatten(order='c') + relative_positions = configuration.relative_coordinates.flatten(order="c") + positions = configuration.cartesian_positions.flatten(order="c") + forces = configuration.cartesian_forces.flatten(order="c") number_of_atoms = len(configuration.ids) box = configuration.cell_dimensions - row = dict(natom=number_of_atoms, - box=box, - type=configuration.types, - potential_energy=configuration.potential_energy, - cartesian_positions=positions, - relative_coordinates=relative_positions, - cartesian_forces=forces, - ) + row = dict( + natom=number_of_atoms, + box=box, + type=configuration.types, + potential_energy=configuration.potential_energy, + cartesian_positions=positions, + relative_coordinates=relative_positions, + cartesian_forces=forces, + ) rows.append(row) return pd.DataFrame(rows) -def find_aligning_permutation(first_2d_array: torch.Tensor, second_2d_array: torch.Tensor, tol=1e-6) -> torch.Tensor: +def find_aligning_permutation( + first_2d_array: torch.Tensor, second_2d_array: torch.Tensor, tol=1e-6 +) -> torch.Tensor: """Find aligning permutation, assuming the input two arrays contain the same information. This function computes and stores all distances. This scales quadratically, which is not good, but it should @@ -185,12 +251,15 @@ def find_aligning_permutation(first_2d_array: torch.Tensor, second_2d_array: tor number_of_vectors = first_2d_array.shape[0] - distances = torch.sum((first_2d_array[:, None, :] - second_2d_array[None, :, :])**2, dim=-1) + distances = torch.sum( + (first_2d_array[:, None, :] - second_2d_array[None, :, :]) ** 2, dim=-1 + ) matching_indices = (distances < tol).nonzero() - assert torch.allclose(matching_indices[:, 0], torch.arange(number_of_vectors)), \ - "There isn't exactly a one-to-one match between the two arrays" + assert torch.allclose( + matching_indices[:, 0], torch.arange(number_of_vectors) + ), "There isn't exactly a one-to-one match between the two arrays" permutation_indices = matching_indices[:, 1] diff --git a/tests/generators/conftest.py b/tests/generators/conftest.py index bed0b402..7699d60d 100644 --- a/tests/generators/conftest.py +++ b/tests/generators/conftest.py @@ -2,15 +2,19 @@ import pytest import torch -from crystal_diffusion.models.score_networks import (ScoreNetwork, - ScoreNetworkParameters) -from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import ( + ScoreNetwork, ScoreNetworkParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import \ + NOISY_RELATIVE_COORDINATES class FakeScoreNetwork(ScoreNetwork): """A fake, smooth score network for the ODE solver.""" - def _forward_unchecked(self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False) -> torch.Tensor: + def _forward_unchecked( + self, batch: Dict[AnyStr, torch.Tensor], conditional: bool = False + ) -> torch.Tensor: return batch[NOISY_RELATIVE_COORDINATES] @@ -35,7 +39,9 @@ def spatial_dimension(self, request): @pytest.fixture() def unit_cell_sample(self, unit_cell_size, spatial_dimension, number_of_samples): - return torch.diag(torch.Tensor([unit_cell_size] * spatial_dimension)).repeat(number_of_samples, 1, 1) + return torch.diag(torch.Tensor([unit_cell_size] * spatial_dimension)).repeat( + number_of_samples, 1, 1 + ) @pytest.fixture() def cell_dimensions(self, unit_cell_size, spatial_dimension): @@ -43,4 +49,8 @@ def cell_dimensions(self, unit_cell_size, spatial_dimension): @pytest.fixture() def sigma_normalized_score_network(self, spatial_dimension): - return FakeScoreNetwork(ScoreNetworkParameters(architecture='dummy', spatial_dimension=spatial_dimension)) + return FakeScoreNetwork( + ScoreNetworkParameters( + architecture="dummy", spatial_dimension=spatial_dimension + ) + ) diff --git a/tests/generators/test_constrained_langevin_generator.py b/tests/generators/test_constrained_langevin_generator.py index 035634f4..d1aa431a 100644 --- a/tests/generators/test_constrained_langevin_generator.py +++ b/tests/generators/test_constrained_langevin_generator.py @@ -1,9 +1,9 @@ import einops import pytest import torch -from crystal_diffusion.generators.constrained_langevin_generator import ( - ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) +from diffusion_for_multi_scale_molecular_dynamics.generators.constrained_langevin_generator import ( + ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) from tests.generators.test_langevin_generator import TestLangevinGenerator @@ -15,40 +15,61 @@ def constrained_relative_coordinates(self, number_of_atoms, spatial_dimension): return torch.rand(number_of_constraints, spatial_dimension).numpy() @pytest.fixture() - def sampling_parameters(self, number_of_atoms, spatial_dimension, number_of_samples, cell_dimensions, - number_of_corrector_steps, unit_cell_size, constrained_relative_coordinates): + def sampling_parameters( + self, + number_of_atoms, + spatial_dimension, + number_of_samples, + cell_dimensions, + number_of_corrector_steps, + unit_cell_size, + constrained_relative_coordinates, + ): sampling_parameters = ConstrainedLangevinGeneratorParameters( number_of_corrector_steps=number_of_corrector_steps, number_of_atoms=number_of_atoms, number_of_samples=number_of_samples, cell_dimensions=cell_dimensions, spatial_dimension=spatial_dimension, - constrained_relative_coordinates=constrained_relative_coordinates) + constrained_relative_coordinates=constrained_relative_coordinates, + ) return sampling_parameters @pytest.fixture() - def pc_generator(self, noise_parameters, sampling_parameters, sigma_normalized_score_network): + def pc_generator( + self, noise_parameters, sampling_parameters, sigma_normalized_score_network + ): generator = ConstrainedLangevinGenerator( noise_parameters=noise_parameters, sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + sigma_normalized_score_network=sigma_normalized_score_network, + ) return generator @pytest.fixture() def x(self, number_of_samples, number_of_atoms, spatial_dimension, device): - return torch.rand(number_of_samples, number_of_atoms, spatial_dimension).to(device) + return torch.rand(number_of_samples, number_of_atoms, spatial_dimension).to( + device + ) - def test_apply_constraint(self, pc_generator, x, constrained_relative_coordinates, device): + def test_apply_constraint( + self, pc_generator, x, constrained_relative_coordinates, device + ): batch_size = x.shape[0] original_x = torch.clone(x) pc_generator._apply_constraint(x, device) number_of_constraints = len(constrained_relative_coordinates) - constrained_x = einops.repeat(torch.from_numpy(constrained_relative_coordinates).to(device), - "n d -> b n d", b=batch_size) + constrained_x = einops.repeat( + torch.from_numpy(constrained_relative_coordinates).to(device), + "n d -> b n d", + b=batch_size, + ) torch.testing.assert_close(x[:, :number_of_constraints], constrained_x) - torch.testing.assert_close(x[:, number_of_constraints:], original_x[:, number_of_constraints:]) + torch.testing.assert_close( + x[:, number_of_constraints:], original_x[:, number_of_constraints:] + ) diff --git a/tests/generators/test_langevin_generator.py b/tests/generators/test_langevin_generator.py index 18e2190f..c1a4c3d0 100644 --- a/tests/generators/test_langevin_generator.py +++ b/tests/generators/test_langevin_generator.py @@ -1,13 +1,14 @@ import pytest import torch -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.predictor_corrector_position_generator import \ + +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell -from src.crystal_diffusion.samplers.variance_sampler import ( +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) - from tests.generators.conftest import BaseTestGenerator @@ -23,42 +24,68 @@ def total_time_steps(self, request): @pytest.fixture() def noise_parameters(self, total_time_steps): - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - time_delta=0.1, - sigma_min=0.15, - corrector_step_epsilon=0.25) + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, + time_delta=0.1, + sigma_min=0.15, + corrector_step_epsilon=0.25, + ) return noise_parameters @pytest.fixture() - def sampling_parameters(self, number_of_atoms, spatial_dimension, cell_dimensions, number_of_samples, - number_of_corrector_steps, unit_cell_size): - sampling_parameters = PredictorCorrectorSamplingParameters(number_of_corrector_steps=number_of_corrector_steps, - number_of_atoms=number_of_atoms, - number_of_samples=number_of_samples, - cell_dimensions=cell_dimensions, - spatial_dimension=spatial_dimension) + def sampling_parameters( + self, + number_of_atoms, + spatial_dimension, + cell_dimensions, + number_of_samples, + number_of_corrector_steps, + unit_cell_size, + ): + sampling_parameters = PredictorCorrectorSamplingParameters( + number_of_corrector_steps=number_of_corrector_steps, + number_of_atoms=number_of_atoms, + number_of_samples=number_of_samples, + cell_dimensions=cell_dimensions, + spatial_dimension=spatial_dimension, + ) return sampling_parameters @pytest.fixture() - def pc_generator(self, noise_parameters, sampling_parameters, sigma_normalized_score_network): - generator = LangevinGenerator(noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + def pc_generator( + self, noise_parameters, sampling_parameters, sigma_normalized_score_network + ): + generator = LangevinGenerator( + noise_parameters=noise_parameters, + sampling_parameters=sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) return generator - def test_smoke_sample(self, pc_generator, device, number_of_samples, unit_cell_sample): + def test_smoke_sample( + self, pc_generator, device, number_of_samples, unit_cell_sample + ): # Just a smoke test that we can sample without crashing. pc_generator.sample(number_of_samples, device, unit_cell_sample) @pytest.fixture() def x_i(self, number_of_samples, number_of_atoms, spatial_dimension, device): return map_relative_coordinates_to_unit_cell( - torch.rand(number_of_samples, number_of_atoms, spatial_dimension)).to(device) - - def test_predictor_step(self, mocker, pc_generator, noise_parameters, x_i, total_time_steps, number_of_samples, - unit_cell_sample): + torch.rand(number_of_samples, number_of_atoms, spatial_dimension) + ).to(device) + + def test_predictor_step( + self, + mocker, + pc_generator, + noise_parameters, + x_i, + total_time_steps, + number_of_samples, + unit_cell_sample, + ): sampler = ExplodingVarianceSampler(noise_parameters) noise, _ = sampler.get_all_sampling_parameters() @@ -71,7 +98,9 @@ def test_predictor_step(self, mocker, pc_generator, noise_parameters, x_i, total mocker.patch.object(pc_generator, "_draw_gaussian_sample", return_value=z) for index_i in range(1, total_time_steps + 1): - computed_sample = pc_generator.predictor_step(x_i, index_i, unit_cell_sample, forces) + computed_sample = pc_generator.predictor_step( + x_i, index_i, unit_cell_sample, forces + ) sigma_i = list_sigma[index_i - 1] t_i = list_time[index_i - 1] @@ -82,14 +111,27 @@ def test_predictor_step(self, mocker, pc_generator, noise_parameters, x_i, total g2 = sigma_i**2 - sigma_im1**2 - s_i = pc_generator._get_sigma_normalized_scores(x_i, t_i, sigma_i, unit_cell_sample, forces) / sigma_i + s_i = ( + pc_generator._get_sigma_normalized_scores( + x_i, t_i, sigma_i, unit_cell_sample, forces + ) + / sigma_i + ) expected_sample = x_i + g2 * s_i + torch.sqrt(g2) * z torch.testing.assert_close(computed_sample, expected_sample) - def test_corrector_step(self, mocker, pc_generator, noise_parameters, x_i, total_time_steps, number_of_samples, - unit_cell_sample): + def test_corrector_step( + self, + mocker, + pc_generator, + noise_parameters, + x_i, + total_time_steps, + number_of_samples, + unit_cell_sample, + ): sampler = ExplodingVarianceSampler(noise_parameters) noise, _ = sampler.get_all_sampling_parameters() @@ -104,19 +146,26 @@ def test_corrector_step(self, mocker, pc_generator, noise_parameters, x_i, total mocker.patch.object(pc_generator, "_draw_gaussian_sample", return_value=z) for index_i in range(0, total_time_steps): - computed_sample = pc_generator.corrector_step(x_i, index_i, unit_cell_sample, forces) + computed_sample = pc_generator.corrector_step( + x_i, index_i, unit_cell_sample, forces + ) if index_i == 0: sigma_i = sigma_min - t_i = 0. + t_i = 0.0 else: sigma_i = list_sigma[index_i - 1] t_i = list_time[index_i - 1] eps_i = 0.5 * epsilon * sigma_i**2 / sigma_1**2 - s_i = pc_generator._get_sigma_normalized_scores(x_i, t_i, sigma_i, unit_cell_sample, forces) / sigma_i + s_i = ( + pc_generator._get_sigma_normalized_scores( + x_i, t_i, sigma_i, unit_cell_sample, forces + ) + / sigma_i + ) - expected_sample = x_i + eps_i * s_i + torch.sqrt(2. * eps_i) * z + expected_sample = x_i + eps_i * s_i + torch.sqrt(2.0 * eps_i) * z torch.testing.assert_close(computed_sample, expected_sample) diff --git a/tests/generators/test_ode_position_generator.py b/tests/generators/test_ode_position_generator.py index 1564aba7..711ad09e 100644 --- a/tests/generators/test_ode_position_generator.py +++ b/tests/generators/test_ode_position_generator.py @@ -1,10 +1,10 @@ import pytest import torch -from crystal_diffusion.generators.ode_position_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) -from src.crystal_diffusion.samplers.variance_sampler import ( +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) - from tests.generators.conftest import BaseTestGenerator @@ -16,29 +16,45 @@ class TestExplodingVarianceODEPositionGenerator(BaseTestGenerator): @pytest.fixture() def noise_parameters(self, total_time_steps, sigma_min): - return NoiseParameters(total_time_steps=total_time_steps, time_delta=0., sigma_min=sigma_min) + return NoiseParameters( + total_time_steps=total_time_steps, time_delta=0.0, sigma_min=sigma_min + ) @pytest.fixture() - def sampling_parameters(self, number_of_atoms, spatial_dimension, cell_dimensions, - number_of_samples, record_samples): - sampling_parameters = ODESamplingParameters(number_of_atoms=number_of_atoms, - spatial_dimension=spatial_dimension, - number_of_samples=number_of_samples, - cell_dimensions=cell_dimensions, - record_samples=record_samples) + def sampling_parameters( + self, + number_of_atoms, + spatial_dimension, + cell_dimensions, + number_of_samples, + record_samples, + ): + sampling_parameters = ODESamplingParameters( + number_of_atoms=number_of_atoms, + spatial_dimension=spatial_dimension, + number_of_samples=number_of_samples, + cell_dimensions=cell_dimensions, + record_samples=record_samples, + ) return sampling_parameters @pytest.fixture() - def ode_generator(self, noise_parameters, sampling_parameters, sigma_normalized_score_network): - generator = ExplodingVarianceODEPositionGenerator(noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + def ode_generator( + self, noise_parameters, sampling_parameters, sigma_normalized_score_network + ): + generator = ExplodingVarianceODEPositionGenerator( + noise_parameters=noise_parameters, + sampling_parameters=sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) return generator def test_get_exploding_variance_sigma(self, ode_generator, noise_parameters): times = ExplodingVarianceSampler._get_time_array(noise_parameters) - expected_sigmas = ExplodingVarianceSampler._create_sigma_array(noise_parameters, times) + expected_sigmas = ExplodingVarianceSampler._create_sigma_array( + noise_parameters, times + ) computed_sigmas = ode_generator._get_exploding_variance_sigma(times) torch.testing.assert_close(expected_sigmas, computed_sigmas) @@ -46,17 +62,32 @@ def test_get_ode_prefactor(self, ode_generator, noise_parameters): times = ExplodingVarianceSampler._get_time_array(noise_parameters) sigmas = ode_generator._get_exploding_variance_sigma(times) - sig_ratio = torch.tensor(noise_parameters.sigma_max / noise_parameters.sigma_min) + sig_ratio = torch.tensor( + noise_parameters.sigma_max / noise_parameters.sigma_min + ) expected_ode_prefactor = torch.log(sig_ratio) * sigmas computed_ode_prefactor = ode_generator._get_ode_prefactor(sigmas) torch.testing.assert_close(expected_ode_prefactor, computed_ode_prefactor) - def test_smoke_sample(self, ode_generator, device, number_of_samples, - number_of_atoms, spatial_dimension, unit_cell_sample): + def test_smoke_sample( + self, + ode_generator, + device, + number_of_samples, + number_of_atoms, + spatial_dimension, + unit_cell_sample, + ): # Just a smoke test that we can sample without crashing. - relative_coordinates = ode_generator.sample(number_of_samples, device, unit_cell_sample) + relative_coordinates = ode_generator.sample( + number_of_samples, device, unit_cell_sample + ) - assert relative_coordinates.shape == (number_of_samples, number_of_atoms, spatial_dimension) + assert relative_coordinates.shape == ( + number_of_samples, + number_of_atoms, + spatial_dimension, + ) - assert relative_coordinates.min() >= 0. - assert relative_coordinates.max() < 1. + assert relative_coordinates.min() >= 0.0 + assert relative_coordinates.max() < 1.0 diff --git a/tests/generators/test_predictor_corrector_position_generator.py b/tests/generators/test_predictor_corrector_position_generator.py index b8ada474..49dd5f0f 100644 --- a/tests/generators/test_predictor_corrector_position_generator.py +++ b/tests/generators/test_predictor_corrector_position_generator.py @@ -1,10 +1,10 @@ import pytest import torch -from crystal_diffusion.generators.predictor_corrector_position_generator import \ + +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorPositionGenerator -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell - from tests.generators.conftest import BaseTestGenerator @@ -18,18 +18,26 @@ def __init__( spatial_dimension: int, initial_sample: torch.Tensor, ): - super().__init__(number_of_discretization_steps, number_of_corrector_steps, spatial_dimension) + super().__init__( + number_of_discretization_steps, number_of_corrector_steps, spatial_dimension + ) self.initial_sample = initial_sample def initialize(self, number_of_samples: int): return self.initial_sample - def predictor_step(self, x_ip1: torch.Tensor, ip1: int, unit_cell: torch.Tensor, forces: torch.Tensor - ) -> torch.Tensor: + def predictor_step( + self, + x_ip1: torch.Tensor, + ip1: int, + unit_cell: torch.Tensor, + forces: torch.Tensor, + ) -> torch.Tensor: return 1.2 * x_ip1 + 3.4 + ip1 / 111.0 - def corrector_step(self, x_i: torch.Tensor, i: int, unit_cell: torch.Tensor, forces: torch.Tensor - ) -> torch.Tensor: + def corrector_step( + self, x_i: torch.Tensor, i: int, unit_cell: torch.Tensor, forces: torch.Tensor + ) -> torch.Tensor: return 0.56 * x_i + 7.89 + i / 117.0 @@ -46,10 +54,17 @@ def initial_sample(self, number_of_samples, number_of_atoms, spatial_dimension): @pytest.fixture def generator( - self, number_of_discretization_steps, number_of_corrector_steps, spatial_dimension, initial_sample + self, + number_of_discretization_steps, + number_of_corrector_steps, + spatial_dimension, + initial_sample, ): generator = FakePCGenerator( - number_of_discretization_steps, number_of_corrector_steps, spatial_dimension, initial_sample + number_of_discretization_steps, + number_of_corrector_steps, + spatial_dimension, + initial_sample, ) return generator @@ -69,14 +84,24 @@ def expected_samples( noisy_sample = map_relative_coordinates_to_unit_cell(initial_sample) x_ip1 = noisy_sample for i in list_i: - xi = map_relative_coordinates_to_unit_cell(generator.predictor_step(x_ip1, i + 1, unit_cell_sample, - torch.zeros_like(x_ip1))) + xi = map_relative_coordinates_to_unit_cell( + generator.predictor_step( + x_ip1, i + 1, unit_cell_sample, torch.zeros_like(x_ip1) + ) + ) for _ in list_j: - xi = map_relative_coordinates_to_unit_cell(generator.corrector_step(xi, i, unit_cell_sample, - torch.zeros_like(xi))) + xi = map_relative_coordinates_to_unit_cell( + generator.corrector_step( + xi, i, unit_cell_sample, torch.zeros_like(xi) + ) + ) x_ip1 = xi return xi - def test_sample(self, generator, number_of_samples, expected_samples, unit_cell_sample): - computed_samples = generator.sample(number_of_samples, torch.device('cpu'), unit_cell_sample) + def test_sample( + self, generator, number_of_samples, expected_samples, unit_cell_sample + ): + computed_samples = generator.sample( + number_of_samples, torch.device("cpu"), unit_cell_sample + ) torch.testing.assert_close(expected_samples, computed_samples) diff --git a/tests/generators/test_sde_position_generator.py b/tests/generators/test_sde_position_generator.py index 7950e90d..9cb36372 100644 --- a/tests/generators/test_sde_position_generator.py +++ b/tests/generators/test_sde_position_generator.py @@ -1,10 +1,10 @@ import pytest import torch -from crystal_diffusion.generators.sde_position_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics.generators.sde_position_generator import ( SDE, ExplodingVarianceSDEPositionGenerator, SDESamplingParameters) -from src.crystal_diffusion.samplers.variance_sampler import ( +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) - from tests.generators.conftest import BaseTestGenerator @@ -14,40 +14,64 @@ class TestExplodingVarianceSDEPositionGenerator(BaseTestGenerator): @pytest.fixture() def initial_diffusion_time(self): - return torch.tensor(0.) + return torch.tensor(0.0) @pytest.fixture() def final_diffusion_time(self): - return torch.tensor(1.) + return torch.tensor(1.0) @pytest.fixture() def noise_parameters(self, total_time_steps, sigma_min): - return NoiseParameters(total_time_steps=total_time_steps, time_delta=0., sigma_min=sigma_min) + return NoiseParameters( + total_time_steps=total_time_steps, time_delta=0.0, sigma_min=sigma_min + ) @pytest.fixture() - def sampling_parameters(self, number_of_atoms, spatial_dimension, cell_dimensions, - number_of_samples, record_samples): - sampling_parameters = SDESamplingParameters(number_of_atoms=number_of_atoms, - spatial_dimension=spatial_dimension, - number_of_samples=number_of_samples, - cell_dimensions=cell_dimensions, - record_samples=record_samples) + def sampling_parameters( + self, + number_of_atoms, + spatial_dimension, + cell_dimensions, + number_of_samples, + record_samples, + ): + sampling_parameters = SDESamplingParameters( + number_of_atoms=number_of_atoms, + spatial_dimension=spatial_dimension, + number_of_samples=number_of_samples, + cell_dimensions=cell_dimensions, + record_samples=record_samples, + ) return sampling_parameters @pytest.fixture() - def sde(self, noise_parameters, sampling_parameters, sigma_normalized_score_network, unit_cell_sample, - initial_diffusion_time, final_diffusion_time): - sde = SDE(noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network, - unit_cells=unit_cell_sample, - initial_diffusion_time=initial_diffusion_time, - final_diffusion_time=final_diffusion_time) + def sde( + self, + noise_parameters, + sampling_parameters, + sigma_normalized_score_network, + unit_cell_sample, + initial_diffusion_time, + final_diffusion_time, + ): + sde = SDE( + noise_parameters=noise_parameters, + sampling_parameters=sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + unit_cells=unit_cell_sample, + initial_diffusion_time=initial_diffusion_time, + final_diffusion_time=final_diffusion_time, + ) return sde - def test_sde_get_diffusion_time(self, sde, initial_diffusion_time, final_diffusion_time): + def test_sde_get_diffusion_time( + self, sde, initial_diffusion_time, final_diffusion_time + ): - diffusion_time = (initial_diffusion_time + torch.rand(1) * (final_diffusion_time - initial_diffusion_time))[0] + diffusion_time = ( + initial_diffusion_time + + torch.rand(1) * (final_diffusion_time - initial_diffusion_time) + )[0] sde_time = final_diffusion_time - diffusion_time @@ -55,15 +79,25 @@ def test_sde_get_diffusion_time(self, sde, initial_diffusion_time, final_diffusi torch.testing.assert_close(computed_diffusion_time, diffusion_time) - def test_sde_g_squared(self, sde, noise_parameters, initial_diffusion_time, final_diffusion_time): + def test_sde_g_squared( + self, sde, noise_parameters, initial_diffusion_time, final_diffusion_time + ): - time_array = initial_diffusion_time + torch.rand(1) * (final_diffusion_time - initial_diffusion_time) + time_array = initial_diffusion_time + torch.rand(1) * ( + final_diffusion_time - initial_diffusion_time + ) - sigma = ExplodingVarianceSampler._create_sigma_array(noise_parameters=noise_parameters, - time_array=time_array)[0] + sigma = ExplodingVarianceSampler._create_sigma_array( + noise_parameters=noise_parameters, time_array=time_array + )[0] - expected_g_squared = (2. * sigma**2 - * torch.log(torch.tensor(noise_parameters.sigma_max / noise_parameters.sigma_min))) + expected_g_squared = ( + 2.0 + * sigma**2 + * torch.log( + torch.tensor(noise_parameters.sigma_max / noise_parameters.sigma_min) + ) + ) diffusion_time = time_array[0] @@ -72,18 +106,35 @@ def test_sde_g_squared(self, sde, noise_parameters, initial_diffusion_time, fina torch.testing.assert_close(computed_g_squared, expected_g_squared) @pytest.fixture() - def sde_generator(self, noise_parameters, sampling_parameters, sigma_normalized_score_network): - generator = ExplodingVarianceSDEPositionGenerator(noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + def sde_generator( + self, noise_parameters, sampling_parameters, sigma_normalized_score_network + ): + generator = ExplodingVarianceSDEPositionGenerator( + noise_parameters=noise_parameters, + sampling_parameters=sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) return generator - def test_smoke_sample(self, sde_generator, device, number_of_samples, - number_of_atoms, spatial_dimension, unit_cell_sample): + def test_smoke_sample( + self, + sde_generator, + device, + number_of_samples, + number_of_atoms, + spatial_dimension, + unit_cell_sample, + ): # Just a smoke test that we can sample without crashing. - relative_coordinates = sde_generator.sample(number_of_samples, device, unit_cell_sample) - - assert relative_coordinates.shape == (number_of_samples, number_of_atoms, spatial_dimension) - - assert relative_coordinates.min() >= 0. - assert relative_coordinates.max() < 1. + relative_coordinates = sde_generator.sample( + number_of_samples, device, unit_cell_sample + ) + + assert relative_coordinates.shape == ( + number_of_samples, + number_of_atoms, + spatial_dimension, + ) + + assert relative_coordinates.min() >= 0.0 + assert relative_coordinates.max() < 1.0 diff --git a/tests/models/score_network/test_force_field_augmented_score_network.py b/tests/models/score_network/test_force_field_augmented_score_network.py index 498b039b..573fe18e 100644 --- a/tests/models/score_network/test_force_field_augmented_score_network.py +++ b/tests/models/score_network/test_force_field_augmented_score_network.py @@ -1,12 +1,12 @@ import pytest import torch -from crystal_diffusion.models.score_networks.force_field_augmented_score_network import ( + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.force_field_augmented_score_network import ( ForceFieldAugmentedScoreNetwork, ForceFieldParameters) -from crystal_diffusion.models.score_networks.mlp_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mlp_score_network import ( MLPScoreNetwork, MLPScoreNetworkParameters) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) @pytest.mark.parametrize("number_of_atoms", [4, 8, 16]) @@ -144,8 +144,7 @@ def test_get_cartesian_pseudo_forces( ) ) cartesian_pseudo_force_contributions = ( - force_field_augmented_score_network._get_cartesian_pseudo_forces_contributions( - cartesian_displacements)) + force_field_augmented_score_network._get_cartesian_pseudo_forces_contributions(cartesian_displacements)) computed_cartesian_pseudo_forces = ( force_field_augmented_score_network._get_cartesian_pseudo_forces( diff --git a/tests/models/score_network/test_score_network.py b/tests/models/score_network/test_score_network.py index d9a60e94..2465523c 100644 --- a/tests/models/score_network/test_score_network.py +++ b/tests/models/score_network/test_score_network.py @@ -6,25 +6,25 @@ import numpy as np import pytest import torch -from crystal_diffusion.models.score_networks.diffusion_mace_score_network import ( + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.diffusion_mace_score_network import ( DiffusionMACEScoreNetwork, DiffusionMACEScoreNetworkParameters) -from crystal_diffusion.models.score_networks.egnn_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.egnn_score_network import ( EGNNScoreNetwork, EGNNScoreNetworkParameters) -from crystal_diffusion.models.score_networks.mace_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mace_score_network import ( MACEScoreNetwork, MACEScoreNetworkParameters) -from crystal_diffusion.models.score_networks.mlp_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mlp_score_network import ( MLPScoreNetwork, MLPScoreNetworkParameters) -from crystal_diffusion.models.score_networks.score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network import ( ScoreNetwork, ScoreNetworkParameters) -from crystal_diffusion.models.score_networks.score_network_factory import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network_factory import \ create_score_network_parameters -from crystal_diffusion.models.score_networks.score_prediction_head import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_prediction_head import ( MaceEquivariantScorePredictionHeadParameters, MaceMLPScorePredictionHeadParameters) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell @@ -53,8 +53,11 @@ def set_random_seed(self): @pytest.fixture() def base_score_network(self, spatial_dimension): - return ScoreNetwork(ScoreNetworkParameters(architecture='dummy', - spatial_dimension=spatial_dimension)) + return ScoreNetwork( + ScoreNetworkParameters( + architecture="dummy", spatial_dimension=spatial_dimension + ) + ) @pytest.fixture() def good_batch(self, spatial_dimension): @@ -63,7 +66,12 @@ def good_batch(self, spatial_dimension): times = torch.rand(batch_size, 1) noises = torch.rand(batch_size, 1) unit_cell = torch.rand(batch_size, spatial_dimension, spatial_dimension) - return {NOISY_RELATIVE_COORDINATES: relative_coordinates, TIME: times, NOISE: noises, UNIT_CELL: unit_cell} + return { + NOISY_RELATIVE_COORDINATES: relative_coordinates, + TIME: times, + NOISE: noises, + UNIT_CELL: unit_cell, + } @pytest.fixture() def bad_batch(self, good_batch, problem): @@ -72,13 +80,16 @@ def bad_batch(self, good_batch, problem): match problem: case "position_name": - bad_batch_dict['bad_position_name'] = bad_batch_dict[NOISY_RELATIVE_COORDINATES] + bad_batch_dict["bad_position_name"] = bad_batch_dict[ + NOISY_RELATIVE_COORDINATES + ] del bad_batch_dict[NOISY_RELATIVE_COORDINATES] case "position_shape": shape = bad_batch_dict[NOISY_RELATIVE_COORDINATES].shape - bad_batch_dict[NOISY_RELATIVE_COORDINATES] = \ - bad_batch_dict[NOISY_RELATIVE_COORDINATES].reshape(shape[0], shape[1] // 2, shape[2] * 2) + bad_batch_dict[NOISY_RELATIVE_COORDINATES] = bad_batch_dict[ + NOISY_RELATIVE_COORDINATES + ].reshape(shape[0], shape[1] // 2, shape[2] * 2) case "position_range1": bad_batch_dict[NOISY_RELATIVE_COORDINATES][0, 0, 0] = 1.01 @@ -87,20 +98,24 @@ def bad_batch(self, good_batch, problem): bad_batch_dict[NOISY_RELATIVE_COORDINATES][1, 0, 0] = -0.01 case "time_name": - bad_batch_dict['bad_time_name'] = bad_batch_dict[TIME] + bad_batch_dict["bad_time_name"] = bad_batch_dict[TIME] del bad_batch_dict[TIME] case "time_shape": shape = bad_batch_dict[TIME].shape - bad_batch_dict[TIME] = bad_batch_dict[TIME].reshape(shape[0] // 2, shape[1] * 2) + bad_batch_dict[TIME] = bad_batch_dict[TIME].reshape( + shape[0] // 2, shape[1] * 2 + ) case "noise_name": - bad_batch_dict['bad_noise_name'] = bad_batch_dict[NOISE] + bad_batch_dict["bad_noise_name"] = bad_batch_dict[NOISE] del bad_batch_dict[NOISE] case "noise_shape": shape = bad_batch_dict[NOISE].shape - bad_batch_dict[NOISE] = bad_batch_dict[NOISE].reshape(shape[0] // 2, shape[1] * 2) + bad_batch_dict[NOISE] = bad_batch_dict[NOISE].reshape( + shape[0] // 2, shape[1] * 2 + ) case "time_range1": bad_batch_dict[TIME][5, 0] = 2.00 @@ -108,21 +123,37 @@ def bad_batch(self, good_batch, problem): bad_batch_dict[TIME][0, 0] = -0.05 case "cell_name": - bad_batch_dict['bad_unit_cell_key'] = bad_batch_dict[UNIT_CELL] + bad_batch_dict["bad_unit_cell_key"] = bad_batch_dict[UNIT_CELL] del bad_batch_dict[UNIT_CELL] case "cell_shape": shape = bad_batch_dict[UNIT_CELL].shape - bad_batch_dict[UNIT_CELL] = bad_batch_dict[UNIT_CELL].reshape(shape[0] // 2, shape[1] * 2, shape[2]) + bad_batch_dict[UNIT_CELL] = bad_batch_dict[UNIT_CELL].reshape( + shape[0] // 2, shape[1] * 2, shape[2] + ) return bad_batch_dict def test_check_batch_good(self, base_score_network, good_batch): base_score_network._check_batch(good_batch) - @pytest.mark.parametrize('problem', ['position_name', 'time_name', 'position_shape', - 'time_shape', 'noise_name', 'noise_shape', 'position_range1', - 'position_range2', 'time_range1', 'time_range2', 'cell_name', 'cell_shape']) + @pytest.mark.parametrize( + "problem", + [ + "position_name", + "time_name", + "position_shape", + "time_shape", + "noise_name", + "noise_shape", + "position_range1", + "position_range2", + "time_range1", + "time_range2", + "cell_name", + "cell_shape", + ], + ) def test_check_batch_bad(self, base_score_network, bad_batch): with pytest.raises(AssertionError): base_score_network._check_batch(bad_batch) @@ -137,11 +168,15 @@ class BaseTestScoreNetwork: @pytest.fixture() def score_network_parameters(self, *args): - raise NotImplementedError("This fixture must be implemented in the derived class.") + raise NotImplementedError( + "This fixture must be implemented in the derived class." + ) @pytest.fixture() def score_network(self, *args): - raise NotImplementedError("This fixture must be implemented in the derived class.") + raise NotImplementedError( + "This fixture must be implemented in the derived class." + ) @pytest.fixture(scope="class", autouse=True) def set_random_seed(self): @@ -158,18 +193,31 @@ def number_of_atoms(self): @pytest.fixture() def basis_vectors(self, batch_size, spatial_dimension): # orthogonal boxes with dimensions between 5 and 10. - orthogonal_boxes = torch.stack([torch.diag(5. + 5. * torch.rand(spatial_dimension)) for _ in range(batch_size)]) + orthogonal_boxes = torch.stack( + [ + torch.diag(5.0 + 5.0 * torch.rand(spatial_dimension)) + for _ in range(batch_size) + ] + ) # add a bit of noise to make the vectors not quite orthogonal - basis_vectors = orthogonal_boxes + 0.1 * torch.randn(batch_size, spatial_dimension, spatial_dimension) + basis_vectors = orthogonal_boxes + 0.1 * torch.randn( + batch_size, spatial_dimension, spatial_dimension + ) return basis_vectors @pytest.fixture - def relative_coordinates(self, batch_size, number_of_atoms, spatial_dimension, basis_vectors): - relative_coordinates = torch.rand(batch_size, number_of_atoms, spatial_dimension) + def relative_coordinates( + self, batch_size, number_of_atoms, spatial_dimension, basis_vectors + ): + relative_coordinates = torch.rand( + batch_size, number_of_atoms, spatial_dimension + ) return relative_coordinates @pytest.fixture - def cartesian_forces(self, batch_size, number_of_atoms, spatial_dimension, basis_vectors): + def cartesian_forces( + self, batch_size, number_of_atoms, spatial_dimension, basis_vectors + ): cartesian_forces = torch.rand(batch_size, number_of_atoms, spatial_dimension) return cartesian_forces @@ -187,16 +235,25 @@ def expected_score_shape(self, batch_size, number_of_atoms, spatial_dimension): return batch_size, number_of_atoms, spatial_dimension @pytest.fixture() - def batch(self, relative_coordinates, cartesian_forces, times, noises, basis_vectors): - return {NOISY_RELATIVE_COORDINATES: relative_coordinates, TIME: times, UNIT_CELL: basis_vectors, NOISE: noises, - CARTESIAN_FORCES: cartesian_forces} + def batch( + self, relative_coordinates, cartesian_forces, times, noises, basis_vectors + ): + return { + NOISY_RELATIVE_COORDINATES: relative_coordinates, + TIME: times, + UNIT_CELL: basis_vectors, + NOISE: noises, + CARTESIAN_FORCES: cartesian_forces, + } @pytest.fixture() def global_parameters_dictionary(self, spatial_dimension): return dict(spatial_dimension=spatial_dimension, irrelevant=123) @pytest.fixture() - def score_network_dictionary(self, score_network_parameters, global_parameters_dictionary): + def score_network_dictionary( + self, score_network_parameters, global_parameters_dictionary + ): dictionary = asdict(score_network_parameters) for key in global_parameters_dictionary.keys(): if key in dictionary: @@ -207,12 +264,18 @@ def test_output_shape(self, score_network, batch, expected_score_shape): scores = score_network(batch) assert scores.shape == expected_score_shape - def test_create_score_network_parameters(self, score_network_parameters, - score_network_dictionary, - global_parameters_dictionary): - computed_score_network_parameters = create_score_network_parameters(score_network_dictionary, - global_parameters_dictionary) - assert_parameters_are_the_same(computed_score_network_parameters, score_network_parameters) + def test_create_score_network_parameters( + self, + score_network_parameters, + score_network_dictionary, + global_parameters_dictionary, + ): + computed_score_network_parameters = create_score_network_parameters( + score_network_dictionary, global_parameters_dictionary + ) + assert_parameters_are_the_same( + computed_score_network_parameters, score_network_parameters + ) @pytest.mark.parametrize("spatial_dimension", [2, 3]) @@ -222,13 +285,21 @@ def test_create_score_network_parameters(self, score_network_parameters, class TestMLPScoreNetwork(BaseTestScoreNetwork): @pytest.fixture() - def score_network_parameters(self, number_of_atoms, spatial_dimension, - embedding_dimensions_size, n_hidden_dimensions, hidden_dimensions_size): - return MLPScoreNetworkParameters(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms, - embedding_dimensions_size=embedding_dimensions_size, - n_hidden_dimensions=n_hidden_dimensions, - hidden_dimensions_size=hidden_dimensions_size) + def score_network_parameters( + self, + number_of_atoms, + spatial_dimension, + embedding_dimensions_size, + n_hidden_dimensions, + hidden_dimensions_size, + ): + return MLPScoreNetworkParameters( + spatial_dimension=spatial_dimension, + number_of_atoms=number_of_atoms, + embedding_dimensions_size=embedding_dimensions_size, + n_hidden_dimensions=n_hidden_dimensions, + hidden_dimensions_size=hidden_dimensions_size, + ) @pytest.fixture() def score_network(self, score_network_parameters): @@ -241,18 +312,26 @@ def score_network(self, score_network_parameters): class TestMACEScoreNetworkMLPHead(BaseTestScoreNetwork): @pytest.fixture() - def prediction_head_parameters(self, spatial_dimension, n_hidden_dimensions, hidden_dimensions_size): - prediction_head_parameters = MaceMLPScorePredictionHeadParameters(spatial_dimension=spatial_dimension, - hidden_dimensions_size=hidden_dimensions_size, - n_hidden_dimensions=n_hidden_dimensions) + def prediction_head_parameters( + self, spatial_dimension, n_hidden_dimensions, hidden_dimensions_size + ): + prediction_head_parameters = MaceMLPScorePredictionHeadParameters( + spatial_dimension=spatial_dimension, + hidden_dimensions_size=hidden_dimensions_size, + n_hidden_dimensions=n_hidden_dimensions, + ) return prediction_head_parameters @pytest.fixture() - def score_network_parameters(self, number_of_atoms, spatial_dimension, prediction_head_parameters): - return MACEScoreNetworkParameters(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms, - r_max=3.0, - prediction_head_parameters=prediction_head_parameters) + def score_network_parameters( + self, number_of_atoms, spatial_dimension, prediction_head_parameters + ): + return MACEScoreNetworkParameters( + spatial_dimension=spatial_dimension, + number_of_atoms=number_of_atoms, + r_max=3.0, + prediction_head_parameters=prediction_head_parameters, + ) @pytest.fixture() def score_network(self, score_network_parameters): @@ -263,16 +342,21 @@ def score_network(self, score_network_parameters): class TestMACEScoreNetworkEquivariantHead(BaseTestScoreNetwork): @pytest.fixture() def prediction_head_parameters(self, spatial_dimension): - prediction_head_parameters = MaceEquivariantScorePredictionHeadParameters(spatial_dimension=spatial_dimension, - number_of_layers=2) + prediction_head_parameters = MaceEquivariantScorePredictionHeadParameters( + spatial_dimension=spatial_dimension, number_of_layers=2 + ) return prediction_head_parameters @pytest.fixture() - def score_network_parameters(self, number_of_atoms, spatial_dimension, prediction_head_parameters): - return MACEScoreNetworkParameters(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms, - r_max=3.0, - prediction_head_parameters=prediction_head_parameters) + def score_network_parameters( + self, number_of_atoms, spatial_dimension, prediction_head_parameters + ): + return MACEScoreNetworkParameters( + spatial_dimension=spatial_dimension, + number_of_atoms=number_of_atoms, + r_max=3.0, + prediction_head_parameters=prediction_head_parameters, + ) @pytest.fixture() def score_network(self, score_network_parameters): @@ -283,17 +367,18 @@ def score_network(self, score_network_parameters): class TestDiffusionMACEScoreNetwork(BaseTestScoreNetwork): @pytest.fixture() def score_network_parameters(self, number_of_atoms, spatial_dimension): - return DiffusionMACEScoreNetworkParameters(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms, - r_max=3.0, - num_bessel=4, - num_polynomial_cutoff=3, - hidden_irreps="8x0e + 8x1o", - mlp_irreps="8x0e", - number_of_mlp_layers=1, - correlation=2, - radial_MLP=[8, 8, 8], - ) + return DiffusionMACEScoreNetworkParameters( + spatial_dimension=spatial_dimension, + number_of_atoms=number_of_atoms, + r_max=3.0, + num_bessel=4, + num_polynomial_cutoff=3, + hidden_irreps="8x0e + 8x1o", + mlp_irreps="8x0e", + number_of_mlp_layers=1, + correlation=2, + radial_MLP=[8, 8, 8], + ) @pytest.fixture() def score_network(self, score_network_parameters): @@ -320,7 +405,12 @@ def basis_vectors(self, batch_size, spatial_dimension): # The basis vectors should form a cube in order to test the equivariance of the current implementation # of the EGNN model. The octaheral point group only applies in this case! acell = 5.5 - cubes = torch.stack([torch.diag(acell * torch.ones(spatial_dimension)) for _ in range(batch_size)]) + cubes = torch.stack( + [ + torch.diag(acell * torch.ones(spatial_dimension)) + for _ in range(batch_size) + ] + ) return cubes @pytest.fixture(params=[("fully_connected", None), ("radial_cutoff", 3.0)]) @@ -335,8 +425,14 @@ def score_network(self, score_network_parameters): @pytest.fixture() def octahedral_point_group_symmetries(self): - permutations = [torch.diag(torch.ones(3))[[idx]] for idx in itertools.permutations([0, 1, 2])] - sign_changes = [torch.diag(torch.tensor(diag)) for diag in itertools.product([-1., 1.], repeat=3)] + permutations = [ + torch.diag(torch.ones(3))[[idx]] + for idx in itertools.permutations([0, 1, 2]) + ] + sign_changes = [ + torch.diag(torch.tensor(diag)) + for diag in itertools.product([-1.0, 1.0], repeat=3) + ] symmetries = [] for permutation in permutations: @@ -345,14 +441,20 @@ def octahedral_point_group_symmetries(self): return symmetries - @pytest.mark.parametrize("edges, radial_cutoff", [("fully_connected", 3.0), ("radial_cutoff", None)]) + @pytest.mark.parametrize( + "edges, radial_cutoff", [("fully_connected", 3.0), ("radial_cutoff", None)] + ) def test_score_network_parameters(self, edges, radial_cutoff): - score_network_parameters = EGNNScoreNetworkParameters(edges=edges, radial_cutoff=radial_cutoff) + score_network_parameters = EGNNScoreNetworkParameters( + edges=edges, radial_cutoff=radial_cutoff + ) with pytest.raises(AssertionError): # Check that the code crashes when inconsistent parameters are fed in. EGNNScoreNetwork(score_network_parameters) - def test_create_block_diagonal_projection_matrices(self, score_network, spatial_dimension): + def test_create_block_diagonal_projection_matrices( + self, score_network, spatial_dimension + ): expected_matrices = [] for space_idx in range(spatial_dimension): matrix = torch.zeros(2 * spatial_dimension, 2 * spatial_dimension) @@ -362,15 +464,18 @@ def test_create_block_diagonal_projection_matrices(self, score_network, spatial_ expected_matrices = torch.stack(expected_matrices) - computed_matrices = score_network._create_block_diagonal_projection_matrices(spatial_dimension) + computed_matrices = score_network._create_block_diagonal_projection_matrices( + spatial_dimension + ) torch.testing.assert_close(computed_matrices, expected_matrices) @pytest.fixture() def flat_relative_coordinates(self, batch): relative_coordinates = batch[NOISY_RELATIVE_COORDINATES] - flat_relative_coordinates = einops.rearrange(relative_coordinates, - "batch natom space -> (batch natom) space") + flat_relative_coordinates = einops.rearrange( + relative_coordinates, "batch natom space -> (batch natom) space" + ) return flat_relative_coordinates @pytest.fixture() @@ -389,18 +494,32 @@ def expected_euclidean_positions(self, flat_relative_coordinates): expected_euclidean_positions = torch.stack(expected_euclidean_positions) return expected_euclidean_positions - def test_get_euclidean_positions(self, score_network, flat_relative_coordinates, expected_euclidean_positions): - computed_euclidean_positions = score_network._get_euclidean_positions(flat_relative_coordinates) - torch.testing.assert_close(expected_euclidean_positions, computed_euclidean_positions) + def test_get_euclidean_positions( + self, score_network, flat_relative_coordinates, expected_euclidean_positions + ): + computed_euclidean_positions = score_network._get_euclidean_positions( + flat_relative_coordinates + ) + torch.testing.assert_close( + expected_euclidean_positions, computed_euclidean_positions + ) @pytest.fixture() def global_translations(self, batch_size, number_of_atoms, spatial_dimension): - translations = einops.repeat(torch.rand(batch_size, spatial_dimension), - "batch spatial_dimension -> batch natoms spatial_dimension", - natoms=number_of_atoms) + translations = einops.repeat( + torch.rand(batch_size, spatial_dimension), + "batch spatial_dimension -> batch natoms spatial_dimension", + natoms=number_of_atoms, + ) return translations - def test_equivariance(self, score_network, batch, octahedral_point_group_symmetries, global_translations): + def test_equivariance( + self, + score_network, + batch, + octahedral_point_group_symmetries, + global_translations, + ): with torch.no_grad(): normalized_scores = score_network(batch) @@ -410,7 +529,9 @@ def test_equivariance(self, score_network, batch, octahedral_point_group_symmetr relative_coordinates = modified_batch[NOISY_RELATIVE_COORDINATES] op_relative_coordinates = relative_coordinates @ op + global_translations - op_relative_coordinates = map_relative_coordinates_to_unit_cell(op_relative_coordinates) + op_relative_coordinates = map_relative_coordinates_to_unit_cell( + op_relative_coordinates + ) modified_batch[NOISY_RELATIVE_COORDINATES] = op_relative_coordinates with torch.no_grad(): @@ -418,4 +539,6 @@ def test_equivariance(self, score_network, batch, octahedral_point_group_symmetr expected_modified_normalized_scores = normalized_scores @ op - torch.testing.assert_close(expected_modified_normalized_scores, modified_normalized_scores) + torch.testing.assert_close( + expected_modified_normalized_scores, modified_normalized_scores + ) diff --git a/tests/models/score_network/test_score_prediction_head.py b/tests/models/score_network/test_score_prediction_head.py index 447cf336..7c7d1b76 100644 --- a/tests/models/score_network/test_score_prediction_head.py +++ b/tests/models/score_network/test_score_prediction_head.py @@ -1,11 +1,12 @@ import pytest import torch -from crystal_diffusion.models.mace_utils import \ +from e3nn import o3 + +from diffusion_for_multi_scale_molecular_dynamics.models.mace_utils import \ build_mace_output_nodes_irreducible_representation -from crystal_diffusion.models.score_networks.score_prediction_head import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_prediction_head import ( MaceEquivariantScorePredictionHead, MaceEquivariantScorePredictionHeadParameters) -from e3nn import o3 class TestMaceEquivariantScorePredictionHead: @@ -33,17 +34,23 @@ def hidden_irreps_string(self): @pytest.fixture() def output_node_features_irreps(self, hidden_irreps_string, num_interactions): output_node_features_irreps = ( - build_mace_output_nodes_irreducible_representation(hidden_irreps_string, num_interactions)) + build_mace_output_nodes_irreducible_representation( + hidden_irreps_string, num_interactions + ) + ) return output_node_features_irreps @pytest.fixture() def parameters(self): - return MaceEquivariantScorePredictionHeadParameters(time_embedding_irreps="4x0e", - number_of_layers=2) + return MaceEquivariantScorePredictionHeadParameters( + time_embedding_irreps="4x0e", number_of_layers=2 + ) @pytest.fixture() def prediction_head(self, output_node_features_irreps, parameters): - head = MaceEquivariantScorePredictionHead(output_node_features_irreps, parameters) + head = MaceEquivariantScorePredictionHead( + output_node_features_irreps, parameters + ) head.eval() return head @@ -56,12 +63,19 @@ def flat_times(self, times, number_of_atoms): return torch.repeat_interleave(times, number_of_atoms).reshape(-1, 1) @pytest.fixture() - def flat_node_features(self, batch_size, number_of_atoms, output_node_features_irreps): + def flat_node_features( + self, batch_size, number_of_atoms, output_node_features_irreps + ): flat_batch_size = batch_size * number_of_atoms return output_node_features_irreps.randn(flat_batch_size, -1) - def test_predictions_are_equivariant(self, prediction_head, flat_node_features, - flat_times, output_node_features_irreps): + def test_predictions_are_equivariant( + self, + prediction_head, + flat_node_features, + flat_times, + output_node_features_irreps, + ): vector_irreps = o3.Irreps("1x1o") random_rotation = o3.rand_matrix() diff --git a/tests/models/test_analytical_score_network.py b/tests/models/test_analytical_score_network.py index 7a14d3e4..b2d0af03 100644 --- a/tests/models/test_analytical_score_network.py +++ b/tests/models/test_analytical_score_network.py @@ -2,11 +2,12 @@ import pytest import torch -from crystal_diffusion.models.score_networks.analytical_score_network import ( + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) -from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, - TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) def factorial(n): @@ -52,33 +53,48 @@ def number_of_atoms(self, request): def equilibrium_relative_coordinates(self, number_of_atoms, spatial_dimension): return torch.rand(number_of_atoms, spatial_dimension) - @pytest.fixture(params=['finite', 'zero']) + @pytest.fixture(params=["finite", "zero"]) def variance_parameter(self, request): - if request.param == 'zero': - return 0. - elif request.param == 'finite': + if request.param == "zero": + return 0.0 + elif request.param == "finite": # Make the spring constants pretty large so that the displacements will be small inverse_variance = float(1000 * torch.rand(1)) - return 1. / inverse_variance + return 1.0 / inverse_variance @pytest.fixture() def batch(self, batch_size, number_of_atoms, spatial_dimension): - relative_coordinates = torch.rand(batch_size, number_of_atoms, spatial_dimension) + relative_coordinates = torch.rand( + batch_size, number_of_atoms, spatial_dimension + ) times = torch.rand(batch_size, 1) noises = torch.rand(batch_size, 1) unit_cell = torch.rand(batch_size, spatial_dimension, spatial_dimension) - return {NOISY_RELATIVE_COORDINATES: relative_coordinates, TIME: times, NOISE: noises, UNIT_CELL: unit_cell} + return { + NOISY_RELATIVE_COORDINATES: relative_coordinates, + TIME: times, + NOISE: noises, + UNIT_CELL: unit_cell, + } @pytest.fixture() - def score_network_parameters(self, number_of_atoms, spatial_dimension, kmax, - equilibrium_relative_coordinates, variance_parameter, use_permutation_invariance): + def score_network_parameters( + self, + number_of_atoms, + spatial_dimension, + kmax, + equilibrium_relative_coordinates, + variance_parameter, + use_permutation_invariance, + ): hyper_params = AnalyticalScoreNetworkParameters( number_of_atoms=number_of_atoms, spatial_dimension=spatial_dimension, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, variance_parameter=variance_parameter, - use_permutation_invariance=use_permutation_invariance) + use_permutation_invariance=use_permutation_invariance, + ) return hyper_params @pytest.fixture() @@ -94,30 +110,46 @@ def test_all_translations(self, kmax): expected_translations = torch.tensor(list(range(-kmax, kmax + 1))) torch.testing.assert_close(expected_translations, computed_translations) - def test_get_all_equilibrium_permutations(self, number_of_atoms, spatial_dimension, - equilibrium_relative_coordinates): + def test_get_all_equilibrium_permutations( + self, number_of_atoms, spatial_dimension, equilibrium_relative_coordinates + ): expected_permutations = [] for permutation_indices in itertools.permutations(range(number_of_atoms)): - expected_permutations.append(equilibrium_relative_coordinates[list(permutation_indices)]) + expected_permutations.append( + equilibrium_relative_coordinates[list(permutation_indices)] + ) expected_permutations = torch.stack(expected_permutations) computed_permutations = ( - AnalyticalScoreNetwork._get_all_equilibrium_permutations(equilibrium_relative_coordinates)) + AnalyticalScoreNetwork._get_all_equilibrium_permutations( + equilibrium_relative_coordinates + ) + ) - assert computed_permutations.shape == (factorial(number_of_atoms), number_of_atoms, spatial_dimension) + assert computed_permutations.shape == ( + factorial(number_of_atoms), + number_of_atoms, + spatial_dimension, + ) torch.testing.assert_close(expected_permutations, computed_permutations) - @pytest.mark.parametrize('use_permutation_invariance', [False]) - def test_compute_unnormalized_log_probability(self, equilibrium_relative_coordinates, variance_parameter, - kmax, batch, score_network): + @pytest.mark.parametrize("use_permutation_invariance", [False]) + def test_compute_unnormalized_log_probability( + self, + equilibrium_relative_coordinates, + variance_parameter, + kmax, + batch, + score_network, + ): sigmas = batch[NOISE] # dimension: [batch_size, 1] xt = batch[NOISY_RELATIVE_COORDINATES] - computed_log_prob = score_network._compute_unnormalized_log_probability(sigmas, - xt, - equilibrium_relative_coordinates) + computed_log_prob = score_network._compute_unnormalized_log_probability( + sigmas, xt, equilibrium_relative_coordinates + ) batch_size = sigmas.shape[0] @@ -131,25 +163,39 @@ def test_compute_unnormalized_log_probability(self, equilibrium_relative_coordin eq_coordinate = equilibrium_relative_coordinates[i, alpha] coordinate = xt[batch_idx, i, alpha] - sum_on_k = torch.tensor(0.) + sum_on_k = torch.tensor(0.0) for k in range(-kmax, kmax + 1): - exponent = -0.5 * (coordinate - eq_coordinate - k)**2 / (sigma**2 + variance_parameter) + exponent = ( + -0.5 + * (coordinate - eq_coordinate - k) ** 2 + / (sigma**2 + variance_parameter) + ) sum_on_k += torch.exp(exponent) expected_log_prob[batch_idx] += torch.log(sum_on_k) torch.testing.assert_close(expected_log_prob, computed_log_prob) - @pytest.mark.parametrize('number_of_atoms, use_permutation_invariance', [(1, False), (1, True), - (2, False), (2, True), (8, False)]) - def test_analytical_score_network(self, score_network, batch, batch_size, number_of_atoms, spatial_dimension): + @pytest.mark.parametrize( + "number_of_atoms, use_permutation_invariance", + [(1, False), (1, True), (2, False), (2, True), (8, False)], + ) + def test_analytical_score_network( + self, score_network, batch, batch_size, number_of_atoms, spatial_dimension + ): normalized_scores = score_network.forward(batch) - assert normalized_scores.shape == (batch_size, number_of_atoms, spatial_dimension) - - @pytest.mark.parametrize('use_permutation_invariance', [False]) - @pytest.mark.parametrize('number_of_atoms', [1, 2, 8]) - def test_compare_score_networks(self, score_network, target_score_based_score_network, batch): + assert normalized_scores.shape == ( + batch_size, + number_of_atoms, + spatial_dimension, + ) + + @pytest.mark.parametrize("use_permutation_invariance", [False]) + @pytest.mark.parametrize("number_of_atoms", [1, 2, 8]) + def test_compare_score_networks( + self, score_network, target_score_based_score_network, batch + ): normalized_scores1 = score_network.forward(batch) normalized_scores2 = target_score_based_score_network.forward(batch) diff --git a/tests/models/test_diffusion_mace.py b/tests/models/test_diffusion_mace.py index db03e61b..4f43f356 100644 --- a/tests/models/test_diffusion_mace.py +++ b/tests/models/test_diffusion_mace.py @@ -1,18 +1,17 @@ import pytest import torch -from crystal_diffusion.models.diffusion_mace import (DiffusionMACE, - LinearVectorReadoutBlock, - input_to_diffusion_mace) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_CARTESIAN_POSITIONS, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import ( +from e3nn import o3 +from mace.modules import gate_dict, interaction_classes + +from diffusion_for_multi_scale_molecular_dynamics.models.diffusion_mace import ( + DiffusionMACE, LinearVectorReadoutBlock, input_to_diffusion_mace) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_CARTESIAN_POSITIONS, + NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import ( get_positions_from_coordinates, get_reciprocal_basis_vectors, get_relative_coordinates_from_cartesian_positions, map_relative_coordinates_to_unit_cell) -from e3nn import o3 -from mace.modules import gate_dict, interaction_classes def test_linear_vector_readout_block(): @@ -44,73 +43,95 @@ def set_seed(self): """Set the random seed.""" torch.manual_seed(234233) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def batch_size(self): return 4 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def number_of_atoms(self): return 8 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def spatial_dimension(self): return 3 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def basis_vectors(self, batch_size, spatial_dimension): # orthogonal boxes with dimensions between 5 and 10. - orthogonal_boxes = torch.stack([torch.diag(5. + 5. * torch.rand(spatial_dimension)) - for _ in range(batch_size)]) + orthogonal_boxes = torch.stack( + [ + torch.diag(5.0 + 5.0 * torch.rand(spatial_dimension)) + for _ in range(batch_size) + ] + ) # add a bit of noise to make the vectors not quite orthogonal - basis_vectors = orthogonal_boxes + 0.1 * torch.randn(batch_size, spatial_dimension, spatial_dimension) + basis_vectors = orthogonal_boxes + 0.1 * torch.randn( + batch_size, spatial_dimension, spatial_dimension + ) return basis_vectors - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def reciprocal_basis_vectors(self, basis_vectors): return get_reciprocal_basis_vectors(basis_vectors) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def relative_coordinates(self, batch_size, number_of_atoms, spatial_dimension): - relative_coordinates = torch.rand(batch_size, number_of_atoms, spatial_dimension) + relative_coordinates = torch.rand( + batch_size, number_of_atoms, spatial_dimension + ) return relative_coordinates - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def cartesian_positions(self, relative_coordinates, basis_vectors): return get_positions_from_coordinates(relative_coordinates, basis_vectors) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def times(self, batch_size): return torch.rand(batch_size, 1) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def noises(self, batch_size): return 0.5 * torch.rand(batch_size, 1) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def forces(self, batch_size, spatial_dimension): return 0.5 * torch.rand(batch_size, spatial_dimension) - @pytest.fixture(scope='class') - def batch(self, relative_coordinates, cartesian_positions, basis_vectors, times, noises, forces): - batch = {NOISY_RELATIVE_COORDINATES: relative_coordinates, - NOISY_CARTESIAN_POSITIONS: cartesian_positions, - TIME: times, - NOISE: noises, - UNIT_CELL: basis_vectors, - CARTESIAN_FORCES: forces} + @pytest.fixture(scope="class") + def batch( + self, + relative_coordinates, + cartesian_positions, + basis_vectors, + times, + noises, + forces, + ): + batch = { + NOISY_RELATIVE_COORDINATES: relative_coordinates, + NOISY_CARTESIAN_POSITIONS: cartesian_positions, + TIME: times, + NOISE: noises, + UNIT_CELL: basis_vectors, + CARTESIAN_FORCES: forces, + } return batch - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def cartesian_rotations(self, batch_size): return o3.rand_matrix(batch_size) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def permutations(self, batch_size, number_of_atoms): return torch.stack([torch.randperm(number_of_atoms) for _ in range(batch_size)]) - @pytest.fixture(scope='class') - def cartesian_translations(self, batch_size, number_of_atoms, spatial_dimension, basis_vectors): - batch_relative_coordinates_translations = torch.rand(batch_size, spatial_dimension) + @pytest.fixture(scope="class") + def cartesian_translations( + self, batch_size, number_of_atoms, spatial_dimension, basis_vectors + ): + batch_relative_coordinates_translations = torch.rand( + batch_size, spatial_dimension + ) batch_cartesian_translations = [] for t, cell in zip(batch_relative_coordinates_translations, basis_vectors): @@ -118,8 +139,9 @@ def cartesian_translations(self, batch_size, number_of_atoms, spatial_dimension, batch_cartesian_translations = torch.stack(batch_cartesian_translations) - cartesian_translations = torch.repeat_interleave(batch_cartesian_translations.unsqueeze(1), - number_of_atoms, dim=1) + cartesian_translations = torch.repeat_interleave( + batch_cartesian_translations.unsqueeze(1), number_of_atoms, dim=1 + ) return cartesian_translations @pytest.fixture() @@ -129,26 +151,27 @@ def r_max(self): @pytest.fixture() def hyperparameters(self, r_max): - hps = dict(r_max=r_max, - num_bessel=8, - num_polynomial_cutoff=5, - num_edge_hidden_layers=0, - edge_hidden_irreps=o3.Irreps("8x0e"), - max_ell=2, - num_elements=1, - atomic_numbers=[14], - interaction_cls=interaction_classes["RealAgnosticResidualInteractionBlock"], - interaction_cls_first=interaction_classes["RealAgnosticInteractionBlock"], - num_interactions=2, - hidden_irreps=o3.Irreps("8x0e + 8x1o + 8x2e"), - mlp_irreps=o3.Irreps("8x0e"), - number_of_mlp_layers=2, - avg_num_neighbors=1, - correlation=2, - gate=gate_dict["silu"], - radial_MLP=[8, 8, 8], - radial_type="bessel", - ) + hps = dict( + r_max=r_max, + num_bessel=8, + num_polynomial_cutoff=5, + num_edge_hidden_layers=0, + edge_hidden_irreps=o3.Irreps("8x0e"), + max_ell=2, + num_elements=1, + atomic_numbers=[14], + interaction_cls=interaction_classes["RealAgnosticResidualInteractionBlock"], + interaction_cls_first=interaction_classes["RealAgnosticInteractionBlock"], + num_interactions=2, + hidden_irreps=o3.Irreps("8x0e + 8x1o + 8x2e"), + mlp_irreps=o3.Irreps("8x0e"), + number_of_mlp_layers=2, + avg_num_neighbors=1, + correlation=2, + gate=gate_dict["silu"], + radial_MLP=[8, 8, 8], + radial_type="bessel", + ) return hps @pytest.fixture() @@ -162,23 +185,44 @@ def graph_input(self, batch, r_max): return input_to_diffusion_mace(batch, radial_cutoff=r_max) @pytest.fixture() - def cartesian_scores(self, graph_input, diffusion_mace, batch_size, number_of_atoms, spatial_dimension): + def cartesian_scores( + self, + graph_input, + diffusion_mace, + batch_size, + number_of_atoms, + spatial_dimension, + ): flat_cartesian_scores = diffusion_mace(graph_input) - return flat_cartesian_scores.reshape(batch_size, number_of_atoms, spatial_dimension) + return flat_cartesian_scores.reshape( + batch_size, number_of_atoms, spatial_dimension + ) @pytest.fixture() - def translated_graph_input(self, batch, r_max, basis_vectors, reciprocal_basis_vectors, cartesian_translations): + def translated_graph_input( + self, + batch, + r_max, + basis_vectors, + reciprocal_basis_vectors, + cartesian_translations, + ): translated_batch = dict(batch) original_cartesian_positions = translated_batch[NOISY_CARTESIAN_POSITIONS] - translated_cartesian_positions = original_cartesian_positions + cartesian_translations + translated_cartesian_positions = ( + original_cartesian_positions + cartesian_translations + ) - rel_coords = get_relative_coordinates_from_cartesian_positions(translated_cartesian_positions, - reciprocal_basis_vectors) + rel_coords = get_relative_coordinates_from_cartesian_positions( + translated_cartesian_positions, reciprocal_basis_vectors + ) new_relative_coordinates = map_relative_coordinates_to_unit_cell(rel_coords) - new_cartesian_positions = get_positions_from_coordinates(new_relative_coordinates, basis_vectors) + new_cartesian_positions = get_positions_from_coordinates( + new_relative_coordinates, basis_vectors + ) translated_batch[NOISY_CARTESIAN_POSITIONS] = new_cartesian_positions translated_batch[NOISY_RELATIVE_COORDINATES] = new_relative_coordinates @@ -186,28 +230,47 @@ def translated_graph_input(self, batch, r_max, basis_vectors, reciprocal_basis_v return input_to_diffusion_mace(translated_batch, radial_cutoff=r_max) @pytest.fixture() - def translated_cartesian_scores(self, diffusion_mace, batch_size, number_of_atoms, - spatial_dimension, basis_vectors, translated_graph_input): + def translated_cartesian_scores( + self, + diffusion_mace, + batch_size, + number_of_atoms, + spatial_dimension, + basis_vectors, + translated_graph_input, + ): flat_translated_cartesian_scores = diffusion_mace(translated_graph_input) - return flat_translated_cartesian_scores.reshape(batch_size, number_of_atoms, spatial_dimension) + return flat_translated_cartesian_scores.reshape( + batch_size, number_of_atoms, spatial_dimension + ) @pytest.fixture() - def rotated_graph_input(self, batch, r_max, basis_vectors, reciprocal_basis_vectors, cartesian_rotations): + def rotated_graph_input( + self, batch, r_max, basis_vectors, reciprocal_basis_vectors, cartesian_rotations + ): rotated_batch = dict(batch) original_cartesian_positions = rotated_batch[NOISY_CARTESIAN_POSITIONS] original_basis_vectors = rotated_batch[UNIT_CELL] - rotated_cartesian_positions = torch.matmul(original_cartesian_positions, - cartesian_rotations.transpose(2, 1)) + rotated_cartesian_positions = torch.matmul( + original_cartesian_positions, cartesian_rotations.transpose(2, 1) + ) - rotated_basis_vectors = torch.matmul(original_basis_vectors, cartesian_rotations.transpose(2, 1)) - rotated_reciprocal_basis_vectors = get_reciprocal_basis_vectors(rotated_basis_vectors) + rotated_basis_vectors = torch.matmul( + original_basis_vectors, cartesian_rotations.transpose(2, 1) + ) + rotated_reciprocal_basis_vectors = get_reciprocal_basis_vectors( + rotated_basis_vectors + ) - rel_coords = get_relative_coordinates_from_cartesian_positions(rotated_cartesian_positions, - rotated_reciprocal_basis_vectors) + rel_coords = get_relative_coordinates_from_cartesian_positions( + rotated_cartesian_positions, rotated_reciprocal_basis_vectors + ) new_relative_coordinates = map_relative_coordinates_to_unit_cell(rel_coords) - new_cartesian_positions = get_positions_from_coordinates(new_relative_coordinates, rotated_basis_vectors) + new_cartesian_positions = get_positions_from_coordinates( + new_relative_coordinates, rotated_basis_vectors + ) rotated_batch[NOISY_CARTESIAN_POSITIONS] = new_cartesian_positions rotated_batch[NOISY_RELATIVE_COORDINATES] = new_relative_coordinates @@ -216,10 +279,18 @@ def rotated_graph_input(self, batch, r_max, basis_vectors, reciprocal_basis_vect return input_to_diffusion_mace(rotated_batch, radial_cutoff=r_max) @pytest.fixture() - def rotated_cartesian_scores(self, diffusion_mace, batch_size, number_of_atoms, - spatial_dimension, rotated_graph_input): + def rotated_cartesian_scores( + self, + diffusion_mace, + batch_size, + number_of_atoms, + spatial_dimension, + rotated_graph_input, + ): flat_rotated_cartesian_scores = diffusion_mace(rotated_graph_input) - return flat_rotated_cartesian_scores.reshape(batch_size, number_of_atoms, spatial_dimension) + return flat_rotated_cartesian_scores.reshape( + batch_size, number_of_atoms, spatial_dimension + ) @pytest.fixture() def permuted_graph_input(self, batch_size, batch, r_max, permutations): @@ -227,34 +298,62 @@ def permuted_graph_input(self, batch_size, batch, r_max, permutations): for position_key in [NOISY_CARTESIAN_POSITIONS, NOISY_RELATIVE_COORDINATES]: pos = permuted_batch[position_key] - permuted_pos = torch.stack([pos[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + permuted_pos = torch.stack( + [ + pos[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) permuted_batch[position_key] = permuted_pos return input_to_diffusion_mace(permuted_batch, radial_cutoff=r_max) @pytest.fixture() - def permuted_cartesian_scores(self, diffusion_mace, batch_size, number_of_atoms, - spatial_dimension, permuted_graph_input): + def permuted_cartesian_scores( + self, + diffusion_mace, + batch_size, + number_of_atoms, + spatial_dimension, + permuted_graph_input, + ): flat_permuted_cartesian_scores = diffusion_mace(permuted_graph_input) - return flat_permuted_cartesian_scores.reshape(batch_size, number_of_atoms, spatial_dimension) + return flat_permuted_cartesian_scores.reshape( + batch_size, number_of_atoms, spatial_dimension + ) - def test_translation_invariance(self, cartesian_scores, translated_cartesian_scores): + def test_translation_invariance( + self, cartesian_scores, translated_cartesian_scores + ): torch.testing.assert_close(translated_cartesian_scores, cartesian_scores) - def test_rotation_equivariance(self, cartesian_scores, rotated_cartesian_scores, cartesian_rotations): - vector_irreps = o3.Irreps('1o') + def test_rotation_equivariance( + self, cartesian_scores, rotated_cartesian_scores, cartesian_rotations + ): + vector_irreps = o3.Irreps("1o") d_matrices = vector_irreps.D_from_matrix(cartesian_rotations) - expected_rotated_cartesian_scores = torch.matmul(cartesian_scores, d_matrices.transpose(2, 1)) - torch.testing.assert_close(expected_rotated_cartesian_scores, rotated_cartesian_scores) + expected_rotated_cartesian_scores = torch.matmul( + cartesian_scores, d_matrices.transpose(2, 1) + ) + torch.testing.assert_close( + expected_rotated_cartesian_scores, rotated_cartesian_scores + ) - def test_permutation_equivariance(self, cartesian_scores, permuted_cartesian_scores, batch_size, permutations): + def test_permutation_equivariance( + self, cartesian_scores, permuted_cartesian_scores, batch_size, permutations + ): - expected_permuted_cartesian_scores = torch.stack([cartesian_scores[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + expected_permuted_cartesian_scores = torch.stack( + [ + cartesian_scores[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) - torch.testing.assert_close(expected_permuted_cartesian_scores, permuted_cartesian_scores) + torch.testing.assert_close( + expected_permuted_cartesian_scores, permuted_cartesian_scores + ) def test_time_dependence(self, batch, r_max, diffusion_mace): @@ -273,4 +372,6 @@ def test_time_dependence(self, batch, r_max, diffusion_mace): # Different times, different results? with pytest.raises(AssertionError): - torch.testing.assert_close(new_flat_cartesian_scores, flat_cartesian_scores1) + torch.testing.assert_close( + new_flat_cartesian_scores, flat_cartesian_scores1 + ) diff --git a/tests/models/test_egnn.py b/tests/models/test_egnn.py index c7e11a67..be93f0d4 100644 --- a/tests/models/test_egnn.py +++ b/tests/models/test_egnn.py @@ -3,7 +3,9 @@ import pytest import torch -from crystal_diffusion.models.egnn import E_GCL, EGNN + +from diffusion_for_multi_scale_molecular_dynamics.models.egnn import (E_GCL, + EGNN) class TestEGNN: @@ -21,77 +23,85 @@ def set_seed(self): """Set the random seed.""" torch.manual_seed(234233) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def batch_size(self): return 4 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def number_of_atoms(self): return 8 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def spatial_dimension(self): return 3 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def relative_coordinates(self, batch_size, number_of_atoms, spatial_dimension): - relative_coordinates = torch.rand(batch_size, number_of_atoms, spatial_dimension) + relative_coordinates = torch.rand( + batch_size, number_of_atoms, spatial_dimension + ) return relative_coordinates - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def node_features_size(self): return 5 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def node_features(self, batch_size, number_of_atoms, node_features_size): node_features = torch.randn(batch_size, number_of_atoms, node_features_size) return node_features - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def num_edges(self, number_of_atoms): return math.floor(number_of_atoms * 1.5) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def edges(self, batch_size, number_of_atoms, num_edges): all_edges = [] for b in range(batch_size): - batch_edges = torch.Tensor([(i, j) for i in range(number_of_atoms) for j in range(number_of_atoms)]) + batch_edges = torch.Tensor( + [(i, j) for i in range(number_of_atoms) for j in range(number_of_atoms)] + ) # select num_edges randomly indices = torch.randperm(len(batch_edges)) shuffled_edges = batch_edges[indices] + b * number_of_atoms all_edges.append(shuffled_edges[:num_edges]) return torch.cat(all_edges, dim=0).long() - @pytest.fixture(scope='class') - def batch(self, relative_coordinates, node_features, edges, batch_size, number_of_atoms): - batch = {'coord': relative_coordinates.view(batch_size * number_of_atoms, -1), - 'node_features': node_features.view(batch_size * number_of_atoms, -1), - 'edges': edges - } + @pytest.fixture(scope="class") + def batch( + self, relative_coordinates, node_features, edges, batch_size, number_of_atoms + ): + batch = { + "coord": relative_coordinates.view(batch_size * number_of_atoms, -1), + "node_features": node_features.view(batch_size * number_of_atoms, -1), + "edges": edges, + } return batch - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def generic_hyperparameters(self, node_features_size): - hps = dict(input_size=node_features_size, - message_n_hidden_dimensions=1, - message_hidden_dimensions_size=4, - node_n_hidden_dimensions=1, - node_hidden_dimensions_size=4, - coordinate_n_hidden_dimensions=1, - coordinate_hidden_dimensions_size=4, - ) + hps = dict( + input_size=node_features_size, + message_n_hidden_dimensions=1, + message_hidden_dimensions_size=4, + node_n_hidden_dimensions=1, + node_hidden_dimensions_size=4, + coordinate_n_hidden_dimensions=1, + coordinate_hidden_dimensions_size=4, + ) return hps @pytest.fixture() def egnn_hyperparameters(self, generic_hyperparameters): hps = copy(generic_hyperparameters) - hps['n_layers'] = 2 + hps["n_layers"] = 2 return hps @pytest.fixture() def egcl_hyperparameters(self, generic_hyperparameters, node_features_size): hps = copy(generic_hyperparameters) - hps['output_size'] = node_features_size + hps["output_size"] = node_features_size return hps @pytest.fixture() @@ -108,38 +118,48 @@ def egnn(self, egnn_hyperparameters): @pytest.fixture() def egnn_scores(self, batch, egnn, batch_size, number_of_atoms, spatial_dimension): - egnn_scores = egnn(batch['node_features'], batch['edges'], batch['coord']) + egnn_scores = egnn(batch["node_features"], batch["edges"], batch["coord"]) return egnn_scores.reshape(batch_size, number_of_atoms, spatial_dimension) @pytest.fixture() def egcl_scores(self, batch, egcl, batch_size, number_of_atoms): - egcl_h, egcl_x = egcl(batch['node_features'], batch['edges'], batch['coord']) - return egcl_h.reshape(batch_size, number_of_atoms, -1), egcl_x.reshape(batch_size, number_of_atoms, -1) + egcl_h, egcl_x = egcl(batch["node_features"], batch["edges"], batch["coord"]) + return egcl_h.reshape(batch_size, number_of_atoms, -1), egcl_x.reshape( + batch_size, number_of_atoms, -1 + ) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def permutations(self, batch_size, number_of_atoms): return torch.stack([torch.randperm(number_of_atoms) for _ in range(batch_size)]) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def permuted_coordinates(self, batch_size, number_of_atoms, batch, permutations): permuted_batch = batch - pos = permuted_batch['coord'].view(batch_size, number_of_atoms, -1) - permuted_pos = torch.stack([pos[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + pos = permuted_batch["coord"].view(batch_size, number_of_atoms, -1) + permuted_pos = torch.stack( + [ + pos[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) return permuted_pos.view(batch_size * number_of_atoms, -1) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def permuted_node_features(self, batch_size, number_of_atoms, batch, permutations): permuted_batch = batch - h = permuted_batch['node_features'].view(batch_size, number_of_atoms, -1) - permuted_h = torch.stack([h[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + h = permuted_batch["node_features"].view(batch_size, number_of_atoms, -1) + permuted_h = torch.stack( + [ + h[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) return permuted_h.view(batch_size * number_of_atoms, -1) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def permuted_edges(self, batch_size, batch, permutations, number_of_atoms): - edges = batch['edges'] + edges = batch["edges"] permuted_edges = edges.clone() for b in range(batch_size): for atom in range(number_of_atoms): @@ -148,58 +168,95 @@ def permuted_edges(self, batch_size, batch, permutations, number_of_atoms): return permuted_edges.long() @pytest.fixture() - def permuted_batch(self, permuted_coordinates, permuted_edges, permuted_node_features): - permuted_batch = {'coord': permuted_coordinates, - 'node_features': permuted_node_features, - 'edges': permuted_edges - } + def permuted_batch( + self, permuted_coordinates, permuted_edges, permuted_node_features + ): + permuted_batch = { + "coord": permuted_coordinates, + "node_features": permuted_node_features, + "edges": permuted_edges, + } return permuted_batch @pytest.fixture() - def permuted_egnn_scores(self, permuted_batch, egnn, batch_size, number_of_atoms, spatial_dimension): - egnn_scores = egnn(permuted_batch['node_features'], permuted_batch['edges'], permuted_batch['coord']) + def permuted_egnn_scores( + self, permuted_batch, egnn, batch_size, number_of_atoms, spatial_dimension + ): + egnn_scores = egnn( + permuted_batch["node_features"], + permuted_batch["edges"], + permuted_batch["coord"], + ) return egnn_scores.reshape(batch_size, number_of_atoms, spatial_dimension) @pytest.fixture() def permuted_egcl_scores(self, permuted_batch, egcl, batch_size, number_of_atoms): - egcl_h, egcl_x = egcl(permuted_batch['node_features'], permuted_batch['edges'], permuted_batch['coord']) - return egcl_h.reshape(batch_size, number_of_atoms, -1), egcl_x.reshape(batch_size, number_of_atoms, -1) - - def test_egcl_permutation_equivariance(self, egcl_scores, permuted_egcl_scores, batch_size, permutations): + egcl_h, egcl_x = egcl( + permuted_batch["node_features"], + permuted_batch["edges"], + permuted_batch["coord"], + ) + return egcl_h.reshape(batch_size, number_of_atoms, -1), egcl_x.reshape( + batch_size, number_of_atoms, -1 + ) + + def test_egcl_permutation_equivariance( + self, egcl_scores, permuted_egcl_scores, batch_size, permutations + ): permuted_egcl_h, permuted_egcl_x = permuted_egcl_scores egcl_h, egcl_x = egcl_scores - expected_permuted_h = torch.stack([egcl_h[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + expected_permuted_h = torch.stack( + [ + egcl_h[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) torch.testing.assert_close(expected_permuted_h, permuted_egcl_h) - expected_permuted_x = torch.stack([egcl_x[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + expected_permuted_x = torch.stack( + [ + egcl_x[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) torch.testing.assert_close(expected_permuted_x, permuted_egcl_x) - def test_egnn_permutation_equivariance(self, egnn_scores, permuted_egnn_scores, batch_size, permutations): - expected_permuted_scores = torch.stack([egnn_scores[batch_idx, permutations[batch_idx], :] - for batch_idx in range(batch_size)]) + def test_egnn_permutation_equivariance( + self, egnn_scores, permuted_egnn_scores, batch_size, permutations + ): + expected_permuted_scores = torch.stack( + [ + egnn_scores[batch_idx, permutations[batch_idx], :] + for batch_idx in range(batch_size) + ] + ) torch.testing.assert_close(expected_permuted_scores, permuted_egnn_scores) - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def single_edge(self): return torch.Tensor([1, 0]).unsqueeze(0).long() - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def fixed_distance(self): return 0.4 - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def simple_pair_coord(self, fixed_distance, spatial_dimension): coord = torch.zeros(2, spatial_dimension) coord[1, 0] = fixed_distance return coord - def test_egcl_coord2radial(self, single_edge, fixed_distance, simple_pair_coord, egcl): - computed_distance_squared, computed_displacement = egcl.coord2radial(single_edge, simple_pair_coord) - torch.testing.assert_close(computed_distance_squared.item(), fixed_distance ** 2) - torch.testing.assert_close(computed_displacement, simple_pair_coord[1, :].unsqueeze(0)) + def test_egcl_coord2radial( + self, single_edge, fixed_distance, simple_pair_coord, egcl + ): + computed_distance_squared, computed_displacement = egcl.coord2radial( + single_edge, simple_pair_coord + ) + torch.testing.assert_close(computed_distance_squared.item(), fixed_distance**2) + torch.testing.assert_close( + computed_displacement, simple_pair_coord[1, :].unsqueeze(0) + ) diff --git a/tests/models/test_egnn_utils.py b/tests/models/test_egnn_utils.py index 8dc211ff..3e15e8bf 100644 --- a/tests/models/test_egnn_utils.py +++ b/tests/models/test_egnn_utils.py @@ -1,7 +1,8 @@ import pytest import torch -from crystal_diffusion.models.egnn_utils import (unsorted_segment_mean, - unsorted_segment_sum) + +from diffusion_for_multi_scale_molecular_dynamics.models.egnn_utils import ( + unsorted_segment_mean, unsorted_segment_sum) @pytest.fixture() @@ -29,7 +30,9 @@ def messages(num_messages, num_message_features): return torch.randn(num_messages, num_message_features) -def test_unsorted_segment_sum(num_messages, num_ids, message_ids, num_message_features, messages): +def test_unsorted_segment_sum( + num_messages, num_ids, message_ids, num_message_features, messages +): expected_message_sums = torch.zeros(num_ids, num_message_features) for i in range(num_messages): m_id = message_ids[i] @@ -41,7 +44,9 @@ def test_unsorted_segment_sum(num_messages, num_ids, message_ids, num_message_fe assert torch.allclose(message_summed, expected_message_sums) -def test_unsorted_segment_mean(num_messages, num_ids, message_ids, num_message_features, messages): +def test_unsorted_segment_mean( + num_messages, num_ids, message_ids, num_message_features, messages +): expected_message_sums = torch.zeros(num_ids, num_message_features) expected_counts = torch.zeros(num_ids, 1) for i in range(num_messages): @@ -49,7 +54,9 @@ def test_unsorted_segment_mean(num_messages, num_ids, message_ids, num_message_f message = messages[i] expected_message_sums[m_id] += message expected_counts[m_id] += 1 - expected_message_average = expected_message_sums / torch.maximum(expected_counts, torch.ones_like(expected_counts)) + expected_message_average = expected_message_sums / torch.maximum( + expected_counts, torch.ones_like(expected_counts) + ) message_averaged = unsorted_segment_mean(messages, message_ids, num_ids) assert message_averaged.size() == torch.Size((num_ids, num_message_features)) diff --git a/tests/models/test_loss.py b/tests/models/test_loss.py index 674ee48e..d130e616 100644 --- a/tests/models/test_loss.py +++ b/tests/models/test_loss.py @@ -1,9 +1,9 @@ import pytest import torch -from crystal_diffusion.models.loss import (MSELossParameters, - WeightedMSELossParameters, - create_loss_calculator) -from crystal_diffusion.utils.tensor_utils import \ + +from diffusion_for_multi_scale_molecular_dynamics.models.loss import ( + MSELossParameters, WeightedMSELossParameters, create_loss_calculator) +from diffusion_for_multi_scale_molecular_dynamics.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions @@ -43,7 +43,9 @@ def predicted_normalized_scores(batch_size, number_of_atoms, spatial_dimension): @pytest.fixture() -def target_normalized_conditional_scores(batch_size, number_of_atoms, spatial_dimension): +def target_normalized_conditional_scores( + batch_size, number_of_atoms, spatial_dimension +): return torch.rand(batch_size, number_of_atoms, spatial_dimension) @@ -51,7 +53,9 @@ def target_normalized_conditional_scores(batch_size, number_of_atoms, spatial_di def sigmas(batch_size, number_of_atoms, spatial_dimension): batch_sigmas = torch.rand(batch_size) shape = (batch_size, number_of_atoms, spatial_dimension) - sigmas = broadcast_batch_tensor_to_all_dimensions(batch_values=batch_sigmas, final_shape=shape) + sigmas = broadcast_batch_tensor_to_all_dimensions( + batch_values=batch_sigmas, final_shape=shape + ) return sigmas @@ -60,7 +64,7 @@ def weights(sigmas, sigma0, exponent): return 1.0 + torch.exp(exponent * (sigmas - sigma0)) -@pytest.fixture(params=['mse', 'weighted_mse']) +@pytest.fixture(params=["mse", "weighted_mse"]) def algorithm(request): return request.param @@ -68,12 +72,12 @@ def algorithm(request): @pytest.fixture() def loss_parameters(algorithm, sigma0, exponent): match algorithm: - case 'mse': + case "mse": parameters = MSELossParameters() - case 'weighted_mse': + case "weighted_mse": parameters = WeightedMSELossParameters(sigma0=sigma0, exponent=exponent) case _: - raise ValueError(f'Unknown loss algorithm {algorithm}') + raise ValueError(f"Unknown loss algorithm {algorithm}") return parameters @@ -83,24 +87,41 @@ def loss_calculator(loss_parameters): @pytest.fixture() -def computed_loss(loss_calculator, predicted_normalized_scores, target_normalized_conditional_scores, sigmas): - unreduced_loss = loss_calculator.calculate_unreduced_loss(predicted_normalized_scores, - target_normalized_conditional_scores, - sigmas) +def computed_loss( + loss_calculator, + predicted_normalized_scores, + target_normalized_conditional_scores, + sigmas, +): + unreduced_loss = loss_calculator.calculate_unreduced_loss( + predicted_normalized_scores, target_normalized_conditional_scores, sigmas + ) return torch.mean(unreduced_loss) @pytest.fixture() -def expected_loss(algorithm, weights, predicted_normalized_scores, target_normalized_conditional_scores, sigmas): +def expected_loss( + algorithm, + weights, + predicted_normalized_scores, + target_normalized_conditional_scores, + sigmas, +): match algorithm: - case 'mse': + case "mse": loss = torch.nn.functional.mse_loss( - predicted_normalized_scores, target_normalized_conditional_scores, reduction="mean" + predicted_normalized_scores, + target_normalized_conditional_scores, + reduction="mean", + ) + case "weighted_mse": + loss = torch.mean( + weights + * (predicted_normalized_scores - target_normalized_conditional_scores) + ** 2 ) - case 'weighted_mse': - loss = torch.mean(weights * (predicted_normalized_scores - target_normalized_conditional_scores)**2) case _: - raise ValueError(f'Unknown loss algorithm {algorithm}') + raise ValueError(f"Unknown loss algorithm {algorithm}") return loss diff --git a/tests/models/test_mace_utils.py b/tests/models/test_mace_utils.py index 71c50751..5cb25480 100644 --- a/tests/models/test_mace_utils.py +++ b/tests/models/test_mace_utils.py @@ -4,17 +4,18 @@ import numpy as np import pytest import torch -from crystal_diffusion.models.mace_utils import ( - get_normalized_irreps_permutation_indices, get_pretrained_mace, - input_to_mace, reshape_from_e3nn_to_mace, reshape_from_mace_to_e3nn) -from crystal_diffusion.namespace import NOISY_CARTESIAN_POSITIONS, UNIT_CELL -from crystal_diffusion.utils.basis_transformations import \ - get_positions_from_coordinates from e3nn import o3 from mace.data import AtomicData, Configuration from mace.tools import get_atomic_number_table_from_zs from mace.tools.torch_geometric.dataloader import Collater +from diffusion_for_multi_scale_molecular_dynamics.models.mace_utils import ( + get_normalized_irreps_permutation_indices, get_pretrained_mace, + input_to_mace, reshape_from_e3nn_to_mace, reshape_from_mace_to_e3nn) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISY_CARTESIAN_POSITIONS, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + get_positions_from_coordinates from tests.fake_data_utils import find_aligning_permutation @@ -50,36 +51,60 @@ def radial_cutoff(self): return 1.1 @pytest.fixture() - def mace_graph(self, cell_size, spatial_dim, atomic_positions, n_atoms, batch_size, radial_cutoff): - unit_cell = np.eye(spatial_dim) * cell_size # box as a spatial_dim x spatial_dim array + def mace_graph( + self, + cell_size, + spatial_dim, + atomic_positions, + n_atoms, + batch_size, + radial_cutoff, + ): + unit_cell = ( + np.eye(spatial_dim) * cell_size + ) # box as a spatial_dim x spatial_dim array atom_type = np.ones(n_atoms) * 14 pbc = np.array([True] * spatial_dim) # periodic boundary conditions - graph_config = Configuration(atomic_numbers=atom_type, - positions=atomic_positions.numpy(), - cell=unit_cell, - pbc=pbc) + graph_config = Configuration( + atomic_numbers=atom_type, + positions=atomic_positions.numpy(), + cell=unit_cell, + pbc=pbc, + ) z_table = get_atomic_number_table_from_zs(list(range(89))) - graph_data = AtomicData.from_config(graph_config, z_table=z_table, cutoff=radial_cutoff) + graph_data = AtomicData.from_config( + graph_config, z_table=z_table, cutoff=radial_cutoff + ) collate_fn = Collater(follow_batch=[None], exclude_keys=[None]) mace_batch = collate_fn([graph_data] * batch_size) return mace_batch @pytest.fixture() def score_network_input(self, batch_size, spatial_dim, cell_size, atomic_positions): - score_network_input = {NOISY_CARTESIAN_POSITIONS: atomic_positions.unsqueeze(0).repeat(batch_size, 1, 1), - UNIT_CELL: torch.eye(spatial_dim).repeat(batch_size, 1, 1) * cell_size} + score_network_input = { + NOISY_CARTESIAN_POSITIONS: atomic_positions.unsqueeze(0).repeat( + batch_size, 1, 1 + ), + UNIT_CELL: torch.eye(spatial_dim).repeat(batch_size, 1, 1) * cell_size, + } return score_network_input def test_input_to_mace(self, score_network_input, radial_cutoff, mace_graph): - crystal_diffusion_graph = input_to_mace(score_network_input, - radial_cutoff=radial_cutoff) + diffusion_for_multi_scale_molecular_dynamics_graph = input_to_mace( + score_network_input, radial_cutoff=radial_cutoff + ) # check the features used in MACE as the same in both our homemade graph and the one native to mace library - for k in ['edge_index', 'node_attrs', 'positions', 'ptr', 'batch', 'cell']: - assert k in crystal_diffusion_graph.keys() - assert crystal_diffusion_graph[k].size() == mace_graph[k].size() - assert torch.equal(crystal_diffusion_graph[k], mace_graph[k]) + for k in ["edge_index", "node_attrs", "positions", "ptr", "batch", "cell"]: + assert k in diffusion_for_multi_scale_molecular_dynamics_graph.keys() + assert ( + diffusion_for_multi_scale_molecular_dynamics_graph[k].size() + == mace_graph[k].size() + ) + assert torch.equal( + diffusion_for_multi_scale_molecular_dynamics_graph[k], mace_graph[k] + ) class TestInputToMaceRandom(TestInputToMaceChain): @@ -95,7 +120,9 @@ def n_atoms(self): @pytest.fixture def basis_vectors(self, batch_size): # orthogonal boxes with dimensions between 5 and 10. - orthogonal_boxes = torch.stack([torch.diag(5. + 5. * torch.rand(3)) for _ in range(batch_size)]) + orthogonal_boxes = torch.stack( + [torch.diag(5.0 + 5.0 * torch.rand(3)) for _ in range(batch_size)] + ) # add a bit of noise to make the vectors not quite orthogonal basis_vectors = orthogonal_boxes + 0.1 * torch.randn(batch_size, 3, 3) return basis_vectors @@ -107,18 +134,24 @@ def cartesian_positions(self, batch_size, n_atoms, spatial_dim, basis_vectors): return positions @pytest.fixture() - def mace_graph(self, basis_vectors, cartesian_positions, spatial_dim, n_atoms, radial_cutoff): + def mace_graph( + self, basis_vectors, cartesian_positions, spatial_dim, n_atoms, radial_cutoff + ): pbc = np.array([True] * spatial_dim) # periodic boundary conditions atom_type = np.ones(n_atoms) * 14 list_graphs = [] for unit_cell, atomic_positions in zip(basis_vectors, cartesian_positions): - graph_config = Configuration(atomic_numbers=atom_type, - positions=atomic_positions.numpy(), - cell=unit_cell, - pbc=pbc) + graph_config = Configuration( + atomic_numbers=atom_type, + positions=atomic_positions.numpy(), + cell=unit_cell, + pbc=pbc, + ) z_table = get_atomic_number_table_from_zs(list(range(89))) - graph_data = AtomicData.from_config(graph_config, z_table=z_table, cutoff=radial_cutoff) + graph_data = AtomicData.from_config( + graph_config, z_table=z_table, cutoff=radial_cutoff + ) list_graphs.append(graph_data) collate_fn = Collater(follow_batch=[None], exclude_keys=[None]) @@ -127,36 +160,55 @@ def mace_graph(self, basis_vectors, cartesian_positions, spatial_dim, n_atoms, r @pytest.fixture() def score_network_input(self, cartesian_positions, basis_vectors): - score_network_input = {NOISY_CARTESIAN_POSITIONS: cartesian_positions, UNIT_CELL: basis_vectors} + score_network_input = { + NOISY_CARTESIAN_POSITIONS: cartesian_positions, + UNIT_CELL: basis_vectors, + } return score_network_input @pytest.mark.parametrize("radial_cutoff", [1.1, 2.2, 4.4]) def test_input_to_mace(self, score_network_input, radial_cutoff, mace_graph): - computed_mace_graph = input_to_mace(score_network_input, - radial_cutoff=radial_cutoff) + computed_mace_graph = input_to_mace( + score_network_input, radial_cutoff=radial_cutoff + ) - for feature_name in ['node_attrs', 'positions', 'ptr', 'batch', 'cell']: - torch.testing.assert_close(mace_graph[feature_name], computed_mace_graph[feature_name]) + for feature_name in ["node_attrs", "positions", "ptr", "batch", "cell"]: + torch.testing.assert_close( + mace_graph[feature_name], computed_mace_graph[feature_name] + ) # The EDGES might not be in the same order. Compute permutations of edges - expected_src_idx = mace_graph['edge_index'][0, :] - expected_dst_idx = mace_graph['edge_index'][1, :] + expected_src_idx = mace_graph["edge_index"][0, :] + expected_dst_idx = mace_graph["edge_index"][1, :] - expected_displacements = (mace_graph['positions'][expected_dst_idx] + mace_graph['shifts'] - - mace_graph['positions'][expected_src_idx]) + expected_displacements = ( + mace_graph["positions"][expected_dst_idx] + + mace_graph["shifts"] + - mace_graph["positions"][expected_src_idx] + ) - computed_src_idx = computed_mace_graph['edge_index'][0, :] - computed_dst_idx = computed_mace_graph['edge_index'][1, :] - computed_displacements = (computed_mace_graph['positions'][computed_dst_idx] + computed_mace_graph['shifts'] - - computed_mace_graph['positions'][computed_src_idx]) + computed_src_idx = computed_mace_graph["edge_index"][0, :] + computed_dst_idx = computed_mace_graph["edge_index"][1, :] + computed_displacements = ( + computed_mace_graph["positions"][computed_dst_idx] + + computed_mace_graph["shifts"] + - computed_mace_graph["positions"][computed_src_idx] + ) # The edge order can be different between the two graphs - edge_permutation_indices = find_aligning_permutation(expected_displacements, computed_displacements) + edge_permutation_indices = find_aligning_permutation( + expected_displacements, computed_displacements + ) - torch.testing.assert_close(computed_mace_graph['shifts'][edge_permutation_indices], mace_graph['shifts']) - torch.testing.assert_close(computed_mace_graph['edge_index'][:, edge_permutation_indices], - mace_graph['edge_index']) + torch.testing.assert_close( + computed_mace_graph["shifts"][edge_permutation_indices], + mace_graph["shifts"], + ) + torch.testing.assert_close( + computed_mace_graph["edge_index"][:, edge_permutation_indices], + mace_graph["edge_index"], + ) @pytest.fixture() @@ -166,26 +218,31 @@ def scrampled_and_expected_irreps(seed): list_irrep_strings = [] for ell in range(4): - for parity in ['o', 'e']: - list_irrep_strings.append(f'{ell}{parity}') + for parity in ["o", "e"]: + list_irrep_strings.append(f"{ell}{parity}") n_irreps = len(list_irrep_strings) list_repetitions = torch.randint(0, 4, (n_irreps,)) list_irreps = [] - expected_irreps = o3.Irreps('') + expected_irreps = o3.Irreps("") for irrep_string, repetitions in zip(list_irrep_strings, list_repetitions): if repetitions == 0: continue multiplicities = torch.randint(1, 8, (repetitions,)) - list_irreps.extend([o3.Irreps(f'{multiplicity}x{irrep_string}') for multiplicity in multiplicities]) + list_irreps.extend( + [ + o3.Irreps(f"{multiplicity}x{irrep_string}") + for multiplicity in multiplicities + ] + ) total_multiplicity = int(multiplicities.sum()) - expected_irreps += o3.Irreps(f'{total_multiplicity}x{irrep_string}') + expected_irreps += o3.Irreps(f"{total_multiplicity}x{irrep_string}") scrambled_indices = torch.randperm(len(list_irreps)) - scrambled_irreps = o3.Irreps('') + scrambled_irreps = o3.Irreps("") for index in scrambled_indices: scrambled_irreps += list_irreps[index] @@ -196,7 +253,9 @@ def scrampled_and_expected_irreps(seed): def test_get_normalized_irreps_permutation_indices(scrampled_and_expected_irreps): scrambled_irreps, expected_irreps = scrampled_and_expected_irreps - normalized_irreps, column_permutation_indices = get_normalized_irreps_permutation_indices(scrambled_irreps) + normalized_irreps, column_permutation_indices = ( + get_normalized_irreps_permutation_indices(scrambled_irreps) + ) assert expected_irreps == normalized_irreps @@ -227,10 +286,16 @@ def mock_model_savedir(self, tmp_path): # Test correctly downloading a small model @patch("os.makedirs") @patch("os.path.isfile", return_value=False) - @patch("urllib.request.urlretrieve", return_value=(None, 'abc')) + @patch("urllib.request.urlretrieve", return_value=(None, "abc")) @patch("torch.load") - def test_download_pretrained_mace_small(self, mock_load, mock_urlretrieve, mock_isfile, mock_makedirs, - mock_model_savedir): + def test_download_pretrained_mace_small( + self, + mock_load, + mock_urlretrieve, + mock_isfile, + mock_makedirs, + mock_model_savedir, + ): mock_model = MagicMock(spec=torch.nn.Module) mock_model.float.return_value = mock_model mock_load.return_value = mock_model @@ -245,7 +310,9 @@ def test_download_pretrained_mace_small(self, mock_load, mock_urlretrieve, mock_ def test_download_pretrained_mace_invalid_model_name(self, mock_model_savedir): with pytest.raises(AssertionError) as e: get_pretrained_mace("invalid_name", mock_model_savedir) - assert "Model name should be small, medium or large. Got invalid_name" in str(e.value) + assert "Model name should be small, medium or large. Got invalid_name" in str( + e.value + ) class TestReshapes: @@ -277,25 +344,36 @@ def mace_format_tensor(self, num_nodes, num_channels, ell_max): def e3nn_format_tensor(self, num_nodes, num_channels, ell_max): return torch.rand(num_nodes, num_channels * (ell_max + 1) ** 2) - def test_reshape_from_mace_to_e3nn(self, mace_format_tensor, irrep, ell_max, num_channels): + def test_reshape_from_mace_to_e3nn( + self, mace_format_tensor, irrep, ell_max, num_channels + ): converted_tensor = reshape_from_mace_to_e3nn(mace_format_tensor, irrep) # mace_format_tensor: (node, channel, (lmax + 1) ** 2) # converted: (node, channel * (lmax +1)**2) # check for each ell that the values match for ell in range(ell_max + 1): - start_idx = ell ** 2 + start_idx = ell**2 end_idx = (ell + 1) ** 2 - expected_values = mace_format_tensor[:, :, start_idx:end_idx].reshape(-1, num_channels * (2 * ell + 1)) - assert torch.allclose(expected_values, - converted_tensor[:, num_channels * start_idx: num_channels * end_idx]) - - def test_reshape_from_e3nn_to_mace(self, e3nn_format_tensor, irrep, ell_max, num_channels): + expected_values = mace_format_tensor[:, :, start_idx:end_idx].reshape( + -1, num_channels * (2 * ell + 1) + ) + assert torch.allclose( + expected_values, + converted_tensor[:, num_channels * start_idx: num_channels * end_idx], + ) + + def test_reshape_from_e3nn_to_mace( + self, e3nn_format_tensor, irrep, ell_max, num_channels + ): converted_tensor = reshape_from_e3nn_to_mace(e3nn_format_tensor, irrep) # e3nn_format_tensor: (node, channel * (lmax +1)**2) # converted: (node, channel, (lmax + 1) ** 2) for ell in range(ell_max + 1): - start_idx = num_channels * (ell ** 2) + start_idx = num_channels * (ell**2) end_idx = num_channels * ((ell + 1) ** 2) - expected_values = e3nn_format_tensor[:, start_idx:end_idx].reshape(-1, num_channels, 2 * ell + 1) - assert torch.allclose(expected_values, - converted_tensor[:, :, ell ** 2:(ell + 1) ** 2]) + expected_values = e3nn_format_tensor[:, start_idx:end_idx].reshape( + -1, num_channels, 2 * ell + 1 + ) + assert torch.allclose( + expected_values, converted_tensor[:, :, ell**2: (ell + 1) ** 2] + ) diff --git a/tests/models/test_mtp.py b/tests/models/test_mtp.py index 0b31c655..be2b26ff 100644 --- a/tests/models/test_mtp.py +++ b/tests/models/test_mtp.py @@ -5,17 +5,20 @@ import pandas as pd import pytest import yaml -from crystal_diffusion.mlip.mtp_utils import ( +from pymatgen.core import Structure +from sklearn.metrics import mean_absolute_error + +from diffusion_for_multi_scale_molecular_dynamics.mlip.mtp_utils import ( MTPInputs, extract_energy_from_thermo_log, extract_structure_and_forces_from_file, get_metrics_from_pred, prepare_mtp_inputs_from_lammps) -from crystal_diffusion.models.mlip.mtp import MTPArguments, MTPWithMLIP3 -from pymatgen.core import Structure -from sklearn.metrics import mean_absolute_error +from diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp import ( + MTPArguments, MTPWithMLIP3) class FakeStructure: """Mock a pymatgen structure""" + def __init__(self, species): self.species = species @@ -32,7 +35,10 @@ def passthrough(*args, **kwargs): def mock_popen(mocker): # mock subprocess calling mlp mock_popen = mocker.patch("subprocess.Popen") - mock_popen.return_value.__enter__.return_value.communicate.return_value = (b'', b'') # stdout, stderr + mock_popen.return_value.__enter__.return_value.communicate.return_value = ( + b"", + b"", + ) # stdout, stderr mock_popen.return_value.__enter__.return_value.returncode = 0 return mock_popen @@ -43,10 +49,16 @@ def test_train(mocker, mock_popen, tmpdir): mocker.patch("os.path.exists", return_value=True) # Mock check_structures_forces_stresses to return a value without needing real input - mocker.patch("crystal_diffusion.models.mlip.mtp.check_structures_forces_stresses", side_effect=passthrough) + mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp.check_structures_forces_stresses", + side_effect=passthrough, + ) # Mock pool_from to return a simplified pool object - mocker.patch("crystal_diffusion.models.mlip.mtp.pool_from", return_value="simple_pool_object") + mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp.pool_from", + return_value="simple_pool_object", + ) # Mock self.write_cfg to simulate creating a config file without file operations mocker.patch.object(MTPWithMLIP3, "write_cfg", return_value="mock_filename.cfg") @@ -58,14 +70,14 @@ def test_train(mocker, mock_popen, tmpdir): mlip_path="/mock/path", name="test_model", unfitted_mtp="08.almtp", - fitted_mtp_savedir=tmpdir + fitted_mtp_savedir=tmpdir, ) model = MTPWithMLIP3(mtp_args) # Call the train method mtp_inputs = MTPInputs( - structure=[FakeStructure(['H', 'O']), FakeStructure(['Si'])], + structure=[FakeStructure(["H", "O"]), FakeStructure(["Si"])], forces=[], - energy=[1, 2] + energy=[1, 2], ) _ = model.train( @@ -80,13 +92,15 @@ def test_train(mocker, mock_popen, tmpdir): def fake_structure(): # Setup a simple mock structure object # Replace with appropriate structure setup for your use case - return Structure(lattice=[1, 0, 0, 0, 1, 0, 0, 0, 1], species=[""], coords=[[0, 0, 0]]) + return Structure( + lattice=[1, 0, 0, 0, 1, 0, 0, 0, 1], species=[""], coords=[[0, 0, 0]] + ) @pytest.fixture def mtp_instance(mocker): # Mock __init__ to not execute its original behavior - mocker.patch.object(MTPWithMLIP3, '__init__', lambda x, y: None) + mocker.patch.object(MTPWithMLIP3, "__init__", lambda x, y: None) # Setup a mocked instance with necessary attributes instance = MTPWithMLIP3("mock_path") instance.mlp_command = "mock_mlp_command" @@ -101,11 +115,16 @@ def test_evaluate(mocker, fake_structure, mtp_instance, mock_popen): test_forces = [[[0, 0, 0]]] # Mock check_structures_forces_stresses to return the arguments unmodified - mocker.patch("crystal_diffusion.models.mlip.mtp.check_structures_forces_stresses", - side_effect=lambda s, f, st: (s, f, st)) + mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp.check_structures_forces_stresses", + side_effect=lambda s, f, st: (s, f, st), + ) # Mock pool_from to return a mocked value - mocker.patch("crystal_diffusion.models.mlip.mtp.pool_from", return_value="mock_pool") + mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.models.mlip.mtp.pool_from", + return_value="mock_pool", + ) # Mock self.write_cfg to simulate creating a config file without file operations mocker.patch.object(MTPWithMLIP3, "write_cfg", return_value="mock_filename.cfg") @@ -119,9 +138,7 @@ def test_evaluate(mocker, fake_structure, mtp_instance, mock_popen): mocker.patch("os.path.exists", return_value=True) mtp_inputs = MTPInputs( - structure=test_structures, - forces=test_forces, - energy=test_energies + structure=test_structures, forces=test_forces, energy=test_energies ) # Perform the test @@ -129,21 +146,22 @@ def test_evaluate(mocker, fake_structure, mtp_instance, mock_popen): # Assertions can vary based on the real output of `read_cfgs` # Here's an example assertion assuming `read_cfgs` returns a string in this mocked scenario - assert df_orig == "mock_dataframe" and df_predict == "mock_dataframe", "Evaluate method should return mock" + \ - "dataframes" + assert df_orig == "mock_dataframe" and df_predict == "mock_dataframe", ( + "Evaluate method should return mock" + "dataframes" + ) def test_read_cfgs(mtp_instance): cfg_path = Path(__file__).parent.joinpath("mtp_cfg_examples.txt") df = mtp_instance.read_cfgs(cfg_path, True) print(df.keys()) - assert np.array_equal(df['x'], [0.1, 0.2, 0.3]) - assert np.array_equal(df['y'], [1.1, 1.2, 1.3]) - assert np.array_equal(df['z'], [2.1, 2.2, 2.3]) - assert np.array_equal(df['fx'], [3.1, 3.2, 3.3]) - assert np.array_equal(df['fy'], [4.1, 4.2, 4.3]) - assert np.array_equal(df['fz'], [5.1, 5.2, 5.3]) - assert np.array_equal(df['nbh_grades'], [6.1, 6.2, 6.3]) + assert np.array_equal(df["x"], [0.1, 0.2, 0.3]) + assert np.array_equal(df["y"], [1.1, 1.2, 1.3]) + assert np.array_equal(df["z"], [2.1, 2.2, 2.3]) + assert np.array_equal(df["fx"], [3.1, 3.2, 3.3]) + assert np.array_equal(df["fy"], [4.1, 4.2, 4.3]) + assert np.array_equal(df["fz"], [5.1, 5.2, 5.3]) + assert np.array_equal(df["nbh_grades"], [6.1, 6.2, 6.3]) def test_extract_structure_and_forces_from_file(tmpdir): @@ -151,16 +169,16 @@ def test_extract_structure_and_forces_from_file(tmpdir): # TODO refactor to move to data/ # Create a mock LAMMPS output yaml_content = { - 'box': [[0, 10], [0, 10], [0, 10]], # x_lim, y_lim, z_lim - 'keywords': ['x', 'y', 'z', 'type', 'fx', 'fy', 'fz'], - 'data': [[1, 1, 1, 1, 0.1, 0.2, 0.3], [2, 2, 2, 2, 0.4, 0.5, 0.6]] + "box": [[0, 10], [0, 10], [0, 10]], # x_lim, y_lim, z_lim + "keywords": ["x", "y", "z", "type", "fx", "fy", "fz"], + "data": [[1, 1, 1, 1, 0.1, 0.2, 0.3], [2, 2, 2, 2, 0.4, 0.5, 0.6]], } yaml_file = os.path.join(tmpdir, "lammps.yaml") with open(yaml_file, "w") as f: yaml.dump(yaml_content, f, sort_keys=False) # Mock atom dict that the function expects - atom_dict = {1: 'H', 2: 'He'} + atom_dict = {1: "H", 2: "He"} # Call the function structures, forces = extract_structure_and_forces_from_file(yaml_file, atom_dict) @@ -175,11 +193,12 @@ def test_extract_structure_and_forces_from_file(tmpdir): # Verify species and positions species = structures[0].species - assert [str(s) for s in species] == ['H', 'He'] + assert [str(s) for s in species] == ["H", "He"] # frac coordinates are reduced coordinates - the values in data are cartesian coordinates # divide by the box length (10) to convert - np.testing.assert_array_almost_equal(structures[0].frac_coords, - [[1 / 10, 1 / 10, 1 / 10], [2 / 10, 2 / 10, 2 / 10]]) + np.testing.assert_array_almost_equal( + structures[0].frac_coords, [[1 / 10, 1 / 10, 1 / 10], [2 / 10, 2 / 10, 2 / 10]] + ) # Verify forces assert isinstance(forces, list) @@ -217,29 +236,46 @@ def test_extract_energy_from_thermo_log(tmpdir): @pytest.fixture def mock_extract_energy_from_thermo_log(mocker): - return mocker.patch('crystal_diffusion.mlip.mtp_utils.extract_energy_from_thermo_log', return_value=[]) + return mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.mlip.mtp_utils.extract_energy_from_thermo_log", + return_value=[], + ) @pytest.fixture def mock_extract_structure_and_forces(mocker): - return mocker.patch('crystal_diffusion.mlip.mtp_utils.extract_structure_and_forces_from_file', - return_value=([], [])) + return mocker.patch( + "diffusion_for_multi_scale_molecular_dynamics.mlip.mtp_utils.extract_structure_and_forces_from_file", + return_value=([], []), + ) -def test_prepare_mtp_inputs_from_lammps(mock_extract_structure_and_forces, mock_extract_energy_from_thermo_log, tmpdir): +def test_prepare_mtp_inputs_from_lammps( + mock_extract_structure_and_forces, mock_extract_energy_from_thermo_log, tmpdir +): # Create mock file paths - output_yaml_files = [os.path.join(tmpdir, "output1.yaml"), os.path.join(tmpdir, "output2.yaml")] - thermo_yaml_files = [os.path.join(tmpdir, "thermo1.yaml"), os.path.join(tmpdir, "thermo2.yaml")] + output_yaml_files = [ + os.path.join(tmpdir, "output1.yaml"), + os.path.join(tmpdir, "output2.yaml"), + ] + thermo_yaml_files = [ + os.path.join(tmpdir, "thermo1.yaml"), + os.path.join(tmpdir, "thermo2.yaml"), + ] # Mock atom dictionary - atom_dict = {1: 'H', 2: 'He'} + atom_dict = {1: "H", 2: "He"} # Call the function - mtp_inputs = prepare_mtp_inputs_from_lammps(output_yaml_files, thermo_yaml_files, atom_dict) + mtp_inputs = prepare_mtp_inputs_from_lammps( + output_yaml_files, thermo_yaml_files, atom_dict + ) # Verify that the mocks were called correctly assert mock_extract_structure_and_forces.call_count == 2 - mock_extract_structure_and_forces.assert_called_with(output_yaml_files[1], atom_dict, True) + mock_extract_structure_and_forces.assert_called_with( + output_yaml_files[1], atom_dict, True + ) assert mock_extract_energy_from_thermo_log.call_count == 2 mock_extract_energy_from_thermo_log.assert_called_with(thermo_yaml_files[1]) @@ -250,42 +286,67 @@ def test_prepare_mtp_inputs_from_lammps(mock_extract_structure_and_forces, mock_ assert isinstance(mtp_inputs.forces, list) # Verify that the data from the mocks is aggregated into the results correctly - assert mtp_inputs.structure == mock_extract_structure_and_forces.return_value[0] * len(output_yaml_files) - assert mtp_inputs.forces == mock_extract_structure_and_forces.return_value[1] * len(output_yaml_files) - assert mtp_inputs.energy == mock_extract_energy_from_thermo_log.return_value * len(thermo_yaml_files) + assert mtp_inputs.structure == mock_extract_structure_and_forces.return_value[ + 0 + ] * len(output_yaml_files) + assert mtp_inputs.forces == mock_extract_structure_and_forces.return_value[1] * len( + output_yaml_files + ) + assert mtp_inputs.energy == mock_extract_energy_from_thermo_log.return_value * len( + thermo_yaml_files + ) def test_get_metrics_from_pred(): # test function from train_mtp # TODO get better metrics and refactor the script # Assuming there are 2 structures, each with 2 atoms (Total 4 readings) - df_orig = pd.DataFrame({ - 'structure_index': [0, 0, 1, 1], - 'atom_index': [0, 1, 0, 1], - 'energy': [1, 1, 3, 3], # Total energy for the structure is the same for both atoms - 'fx': [0.1, -0.1, 0.2, -0.2], - 'fy': [0.1, -0.1, 0.2, -0.2], - 'fz': [0.1, -0.1, 0.2, -0.2] - }) + df_orig = pd.DataFrame( + { + "structure_index": [0, 0, 1, 1], + "atom_index": [0, 1, 0, 1], + "energy": [ + 1, + 1, + 3, + 3, + ], # Total energy for the structure is the same for both atoms + "fx": [0.1, -0.1, 0.2, -0.2], + "fy": [0.1, -0.1, 0.2, -0.2], + "fz": [0.1, -0.1, 0.2, -0.2], + } + ) # Predicted data with some error introduced - df_predict = pd.DataFrame({ - 'structure_index': [0, 0, 1, 1], - 'atom_index': [0, 1, 0, 1], - 'energy': [1.1, 1.1, 3.3, 3.3], # energy cannot be different per atom for a given structure - 'fx': [0.12, -0.08, 0.23, -0.17], - 'fy': [0.09, -0.11, 0.19, -0.21], - 'fz': [0.11, -0.09, 0.18, -0.22] - }) + df_predict = pd.DataFrame( + { + "structure_index": [0, 0, 1, 1], + "atom_index": [0, 1, 0, 1], + "energy": [ + 1.1, + 1.1, + 3.3, + 3.3, + ], # energy cannot be different per atom for a given structure + "fx": [0.12, -0.08, 0.23, -0.17], + "fy": [0.09, -0.11, 0.19, -0.21], + "fz": [0.11, -0.09, 0.18, -0.22], + } + ) # Calculate expected MAE for energy and forces # 1 value per structure - here, we take indices 0 and 2 - expected_mae_energy = np.abs(df_orig['energy'].iloc[[0, 2]] - df_predict['energy'].iloc[[0, 2]]).mean() / 2 + expected_mae_energy = ( + np.abs( + df_orig["energy"].iloc[[0, 2]] - df_predict["energy"].iloc[[0, 2]] + ).mean() + / 2 + ) # we take the energy per atom. 2 atoms per structure here, so we can simply divide by 2 expected_mae_forces = mean_absolute_error( - df_orig[['fx', 'fy', 'fz']].values.flatten(), - df_predict[['fx', 'fy', 'fz']].values.flatten() + df_orig[["fx", "fy", "fz"]].values.flatten(), + df_predict[["fx", "fy", "fz"]].values.flatten(), ) # Call the function under test diff --git a/tests/models/test_optimizer.py b/tests/models/test_optimizer.py index 11e3e98b..384f0305 100644 --- a/tests/models/test_optimizer.py +++ b/tests/models/test_optimizer.py @@ -1,11 +1,13 @@ import pytest import torch -from crystal_diffusion.models.optimizer import (OptimizerParameters, - load_optimizer) + +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import ( + OptimizerParameters, load_optimizer) class FakeNeuralNet(torch.nn.Module): """A fake neural net for testing that we can attach an optimizer.""" + def __init__(self): super(FakeNeuralNet, self).__init__() self.linear_layer = torch.nn.Linear(in_features=4, out_features=1) @@ -24,7 +26,7 @@ def weight_decay(request): return request.param -@pytest.fixture(params=['adam', 'adamw']) +@pytest.fixture(params=["adam", "adamw"]) def optimizer_name(request): return request.param @@ -32,7 +34,9 @@ def optimizer_name(request): @pytest.fixture() def optimizer_parameters(optimizer_name, weight_decay): if weight_decay: - return OptimizerParameters(name=optimizer_name, learning_rate=0.01, weight_decay=weight_decay) + return OptimizerParameters( + name=optimizer_name, learning_rate=0.01, weight_decay=weight_decay + ) else: return OptimizerParameters(name=optimizer_name, learning_rate=0.01) diff --git a/tests/models/test_position_diffusion_lightning_model.py b/tests/models/test_position_diffusion_lightning_model.py index 6d3bd49c..06314532 100644 --- a/tests/models/test_position_diffusion_lightning_model.py +++ b/tests/models/test_position_diffusion_lightning_model.py @@ -1,27 +1,32 @@ import pytest import torch -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from pytorch_lightning import LightningDataModule, Trainer +from torch.utils.data import DataLoader, random_split + +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.metrics.sampling_metrics_parameters import \ +from diffusion_for_multi_scale_molecular_dynamics.metrics.sampling_metrics_parameters import \ SamplingMetricsParameters -from crystal_diffusion.models.loss import create_loss_parameters -from crystal_diffusion.models.optimizer import OptimizerParameters -from crystal_diffusion.models.position_diffusion_lightning_model import ( +from diffusion_for_multi_scale_molecular_dynamics.models.loss import \ + create_loss_parameters +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import \ + OptimizerParameters +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import ( PositionDiffusionLightningModel, PositionDiffusionParameters) -from crystal_diffusion.models.scheduler import ( +from diffusion_for_multi_scale_molecular_dynamics.models.scheduler import ( CosineAnnealingLRSchedulerParameters, ReduceLROnPlateauSchedulerParameters) -from crystal_diffusion.models.score_networks.mlp_score_network import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mlp_score_network import \ MLPScoreNetworkParameters -from crystal_diffusion.namespace import CARTESIAN_FORCES, RELATIVE_COORDINATES -from crystal_diffusion.samples.diffusion_sampling_parameters import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, RELATIVE_COORDINATES) +from diffusion_for_multi_scale_molecular_dynamics.samples.diffusion_sampling_parameters import \ DiffusionSamplingParameters -from crystal_diffusion.score.wrapped_gaussian_score import \ +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import \ get_sigma_normalized_score_brute_force -from crystal_diffusion.utils.tensor_utils import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions -from pytorch_lightning import LightningDataModule, Trainer -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters -from torch.utils.data import DataLoader, random_split +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters class FakePositionsDataModule(LightningDataModule): @@ -34,10 +39,16 @@ def __init__( ): super().__init__() self.batch_size = batch_size - all_relative_coordinates = torch.rand(dataset_size, number_of_atoms, spatial_dimension) + all_relative_coordinates = torch.rand( + dataset_size, number_of_atoms, spatial_dimension + ) box = torch.rand(spatial_dimension) self.data = [ - {RELATIVE_COORDINATES: configuration, 'box': box, CARTESIAN_FORCES: torch.zeros_like(configuration)} + { + RELATIVE_COORDINATES: configuration, + "box": box, + CARTESIAN_FORCES: torch.zeros_like(configuration), + } for configuration in all_relative_coordinates ] self.train_data, self.val_data, self.test_data = None, None, None @@ -75,25 +86,31 @@ def number_of_atoms(self): def unit_cell_size(self): return 10.1 - @pytest.fixture(params=['adam', 'adamw']) + @pytest.fixture(params=["adam", "adamw"]) def optimizer_parameters(self, request): - return OptimizerParameters(name=request.param, learning_rate=0.01, weight_decay=1e-6) + return OptimizerParameters( + name=request.param, learning_rate=0.01, weight_decay=1e-6 + ) - @pytest.fixture(params=[None, 'ReduceLROnPlateau', 'CosineAnnealingLR']) + @pytest.fixture(params=[None, "ReduceLROnPlateau", "CosineAnnealingLR"]) def scheduler_parameters(self, request): match request.param: case None: scheduler_parameters = None - case 'ReduceLROnPlateau': - scheduler_parameters = ReduceLROnPlateauSchedulerParameters(factor=0.5, patience=2) - case 'CosineAnnealingLR': - scheduler_parameters = CosineAnnealingLRSchedulerParameters(T_max=5, eta_min=1e-5) + case "ReduceLROnPlateau": + scheduler_parameters = ReduceLROnPlateauSchedulerParameters( + factor=0.5, patience=2 + ) + case "CosineAnnealingLR": + scheduler_parameters = CosineAnnealingLRSchedulerParameters( + T_max=5, eta_min=1e-5 + ) case _: raise ValueError(f"Untested case {request.param}") return scheduler_parameters - @pytest.fixture(params=['mse', 'weighted_mse']) + @pytest.fixture(params=["mse", "weighted_mse"]) def loss_parameters(self, request): model_dict = dict(loss=dict(algorithm=request.param)) return create_loss_parameters(model_dictionary=model_dict) @@ -107,27 +124,41 @@ def cell_dimensions(self, unit_cell_size, spatial_dimension): return spatial_dimension * [unit_cell_size] @pytest.fixture() - def sampling_parameters(self, number_of_atoms, spatial_dimension, number_of_samples, cell_dimensions): - sampling_parameters = PredictorCorrectorSamplingParameters(number_of_atoms=number_of_atoms, - spatial_dimension=spatial_dimension, - number_of_samples=number_of_samples, - cell_dimensions=cell_dimensions) + def sampling_parameters( + self, number_of_atoms, spatial_dimension, number_of_samples, cell_dimensions + ): + sampling_parameters = PredictorCorrectorSamplingParameters( + number_of_atoms=number_of_atoms, + spatial_dimension=spatial_dimension, + number_of_samples=number_of_samples, + cell_dimensions=cell_dimensions, + ) return sampling_parameters @pytest.fixture() def diffusion_sampling_parameters(self, sampling_parameters): noise_parameters = NoiseParameters(total_time_steps=5) - metrics_parameters = SamplingMetricsParameters(structure_factor_max_distance=1.) + metrics_parameters = SamplingMetricsParameters( + structure_factor_max_distance=1.0 + ) diffusion_sampling_parameters = DiffusionSamplingParameters( sampling_parameters=sampling_parameters, noise_parameters=noise_parameters, - metrics_parameters=metrics_parameters) + metrics_parameters=metrics_parameters, + ) return diffusion_sampling_parameters @pytest.fixture() - def hyper_params(self, number_of_atoms, spatial_dimension, - optimizer_parameters, scheduler_parameters, - loss_parameters, sampling_parameters, diffusion_sampling_parameters): + def hyper_params( + self, + number_of_atoms, + spatial_dimension, + optimizer_parameters, + scheduler_parameters, + loss_parameters, + sampling_parameters, + diffusion_sampling_parameters, + ): score_network_parameters = MLPScoreNetworkParameters( number_of_atoms=number_of_atoms, n_hidden_dimensions=3, @@ -150,11 +181,15 @@ def hyper_params(self, number_of_atoms, spatial_dimension, @pytest.fixture() def real_relative_coordinates(self, batch_size, number_of_atoms, spatial_dimension): - relative_coordinates = torch.rand(batch_size, number_of_atoms, spatial_dimension) + relative_coordinates = torch.rand( + batch_size, number_of_atoms, spatial_dimension + ) return relative_coordinates @pytest.fixture() - def noisy_relative_coordinates(self, batch_size, number_of_atoms, spatial_dimension): + def noisy_relative_coordinates( + self, batch_size, number_of_atoms, spatial_dimension + ): noisy_relative_coordinates = torch.rand( batch_size, number_of_atoms, spatial_dimension ) @@ -216,7 +251,9 @@ def brute_force_target_normalized_score( @pytest.fixture() def unit_cell_sample(self, unit_cell_size, spatial_dimension, batch_size): - return torch.diag(torch.Tensor([unit_cell_size] * spatial_dimension)).repeat(batch_size, 1, 1) + return torch.diag(torch.Tensor([unit_cell_size] * spatial_dimension)).repeat( + batch_size, 1, 1 + ) # The brute force target normalized scores are *fragile*; they can return NaNs easily. # There is no point in running this test for all possible component combinations. @@ -230,7 +267,7 @@ def test_get_target_normalized_score( real_relative_coordinates, sigmas, brute_force_target_normalized_score, - unit_cell_sample + unit_cell_sample, ): computed_target_normalized_scores = ( lightning_model._get_target_normalized_score( @@ -238,16 +275,24 @@ def test_get_target_normalized_score( ) ) - torch.testing.assert_close(computed_target_normalized_scores, - brute_force_target_normalized_score, - atol=1e-7, - rtol=1e-4) + torch.testing.assert_close( + computed_target_normalized_scores, + brute_force_target_normalized_score, + atol=1e-7, + rtol=1e-4, + ) def test_smoke_test(self, lightning_model, fake_datamodule, accelerator): trainer = Trainer(fast_dev_run=3, accelerator=accelerator) trainer.fit(lightning_model, fake_datamodule) trainer.test(lightning_model, fake_datamodule) - def test_generate_sample(self, lightning_model, number_of_samples, number_of_atoms, spatial_dimension): + def test_generate_sample( + self, lightning_model, number_of_samples, number_of_atoms, spatial_dimension + ): samples_batch = lightning_model.generate_samples() - assert samples_batch[RELATIVE_COORDINATES].shape == (number_of_samples, number_of_atoms, spatial_dimension) + assert samples_batch[RELATIVE_COORDINATES].shape == ( + number_of_samples, + number_of_atoms, + spatial_dimension, + ) diff --git a/tests/models/test_scheduler.py b/tests/models/test_scheduler.py index 57b141eb..a4e75af5 100644 --- a/tests/models/test_scheduler.py +++ b/tests/models/test_scheduler.py @@ -1,14 +1,16 @@ import pytest import torch -from crystal_diffusion.models.optimizer import (OptimizerParameters, - load_optimizer) -from crystal_diffusion.models.scheduler import ( + +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import ( + OptimizerParameters, load_optimizer) +from diffusion_for_multi_scale_molecular_dynamics.models.scheduler import ( CosineAnnealingLRSchedulerParameters, ReduceLROnPlateauSchedulerParameters, load_scheduler_dictionary) class FakeNeuralNet(torch.nn.Module): """A fake neural net for testing that we can attach an optimizer.""" + def __init__(self): super(FakeNeuralNet, self).__init__() self.linear_layer = torch.nn.Linear(in_features=4, out_features=1) @@ -20,12 +22,14 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: @pytest.fixture def optimizer(): model = FakeNeuralNet() - optimizer_parameters = OptimizerParameters(name='adam', learning_rate=0.001, weight_decay=1e-6) + optimizer_parameters = OptimizerParameters( + name="adam", learning_rate=0.001, weight_decay=1e-6 + ) optimizer = load_optimizer(optimizer_parameters, model) return optimizer -@pytest.fixture(params=['CosineAnnealingLR', 'ReduceLROnPlateau']) +@pytest.fixture(params=["CosineAnnealingLR", "ReduceLROnPlateau"]) def scheduler_name(request): return request.param @@ -33,14 +37,20 @@ def scheduler_name(request): @pytest.fixture def scheduler_parameters(scheduler_name): match scheduler_name: - case 'CosineAnnealingLR': - parameters = CosineAnnealingLRSchedulerParameters(name=scheduler_name, T_max=1000, eta_min=0.001) - case 'ReduceLROnPlateau': - parameters = ReduceLROnPlateauSchedulerParameters(name=scheduler_name, factor=0.243, patience=17) + case "CosineAnnealingLR": + parameters = CosineAnnealingLRSchedulerParameters( + name=scheduler_name, T_max=1000, eta_min=0.001 + ) + case "ReduceLROnPlateau": + parameters = ReduceLROnPlateauSchedulerParameters( + name=scheduler_name, factor=0.243, patience=17 + ) case _: raise ValueError(f"Untested case: {scheduler_name}") return parameters def test_load_scheduler(optimizer, scheduler_parameters): - _ = load_scheduler_dictionary(scheduler_parameters=scheduler_parameters, optimizer=optimizer) + _ = load_scheduler_dictionary( + scheduler_parameters=scheduler_parameters, optimizer=optimizer + ) diff --git a/tests/models/test_score_fokker_planck_error.py b/tests/models/test_score_fokker_planck_error.py index 672d5a90..095a7c2a 100644 --- a/tests/models/test_score_fokker_planck_error.py +++ b/tests/models/test_score_fokker_planck_error.py @@ -3,16 +3,19 @@ import einops import pytest import torch -from crystal_diffusion.models.score_networks.egnn_score_network import \ + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.egnn_score_network import \ EGNNScoreNetworkParameters -from crystal_diffusion.models.score_networks.score_network_factory import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_network_factory import \ create_score_network -from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, - TIME, UNIT_CELL) -from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from src.crystal_diffusion.models.normalized_score_fokker_planck_error import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.exploding_variance import \ + ExplodingVariance +from src.diffusion_for_multi_scale_molecular_dynamics.models.normalized_score_fokker_planck_error import \ NormalizedScoreFokkerPlanckError -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters def get_finite_difference_time_derivative( diff --git a/tests/oracle/test_lammps.py b/tests/oracle/test_lammps.py index cb342d90..77947b49 100644 --- a/tests/oracle/test_lammps.py +++ b/tests/oracle/test_lammps.py @@ -1,6 +1,8 @@ import numpy as np import pytest -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps + +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps @pytest.fixture @@ -18,10 +20,11 @@ def high_symmetry_positions(): # do not run on github because no lammps @pytest.mark.not_on_github def test_high_symmetry(high_symmetry_positions, high_symmetry_lattice): - energy, forces = get_energy_and_forces_from_lammps(high_symmetry_positions, high_symmetry_lattice, - atom_types=np.array([1, 1])) - for x in ['x', 'y', 'z']: - assert np.allclose(forces[f'f{x}'], [0, 0]) + energy, forces = get_energy_and_forces_from_lammps( + high_symmetry_positions, high_symmetry_lattice, atom_types=np.array([1, 1]) + ) + for x in ["x", "y", "z"]: + assert np.allclose(forces[f"f{x}"], [0, 0]) assert energy < 0 @@ -33,8 +36,9 @@ def low_symmetry_positions(): @pytest.mark.not_on_github def test_low_symmetry(low_symmetry_positions, high_symmetry_lattice): - energy, forces = get_energy_and_forces_from_lammps(low_symmetry_positions, high_symmetry_lattice, - atom_types=np.array([1, 1])) - for x in ['x', 'y', 'z']: - assert not np.allclose(forces[f'f{x}'], [0, 0]) + energy, forces = get_energy_and_forces_from_lammps( + low_symmetry_positions, high_symmetry_lattice, atom_types=np.array([1, 1]) + ) + for x in ["x", "y", "z"]: + assert not np.allclose(forces[f"f{x}"], [0, 0]) assert energy < 0 diff --git a/tests/samplers/test_exploding_variance.py b/tests/samplers/test_exploding_variance.py index 551471fb..e588e31a 100644 --- a/tests/samplers/test_exploding_variance.py +++ b/tests/samplers/test_exploding_variance.py @@ -1,7 +1,10 @@ import pytest import torch -from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + +from diffusion_for_multi_scale_molecular_dynamics.samplers.exploding_variance import \ + ExplodingVariance +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters class TestExplodingVariance: @@ -20,9 +23,9 @@ def sigma_max(self): @pytest.fixture() def noise_parameters(self, sigma_min, sigma_max): - return NoiseParameters(total_time_steps=10, - sigma_min=sigma_min, - sigma_max=sigma_max) + return NoiseParameters( + total_time_steps=10, sigma_min=sigma_min, sigma_max=sigma_max + ) @pytest.fixture() def times(self): @@ -36,7 +39,9 @@ def exploding_variance(self, noise_parameters): def expected_sigmas(self, noise_parameters, times): expected_sigmas = [] for t in times: - sigma = noise_parameters.sigma_min**(1. - t) * noise_parameters.sigma_max**t + sigma = ( + noise_parameters.sigma_min ** (1.0 - t) * noise_parameters.sigma_max**t + ) expected_sigmas.append(sigma) return torch.tensor(expected_sigmas) @@ -63,7 +68,7 @@ def test_get_g_squared(self, exploding_variance, times): t = torch.tensor(times, requires_grad=True) sigma = exploding_variance.get_sigma(t) - sigma_squared = sigma ** 2 + sigma_squared = sigma**2 gradients = torch.ones_like(sigma_squared) sigma_squared.backward(gradients) diff --git a/tests/samplers/test_noisy_relative_coordinates_sampler.py b/tests/samplers/test_noisy_relative_coordinates_sampler.py index a087b8c6..b18b5f76 100644 --- a/tests/samplers/test_noisy_relative_coordinates_sampler.py +++ b/tests/samplers/test_noisy_relative_coordinates_sampler.py @@ -1,7 +1,8 @@ import numpy as np import pytest import torch -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ + +from diffusion_for_multi_scale_molecular_dynamics.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler @@ -47,8 +48,10 @@ def test_get_noisy_relative_coordinates_sample( return_value=fake_gaussian_sample, ) - computed_samples = NoisyRelativeCoordinatesSampler.get_noisy_relative_coordinates_sample( - real_relative_coordinates, sigmas + computed_samples = ( + NoisyRelativeCoordinatesSampler.get_noisy_relative_coordinates_sample( + real_relative_coordinates, sigmas + ) ) flat_sigmas = sigmas.flatten() diff --git a/tests/samplers/test_variance_sampler.py b/tests/samplers/test_variance_sampler.py index 50779272..bfe4c5ea 100644 --- a/tests/samplers/test_variance_sampler.py +++ b/tests/samplers/test_variance_sampler.py @@ -1,6 +1,7 @@ import pytest import torch -from src.crystal_diffusion.samplers.variance_sampler import ( + +from src.diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) @@ -10,11 +11,15 @@ @pytest.mark.parametrize("corrector_step_epsilon", [2e-5, 0.1]) class TestExplodingVarianceSampler: @pytest.fixture() - def noise_parameters(self, total_time_steps, time_delta, sigma_min, corrector_step_epsilon): - return NoiseParameters(total_time_steps=total_time_steps, - time_delta=time_delta, - sigma_min=sigma_min, - corrector_step_epsilon=corrector_step_epsilon) + def noise_parameters( + self, total_time_steps, time_delta, sigma_min, corrector_step_epsilon + ): + return NoiseParameters( + total_time_steps=total_time_steps, + time_delta=time_delta, + sigma_min=sigma_min, + corrector_step_epsilon=corrector_step_epsilon, + ) @pytest.fixture() def variance_sampler(self, noise_parameters): @@ -24,7 +29,7 @@ def variance_sampler(self, noise_parameters): def expected_times(self, total_time_steps, time_delta): times = [] for i in range(total_time_steps): - t = i / (total_time_steps - 1) * (1. - time_delta) + time_delta + t = i / (total_time_steps - 1) * (1.0 - time_delta) + time_delta times.append(t) times = torch.tensor(times) return times @@ -60,7 +65,9 @@ def test_time_array(self, variance_sampler, expected_times): def test_sigma_and_sigma_squared_arrays(self, variance_sampler, expected_sigmas): torch.testing.assert_close(variance_sampler._sigma_array, expected_sigmas) - torch.testing.assert_close(variance_sampler._sigma_squared_array, expected_sigmas**2) + torch.testing.assert_close( + variance_sampler._sigma_squared_array, expected_sigmas**2 + ) def test_g_and_g_square_array(self, variance_sampler, expected_sigmas, sigma_min): expected_sigmas_square = expected_sigmas**2 @@ -78,11 +85,16 @@ def test_g_and_g_square_array(self, variance_sampler, expected_sigmas, sigma_min expected_g_array = torch.sqrt(expected_g_squared_array) torch.testing.assert_close(variance_sampler._g_array, expected_g_array) - torch.testing.assert_close(variance_sampler._g_squared_array, expected_g_squared_array) + torch.testing.assert_close( + variance_sampler._g_squared_array, expected_g_squared_array + ) def test_epsilon_arrays(self, variance_sampler, expected_epsilons): torch.testing.assert_close(variance_sampler._epsilon_array, expected_epsilons) - torch.testing.assert_close(variance_sampler._sqrt_two_epsilon_array, torch.sqrt(2. * expected_epsilons)) + torch.testing.assert_close( + variance_sampler._sqrt_two_epsilon_array, + torch.sqrt(2.0 * expected_epsilons), + ) def test_get_random_time_step_indices(self, variance_sampler, total_time_steps): random_indices = variance_sampler._get_random_time_step_indices(shape=(1000,)) @@ -120,9 +132,15 @@ def test_get_all_sampling_parameters(self, variance_sampler): noise, langevin_dynamics = variance_sampler.get_all_sampling_parameters() torch.testing.assert_close(noise.time, variance_sampler._time_array) torch.testing.assert_close(noise.sigma, variance_sampler._sigma_array) - torch.testing.assert_close(noise.sigma_squared, variance_sampler._sigma_squared_array) + torch.testing.assert_close( + noise.sigma_squared, variance_sampler._sigma_squared_array + ) torch.testing.assert_close(noise.g, variance_sampler._g_array) torch.testing.assert_close(noise.g_squared, variance_sampler._g_squared_array) - torch.testing.assert_close(langevin_dynamics.epsilon, variance_sampler._epsilon_array) - torch.testing.assert_close(langevin_dynamics.sqrt_2_epsilon, variance_sampler._sqrt_two_epsilon_array) + torch.testing.assert_close( + langevin_dynamics.epsilon, variance_sampler._epsilon_array + ) + torch.testing.assert_close( + langevin_dynamics.sqrt_2_epsilon, variance_sampler._sqrt_two_epsilon_array + ) diff --git a/tests/samples_and_metrics/test_sampling.py b/tests/samples_and_metrics/test_sampling.py index fb939520..c7cdc993 100644 --- a/tests/samples_and_metrics/test_sampling.py +++ b/tests/samples_and_metrics/test_sampling.py @@ -1,13 +1,15 @@ import einops import pytest import torch -from crystal_diffusion.namespace import (CARTESIAN_POSITIONS, - RELATIVE_COORDINATES, UNIT_CELL) -from crystal_diffusion.utils.basis_transformations import \ + +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_POSITIONS, RELATIVE_COORDINATES, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ get_positions_from_coordinates -from src.crystal_diffusion.generators.position_generator import ( +from src.diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import ( PositionGenerator, SamplingParameters) -from src.crystal_diffusion.samples.sampling import create_batch_of_samples +from src.diffusion_for_multi_scale_molecular_dynamics.samples.sampling import \ + create_batch_of_samples class DummyGenerator(PositionGenerator): @@ -22,7 +24,9 @@ def sample( self, number_of_samples: int, device: torch.device, unit_cell: torch.Tensor ) -> torch.Tensor: self._counter += number_of_samples - return self._relative_coordinates[self._counter - number_of_samples:self._counter] + return self._relative_coordinates[ + self._counter - number_of_samples: self._counter + ] @pytest.fixture diff --git a/tests/score/test_wrapped_gaussian_score.py b/tests/score/test_wrapped_gaussian_score.py index 0787bede..adb77f34 100644 --- a/tests/score/test_wrapped_gaussian_score.py +++ b/tests/score/test_wrapped_gaussian_score.py @@ -1,7 +1,8 @@ import numpy as np import pytest import torch -from crystal_diffusion.score.wrapped_gaussian_score import ( + +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import ( SIGMA_THRESHOLD, _get_large_sigma_mask, _get_s1a_exponential, _get_s1b_exponential, _get_sigma_normalized_s2, _get_sigma_square_times_score_1_from_exponential, @@ -44,7 +45,9 @@ def expected_sigma_normalized_scores(relative_coordinates, sigmas): shape = relative_coordinates.shape list_sigma_normalized_scores = [] - for u, sigma in zip(relative_coordinates.numpy().flatten(), sigmas.numpy().flatten()): + for u, sigma in zip( + relative_coordinates.numpy().flatten(), sigmas.numpy().flatten() + ): s = get_sigma_normalized_score_brute_force(u, sigma) list_sigma_normalized_scores.append(s) @@ -162,10 +165,14 @@ def test_get_sigma_normalized_s2(list_u, list_sigma, list_k, numerical_type): deriv_z2 = torch.tensor(0.0, dtype=numerical_type) for k in list_k: - g_term = torch.sqrt(2.0 * pi) * sigma * (-2.0 * pi**2 * sigma**2 * k**2).exp() - (-pi * k**2).exp() + g_term = ( + torch.sqrt(2.0 * pi) * sigma * (-2.0 * pi**2 * sigma**2 * k**2).exp() + - (-pi * k**2).exp() + ) z2 += (-pi * (u + k) ** 2).exp() + g_term * torch.cos(2 * pi * k * u) - deriv_z2 += (-2.0 * pi * (u + k) * (-pi * (u + k) ** 2).exp() - - 2.0 * pi * k * g_term * torch.sin(2.0 * pi * k * u)) + deriv_z2 += -2.0 * pi * (u + k) * ( + -pi * (u + k) ** 2 + ).exp() - 2.0 * pi * k * g_term * torch.sin(2.0 * pi * k * u) expected_value = sigma * deriv_z2 / z2 list_expected_s2.append(expected_value) @@ -178,11 +185,13 @@ def test_get_sigma_normalized_s2(list_u, list_sigma, list_k, numerical_type): @pytest.mark.parametrize("kmax", [4]) @pytest.mark.parametrize("shape", test_shapes) def test_get_sigma_normalized_score( - relative_coordinates, sigmas, kmax, expected_sigma_normalized_scores + relative_coordinates, sigmas, kmax, expected_sigma_normalized_scores ): sigma_normalized_score_small_sigma = get_sigma_normalized_score( relative_coordinates, sigmas, kmax ) torch.testing.assert_close( - sigma_normalized_score_small_sigma, expected_sigma_normalized_scores, check_dtype=False + sigma_normalized_score_small_sigma, + expected_sigma_normalized_scores, + check_dtype=False, ) diff --git a/tests/test_hp_utils.py b/tests/test_hp_utils.py index d35c8b71..d136417f 100644 --- a/tests/test_hp_utils.py +++ b/tests/test_hp_utils.py @@ -1,28 +1,30 @@ import pytest -from crystal_diffusion.utils.hp_utils import check_hp + +from diffusion_for_multi_scale_molecular_dynamics.utils.hp_utils import \ + check_hp def test_check_hp__all_params_are_there(): - names = ['a', 'b'] - hps = {'a': 0, 'b': 1} + names = ["a", "b"] + hps = {"a": 0, "b": 1} check_hp(names, hps) def test_check_hp__param_is_missing(): - names = ['a', 'b'] - hps = {'a': 0} + names = ["a", "b"] + hps = {"a": 0} with pytest.raises(ValueError): check_hp(names, hps) def test_check_hp__extra_param_allowed(): - names = ['a'] - hps = {'a': 0, 'b': 1} + names = ["a"] + hps = {"a": 0, "b": 1} check_hp(names, hps) def test_check_hp__extra_param_not_allowed(): - names = ['a'] - hps = {'a': 0, 'b': 1} + names = ["a"] + hps = {"a": 0, "b": 1} with pytest.raises(ValueError): check_hp(names, hps, allow_extra=False) diff --git a/tests/test_sample_diffusion.py b/tests/test_sample_diffusion.py index 56c6e07a..d6f8d0ab 100644 --- a/tests/test_sample_diffusion.py +++ b/tests/test_sample_diffusion.py @@ -3,17 +3,22 @@ import pytest import torch import yaml -from crystal_diffusion import sample_diffusion -from crystal_diffusion.generators.predictor_corrector_position_generator import \ + +from diffusion_for_multi_scale_molecular_dynamics import sample_diffusion +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.models.loss import MSELossParameters -from crystal_diffusion.models.optimizer import OptimizerParameters -from crystal_diffusion.models.position_diffusion_lightning_model import ( +from diffusion_for_multi_scale_molecular_dynamics.models.loss import \ + MSELossParameters +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import \ + OptimizerParameters +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import ( PositionDiffusionLightningModel, PositionDiffusionParameters) -from crystal_diffusion.models.score_networks.mlp_score_network import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mlp_score_network import \ MLPScoreNetworkParameters -from crystal_diffusion.namespace import RELATIVE_COORDINATES -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.namespace import \ + RELATIVE_COORDINATES +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters @pytest.fixture() @@ -139,7 +144,7 @@ def test_sample_diffusion( record_samples, ): mocker.patch( - "crystal_diffusion.sample_diffusion.get_sigma_normalized_score_network", + "diffusion_for_multi_scale_molecular_dynamics.sample_diffusion.get_sigma_normalized_score_network", return_value=sigma_normalized_score_network, ) diff --git a/tests/test_train_diffusion.py b/tests/test_train_diffusion.py index e7866b6f..16f10d23 100644 --- a/tests/test_train_diffusion.py +++ b/tests/test_train_diffusion.py @@ -4,6 +4,7 @@ to make sure that the code can run without crashing when given appropriate inputs: we are not testing that the results are correct. """ + import glob import os import re @@ -12,10 +13,10 @@ import numpy as np import pytest import yaml -from crystal_diffusion import train_diffusion -from crystal_diffusion.callbacks.standard_callbacks import (BEST_MODEL_NAME, - LAST_MODEL_NAME) +from diffusion_for_multi_scale_molecular_dynamics import train_diffusion +from diffusion_for_multi_scale_molecular_dynamics.callbacks.standard_callbacks import ( + BEST_MODEL_NAME, LAST_MODEL_NAME) from tests.conftest import TestDiffusionDataBase best_model_regex = re.compile(r"best_model-epoch=(?P(\d+)).*.ckpt") @@ -24,6 +25,7 @@ class DelayedInterrupt: """This class will raise a KeyboardInterrupt when its method is called a certain number of times.""" + def __init__(self, how_many_epochs_before_interrupt: int): self.how_many_epochs_before_interrupt = how_many_epochs_before_interrupt self.counter = 0 @@ -36,130 +38,174 @@ def delayed_interrupt(self): def get_prediction_head_parameters(name: str): - if name == 'mlp': - head_parameters = dict(name='mlp', - hidden_dimensions_size=16, - n_hidden_dimensions=3) - - elif name == 'equivariant': - head_parameters = dict(name='equivariant', - time_embedding_irreps="16x0e", - number_of_layers=2, - gate="silu") + if name == "mlp": + head_parameters = dict( + name="mlp", hidden_dimensions_size=16, n_hidden_dimensions=3 + ) + + elif name == "equivariant": + head_parameters = dict( + name="equivariant", + time_embedding_irreps="16x0e", + number_of_layers=2, + gate="silu", + ) else: raise NotImplementedError("This score network is not implemented") return head_parameters -def get_score_network(architecture: str, head_name: Union[str, None], number_of_atoms: int): - if architecture == 'mlp': +def get_score_network( + architecture: str, head_name: Union[str, None], number_of_atoms: int +): + if architecture == "mlp": assert head_name is None, "There are no head options for a MLP score network." - score_network = dict(architecture='mlp', - number_of_atoms=number_of_atoms, - embedding_dimensions_size=8, - n_hidden_dimensions=2, - hidden_dimensions_size=16) - elif architecture == 'mace': - score_network = dict(architecture='mace', - r_max=3.0, - num_bessel=4, - hidden_irreps="8x0e + 8x1o", - number_of_atoms=number_of_atoms, - radial_MLP=[4, 4, 4], - prediction_head_parameters=get_prediction_head_parameters(head_name)) - - elif architecture == 'diffusion_mace': + score_network = dict( + architecture="mlp", + number_of_atoms=number_of_atoms, + embedding_dimensions_size=8, + n_hidden_dimensions=2, + hidden_dimensions_size=16, + ) + elif architecture == "mace": + score_network = dict( + architecture="mace", + r_max=3.0, + num_bessel=4, + hidden_irreps="8x0e + 8x1o", + number_of_atoms=number_of_atoms, + radial_MLP=[4, 4, 4], + prediction_head_parameters=get_prediction_head_parameters(head_name), + ) + + elif architecture == "diffusion_mace": assert head_name is None, "There are no head options for a MLP score network." - score_network = dict(architecture='diffusion_mace', - r_max=3.0, - num_bessel=4, - hidden_irreps="8x0e + 8x1o", - mlp_irreps="8x0e", - number_of_mlp_layers=1, - number_of_atoms=number_of_atoms, - radial_MLP=[4, 4, 4]) - - elif architecture == 'egnn': - score_network = dict(architecture='egnn') + score_network = dict( + architecture="diffusion_mace", + r_max=3.0, + num_bessel=4, + hidden_irreps="8x0e + 8x1o", + mlp_irreps="8x0e", + number_of_mlp_layers=1, + number_of_atoms=number_of_atoms, + radial_MLP=[4, 4, 4], + ) + + elif architecture == "egnn": + score_network = dict(architecture="egnn") else: raise NotImplementedError("This score network is not implemented") return score_network -def get_config(number_of_atoms: int, max_epoch: int, architecture: str, head_name: Union[str, None], - sampling_algorithm: str): +def get_config( + number_of_atoms: int, + max_epoch: int, + architecture: str, + head_name: Union[str, None], + sampling_algorithm: str, +): data_config = dict(batch_size=4, num_workers=0, max_atom=number_of_atoms) - model_config = dict(score_network=get_score_network(architecture, head_name, number_of_atoms), - loss={'algorithm': 'mse'}, - noise={'total_time_steps': 10}) - - optimizer_config = dict(name='adam', learning_rate=0.001) - scheduler_config = dict(name='ReduceLROnPlateau', factor=0.6, patience=2) - - sampling_dict = dict(algorithm=sampling_algorithm, - spatial_dimension=3, - number_of_atoms=number_of_atoms, - number_of_samples=4, - record_samples=True, - cell_dimensions=[10., 10., 10.]) - if sampling_algorithm == 'predictor_corrector': + model_config = dict( + score_network=get_score_network(architecture, head_name, number_of_atoms), + loss={"algorithm": "mse"}, + noise={"total_time_steps": 10}, + ) + + optimizer_config = dict(name="adam", learning_rate=0.001) + scheduler_config = dict(name="ReduceLROnPlateau", factor=0.6, patience=2) + + sampling_dict = dict( + algorithm=sampling_algorithm, + spatial_dimension=3, + number_of_atoms=number_of_atoms, + number_of_samples=4, + record_samples=True, + cell_dimensions=[10.0, 10.0, 10.0], + ) + if sampling_algorithm == "predictor_corrector": sampling_dict["number_of_corrector_steps"] = 1 - early_stopping_config = dict(metric='validation_epoch_loss', mode='min', patience=max_epoch) - model_checkpoint_config = dict(monitor='validation_epoch_loss', mode='min') - diffusion_sampling_config = dict(noise={'total_time_steps': 10}, - sampling=sampling_dict, - metrics={'compute_energies': False, - 'compute_structure_factor': True, - 'structure_factor_max_distance': 5.0}) - - config = dict(max_epoch=max_epoch, - exp_name='smoke_test', - seed=9999, - spatial_dimension=3, - data=data_config, - model=model_config, - optimizer=optimizer_config, - scheduler=scheduler_config, - early_stopping=early_stopping_config, - model_checkpoint=model_checkpoint_config, - loss_monitoring=dict(number_of_bins=10, sample_every_n_epochs=3), - diffusion_sampling=diffusion_sampling_config, - logging=['csv', 'tensorboard']) + early_stopping_config = dict( + metric="validation_epoch_loss", mode="min", patience=max_epoch + ) + model_checkpoint_config = dict(monitor="validation_epoch_loss", mode="min") + diffusion_sampling_config = dict( + noise={"total_time_steps": 10}, + sampling=sampling_dict, + metrics={ + "compute_energies": False, + "compute_structure_factor": True, + "structure_factor_max_distance": 5.0, + }, + ) + + config = dict( + max_epoch=max_epoch, + exp_name="smoke_test", + seed=9999, + spatial_dimension=3, + data=data_config, + model=model_config, + optimizer=optimizer_config, + scheduler=scheduler_config, + early_stopping=early_stopping_config, + model_checkpoint=model_checkpoint_config, + loss_monitoring=dict(number_of_bins=10, sample_every_n_epochs=3), + diffusion_sampling=diffusion_sampling_config, + logging=["csv", "tensorboard"], + ) return config @pytest.mark.parametrize("sampling_algorithm", ["ode", "predictor_corrector"]) -@pytest.mark.parametrize("architecture, head_name", - [('egnn', None), - ('diffusion_mace', None), - ('mlp', None), - ('mace', 'equivariant'), - ('mace', 'mlp')]) +@pytest.mark.parametrize( + "architecture, head_name", + [ + ("egnn", None), + ("diffusion_mace", None), + ("mlp", None), + ("mace", "equivariant"), + ("mace", "mlp"), + ], +) class TestTrainDiffusion(TestDiffusionDataBase): @pytest.fixture() def max_epoch(self): return 5 @pytest.fixture() - def config(self, number_of_atoms, max_epoch, architecture, head_name, sampling_algorithm): - return get_config(number_of_atoms, max_epoch=max_epoch, - architecture=architecture, head_name=head_name, sampling_algorithm=sampling_algorithm) + def config( + self, number_of_atoms, max_epoch, architecture, head_name, sampling_algorithm + ): + return get_config( + number_of_atoms, + max_epoch=max_epoch, + architecture=architecture, + head_name=head_name, + sampling_algorithm=sampling_algorithm, + ) @pytest.fixture() def all_paths(self, paths, tmpdir, config): - all_paths = dict(data=paths['raw_data_dir'], processed_datadir=paths['processed_data_dir']) + all_paths = dict( + data=paths["raw_data_dir"], processed_datadir=paths["processed_data_dir"] + ) - for directory_name in ['dataset_working_dir', 'output']: + for directory_name in ["dataset_working_dir", "output"]: # use random names to make sure we didn't accidentally hardcode a folder name. - directory = os.path.join(tmpdir, f"{directory_name}_{np.random.randint(99999999)}") + directory = os.path.join( + tmpdir, f"{directory_name}_{np.random.randint(99999999)}" + ) all_paths[directory_name] = directory - all_paths['config'] = os.path.join(tmpdir, f"config_{np.random.randint(99999999)}.yaml") + all_paths["config"] = os.path.join( + tmpdir, f"config_{np.random.randint(99999999)}.yaml" + ) - with open(all_paths['config'], 'w') as fd: + with open(all_paths["config"], "w") as fd: yaml.dump(config, fd) return all_paths @@ -167,58 +213,71 @@ def all_paths(self, paths, tmpdir, config): @pytest.fixture() def args(self, all_paths, accelerator): """Input arguments for main.""" - input_args = [f"--config={all_paths['config']}", - f"--data={all_paths['data']}", - f"--processed_datadir={all_paths['processed_datadir']}", - f"--dataset_working_dir={all_paths['dataset_working_dir']}", - f"--output={all_paths['output']}", - f"--accelerator={accelerator}", - f"--devices={1}"] + input_args = [ + f"--config={all_paths['config']}", + f"--data={all_paths['data']}", + f"--processed_datadir={all_paths['processed_datadir']}", + f"--dataset_working_dir={all_paths['dataset_working_dir']}", + f"--output={all_paths['output']}", + f"--accelerator={accelerator}", + f"--devices={1}", + ] return input_args @pytest.mark.slow def test_checkpoint_callback(self, args, all_paths, max_epoch): train_diffusion.main(args) - best_model_path = os.path.join(all_paths['output'], BEST_MODEL_NAME) - last_model_path = os.path.join(all_paths['output'], LAST_MODEL_NAME) + best_model_path = os.path.join(all_paths["output"], BEST_MODEL_NAME) + last_model_path = os.path.join(all_paths["output"], LAST_MODEL_NAME) model_paths = [best_model_path, last_model_path] regexes = [best_model_regex, last_model_regex] - should_test_epoch_number = [False, True] # the 'best' model epoch is ill-defined. Don't test! + should_test_epoch_number = [ + False, + True, + ] # the 'best' model epoch is ill-defined. Don't test! - for model_path, regex, test_epoch_number in zip(model_paths, regexes, should_test_epoch_number): - paths_in_directory = glob.glob(model_path + '/*.ckpt') + for model_path, regex, test_epoch_number in zip( + model_paths, regexes, should_test_epoch_number + ): + paths_in_directory = glob.glob(model_path + "/*.ckpt") assert len(paths_in_directory) == 1 model_filename = os.path.basename(paths_in_directory[0]) match_object = regex.match(model_filename) assert match_object is not None if test_epoch_number: - model_epoch = int(match_object.group('epoch')) + model_epoch = int(match_object.group("epoch")) assert model_epoch == max_epoch - 1 # the epoch counter starts at zero! - @pytest.mark.skip(reason="This test fails because of some obscure change in the Pytorch-Lightning library. " - "'Restart' is such a low value proposition at this time that it is not worth the " - "time and effort to fight with a subtle library issue.") + @pytest.mark.skip( + reason="This test fails because of some obscure change in the Pytorch-Lightning library. " + "'Restart' is such a low value proposition at this time that it is not worth the " + "time and effort to fight with a subtle library issue." + ) def test_restart(self, args, all_paths, max_epoch, mocker): - last_model_path = os.path.join(all_paths['output'], LAST_MODEL_NAME) + last_model_path = os.path.join(all_paths["output"], LAST_MODEL_NAME) - method_to_patch = ("crystal_diffusion.models.position_diffusion_lightning_model." - "PositionDiffusionLightningModel.on_train_epoch_start") + method_to_patch = ( + "diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model." + "PositionDiffusionLightningModel.on_train_epoch_start" + ) interruption_epoch = max_epoch // 2 - interruptor = DelayedInterrupt(how_many_epochs_before_interrupt=interruption_epoch) + interruptor = DelayedInterrupt( + how_many_epochs_before_interrupt=interruption_epoch + ) mocker.patch(method_to_patch, side_effect=interruptor.delayed_interrupt) train_diffusion.main(args) # Check that there is only one last model, and that it fits expectation - paths_in_directory = glob.glob(last_model_path + '/*.ckpt') + paths_in_directory = glob.glob(last_model_path + "/*.ckpt") last_model_filename = os.path.basename(paths_in_directory[0]) match_object = last_model_regex.match(last_model_filename) assert match_object is not None - model_epoch = int(match_object.group('epoch')) + model_epoch = int(match_object.group("epoch")) assert model_epoch == interruption_epoch - 1 # Restart training @@ -233,10 +292,10 @@ def test_restart(self, args, all_paths, max_epoch, mocker): # deleting all checkpoints. Interruptions are rare: it is best to deal with # multiple checkpoints by trusting the largest epoch number. list_epoch_numbers = [] - for path in glob.glob(last_model_path + '/*.ckpt'): + for path in glob.glob(last_model_path + "/*.ckpt"): last_model_filename = os.path.basename(path) match_object = last_model_regex.match(last_model_filename) assert match_object is not None - model_epoch = int(match_object.group('epoch')) + model_epoch = int(match_object.group("epoch")) list_epoch_numbers.append(model_epoch) assert np.max(list_epoch_numbers) == max_epoch - 1 diff --git a/tests/utils/test_basis_transformations.py b/tests/utils/test_basis_transformations.py index 62541103..e14bc7a8 100644 --- a/tests/utils/test_basis_transformations.py +++ b/tests/utils/test_basis_transformations.py @@ -1,6 +1,7 @@ import pytest import torch -from crystal_diffusion.utils.basis_transformations import ( + +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import ( get_positions_from_coordinates, get_reciprocal_basis_vectors, get_relative_coordinates_from_cartesian_positions, map_relative_coordinates_to_unit_cell) @@ -32,9 +33,13 @@ def test_get_reciprocal_basis_vectors(basis_vectors): torch.testing.assert_allclose(b_matrix @ a_matrix, identity) -def test_get_positions_from_coordinates(batch_size, relative_coordinates, basis_vectors): +def test_get_positions_from_coordinates( + batch_size, relative_coordinates, basis_vectors +): - computed_positions = get_positions_from_coordinates(relative_coordinates, basis_vectors) + computed_positions = get_positions_from_coordinates( + relative_coordinates, basis_vectors + ) expected_positions = torch.empty(relative_coordinates.shape, dtype=torch.float32) for batch_idx, (a1, a2, a3) in enumerate(basis_vectors): @@ -44,12 +49,17 @@ def test_get_positions_from_coordinates(batch_size, relative_coordinates, basis_ torch.testing.assert_close(expected_positions, computed_positions) -def test_get_relative_coordinates_from_cartesian_positions(relative_coordinates, basis_vectors): - cartesian_positions = get_positions_from_coordinates(relative_coordinates, basis_vectors) +def test_get_relative_coordinates_from_cartesian_positions( + relative_coordinates, basis_vectors +): + cartesian_positions = get_positions_from_coordinates( + relative_coordinates, basis_vectors + ) reciprocal_basis_vectors = get_reciprocal_basis_vectors(basis_vectors) - computed_relative_coordinates = get_relative_coordinates_from_cartesian_positions(cartesian_positions, - reciprocal_basis_vectors) + computed_relative_coordinates = get_relative_coordinates_from_cartesian_positions( + cartesian_positions, reciprocal_basis_vectors + ) torch.testing.assert_close(computed_relative_coordinates, relative_coordinates) @@ -57,7 +67,7 @@ def test_get_relative_coordinates_from_cartesian_positions(relative_coordinates, def test_remainder_failure(): # This test demonstrates how torch.remainder does not do what we want, which is why we need # to define the function "map_relative_coordinates_to_unit_cell". - epsilon = -torch.tensor(1.e-8) + epsilon = -torch.tensor(1.0e-8) relative_coordinates_not_in_unit_cell = torch.remainder(epsilon, 1.0) assert relative_coordinates_not_in_unit_cell == 1.0 @@ -65,19 +75,27 @@ def test_remainder_failure(): @pytest.mark.parametrize("shape", [(10,), (10, 20), (3, 4, 5)]) def test_map_relative_coordinates_to_unit_cell_hard(shape): relative_coordinates = 1e-8 * (torch.rand((10,)) - 0.5) - computed_relative_coordinates = map_relative_coordinates_to_unit_cell(relative_coordinates) - - positive_relative_coordinates_mask = relative_coordinates >= 0. - assert torch.all(relative_coordinates[positive_relative_coordinates_mask] - == computed_relative_coordinates[positive_relative_coordinates_mask]) - torch.testing.assert_close(computed_relative_coordinates[~positive_relative_coordinates_mask], - torch.zeros_like(computed_relative_coordinates[~positive_relative_coordinates_mask])) + computed_relative_coordinates = map_relative_coordinates_to_unit_cell( + relative_coordinates + ) + + positive_relative_coordinates_mask = relative_coordinates >= 0.0 + assert torch.all( + relative_coordinates[positive_relative_coordinates_mask] + == computed_relative_coordinates[positive_relative_coordinates_mask] + ) + torch.testing.assert_close( + computed_relative_coordinates[~positive_relative_coordinates_mask], + torch.zeros_like( + computed_relative_coordinates[~positive_relative_coordinates_mask] + ), + ) @pytest.mark.parametrize("shape", [(100, 8, 16)]) def test_map_relative_coordinates_to_unit_cell_easy(shape): # Very unlikely to hit the edge cases. - relative_coordinates = 10. * (torch.rand((10,)) - 0.5) - expected_values = torch.remainder(relative_coordinates, 1.) + relative_coordinates = 10.0 * (torch.rand((10,)) - 0.5) + expected_values = torch.remainder(relative_coordinates, 1.0) computed_values = map_relative_coordinates_to_unit_cell(relative_coordinates) torch.testing.assert_close(computed_values, expected_values) diff --git a/tests/utils/test_configuration_parsing.py b/tests/utils/test_configuration_parsing.py index c72fc92c..f74b6a62 100644 --- a/tests/utils/test_configuration_parsing.py +++ b/tests/utils/test_configuration_parsing.py @@ -1,28 +1,32 @@ from dataclasses import asdict, dataclass import pytest -from crystal_diffusion.utils.configuration_parsing import \ + +from diffusion_for_multi_scale_molecular_dynamics.utils.configuration_parsing import \ create_parameters_from_configuration_dictionary @dataclass(kw_only=True) class DummyParameters: """Base dataclass for some set of parameters""" + name: str @dataclass(kw_only=True) class FirstKindDummyParameters(DummyParameters): """Base dataclass for some set of parameters""" - name: str = 'first_kind' - a: float = 1. - b: float = 2. + + name: str = "first_kind" + a: float = 1.0 + b: float = 2.0 @dataclass(kw_only=True) class SecondKindDummyParameters(DummyParameters): """Base dataclass for some set of parameters""" - name: str = 'second_kind' + + name: str = "second_kind" x: float = 0.1 y: float = 0.2 z: float = 0.3 @@ -40,12 +44,16 @@ def configuration(parameters): @pytest.fixture() def options(): - return dict(first_kind=FirstKindDummyParameters, second_kind=SecondKindDummyParameters) + return dict( + first_kind=FirstKindDummyParameters, second_kind=SecondKindDummyParameters + ) -def test_create_parameters_from_configuration_dictionary(configuration, options, parameters): +def test_create_parameters_from_configuration_dictionary( + configuration, options, parameters +): - computed_parameters = create_parameters_from_configuration_dictionary(configuration=configuration, - identifier='name', - options=options) + computed_parameters = create_parameters_from_configuration_dictionary( + configuration=configuration, identifier="name", options=options + ) assert computed_parameters == parameters diff --git a/tests/utils/test_neighbors.py b/tests/utils/test_neighbors.py index aee6dc0e..b58c1f8e 100644 --- a/tests/utils/test_neighbors.py +++ b/tests/utils/test_neighbors.py @@ -3,18 +3,20 @@ import numpy as np import pytest import torch -from crystal_diffusion.utils.basis_transformations import \ +from pymatgen.core import Lattice, Structure + +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ get_positions_from_coordinates -from crystal_diffusion.utils.neighbors import ( +from diffusion_for_multi_scale_molecular_dynamics.utils.neighbors import ( AdjacencyInfo, _get_relative_coordinates_lattice_vectors, _get_shifted_positions, _get_shortest_distance_that_crosses_unit_cell, _get_vectors_from_multiple_indices, get_periodic_adjacency_information, shift_adjacency_matrix_indices_for_graph_batching) -from pymatgen.core import Lattice, Structure - from tests.fake_data_utils import find_aligning_permutation -Neighbors = namedtuple("Neighbors", ["source_index", "destination_index", "displacement", "shift"]) +Neighbors = namedtuple( + "Neighbors", ["source_index", "destination_index", "displacement", "shift"] +) @pytest.fixture(scope="module", autouse=True) @@ -46,9 +48,13 @@ def positions(relative_coordinates, basis_vectors): @pytest.fixture def lattice_vectors(batch_size, basis_vectors, number_of_shells): - relative_lattice_vectors = _get_relative_coordinates_lattice_vectors(number_of_shells) + relative_lattice_vectors = _get_relative_coordinates_lattice_vectors( + number_of_shells + ) batched_relative_lattice_vectors = relative_lattice_vectors.repeat(batch_size, 1, 1) - lattice_vectors = get_positions_from_coordinates(batched_relative_lattice_vectors, basis_vectors) + lattice_vectors = get_positions_from_coordinates( + batched_relative_lattice_vectors, basis_vectors + ) return lattice_vectors @@ -58,14 +64,18 @@ def structures(basis_vectors, relative_coordinates): list_structures = [] for basis, coordinates in zip(basis_vectors, relative_coordinates): number_of_atoms = coordinates.shape[0] - species = number_of_atoms * ["Si"] # this is a dummy variable. It doesn't matter what the atom types are... + species = number_of_atoms * [ + "Si" + ] # this is a dummy variable. It doesn't matter what the atom types are... lattice = Lattice(matrix=basis.cpu().numpy(), pbc=(True, True, True)) - structure = Structure(lattice=lattice, - species=species, - coords=coordinates.cpu().numpy(), - to_unit_cell=False, # already in [0, 1) range. - coords_are_cartesian=False) + structure = Structure( + lattice=lattice, + species=species, + coords=coordinates.cpu().numpy(), + to_unit_cell=False, # already in [0, 1) range. + coords_are_cartesian=False, + ) list_structures.append(structure) return list_structures @@ -83,18 +93,21 @@ def expected_neighbors(structures, radial_cutoff): source_site = structure.sites[src_idx] for neighbor_site in neighbor_sites: # Make sure the neighbors are no more than one shell away. - assert np.all(np.abs(np.array(neighbor_site.image)) <= 1.), \ - "A neighbor site is out of range. Review test code!" + assert np.all( + np.abs(np.array(neighbor_site.image)) <= 1.0 + ), "A neighbor site is out of range. Review test code!" dst_idx = neighbor_site.index displacement = neighbor_site.coords - source_site.coords shift = np.dot(np.array(neighbor_site.image), structure.lattice.matrix) - neighbors = Neighbors(source_index=src_idx, - destination_index=dst_idx, - displacement=displacement, - shift=shift) + neighbors = Neighbors( + source_index=src_idx, + destination_index=dst_idx, + displacement=displacement, + shift=shift, + ) list_neighbors.append(neighbors) structure_neighbors.append(list_neighbors) @@ -109,74 +122,123 @@ def expected_adjacency_info(structures, expected_neighbors): shifts = [] adj_matrix = [] number_of_edges = [] - for batch_index, (structure, list_neighbors) in enumerate(zip(structures, expected_neighbors)): + for batch_index, (structure, list_neighbors) in enumerate( + zip(structures, expected_neighbors) + ): number_of_atoms = len(structure) - batch_node_batch_indices = torch.tensor(number_of_atoms * [batch_index], dtype=torch.long) + batch_node_batch_indices = torch.tensor( + number_of_atoms * [batch_index], dtype=torch.long + ) node_batch_indices.append(batch_node_batch_indices) - batch_shifts = torch.stack([torch.from_numpy(neigh.shift) for neigh in list_neighbors]) + batch_shifts = torch.stack( + [torch.from_numpy(neigh.shift) for neigh in list_neighbors] + ) shifts.append(batch_shifts) source_indices = torch.tensor([neigh.source_index for neigh in list_neighbors]) - dest_indices = torch.tensor([neigh.destination_index for neigh in list_neighbors]) + dest_indices = torch.tensor( + [neigh.destination_index for neigh in list_neighbors] + ) batch_adj_matrix = torch.stack([source_indices, dest_indices]) adj_matrix.append(batch_adj_matrix) number_of_edges.append(len(source_indices)) - batch_edge_batch_indices = torch.tensor(len(source_indices) * [batch_index], dtype=torch.long) + batch_edge_batch_indices = torch.tensor( + len(source_indices) * [batch_index], dtype=torch.long + ) edge_batch_indices.append(batch_edge_batch_indices) - return AdjacencyInfo(adjacency_matrix=torch.cat(adj_matrix, dim=1), - shifts=torch.cat(shifts, dim=0), - node_batch_indices=torch.cat(node_batch_indices), - edge_batch_indices=torch.cat(edge_batch_indices), - number_of_edges=torch.tensor(number_of_edges)) + return AdjacencyInfo( + adjacency_matrix=torch.cat(adj_matrix, dim=1), + shifts=torch.cat(shifts, dim=0), + node_batch_indices=torch.cat(node_batch_indices), + edge_batch_indices=torch.cat(edge_batch_indices), + number_of_edges=torch.tensor(number_of_edges), + ) @pytest.mark.parametrize("radial_cutoff", [1.1, 2.2, 3.3]) -def test_get_periodic_adjacency_information(basis_vectors, positions, radial_cutoff, - batch_size, expected_adjacency_info): - computed_adjacency_info = get_periodic_adjacency_information(positions, basis_vectors, radial_cutoff) - - torch.testing.assert_close(expected_adjacency_info.number_of_edges, computed_adjacency_info.number_of_edges) - torch.testing.assert_close(expected_adjacency_info.node_batch_indices, computed_adjacency_info.node_batch_indices) - torch.testing.assert_close(expected_adjacency_info.edge_batch_indices, computed_adjacency_info.edge_batch_indices) +def test_get_periodic_adjacency_information( + basis_vectors, positions, radial_cutoff, batch_size, expected_adjacency_info +): + computed_adjacency_info = get_periodic_adjacency_information( + positions, basis_vectors, radial_cutoff + ) + + torch.testing.assert_close( + expected_adjacency_info.number_of_edges, computed_adjacency_info.number_of_edges + ) + torch.testing.assert_close( + expected_adjacency_info.node_batch_indices, + computed_adjacency_info.node_batch_indices, + ) + torch.testing.assert_close( + expected_adjacency_info.edge_batch_indices, + computed_adjacency_info.edge_batch_indices, + ) # The edges might not be in the same order; build the corresponding permutation. for batch_idx in range(batch_size): expected_mask = expected_adjacency_info.edge_batch_indices == batch_idx - expected_src_idx, expected_dst_idx = expected_adjacency_info.adjacency_matrix[:, expected_mask] + expected_src_idx, expected_dst_idx = expected_adjacency_info.adjacency_matrix[ + :, expected_mask + ] expected_shifts = expected_adjacency_info.shifts[expected_mask, :] - expected_displacements = (positions[batch_idx, expected_dst_idx] + expected_shifts - - positions[batch_idx, expected_src_idx]) + expected_displacements = ( + positions[batch_idx, expected_dst_idx] + + expected_shifts + - positions[batch_idx, expected_src_idx] + ) computed_mask = computed_adjacency_info.edge_batch_indices == batch_idx - computed_src_idx, computed_dst_idx = computed_adjacency_info.adjacency_matrix[:, computed_mask] + computed_src_idx, computed_dst_idx = computed_adjacency_info.adjacency_matrix[ + :, computed_mask + ] computed_shifts = computed_adjacency_info.shifts[computed_mask, :] - computed_displacements = (positions[batch_idx, computed_dst_idx] + computed_shifts - - positions[batch_idx, computed_src_idx]) - - permutation_indices = find_aligning_permutation(expected_displacements, computed_displacements, tol=1e-5) - - torch.testing.assert_close(computed_shifts[permutation_indices], expected_shifts, check_dtype=False) - torch.testing.assert_close(computed_src_idx[permutation_indices], expected_src_idx) - torch.testing.assert_close(computed_dst_idx[permutation_indices], expected_dst_idx) - - -def test_get_periodic_neighbour_indices_and_displacements_large_cutoff(basis_vectors, relative_coordinates): + computed_displacements = ( + positions[batch_idx, computed_dst_idx] + + computed_shifts + - positions[batch_idx, computed_src_idx] + ) + + permutation_indices = find_aligning_permutation( + expected_displacements, computed_displacements, tol=1e-5 + ) + + torch.testing.assert_close( + computed_shifts[permutation_indices], expected_shifts, check_dtype=False + ) + torch.testing.assert_close( + computed_src_idx[permutation_indices], expected_src_idx + ) + torch.testing.assert_close( + computed_dst_idx[permutation_indices], expected_dst_idx + ) + + +def test_get_periodic_neighbour_indices_and_displacements_large_cutoff( + basis_vectors, relative_coordinates +): # Check that the code crashes if the radial cutoff is too big! - shortest_cell_crossing_distances = _get_shortest_distance_that_crosses_unit_cell(basis_vectors).min() + shortest_cell_crossing_distances = _get_shortest_distance_that_crosses_unit_cell( + basis_vectors + ).min() large_radial_cutoff = shortest_cell_crossing_distances + 0.1 small_radial_cutoff = shortest_cell_crossing_distances - 0.1 # Should run - get_periodic_adjacency_information(relative_coordinates, basis_vectors, small_radial_cutoff) + get_periodic_adjacency_information( + relative_coordinates, basis_vectors, small_radial_cutoff + ) with pytest.raises(AssertionError): # Should crash - get_periodic_adjacency_information(relative_coordinates, basis_vectors, large_radial_cutoff) + get_periodic_adjacency_information( + relative_coordinates, basis_vectors, large_radial_cutoff + ) @pytest.mark.parametrize("number_of_shells", [1, 2, 3]) @@ -190,8 +252,12 @@ def test_get_relative_coordinates_lattice_vectors(number_of_shells): lattice_vector = torch.tensor([nx, ny, nz]) expected_lattice_vectors.append(lattice_vector) - expected_lattice_vectors = torch.stack(expected_lattice_vectors).to(dtype=torch.float32) - computed_lattice_vectors = _get_relative_coordinates_lattice_vectors(number_of_shells) + expected_lattice_vectors = torch.stack(expected_lattice_vectors).to( + dtype=torch.float32 + ) + computed_lattice_vectors = _get_relative_coordinates_lattice_vectors( + number_of_shells + ) torch.testing.assert_close(expected_lattice_vectors, computed_lattice_vectors) @@ -214,9 +280,13 @@ def test_get_shifted_positions(positions, lattice_vectors): lattice_vector = lattice_vectors[batch_idx, lat_idx, :] expected_cell_shift_positions = position + lattice_vector - computed_cell_shift_positions = computed_shifted_positions[batch_idx, lat_idx, atom_idx, :] + computed_cell_shift_positions = computed_shifted_positions[ + batch_idx, lat_idx, atom_idx, : + ] - torch.testing.assert_close(expected_cell_shift_positions, computed_cell_shift_positions) + torch.testing.assert_close( + expected_cell_shift_positions, computed_cell_shift_positions + ) def test_get_shortest_distance_that_crosses_unit_cell(basis_vectors): @@ -235,7 +305,9 @@ def test_get_shortest_distance_that_crosses_unit_cell(basis_vectors): expected_shortest_distances.append(np.min([d1, d2, d3])) expected_shortest_distances = torch.Tensor(expected_shortest_distances) - computed_shortest_distances = _get_shortest_distance_that_crosses_unit_cell(basis_vectors) + computed_shortest_distances = _get_shortest_distance_that_crosses_unit_cell( + basis_vectors + ) torch.testing.assert_close(expected_shortest_distances, computed_shortest_distances) @@ -250,12 +322,16 @@ def test_get_vectors_from_multiple_indices(batch_size, number_of_atoms): batch_indices = torch.randint(0, batch_size, (number_of_indices,)) vector_indices = torch.randint(0, number_of_atoms, (number_of_indices,)) - expected_vectors = torch.empty(number_of_indices, spatial_dimension, dtype=torch.float32) + expected_vectors = torch.empty( + number_of_indices, spatial_dimension, dtype=torch.float32 + ) for idx, (batch_idx, vector_idx) in enumerate(zip(batch_indices, vector_indices)): expected_vectors[idx] = vectors[batch_idx, vector_idx] - computed_vectors = _get_vectors_from_multiple_indices(vectors, batch_indices, vector_indices) + computed_vectors = _get_vectors_from_multiple_indices( + vectors, batch_indices, vector_indices + ) torch.testing.assert_close(expected_vectors, computed_vectors) @@ -273,8 +349,8 @@ def test_shift_adjacency_matrix_indices_for_graph_batching(batch_size, number_of expected_shifted_adj[:, bottom_idx:top_idx] += index_shift index_shift += number_of_atoms - computed_shifted_adj = shift_adjacency_matrix_indices_for_graph_batching(adjacency_matrix, - num_edges, - number_of_atoms) + computed_shifted_adj = shift_adjacency_matrix_indices_for_graph_batching( + adjacency_matrix, num_edges, number_of_atoms + ) torch.testing.assert_close(expected_shifted_adj, computed_shifted_adj) diff --git a/tests/utils/test_sample_trajectory.py b/tests/utils/test_sample_trajectory.py index bef3e266..e8699dee 100644 --- a/tests/utils/test_sample_trajectory.py +++ b/tests/utils/test_sample_trajectory.py @@ -3,165 +3,250 @@ import einops import pytest import torch -from crystal_diffusion.utils.sample_trajectory import \ + +from diffusion_for_multi_scale_molecular_dynamics.utils.sample_trajectory import \ PredictorCorrectorSampleTrajectory -@pytest.fixture(autouse=True, scope='module') +@pytest.fixture(autouse=True, scope="module") def set_seed(): torch.manual_seed(32434) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def number_of_predictor_steps(): return 8 -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def number_of_corrector_steps(): return 3 -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def batch_size(): return 4 -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def number_of_atoms(): return 8 -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def spatial_dimension(): return 3 -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def basis_vectors(batch_size): # orthogonal boxes with dimensions between 5 and 10. - orthogonal_boxes = torch.stack([torch.diag(5. + 5. * torch.rand(3)) for _ in range(batch_size)]) + orthogonal_boxes = torch.stack( + [torch.diag(5.0 + 5.0 * torch.rand(3)) for _ in range(batch_size)] + ) # add a bit of noise to make the vectors not quite orthogonal basis_vectors = orthogonal_boxes + 0.1 * torch.randn(batch_size, 3, 3) return basis_vectors -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def list_i_indices(number_of_predictor_steps): return torch.arange(number_of_predictor_steps - 1, -1, -1) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def list_sigmas(number_of_predictor_steps): return torch.rand(number_of_predictor_steps) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def list_times(number_of_predictor_steps): return torch.rand(number_of_predictor_steps) -@pytest.fixture(scope='module') -def predictor_scores(number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension): - return torch.rand(number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension) +@pytest.fixture(scope="module") +def predictor_scores( + number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension +): + return torch.rand( + number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension + ) -@pytest.fixture(scope='module') +@pytest.fixture(scope="module") def list_x_i(number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension): - return torch.rand(number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension) - - -@pytest.fixture(scope='module') -def list_x_im1(number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension): - return torch.rand(number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension) - - -@pytest.fixture(scope='module') -def corrector_scores(number_of_predictor_steps, number_of_corrector_steps, batch_size, - number_of_atoms, spatial_dimension): + return torch.rand( + number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension + ) + + +@pytest.fixture(scope="module") +def list_x_im1( + number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension +): + return torch.rand( + number_of_predictor_steps, batch_size, number_of_atoms, spatial_dimension + ) + + +@pytest.fixture(scope="module") +def corrector_scores( + number_of_predictor_steps, + number_of_corrector_steps, + batch_size, + number_of_atoms, + spatial_dimension, +): number_of_scores = number_of_predictor_steps * number_of_corrector_steps return torch.rand(number_of_scores, batch_size, number_of_atoms, spatial_dimension) -@pytest.fixture(scope='module') -def list_x_i_corr(number_of_predictor_steps, number_of_corrector_steps, batch_size, number_of_atoms, spatial_dimension): +@pytest.fixture(scope="module") +def list_x_i_corr( + number_of_predictor_steps, + number_of_corrector_steps, + batch_size, + number_of_atoms, + spatial_dimension, +): number_of_scores = number_of_predictor_steps * number_of_corrector_steps return torch.rand(number_of_scores, batch_size, number_of_atoms, spatial_dimension) -@pytest.fixture(scope='module') -def list_corrected_x_i(number_of_predictor_steps, number_of_corrector_steps, batch_size, - number_of_atoms, spatial_dimension): +@pytest.fixture(scope="module") +def list_corrected_x_i( + number_of_predictor_steps, + number_of_corrector_steps, + batch_size, + number_of_atoms, + spatial_dimension, +): number_of_scores = number_of_predictor_steps * number_of_corrector_steps return torch.rand(number_of_scores, batch_size, number_of_atoms, spatial_dimension) -@pytest.fixture(scope='module') -def sample_trajectory(number_of_corrector_steps, list_i_indices, list_times, list_sigmas, basis_vectors, - list_x_i, list_x_im1, predictor_scores, list_x_i_corr, list_corrected_x_i, corrector_scores): +@pytest.fixture(scope="module") +def sample_trajectory( + number_of_corrector_steps, + list_i_indices, + list_times, + list_sigmas, + basis_vectors, + list_x_i, + list_x_im1, + predictor_scores, + list_x_i_corr, + list_corrected_x_i, + corrector_scores, +): sample_trajectory = PredictorCorrectorSampleTrajectory() sample_trajectory.record_unit_cell(basis_vectors) total_corrector_index = 0 - for i_index, time, sigma, x_i, x_im1, scores in zip(list_i_indices, list_times, list_sigmas, - list_x_i, list_x_im1, predictor_scores): - sample_trajectory.record_predictor_step(i_index=i_index, time=time, sigma=sigma, - x_i=x_i, x_im1=x_im1, scores=scores) + for i_index, time, sigma, x_i, x_im1, scores in zip( + list_i_indices, list_times, list_sigmas, list_x_i, list_x_im1, predictor_scores + ): + sample_trajectory.record_predictor_step( + i_index=i_index, time=time, sigma=sigma, x_i=x_i, x_im1=x_im1, scores=scores + ) for _ in range(number_of_corrector_steps): x_i = list_x_i_corr[total_corrector_index] corrected_x_i = list_corrected_x_i[total_corrector_index] scores = corrector_scores[total_corrector_index] - sample_trajectory.record_corrector_step(i_index=i_index, time=time, sigma=sigma, - x_i=x_i, corrected_x_i=corrected_x_i, scores=scores) + sample_trajectory.record_corrector_step( + i_index=i_index, + time=time, + sigma=sigma, + x_i=x_i, + corrected_x_i=corrected_x_i, + scores=scores, + ) total_corrector_index += 1 return sample_trajectory def test_sample_trajectory_unit_cell(sample_trajectory, basis_vectors): - torch.testing.assert_close(sample_trajectory.data['unit_cell'], basis_vectors) - - -def test_record_predictor(sample_trajectory, list_times, list_sigmas, list_x_i, list_x_im1, predictor_scores): - torch.testing.assert_close(torch.tensor(sample_trajectory.data['predictor_time']), list_times) - torch.testing.assert_close(torch.tensor(sample_trajectory.data['predictor_sigma']), list_sigmas) - torch.testing.assert_close(torch.stack(sample_trajectory.data['predictor_x_i'], dim=0), list_x_i) - torch.testing.assert_close(torch.stack(sample_trajectory.data['predictor_x_im1'], dim=0), list_x_im1) - torch.testing.assert_close(torch.stack(sample_trajectory.data['predictor_scores'], dim=0), predictor_scores) - - -def test_record_corrector(sample_trajectory, number_of_corrector_steps, list_times, list_sigmas, list_x_i_corr, - list_corrected_x_i, corrector_scores): - - torch.testing.assert_close(torch.tensor(sample_trajectory.data['corrector_time']), - torch.repeat_interleave(list_times, number_of_corrector_steps)) - torch.testing.assert_close(torch.tensor(sample_trajectory.data['corrector_sigma']), - torch.repeat_interleave(list_sigmas, number_of_corrector_steps)) - torch.testing.assert_close(torch.stack(sample_trajectory.data['corrector_x_i'], dim=0), - list_x_i_corr) - torch.testing.assert_close(torch.stack(sample_trajectory.data['corrector_corrected_x_i'], dim=0), - list_corrected_x_i) - torch.testing.assert_close(torch.stack(sample_trajectory.data['corrector_scores'], dim=0), corrector_scores) - - -def test_standardize_data_and_write_pickle(sample_trajectory, basis_vectors, list_times, list_sigmas, - list_x_i, predictor_scores, tmp_path): - pickle_path = str(tmp_path / 'test_pickle_path.pkl') + torch.testing.assert_close(sample_trajectory.data["unit_cell"], basis_vectors) + + +def test_record_predictor( + sample_trajectory, list_times, list_sigmas, list_x_i, list_x_im1, predictor_scores +): + torch.testing.assert_close( + torch.tensor(sample_trajectory.data["predictor_time"]), list_times + ) + torch.testing.assert_close( + torch.tensor(sample_trajectory.data["predictor_sigma"]), list_sigmas + ) + torch.testing.assert_close( + torch.stack(sample_trajectory.data["predictor_x_i"], dim=0), list_x_i + ) + torch.testing.assert_close( + torch.stack(sample_trajectory.data["predictor_x_im1"], dim=0), list_x_im1 + ) + torch.testing.assert_close( + torch.stack(sample_trajectory.data["predictor_scores"], dim=0), predictor_scores + ) + + +def test_record_corrector( + sample_trajectory, + number_of_corrector_steps, + list_times, + list_sigmas, + list_x_i_corr, + list_corrected_x_i, + corrector_scores, +): + + torch.testing.assert_close( + torch.tensor(sample_trajectory.data["corrector_time"]), + torch.repeat_interleave(list_times, number_of_corrector_steps), + ) + torch.testing.assert_close( + torch.tensor(sample_trajectory.data["corrector_sigma"]), + torch.repeat_interleave(list_sigmas, number_of_corrector_steps), + ) + torch.testing.assert_close( + torch.stack(sample_trajectory.data["corrector_x_i"], dim=0), list_x_i_corr + ) + torch.testing.assert_close( + torch.stack(sample_trajectory.data["corrector_corrected_x_i"], dim=0), + list_corrected_x_i, + ) + torch.testing.assert_close( + torch.stack(sample_trajectory.data["corrector_scores"], dim=0), corrector_scores + ) + + +def test_standardize_data_and_write_pickle( + sample_trajectory, + basis_vectors, + list_times, + list_sigmas, + list_x_i, + predictor_scores, + tmp_path, +): + pickle_path = str(tmp_path / "test_pickle_path.pkl") sample_trajectory.write_to_pickle(pickle_path) - with open(pickle_path, 'rb') as fd: + with open(pickle_path, "rb") as fd: standardized_data = torch.load(fd) reordered_scores = einops.rearrange(predictor_scores, "t b n d -> b t n d") reordered_relative_coordinates = einops.rearrange(list_x_i, "t b n d -> b t n d") - torch.testing.assert_close(standardized_data['unit_cell'], basis_vectors) - torch.testing.assert_close(standardized_data['time'], list_times) - torch.testing.assert_close(standardized_data['sigma'], list_sigmas) - torch.testing.assert_close(standardized_data['relative_coordinates'], reordered_relative_coordinates) - torch.testing.assert_close(standardized_data['normalized_scores'], reordered_scores) + torch.testing.assert_close(standardized_data["unit_cell"], basis_vectors) + torch.testing.assert_close(standardized_data["time"], list_times) + torch.testing.assert_close(standardized_data["sigma"], list_sigmas) + torch.testing.assert_close( + standardized_data["relative_coordinates"], reordered_relative_coordinates + ) + torch.testing.assert_close(standardized_data["normalized_scores"], reordered_scores) def test_reset(sample_trajectory, tmp_path): diff --git a/tests/utils/test_structure_utils.py b/tests/utils/test_structure_utils.py index d73cab76..250b54a5 100644 --- a/tests/utils/test_structure_utils.py +++ b/tests/utils/test_structure_utils.py @@ -1,8 +1,9 @@ import pytest import torch -from crystal_diffusion.utils.basis_transformations import \ + +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ get_positions_from_coordinates -from crystal_diffusion.utils.structure_utils import ( +from diffusion_for_multi_scale_molecular_dynamics.utils.structure_utils import ( compute_distances, compute_distances_in_batch, get_orthogonal_basis_vectors) diff --git a/tests/utils/test_tensor_utils.py b/tests/utils/test_tensor_utils.py index dee8184c..4d2d5253 100644 --- a/tests/utils/test_tensor_utils.py +++ b/tests/utils/test_tensor_utils.py @@ -1,6 +1,7 @@ import pytest import torch -from crystal_diffusion.utils.tensor_utils import \ + +from diffusion_for_multi_scale_molecular_dynamics.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions @@ -23,8 +24,12 @@ def final_shape(batch_size, number_of_dimensions): @pytest.mark.parametrize("batch_size", [4, 8]) @pytest.mark.parametrize("number_of_dimensions", [1, 2, 3]) -def test_broadcast_batch_tensor_to_all_dimensions(batch_size, batch_values, final_shape): - broadcast_values = broadcast_batch_tensor_to_all_dimensions(batch_values, final_shape) +def test_broadcast_batch_tensor_to_all_dimensions( + batch_size, batch_values, final_shape +): + broadcast_values = broadcast_batch_tensor_to_all_dimensions( + batch_values, final_shape + ) value_arrays = broadcast_values.reshape(batch_size, -1) From 4abda7f0b8cde2539b5478e31d7a883f9e7dcde7 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 07:35:27 -0400 Subject: [PATCH 20/29] Fixed imports in the experiments folder. --- .../analysis/analytic_score/__init__.py | 2 +- .../analytical_score_sampling_and_plotting.py | 301 ++++++++++-------- .../generate_sample_energies.py | 190 ++++++----- .../plot_sample_energies.py | 53 +-- .../perfect_score_loss_analysis.py | 169 ++++++---- ...t_repaint_analytical_score_trajectories.py | 134 +++++--- .../repaint/repaint_with_analytic_score.py | 105 +++--- .../score_convergence_analysis.py | 82 +++-- experiments/analysis/analytic_score/utils.py | 127 +++++--- .../diffusion_sample_position_analysis.py | 127 ++++++-- .../analysis/exploding_variance_analysis.py | 40 ++- .../analysis/perturbation_kernel_analysis.py | 41 ++- experiments/analysis/plot_repulsive_force.py | 11 +- .../plotting_gaussian_vs_wrapped_gaussian.py | 36 ++- experiments/analysis/target_score_analysis.py | 89 ++++-- experiments/analysis_utils.py | 13 +- .../dataset_analysis/dataset_covariance.py | 36 ++- .../energy_consistency_analysis.py | 59 ++-- .../dataset_analysis/plot_si_phonon_DOS.py | 7 +- .../dataset_analysis/si_diffusion_analysis.py | 162 ++++++---- .../si_diffusion_v1_analysis.py | 100 ++++-- ...experiments_with_various_score_networks.py | 244 ++++++++------ .../analysis_callbacks.py | 53 +-- .../overfit_diffusion_mace.py | 121 ++++--- .../generators/sde_generator_sanity_check.py | 167 ++++++---- .../plot_repaint_score_trajectories.py | 118 ++++--- .../repaint_with_sota_score.py | 77 +++-- .../sota_score_sampling_and_plotting.py | 256 ++++++++------- .../draw_samples_from_equilibrium.py | 28 +- .../plot_hessian_eigenvalues.py | 16 +- .../plot_score_norm.py | 28 +- experiments/score_stability_analysis/util.py | 15 +- 32 files changed, 1887 insertions(+), 1120 deletions(-) diff --git a/experiments/analysis/analytic_score/__init__.py b/experiments/analysis/analytic_score/__init__.py index edadef72..d97a9bed 100644 --- a/experiments/analysis/analytic_score/__init__.py +++ b/experiments/analysis/analytic_score/__init__.py @@ -1,4 +1,4 @@ -from crystal_diffusion import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR ANALYTIC_SCORE_RESULTS_DIR = ANALYSIS_RESULTS_DIR / "ANALYTIC_SCORE" ANALYTIC_SCORE_RESULTS_DIR.mkdir(exist_ok=True) diff --git a/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py b/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py index 44f2b452..c28bd3b1 100644 --- a/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py +++ b/experiments/analysis/analytic_score/analytical_score_sampling_and_plotting.py @@ -11,18 +11,22 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.ode_position_generator import ( +from einops import einops + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.models.score_networks.analytical_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from einops import einops -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters - +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger from experiments.analysis.analytic_score import ANALYTIC_SCORE_RESULTS_DIR from experiments.analysis.analytic_score.utils import ( get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell, @@ -35,13 +39,13 @@ plt.style.use(PLOT_STYLE_PATH) if torch.cuda.is_available(): - device = torch.device('cuda') + device = torch.device("cuda") else: - device = torch.device('cpu') + device = torch.device("cpu") # Change these parameters as needed! # sampling_algorithm = 'ode' -sampling_algorithm = 'langevin' +sampling_algorithm = "langevin" supercell_factor = 1 kmax = 1 @@ -51,66 +55,75 @@ total_time_steps = 101 number_of_corrector_steps = 1 -if __name__ == '__main__': +if __name__ == "__main__": logger.info("Setting up parameters") equilibrium_relative_coordinates = torch.from_numpy( - get_silicon_supercell(supercell_factor=supercell_factor)).to(device) + get_silicon_supercell(supercell_factor=supercell_factor) + ).to(device) number_of_atoms, spatial_dimension = equilibrium_relative_coordinates.shape nd = number_of_atoms * spatial_dimension - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - sigma_min=0.001, - sigma_max=0.5) + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, sigma_min=0.001, sigma_max=0.5 + ) score_network_parameters = AnalyticalScoreNetworkParameters( number_of_atoms=number_of_atoms, spatial_dimension=spatial_dimension, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - variance_parameter=variance_parameter) + variance_parameter=variance_parameter, + ) sigma_normalized_score_network = AnalyticalScoreNetwork(score_network_parameters) - if sampling_algorithm == 'ode': - ode_sampling_parameters = ODESamplingParameters(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms, - number_of_samples=number_of_samples, - cell_dimensions=[1., 1., 1.], - record_samples=True, - absolute_solver_tolerance=1.0e-5, - relative_solver_tolerance=1.0e-5) - - position_generator = ( - ExplodingVarianceODEPositionGenerator(noise_parameters=noise_parameters, - sampling_parameters=ode_sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network)) - - elif sampling_algorithm == 'langevin': + if sampling_algorithm == "ode": + ode_sampling_parameters = ODESamplingParameters( + spatial_dimension=spatial_dimension, + number_of_atoms=number_of_atoms, + number_of_samples=number_of_samples, + cell_dimensions=[1.0, 1.0, 1.0], + record_samples=True, + absolute_solver_tolerance=1.0e-5, + relative_solver_tolerance=1.0e-5, + ) + + position_generator = ExplodingVarianceODEPositionGenerator( + noise_parameters=noise_parameters, + sampling_parameters=ode_sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) + + elif sampling_algorithm == "langevin": pc_sampling_parameters = PredictorCorrectorSamplingParameters( number_of_corrector_steps=number_of_corrector_steps, spatial_dimension=spatial_dimension, number_of_atoms=number_of_atoms, number_of_samples=number_of_samples, - cell_dimensions=[1., 1., 1.], - record_samples=True) + cell_dimensions=[1.0, 1.0, 1.0], + record_samples=True, + ) position_generator = LangevinGenerator( noise_parameters=noise_parameters, sampling_parameters=pc_sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + sigma_normalized_score_network=sigma_normalized_score_network, + ) # Draw some samples, create some plots - unit_cell = get_unit_cells(acell=1., - spatial_dimension=spatial_dimension, - number_of_samples=number_of_samples).to(device) + unit_cell = get_unit_cells( + acell=1.0, + spatial_dimension=spatial_dimension, + number_of_samples=number_of_samples, + ).to(device) logger.info("Drawing samples") - samples = position_generator.sample(number_of_samples=number_of_samples, - device=device, - unit_cell=unit_cell).detach() + samples = position_generator.sample( + number_of_samples=number_of_samples, device=device, unit_cell=unit_cell + ).detach() - if sampling_algorithm == 'ode': + if sampling_algorithm == "ode": # Plot the ODE parameters logger.info("Plotting ODE parameters") times = torch.linspace(0, 1, 1001) @@ -118,19 +131,19 @@ ode_prefactor = position_generator._get_ode_prefactor(sigmas) fig0 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig0.suptitle('ODE parameters') + fig0.suptitle("ODE parameters") ax1 = fig0.add_subplot(121) ax2 = fig0.add_subplot(122) - ax1.set_title('$\\sigma$ Parameter') - ax2.set_title('$\\gamma$ Parameter') - ax1.plot(times, sigmas, '-') - ax2.plot(times, ode_prefactor, '-') + ax1.set_title("$\\sigma$ Parameter") + ax2.set_title("$\\gamma$ Parameter") + ax1.plot(times, sigmas, "-") + ax2.plot(times, ode_prefactor, "-") - ax1.set_ylabel('$\\sigma(t)$') - ax2.set_ylabel('$\\gamma(t)$') + ax1.set_ylabel("$\\sigma(t)$") + ax2.set_ylabel("$\\gamma(t)$") for ax in [ax1, ax2]: - ax.set_xlabel('Diffusion Time') + ax.set_xlabel("Diffusion Time") ax.set_xlim([-0.01, 1.01]) fig0.tight_layout() @@ -139,74 +152,86 @@ logger.info("Extracting data artifacts") raw_data = position_generator.sample_trajectory_recorder.data - recorded_data = position_generator.sample_trajectory_recorder.standardize_data(raw_data) + recorded_data = position_generator.sample_trajectory_recorder.standardize_data( + raw_data + ) - sampling_times = recorded_data['time'] - relative_coordinates = recorded_data['relative_coordinates'] - batch_flat_relative_coordinates = einops.rearrange(relative_coordinates, "b t n d -> b t (n d)") + sampling_times = recorded_data["time"] + relative_coordinates = recorded_data["relative_coordinates"] + batch_flat_relative_coordinates = einops.rearrange( + relative_coordinates, "b t n d -> b t (n d)" + ) - normalized_scores = recorded_data['normalized_scores'] - batch_flat_normalized_scores = einops.rearrange(normalized_scores, "b t n d -> b t (n d)") + normalized_scores = recorded_data["normalized_scores"] + batch_flat_normalized_scores = einops.rearrange( + normalized_scores, "b t n d -> b t (n d)" + ) # ============================================================================ logger.info("Creating samples from the exact distribution") - inverse_covariance = (torch.diag(torch.ones(nd)) / variance_parameter).to(equilibrium_relative_coordinates) - inverse_covariance = inverse_covariance.reshape(number_of_atoms, spatial_dimension, - number_of_atoms, spatial_dimension) - exact_samples = get_exact_samples(equilibrium_relative_coordinates, - inverse_covariance, - number_of_samples).cpu() + inverse_covariance = (torch.diag(torch.ones(nd)) / variance_parameter).to( + equilibrium_relative_coordinates + ) + inverse_covariance = inverse_covariance.reshape( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) + exact_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, number_of_samples + ).cpu() logger.info("Computing harmonic energies") - exact_harmonic_energies = get_samples_harmonic_energy(equilibrium_relative_coordinates, - inverse_covariance, - exact_samples) + exact_harmonic_energies = get_samples_harmonic_energy( + equilibrium_relative_coordinates, inverse_covariance, exact_samples + ) - sampled_harmonic_energies = get_samples_harmonic_energy(equilibrium_relative_coordinates, - inverse_covariance, - samples) + sampled_harmonic_energies = get_samples_harmonic_energy( + equilibrium_relative_coordinates, inverse_covariance, samples + ) # ======================== Figure 1 ====================================== logger.info("Plotting relative coordinates trajectories") fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle('Sampling Trajectories') + fig1.suptitle("Sampling Trajectories") ax = fig1.add_subplot(111) - ax.set_xlabel('Diffusion Time') - ax.set_ylabel('Raw Relative Coordinate') + ax.set_xlabel("Diffusion Time") + ax.set_ylabel("Raw Relative Coordinate") ax.yaxis.tick_right() - ax.spines['top'].set_visible(True) - ax.spines['right'].set_visible(True) - ax.spines['bottom'].set_visible(True) - ax.spines['left'].set_visible(True) + ax.spines["top"].set_visible(True) + ax.spines["right"].set_visible(True) + ax.spines["bottom"].set_visible(True) + ax.spines["left"].set_visible(True) alpha = 1.0 for flat_relative_coordinates in batch_flat_relative_coordinates[::100]: for i in range(number_of_atoms * spatial_dimension): coordinate = flat_relative_coordinates[:, i] - ax.plot(sampling_times.cpu(), coordinate.cpu(), '-', color='b', alpha=alpha) + ax.plot(sampling_times.cpu(), coordinate.cpu(), "-", color="b", alpha=alpha) alpha = 0.05 ax.set_xlim([1.01, -0.01]) - fig1.savefig(ANALYTIC_SCORE_RESULTS_DIR / f"sampling_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png") + fig1.savefig( + ANALYTIC_SCORE_RESULTS_DIR + / f"sampling_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig1) # ======================== Figure 2 ====================================== logger.info("Plotting scores along trajectories") fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig2.suptitle('Root Mean Squared Normalized Scores Along Sample Trajectories') - rms_norm_score = (batch_flat_normalized_scores ** 2).mean(dim=-1).sqrt().numpy() + fig2.suptitle("Root Mean Squared Normalized Scores Along Sample Trajectories") + rms_norm_score = (batch_flat_normalized_scores**2).mean(dim=-1).sqrt().numpy() ax1 = fig2.add_subplot(121) ax2 = fig2.add_subplot(122) for y in rms_norm_score[::10]: - ax1.plot(sampling_times, y, '-', color='gray', alpha=0.2, label='__nolabel__') - ax2.plot(sampling_times, y, '-', color='gray', alpha=0.2, label='__nolabel__') + ax1.plot(sampling_times, y, "-", color="gray", alpha=0.2, label="__nolabel__") + ax2.plot(sampling_times, y, "-", color="gray", alpha=0.2, label="__nolabel__") - ax2.set_yscale('log') + ax2.set_yscale("log") list_quantiles = [0.0, 0.10, 0.5, 1.0] - list_colors = ['green', 'yellow', 'orange', 'red'] + list_colors = ["green", "yellow", "orange", "red"] energies = sampled_harmonic_energies.numpy() @@ -215,12 +240,18 @@ idx = np.argmin(np.abs(energies - energy_quantile)) e = energies[idx] for ax in [ax1, ax2]: - ax.plot(sampling_times, rms_norm_score[idx], '-', - color=c, alpha=1., label=f'{100 * q:2.0f}% Percentile Energy: {e:5.1f}') + ax.plot( + sampling_times, + rms_norm_score[idx], + "-", + color=c, + alpha=1.0, + label=f"{100 * q:2.0f}% Percentile Energy: {e:5.1f}", + ) for ax in [ax1, ax2]: - ax.set_xlabel('Diffusion Time') - ax.set_ylabel(r'$\langle \sqrt{(\sigma(t) S_{\theta})^2} \rangle$') + ax.set_xlabel("Diffusion Time") + ax.set_ylabel(r"$\langle \sqrt{(\sigma(t) S_{\theta})^2} \rangle$") ax.set_xlim(1, 0) ax1.legend(loc=0, fontsize=6) ax1.set_title("Normal Scale") @@ -229,41 +260,45 @@ fig2.tight_layout() fig2.savefig( - ANALYTIC_SCORE_RESULTS_DIR / f"sampling_score_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png") + ANALYTIC_SCORE_RESULTS_DIR + / f"sampling_score_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig2) # ======================== Figure 3 ====================================== logger.info("Plotting Marginal distribution in 2D") fig3 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig3.suptitle('Comparing Sampling and Expected Marginal Distributions') - ax1 = fig3.add_subplot(131, aspect='equal') - ax2 = fig3.add_subplot(132, aspect='equal') - ax3 = fig3.add_subplot(133, aspect='equal') + fig3.suptitle("Comparing Sampling and Expected Marginal Distributions") + ax1 = fig3.add_subplot(131, aspect="equal") + ax2 = fig3.add_subplot(132, aspect="equal") + ax3 = fig3.add_subplot(133, aspect="equal") - xs = einops.rearrange(samples, 'b n d -> (b n) d').cpu() - zs = einops.rearrange(exact_samples, 'b n d -> (b n) d').cpu() - ax1.set_title('XY Projection') - ax1.plot(xs[:, 0], xs[:, 1], 'ro', alpha=0.5, mew=0, label='ODE Solver') - ax1.plot(zs[:, 0], zs[:, 1], 'go', alpha=0.05, mew=0, label='Exact Samples') + xs = einops.rearrange(samples, "b n d -> (b n) d").cpu() + zs = einops.rearrange(exact_samples, "b n d -> (b n) d").cpu() + ax1.set_title("XY Projection") + ax1.plot(xs[:, 0], xs[:, 1], "ro", alpha=0.5, mew=0, label="ODE Solver") + ax1.plot(zs[:, 0], zs[:, 1], "go", alpha=0.05, mew=0, label="Exact Samples") - ax2.set_title('XZ Projection') - ax2.plot(xs[:, 0], xs[:, 2], 'ro', alpha=0.5, mew=0, label='ODE Solver') - ax2.plot(zs[:, 0], zs[:, 2], 'go', alpha=0.05, mew=0, label='Exact Samples') + ax2.set_title("XZ Projection") + ax2.plot(xs[:, 0], xs[:, 2], "ro", alpha=0.5, mew=0, label="ODE Solver") + ax2.plot(zs[:, 0], zs[:, 2], "go", alpha=0.05, mew=0, label="Exact Samples") - ax3.set_title('YZ Projection') - ax3.plot(xs[:, 1], xs[:, 2], 'ro', alpha=0.5, mew=0, label='ODE Solver') - ax3.plot(zs[:, 1], zs[:, 2], 'go', alpha=0.05, mew=0, label='Exact Samples') + ax3.set_title("YZ Projection") + ax3.plot(xs[:, 1], xs[:, 2], "ro", alpha=0.5, mew=0, label="ODE Solver") + ax3.plot(zs[:, 1], zs[:, 2], "go", alpha=0.05, mew=0, label="Exact Samples") for ax in [ax1, ax2, ax3]: ax.set_xlim(-0.01, 1.01) ax.set_ylim(-0.01, 1.01) - ax.vlines(x=[0, 1], ymin=0, ymax=1, color='k', lw=2) - ax.hlines(y=[0, 1], xmin=0, xmax=1, color='k', lw=2) + ax.vlines(x=[0, 1], ymin=0, ymax=1, color="k", lw=2) + ax.hlines(y=[0, 1], xmin=0, xmax=1, color="k", lw=2) ax1.legend(loc=0) fig3.tight_layout() fig3.savefig( - ANALYTIC_SCORE_RESULTS_DIR / f"marginal_2D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png") + ANALYTIC_SCORE_RESULTS_DIR + / f"marginal_2D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig3) # ======================== Figure 4 ====================================== @@ -272,21 +307,21 @@ ax1 = fig4.add_subplot(131) ax2 = fig4.add_subplot(132) ax3 = fig4.add_subplot(133) - fig4.suptitle('Comparing Sampling and Expected Marginal Distributions') + fig4.suptitle("Comparing Sampling and Expected Marginal Distributions") - common_params = dict(histtype='stepfilled', alpha=0.5, bins=50) + common_params = dict(histtype="stepfilled", alpha=0.5, bins=50) - ax1.hist(xs[:, 0], **common_params, facecolor='r', label='ODE solver') - ax2.hist(xs[:, 1], **common_params, facecolor='r', label='ODE solver') - ax3.hist(xs[:, 2], **common_params, facecolor='r', label='ODE solver') + ax1.hist(xs[:, 0], **common_params, facecolor="r", label="ODE solver") + ax2.hist(xs[:, 1], **common_params, facecolor="r", label="ODE solver") + ax3.hist(xs[:, 2], **common_params, facecolor="r", label="ODE solver") - ax1.hist(zs[:, 0], **common_params, facecolor='g', label='Exact') - ax2.hist(zs[:, 1], **common_params, facecolor='g', label='Exact') - ax3.hist(zs[:, 2], **common_params, facecolor='g', label='Exact') + ax1.hist(zs[:, 0], **common_params, facecolor="g", label="Exact") + ax2.hist(zs[:, 1], **common_params, facecolor="g", label="Exact") + ax3.hist(zs[:, 2], **common_params, facecolor="g", label="Exact") - ax1.set_xlabel('X') - ax2.set_xlabel('Y') - ax3.set_xlabel('Z') + ax1.set_xlabel("X") + ax2.set_xlabel("Y") + ax3.set_xlabel("Z") for ax in [ax1, ax2, ax3]: ax.set_xlim(-0.01, 1.01) @@ -294,27 +329,41 @@ ax1.legend(loc=0) fig4.tight_layout() fig4.savefig( - ANALYTIC_SCORE_RESULTS_DIR / f"marginal_1D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png") + ANALYTIC_SCORE_RESULTS_DIR + / f"marginal_1D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig4) # ======================== Figure 5 ====================================== logger.info("Plotting harmonic energy distributions") fig5 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig5.suptitle(f'Energy Distribution, sampling algorithm {sampling_algorithm}') + fig5.suptitle(f"Energy Distribution, sampling algorithm {sampling_algorithm}") common_params = dict(density=True, bins=50, histtype="stepfilled", alpha=0.25) ax1 = fig5.add_subplot(111) - ax1.hist(sampled_harmonic_energies, **common_params, label='Sampled Energies', color='red') - ax1.hist(exact_harmonic_energies, **common_params, label='Theoretical Energies', color='green') + ax1.hist( + sampled_harmonic_energies, + **common_params, + label="Sampled Energies", + color="red", + ) + ax1.hist( + exact_harmonic_energies, + **common_params, + label="Theoretical Energies", + color="green", + ) ax1.set_xlim(xmin=-0.01) - ax1.set_xlabel('Unitless Harmonic Energy') - ax1.set_ylabel('Density') - ax1.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=12) + ax1.set_xlabel("Unitless Harmonic Energy") + ax1.set_ylabel("Density") + ax1.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=12) fig5.tight_layout() fig5.savefig( - ANALYTIC_SCORE_RESULTS_DIR / f"harmonic_energy_samples_{sampling_algorithm}_{number_of_atoms}_atoms.png") + ANALYTIC_SCORE_RESULTS_DIR + / f"harmonic_energy_samples_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig5) logger.info("Done!") diff --git a/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py b/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py index 17ae45ed..2a016f5f 100644 --- a/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py +++ b/experiments/analysis/analytic_score/exploring_langevin_generator/generate_sample_energies.py @@ -5,18 +5,22 @@ import einops import numpy as np import torch -from crystal_diffusion.callbacks.sampling_visualization_callback import logger -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from tqdm import tqdm + +from diffusion_for_multi_scale_molecular_dynamics.callbacks.sampling_visualization_callback import \ + logger +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.models.score_networks.analytical_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.utils.basis_transformations import ( +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import ( get_positions_from_coordinates, map_relative_coordinates_to_unit_cell) -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters -from tqdm import tqdm - from experiments.analysis.analytic_score.exploring_langevin_generator import \ LANGEVIN_EXPLORATION_DIRECTORY from experiments.analysis.analytic_score.utils import (get_exact_samples, @@ -37,45 +41,59 @@ def _get_basis_vectors(self, batch_size: int) -> torch.Tensor: basis_vectors = self.unit_cell.unsqueeze(0).repeat(batch_size, 1, 1) return basis_vectors - def compute_oracle_energies(self, batch_relative_coordinates: torch.Tensor) -> np.ndarray: + def compute_oracle_energies( + self, batch_relative_coordinates: torch.Tensor + ) -> np.ndarray: """Compute oracle energies.""" batch_size = batch_relative_coordinates.shape[0] - batched_unit_cells = einops.repeat(self.unit_cell, "d1 d2 -> b d1 d2", b=batch_size) + batched_unit_cells = einops.repeat( + self.unit_cell, "d1 d2 -> b d1 d2", b=batch_size + ) - batch_cartesian_positions = get_positions_from_coordinates(batch_relative_coordinates, - batched_unit_cells) + batch_cartesian_positions = get_positions_from_coordinates( + batch_relative_coordinates, batched_unit_cells + ) list_energy = [] logger.info("Compute energy from Oracle") with tempfile.TemporaryDirectory() as tmp_work_dir: - for positions, box in zip(batch_cartesian_positions.cpu().numpy(), batched_unit_cells.cpu().numpy()): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - self.atom_types, - tmp_work_dir=tmp_work_dir) + for positions, box in zip( + batch_cartesian_positions.cpu().numpy(), + batched_unit_cells.cpu().numpy(), + ): + energy, forces = get_energy_and_forces_from_lammps( + positions, box, self.atom_types, tmp_work_dir=tmp_work_dir + ) list_energy.append(energy) return np.array(list_energy) -def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma_d: float, number_of_samples: int): +def generate_exact_samples( + equilibrium_relative_coordinates: torch.Tensor, + sigma_d: float, + number_of_samples: int, +): """Generate Gaussian samples about the equilibrium relative coordinates.""" - variance_parameter = sigma_d ** 2 + variance_parameter = sigma_d**2 number_of_atoms, spatial_dimension = equilibrium_relative_coordinates.shape nd = number_of_atoms * spatial_dimension inverse_covariance = torch.diag(torch.ones(nd)) / variance_parameter - inverse_covariance = inverse_covariance.reshape(number_of_atoms, spatial_dimension, - number_of_atoms, spatial_dimension) + inverse_covariance = inverse_covariance.reshape( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) - exact_samples = get_exact_samples(equilibrium_relative_coordinates, inverse_covariance, number_of_samples) + exact_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, number_of_samples + ) exact_samples = map_relative_coordinates_to_unit_cell(exact_samples) return exact_samples -device = torch.device('cpu') +device = torch.device("cpu") number_of_atoms = 64 spatial_dimension = 3 @@ -84,7 +102,10 @@ def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma supercell_factor = 2 cell_dimensions = 3 * [acell * supercell_factor] -pickle_directory = LANGEVIN_EXPLORATION_DIRECTORY / f"Si_{supercell_factor}x{supercell_factor}x{supercell_factor}" +pickle_directory = ( + LANGEVIN_EXPLORATION_DIRECTORY + / f"Si_{supercell_factor}x{supercell_factor}x{supercell_factor}" +) pickle_directory.mkdir(exist_ok=True, parents=True) number_of_samples = 1000 @@ -95,37 +116,53 @@ def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma list_total_time_steps = [10, 100, 1000] -if __name__ == '__main__': +if __name__ == "__main__": unit_cell = torch.diag(torch.tensor(cell_dimensions)) - batched_unit_cells = einops.repeat(unit_cell, "d1 d2 -> b d1 d2", b=number_of_samples) + batched_unit_cells = einops.repeat( + unit_cell, "d1 d2 -> b d1 d2", b=number_of_samples + ) - energy_calculator = EnergyCalculator(unit_cell=unit_cell, number_of_atoms=number_of_atoms) + energy_calculator = EnergyCalculator( + unit_cell=unit_cell, number_of_atoms=number_of_atoms + ) equilibrium_relative_coordinates = torch.from_numpy( - get_silicon_supercell(supercell_factor=supercell_factor)).to(torch.float32) + get_silicon_supercell(supercell_factor=supercell_factor) + ).to(torch.float32) for sigma_d in tqdm(list_sigma_d, "exact energies"): name = f"Sd={sigma_d}" - output_path = pickle_directory / f'exact_energies_{name}.pkl' + output_path = pickle_directory / f"exact_energies_{name}.pkl" if output_path.is_file(): logger.info(f"file {name} already exists. Moving on.") continue - exact_samples = generate_exact_samples(equilibrium_relative_coordinates, - sigma_d=sigma_d, - number_of_samples=number_of_samples) + exact_samples = generate_exact_samples( + equilibrium_relative_coordinates, + sigma_d=sigma_d, + number_of_samples=number_of_samples, + ) - exact_samples_energies = energy_calculator.compute_oracle_energies(exact_samples) + exact_samples_energies = energy_calculator.compute_oracle_energies( + exact_samples + ) result = dict(sigma_d=sigma_d, energies=exact_samples_energies) - with open(output_path, 'wb') as fd: + with open(output_path, "wb") as fd: pickle.dump(result, fd) - prod = itertools.product(list_number_of_corrector_steps, list_sigma_d, list_sigma_min, list_total_time_steps) - for number_of_corrector_steps, sigma_d, sigma_min, total_time_steps in tqdm(prod, "sample energies"): + prod = itertools.product( + list_number_of_corrector_steps, + list_sigma_d, + list_sigma_min, + list_total_time_steps, + ) + for number_of_corrector_steps, sigma_d, sigma_min, total_time_steps in tqdm( + prod, "sample energies" + ): name = f"Sd={sigma_d}_Sm={sigma_min}_S={total_time_steps}_C={number_of_corrector_steps}" - output_path = pickle_directory / f'sampled_energies_{name}.pkl' + output_path = pickle_directory / f"sampled_energies_{name}.pkl" if output_path.is_file(): logger.info(f"file {name} already exists. Moving on.") continue @@ -136,39 +173,50 @@ def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma use_permutation_invariance=False, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - variance_parameter=sigma_d**2) - - sigma_normalized_score_network = TargetScoreBasedAnalyticalScoreNetwork(score_network_parameters) - - sampling_parameters = PredictorCorrectorSamplingParameters(number_of_atoms=number_of_atoms, - number_of_corrector_steps=number_of_corrector_steps, - number_of_samples=number_of_samples, - sample_batchsize=number_of_samples, - sample_every_n_epochs=1, - first_sampling_epoch=0, - cell_dimensions=cell_dimensions, - record_samples=False) - - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - sigma_min=sigma_min, - sigma_max=0.5) - - generator = LangevinGenerator(noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) - - generated_samples = generator.sample(number_of_samples, - device=device, - unit_cell=batched_unit_cells) - - generated_samples_energies = energy_calculator.compute_oracle_energies(generated_samples) - - result = dict(sigma_d=sigma_d, - sigma_min=sigma_min, - total_time_steps=total_time_steps, - number_of_corrector_steps=number_of_corrector_steps, - energies=generated_samples_energies) + variance_parameter=sigma_d**2, + ) + + sigma_normalized_score_network = TargetScoreBasedAnalyticalScoreNetwork( + score_network_parameters + ) + + sampling_parameters = PredictorCorrectorSamplingParameters( + number_of_atoms=number_of_atoms, + number_of_corrector_steps=number_of_corrector_steps, + number_of_samples=number_of_samples, + sample_batchsize=number_of_samples, + sample_every_n_epochs=1, + first_sampling_epoch=0, + cell_dimensions=cell_dimensions, + record_samples=False, + ) + + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, sigma_min=sigma_min, sigma_max=0.5 + ) + + generator = LangevinGenerator( + noise_parameters=noise_parameters, + sampling_parameters=sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) + + generated_samples = generator.sample( + number_of_samples, device=device, unit_cell=batched_unit_cells + ) + + generated_samples_energies = energy_calculator.compute_oracle_energies( + generated_samples + ) + + result = dict( + sigma_d=sigma_d, + sigma_min=sigma_min, + total_time_steps=total_time_steps, + number_of_corrector_steps=number_of_corrector_steps, + energies=generated_samples_energies, + ) name = f"Sd={sigma_d}_Sm={sigma_min}_S={total_time_steps}_C={number_of_corrector_steps}" # Pickle a lot of intermediate things to avoid losing everything in a crash. - with open(output_path, 'wb') as fd: + with open(output_path, "wb") as fd: pickle.dump(result, fd) diff --git a/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py b/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py index 4d897836..0a943507 100644 --- a/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py +++ b/experiments/analysis/analytic_score/exploring_langevin_generator/plot_sample_energies.py @@ -2,24 +2,28 @@ import matplotlib.pyplot as plt import numpy as np -from src.crystal_diffusion.analysis import PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.analysis import \ + PLOT_STYLE_PATH from experiments.analysis.analytic_score.exploring_langevin_generator import \ LANGEVIN_EXPLORATION_DIRECTORY plt.style.use(PLOT_STYLE_PATH) supercell_factor = 2 -pickle_directory = LANGEVIN_EXPLORATION_DIRECTORY / f"Si_{supercell_factor}x{supercell_factor}x{supercell_factor}" +pickle_directory = ( + LANGEVIN_EXPLORATION_DIRECTORY + / f"Si_{supercell_factor}x{supercell_factor}x{supercell_factor}" +) title = f"Si {supercell_factor}x{supercell_factor}x{supercell_factor}" sigma_d = 0.001 sigma_min = 0.001 -if __name__ == '__main__': +if __name__ == "__main__": list_q = np.linspace(0, 1, 101) - with open(pickle_directory / f'exact_energies_Sd={sigma_d}.pkl', 'rb') as fd: + with open(pickle_directory / f"exact_energies_Sd={sigma_d}.pkl", "rb") as fd: exact_results = pickle.load(fd) FIG_SIZE = (7.2, 7.2) @@ -30,9 +34,16 @@ fig.suptitle(f"{title} : $\\sigma_d$ = {sigma_d}, $\\sigma_m$ = {sigma_min}") - list_energy_quantiles = np.quantile(exact_results['energies'], list_q) + list_energy_quantiles = np.quantile(exact_results["energies"], list_q) for ax in [ax1, ax2, ax3]: - ax.plot(100 * list_q, list_energy_quantiles, '-', lw=10, color='green', label='Exact') + ax.plot( + 100 * list_q, + list_energy_quantiles, + "-", + lw=10, + color="green", + label="Exact", + ) min = list_energy_quantiles[0] max = list_energy_quantiles[-1] @@ -45,27 +56,31 @@ ax.set_title(f"Corrector steps: {number_of_corrector_steps}") lw = 8 - for total_time_steps, color in zip([10, 100, 1000], ['r', 'y', 'b']): - name = (f"sampled_energies_Sd={sigma_d}_Sm={sigma_min}" - f"_S={total_time_steps}_C={number_of_corrector_steps}.pkl") + for total_time_steps, color in zip([10, 100, 1000], ["r", "y", "b"]): + name = ( + f"sampled_energies_Sd={sigma_d}_Sm={sigma_min}" + f"_S={total_time_steps}_C={number_of_corrector_steps}.pkl" + ) filepath = pickle_directory / name if not filepath.is_file(): print(f"File {name} is missing. Moving on.") continue - with open(filepath, 'rb') as fd: + with open(filepath, "rb") as fd: sampled_results = pickle.load(fd) - energies = sampled_results.pop('energies') + energies = sampled_results.pop("energies") list_energy_quantiles = np.quantile(energies, list_q) - ax.plot(100 * list_q, - list_energy_quantiles, - '-.', - lw=lw, - c=color, - label=f'total time steps = {total_time_steps}') + ax.plot( + 100 * list_q, + list_energy_quantiles, + "-.", + lw=lw, + c=color, + label=f"total time steps = {total_time_steps}", + ) lw = lw / 1.5 - ax.set_xlabel('Quantile (%)') - ax.set_ylabel('Energy Quantile (eV)') + ax.set_xlabel("Quantile (%)") + ax.set_ylabel("Energy Quantile (eV)") ax.set_xlim([-0.01, 100.01]) ax.set_ylim([min, max]) diff --git a/experiments/analysis/analytic_score/perfect_score_loss_analysis.py b/experiments/analysis/analytic_score/perfect_score_loss_analysis.py index d9cdf106..085e7c3b 100644 --- a/experiments/analysis/analytic_score/perfect_score_loss_analysis.py +++ b/experiments/analysis/analytic_score/perfect_score_loss_analysis.py @@ -6,36 +6,40 @@ import numpy as np import pytorch_lightning as pl import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.callbacks.loss_monitoring_callback import \ +from pytorch_lightning.loggers import CSVLogger +from torch.utils.data import DataLoader +from tqdm import tqdm + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import \ + PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.callbacks.loss_monitoring_callback import \ LossMonitoringCallback -from crystal_diffusion.callbacks.sampling_visualization_callback import \ +from diffusion_for_multi_scale_molecular_dynamics.callbacks.sampling_visualization_callback import \ PredictorCorrectorDiffusionSamplingCallback -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.models.loss import (MSELossParameters, - create_loss_calculator) -from crystal_diffusion.models.optimizer import OptimizerParameters -from crystal_diffusion.models.position_diffusion_lightning_model import ( +from diffusion_for_multi_scale_molecular_dynamics.models.loss import ( + MSELossParameters, create_loss_calculator) +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import \ + OptimizerParameters +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import ( PositionDiffusionLightningModel, PositionDiffusionParameters) -from crystal_diffusion.models.scheduler import \ +from diffusion_for_multi_scale_molecular_dynamics.models.scheduler import \ CosineAnnealingLRSchedulerParameters -from crystal_diffusion.models.score_networks.analytical_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) -from crystal_diffusion.namespace import CARTESIAN_FORCES, RELATIVE_COORDINATES -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, RELATIVE_COORDINATES) +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps +from diffusion_for_multi_scale_molecular_dynamics.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from pytorch_lightning.loggers import CSVLogger -from src.crystal_diffusion.analysis import PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import ( +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) -from torch.utils.data import DataLoader -from tqdm import tqdm - +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + map_relative_coordinates_to_unit_cell from experiments.analysis.analytic_score.utils import (get_exact_samples, get_silicon_supercell) @@ -59,13 +63,20 @@ def __init__(self, hyper_params: PositionDiffusionParameters): self.hyper_params = hyper_params self.save_hyperparameters(logger=False) - self.use_permutation_invariance = hyper_params.score_network_parameters.use_permutation_invariance + self.use_permutation_invariance = ( + hyper_params.score_network_parameters.use_permutation_invariance + ) if self.use_permutation_invariance: - self.sigma_normalized_score_network = AnalyticalScoreNetwork(hyper_params.score_network_parameters) + self.sigma_normalized_score_network = AnalyticalScoreNetwork( + hyper_params.score_network_parameters + ) else: - self.sigma_normalized_score_network = TargetScoreBasedAnalyticalScoreNetwork( - hyper_params.score_network_parameters) + self.sigma_normalized_score_network = ( + TargetScoreBasedAnalyticalScoreNetwork( + hyper_params.score_network_parameters + ) + ) self.loss_calculator = create_loss_calculator(hyper_params.loss_parameters) self.noisy_relative_coordinates_sampler = NoisyRelativeCoordinatesSampler() @@ -80,7 +91,7 @@ def on_validation_start(self) -> None: plt.style.use(PLOT_STYLE_PATH) -device = torch.device('cpu') +device = torch.device("cpu") dataset_size = 1024 batch_size = 1024 @@ -101,82 +112,98 @@ def on_validation_start(self) -> None: else: # samples will be created sigma_d = 0.0025 / np.sqrt(supercell_factor) - variance_parameter = sigma_d ** 2 + variance_parameter = sigma_d**2 model_variance_parameter = 1.0 * variance_parameter -noise_parameters = NoiseParameters(total_time_steps=1000, - sigma_min=0.0001, - sigma_max=0.5) +noise_parameters = NoiseParameters( + total_time_steps=1000, sigma_min=0.0001, sigma_max=0.5 +) # We will not optimize, so this doesn't matter -dummy_optimizer_parameters = OptimizerParameters(name='adam', learning_rate=0.001, weight_decay=0.0) +dummy_optimizer_parameters = OptimizerParameters( + name="adam", learning_rate=0.001, weight_decay=0.0 +) dummy_scheduler_parameters = CosineAnnealingLRSchedulerParameters(T_max=10) -loss_monitoring_callback = LossMonitoringCallback(number_of_bins=100, - sample_every_n_epochs=1, - spatial_dimension=spatial_dimension) +loss_monitoring_callback = LossMonitoringCallback( + number_of_bins=100, sample_every_n_epochs=1, spatial_dimension=spatial_dimension +) -experiment_name = (f"cell_{supercell_factor}x{supercell_factor}x{supercell_factor}" - f"_permutation_{use_permutation_invariance}" - f"_sigma_d={np.sqrt(model_variance_parameter):5.4f}_super_noise") +experiment_name = ( + f"cell_{supercell_factor}x{supercell_factor}x{supercell_factor}" + f"_permutation_{use_permutation_invariance}" + f"_sigma_d={np.sqrt(model_variance_parameter):5.4f}_super_noise" +) -output_dir = ANALYSIS_RESULTS_DIR / 'PERFECT_SCORE_LOSS' +output_dir = ANALYSIS_RESULTS_DIR / "PERFECT_SCORE_LOSS" csv_logger = CSVLogger(save_dir=str(output_dir), name=experiment_name) -if __name__ == '__main__': +if __name__ == "__main__": box = torch.diag(torch.tensor(cell_dimensions)) equilibrium_relative_coordinates = torch.from_numpy( - get_silicon_supercell(supercell_factor=supercell_factor)).to(torch.float32) + get_silicon_supercell(supercell_factor=supercell_factor) + ).to(torch.float32) number_of_atoms, _ = equilibrium_relative_coordinates.shape nd = number_of_atoms * spatial_dimension atom_types = np.array(number_of_atoms * [1]) - sampling_parameters = PredictorCorrectorSamplingParameters(number_of_atoms=number_of_atoms, - number_of_corrector_steps=10, - number_of_samples=batch_size, - sample_batchsize=batch_size, - sample_every_n_epochs=1, - first_sampling_epoch=0, - cell_dimensions=cell_dimensions, - record_samples=False) + sampling_parameters = PredictorCorrectorSamplingParameters( + number_of_atoms=number_of_atoms, + number_of_corrector_steps=10, + number_of_samples=batch_size, + sample_batchsize=batch_size, + sample_every_n_epochs=1, + first_sampling_epoch=0, + cell_dimensions=cell_dimensions, + record_samples=False, + ) diffusion_sampling_callback = PredictorCorrectorDiffusionSamplingCallback( noise_parameters=noise_parameters, sampling_parameters=sampling_parameters, - output_directory=output_dir / experiment_name + output_directory=output_dir / experiment_name, ) if use_equilibrium: - exact_samples = einops.repeat(equilibrium_relative_coordinates, "n d -> b n d", b=dataset_size) + exact_samples = einops.repeat( + equilibrium_relative_coordinates, "n d -> b n d", b=dataset_size + ) else: inverse_covariance = torch.diag(torch.ones(nd)) / variance_parameter - inverse_covariance = inverse_covariance.reshape(number_of_atoms, spatial_dimension, - number_of_atoms, spatial_dimension) + inverse_covariance = inverse_covariance.reshape( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) # Create a dataloader - exact_samples = get_exact_samples(equilibrium_relative_coordinates, inverse_covariance, dataset_size) + exact_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, dataset_size + ) exact_samples = map_relative_coordinates_to_unit_cell(exact_samples) list_oracle_energies = [] list_positions = (exact_samples @ box).cpu().numpy() with tempfile.TemporaryDirectory() as tmp_work_dir: for positions in tqdm(list_positions, "LAMMPS energies"): - energy, forces = get_energy_and_forces_from_lammps(positions, - box.numpy(), - atom_types, - tmp_work_dir=tmp_work_dir) + energy, forces = get_energy_and_forces_from_lammps( + positions, box.numpy(), atom_types, tmp_work_dir=tmp_work_dir + ) list_oracle_energies.append(energy) - dataset = [{RELATIVE_COORDINATES: x, - CARTESIAN_FORCES: torch.zeros_like(x), - 'potential_energy': energy, - 'box': torch.tensor(cell_dimensions)} for x, energy in zip(exact_samples, list_oracle_energies)] + dataset = [ + { + RELATIVE_COORDINATES: x, + CARTESIAN_FORCES: torch.zeros_like(x), + "potential_energy": energy, + "box": torch.tensor(cell_dimensions), + } + for x, energy in zip(exact_samples, list_oracle_energies) + ] dataloader = DataLoader(dataset, batch_size=batch_size) @@ -186,7 +213,8 @@ def on_validation_start(self) -> None: use_permutation_invariance=use_permutation_invariance, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - variance_parameter=model_variance_parameter) + variance_parameter=model_variance_parameter, + ) diffusion_params = PositionDiffusionParameters( score_network_parameters=score_network_parameters, @@ -194,18 +222,19 @@ def on_validation_start(self) -> None: optimizer_parameters=dummy_optimizer_parameters, scheduler_parameters=dummy_scheduler_parameters, noise_parameters=noise_parameters, - kmax_target_score=kmax_target_score + kmax_target_score=kmax_target_score, ) model = AnalyticalScorePositionDiffusionLightningModel(diffusion_params) - trainer = pl.Trainer(callbacks=[loss_monitoring_callback, diffusion_sampling_callback], - max_epochs=1, - log_every_n_steps=1, - fast_dev_run=False, - logger=csv_logger, - inference_mode=not use_equilibrium # Do this or else the gradients don't work! - ) + trainer = pl.Trainer( + callbacks=[loss_monitoring_callback, diffusion_sampling_callback], + max_epochs=1, + log_every_n_steps=1, + fast_dev_run=False, + logger=csv_logger, + inference_mode=not use_equilibrium, # Do this or else the gradients don't work! + ) # Run a validation epoch trainer.validate(model, dataloaders=[dataloader]) diff --git a/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py b/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py index aa23103e..f1043a42 100644 --- a/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py +++ b/experiments/analysis/analytic_score/repaint/plot_repaint_analytical_score_trajectories.py @@ -5,14 +5,15 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.utils.logging_utils import setup_analysis_logger from einops import einops -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from experiments.analysis.analytic_score import (get_exact_samples, - get_samples_harmonic_energy, - get_silicon_supercell) +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger +from experiments.analysis.analytic_score.utils import ( + get_exact_samples, get_samples_harmonic_energy, get_silicon_supercell) logger = logging.getLogger(__name__) setup_analysis_logger() @@ -34,99 +35,140 @@ number_of_atoms, spatial_dimension = 8, 3 nd = number_of_atoms * spatial_dimension -if __name__ == '__main__': +if __name__ == "__main__": - inverse_covariance = (torch.diag(torch.ones(nd)) / variance_parameter) - inverse_covariance = inverse_covariance.reshape(number_of_atoms, spatial_dimension, - number_of_atoms, spatial_dimension) + inverse_covariance = torch.diag(torch.ones(nd)) / variance_parameter + inverse_covariance = inverse_covariance.reshape( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) equilibrium_relative_coordinates = torch.from_numpy( - get_silicon_supercell(supercell_factor=supercell_factor).astype(np.float32)) - exact_samples = get_exact_samples(equilibrium_relative_coordinates, - inverse_covariance, - number_of_samples).cpu() + get_silicon_supercell(supercell_factor=supercell_factor).astype(np.float32) + ) + exact_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, number_of_samples + ).cpu() logger.info("Computing harmonic energies") - exact_harmonic_energies = get_samples_harmonic_energy(equilibrium_relative_coordinates, - inverse_covariance, - exact_samples) + exact_harmonic_energies = get_samples_harmonic_energy( + equilibrium_relative_coordinates, inverse_covariance, exact_samples + ) logger.info("Extracting data artifacts") - with open(data_pickle_path, 'rb') as fd: - recorded_data = torch.load(fd, map_location=torch.device('cpu')) + with open(data_pickle_path, "rb") as fd: + recorded_data = torch.load(fd, map_location=torch.device("cpu")) energies = torch.load(energy_pickle_path) # ============================================================== logger.info("Plotting energy distributions") fig0 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig0.suptitle('Energy Distribution: Constrained vs Free') + fig0.suptitle("Energy Distribution: Constrained vs Free") common_params = dict(density=True, bins=50, histtype="stepfilled", alpha=0.25) ax1 = fig0.add_subplot(111) - ax1.hist(energies, **common_params, label='Repaint Samples Energies', color='red') - ax1.hist(exact_harmonic_energies, **common_params, label='Unconstrained Samples Energies', color='green') + ax1.hist(energies, **common_params, label="Repaint Samples Energies", color="red") + ax1.hist( + exact_harmonic_energies, + **common_params, + label="Unconstrained Samples Energies", + color="green", + ) ax1.set_xlim(xmin=-0.01) - ax1.set_xlabel('Unitless Harmonic Energy') - ax1.set_ylabel('Density') - ax1.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=12) + ax1.set_xlabel("Unitless Harmonic Energy") + ax1.set_ylabel("Density") + ax1.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=12) fig0.tight_layout() fig0.savefig(base_path / "comparing_free_and_repaint_energies.png") # ============================================================== - sampling_times = recorded_data['time'] - relative_coordinates = recorded_data['relative_coordinates'] - batch_flat_relative_coordinates = einops.rearrange(relative_coordinates, "b t n d -> b t (n d)") + sampling_times = recorded_data["time"] + relative_coordinates = recorded_data["relative_coordinates"] + batch_flat_relative_coordinates = einops.rearrange( + relative_coordinates, "b t n d -> b t (n d)" + ) - normalized_scores = recorded_data['normalized_scores'] - batch_flat_normalized_scores = einops.rearrange(normalized_scores, "b t n d -> b t (n d)") + normalized_scores = recorded_data["normalized_scores"] + batch_flat_normalized_scores = einops.rearrange( + normalized_scores, "b t n d -> b t (n d)" + ) logger.info("Plotting scores along trajectories") fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle('Root Mean Square Normalized Scores Along Sample Trajectories') - rms_norm_score = (batch_flat_normalized_scores ** 2).mean(dim=-1).sqrt().numpy() + fig.suptitle("Root Mean Square Normalized Scores Along Sample Trajectories") + rms_norm_score = (batch_flat_normalized_scores**2).mean(dim=-1).sqrt().numpy() constrained_rms_norm_score = ( - (batch_flat_normalized_scores[:, :, :number_of_constrained_coordinates] ** 2).mean(dim=-1).sqrt().numpy()) + (batch_flat_normalized_scores[:, :, :number_of_constrained_coordinates] ** 2) + .mean(dim=-1) + .sqrt() + .numpy() + ) free_rms_norm_score = ( - (batch_flat_normalized_scores[:, :, number_of_constrained_coordinates:] ** 2).mean(dim=-1).sqrt().numpy()) + (batch_flat_normalized_scores[:, :, number_of_constrained_coordinates:] ** 2) + .mean(dim=-1) + .sqrt() + .numpy() + ) ax1 = fig.add_subplot(221) ax2 = fig.add_subplot(222) ax3 = fig.add_subplot(223) ax4 = fig.add_subplot(224) - for ax, scores in zip([ax1, ax2, ax3, ax4], - [rms_norm_score, rms_norm_score, constrained_rms_norm_score, free_rms_norm_score]): + for ax, scores in zip( + [ax1, ax2, ax3, ax4], + [ + rms_norm_score, + rms_norm_score, + constrained_rms_norm_score, + free_rms_norm_score, + ], + ): for y in scores[::10]: - ax.plot(sampling_times, y, '-', color='gray', alpha=0.2, label='__nolabel__') + ax.plot( + sampling_times, y, "-", color="gray", alpha=0.2, label="__nolabel__" + ) list_quantiles = [0.0, 0.10, 0.5, 1.0] - list_colors = ['green', 'yellow', 'orange', 'red'] + list_colors = ["green", "yellow", "orange", "red"] for q, c in zip(list_quantiles, list_colors): energy_quantile = np.quantile(energies, q) idx = np.argmin(np.abs(energies - energy_quantile)) e = energies[idx] - for ax, scores in zip([ax1, ax2, ax3, ax4], - [rms_norm_score, rms_norm_score, constrained_rms_norm_score, free_rms_norm_score]): - ax.plot(sampling_times, scores[idx], '-', - color=c, alpha=1., label=f'Q = {100 * q:2.0f}%, Energy: {e:5.1f}') + for ax, scores in zip( + [ax1, ax2, ax3, ax4], + [ + rms_norm_score, + rms_norm_score, + constrained_rms_norm_score, + free_rms_norm_score, + ], + ): + ax.plot( + sampling_times, + scores[idx], + "-", + color=c, + alpha=1.0, + label=f"Q = {100 * q:2.0f}%, Energy: {e:5.1f}", + ) for ax in [ax1, ax2, ax3, ax4]: - ax.set_xlabel('Diffusion Time') - ax.set_ylabel(r'$\sqrt{\langle (\sigma(t) S_{\theta} )^2 \rangle}$') + ax.set_xlabel("Diffusion Time") + ax.set_ylabel(r"$\sqrt{\langle (\sigma(t) S_{\theta} )^2 \rangle}$") ax.set_xlim(1, 0) for ax in [ax2, ax3, ax4]: - ax.set_yscale('log') + ax.set_yscale("log") ax1.set_title("All Coordinates (Not Log Scale)") - ax1.set_ylim(ymin=0.) + ax1.set_ylim(ymin=0.0) ax2.set_title("All Coordinates") ax3.set_title("Restrained Coordinates") diff --git a/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py b/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py index c4667eb4..1ee22cc2 100644 --- a/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py +++ b/experiments/analysis/analytic_score/repaint/repaint_with_analytic_score.py @@ -3,16 +3,20 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.generators.constrained_langevin_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import \ + PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.generators.constrained_langevin_generator import ( ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) -from crystal_diffusion.models.score_networks.analytical_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from crystal_diffusion.utils.structure_utils import create_structure -from src.crystal_diffusion.analysis import PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters - +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger +from diffusion_for_multi_scale_molecular_dynamics.utils.structure_utils import \ + create_structure from experiments.analysis.analytic_score import (get_samples_harmonic_energy, get_silicon_supercell, get_unit_cells) @@ -26,9 +30,9 @@ plt.style.use(PLOT_STYLE_PATH) if torch.cuda.is_available(): - device = torch.device('cuda') + device = torch.device("cuda") else: - device = torch.device('cpu') + device = torch.device("cpu") kmax = 1 supercell_factor = 1 @@ -39,40 +43,49 @@ acell = 5.43 # Angstroms. -constrained_relative_coordinates = np.array([[0.5, 0.5, 0.25], - [0.5, 0.5, 0.5], - [0.5, 0.5, 0.75]], dtype=np.float32) +constrained_relative_coordinates = np.array( + [[0.5, 0.5, 0.25], [0.5, 0.5, 0.5], [0.5, 0.5, 0.75]], dtype=np.float32 +) translation = np.array([0.125, 0.125, 0.125]).astype(np.float32) -if __name__ == '__main__': +if __name__ == "__main__": logger.info("Setting up parameters") - equilibrium_relative_coordinates = get_silicon_supercell(supercell_factor=supercell_factor).astype(np.float32) + equilibrium_relative_coordinates = get_silicon_supercell( + supercell_factor=supercell_factor + ).astype(np.float32) # Translate to avoid atoms right on the cell boundary equilibrium_relative_coordinates = equilibrium_relative_coordinates + translation number_of_atoms, spatial_dimension = equilibrium_relative_coordinates.shape logger.info("Creating samples from the exact distribution") - inverse_covariance = ((torch.diag(torch.ones(number_of_atoms * spatial_dimension)) / variance_parameter) - .to(device)) - inverse_covariance = inverse_covariance.reshape(number_of_atoms, spatial_dimension, - number_of_atoms, spatial_dimension) - - unit_cells = get_unit_cells(acell=acell, - spatial_dimension=spatial_dimension, - number_of_samples=number_of_samples) + inverse_covariance = ( + torch.diag(torch.ones(number_of_atoms * spatial_dimension)) / variance_parameter + ).to(device) + inverse_covariance = inverse_covariance.reshape( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) + + unit_cells = get_unit_cells( + acell=acell, + spatial_dimension=spatial_dimension, + number_of_samples=number_of_samples, + ) - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - sigma_min=0.001, - sigma_max=0.5) + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, sigma_min=0.001, sigma_max=0.5 + ) score_network_parameters = AnalyticalScoreNetworkParameters( number_of_atoms=number_of_atoms, spatial_dimension=spatial_dimension, kmax=kmax, - equilibrium_relative_coordinates=torch.from_numpy(equilibrium_relative_coordinates).to(device), - variance_parameter=variance_parameter) + equilibrium_relative_coordinates=torch.from_numpy( + equilibrium_relative_coordinates + ).to(device), + variance_parameter=variance_parameter, + ) sigma_normalized_score_network = AnalyticalScoreNetwork(score_network_parameters) @@ -83,24 +96,28 @@ number_of_samples=number_of_samples, cell_dimensions=3 * [acell], constrained_relative_coordinates=constrained_relative_coordinates, - record_samples=True) + record_samples=True, + ) position_generator = ConstrainedLangevinGenerator( noise_parameters=noise_parameters, sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + sigma_normalized_score_network=sigma_normalized_score_network, + ) logger.info("Drawing constrained samples") - samples = position_generator.sample(number_of_samples=number_of_samples, - device=device, - unit_cell=unit_cells).detach() + samples = position_generator.sample( + number_of_samples=number_of_samples, device=device, unit_cell=unit_cells + ).detach() - position_generator.sample_trajectory_recorder.write_to_pickle(repaint_dir / "repaint_trajectories.pkl") + position_generator.sample_trajectory_recorder.write_to_pickle( + repaint_dir / "repaint_trajectories.pkl" + ) logger.info("Computing harmonic energies") - sampled_harmonic_energies = get_samples_harmonic_energy(equilibrium_relative_coordinates, - inverse_covariance, - samples) + sampled_harmonic_energies = get_samples_harmonic_energy( + equilibrium_relative_coordinates, inverse_covariance, samples + ) with open(repaint_dir / "harmonic_energies.pt", "wb") as fd: torch.save(sampled_harmonic_energies, fd) @@ -108,14 +125,20 @@ logger.info("Creating CIF files") lattice_basis_vectors = np.diag([acell, acell, acell]) - species = number_of_atoms * ['Si'] + species = number_of_atoms * ["Si"] relative_coordinates = equilibrium_relative_coordinates - equilibrium_structure = create_structure(lattice_basis_vectors, relative_coordinates, species) + equilibrium_structure = create_structure( + lattice_basis_vectors, relative_coordinates, species + ) equilibrium_structure.to_file(str(repaint_dir / "equilibrium_positions.cif")) - relative_coordinates[:len(constrained_relative_coordinates)] = constrained_relative_coordinates - forced_structure = create_structure(lattice_basis_vectors, relative_coordinates, species) + relative_coordinates[: len(constrained_relative_coordinates)] = ( + constrained_relative_coordinates + ) + forced_structure = create_structure( + lattice_basis_vectors, relative_coordinates, species + ) forced_structure.to_file(str(repaint_dir / "forced_constraint_positions.cif")) samples_dir = repaint_dir / "samples" diff --git a/experiments/analysis/analytic_score/score_convergence_analysis.py b/experiments/analysis/analytic_score/score_convergence_analysis.py index 1e61ff45..8fecb26f 100644 --- a/experiments/analysis/analytic_score/score_convergence_analysis.py +++ b/experiments/analysis/analytic_score/score_convergence_analysis.py @@ -2,17 +2,19 @@ This module seeks to estimate the "dynamical matrix" for Si 1x1x1 from data. """ + import logging import matplotlib.pyplot as plt import torch -from crystal_diffusion.models.score_networks.analytical_score_network import ( - AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) -from crystal_diffusion.namespace import (NOISE, NOISY_RELATIVE_COORDINATES, - TIME, UNIT_CELL) from einops import einops -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( + AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) from experiments.analysis.analytic_score import ANALYTIC_SCORE_RESULTS_DIR from experiments.analysis.analytic_score.utils import get_unit_cells @@ -20,23 +22,31 @@ plt.style.use(PLOT_STYLE_PATH) -device = torch.device('cpu') +device = torch.device("cpu") number_of_atoms = 1 spatial_dimension = 1 -variance_parameter = 1. / 250. +variance_parameter = 1.0 / 250.0 x_eq = 0.5 -if __name__ == '__main__': +if __name__ == "__main__": - grid = torch.linspace(0., 1., 201)[:-1] + grid = torch.linspace(0.0, 1.0, 201)[:-1] batch_size = len(grid) - relative_coordinates = einops.repeat(grid, 'b -> b n d', n=number_of_atoms, d=spatial_dimension) + relative_coordinates = einops.repeat( + grid, "b -> b n d", n=number_of_atoms, d=spatial_dimension + ) times = torch.rand(batch_size, 1) # doesn't matter. Not used by analytical score. - unit_cell = get_unit_cells(acell=1., spatial_dimension=spatial_dimension, number_of_samples=batch_size) - batch = {NOISY_RELATIVE_COORDINATES: relative_coordinates, TIME: times, UNIT_CELL: unit_cell} + unit_cell = get_unit_cells( + acell=1.0, spatial_dimension=spatial_dimension, number_of_samples=batch_size + ) + batch = { + NOISY_RELATIVE_COORDINATES: relative_coordinates, + TIME: times, + UNIT_CELL: unit_cell, + } equilibrium_relative_coordinates = torch.tensor([[x_eq]]) @@ -46,33 +56,40 @@ spatial_dimension=spatial_dimension, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - variance_parameter=variance_parameter) + variance_parameter=variance_parameter, + ) sigma_normalized_score_network = AnalyticalScoreNetwork(score_network_parameters) fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle(f'Sigma normalized Score, 1 atom in 1D\n Equilibrium Position: ({x_eq}), ' - f'$\\sigma_d^2$ = {variance_parameter}, kmax = {kmax}') + fig.suptitle( + f"Sigma normalized Score, 1 atom in 1D\n Equilibrium Position: ({x_eq}), " + f"$\\sigma_d^2$ = {variance_parameter}, kmax = {kmax}" + ) ax1 = fig.add_subplot(111) for sigma in [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5]: batch[NOISE] = sigma * torch.ones(batch_size, 1) - sigma_normalized_scores = sigma_normalized_score_network(batch).detach().flatten() + sigma_normalized_scores = ( + sigma_normalized_score_network(batch).detach().flatten() + ) x = relative_coordinates.detach().flatten() - ax1.plot(x, sigma_normalized_scores, label=f'$\\sigma$ = {sigma}') + ax1.plot(x, sigma_normalized_scores, label=f"$\\sigma$ = {sigma}") - ax1.set_xlabel('Relative Coordinates') - ax1.set_ylabel('$\\sigma \\times$ Score') - ax1.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=9) + ax1.set_xlabel("Relative Coordinates") + ax1.set_ylabel("$\\sigma \\times$ Score") + ax1.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=9) ax1.set_xlim([-0.01, 1.01]) fig.tight_layout() fig.savefig(ANALYTIC_SCORE_RESULTS_DIR / "analytical_score_network_1D.png") plt.close(fig) fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig2.suptitle(f'Sigma normalized Score, 1 atom in 1D\n Equilibrium Position: ({x_eq}), ' - f'$\\sigma_d^2$ = {variance_parameter}') + fig2.suptitle( + f"Sigma normalized Score, 1 atom in 1D\n Equilibrium Position: ({x_eq}), " + f"$\\sigma_d^2$ = {variance_parameter}" + ) ax2 = fig2.add_subplot(131) ax3 = fig2.add_subplot(132) @@ -86,20 +103,27 @@ spatial_dimension=spatial_dimension, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - variance_parameter=variance_parameter) - sigma_normalized_score_network = AnalyticalScoreNetwork(score_network_parameters) + variance_parameter=variance_parameter, + ) + sigma_normalized_score_network = AnalyticalScoreNetwork( + score_network_parameters + ) for sigma, ax in zip([0.001, 0.1, 0.5], [ax2, ax3, ax4]): batch[NOISE] = sigma * torch.ones(batch_size, 1) - sigma_normalized_scores = sigma_normalized_score_network(batch).detach().flatten() + sigma_normalized_scores = ( + sigma_normalized_score_network(batch).detach().flatten() + ) - ax.plot(x, sigma_normalized_scores, label=f'kmax = {kmax}', lw=lw) + ax.plot(x, sigma_normalized_scores, label=f"kmax = {kmax}", lw=lw) lw = 0.5 * lw - ax2.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=9) + ax2.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=9) for sigma, ax in zip([0.001, 0.1, 0.5], [ax2, ax3, ax4]): - ax.set_ylabel('$\\sigma \\times$ Score') + ax.set_ylabel("$\\sigma \\times$ Score") ax.set_xlim([-0.01, 1.01]) ax.set_title(f"$\\sigma$={sigma}") fig2.tight_layout() - fig2.savefig(ANALYTIC_SCORE_RESULTS_DIR / "analytical_score_network_1D_convergence.png") + fig2.savefig( + ANALYTIC_SCORE_RESULTS_DIR / "analytical_score_network_1D_convergence.png" + ) plt.close(fig2) diff --git a/experiments/analysis/analytic_score/utils.py b/experiments/analysis/analytic_score/utils.py index d166a71f..17a4736f 100644 --- a/experiments/analysis/analytic_score/utils.py +++ b/experiments/analysis/analytic_score/utils.py @@ -5,7 +5,9 @@ from pymatgen.symmetry.analyzer import SpacegroupAnalyzer -def get_unit_cells(acell: float, spatial_dimension: int, number_of_samples: int) -> torch.Tensor: +def get_unit_cells( + acell: float, spatial_dimension: int, number_of_samples: int +) -> torch.Tensor: """Generate cubic unit cells.""" unit_cell = torch.diag(acell * torch.ones(spatial_dimension)) return unit_cell.repeat(number_of_samples, 1, 1) @@ -14,33 +16,45 @@ def get_unit_cells(acell: float, spatial_dimension: int, number_of_samples: int) def get_silicon_supercell(supercell_factor: int): """Get silicon supercell.""" primitive_cell_a = 3.84 - lattice = Lattice.from_parameters(a=primitive_cell_a, - b=primitive_cell_a, - c=primitive_cell_a, - alpha=60., beta=60., gamma=60.) - - species = ['Si', 'Si'] + lattice = Lattice.from_parameters( + a=primitive_cell_a, + b=primitive_cell_a, + c=primitive_cell_a, + alpha=60.0, + beta=60.0, + gamma=60.0, + ) + + species = ["Si", "Si"] coordinates = np.array([[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]]) - primitize_structure = Structure(lattice=lattice, - species=species, - coords=coordinates, - coords_are_cartesian=False) - conventional_structure = SpacegroupAnalyzer(primitize_structure).get_symmetrized_structure().to_conventional() + primitize_structure = Structure( + lattice=lattice, species=species, coords=coordinates, coords_are_cartesian=False + ) + conventional_structure = ( + SpacegroupAnalyzer(primitize_structure) + .get_symmetrized_structure() + .to_conventional() + ) - super_structure = conventional_structure.make_supercell([supercell_factor, supercell_factor, supercell_factor]) + super_structure = conventional_structure.make_supercell( + [supercell_factor, supercell_factor, supercell_factor] + ) return super_structure.frac_coords -def get_random_equilibrium_relative_coordinates(number_of_atoms: int, spatial_dimension: int) -> torch.Tensor: +def get_random_equilibrium_relative_coordinates( + number_of_atoms: int, spatial_dimension: int +) -> torch.Tensor: """Random equilibrium coordinates inside the unit cell.""" # Make sure the positions are far from the edges. return 0.6 * torch.rand(number_of_atoms, spatial_dimension) + 0.2 -def get_random_inverse_covariance(spring_constant_scale: float, number_of_atoms: int, - spatial_dimension: int) -> torch.Tensor: +def get_random_inverse_covariance( + spring_constant_scale: float, number_of_atoms: int, spatial_dimension: int +) -> torch.Tensor: """Get a random inverse covariance.""" flat_dim = number_of_atoms * spatial_dimension @@ -49,23 +63,35 @@ def get_random_inverse_covariance(spring_constant_scale: float, number_of_atoms: random_matrix = torch.rand(flat_dim, flat_dim) orthogonal_matrix, _, _ = torch.svd(random_matrix) - flat_inverse_covariance = orthogonal_matrix @ (torch.diag(diagonal_values) @ orthogonal_matrix.T) + flat_inverse_covariance = orthogonal_matrix @ ( + torch.diag(diagonal_values) @ orthogonal_matrix.T + ) - inverse_covariance = einops.rearrange(flat_inverse_covariance, "(n1 d1) (n2 d2) -> n1 d1 n2 d2", - n1=number_of_atoms, n2=number_of_atoms, - d1=spatial_dimension, d2=spatial_dimension) + inverse_covariance = einops.rearrange( + flat_inverse_covariance, + "(n1 d1) (n2 d2) -> n1 d1 n2 d2", + n1=number_of_atoms, + n2=number_of_atoms, + d1=spatial_dimension, + d2=spatial_dimension, + ) return inverse_covariance -def get_exact_samples(equilibrium_relative_coordinates: torch.Tensor, inverse_covariance: torch.Tensor, - number_of_samples: int) -> torch.Tensor: +def get_exact_samples( + equilibrium_relative_coordinates: torch.Tensor, + inverse_covariance: torch.Tensor, + number_of_samples: int, +) -> torch.Tensor: """Sample the exact harmonic energy.""" device = equilibrium_relative_coordinates.device natom, spatial_dimension, _, _ = inverse_covariance.shape flat_dim = natom * spatial_dimension - flat_equilibrium_relative_coordinates = einops.rearrange(equilibrium_relative_coordinates, "n d -> (n d)") + flat_equilibrium_relative_coordinates = einops.rearrange( + equilibrium_relative_coordinates, "n d -> (n d)" + ) matrix = einops.rearrange(inverse_covariance, "n1 d1 n2 d2 -> (n1 d1) (n2 d2)") @@ -73,7 +99,7 @@ def get_exact_samples(equilibrium_relative_coordinates: torch.Tensor, inverse_co eigenvalues = eigen.eigenvalues eigenvectors_as_columns = eigen.eigenvectors - sigmas = 1. / torch.sqrt(eigenvalues) + sigmas = 1.0 / torch.sqrt(eigenvalues) z_scores = torch.randn(number_of_samples, flat_dim).to(device) @@ -81,16 +107,25 @@ def get_exact_samples(equilibrium_relative_coordinates: torch.Tensor, inverse_co flat_displacements = sigma_z_scores @ eigenvectors_as_columns.T - flat_relative_coordinates = flat_equilibrium_relative_coordinates.unsqueeze(0) + flat_displacements + flat_relative_coordinates = ( + flat_equilibrium_relative_coordinates.unsqueeze(0) + flat_displacements + ) - relative_coordinates_sample = einops.rearrange(flat_relative_coordinates, - "batch (n d) -> batch n d", n=natom, d=spatial_dimension) + relative_coordinates_sample = einops.rearrange( + flat_relative_coordinates, + "batch (n d) -> batch n d", + n=natom, + d=spatial_dimension, + ) return relative_coordinates_sample -def get_samples_harmonic_energy(equilibrium_relative_coordinates: torch.Tensor, inverse_covariance: torch.Tensor, - samples: torch.Tensor) -> torch.Tensor: +def get_samples_harmonic_energy( + equilibrium_relative_coordinates: torch.Tensor, + inverse_covariance: torch.Tensor, + samples: torch.Tensor, +) -> torch.Tensor: """Get samples harmonic energy.""" flat_x_eq = einops.rearrange(equilibrium_relative_coordinates, "n d -> (n d)") flat_x = einops.rearrange(samples, "batch n d -> batch (n d)") @@ -100,27 +135,41 @@ def get_samples_harmonic_energy(equilibrium_relative_coordinates: torch.Tensor, # Account for periodicity: the closest equilibrium position might be a lattice vector away from the origin. flat_displacements = flat_displacements - flat_displacements.round() - flat_inverse_covariance = einops.rearrange(inverse_covariance, "n1 d1 n2 d2 -> (n1 d1) (n2 d2)") + flat_inverse_covariance = einops.rearrange( + inverse_covariance, "n1 d1 n2 d2 -> (n1 d1) (n2 d2)" + ) - m_u = einops.einsum(flat_inverse_covariance, flat_displacements, "flat1 flat2, batch flat2 -> batch flat1") + m_u = einops.einsum( + flat_inverse_covariance, + flat_displacements, + "flat1 flat2, batch flat2 -> batch flat1", + ) u_m_u = einops.einsum(flat_displacements, m_u, "batch flat, batch flat-> batch") energies = 0.5 * u_m_u return energies -def get_relative_harmonic_energy(batch_relative_coordinates: torch.Tensor, - equilibrium_relative_coordinates: torch.Tensor, - spring_constant: float): +def get_relative_harmonic_energy( + batch_relative_coordinates: torch.Tensor, + equilibrium_relative_coordinates: torch.Tensor, + spring_constant: float, +): """Get relative harmonic energy. This is the harmonic energy without the center of mass term when there are only two atoms and an isotropic dynamical matrix described by a spring constant. """ - assert batch_relative_coordinates.shape[1] == 2, "This method is specialized to 2 atoms only." - assert equilibrium_relative_coordinates.shape[0] == 2, "This method is specialized to 2 atoms only." - - batch_displacements = batch_relative_coordinates[:, 1, :] - batch_relative_coordinates[:, 0, :] - energies = spring_constant * (batch_displacements ** 2).sum(dim=1) + assert ( + batch_relative_coordinates.shape[1] == 2 + ), "This method is specialized to 2 atoms only." + assert ( + equilibrium_relative_coordinates.shape[0] == 2 + ), "This method is specialized to 2 atoms only." + + batch_displacements = ( + batch_relative_coordinates[:, 1, :] - batch_relative_coordinates[:, 0, :] + ) + energies = spring_constant * (batch_displacements**2).sum(dim=1) return energies diff --git a/experiments/analysis/diffusion_sample_position_analysis.py b/experiments/analysis/diffusion_sample_position_analysis.py index 1986ed48..58c3302e 100644 --- a/experiments/analysis/diffusion_sample_position_analysis.py +++ b/experiments/analysis/diffusion_sample_position_analysis.py @@ -3,13 +3,16 @@ A simple script to extract the diffusion positions from a pickle on disk and generate some distributions. """ + import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion import TOP_DIR -from crystal_diffusion.utils.sample_trajectory import \ + +from diffusion_for_multi_scale_molecular_dynamics import TOP_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.utils.sample_trajectory import \ PredictorCorrectorSampleTrajectory -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) @@ -21,25 +24,35 @@ # experiment_name = "mace/run1/" -trajectory_top_output_directory = TOP_DIR / "experiments/diffusion_mace_harmonic_data/output/" +trajectory_top_output_directory = ( + TOP_DIR / "experiments/diffusion_mace_harmonic_data/output/" +) -trajectory_data_directory = trajectory_top_output_directory / experiment_name / "diffusion_position_samples" -energy_sample_directory = trajectory_top_output_directory / experiment_name / "energy_samples" +trajectory_data_directory = ( + trajectory_top_output_directory / experiment_name / "diffusion_position_samples" +) +energy_sample_directory = ( + trajectory_top_output_directory / experiment_name / "energy_samples" +) output_top_dir = trajectory_data_directory.parent / "visualization" -if __name__ == '__main__': +if __name__ == "__main__": energies = torch.load(energy_sample_directory / f"energies_sample_epoch={epoch}.pt") - pickle_path = trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}.pt" + pickle_path = ( + trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}.pt" + ) sample_trajectory = PredictorCorrectorSampleTrajectory.read_from_pickle(pickle_path) - pickle_path = trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}.pt" + pickle_path = ( + trajectory_data_directory / f"diffusion_position_sample_epoch={epoch}.pt" + ) sample_trajectory = PredictorCorrectorSampleTrajectory.read_from_pickle(pickle_path) - list_predictor_coordinates = sample_trajectory.data['predictor_x_i'] + list_predictor_coordinates = sample_trajectory.data["predictor_x_i"] float_datatype = list_predictor_coordinates[0].dtype fig = plt.figure(figsize=PLEASANT_FIG_SIZE) @@ -57,37 +70,85 @@ list_indices = [0, 40, 80, 99] equilibrium_delta_squared = 0.75 - default_orientation = torch.tensor([1., 1., 1.], dtype=float_datatype) / np.sqrt(3.) + default_orientation = torch.tensor([1.0, 1.0, 1.0], dtype=float_datatype) / np.sqrt( + 3.0 + ) for idx in list_indices: time_idx = list_t[idx] relative_coordinates = list_predictor_coordinates[idx] displacements = relative_coordinates[:, 1, :] - relative_coordinates[:, 0, :] - center_of_mass = 0.5 * (relative_coordinates[:, 1, :] + relative_coordinates[:, 0, :]) - delta_squared = (displacements ** 2).sum(dim=1) - - normalized_angle = torch.arccos(displacements @ default_orientation / torch.sqrt(delta_squared)) / (np.pi) - - ax1.hist(delta_squared, bins=nbins, alpha=0.5, histtype="stepfilled", label=f't = {time_idx}') - ax2.hist(normalized_angle, bins=nbins, alpha=0.5, histtype="stepfilled", label=f't = {time_idx}') - - ax3.hist(center_of_mass[:, 0], bins=nbins, alpha=0.5, histtype="stepfilled", label=f't = {time_idx}') - ax4.hist(center_of_mass[:, 1], bins=nbins, alpha=0.5, histtype="stepfilled", label=f't = {time_idx}') - ax5.hist(center_of_mass[:, 2], bins=nbins, alpha=0.5, histtype="stepfilled", label=f't = {time_idx}') - - ax1.vlines(x=equilibrium_delta_squared, ymin=0, ymax=75, - linestyle='--', color='black', label='Equilibrium Distance') - ax2.vlines(x=torch.arccos(torch.tensor([-1, -1 / 3, 1 / 3, 1])) / np.pi, ymin=0, ymax=180, - linestyle='--', color='black', label='Equilibrium Angles') + center_of_mass = 0.5 * ( + relative_coordinates[:, 1, :] + relative_coordinates[:, 0, :] + ) + delta_squared = (displacements**2).sum(dim=1) + + normalized_angle = torch.arccos( + displacements @ default_orientation / torch.sqrt(delta_squared) + ) / (np.pi) + + ax1.hist( + delta_squared, + bins=nbins, + alpha=0.5, + histtype="stepfilled", + label=f"t = {time_idx}", + ) + ax2.hist( + normalized_angle, + bins=nbins, + alpha=0.5, + histtype="stepfilled", + label=f"t = {time_idx}", + ) + + ax3.hist( + center_of_mass[:, 0], + bins=nbins, + alpha=0.5, + histtype="stepfilled", + label=f"t = {time_idx}", + ) + ax4.hist( + center_of_mass[:, 1], + bins=nbins, + alpha=0.5, + histtype="stepfilled", + label=f"t = {time_idx}", + ) + ax5.hist( + center_of_mass[:, 2], + bins=nbins, + alpha=0.5, + histtype="stepfilled", + label=f"t = {time_idx}", + ) + + ax1.vlines( + x=equilibrium_delta_squared, + ymin=0, + ymax=75, + linestyle="--", + color="black", + label="Equilibrium Distance", + ) + ax2.vlines( + x=torch.arccos(torch.tensor([-1, -1 / 3, 1 / 3, 1])) / np.pi, + ymin=0, + ymax=180, + linestyle="--", + color="black", + label="Equilibrium Angles", + ) for ax in [ax1, ax2, ax3, ax4, ax5]: - ax.set_ylabel('Counts') + ax.set_ylabel("Counts") ax1.legend(loc=0, fontsize=8) - ax1.set_xlabel('$\\delta^2$') - ax2.set_xlabel('Angle ($\\pi$)') - ax3.set_xlabel('CM x direction') - ax4.set_xlabel('CM y direction') - ax5.set_xlabel('CM z direction') + ax1.set_xlabel("$\\delta^2$") + ax2.set_xlabel("Angle ($\\pi$)") + ax3.set_xlabel("CM x direction") + ax4.set_xlabel("CM y direction") + ax5.set_xlabel("CM z direction") ax1.set_xlim(xmin=-0.01) for ax in [ax2, ax3, ax4, ax5]: diff --git a/experiments/analysis/exploding_variance_analysis.py b/experiments/analysis/exploding_variance_analysis.py index 0520683b..197df4d6 100644 --- a/experiments/analysis/exploding_variance_analysis.py +++ b/experiments/analysis/exploding_variance_analysis.py @@ -3,18 +3,21 @@ This script computes and plots the variance schedule used to noise and denoise the relative positions. """ + import matplotlib.pyplot as plt import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.score.wrapped_gaussian_score import \ - get_sigma_normalized_score -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import ( + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( ExplodingVarianceSampler, NoiseParameters) +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import \ + get_sigma_normalized_score plt.style.use(PLOT_STYLE_PATH) -if __name__ == '__main__': +if __name__ == "__main__": noise_parameters = NoiseParameters(total_time_steps=1000) variance_sampler = ExplodingVarianceSampler(noise_parameters=noise_parameters) @@ -27,14 +30,21 @@ ax1 = fig1.add_subplot(121) ax2 = fig1.add_subplot(122) - ax1.plot(noise.time, noise.sigma, '-', c='b', lw=2, label='$\\sigma(t)$') - ax1.plot(noise.time, noise.g, '-', c='g', lw=2, label="$g(t)$") + ax1.plot(noise.time, noise.sigma, "-", c="b", lw=2, label="$\\sigma(t)$") + ax1.plot(noise.time, noise.g, "-", c="g", lw=2, label="$g(t)$") shifted_time = torch.cat([torch.tensor([0]), noise.time[:-1]]) - ax1.plot(shifted_time, langevin_dynamics.epsilon, '-', c='r', lw=2, label="$\\epsilon(t)$") + ax1.plot( + shifted_time, + langevin_dynamics.epsilon, + "-", + c="r", + lw=2, + label="$\\epsilon(t)$", + ) ax1.legend(loc=0) - ax1.set_xlabel('time') + ax1.set_xlabel("time") ax1.set_xlim([-0.01, 1.01]) ax1.set_title("$\\sigma, g, \\epsilon$ schedules") @@ -49,10 +59,12 @@ gs_squared = noise.g_squared.take(indices) for t, sigma in zip(times, sigmas): - target_sigma_normalized_scores = get_sigma_normalized_score(relative_positions, - torch.ones_like(relative_positions) * sigma, - kmax=kmax) - ax2.plot(relative_positions, target_sigma_normalized_scores, label=f"t = {t:3.2f}") + target_sigma_normalized_scores = get_sigma_normalized_score( + relative_positions, torch.ones_like(relative_positions) * sigma, kmax=kmax + ) + ax2.plot( + relative_positions, target_sigma_normalized_scores, label=f"t = {t:3.2f}" + ) ax2.set_title("Target Normalized Score") ax2.set_xlabel("relative position, u") diff --git a/experiments/analysis/perturbation_kernel_analysis.py b/experiments/analysis/perturbation_kernel_analysis.py index 25d2de3a..d4cff243 100644 --- a/experiments/analysis/perturbation_kernel_analysis.py +++ b/experiments/analysis/perturbation_kernel_analysis.py @@ -3,23 +3,26 @@ This script computes and plots the conditional target score based on the perturbation kernel K for various values of sigma, showing the behavior for different normalizations. """ + import matplotlib.pyplot as plt import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.score.wrapped_gaussian_score import \ + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import \ get_sigma_normalized_score -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) sigma_max = 0.5 -if __name__ == '__main__': +if __name__ == "__main__": coordinates = torch.linspace(start=0, end=1, steps=1001)[:-1] sigmas = torch.linspace(start=0, end=sigma_max, steps=101)[1:] - X, SIG = torch.meshgrid(coordinates, sigmas, indexing='xy') + X, SIG = torch.meshgrid(coordinates, sigmas, indexing="xy") # Avoid non-contiguous bjorks X = X.clone() @@ -33,7 +36,7 @@ fig = plt.figure(figsize=fig_size) fig.suptitle(r"$\alpha$ Component of Conditional Score") - left, right, bottom, top = 0, 1., 0., sigma_max + left, right, bottom, top = 0, 1.0, 0.0, sigma_max extent = [left, right, bottom, top] ax1 = fig.add_subplot(221) @@ -49,18 +52,30 @@ xlabel = r"$x^\alpha - x_0^\alpha$" - for label, scores, ax in zip([label1, label2], [sigma_normalized_scores, sigma2_normalized_scores], [ax1, ax3]): + for label, scores, ax in zip( + [label1, label2], + [sigma_normalized_scores, sigma2_normalized_scores], + [ax1, ax3], + ): ax.set_title(label) - image = ax.imshow(scores, origin="lower", cmap='jet', extent=extent) + image = ax.imshow(scores, origin="lower", cmap="jet", extent=extent) ax.set_xlabel(xlabel) ax.set_ylabel(r"$\sigma$") _ = fig.colorbar(image, shrink=0.75, ax=ax) for index in [19, 39, 99]: sigma = sigmas[index] - for ax, scores in zip([ax2, ax4], [sigma_normalized_scores, sigma2_normalized_scores]): - ax.plot(coordinates, scores[index], ls='-', lw=2, label=f'$\\sigma = {sigma:4.3f}$') + for ax, scores in zip( + [ax2, ax4], [sigma_normalized_scores, sigma2_normalized_scores] + ): + ax.plot( + coordinates, + scores[index], + ls="-", + lw=2, + label=f"$\\sigma = {sigma:4.3f}$", + ) for ax in [ax2, ax4]: ax.legend(loc=0) @@ -70,4 +85,8 @@ ax4.set_ylabel(label2) fig.tight_layout() - fig.savefig(ANALYSIS_RESULTS_DIR.joinpath("perturbation_kernel_with_different_normalizations.png")) + fig.savefig( + ANALYSIS_RESULTS_DIR.joinpath( + "perturbation_kernel_with_different_normalizations.png" + ) + ) diff --git a/experiments/analysis/plot_repulsive_force.py b/experiments/analysis/plot_repulsive_force.py index d5e50b97..1d16572a 100644 --- a/experiments/analysis/plot_repulsive_force.py +++ b/experiments/analysis/plot_repulsive_force.py @@ -1,9 +1,12 @@ import torch -from crystal_diffusion.models.score_networks.force_field_augmented_score_network import ( - ForceFieldAugmentedScoreNetwork, ForceFieldParameters) -from crystal_diffusion.namespace import NOISY_RELATIVE_COORDINATES, UNIT_CELL from matplotlib import pyplot as plt -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.force_field_augmented_score_network import ( + ForceFieldAugmentedScoreNetwork, ForceFieldParameters) +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + NOISY_RELATIVE_COORDINATES, UNIT_CELL) plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py b/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py index 7b13971a..6edba2ec 100644 --- a/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py +++ b/experiments/analysis/plotting_gaussian_vs_wrapped_gaussian.py @@ -1,24 +1,26 @@ - """Plotting Gaussians vs. Wrapped Gaussians. This script plots a comparison between a 1D Gaussian centered at 0.5 and the sum of its periodic images. """ + import matplotlib.pyplot as plt import numpy as np -from crystal_diffusion import ANALYSIS_RESULTS_DIR from scipy.stats import norm -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) plt.style.use(PLOT_STYLE_PATH) sigma_max = 0.5 list_sigmas = [0.1, 0.25, 0.5] -list_colors = ['r', 'b', 'g'] +list_colors = ["r", "b", "g"] imin = -1 imax = 2 -if __name__ == '__main__': +if __name__ == "__main__": fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) @@ -36,28 +38,36 @@ ax = fig.add_subplot(111) in_cell_gaussian = norm(loc=0.5, scale=sigma).pdf(x_in_cell) ymax1 = np.max(in_cell_gaussian) + 0.1 - ax.plot(x_in_cell, in_cell_gaussian, lw=2, color=color, label='Gaussian') + ax.plot(x_in_cell, in_cell_gaussian, lw=2, color=color, label="Gaussian") wrapped_gaussians = np.zeros(len(x)) - label = 'Periodic Images' + label = "Periodic Images" for loc in list_locs: gaussian = norm(loc=loc, scale=sigma).pdf(x) wrapped_gaussians += gaussian - ax.plot(x, gaussian, color=color, lw=1, ls='-', alpha=0.5, label=label) - label = '__nolabel__' + ax.plot(x, gaussian, color=color, lw=1, ls="-", alpha=0.5, label=label) + label = "__nolabel__" ymax2 = np.max(wrapped_gaussians) + 0.1 ymax = np.max([ymax1, ymax2]) - ax.plot(x, wrapped_gaussians, color=color, lw=2, ls='--', label='Wrapped Gaussian') + ax.plot( + x, wrapped_gaussians, color=color, lw=2, ls="--", label="Wrapped Gaussian" + ) - ax.vlines(np.arange(imin, imax + 1), 0, ymax, color='k') + ax.vlines(np.arange(imin, imax + 1), 0, ymax, color="k") - ax.fill_betweenx([0, ymax], x1=0, x2=1, color='grey', alpha=0.25, label='Unit Cell') + ax.fill_betweenx( + [0, ymax], x1=0, x2=1, color="grey", alpha=0.25, label="Unit Cell" + ) ax.set_ylim(0, ymax) ax.set_xlim(imin - 0.1, imax + 0.1) ax.legend(loc=0) fig.tight_layout() - fig.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"Gaussian_vs_Wrapped_Gaussian_sigma={sigma}.png")) + fig.savefig( + ANALYSIS_RESULTS_DIR.joinpath( + f"Gaussian_vs_Wrapped_Gaussian_sigma={sigma}.png" + ) + ) diff --git a/experiments/analysis/target_score_analysis.py b/experiments/analysis/target_score_analysis.py index 0bca3889..a3d5e736 100644 --- a/experiments/analysis/target_score_analysis.py +++ b/experiments/analysis/target_score_analysis.py @@ -3,18 +3,21 @@ This script computes and plots the target score for various values of sigma, showing that the 'smart' implementation converges quickly and is equal to the expected brute force value. """ + import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from crystal_diffusion.score.wrapped_gaussian_score import ( + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.score.wrapped_gaussian_score import ( SIGMA_THRESHOLD, get_sigma_normalized_score, get_sigma_normalized_score_brute_force) -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH plt.style.use(PLOT_STYLE_PATH) -if __name__ == '__main__': +if __name__ == "__main__": list_u = np.linspace(0, 1, 101)[:-1] relative_positions = torch.from_numpy(list_u) @@ -27,25 +30,35 @@ ax1 = fig1.add_subplot(121) ax2 = fig1.add_subplot(122) - for sigma_factor, color in zip([0.1, 1., 2.], ['r', 'g', 'b']): + for sigma_factor, color in zip([0.1, 1.0, 2.0], ["r", "g", "b"]): sigma = sigma_factor * SIGMA_THRESHOLD sigmas = torch.ones_like(relative_positions) * sigma - list_scores_brute = np.array([get_sigma_normalized_score_brute_force(u, sigma) for u in list_u]) - list_scores = get_sigma_normalized_score(relative_positions, sigmas, kmax=kmax).numpy() + list_scores_brute = np.array( + [get_sigma_normalized_score_brute_force(u, sigma) for u in list_u] + ) + list_scores = get_sigma_normalized_score( + relative_positions, sigmas, kmax=kmax + ).numpy() error = list_scores - list_scores_brute - ax1.plot(list_u, list_scores_brute, '--', c=color, lw=4, label='brute force') - ax1.plot(list_u, list_scores, '-', c=color, lw=2, label='smart') + ax1.plot(list_u, list_scores_brute, "--", c=color, lw=4, label="brute force") + ax1.plot(list_u, list_scores, "-", c=color, lw=2, label="smart") - ax2.plot(list_u, error, '-', c=color, label=f'$\\sigma$ = {sigma_factor} $\\sigma_{{th}}$') + ax2.plot( + list_u, + error, + "-", + c=color, + label=f"$\\sigma$ = {sigma_factor} $\\sigma_{{th}}$", + ) - ax1.set_ylabel('$\\sigma^2\\times S$') - ax2.set_xlabel('u') - ax2.set_ylabel('Error') + ax1.set_ylabel("$\\sigma^2\\times S$") + ax2.set_xlabel("u") + ax2.set_ylabel("Error") for ax in [ax1, ax2]: - ax.set_xlabel('u') + ax.set_xlabel("u") ax.set_xlim([0, 1]) ax.legend(loc=0) @@ -59,29 +72,51 @@ ax3 = fig2.add_subplot(121) ax4 = fig2.add_subplot(122) - sigma_factors = torch.linspace(0.001, 4., 40).to(torch.double) + sigma_factors = torch.linspace(0.001, 4.0, 40).to(torch.double) sigmas = sigma_factors * SIGMA_THRESHOLD u = 0.6 relative_positions = torch.ones_like(sigmas).to(torch.double) * u ms = 8 - for kmax, color in zip([1, 2, 3, 4, 5], ['y', 'r', 'g', 'b', 'k']): - list_scores = get_sigma_normalized_score(relative_positions, sigmas, kmax=kmax).numpy() - ax3.semilogy(sigma_factors, list_scores, 'o-', - ms=ms, c=color, lw=2, alpha=0.25, label=f'kmax = {kmax}') - - list_scores_brute = np.array([ - get_sigma_normalized_score_brute_force(u, sigma, kmax=4 * kmax) for sigma in sigmas]) - ax4.semilogy(sigma_factors, list_scores_brute, 'o-', - ms=ms, c=color, lw=2, alpha=0.25, label=f'kmax = {4 * kmax}') + for kmax, color in zip([1, 2, 3, 4, 5], ["y", "r", "g", "b", "k"]): + list_scores = get_sigma_normalized_score( + relative_positions, sigmas, kmax=kmax + ).numpy() + ax3.semilogy( + sigma_factors, + list_scores, + "o-", + ms=ms, + c=color, + lw=2, + alpha=0.25, + label=f"kmax = {kmax}", + ) + + list_scores_brute = np.array( + [ + get_sigma_normalized_score_brute_force(u, sigma, kmax=4 * kmax) + for sigma in sigmas + ] + ) + ax4.semilogy( + sigma_factors, + list_scores_brute, + "o-", + ms=ms, + c=color, + lw=2, + alpha=0.25, + label=f"kmax = {4 * kmax}", + ) ms = 0.75 * ms for ax in [ax3, ax4]: - ax.set_xlabel('$\\sigma$ ($\\sigma_{th}$)') - ax.set_ylabel(f'$\\sigma^2 \\times S(u={u})$') - ax.set_xlim([0., 1.1 * sigma_factors.max()]) + ax.set_xlabel("$\\sigma$ ($\\sigma_{th}$)") + ax.set_ylabel(f"$\\sigma^2 \\times S(u={u})$") + ax.set_xlim([0.0, 1.1 * sigma_factors.max()]) ax.legend(loc=0) ax3.set_title("Smart implementation") diff --git a/experiments/analysis_utils.py b/experiments/analysis_utils.py index 3f92569f..3a5c38d8 100644 --- a/experiments/analysis_utils.py +++ b/experiments/analysis_utils.py @@ -31,8 +31,9 @@ def get_thermo_dataset(dataset_name: str) -> Tuple[pd.DataFrame, pd.DataFrame]: """ lammps_dataset_dir = DATA_DIR.joinpath(dataset_name) - assert lammps_dataset_dir.is_dir(), \ - f"The folder {lammps_dataset_dir} does not exist! Data must be present to execute this function." + assert ( + lammps_dataset_dir.is_dir() + ), f"The folder {lammps_dataset_dir} does not exist! Data must be present to execute this function." cache_dir = EXPERIMENT_ANALYSIS_DIR.joinpath(f"cache/{dataset_name}") cache_dir.mkdir(parents=True, exist_ok=True) @@ -41,7 +42,7 @@ def get_thermo_dataset(dataset_name: str) -> Tuple[pd.DataFrame, pd.DataFrame]: list_valid_df = [] logging.info("Parsing the thermo logs") - run_directories = glob.glob(str(lammps_dataset_dir.joinpath('*_run_*'))) + run_directories = glob.glob(str(lammps_dataset_dir.joinpath("*_run_*"))) for run_directory in run_directories: basename = os.path.basename(run_directory) @@ -50,13 +51,15 @@ def get_thermo_dataset(dataset_name: str) -> Tuple[pd.DataFrame, pd.DataFrame]: logging.info(f"Pickle file {pickle_path} exists. Reading in...") else: logging.info(f"Pickle file {pickle_path} does not exist: creating...") - lammps_thermo_log = lammps_dataset_dir.joinpath(f"{basename}/lammps_thermo.yaml") + lammps_thermo_log = lammps_dataset_dir.joinpath( + f"{basename}/lammps_thermo.yaml" + ) df = pd.DataFrame(parse_lammps_thermo_log(lammps_thermo_log)) df.to_pickle(pickle_path) logging.info("Done creating pickle file") df = pd.read_pickle(pickle_path) - if 'train' in basename: + if "train" in basename: list_train_df.append(df) else: list_valid_df.append(df) diff --git a/experiments/dataset_analysis/dataset_covariance.py b/experiments/dataset_analysis/dataset_covariance.py index 83b95dc6..70994a7e 100644 --- a/experiments/dataset_analysis/dataset_covariance.py +++ b/experiments/dataset_analysis/dataset_covariance.py @@ -4,30 +4,34 @@ actual datasets, that is, the standard deviation of the displacement from equilibrium, in fractional coordinates. """ + import logging import einops import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR -from crystal_diffusion.data.diffusion.data_loader import ( +from tqdm import tqdm + +from diffusion_for_multi_scale_molecular_dynamics import (ANALYSIS_RESULTS_DIR, + DATA_DIR) +from diffusion_for_multi_scale_molecular_dynamics.data.diffusion.data_loader import ( LammpsForDiffusionDataModule, LammpsLoaderParameters) -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from tqdm import tqdm +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger logger = logging.getLogger(__name__) -dataset_name = 'si_diffusion_2x2x2' +dataset_name = "si_diffusion_2x2x2" # dataset_name = 'si_diffusion_1x1x1' output_dir = ANALYSIS_RESULTS_DIR / "covariances" output_dir.mkdir(exist_ok=True) -if dataset_name == 'si_diffusion_1x1x1': +if dataset_name == "si_diffusion_1x1x1": max_atom = 8 translation = torch.tensor([0.125, 0.125, 0.125]) -elif dataset_name == 'si_diffusion_2x2x2': +elif dataset_name == "si_diffusion_2x2x2": max_atom = 64 translation = torch.tensor([0.0625, 0.0625, 0.0625]) @@ -38,7 +42,7 @@ data_params = LammpsLoaderParameters(batch_size=2048, max_atom=max_atom) -if __name__ == '__main__': +if __name__ == "__main__": setup_analysis_logger() logger.info(f"Computing the covariance matrix for {dataset_name}") @@ -54,7 +58,9 @@ list_means = [] for batch in tqdm(datamodule.train_dataloader(), "Mean"): - x = map_relative_coordinates_to_unit_cell(batch['relative_coordinates'] + translation) + x = map_relative_coordinates_to_unit_cell( + batch["relative_coordinates"] + translation + ) list_means.append(x.mean(dim=0)) # Drop the last batch, which might not have dimension batch_size @@ -63,9 +69,13 @@ list_covariances = [] list_sizes = [] for batch in tqdm(datamodule.train_dataloader(), "displacements"): - x = map_relative_coordinates_to_unit_cell(batch['relative_coordinates'] + translation) + x = map_relative_coordinates_to_unit_cell( + batch["relative_coordinates"] + translation + ) list_sizes.append(x.shape[0]) - displacements = einops.rearrange(x - x0, "batch natoms space -> batch (natoms space)") + displacements = einops.rearrange( + x - x0, "batch natoms space -> batch (natoms space)" + ) covariance = (displacements[:, None, :] * displacements[:, :, None]).sum(dim=0) list_covariances.append(covariance) @@ -73,5 +83,5 @@ output_file = output_dir / f"covariance_{dataset_name}.pkl" logger.info(f"Writing to file {output_file}...") - with open(output_file, 'wb') as fd: + with open(output_file, "wb") as fd: torch.save(covariance, fd) diff --git a/experiments/dataset_analysis/energy_consistency_analysis.py b/experiments/dataset_analysis/energy_consistency_analysis.py index 4c32c45d..35762c04 100644 --- a/experiments/dataset_analysis/energy_consistency_analysis.py +++ b/experiments/dataset_analysis/energy_consistency_analysis.py @@ -6,31 +6,35 @@ This is to ensure that the configurations are exactly equivalent between these two distinct ways of calling LAMMPS. """ + import logging import tempfile import matplotlib.pyplot as plt import numpy as np -from crystal_diffusion import DATA_DIR -from crystal_diffusion.callbacks.sampling_visualization_callback import ( - LOGGER_FIGSIZE, SamplingVisualizationCallback) -from crystal_diffusion.data.diffusion.data_loader import ( - LammpsForDiffusionDataModule, LammpsLoaderParameters) -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from src.crystal_diffusion.analysis import PLOT_STYLE_PATH from tqdm import tqdm +from diffusion_for_multi_scale_molecular_dynamics import DATA_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import \ + PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.callbacks.sampling_visualization_callback import ( + LOGGER_FIGSIZE, SamplingVisualizationCallback) +from diffusion_for_multi_scale_molecular_dynamics.data.diffusion.data_loader import ( + LammpsForDiffusionDataModule, LammpsLoaderParameters) +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger from experiments import EXPERIMENT_ANALYSIS_DIR from experiments.analysis_utils import get_thermo_dataset plt.style.use(PLOT_STYLE_PATH) logger = logging.getLogger(__name__) -dataset_name = 'si_diffusion_1x1x1' +dataset_name = "si_diffusion_1x1x1" lammps_run_dir = str(DATA_DIR / dataset_name) -processed_dataset_dir = str(DATA_DIR / dataset_name / 'processed') +processed_dataset_dir = str(DATA_DIR / dataset_name / "processed") cache_dir = str(EXPERIMENT_ANALYSIS_DIR / "cache" / dataset_name) @@ -38,7 +42,7 @@ sample_size = 1000 -if __name__ == '__main__': +if __name__ == "__main__": setup_analysis_logger() logging.info(f"Starting {dataset_name} analysis") @@ -57,32 +61,35 @@ train_dataset = datamodule.train_dataset data = train_dataset[0] - box = np.diag(data['box'].cpu().numpy()) - atom_types = data['type'].cpu().numpy() + box = np.diag(data["box"].cpu().numpy()) + atom_types = data["type"].cpu().numpy() - list_relative_positions = train_dataset[:sample_size]['relative_positions'].cpu().numpy() - list_positions = train_dataset[:sample_size]['position'].cpu().numpy() + list_relative_positions = ( + train_dataset[:sample_size]["relative_positions"].cpu().numpy() + ) + list_positions = train_dataset[:sample_size]["position"].cpu().numpy() list_positions_translated_back_in_box = list_positions % box[0, 0] list_expected_positions = np.dot(list_relative_positions, box) - list_dataset_potential_energies = train_df['potential_energy'][:sample_size].values + list_dataset_potential_energies = train_df["potential_energy"][:sample_size].values logger.info("Compute energy from Oracle") list_oracle_energies = [] with tempfile.TemporaryDirectory() as tmp_work_dir: for positions in tqdm(list_positions_translated_back_in_box, "LAMMPS energies"): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - atom_types, - tmp_work_dir=tmp_work_dir) + energy, forces = get_energy_and_forces_from_lammps( + positions, box, atom_types, tmp_work_dir=tmp_work_dir + ) list_oracle_energies.append(energy) list_oracle_energies = np.array(list_oracle_energies) - fig = SamplingVisualizationCallback._plot_energy_histogram(list_oracle_energies, list_dataset_potential_energies) + fig = SamplingVisualizationCallback._plot_energy_histogram( + list_oracle_energies, list_dataset_potential_energies + ) plt.show() fig2 = plt.figure(figsize=LOGGER_FIGSIZE) @@ -90,9 +97,11 @@ errors = list_oracle_energies - list_dataset_potential_energies - fig2.suptitle('Error Distribution between Dataset and Oracle') - ax2.hist(errors, density=True, bins=20, histtype="stepfilled", alpha=0.25, color='red') - ax2.set_xlabel('Energy (eV)') - ax2.set_ylabel('Density') + fig2.suptitle("Error Distribution between Dataset and Oracle") + ax2.hist( + errors, density=True, bins=20, histtype="stepfilled", alpha=0.25, color="red" + ) + ax2.set_xlabel("Energy (eV)") + ax2.set_ylabel("Density") fig2.tight_layout() plt.show() diff --git a/experiments/dataset_analysis/plot_si_phonon_DOS.py b/experiments/dataset_analysis/plot_si_phonon_DOS.py index efda2862..14fc4e86 100644 --- a/experiments/dataset_analysis/plot_si_phonon_DOS.py +++ b/experiments/dataset_analysis/plot_si_phonon_DOS.py @@ -4,10 +4,13 @@ Here we extract the corresponding phonon density of state, based on this covariance, to see if the energy scales match up. """ + import matplotlib.pyplot as plt import torch -from crystal_diffusion import ANALYSIS_RESULTS_DIR -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + +from diffusion_for_multi_scale_molecular_dynamics import ANALYSIS_RESULTS_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) plt.style.use(PLOT_STYLE_PATH) diff --git a/experiments/dataset_analysis/si_diffusion_analysis.py b/experiments/dataset_analysis/si_diffusion_analysis.py index 3bb92a53..1deb3916 100644 --- a/experiments/dataset_analysis/si_diffusion_analysis.py +++ b/experiments/dataset_analysis/si_diffusion_analysis.py @@ -9,16 +9,20 @@ It is still early days in the project, so the starting point format for these kinds of analyses is still in flux. """ + import logging import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy -from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics import (ANALYSIS_RESULTS_DIR, + DATA_DIR) +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger from experiments.analysis_utils import get_thermo_dataset plt.style.use(PLOT_STYLE_PATH) @@ -28,25 +32,25 @@ # Taken from this table: http://wild.life.nctu.edu.tw/class/common/energy-unit-conv-table.html kelvin_in_ev = 0.0000861705 -dataset_name = 'si_diffusion_1x1x1' +dataset_name = "si_diffusion_1x1x1" -if dataset_name == 'si_diffusion_1x1x1': +if dataset_name == "si_diffusion_1x1x1": number_of_atoms = 8 -elif dataset_name == 'si_diffusion_2x2x2': +elif dataset_name == "si_diffusion_2x2x2": number_of_atoms = 64 -elif dataset_name == 'si_diffusion_3x3x3': +elif dataset_name == "si_diffusion_3x3x3": number_of_atoms = 216 lammps_dataset_dir = DATA_DIR.joinpath(dataset_name) -if __name__ == '__main__': +if __name__ == "__main__": setup_analysis_logger() logging.info(f"Starting {dataset_name} analysis") train_df, valid_df = get_thermo_dataset(dataset_name) - temperatures = train_df['temperature'].values # Kelvin + temperatures = train_df["temperature"].values # Kelvin mu_temp = np.mean(temperatures) kB_T = mu_temp * kelvin_in_ev sigma_temp = np.std(temperatures) @@ -54,123 +58,157 @@ list_t = np.linspace(100, 500, 1001) list_pt = scipy.stats.norm(loc=mu_temp, scale=sigma_temp).pdf(list_t) - beta = 1. / kB_T + beta = 1.0 / kB_T - relative_energies = train_df['energy'].values + relative_energies = train_df["energy"].values mu_e = np.mean(relative_energies) - e0 = mu_e - 3. * number_of_atoms * kB_T + e0 = mu_e - 3.0 * number_of_atoms * kB_T - expected_sigma_e = np.sqrt((3. * number_of_atoms) / beta**2) # in eV + expected_sigma_e = np.sqrt((3.0 * number_of_atoms) / beta**2) # in eV computed_sigma_e = np.std(relative_energies) std_relative_error = (expected_sigma_e - computed_sigma_e) / expected_sigma_e - list_de = np.linspace(mu_e - 5 * computed_sigma_e, mu_e + 5 * computed_sigma_e, 1001) + list_de = np.linspace( + mu_e - 5 * computed_sigma_e, mu_e + 5 * computed_sigma_e, 1001 + ) list_pe = scipy.stats.norm(loc=mu_e, scale=computed_sigma_e).pdf(list_de) fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle(f'Distributions for dataset {dataset_name}') + fig1.suptitle(f"Distributions for dataset {dataset_name}") ax1 = fig1.add_subplot(211) ax2 = fig1.add_subplot(212) common_params = dict(density=True, bins=50, histtype="stepfilled", alpha=0.25) - ax1.set_title('Energy distribution') - ax1.hist(train_df['energy'], **common_params, label='Train', color='green') - ax1.hist(valid_df['energy'], **common_params, label='Valid', color='yellow') - ax1.vlines(e0, ymin=0, ymax=1, color='k', linestyle='--', label=f'$E_0$ = {e0:5.3f} eV') - ax1.vlines(e0 + 3 * number_of_atoms * kB_T, ymin=0, ymax=1, color='r', - linestyle='--', label=f'$E_0 + 3 N k_B T$ = {np.mean(relative_energies):5.3f} eV') - - label = f'Normal Fit: $\\sigma_{{emp}}$ = {computed_sigma_e:5.3f} eV, $\\sigma_{{th}}$ = {expected_sigma_e:5.3f} eV' - ax1.plot(list_de, list_pe, '-', color='blue', label=label) - - ax1.set_xlabel('Energy (eV)') - ax1.set_ylabel('Density') + ax1.set_title("Energy distribution") + ax1.hist(train_df["energy"], **common_params, label="Train", color="green") + ax1.hist(valid_df["energy"], **common_params, label="Valid", color="yellow") + ax1.vlines( + e0, ymin=0, ymax=1, color="k", linestyle="--", label=f"$E_0$ = {e0:5.3f} eV" + ) + ax1.vlines( + e0 + 3 * number_of_atoms * kB_T, + ymin=0, + ymax=1, + color="r", + linestyle="--", + label=f"$E_0 + 3 N k_B T$ = {np.mean(relative_energies):5.3f} eV", + ) + + label = f"Normal Fit: $\\sigma_{{emp}}$ = {computed_sigma_e:5.3f} eV, $\\sigma_{{th}}$ = {expected_sigma_e:5.3f} eV" + ax1.plot(list_de, list_pe, "-", color="blue", label=label) + + ax1.set_xlabel("Energy (eV)") + ax1.set_ylabel("Density") ax1.legend(loc=0) - ax2.set_title('Temperature Distribution') - ax2.hist(train_df['temperature'], **common_params, label='Train', color='green') - ax2.hist(valid_df['temperature'], **common_params, label='Valid', color='yellow') - label = f'Normal Fit: $\\sigma_T$ = {sigma_temp:5.1f} K, $k_B \\sigma_T$ = {kelvin_in_ev * sigma_temp:5.4f} eV' - ax2.plot(list_t, list_pt, '-', color='blue', label=label) - ax2.set_xlabel('Temperature (K)') - ax2.set_ylabel('Density') + ax2.set_title("Temperature Distribution") + ax2.hist(train_df["temperature"], **common_params, label="Train", color="green") + ax2.hist(valid_df["temperature"], **common_params, label="Valid", color="yellow") + label = f"Normal Fit: $\\sigma_T$ = {sigma_temp:5.1f} K, $k_B \\sigma_T$ = {kelvin_in_ev * sigma_temp:5.4f} eV" + ax2.plot(list_t, list_pt, "-", color="blue", label=label) + ax2.set_xlabel("Temperature (K)") + ax2.set_ylabel("Density") ax2.legend(loc=0) fig1.tight_layout() - fig1.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_distribution_analysis.png")) + fig1.savefig( + ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_distribution_analysis.png") + ) common_params = dict(density=True, bins=50, histtype="stepfilled", alpha=0.25) fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) ax3 = fig2.add_subplot(121) ax4 = fig2.add_subplot(122) - ax3.set_title('Energy distribution') - ax4.set_title('Average Energy') + ax3.set_title("Energy distribution") + ax4.set_title("Average Energy") - ax3.set_xlabel('$E- E_0$ (eV)') - ax3.set_ylabel('Density') + ax3.set_xlabel("$E- E_0$ (eV)") + ax3.set_ylabel("Density") - ax4.set_xlabel('$k_B T$ (eV)') - ax4.set_ylabel('Energy (eV)') + ax4.set_xlabel("$k_B T$ (eV)") + ax4.set_ylabel("Energy (eV)") - df = train_df[['temperature', 'energy']] - temperature_bins = pd.cut(df['temperature'], bins=5) + df = train_df[["temperature", "energy"]] + temperature_bins = pd.cut(df["temperature"], bins=5) list_kt = [] list_kt_err = [] list_de = [] list_de_err = [] for temp_bin, group_df in df.groupby(temperature_bins): - relative_energies = group_df['energy'].values - e0 - kB_temperatures = kelvin_in_ev * group_df['temperature'].values - ax3.hist(relative_energies, **common_params, label=f'$T \\in$ {temp_bin} K') + relative_energies = group_df["energy"].values - e0 + kB_temperatures = kelvin_in_ev * group_df["temperature"].values + ax3.hist(relative_energies, **common_params, label=f"$T \\in$ {temp_bin} K") list_kt.append(np.mean(kB_temperatures)) list_kt_err.append(np.std(kB_temperatures)) list_de.append(np.mean(relative_energies)) list_de_err.append(np.std(relative_energies)) - ax4.errorbar(list_kt, list_de, xerr=list_kt_err, yerr=list_de_err, fmt='o', label='Data') + ax4.errorbar( + list_kt, list_de, xerr=list_kt_err, yerr=list_de_err, fmt="o", label="Data" + ) list_kt_fit = np.linspace(0.01, 0.04, 101) - list_de_ideal_fit = 3. * number_of_atoms * list_kt_fit - ax4.plot(list_kt_fit, list_de_ideal_fit, 'r-', label='$E-E_0 = 3 N k_B T$') + list_de_ideal_fit = 3.0 * number_of_atoms * list_kt_fit + ax4.plot(list_kt_fit, list_de_ideal_fit, "r-", label="$E-E_0 = 3 N k_B T$") fit = np.polyfit(list_kt, list_de, deg=1) m = fit[0] b = fit[1] list_de_fit = np.poly1d(fit)(list_kt_fit) - ax4.plot(list_kt_fit, list_de_fit, 'g-', label=f'$E-E_0 = m k_B T + b$\n$m = {m:4.2f}, b={b:4.2f}$ eV') + ax4.plot( + list_kt_fit, + list_de_fit, + "g-", + label=f"$E-E_0 = m k_B T + b$\n$m = {m:4.2f}, b={b:4.2f}$ eV", + ) ax3.legend(loc=0) ax4.legend(loc=0) fig2.tight_layout() - fig2.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_temperature_bins_analysis.png")) + fig2.savefig( + ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_temperature_bins_analysis.png") + ) fig3 = plt.figure(figsize=PLEASANT_FIG_SIZE) ax5 = fig3.add_subplot(111) - ax5.hist2d(kelvin_in_ev * df['temperature'], df['energy'], bins=100) - ax5.set_xlabel('$k_B T$ (eV)') - ax5.set_ylabel('$E$ (eV)') + ax5.hist2d(kelvin_in_ev * df["temperature"], df["energy"], bins=100) + ax5.set_xlabel("$k_B T$ (eV)") + ax5.set_ylabel("$E$ (eV)") list_kb_T = kelvin_in_ev * np.linspace(100, 500, 101) list_e = e0 + 3 * number_of_atoms * list_kb_T - ax5.plot(list_kb_T, list_e, 'r-', label='$E = E_0 + 3 N k_B T$') + ax5.plot(list_kb_T, list_e, "r-", label="$E = E_0 + 3 N k_B T$") ax5.legend(loc=0) fig3.tight_layout() - fig3.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_energy_temperature_2D_histogram.png")) + fig3.savefig( + ANALYSIS_RESULTS_DIR.joinpath( + f"{dataset_name}_energy_temperature_2D_histogram.png" + ) + ) fig4 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig4.suptitle('Total Energy and Potential Energy') + fig4.suptitle("Total Energy and Potential Energy") ax6 = fig4.add_subplot(111) - ax6.hist(train_df['potential_energy'].values, **common_params, color='green', label='Potential Energy') - ax6.hist(train_df['energy'].values, **common_params, color='red', label='Total Energy') - ax6.set_xlabel('Energy (eV)') - ax6.set_ylabel('Density') + ax6.hist( + train_df["potential_energy"].values, + **common_params, + color="green", + label="Potential Energy", + ) + ax6.hist( + train_df["energy"].values, **common_params, color="red", label="Total Energy" + ) + ax6.set_xlabel("Energy (eV)") + ax6.set_ylabel("Density") ax6.legend(loc=0) fig4.tight_layout() - fig4.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_potential_vs_total_energy.png")) + fig4.savefig( + ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_potential_vs_total_energy.png") + ) diff --git a/experiments/dataset_analysis/si_diffusion_v1_analysis.py b/experiments/dataset_analysis/si_diffusion_v1_analysis.py index 81c1e1f4..a1be7ad4 100644 --- a/experiments/dataset_analysis/si_diffusion_v1_analysis.py +++ b/experiments/dataset_analysis/si_diffusion_v1_analysis.py @@ -2,17 +2,22 @@ This script computes and plots different features of a dataset used to train a diffusion model. """ + import os from typing import Dict import matplotlib.pyplot as plt import numpy as np import pandas as pd -from crystal_diffusion import ANALYSIS_RESULTS_DIR, DATA_DIR -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.data.parse_lammps_outputs import parse_lammps_output -DATASET_NAME = 'si_diffusion_v1' +from diffusion_for_multi_scale_molecular_dynamics import (ANALYSIS_RESULTS_DIR, + DATA_DIR) +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.data.parse_lammps_outputs import \ + parse_lammps_output + +DATASET_NAME = "si_diffusion_v1" def read_lammps_run(run_path: str) -> pd.DataFrame: @@ -24,10 +29,14 @@ def read_lammps_run(run_path: str) -> pd.DataFrame: Returns: output as """ - dump_file = [d for d in os.listdir(run_path) if 'dump' in d] - thermo_file = [d for d in os.listdir(run_path) if 'thermo' in d] + dump_file = [d for d in os.listdir(run_path) if "dump" in d] + thermo_file = [d for d in os.listdir(run_path) if "thermo" in d] - df = parse_lammps_output(os.path.join(run_path, dump_file[0]), os.path.join(run_path, thermo_file[0]), None) + df = parse_lammps_output( + os.path.join(run_path, dump_file[0]), + os.path.join(run_path, thermo_file[0]), + None, + ) return df @@ -42,26 +51,40 @@ def compute_metrics_for_a_run(df: pd.DataFrame) -> Dict[str, pd.Series]: metrics evaluated at each MD step organized in a dict """ metrics = {} - metrics['energy'] = df['energy'] - force_norm_mean = df.apply(lambda row: np.mean([np.sqrt(fx**2 + fy**2 + fz**2) for fx, fy, fz in - zip(row['fx'], row['fy'], row['fz'])]), axis=1) - metrics['force_norm_average'] = force_norm_mean - - x0s = df['x'][0] - y0s = df['y'][0] - z0s = df['z'][0] - - square_displacement = df.apply(lambda row: [(x - x0) ** 2 + (y - y0) ** 2 + (z - z0) ** 2 for x, y, z, x0, y0, z0 in - zip(row['x'], row['y'], row['z'], x0s, y0s, z0s)], axis=1) - - metrics['root_mean_square_displacement'] = square_displacement.apply(lambda row: np.sqrt(np.mean(row))) - - metrics['std_displacement'] = np.std(square_displacement.apply(np.sqrt)) + metrics["energy"] = df["energy"] + force_norm_mean = df.apply( + lambda row: np.mean( + [ + np.sqrt(fx**2 + fy**2 + fz**2) + for fx, fy, fz in zip(row["fx"], row["fy"], row["fz"]) + ] + ), + axis=1, + ) + metrics["force_norm_average"] = force_norm_mean + + x0s = df["x"][0] + y0s = df["y"][0] + z0s = df["z"][0] + + square_displacement = df.apply( + lambda row: [ + (x - x0) ** 2 + (y - y0) ** 2 + (z - z0) ** 2 + for x, y, z, x0, y0, z0 in zip(row["x"], row["y"], row["z"], x0s, y0s, z0s) + ], + axis=1, + ) + + metrics["root_mean_square_displacement"] = square_displacement.apply( + lambda row: np.sqrt(np.mean(row)) + ) + + metrics["std_displacement"] = np.std(square_displacement.apply(np.sqrt)) return metrics -def plot_metrics_runs(dataset_name: str, mode: str = 'train'): +def plot_metrics_runs(dataset_name: str, mode: str = "train"): """Compute and plot metrics for a dataset made up of several MD runs. Args: @@ -71,8 +94,13 @@ def plot_metrics_runs(dataset_name: str, mode: str = 'train'): assert mode in ["train", "valid"], f"Mode should be train or valid. Got {mode}" dataset_path = os.path.join(DATA_DIR, dataset_name) - list_runs = [d for d in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, d)) - and d.startswith(f"{mode}_run") and not d.endswith('backup')] + list_runs = [ + d + for d in os.listdir(dataset_path) + if os.path.isdir(os.path.join(dataset_path, d)) + and d.startswith(f"{mode}_run") + and not d.endswith("backup") + ] metrics = {} for run in list_runs: @@ -82,7 +110,9 @@ def plot_metrics_runs(dataset_name: str, mode: str = 'train'): plt.style.use(PLOT_STYLE_PATH) - fig, axs = plt.subplots(4, 1, figsize=(PLEASANT_FIG_SIZE[0], 4 * PLEASANT_FIG_SIZE[1])) + fig, axs = plt.subplots( + 4, 1, figsize=(PLEASANT_FIG_SIZE[0], 4 * PLEASANT_FIG_SIZE[1]) + ) fig.suptitle("MD runs properties") # energy @@ -100,10 +130,10 @@ def plot_metrics_runs(dataset_name: str, mode: str = 'train'): legend = [] for k, m in metrics.items(): - axs[0].plot(m['energy'], '-', lw=2) - axs[1].plot(m['force_norm_average'], ':', lw=2) - axs[2].plot(m['root_mean_square_displacement'], lw=2) - axs[3].plot(m['std_displacement'], lw=2) + axs[0].plot(m["energy"], "-", lw=2) + axs[1].plot(m["force_norm_average"], ":", lw=2) + axs[2].plot(m["root_mean_square_displacement"], lw=2) + axs[3].plot(m["std_displacement"], lw=2) legend.append(k) for ax in axs: @@ -112,14 +142,16 @@ def plot_metrics_runs(dataset_name: str, mode: str = 'train'): fig.tight_layout() - fig.savefig(ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_{mode}_analysis.png"), dpi=300) + fig.savefig( + ANALYSIS_RESULTS_DIR.joinpath(f"{dataset_name}_{mode}_analysis.png"), dpi=300 + ) def main(): """Analyze training and validation set of a dataset.""" - plot_metrics_runs(DATASET_NAME, mode='train') - plot_metrics_runs(DATASET_NAME, mode='valid') + plot_metrics_runs(DATASET_NAME, mode="train") + plot_metrics_runs(DATASET_NAME, mode="valid") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py b/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py index 209cd31e..1403ffa4 100644 --- a/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py +++ b/experiments/diffusion_mace_harmonic_data/ad_hoc_experiments_with_various_score_networks.py @@ -4,28 +4,33 @@ import matplotlib.pyplot as plt import pytorch_lightning as pl import torch -from crystal_diffusion.callbacks.callback_loader import create_all_callbacks -from crystal_diffusion.models.optimizer import OptimizerParameters -from crystal_diffusion.models.position_diffusion_lightning_model import ( +from pytorch_lightning.loggers import TensorBoardLogger +from torch.utils.data import DataLoader + +from diffusion_for_multi_scale_molecular_dynamics.analysis import \ + PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.callbacks.callback_loader import \ + create_all_callbacks +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import \ + SamplingParameters +from diffusion_for_multi_scale_molecular_dynamics.models.optimizer import \ + OptimizerParameters +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import ( PositionDiffusionLightningModel, PositionDiffusionParameters) -from crystal_diffusion.models.scheduler import \ +from diffusion_for_multi_scale_molecular_dynamics.models.scheduler import \ ReduceLROnPlateauSchedulerParameters -from crystal_diffusion.models.score_networks.diffusion_mace_score_network import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.diffusion_mace_score_network import \ DiffusionMACEScoreNetworkParameters -from crystal_diffusion.models.score_networks.mace_score_network import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mace_score_network import \ MACEScoreNetworkParameters -from crystal_diffusion.models.score_networks.mlp_score_network import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.mlp_score_network import \ MLPScoreNetworkParameters -from crystal_diffusion.models.score_networks.score_prediction_head import \ +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.score_prediction_head import \ MaceEquivariantScorePredictionHeadParameters -from crystal_diffusion.namespace import CARTESIAN_FORCES, RELATIVE_COORDINATES -from pytorch_lightning.loggers import TensorBoardLogger -from src.crystal_diffusion.analysis import PLOT_STYLE_PATH -from src.crystal_diffusion.generators.position_generator import \ - SamplingParameters -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters -from torch.utils.data import DataLoader - +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, RELATIVE_COORDINATES) +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters from experiments.analysis.analytic_score import (get_exact_samples, get_relative_harmonic_energy) from experiments.diffusion_mace_harmonic_data.analysis_callbacks import \ @@ -37,7 +42,7 @@ plt.style.use(PLOT_STYLE_PATH) # model = 'mlp' -model = 'diffusion_mace' +model = "diffusion_mace" # model = 'mace_plus_prediction_head' run_id = 1 @@ -48,50 +53,56 @@ batch_size = 1000 gradient_clip_val = 0.0 -spring_constant = 1000. +spring_constant = 1000.0 dim = 16 num_interactions = 2 correlation = 2 number_of_mlp_layers = 0 -common_mace_parameters = dict(number_of_atoms=number_of_atoms, - r_max=5.0, - num_bessel=8, - num_polynomial_cutoff=5, - max_ell=2, - interaction_cls="RealAgnosticResidualInteractionBlock", - interaction_cls_first="RealAgnosticInteractionBlock", - num_interactions=num_interactions, - hidden_irreps=f"{dim}x0e + {dim}x1o + {dim}x2e", - avg_num_neighbors=1, - correlation=correlation, - gate="silu", - radial_MLP=[dim, dim, dim], - radial_type="gaussian") - - -prediction_head_parameters = MaceEquivariantScorePredictionHeadParameters(time_embedding_irreps="32x0e", - gate="silu", - number_of_layers=3) - -mace_score_network_parameters = MACEScoreNetworkParameters(prediction_head_parameters=prediction_head_parameters, - MLP_irreps=f"{dim}x0e", - **common_mace_parameters) - -diffusion_mace_score_network_parameters = DiffusionMACEScoreNetworkParameters(mlp_irreps=f"{dim}x0e", - number_of_mlp_layers=number_of_mlp_layers, - **common_mace_parameters) - -mlp_score_network_parameters = MLPScoreNetworkParameters(number_of_atoms=number_of_atoms, - n_hidden_dimensions=3, - hidden_dimensions_size=64) - -if model == 'diffusion_mace': +common_mace_parameters = dict( + number_of_atoms=number_of_atoms, + r_max=5.0, + num_bessel=8, + num_polynomial_cutoff=5, + max_ell=2, + interaction_cls="RealAgnosticResidualInteractionBlock", + interaction_cls_first="RealAgnosticInteractionBlock", + num_interactions=num_interactions, + hidden_irreps=f"{dim}x0e + {dim}x1o + {dim}x2e", + avg_num_neighbors=1, + correlation=correlation, + gate="silu", + radial_MLP=[dim, dim, dim], + radial_type="gaussian", +) + + +prediction_head_parameters = MaceEquivariantScorePredictionHeadParameters( + time_embedding_irreps="32x0e", gate="silu", number_of_layers=3 +) + +mace_score_network_parameters = MACEScoreNetworkParameters( + prediction_head_parameters=prediction_head_parameters, + MLP_irreps=f"{dim}x0e", + **common_mace_parameters, +) + +diffusion_mace_score_network_parameters = DiffusionMACEScoreNetworkParameters( + mlp_irreps=f"{dim}x0e", + number_of_mlp_layers=number_of_mlp_layers, + **common_mace_parameters, +) + +mlp_score_network_parameters = MLPScoreNetworkParameters( + number_of_atoms=number_of_atoms, n_hidden_dimensions=3, hidden_dimensions_size=64 +) + +if model == "diffusion_mace": score_network_parameters = diffusion_mace_score_network_parameters -elif model == 'mace_plus_prediction_head': +elif model == "mace_plus_prediction_head": score_network_parameters = mace_score_network_parameters -elif model == 'mlp': +elif model == "mlp": score_network_parameters = mlp_score_network_parameters @@ -99,19 +110,25 @@ acell = 5.5 noise_parameters = NoiseParameters(total_time_steps=100, sigma_min=0.001, sigma_max=0.5) -sampling_parameters = SamplingParameters(spatial_dimension=3, - number_of_corrector_steps=1, - number_of_atoms=number_of_atoms, - number_of_samples=1000, - sample_every_n_epochs=1, - record_samples=True, - cell_dimensions=[acell, acell, acell]) - - -optimizer_parameters = OptimizerParameters(name='adamw', learning_rate=0.001, weight_decay=0.0) +sampling_parameters = SamplingParameters( + spatial_dimension=3, + number_of_corrector_steps=1, + number_of_atoms=number_of_atoms, + number_of_samples=1000, + sample_every_n_epochs=1, + record_samples=True, + cell_dimensions=[acell, acell, acell], +) + + +optimizer_parameters = OptimizerParameters( + name="adamw", learning_rate=0.001, weight_decay=0.0 +) scheduler_parameters = ReduceLROnPlateauSchedulerParameters(factor=0.5, patience=10) -loss_monitoring_parameters = dict(number_of_bins=50, sample_every_n_epochs=1, spatial_dimension=spatial_dimension) +loss_monitoring_parameters = dict( + number_of_bins=50, sample_every_n_epochs=1, spatial_dimension=spatial_dimension +) callback_parameters = dict(loss_monitoring=loss_monitoring_parameters) @@ -120,49 +137,79 @@ output_directory = str(Path(__file__).parent / "output" / experiment_name) -tensorboard_logger = TensorBoardLogger(save_dir=output_directory, - default_hp_metric=False, - name=experiment_name, - version=0, - ) +tensorboard_logger = TensorBoardLogger( + save_dir=output_directory, + default_hp_metric=False, + name=experiment_name, + version=0, +) -if __name__ == '__main__': +if __name__ == "__main__": torch.manual_seed(42) # torch.set_default_dtype(torch.float64) box = acell * torch.ones(spatial_dimension) if number_of_atoms == 1: - equilibrium_relative_coordinates = (0.5 * torch.ones(spatial_dimension)).unsqueeze(0) + equilibrium_relative_coordinates = ( + 0.5 * torch.ones(spatial_dimension) + ).unsqueeze(0) elif number_of_atoms == 2: - equilibrium_relative_coordinates = torch.stack([0.25 * torch.ones(spatial_dimension), - 0.75 * torch.ones(spatial_dimension)]) + equilibrium_relative_coordinates = torch.stack( + [0.25 * torch.ones(spatial_dimension), 0.75 * torch.ones(spatial_dimension)] + ) - inverse_covariance = torch.zeros(number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension) + inverse_covariance = torch.zeros( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) for atom_i in range(number_of_atoms): for alpha in range(spatial_dimension): inverse_covariance[atom_i, alpha, atom_i, alpha] = spring_constant - diffusion_sampling_callback = HarmonicEnergyDiffusionSamplingCallback(noise_parameters, - sampling_parameters, - equilibrium_relative_coordinates, - inverse_covariance, - output_directory) + diffusion_sampling_callback = HarmonicEnergyDiffusionSamplingCallback( + noise_parameters, + sampling_parameters, + equilibrium_relative_coordinates, + inverse_covariance, + output_directory, + ) # Create a dataloader - train_samples = get_exact_samples(equilibrium_relative_coordinates, inverse_covariance, dataset_size) + train_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, dataset_size + ) # train_energies = get_samples_harmonic_energy(equilibrium_relative_coordinates, inverse_covariance, train_samples) - train_energies = get_relative_harmonic_energy(train_samples, equilibrium_relative_coordinates, spring_constant) - train_dataset = [{RELATIVE_COORDINATES: x, 'box': box, 'potential_energy': e, CARTESIAN_FORCES: torch.zeros_like(x)} - for x, e in zip(train_samples, train_energies)] + train_energies = get_relative_harmonic_energy( + train_samples, equilibrium_relative_coordinates, spring_constant + ) + train_dataset = [ + { + RELATIVE_COORDINATES: x, + "box": box, + "potential_energy": e, + CARTESIAN_FORCES: torch.zeros_like(x), + } + for x, e in zip(train_samples, train_energies) + ] train_dataloader = DataLoader(train_dataset, batch_size=batch_size) - valid_samples = get_exact_samples(equilibrium_relative_coordinates, inverse_covariance, dataset_size) - valid_energies = get_relative_harmonic_energy(valid_samples, equilibrium_relative_coordinates, spring_constant) + valid_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, dataset_size + ) + valid_energies = get_relative_harmonic_energy( + valid_samples, equilibrium_relative_coordinates, spring_constant + ) # valid_energies = get_samples_harmonic_energy(equilibrium_relative_coordinates, inverse_covariance, valid_samples) - valid_dataset = [{RELATIVE_COORDINATES: x, 'box': box, 'potential_energy': e, CARTESIAN_FORCES: torch.zeros_like(x)} - for x, e in zip(valid_samples, valid_energies)] + valid_dataset = [ + { + RELATIVE_COORDINATES: x, + "box": box, + "potential_energy": e, + CARTESIAN_FORCES: torch.zeros_like(x), + } + for x, e in zip(valid_samples, valid_energies) + ] valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size) diffusion_params = PositionDiffusionParameters( @@ -174,17 +221,22 @@ model = PositionDiffusionLightningModel(diffusion_params) - callbacks_dict = create_all_callbacks(callback_parameters, output_directory=output_directory, verbose=True) + callbacks_dict = create_all_callbacks( + callback_parameters, output_directory=output_directory, verbose=True + ) callbacks = list(callbacks_dict.values()) callbacks.append(diffusion_sampling_callback) - trainer = pl.Trainer(callbacks=callbacks, - max_epochs=max_epochs, - log_every_n_steps=1, - fast_dev_run=False, - gradient_clip_val=gradient_clip_val, - logger=tensorboard_logger, - ) + trainer = pl.Trainer( + callbacks=callbacks, + max_epochs=max_epochs, + log_every_n_steps=1, + fast_dev_run=False, + gradient_clip_val=gradient_clip_val, + logger=tensorboard_logger, + ) - trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=valid_dataloader) + trainer.fit( + model, train_dataloaders=train_dataloader, val_dataloaders=valid_dataloader + ) diff --git a/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py b/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py index 4bdb5e24..a7576c66 100644 --- a/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py +++ b/experiments/diffusion_mace_harmonic_data/analysis_callbacks.py @@ -3,18 +3,21 @@ The callbacks in this module are not designed for 'production'. They are to be used in ad hoc / debugging / analysis experiments. """ + import logging import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion.callbacks.sampling_visualization_callback import \ + +from diffusion_for_multi_scale_molecular_dynamics.analysis import \ + PLOT_STYLE_PATH +from diffusion_for_multi_scale_molecular_dynamics.callbacks.sampling_visualization_callback import \ SamplingVisualizationCallback -from src.crystal_diffusion.analysis import PLOT_STYLE_PATH -from src.crystal_diffusion.generators.position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.position_generator import \ SamplingParameters -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters - +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters from experiments.analysis.analytic_score import get_relative_harmonic_energy logger = logging.getLogger(__name__) @@ -25,26 +28,33 @@ class HarmonicEnergyDiffusionSamplingCallback(SamplingVisualizationCallback): """Callback class to periodically generate samples and log their energies.""" - def __init__(self, noise_parameters: NoiseParameters, - sampling_parameters: SamplingParameters, - equilibrium_relative_coordinates: torch.Tensor, - inverse_covariance: torch.Tensor, - output_directory: str): + def __init__( + self, + noise_parameters: NoiseParameters, + sampling_parameters: SamplingParameters, + equilibrium_relative_coordinates: torch.Tensor, + inverse_covariance: torch.Tensor, + output_directory: str, + ): """Init method.""" super().__init__(noise_parameters, sampling_parameters, output_directory) self.equilibrium_relative_coordinates = equilibrium_relative_coordinates self.inverse_covariance = inverse_covariance - def _compute_oracle_energies(self, batch_relative_coordinates: torch.Tensor) -> np.ndarray: + def _compute_oracle_energies( + self, batch_relative_coordinates: torch.Tensor + ) -> np.ndarray: """Compute energies from samples.""" logger.info("Compute harmonic energy from Oracle") spring_constant = self.inverse_covariance[0, 0, 0, 0] - energies = get_relative_harmonic_energy(batch_relative_coordinates, - self.equilibrium_relative_coordinates, - spring_constant) + energies = get_relative_harmonic_energy( + batch_relative_coordinates, + self.equilibrium_relative_coordinates, + spring_constant, + ) # energies = get_samples_harmonic_energy(self.equilibrium_relative_coordinates, # self.inverse_covariance, @@ -52,11 +62,16 @@ def _compute_oracle_energies(self, batch_relative_coordinates: torch.Tensor) -> return energies.cpu().detach().numpy() @staticmethod - def _plot_energy_histogram(sample_energies: np.ndarray, validation_dataset_energies: np.array, - epoch: int) -> plt.figure: - fig = SamplingVisualizationCallback._plot_energy_histogram(sample_energies, validation_dataset_energies, epoch) + def _plot_energy_histogram( + sample_energies: np.ndarray, validation_dataset_energies: np.array, epoch: int + ) -> plt.figure: + fig = SamplingVisualizationCallback._plot_energy_histogram( + sample_energies, validation_dataset_energies, epoch + ) - fig.suptitle(f'Sampling Unitless Harmonic Potential Energy Distributions\nEpoch {epoch}') + fig.suptitle( + f"Sampling Unitless Harmonic Potential Energy Distributions\nEpoch {epoch}" + ) ax1 = fig.axes[0] - ax1.set_xlabel('Unitless Harmonic Energy') + ax1.set_xlabel("Unitless Harmonic Energy") return fig diff --git a/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py b/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py index b9d5cbc4..03b2e519 100644 --- a/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py +++ b/experiments/diffusion_mace_harmonic_data/overfit_diffusion_mace.py @@ -3,30 +3,31 @@ This script helps in the exploration of the Diffusion MACE architecture, trying different ideas quickly to see if we can overfit the analytical score for a single example. """ + from pathlib import Path import pytorch_lightning as pl import torch -from crystal_diffusion.callbacks.standard_callbacks import CustomProgressBar -from crystal_diffusion.models.score_networks.analytical_score_network import ( +from pytorch_lightning.loggers import TensorBoardLogger +from torch import optim +from torch.utils.data import DataLoader + +from diffusion_for_multi_scale_molecular_dynamics.callbacks.standard_callbacks import \ + CustomProgressBar +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetwork, AnalyticalScoreNetworkParameters) -from crystal_diffusion.models.score_networks.diffusion_mace_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.diffusion_mace_score_network import ( DiffusionMACEScoreNetwork, DiffusionMACEScoreNetworkParameters) -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.samplers.noisy_relative_coordinates_sampler import \ +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.noisy_relative_coordinates_sampler import \ NoisyRelativeCoordinatesSampler -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import ( + ExplodingVarianceSampler, NoiseParameters) +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.tensor_utils import \ +from diffusion_for_multi_scale_molecular_dynamics.utils.tensor_utils import \ broadcast_batch_tensor_to_all_dimensions -from pytorch_lightning.loggers import TensorBoardLogger -from src.crystal_diffusion.samplers.variance_sampler import ( - ExplodingVarianceSampler, NoiseParameters) -from torch import optim -from torch.utils.data import DataLoader - from experiments.analysis.analytic_score import (get_exact_samples, get_unit_cells) @@ -44,21 +45,27 @@ output_directory = root_dir / experiment_name -tensorboard_logger = TensorBoardLogger(save_dir=str(output_directory), - default_hp_metric=False, - name=experiment_name, - version=0, - ) +tensorboard_logger = TensorBoardLogger( + save_dir=str(output_directory), + default_hp_metric=False, + name=experiment_name, + version=0, +) class DevDiffusionMaceLightningModel(pl.LightningModule): """This is a stub lightning module for the purpose of trying to overfit Diffusion Mace to the analytical score.""" - def __init__(self, diffusion_mace_score_network_parameters: DiffusionMACEScoreNetworkParameters): + def __init__( + self, + diffusion_mace_score_network_parameters: DiffusionMACEScoreNetworkParameters, + ): """Init method.""" super().__init__() self.save_hyperparameters(logger=True) - self.sigma_normalized_score_network = DiffusionMACEScoreNetwork(diffusion_mace_score_network_parameters) + self.sigma_normalized_score_network = DiffusionMACEScoreNetwork( + diffusion_mace_score_network_parameters + ) def configure_optimizers(self): """Configure optimizers.""" @@ -69,8 +76,10 @@ def configure_optimizers(self): def training_step(self, batch, batch_idx): """Training step.""" predicted_normalized_scores = self.sigma_normalized_score_network(batch) - target_normalized_scores = batch['TARGET'] - loss = torch.nn.functional.mse_loss(predicted_normalized_scores, target_normalized_scores, reduction="mean") + target_normalized_scores = batch["TARGET"] + loss = torch.nn.functional.mse_loss( + predicted_normalized_scores, target_normalized_scores, reduction="mean" + ) output = dict(loss=loss) self.log("train_step_loss", loss, on_step=True, on_epoch=False, prog_bar=True) return output @@ -98,7 +107,8 @@ def training_step(self, batch, batch_idx): correlation=2, gate="silu", radial_MLP=[dim, dim, dim], - radial_type="gaussian") + radial_type="gaussian", +) max_epochs = 1000 @@ -110,30 +120,39 @@ def training_step(self, batch, batch_idx): variance_sampler = ExplodingVarianceSampler(noise_parameters) -if __name__ == '__main__': +if __name__ == "__main__": torch.manual_seed(42) # ====================== Generate Harmonic potential samples ==================================== - spring_constant = 1000. + spring_constant = 1000.0 box = acell * torch.ones(spatial_dimension) - equilibrium_relative_coordinates = torch.stack([0.25 * torch.ones(spatial_dimension), - 0.75 * torch.ones(spatial_dimension)]) + equilibrium_relative_coordinates = torch.stack( + [0.25 * torch.ones(spatial_dimension), 0.75 * torch.ones(spatial_dimension)] + ) - inverse_covariance = torch.zeros(number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension) + inverse_covariance = torch.zeros( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) for atom_i in range(number_of_atoms): for alpha in range(spatial_dimension): inverse_covariance[atom_i, alpha, atom_i, alpha] = spring_constant # ====================== Generate Random Data ==================================== - x0 = get_exact_samples(equilibrium_relative_coordinates, inverse_covariance, dataset_size) + x0 = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, dataset_size + ) noise_sample = variance_sampler.get_random_noise_sample(dataset_size) - sigmas = broadcast_batch_tensor_to_all_dimensions(batch_values=noise_sample.sigma, final_shape=x0.shape) + sigmas = broadcast_batch_tensor_to_all_dimensions( + batch_values=noise_sample.sigma, final_shape=x0.shape + ) sigmas = torch.ones_like(sigmas) - xt = noisy_relative_coordinates_sampler.get_noisy_relative_coordinates_sample(x0, sigmas) + xt = noisy_relative_coordinates_sampler.get_noisy_relative_coordinates_sample( + x0, sigmas + ) cm = 0.5 * xt.sum(dim=1) xt = map_relative_coordinates_to_unit_cell(xt - cm) @@ -145,19 +164,26 @@ def training_step(self, batch, batch_idx): spatial_dimension=spatial_dimension, kmax=1, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - inverse_covariance=inverse_covariance) + inverse_covariance=inverse_covariance, + ) - analytical_score_network = AnalyticalScoreNetwork(analytical_score_network_parameters) + analytical_score_network = AnalyticalScoreNetwork( + analytical_score_network_parameters + ) - unit_cells = get_unit_cells(acell, spatial_dimension, number_of_samples=dataset_size) + unit_cells = get_unit_cells( + acell, spatial_dimension, number_of_samples=dataset_size + ) sigma = noise_sample.sigma.reshape(-1, 1) sigma = sigma_value * torch.ones_like(sigma) - augmented_batch = {NOISY_RELATIVE_COORDINATES: xt, - TIME: noise_sample.time.reshape(-1, 1), - NOISE: sigma, - UNIT_CELL: unit_cells, - CARTESIAN_FORCES: torch.zeros_like(xt)} + augmented_batch = { + NOISY_RELATIVE_COORDINATES: xt, + TIME: noise_sample.time.reshape(-1, 1), + NOISE: sigma, + UNIT_CELL: unit_cells, + CARTESIAN_FORCES: torch.zeros_like(xt), + } analytical_scores = analytical_score_network(augmented_batch) @@ -176,11 +202,12 @@ def training_step(self, batch, batch_idx): callbacks = [CustomProgressBar()] - trainer = pl.Trainer(callbacks=callbacks, - max_epochs=max_epochs, - log_every_n_steps=1, - fast_dev_run=False, - logger=tensorboard_logger, - ) + trainer = pl.Trainer( + callbacks=callbacks, + max_epochs=max_epochs, + log_every_n_steps=1, + fast_dev_run=False, + logger=tensorboard_logger, + ) trainer.fit(model, train_dataloaders=train_dataloader) diff --git a/experiments/generators/sde_generator_sanity_check.py b/experiments/generators/sde_generator_sanity_check.py index 075f441b..ff140d37 100644 --- a/experiments/generators/sde_generator_sanity_check.py +++ b/experiments/generators/sde_generator_sanity_check.py @@ -6,16 +6,18 @@ import einops import numpy as np import torch -from crystal_diffusion.generators.sde_position_generator import ( +from matplotlib import pyplot as plt + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.generators.sde_position_generator import ( ExplodingVarianceSDEPositionGenerator, SDESamplingParameters) -from crystal_diffusion.models.score_networks.analytical_score_network import ( +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks.analytical_score_network import ( AnalyticalScoreNetworkParameters, TargetScoreBasedAnalyticalScoreNetwork) -from crystal_diffusion.utils.basis_transformations import \ +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ map_relative_coordinates_to_unit_cell -from matplotlib import pyplot as plt -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters - from experiments.analysis.analytic_score.utils import get_exact_samples from experiments.generators import GENERATOR_SANITY_CHECK_DIRECTORY @@ -29,33 +31,42 @@ def __init__(self, equilibrium_relative_coordinates: torch.Tensor): """Init method.""" self.equilibrium_relative_coordinates = equilibrium_relative_coordinates - def compute_displacements(self, batch_relative_coordinates: torch.Tensor) -> np.ndarray: + def compute_displacements( + self, batch_relative_coordinates: torch.Tensor + ) -> np.ndarray: """Compute displacements.""" return (batch_relative_coordinates - equilibrium_relative_coordinates).flatten() -def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma_d: float, number_of_samples: int): +def generate_exact_samples( + equilibrium_relative_coordinates: torch.Tensor, + sigma_d: float, + number_of_samples: int, +): """Generate Gaussian samples about the equilibrium relative coordinates.""" - variance_parameter = sigma_d ** 2 + variance_parameter = sigma_d**2 number_of_atoms, spatial_dimension = equilibrium_relative_coordinates.shape nd = number_of_atoms * spatial_dimension inverse_covariance = torch.diag(torch.ones(nd)) / variance_parameter - inverse_covariance = inverse_covariance.reshape(number_of_atoms, spatial_dimension, - number_of_atoms, spatial_dimension) + inverse_covariance = inverse_covariance.reshape( + number_of_atoms, spatial_dimension, number_of_atoms, spatial_dimension + ) - exact_samples = get_exact_samples(equilibrium_relative_coordinates, inverse_covariance, number_of_samples) + exact_samples = get_exact_samples( + equilibrium_relative_coordinates, inverse_covariance, number_of_samples + ) exact_samples = map_relative_coordinates_to_unit_cell(exact_samples) return exact_samples -device = torch.device('cpu') +device = torch.device("cpu") number_of_atoms = 1 spatial_dimension = 1 kmax = 8 -acell = 1. +acell = 1.0 cell_dimensions = [acell] output_dir = GENERATOR_SANITY_CHECK_DIRECTORY / "figures" @@ -67,21 +78,29 @@ def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma sigma_min = 0.001 total_time_steps = 101 -if __name__ == '__main__': +if __name__ == "__main__": unit_cell = torch.diag(torch.tensor(cell_dimensions)) - batched_unit_cells = einops.repeat(unit_cell, "d1 d2 -> b d1 d2", b=number_of_samples) + batched_unit_cells = einops.repeat( + unit_cell, "d1 d2 -> b d1 d2", b=number_of_samples + ) equilibrium_relative_coordinates = torch.tensor([[0.5]]) - displacement_calculator = DisplacementCalculator(equilibrium_relative_coordinates=equilibrium_relative_coordinates) + displacement_calculator = DisplacementCalculator( + equilibrium_relative_coordinates=equilibrium_relative_coordinates + ) for sigma_d in list_sigma_d: - exact_samples = generate_exact_samples(equilibrium_relative_coordinates, - sigma_d=sigma_d, - number_of_samples=number_of_samples) + exact_samples = generate_exact_samples( + equilibrium_relative_coordinates, + sigma_d=sigma_d, + number_of_samples=number_of_samples, + ) - exact_samples_displacements = displacement_calculator.compute_displacements(exact_samples) + exact_samples_displacements = displacement_calculator.compute_displacements( + exact_samples + ) score_network_parameters = AnalyticalScoreNetworkParameters( number_of_atoms=number_of_atoms, @@ -89,63 +108,85 @@ def generate_exact_samples(equilibrium_relative_coordinates: torch.Tensor, sigma use_permutation_invariance=False, kmax=kmax, equilibrium_relative_coordinates=equilibrium_relative_coordinates, - variance_parameter=sigma_d**2) - - sigma_normalized_score_network = TargetScoreBasedAnalyticalScoreNetwork(score_network_parameters) - - sampling_parameters = SDESamplingParameters(method='euler', - adaptative=False, - absolute_solver_tolerance=1.0e-7, - relative_solver_tolerance=1.0e-5, - number_of_atoms=number_of_atoms, - spatial_dimension=spatial_dimension, - number_of_samples=number_of_samples, - sample_batchsize=number_of_samples, - cell_dimensions=cell_dimensions, - record_samples=True) - - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - sigma_min=sigma_min, - sigma_max=0.5) - - generator = ExplodingVarianceSDEPositionGenerator(noise_parameters=noise_parameters, - sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) - - generated_samples = generator.sample(number_of_samples, - device=device, - unit_cell=batched_unit_cells) + variance_parameter=sigma_d**2, + ) + + sigma_normalized_score_network = TargetScoreBasedAnalyticalScoreNetwork( + score_network_parameters + ) + + sampling_parameters = SDESamplingParameters( + method="euler", + adaptative=False, + absolute_solver_tolerance=1.0e-7, + relative_solver_tolerance=1.0e-5, + number_of_atoms=number_of_atoms, + spatial_dimension=spatial_dimension, + number_of_samples=number_of_samples, + sample_batchsize=number_of_samples, + cell_dimensions=cell_dimensions, + record_samples=True, + ) + + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, sigma_min=sigma_min, sigma_max=0.5 + ) + + generator = ExplodingVarianceSDEPositionGenerator( + noise_parameters=noise_parameters, + sampling_parameters=sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) + + generated_samples = generator.sample( + number_of_samples, device=device, unit_cell=batched_unit_cells + ) sampled_x = generated_samples[:, 0, 0] trajectory_data = generator.sample_trajectory_recorder.standardize_data( - generator.sample_trajectory_recorder.data) + generator.sample_trajectory_recorder.data + ) - times = trajectory_data['time'] - x = trajectory_data['relative_coordinates'][:, :, 0, 0] + times = trajectory_data["time"] + x = trajectory_data["relative_coordinates"][:, :, 0, 0] fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle("1 Atom in 1D, Analytical Score, SDE solver" - f"\n $\\sigma_d$ = {sigma_d:4.3f}, time steps = {total_time_steps}") + fig.suptitle( + "1 Atom in 1D, Analytical Score, SDE solver" + f"\n $\\sigma_d$ = {sigma_d:4.3f}, time steps = {total_time_steps}" + ) ax1 = fig.add_subplot(121) ax1.set_title("Subset of Sampling Trajectories") for i in range(50): - ax1.plot(times, x[i, :], '-', color='gray', alpha=0.5) + ax1.plot(times, x[i, :], "-", color="gray", alpha=0.5) ax1.set_xlim(1, 0) - ax1.set_xlabel('Diffusion Time') - ax1.set_ylabel('$x(t)$') + ax1.set_xlabel("Diffusion Time") + ax1.set_ylabel("$x(t)$") - generated_samples_displacements = displacement_calculator.compute_displacements(generated_samples) + generated_samples_displacements = displacement_calculator.compute_displacements( + generated_samples + ) ax2 = fig.add_subplot(122) ax2.set_title("Distribution of Displacements") common_params = dict(density=True, bins=101, histtype="stepfilled", alpha=0.25) - ax2.hist(exact_samples_displacements, **common_params, label='Exact Samples Displacements', color='green') - ax2.hist(generated_samples_displacements, **common_params, label='Generated Samples Displacements', color='red') - - ax2.set_xlabel('$x - x_0$') - ax2.set_ylabel('Density') - ax2.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=8) + ax2.hist( + exact_samples_displacements, + **common_params, + label="Exact Samples Displacements", + color="green", + ) + ax2.hist( + generated_samples_displacements, + **common_params, + label="Generated Samples Displacements", + color="red", + ) + + ax2.set_xlabel("$x - x_0$") + ax2.set_ylabel("Density") + ax2.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=8) fig.tight_layout() fig.savefig(output_dir / f"sde_solution_sigma_d={sigma_d}.png") diff --git a/experiments/sampling_sota_model/plot_repaint_score_trajectories.py b/experiments/sampling_sota_model/plot_repaint_score_trajectories.py index c9f6252a..c8e054ea 100644 --- a/experiments/sampling_sota_model/plot_repaint_score_trajectories.py +++ b/experiments/sampling_sota_model/plot_repaint_score_trajectories.py @@ -6,9 +6,12 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion.utils.logging_utils import setup_analysis_logger from einops import einops -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger logger = logging.getLogger(__name__) setup_analysis_logger() @@ -19,17 +22,19 @@ energy_pickle_path = base_path / "energies.pt" -unconstrained_energy_pickle_path = Path("/Users/bruno/courtois/draw_sota_samples/1x1x1/LANGEVIN/energy_samples.pt") +unconstrained_energy_pickle_path = Path( + "/Users/bruno/courtois/draw_sota_samples/1x1x1/LANGEVIN/energy_samples.pt" +) number_of_constrained_coordinates = 9 plt.style.use(PLOT_STYLE_PATH) -if __name__ == '__main__': +if __name__ == "__main__": logger.info("Extracting data artifacts") - with open(data_pickle_path, 'rb') as fd: - recorded_data = torch.load(fd, map_location=torch.device('cpu')) + with open(data_pickle_path, "rb") as fd: + recorded_data = torch.load(fd, map_location=torch.device("cpu")) repaint_energies = torch.load(energy_pickle_path) unconstrained_energies = torch.load(unconstrained_energy_pickle_path) @@ -37,72 +42,113 @@ # ============================================================== logger.info("Plotting energy distributions") fig0 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig0.suptitle('Comparing Unconstrained and Repaint Sample Energy Distributions') + fig0.suptitle("Comparing Unconstrained and Repaint Sample Energy Distributions") common_params = dict(density=True, bins=80, histtype="stepfilled", alpha=0.25) ax1 = fig0.add_subplot(111) - ax1.hist(unconstrained_energies, **common_params, label='Unconstrained Energies', color='green') - ax1.hist(repaint_energies, **common_params, label='Repaint Energies', color='red') - - ax1.set_xlabel('Energy (eV)') - ax1.set_ylabel('Density') - ax1.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=12) - ax1.set_yscale('log') + ax1.hist( + unconstrained_energies, + **common_params, + label="Unconstrained Energies", + color="green", + ) + ax1.hist(repaint_energies, **common_params, label="Repaint Energies", color="red") + + ax1.set_xlabel("Energy (eV)") + ax1.set_ylabel("Density") + ax1.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=12) + ax1.set_yscale("log") fig0.tight_layout() - fig0.savefig(base_path / "comparing_energy_distributions_unconstrained_vs_repaint.png") + fig0.savefig( + base_path / "comparing_energy_distributions_unconstrained_vs_repaint.png" + ) plt.close(fig0) - sampling_times = recorded_data['time'] - relative_coordinates = recorded_data['relative_coordinates'] - batch_flat_relative_coordinates = einops.rearrange(relative_coordinates, "b t n d -> b t (n d)") + sampling_times = recorded_data["time"] + relative_coordinates = recorded_data["relative_coordinates"] + batch_flat_relative_coordinates = einops.rearrange( + relative_coordinates, "b t n d -> b t (n d)" + ) - normalized_scores = recorded_data['normalized_scores'] - batch_flat_normalized_scores = einops.rearrange(normalized_scores, "b t n d -> b t (n d)") + normalized_scores = recorded_data["normalized_scores"] + batch_flat_normalized_scores = einops.rearrange( + normalized_scores, "b t n d -> b t (n d)" + ) logger.info("Plotting scores along trajectories") fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle('Root Mean Square Normalized Scores Along Sample Trajectories') - rms_norm_score = (batch_flat_normalized_scores ** 2).mean(dim=-1).sqrt().numpy() + fig.suptitle("Root Mean Square Normalized Scores Along Sample Trajectories") + rms_norm_score = (batch_flat_normalized_scores**2).mean(dim=-1).sqrt().numpy() constrained_rms_norm_score = ( - (batch_flat_normalized_scores[:, :, :number_of_constrained_coordinates] ** 2).mean(dim=-1).sqrt().numpy()) + (batch_flat_normalized_scores[:, :, :number_of_constrained_coordinates] ** 2) + .mean(dim=-1) + .sqrt() + .numpy() + ) free_rms_norm_score = ( - (batch_flat_normalized_scores[:, :, number_of_constrained_coordinates:] ** 2).mean(dim=-1).sqrt().numpy()) + (batch_flat_normalized_scores[:, :, number_of_constrained_coordinates:] ** 2) + .mean(dim=-1) + .sqrt() + .numpy() + ) ax1 = fig.add_subplot(221) ax2 = fig.add_subplot(222) ax3 = fig.add_subplot(223) ax4 = fig.add_subplot(224) - for ax, scores in zip([ax1, ax2, ax3, ax4], - [rms_norm_score, rms_norm_score, constrained_rms_norm_score, free_rms_norm_score]): + for ax, scores in zip( + [ax1, ax2, ax3, ax4], + [ + rms_norm_score, + rms_norm_score, + constrained_rms_norm_score, + free_rms_norm_score, + ], + ): for y in scores[::10]: - ax.plot(sampling_times, y, '-', color='gray', alpha=0.2, label='__nolabel__') + ax.plot( + sampling_times, y, "-", color="gray", alpha=0.2, label="__nolabel__" + ) list_quantiles = [0.0, 0.10, 0.5, 1.0] - list_colors = ['green', 'yellow', 'orange', 'red'] + list_colors = ["green", "yellow", "orange", "red"] for q, c in zip(list_quantiles, list_colors): energy_quantile = np.quantile(repaint_energies, q) idx = np.argmin(np.abs(repaint_energies - energy_quantile)) e = repaint_energies[idx] - for ax, scores in zip([ax1, ax2, ax3, ax4], - [rms_norm_score, rms_norm_score, constrained_rms_norm_score, free_rms_norm_score]): - ax.plot(sampling_times, scores[idx], '-', - color=c, alpha=1., label=f'Q = {100 * q:2.0f}%, Energy: {e:5.1f}') + for ax, scores in zip( + [ax1, ax2, ax3, ax4], + [ + rms_norm_score, + rms_norm_score, + constrained_rms_norm_score, + free_rms_norm_score, + ], + ): + ax.plot( + sampling_times, + scores[idx], + "-", + color=c, + alpha=1.0, + label=f"Q = {100 * q:2.0f}%, Energy: {e:5.1f}", + ) for ax in [ax1, ax2, ax3, ax4]: - ax.set_xlabel('Diffusion Time') - ax.set_ylabel(r'$\sqrt{\langle (\sigma(t) S_{\theta} )^2 \rangle}$') + ax.set_xlabel("Diffusion Time") + ax.set_ylabel(r"$\sqrt{\langle (\sigma(t) S_{\theta} )^2 \rangle}$") ax.set_xlim(1, 0) for ax in [ax2, ax3, ax4]: - ax.set_yscale('log') + ax.set_yscale("log") ax1.set_title("All Coordinates (Not Log Scale)") - ax1.set_ylim(ymin=0.) + ax1.set_ylim(ymin=0.0) ax2.set_title("All Coordinates") ax3.set_title("Restrained Coordinates") diff --git a/experiments/sampling_sota_model/repaint_with_sota_score.py b/experiments/sampling_sota_model/repaint_with_sota_score.py index 9a96d80d..935781b3 100644 --- a/experiments/sampling_sota_model/repaint_with_sota_score.py +++ b/experiments/sampling_sota_model/repaint_with_sota_score.py @@ -6,15 +6,20 @@ import numpy as np import torch import yaml -from crystal_diffusion import DATA_DIR -from crystal_diffusion.generators.constrained_langevin_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics import DATA_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.generators.constrained_langevin_generator import ( ConstrainedLangevinGenerator, ConstrainedLangevinGeneratorParameters) -from crystal_diffusion.models.instantiate_diffusion_model import \ +from diffusion_for_multi_scale_molecular_dynamics.models.instantiate_diffusion_model import \ load_diffusion_model -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger logger = logging.getLogger(__name__) setup_analysis_logger() @@ -30,7 +35,7 @@ plt.style.use(PLOT_STYLE_PATH) -device = torch.device('cuda') +device = torch.device("cuda") number_of_samples = 1000 total_time_steps = 100 @@ -42,24 +47,24 @@ number_of_atoms, spatial_dimension = 8, 3 atom_types = np.ones(number_of_atoms, dtype=int) -constrained_relative_coordinates = np.array([[0.5, 0.5, 0.25], - [0.5, 0.5, 0.5], - [0.5, 0.5, 0.75]], dtype=np.float32) +constrained_relative_coordinates = np.array( + [[0.5, 0.5, 0.25], [0.5, 0.5, 0.5], [0.5, 0.5, 0.75]], dtype=np.float32 +) -if __name__ == '__main__': +if __name__ == "__main__": logger.info("Setting up parameters") unit_cells = torch.Tensor(box).repeat(number_of_samples, 1, 1).to(device) - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - sigma_min=0.001, - sigma_max=0.5) + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, sigma_min=0.001, sigma_max=0.5 + ) logger.info("Loading state dict") - with open(str(state_dict_path), 'rb') as fd: + with open(str(state_dict_path), "rb") as fd: state_dict = torch.load(fd) - with open(str(config_path), 'r') as fd: + with open(str(config_path), "r") as fd: hyper_params = yaml.load(fd, Loader=yaml.FullLoader) logger.info("Instantiate model") pl_model = load_diffusion_model(hyper_params) @@ -76,24 +81,28 @@ number_of_samples=number_of_samples, cell_dimensions=3 * [acell], constrained_relative_coordinates=constrained_relative_coordinates, - record_samples=True) + record_samples=True, + ) position_generator = ConstrainedLangevinGenerator( noise_parameters=noise_parameters, sampling_parameters=sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + sigma_normalized_score_network=sigma_normalized_score_network, + ) logger.info("Drawing constrained samples") with torch.no_grad(): - samples = position_generator.sample(number_of_samples=number_of_samples, - device=device, - unit_cell=unit_cells) + samples = position_generator.sample( + number_of_samples=number_of_samples, device=device, unit_cell=unit_cells + ) batch_relative_positions = samples.cpu().numpy() batch_positions = np.dot(batch_relative_positions, box) - position_generator.sample_trajectory_recorder.write_to_pickle(repaint_dir / "repaint_trajectories.pkl") + position_generator.sample_trajectory_recorder.write_to_pickle( + repaint_dir / "repaint_trajectories.pkl" + ) logger.info("Compute energy from Oracle") list_energy = [] @@ -102,11 +111,13 @@ lammps_work_directory.mkdir(exist_ok=True) for idx, positions in enumerate(batch_positions): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - atom_types, - tmp_work_dir=str(lammps_work_directory), - pair_coeff_dir=DATA_DIR) + energy, forces = get_energy_and_forces_from_lammps( + positions, + box, + atom_types, + tmp_work_dir=str(lammps_work_directory), + pair_coeff_dir=DATA_DIR, + ) list_energy.append(energy) src = os.path.join(lammps_work_directory, "dump.yaml") dst = os.path.join(lammps_work_directory, f"dump_{idx}.yaml") @@ -119,16 +130,16 @@ logger.info("Plotting energy distributions") fig = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig.suptitle('Energy Distribution for Repaint Structures,') + fig.suptitle("Energy Distribution for Repaint Structures,") common_params = dict(density=True, bins=50, histtype="stepfilled", alpha=0.25) ax1 = fig.add_subplot(111) - ax1.hist(energies, **common_params, label='Sampled Energies', color='red') + ax1.hist(energies, **common_params, label="Sampled Energies", color="red") - ax1.set_xlabel('Energy (eV)') - ax1.set_ylabel('Density') - ax1.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=12) + ax1.set_xlabel("Energy (eV)") + ax1.set_ylabel("Density") + ax1.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=12) fig.tight_layout() fig.savefig(repaint_dir / f"energy_samples_repaint_{number_of_atoms}_atoms.png") plt.close(fig) diff --git a/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py b/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py index 2867eb28..be33daab 100644 --- a/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py +++ b/experiments/sampling_sota_model/sota_score_sampling_and_plotting.py @@ -8,19 +8,25 @@ import numpy as np import torch import yaml -from crystal_diffusion import DATA_DIR -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.ode_position_generator import ( +from einops import einops + +from diffusion_for_multi_scale_molecular_dynamics import DATA_DIR +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.models.instantiate_diffusion_model import \ +from diffusion_for_multi_scale_molecular_dynamics.models.instantiate_diffusion_model import \ load_diffusion_model -from crystal_diffusion.oracle.lammps import get_energy_and_forces_from_lammps -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from einops import einops -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.oracle.lammps import \ + get_energy_and_forces_from_lammps +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger logger = logging.getLogger(__name__) setup_analysis_logger() @@ -30,18 +36,20 @@ state_dict_path = model_dir / "last_model-epoch=199-step=019600_state_dict.ckpt" config_path = model_dir / "config_backup.yaml" -SOTA_SCORE_RESULTS_DIR = Path("/home/mila/r/rousseab/experiments/draw_sota_samples/figures") +SOTA_SCORE_RESULTS_DIR = Path( + "/home/mila/r/rousseab/experiments/draw_sota_samples/figures" +) SOTA_SCORE_RESULTS_DIR.mkdir(exist_ok=True) plt.style.use(PLOT_STYLE_PATH) -device = torch.device('cuda') +device = torch.device("cuda") # Change these parameters as needed! # sampling_algorithm = 'ode' -sampling_algorithm = 'langevin' +sampling_algorithm = "langevin" spatial_dimension = 3 number_of_atoms = 8 @@ -54,13 +62,13 @@ total_time_steps = 100 number_of_corrector_steps = 1 -if __name__ == '__main__': +if __name__ == "__main__": logger.info("Loading state dict") - with open(str(state_dict_path), 'rb') as fd: + with open(str(state_dict_path), "rb") as fd: state_dict = torch.load(fd) - with open(str(config_path), 'r') as fd: + with open(str(config_path), "r") as fd: hyper_params = yaml.load(fd, Loader=yaml.FullLoader) logger.info("Instantiate model") pl_model = load_diffusion_model(hyper_params) @@ -71,37 +79,42 @@ sigma_normalized_score_network = pl_model.sigma_normalized_score_network logger.info("Setting up parameters") - noise_parameters = NoiseParameters(total_time_steps=total_time_steps, - sigma_min=0.001, - sigma_max=0.5) - - if sampling_algorithm == 'ode': - ode_sampling_parameters = ODESamplingParameters(spatial_dimension=spatial_dimension, - number_of_atoms=number_of_atoms, - number_of_samples=number_of_samples, - cell_dimensions=[acell, acell, acell], - record_samples=True, - absolute_solver_tolerance=1.0e-5, - relative_solver_tolerance=1.0e-5) - - position_generator = ( - ExplodingVarianceODEPositionGenerator(noise_parameters=noise_parameters, - sampling_parameters=ode_sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network)) - - elif sampling_algorithm == 'langevin': + noise_parameters = NoiseParameters( + total_time_steps=total_time_steps, sigma_min=0.001, sigma_max=0.5 + ) + + if sampling_algorithm == "ode": + ode_sampling_parameters = ODESamplingParameters( + spatial_dimension=spatial_dimension, + number_of_atoms=number_of_atoms, + number_of_samples=number_of_samples, + cell_dimensions=[acell, acell, acell], + record_samples=True, + absolute_solver_tolerance=1.0e-5, + relative_solver_tolerance=1.0e-5, + ) + + position_generator = ExplodingVarianceODEPositionGenerator( + noise_parameters=noise_parameters, + sampling_parameters=ode_sampling_parameters, + sigma_normalized_score_network=sigma_normalized_score_network, + ) + + elif sampling_algorithm == "langevin": pc_sampling_parameters = PredictorCorrectorSamplingParameters( number_of_corrector_steps=number_of_corrector_steps, spatial_dimension=spatial_dimension, number_of_atoms=number_of_atoms, number_of_samples=number_of_samples, cell_dimensions=[acell, acell, acell], - record_samples=True) + record_samples=True, + ) position_generator = LangevinGenerator( noise_parameters=noise_parameters, sampling_parameters=pc_sampling_parameters, - sigma_normalized_score_network=sigma_normalized_score_network) + sigma_normalized_score_network=sigma_normalized_score_network, + ) # Draw some samples, create some plots unit_cells = torch.Tensor(box).repeat(number_of_samples, 1, 1).to(device) @@ -109,9 +122,9 @@ logger.info("Drawing samples") with torch.no_grad(): - samples = position_generator.sample(number_of_samples=number_of_samples, - device=device, - unit_cell=unit_cells) + samples = position_generator.sample( + number_of_samples=number_of_samples, device=device, unit_cell=unit_cells + ) batch_relative_positions = samples.cpu().numpy() batch_positions = np.dot(batch_relative_positions, box) @@ -120,15 +133,17 @@ logger.info("Compute energy from Oracle") with tempfile.TemporaryDirectory() as lammps_work_directory: for idx, positions in enumerate(batch_positions): - energy, forces = get_energy_and_forces_from_lammps(positions, - box, - atom_types, - tmp_work_dir=lammps_work_directory, - pair_coeff_dir=DATA_DIR) + energy, forces = get_energy_and_forces_from_lammps( + positions, + box, + atom_types, + tmp_work_dir=lammps_work_directory, + pair_coeff_dir=DATA_DIR, + ) list_energy.append(energy) energies = np.array(list_energy) - if sampling_algorithm == 'ode': + if sampling_algorithm == "ode": # Plot the ODE parameters logger.info("Plotting ODE parameters") times = torch.linspace(0, 1, 1001) @@ -136,19 +151,19 @@ ode_prefactor = position_generator._get_ode_prefactor(sigmas) fig0 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig0.suptitle('ODE parameters') + fig0.suptitle("ODE parameters") ax1 = fig0.add_subplot(121) ax2 = fig0.add_subplot(122) - ax1.set_title('$\\sigma$ Parameter') - ax2.set_title('$\\gamma$ Parameter') - ax1.plot(times, sigmas, '-') - ax2.plot(times, ode_prefactor, '-') + ax1.set_title("$\\sigma$ Parameter") + ax2.set_title("$\\gamma$ Parameter") + ax1.plot(times, sigmas, "-") + ax2.plot(times, ode_prefactor, "-") - ax1.set_ylabel('$\\sigma(t)$') - ax2.set_ylabel('$\\gamma(t)$') + ax1.set_ylabel("$\\sigma(t)$") + ax2.set_ylabel("$\\gamma(t)$") for ax in [ax1, ax2]: - ax.set_xlabel('Diffusion Time') + ax.set_xlabel("Diffusion Time") ax.set_xlim([-0.01, 1.01]) fig0.tight_layout() @@ -157,103 +172,124 @@ logger.info("Extracting data artifacts") raw_data = position_generator.sample_trajectory_recorder.data - recorded_data = position_generator.sample_trajectory_recorder.standardize_data(raw_data) + recorded_data = position_generator.sample_trajectory_recorder.standardize_data( + raw_data + ) - sampling_times = recorded_data['time'].cpu() - relative_coordinates = recorded_data['relative_coordinates'] - batch_flat_relative_coordinates = einops.rearrange(relative_coordinates, "b t n d -> b t (n d)") + sampling_times = recorded_data["time"].cpu() + relative_coordinates = recorded_data["relative_coordinates"] + batch_flat_relative_coordinates = einops.rearrange( + relative_coordinates, "b t n d -> b t (n d)" + ) - normalized_scores = recorded_data['normalized_scores'] - batch_flat_normalized_scores = einops.rearrange(normalized_scores, "b t n d -> b t (n d)") + normalized_scores = recorded_data["normalized_scores"] + batch_flat_normalized_scores = einops.rearrange( + normalized_scores, "b t n d -> b t (n d)" + ) # ============================================================================ logger.info("Plotting relative coordinates trajectories") fig1 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig1.suptitle('Sampling Trajectories') + fig1.suptitle("Sampling Trajectories") ax = fig1.add_subplot(111) - ax.set_xlabel('Diffusion Time') - ax.set_ylabel('Raw Relative Coordinate') + ax.set_xlabel("Diffusion Time") + ax.set_ylabel("Raw Relative Coordinate") ax.yaxis.tick_right() - ax.spines['top'].set_visible(True) - ax.spines['right'].set_visible(True) - ax.spines['bottom'].set_visible(True) - ax.spines['left'].set_visible(True) + ax.spines["top"].set_visible(True) + ax.spines["right"].set_visible(True) + ax.spines["bottom"].set_visible(True) + ax.spines["left"].set_visible(True) alpha = 1.0 for flat_relative_coordinates in batch_flat_relative_coordinates[::100]: for i in range(number_of_atoms * spatial_dimension): coordinate = flat_relative_coordinates[:, i].cpu() - ax.plot(sampling_times, coordinate, '-', color='b', alpha=alpha) + ax.plot(sampling_times, coordinate, "-", color="b", alpha=alpha) alpha = 0.05 ax.set_xlim([1.01, -0.01]) - fig1.savefig(SOTA_SCORE_RESULTS_DIR / f"sampling_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png") + fig1.savefig( + SOTA_SCORE_RESULTS_DIR + / f"sampling_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig1) # ======================== Figure 2 ====================================== logger.info("Plotting scores along trajectories") fig2 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig2.suptitle('Root Mean Squared Normalized Scores Along Sample Trajectories') - rms_norm_score = (batch_flat_normalized_scores ** 2).mean(dim=-1).sqrt().cpu().numpy() + fig2.suptitle("Root Mean Squared Normalized Scores Along Sample Trajectories") + rms_norm_score = (batch_flat_normalized_scores**2).mean(dim=-1).sqrt().cpu().numpy() ax1 = fig2.add_subplot(121) ax2 = fig2.add_subplot(122) for y in rms_norm_score[::10]: for ax in [ax1, ax2]: - ax.plot(sampling_times, y, '-', color='gray', alpha=0.2, label='__nolabel__') + ax.plot( + sampling_times, y, "-", color="gray", alpha=0.2, label="__nolabel__" + ) list_quantiles = [0.0, 0.10, 0.5, 1.0] - list_colors = ['green', 'yellow', 'orange', 'red'] + list_colors = ["green", "yellow", "orange", "red"] for q, c in zip(list_quantiles, list_colors): energy_quantile = np.quantile(energies, q) idx = np.argmin(np.abs(energies - energy_quantile)) e = energies[idx] for ax in [ax1, ax2]: - ax.plot(sampling_times, rms_norm_score[idx], '-', - color=c, alpha=1., label=f'{100 * q:2.0f}% Percentile Energy: {e:5.1f}') + ax.plot( + sampling_times, + rms_norm_score[idx], + "-", + color=c, + alpha=1.0, + label=f"{100 * q:2.0f}% Percentile Energy: {e:5.1f}", + ) for ax in [ax1, ax2]: - ax.set_xlabel('Diffusion Time') - ax.set_ylabel(r'$ \sqrt{\langle (\sigma(t) S_{\theta} )^2\rangle}$') + ax.set_xlabel("Diffusion Time") + ax.set_ylabel(r"$ \sqrt{\langle (\sigma(t) S_{\theta} )^2\rangle}$") ax.set_xlim(1, 0) ax1.legend(loc=0, fontsize=6) - ax1.set_yscale('log') + ax1.set_yscale("log") fig2.savefig( - SOTA_SCORE_RESULTS_DIR / f"sampling_score_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png") + SOTA_SCORE_RESULTS_DIR + / f"sampling_score_trajectories_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig2) # ======================== Figure 3 ====================================== logger.info("Plotting Marginal distribution in 2D") fig3 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig3.suptitle('Sampling Marginal Distributions') - ax1 = fig3.add_subplot(131, aspect='equal') - ax2 = fig3.add_subplot(132, aspect='equal') - ax3 = fig3.add_subplot(133, aspect='equal') + fig3.suptitle("Sampling Marginal Distributions") + ax1 = fig3.add_subplot(131, aspect="equal") + ax2 = fig3.add_subplot(132, aspect="equal") + ax3 = fig3.add_subplot(133, aspect="equal") - xs = einops.rearrange(samples, 'b n d -> (b n) d').cpu() - ax1.set_title('XY Projection') - ax1.plot(xs[:, 0], xs[:, 1], 'ro', alpha=0.5, mew=0, label='ODE Solver') + xs = einops.rearrange(samples, "b n d -> (b n) d").cpu() + ax1.set_title("XY Projection") + ax1.plot(xs[:, 0], xs[:, 1], "ro", alpha=0.5, mew=0, label="ODE Solver") - ax2.set_title('XZ Projection') - ax2.plot(xs[:, 0], xs[:, 2], 'ro', alpha=0.5, mew=0, label='ODE Solver') + ax2.set_title("XZ Projection") + ax2.plot(xs[:, 0], xs[:, 2], "ro", alpha=0.5, mew=0, label="ODE Solver") - ax3.set_title('YZ Projection') - ax3.plot(xs[:, 1], xs[:, 2], 'ro', alpha=0.5, mew=0, label='ODE Solver') + ax3.set_title("YZ Projection") + ax3.plot(xs[:, 1], xs[:, 2], "ro", alpha=0.5, mew=0, label="ODE Solver") for ax in [ax1, ax2, ax3]: ax.set_xlim(-0.01, 1.01) ax.set_ylim(-0.01, 1.01) - ax.vlines(x=[0, 1], ymin=0, ymax=1, color='k', lw=2) - ax.hlines(y=[0, 1], xmin=0, xmax=1, color='k', lw=2) + ax.vlines(x=[0, 1], ymin=0, ymax=1, color="k", lw=2) + ax.hlines(y=[0, 1], xmin=0, xmax=1, color="k", lw=2) ax1.legend(loc=0) fig3.tight_layout() fig3.savefig( - SOTA_SCORE_RESULTS_DIR / f"marginal_2D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png") + SOTA_SCORE_RESULTS_DIR + / f"marginal_2D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig3) # ======================== Figure 4 ====================================== @@ -262,42 +298,46 @@ ax1 = fig4.add_subplot(131) ax2 = fig4.add_subplot(132) ax3 = fig4.add_subplot(133) - fig4.suptitle('Comparing Sampling and Expected Marginal Distributions') + fig4.suptitle("Comparing Sampling and Expected Marginal Distributions") - common_params = dict(histtype='stepfilled', alpha=0.5, bins=50) + common_params = dict(histtype="stepfilled", alpha=0.5, bins=50) - ax1.hist(xs[:, 0], **common_params, facecolor='r') - ax2.hist(xs[:, 1], **common_params, facecolor='r') - ax3.hist(xs[:, 2], **common_params, facecolor='r') + ax1.hist(xs[:, 0], **common_params, facecolor="r") + ax2.hist(xs[:, 1], **common_params, facecolor="r") + ax3.hist(xs[:, 2], **common_params, facecolor="r") - ax1.set_xlabel('X') - ax2.set_xlabel('Y') - ax3.set_xlabel('Z') + ax1.set_xlabel("X") + ax2.set_xlabel("Y") + ax3.set_xlabel("Z") for ax in [ax1, ax2, ax3]: ax.set_xlim(-0.01, 1.01) fig4.tight_layout() fig4.savefig( - SOTA_SCORE_RESULTS_DIR / f"marginal_1D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png") + SOTA_SCORE_RESULTS_DIR + / f"marginal_1D_distributions_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig4) # ======================== Figure 5 ====================================== logger.info("Plotting energy distributions") fig5 = plt.figure(figsize=PLEASANT_FIG_SIZE) - fig5.suptitle(f'Energy Distribution, sampling algorithm {sampling_algorithm}') + fig5.suptitle(f"Energy Distribution, sampling algorithm {sampling_algorithm}") common_params = dict(density=True, bins=50, histtype="stepfilled", alpha=0.25) ax1 = fig5.add_subplot(111) - ax1.hist(energies, **common_params, label='Sampled Energies', color='red') + ax1.hist(energies, **common_params, label="Sampled Energies", color="red") - ax1.set_xlabel('Energy (eV)') - ax1.set_ylabel('Density') - ax1.legend(loc='upper right', fancybox=True, shadow=True, ncol=1, fontsize=12) + ax1.set_xlabel("Energy (eV)") + ax1.set_ylabel("Density") + ax1.legend(loc="upper right", fancybox=True, shadow=True, ncol=1, fontsize=12) fig5.tight_layout() fig5.savefig( - SOTA_SCORE_RESULTS_DIR / f"energy_samples_{sampling_algorithm}_{number_of_atoms}_atoms.png") + SOTA_SCORE_RESULTS_DIR + / f"energy_samples_{sampling_algorithm}_{number_of_atoms}_atoms.png" + ) plt.close(fig5) logger.info("Done!") diff --git a/experiments/score_stability_analysis/draw_samples_from_equilibrium.py b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py index 303f92fb..aa54a67a 100644 --- a/experiments/score_stability_analysis/draw_samples_from_equilibrium.py +++ b/experiments/score_stability_analysis/draw_samples_from_equilibrium.py @@ -4,20 +4,25 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion.generators.langevin_generator import LangevinGenerator -from crystal_diffusion.generators.ode_position_generator import ( + +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.generators.langevin_generator import \ + LangevinGenerator +from diffusion_for_multi_scale_molecular_dynamics.generators.ode_position_generator import ( ExplodingVarianceODEPositionGenerator, ODESamplingParameters) -from crystal_diffusion.generators.predictor_corrector_position_generator import \ +from diffusion_for_multi_scale_molecular_dynamics.generators.predictor_corrector_position_generator import \ PredictorCorrectorSamplingParameters -from crystal_diffusion.generators.sde_position_generator import ( +from diffusion_for_multi_scale_molecular_dynamics.generators.sde_position_generator import ( ExplodingVarianceSDEPositionGenerator, SDESamplingParameters) -from crystal_diffusion.models.position_diffusion_lightning_model import \ +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import \ PositionDiffusionLightningModel -from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters - +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger from experiments.analysis.analytic_score.exploring_langevin_generator.generate_sample_energies import \ EnergyCalculator from experiments.analysis.analytic_score.utils import get_silicon_supercell @@ -30,6 +35,7 @@ class ForcedStartingPointLangevinGenerator(LangevinGenerator): """Langevin Generator with Forced Starting point.""" + def __init__( self, noise_parameters: NoiseParameters, @@ -56,6 +62,7 @@ def initialize(self, number_of_samples: int): class ForcedStartingPointODEPositionGenerator(ExplodingVarianceODEPositionGenerator): """Forced starting point ODE position generator.""" + def __init__( self, noise_parameters: NoiseParameters, @@ -82,6 +89,7 @@ def initialize(self, number_of_samples: int): class ForcedStartingPointSDEPositionGenerator(ExplodingVarianceSDEPositionGenerator): """Forced Starting Point SDE position generator.""" + def __init__( self, noise_parameters: NoiseParameters, diff --git a/experiments/score_stability_analysis/plot_hessian_eigenvalues.py b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py index b9508d33..543a9deb 100644 --- a/experiments/score_stability_analysis/plot_hessian_eigenvalues.py +++ b/experiments/score_stability_analysis/plot_hessian_eigenvalues.py @@ -5,15 +5,19 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion.models.position_diffusion_lightning_model import \ - PositionDiffusionLightningModel -from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters from torch.func import jacrev from tqdm import tqdm +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import \ + PositionDiffusionLightningModel +from diffusion_for_multi_scale_molecular_dynamics.samplers.exploding_variance import \ + ExplodingVariance +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger from experiments import get_normalized_score_function from experiments.analysis.analytic_score.utils import get_silicon_supercell diff --git a/experiments/score_stability_analysis/plot_score_norm.py b/experiments/score_stability_analysis/plot_score_norm.py index 1cfd8399..706e6bbd 100644 --- a/experiments/score_stability_analysis/plot_score_norm.py +++ b/experiments/score_stability_analysis/plot_score_norm.py @@ -4,16 +4,20 @@ import matplotlib.pyplot as plt import numpy as np import torch -from crystal_diffusion.models.position_diffusion_lightning_model import \ - PositionDiffusionLightningModel -from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from crystal_diffusion.utils.basis_transformations import \ - map_relative_coordinates_to_unit_cell -from crystal_diffusion.utils.logging_utils import setup_analysis_logger -from src.crystal_diffusion.analysis import PLEASANT_FIG_SIZE, PLOT_STYLE_PATH -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters from tqdm import tqdm +from diffusion_for_multi_scale_molecular_dynamics.analysis import ( + PLEASANT_FIG_SIZE, PLOT_STYLE_PATH) +from diffusion_for_multi_scale_molecular_dynamics.models.position_diffusion_lightning_model import \ + PositionDiffusionLightningModel +from diffusion_for_multi_scale_molecular_dynamics.samplers.exploding_variance import \ + ExplodingVariance +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters +from diffusion_for_multi_scale_molecular_dynamics.utils.basis_transformations import \ + map_relative_coordinates_to_unit_cell +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger from experiments import create_fixed_time_normalized_score_function from experiments.analysis.analytic_score.utils import get_silicon_supercell @@ -23,8 +27,10 @@ setup_analysis_logger() -checkpoint_path = ("/home/mila/r/rousseab/scratch/experiments/oct2_egnn_1x1x1/run1/" - "output/last_model/last_model-epoch=049-step=039100.ckpt") +checkpoint_path = ( + "/home/mila/r/rousseab/scratch/experiments/oct2_egnn_1x1x1/run1/" + "output/last_model/last_model-epoch=049-step=039100.ckpt" +) spatial_dimension = 3 number_of_atoms = 8 @@ -67,7 +73,7 @@ dv = equilibrium_relative_coordinates[0] - equilibrium_relative_coordinates[1] direction[0] = -0.5 * dv direction[1] = 0.5 * dv - list_delta = torch.linspace(0., 2.0, 201) + list_delta = torch.linspace(0.0, 2.0, 201) relative_coordinates = [] for delta in list_delta: diff --git a/experiments/score_stability_analysis/util.py b/experiments/score_stability_analysis/util.py index 842107de..373cc761 100644 --- a/experiments/score_stability_analysis/util.py +++ b/experiments/score_stability_analysis/util.py @@ -3,12 +3,15 @@ import einops import torch -from crystal_diffusion.models.score_networks import ScoreNetwork -from crystal_diffusion.namespace import (CARTESIAN_FORCES, NOISE, - NOISY_RELATIVE_COORDINATES, TIME, - UNIT_CELL) -from crystal_diffusion.samplers.exploding_variance import ExplodingVariance -from src.crystal_diffusion.samplers.variance_sampler import NoiseParameters + +from diffusion_for_multi_scale_molecular_dynamics.models.score_networks import \ + ScoreNetwork +from diffusion_for_multi_scale_molecular_dynamics.namespace import ( + CARTESIAN_FORCES, NOISE, NOISY_RELATIVE_COORDINATES, TIME, UNIT_CELL) +from diffusion_for_multi_scale_molecular_dynamics.samplers.exploding_variance import \ + ExplodingVariance +from diffusion_for_multi_scale_molecular_dynamics.samplers.variance_sampler import \ + NoiseParameters def get_normalized_score_function( From 87dcdcd3919d22f7f6bad4d50b189e9471d7caf0 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 07:44:20 -0400 Subject: [PATCH 21/29] the toml file and uv lock --- pyproject.toml | 59 + uv.lock | 4932 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 4991 insertions(+) create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..49ab5007 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,59 @@ +[project] +name = "diffusion-for-multi-scale-molecular-dynamics" +version = "0.1.0" +description = "This project trains and samples from diffusion models adapted to periodic materials." +readme = "README.md" +requires-python = ">=3.10" +authors = [ + {name = "Simon Blackburn", email = "simon.blackburn@mila.quebec"}, + {name = "Bruno Rousseau", email = "rousseau.bruno@mila.quebec"}, +] +dependencies = [ + "black>=24.10.0", + "comet-ml>=3.47.0", + "datasets==2.17.1", + "deepdiff==7.0.1", + "einops==0.8.0", + "flake8-docstrings==1.6.0", + "flake8==4.0.1", + "gitpython==3.1.27", + "hydra-core==1.3.2", + "isort==5.13.2", + 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22/29] Updating the README file. --- README.md | 28 ++++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 0e7cece3..1506ba4b 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,30 @@ -# Crystal Diffusion +# Diffusion for Multiscale Molecular Dynamics -Replace this line with a short description about your project! +This project implements diffusion-based generative models for periodic atomistic systems (i.e., crystals). +The aim of this project is to be able to train such a model and use it as part of an active learning +framework, where a Machine Learning Interatomic Potential (MLIP) is continually fine-tuned on labels obtained +from a costly oracle such as Density Functional Theory (DFT). The generative model is used to create +few-atom configurations that are computationally tractable for the costly oracle by inpainting +around problematic atomic configurations. -## Instructions to setup the project +# Instructions to set up the project for development -### Install the dependencies: +## Creating a Virtual Environment +The project dependencies are stated in the `pyproject.toml` file. They must be installed in a virtual environment. + +### uv +The recommended way of creating a virtual environment is to use the tool [`uv`](https://docs.astral.sh/uv/). +Once `uv` is installed locally, the virtual environment can be created with the command + + uv sync + +which will install the exact environment described in file `uv.lock`. The environment can then be activated with +the command + + source .venv/bin/activate + + +### pip First, activate a virtual environment (recommended). Install the package in `editable` mode so you can modify the source directly: From ccdf8fba720041437c3d4d31dc3c16092ac75cf3 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 10:36:11 -0400 Subject: [PATCH 23/29] Udpated the README file. --- README.md | 193 +++++++++++------------------------------------------- 1 file changed, 38 insertions(+), 155 deletions(-) diff --git a/README.md b/README.md index 1506ba4b..bba7f8cb 100644 --- a/README.md +++ b/README.md @@ -23,182 +23,65 @@ the command source .venv/bin/activate - ### pip -First, activate a virtual environment (recommended). -Install the package in `editable` mode so you can modify the source directly: - - pip install -e . - -To add new dependencies, simply add them to the setup.py. - -### Setup pre-commit hooks: -These hooks will: -* validate flake8 before any commit -* check that jupyter notebook outputs have been stripped - - cd .git/hooks/ && ln -s ../../hooks/pre-commit . - -Alternatively, to only lint files that have been staged in git, use - cd .git/hooks/ && ln -s ../../hooks/pre-commit_staged pre-commit - -### Setup Continuous Integration - -Continuous integration will run the following: -- Unit tests under `tests`. -- End-to-end test under `exmaples/local`. -- `flake8` to check the code syntax. -- Checks on documentation presence and format (using `sphinx`). - -We support the GitHub Actions for running CI. - -Github actions are already configured in `.github/workflows/tests.yml`. -Github actions are already enabled by default when using Github, so, when -pushing to github, they will be executed automatically for pull requests to -`main` and to `develop`. - -## Running the code - -### Run the tests -Just run (from the root folder): - - pytest - -### Run the code/examples. -Note that the code should already compile at this point. - -Running examples can be found under the `examples` folder. - -In particular, you will find examples for: -* local machine (e.g., your laptop). -* a slurm cluster. - -For both these cases, there is the possibility to run with or without Orion. -(Orion is a hyper-parameter search tool - see https://github.com/Epistimio/orion - -that is already configured in this project) - -#### Run locally - -For example, to run on your local machine without Orion: - - cd examples/local - sh run.sh - -This will run a simple MLP on a simple toy task: sum 5 float numbers. -You should see an almost perfect loss of 0 after a few epochs. -Note you have a new `output` folder which contains models and a summary of results: -* best_model: the best model checkpoint during training -* last_model: the last model checkpoint during training -* lightning_logs: contains the tensorboard logs. +Alternatively, `pip` can be used to create the virtual environment. Assuming `python` and `pip` are already +available on the system, create a virtual env folder in the root directory with the command -To view tensorboard logs, simply run: + python -m venv ./.venv/ - tensorboard --logdir output +The environment must then be activated with the command -#### Run on a remote cluster (with Slurm) - -First, bring you project on the cluster (assuming you didn't create your -project directly there). To do so, simply login on the cluster and git -clone your project: - - git clone git@github.com:${GITHUB_USERNAME}/${PROJECT_NAME}.git + source .venv/bin/activate -Then activate your virtual env, and install the dependencies: +and the environment should be created in `editable` mode so that the source code can be modified directly, - cd crystal_diffusion pip install -e . -To run with Slurm, just: - - cd examples/slurm - sh run.sh - -Check the log to see that you got an almost perfect loss (i.e., 0). - -#### Measure GPU time (and others) on the Mila cluster - -You can track down the GPU time (and other resources) of your jobs by -associating a tag to the job (when using `sbatch`). -To associate a tag to a job, replace `my_tag` with a proper tag, -and uncomment the line (i.e., remove one #) from the line: - - ##SBATCH --wckey=my_tag - -This line is inside the file `examples/slurm_mila/to_submit.sh`. - -To get a sumary for a particular tag, just run: - - sacct --allusers --wckeys=my_tag --format=JobID,JobName,Start,Elapsed -X -P --delimiter=',' - -(again, remember to change `my_tag` into the real tag name) - -#### GPU profiling on the Mila cluster +### Testing the Installation +The test suite should be executed to make sure that the environment is properly installed. After activating the +environment, the tests can be executed with the command -It can be useful to monitor and profile how you utilise your GPU (usage, memory, etc.). For the -time being, you can only monitor your profiling in real-time from the Mila cluster, i.e. while your -experiments are running. To monitor your GPU, you need to setup port-forwarding on the host your -experiments are running on. This can be done in the following way: + pytest [--quick] -Once you have launched your job on the mila cluster, open the log for your current experiment: +the argument `--quick` is optional; a few tests are a bit slow and will be skipped if this flag is present. -`head logs/crystal_diffusion/__.err` -You should see printed in the first few lines the hostname of your machine, e.g., +## Setting up the Development Tools +Various automated tools are used in order to maintain a high quality code base. These must be set up +to start developing. We use -``` -INFO:crystal_diffusion.utils.logging_utils:Experiment info: -hostname: leto35 -git code hash: a51bfc5447d188bd6d31fac3afbd5757650ef524 -data folder: ../data -data folder (abs): /network/tmp1/bronzimi/20191105_cookiecutter/crystal_diffusion/examples/data -``` +* [flake8](https://flake8.pycqa.org/en/latest/) to insure the coding style is enforced. +* [isort](https://pycqa.github.io/isort/) to insure that the imports are properly ordered. +* [black](https://pypi.org/project/black/) to format the code. -In a separate shell on your local computer, run the following command: +### Setup pre-commit hooks +The folder `./hooks/` contain "pre-commit" scripts that automate various checks at every git commit. +These hooks will +* validate flake8 before any commit; +* check that jupyter notebook outputs have been stripped. -`ssh -L 19999:.server.mila.quebec:19999 @login.server.mila.quebec -p 2222` +There are two pre-commit scripts, `pre-commit` and `pre-commit_staged`. Both scripts perform the same +checks; `pre-commit` is used within the continuous integration (CI), while `pre-commit_staged` only +validates files that are staged in git, making it more developer-friendly. -where `` is your user name on the Mila cluster and `` is the name of the machine your job is currenty running on (`leto35` in our example). You can then navigate your local browser to `http://localhost:19999/` to view the ressources being used on the cluster and monitor your job. You should see something like this: +To activate the pre-commit hook, -![image](https://user-images.githubusercontent.com/18450628/88088807-fe2acd80-cb58-11ea-8ab2-bd090e8a826c.png) -{%- endif %} - -#### Run with Orion on the Slurm cluster - -This example will run orion for 2 trials (see the orion config file). -To do so, go into `examples/slurm_orion`. -Here you can find the orion config file (`orion_config.yaml`), as well as the config -file (`config.yaml`) for your project (that contains the hyper-parameters). - -In general, you will want to run Orion in parallel over N slurm jobs. -To do so, simply run `sh run.sh` N times. - -When Orion has completed the trials, you will find the orion db file. - -You will also find the output of your experiments in `orion_working_dir`, which -will contain a folder for every trial. -Inside these folders, you can find the models (the best one and the last one), the config file with -the hyper-parameters for this trial, and the log file. - -You can check orion status with the following commands: -(to be run from `examples/slurm_orion`) - - export ORION_DB_ADDRESS='orion_db.pkl' - export ORION_DB_TYPE='pickleddb' - orion status - orion info --name my_exp - -### Building docs: + cd .git/hooks/ && ln -s ../../hooks/pre-commit . -Documentation is built using sphinx. It will automatically document all functions based on docstrings. -To automatically generate docs for your project, navigate to the `docs` folder and build the documentation: +Alternatively, to only lint files that have been staged in git, use - cd docs - make html + cd .git/hooks/ && ln -s ../../hooks/pre-commit_staged pre-commit -To view the docs locally, open `docs/_build/html/index.html` in your browser. +### Setup Continuous Integration +GitHub Actions is used for running continuous integration (CI) checks. +The cI workflow is described in `.github/workflows/ci.yml`. -## YOUR PROJECT README: +CI will run the following: +- check the code syntax with `flake8` +- execute the unit tests in `./tests/`. +- Checks on documentation presence and format (using `sphinx`). -* __TODO__ +Since the various tests are relatively costly, the CI actions will only be executed for +pull requests to the `main` branch. From aabb4cedca334f13f2762191c95057f6b91a3d5b Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 10:40:21 -0400 Subject: [PATCH 24/29] Only deal with the main branch. --- .github/workflows/ci.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 201faa28..f2d416b9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -5,11 +5,9 @@ on: push: branches: - main - - development pull_request: branches: - main - - development jobs: build: runs-on: ubuntu-latest From 1ea21006691afee5639f8306af26633b13d613bc Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 10:42:52 -0400 Subject: [PATCH 25/29] Add the UV default python version file. --- .python-version | 1 + 1 file changed, 1 insertion(+) create mode 100644 .python-version diff --git a/.python-version b/.python-version new file mode 100644 index 00000000..c8cfe395 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 From 0fbd6594dc385bc54f130aa3125fc799e7c69f23 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 10:44:58 -0400 Subject: [PATCH 26/29] removed needless files --- requirements.txt | 42 ------------------------------------------ setup.py | 18 ------------------ 2 files changed, 60 deletions(-) delete mode 100644 requirements.txt delete mode 100644 setup.py diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index dd631c5b..00000000 --- a/requirements.txt +++ /dev/null @@ -1,42 +0,0 @@ -datasets==2.17.1 -flake8==4.0.1 -flake8-docstrings==1.6.0 -gitpython==3.1.27 -hydra-core==1.3.2 -isort==5.13.2 -jupyter==1.0.0 -jinja2==3.1.2 -lammps -mace-torch==0.3.4 -maml==2023.9.9 -monty==2024.2.2 -myst-parser==2.0.0 -orion==0.2.7 -pyarrow==15.0.1 -pymatgen==2024.2.23 -pyyaml==6.0.1 -pytest==7.1.2 -pytest-cov==3.0.0 -pytest-mock==3.12.0 -pytorch_lightning==2.2.1 -pytype==2024.2.13 -sphinx==7.2.6 -sphinx-autoapi==3.0.0 -sphinx-rtd-theme==2.0.0 -sphinxcontrib-napoleon==0.7 -sphinxcontrib-katex==0.8.6 -tensorboard==2.16.2 -tqdm==4.64.0 -torch==2.2.0 -torchvision>=0.17.0 -torch_geometric==2.5.3 -matplotlib==3.8.3 -rich==13.7.1 -deepdiff==7.0.1 -pykeops==2.2.3 -comet_ml -einops==0.8.0 -torchode==0.2.0 -torchsde==0.2.6 -ovito==3.10.6.post2 - diff --git a/setup.py b/setup.py deleted file mode 100644 index 90bab9aa..00000000 --- a/setup.py +++ /dev/null @@ -1,18 +0,0 @@ -from setuptools import find_packages, setup - -with open('requirements.txt', 'r') as f: - requirements = f.readlines() - -setup( - name='crystal_diffusion', - version='0.0.1', - packages=find_packages(include=['crystal_diffusion', 'crystal_diffusion.*']), - python_requires='>=3.10', - install_requires=requirements, - entry_points={ - 'console_scripts': [ - 'cd-train=crystal_diffusion.train:main', - 'cd-eval=crystal_diffusion.evaluate:main', - ], - } -) From 9dbf6a0d1450b950ca3f064ad2ae0e984a8f92b8 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 10:46:39 -0400 Subject: [PATCH 27/29] a few init files --- .../active_learning_loop/__init__.py | 0 src/diffusion_for_multi_scale_molecular_dynamics/mlip/__init__.py | 0 .../models/mlip/__init__.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/__init__.py create mode 100644 src/diffusion_for_multi_scale_molecular_dynamics/mlip/__init__.py create mode 100644 src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/__init__.py diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/active_learning_loop/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/mlip/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/mlip/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/__init__.py b/src/diffusion_for_multi_scale_molecular_dynamics/models/mlip/__init__.py new file mode 100644 index 00000000..e69de29b From 56bb596c6e8ea20f1e31019932fe429c0fca8d46 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 10:55:19 -0400 Subject: [PATCH 28/29] Fix imports. --- data/crop_lammps_outputs.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/data/crop_lammps_outputs.py b/data/crop_lammps_outputs.py index fee07fbf..79a75f78 100644 --- a/data/crop_lammps_outputs.py +++ b/data/crop_lammps_outputs.py @@ -5,8 +5,10 @@ import yaml -from crystal_diffusion.data.utils import crop_lammps_yaml -from crystal_diffusion.utils.logging_utils import setup_analysis_logger +from diffusion_for_multi_scale_molecular_dynamics.data.utils import \ + crop_lammps_yaml +from diffusion_for_multi_scale_molecular_dynamics.utils.logging_utils import \ + setup_analysis_logger logger = logging.getLogger(__name__) From 706a5b172124d52c78dbeff8d5dc15d285397ef8 Mon Sep 17 00:00:00 2001 From: Bruno Rousseau Date: Tue, 22 Oct 2024 11:17:20 -0400 Subject: [PATCH 29/29] fixed docs --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index f46158c0..c0a5b517 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ # -- Project information ----------------------------------------------------- -project = 'crystal_diffusion' +project = 'diffusion_for_multi_scale_molecular_dynamics' copyright = '2023, amlrt_team' author = 'amlrt_team' @@ -39,7 +39,7 @@ # autoapi extension for doc strings extensions.append('autoapi.extension') autoapi_type = 'python' -autoapi_dirs = ['../crystal_diffusion/'] +autoapi_dirs = ['../src/'] # Skip docstrings for loggers and tests