From 7b56afd50cf2b696da8f96b4cfe166a7dc717f91 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 15:35:28 +0000 Subject: [PATCH 01/37] Energy and Force Prediction changes (loss function in base, and option in train_val_test) --- hydragnn/models/Base.py | 29 +++++++++++++++++++++++++++ hydragnn/train/train_validate_test.py | 10 +++++++-- 2 files changed, 37 insertions(+), 2 deletions(-) diff --git a/hydragnn/models/Base.py b/hydragnn/models/Base.py index 254461b76..eb86e86af 100644 --- a/hydragnn/models/Base.py +++ b/hydragnn/models/Base.py @@ -354,6 +354,35 @@ def loss(self, pred, value, head_index): return self.loss_nll(pred, value, head_index, var=var) elif self.ilossweights_hyperp == 1: return self.loss_hpweighted(pred, value, head_index, var=var) + + def energy_force_loss(self, pred, data): + # Initialize loss + tot_loss = 0 + tasks_loss = [] + # Energies + node_energy_pred = pred[0] + energy_pred = torch_scatter.scatter_add(node_energy_pred, data.batch, dim=0).float() + energy_true = data.energy + tot_loss += ( + self.loss_function(energy_pred, energy_true) * self.loss_weights[0] # There should only be one loss-weight for energy + ) + tasks_loss.append(self.loss_function(energy_pred, energy_true)) + # Forces + forces_true = data.forces + forces_pred = (torch.autograd.grad( + energy_pred, + data.pos, + grad_outputs=torch.ones_like(energy_pred), + retain_graph=True, create_graph=True + )[0]).float() + forces_pred = -forces_pred + tot_loss += ( + self.loss_function(forces_pred, forces_true) * (1-self.loss_weights[0]) # Have force-weight be the complement to energy-weight + ) + tasks_loss.append(self.loss_function(forces_pred, forces_true)) + ## FixMe: current loss functions require the number of heads to be the number of things being predicted + ## so, we need to do this manually without calling the other functions. + def loss_nll(self, pred, value, head_index, var=None): # negative log likelihood loss diff --git a/hydragnn/train/train_validate_test.py b/hydragnn/train/train_validate_test.py index fee6e7ea2..3ea43f1a3 100644 --- a/hydragnn/train/train_validate_test.py +++ b/hydragnn/train/train_validate_test.py @@ -66,6 +66,7 @@ def train_validate_test( plot_hist_solution=False, create_plots=False, use_deepspeed=False, + compute_grad_energy=False, ): num_epoch = config["Training"]["num_epoch"] EarlyStop = ( @@ -492,8 +493,13 @@ def train(loader, model, opt, verbosity, profiler=None, use_deepspeed=False): data = data.to(get_device()) if trace_level > 0: tr.stop("h2d", **syncopt) - pred = model(data) - loss, tasks_loss = model.module.loss(pred, data.y, head_index) + if compute_grad_energy: + data.pos.requires_grad = True + pred = model(data) + loss, tasks_loss = model.module.energy_force_loss(pred, data) + else: + pred = model(data) + loss, tasks_loss = model.module.loss(pred, data.y, head_index) if trace_level > 0: tr.start("forward_sync", **syncopt) MPI.COMM_WORLD.Barrier() From a277ee50e47bcf49eee9432d3581ca65283e692f Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 15:47:00 +0000 Subject: [PATCH 02/37] comments and renamings --- hydragnn/models/Base.py | 10 +++++----- hydragnn/run_training.py | 1 + hydragnn/train/train_validate_test.py | 4 ++-- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/hydragnn/models/Base.py b/hydragnn/models/Base.py index eb86e86af..3c5f6ec1e 100644 --- a/hydragnn/models/Base.py +++ b/hydragnn/models/Base.py @@ -361,18 +361,18 @@ def energy_force_loss(self, pred, data): tasks_loss = [] # Energies node_energy_pred = pred[0] - energy_pred = torch_scatter.scatter_add(node_energy_pred, data.batch, dim=0).float() - energy_true = data.energy + graph_energy_pred = torch_scatter.scatter_add(node_energy_pred, data.batch, dim=0).float() + graph_energy_true = data.energy tot_loss += ( - self.loss_function(energy_pred, energy_true) * self.loss_weights[0] # There should only be one loss-weight for energy + self.loss_function(graph_energy_pred, graph_energy_true) * self.loss_weights[0] # There should only be one loss-weight for energy ) - tasks_loss.append(self.loss_function(energy_pred, energy_true)) + tasks_loss.append(self.loss_function(graph_energy_pred, graph_energy_true)) # Forces forces_true = data.forces forces_pred = (torch.autograd.grad( energy_pred, data.pos, - grad_outputs=torch.ones_like(energy_pred), + grad_outputs=torch.ones_like(graph_energy_pred), retain_graph=True, create_graph=True )[0]).float() forces_pred = -forces_pred diff --git a/hydragnn/run_training.py b/hydragnn/run_training.py index c702074f9..d1187c89f 100644 --- a/hydragnn/run_training.py +++ b/hydragnn/run_training.py @@ -170,6 +170,7 @@ def _(config: dict, use_deepspeed=False): scheduler, config["NeuralNetwork"], log_name, + compute_grad_energy=config["NeuralNetwork"]["Training"]["compute_grad_energy"], config["Verbosity"]["level"], plot_init_solution, plot_hist_solution, diff --git a/hydragnn/train/train_validate_test.py b/hydragnn/train/train_validate_test.py index 3ea43f1a3..1d187d87a 100644 --- a/hydragnn/train/train_validate_test.py +++ b/hydragnn/train/train_validate_test.py @@ -61,12 +61,12 @@ def train_validate_test( scheduler, config, model_with_config_name, + compute_grad_energy=False, verbosity=0, plot_init_solution=True, plot_hist_solution=False, create_plots=False, use_deepspeed=False, - compute_grad_energy=False, ): num_epoch = config["Training"]["num_epoch"] EarlyStop = ( @@ -493,7 +493,7 @@ def train(loader, model, opt, verbosity, profiler=None, use_deepspeed=False): data = data.to(get_device()) if trace_level > 0: tr.stop("h2d", **syncopt) - if compute_grad_energy: + if compute_grad_energy: # for force and energy prediction data.pos.requires_grad = True pred = model(data) loss, tasks_loss = model.module.energy_force_loss(pred, data) From fd248b5ed92be21f0eb96a567e849a048a68b9f5 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 17:00:00 +0000 Subject: [PATCH 03/37] Black formatting and mak computational graph fixes --- hydragnn/models/Base.py | 52 ++++++++++++++++-------- hydragnn/run_training.py | 2 +- hydragnn/train/train_validate_test.py | 57 +++++++++++++++++++++------ 3 files changed, 81 insertions(+), 30 deletions(-) diff --git a/hydragnn/models/Base.py b/hydragnn/models/Base.py index 3c5f6ec1e..29157a85e 100644 --- a/hydragnn/models/Base.py +++ b/hydragnn/models/Base.py @@ -15,6 +15,7 @@ from torch_geometric.nn import global_mean_pool, BatchNorm from torch.nn import GaussianNLLLoss from torch.utils.checkpoint import checkpoint +import torch_scatter from hydragnn.utils.model import activation_function_selection, loss_function_selection import sys from hydragnn.utils.distributed import get_device @@ -354,35 +355,52 @@ def loss(self, pred, value, head_index): return self.loss_nll(pred, value, head_index, var=var) elif self.ilossweights_hyperp == 1: return self.loss_hpweighted(pred, value, head_index, var=var) - + def energy_force_loss(self, pred, data): + # Asserts + assert ( + data.pos is not None and data.energy is not None and data.forces is not None + ), "data.pos, data.energy, data.forces must be provided for energy-force loss. Check your dataset creation and naming." + assert ( + data.pos.requires_grad + ), "data.pos does not have grad, so force predictions cannot be computed. Check that data.pos has grad set to true before prediction." + assert ( + self.num_heads == 1 and self.head_type[0] == "node" + ), "Force predictions are only supported for models with one head that predict nodal energy. Check your num_heads and head_types." # Initialize loss tot_loss = 0 tasks_loss = [] # Energies node_energy_pred = pred[0] - graph_energy_pred = torch_scatter.scatter_add(node_energy_pred, data.batch, dim=0).float() + graph_energy_pred = torch_scatter.scatter_add( + node_energy_pred, data.batch, dim=0 + ).float() graph_energy_true = data.energy tot_loss += ( - self.loss_function(graph_energy_pred, graph_energy_true) * self.loss_weights[0] # There should only be one loss-weight for energy - ) + self.loss_function(graph_energy_pred, graph_energy_true) + * self.loss_weights[0] + ) # There should only be one loss-weight for energy tasks_loss.append(self.loss_function(graph_energy_pred, graph_energy_true)) # Forces - forces_true = data.forces - forces_pred = (torch.autograd.grad( - energy_pred, - data.pos, - grad_outputs=torch.ones_like(graph_energy_pred), - retain_graph=True, create_graph=True - )[0]).float() + forces_true = data.forces.float() + forces_pred = torch.autograd.grad( + graph_energy_pred, + data.pos, + grad_outputs=torch.ones_like(graph_energy_pred), + retain_graph=graph_energy_pred.requires_grad, # Retain graph only if needed (it will be needed during training, but not during validation/testing) + create_graph=True, + )[0].float() + assert ( + forces_pred is not None + ), "No gradients were found for data.pos. Does your model use positions for prediction?" forces_pred = -forces_pred - tot_loss += ( - self.loss_function(forces_pred, forces_true) * (1-self.loss_weights[0]) # Have force-weight be the complement to energy-weight - ) - tasks_loss.append(self.loss_function(forces_pred, forces_true)) + tot_loss += self.loss_function(forces_pred, forces_true) * ( + 1 - self.loss_weights[0] + ) # Have force-weight be the complement to energy-weight ## FixMe: current loss functions require the number of heads to be the number of things being predicted - ## so, we need to do this manually without calling the other functions. - + ## so, we need to do loss calculation manually without calling the other functions. + + return tot_loss, tasks_loss def loss_nll(self, pred, value, head_index, var=None): # negative log likelihood loss diff --git a/hydragnn/run_training.py b/hydragnn/run_training.py index d1187c89f..035693380 100644 --- a/hydragnn/run_training.py +++ b/hydragnn/run_training.py @@ -170,12 +170,12 @@ def _(config: dict, use_deepspeed=False): scheduler, config["NeuralNetwork"], log_name, - compute_grad_energy=config["NeuralNetwork"]["Training"]["compute_grad_energy"], config["Verbosity"]["level"], plot_init_solution, plot_hist_solution, create_plots, use_deepspeed=use_deepspeed, + compute_grad_energy=config["NeuralNetwork"]["Training"]["compute_grad_energy"], ) save_model(model, optimizer, log_name, use_deepspeed=use_deepspeed) diff --git a/hydragnn/train/train_validate_test.py b/hydragnn/train/train_validate_test.py index 1d187d87a..d33e99d21 100644 --- a/hydragnn/train/train_validate_test.py +++ b/hydragnn/train/train_validate_test.py @@ -61,12 +61,12 @@ def train_validate_test( scheduler, config, model_with_config_name, - compute_grad_energy=False, verbosity=0, plot_init_solution=True, plot_hist_solution=False, create_plots=False, use_deepspeed=False, + compute_grad_energy=False, ): num_epoch = config["Training"]["num_epoch"] EarlyStop = ( @@ -163,6 +163,7 @@ def train_validate_test( verbosity, profiler=prof, use_deepspeed=use_deepspeed, + compute_grad_energy=compute_grad_energy, ) tr.stop("train") tr.disable() @@ -173,7 +174,11 @@ def train_validate_test( continue val_loss, val_taskserr = validate( - val_loader, model, verbosity, reduce_ranks=True + val_loader, + model, + verbosity, + reduce_ranks=True, + compute_grad_energy=compute_grad_energy, ) test_loss, test_taskserr, true_values, predicted_values = test( test_loader, @@ -181,6 +186,7 @@ def train_validate_test( verbosity, reduce_ranks=True, return_samples=plot_hist_solution, + compute_grad_energy=compute_grad_energy, ) scheduler.step(val_loss) if writer is not None: @@ -428,14 +434,22 @@ def gather_tensor_ranks(head_values): start_idx = i * max_size end_idx = start_idx + size.item() if end_idx > start_idx: - head_values[ - size_all[:i].sum() : size_all[:i].sum() + size.item() - ] = tensor_list[start_idx:end_idx] + head_values[size_all[:i].sum() : size_all[:i].sum() + size.item()] = ( + tensor_list[start_idx:end_idx] + ) return head_values -def train(loader, model, opt, verbosity, profiler=None, use_deepspeed=False): +def train( + loader, + model, + opt, + verbosity, + profiler=None, + use_deepspeed=False, + compute_grad_energy=False, +): if profiler is None: profiler = Profiler() @@ -547,7 +561,7 @@ def train(loader, model, opt, verbosity, profiler=None, use_deepspeed=False): @torch.no_grad() -def validate(loader, model, verbosity, reduce_ranks=True): +def validate(loader, model, verbosity, reduce_ranks=True, compute_grad_energy=False): total_error = torch.tensor(0.0, device=get_device()) tasks_error = torch.zeros(model.module.num_heads, device=get_device()) @@ -571,8 +585,14 @@ def validate(loader, model, verbosity, reduce_ranks=True): loader.dataset.ddstore.epoch_end() head_index = get_head_indices(model, data) data = data.to(get_device()) - pred = model(data) - error, tasks_loss = model.module.loss(pred, data.y, head_index) + if compute_grad_energy: # for force and energy prediction + with torch.enable_grad(): + data.pos.requires_grad = True + pred = model(data) + error, tasks_loss = model.module.energy_force_loss(pred, data) + else: + pred = model(data) + error, tasks_loss = model.module.loss(pred, data.y, head_index) total_error += error * data.num_graphs num_samples_local += data.num_graphs for itask in range(len(tasks_loss)): @@ -591,7 +611,14 @@ def validate(loader, model, verbosity, reduce_ranks=True): @torch.no_grad() -def test(loader, model, verbosity, reduce_ranks=True, return_samples=True): +def test( + loader, + model, + verbosity, + reduce_ranks=True, + return_samples=True, + compute_grad_energy=False, +): total_error = torch.tensor(0.0, device=get_device()) tasks_error = torch.zeros(model.module.num_heads, device=get_device()) @@ -618,8 +645,14 @@ def test(loader, model, verbosity, reduce_ranks=True, return_samples=True): loader.dataset.ddstore.epoch_end() head_index = get_head_indices(model, data) data = data.to(get_device()) - pred = model(data) - error, tasks_loss = model.module.loss(pred, data.y, head_index) + if compute_grad_energy: # for force and energy prediction + with torch.enable_grad(): + data.pos.requires_grad = True + pred = model(data) + error, tasks_loss = model.module.energy_force_loss(pred, data) + else: + pred = model(data) + error, tasks_loss = model.module.loss(pred, data.y, head_index) ## FIXME: temporary if int(os.getenv("HYDRAGNN_DUMP_TESTDATA", "0")) == 1: if model.module.var_output: From c3eaedcff00c490d9638bc05f10ba6aa2c3ed44c Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 17:53:32 +0000 Subject: [PATCH 04/37] fix loss weighting --- hydragnn/models/Base.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/hydragnn/models/Base.py b/hydragnn/models/Base.py index 29157a85e..3ae4cddff 100644 --- a/hydragnn/models/Base.py +++ b/hydragnn/models/Base.py @@ -376,10 +376,11 @@ def energy_force_loss(self, pred, data): node_energy_pred, data.batch, dim=0 ).float() graph_energy_true = data.energy + energy_loss_weight = self.loss_weights[0] # There should only be one loss-weight for energy tot_loss += ( self.loss_function(graph_energy_pred, graph_energy_true) - * self.loss_weights[0] - ) # There should only be one loss-weight for energy + * energy_loss_weight + ) tasks_loss.append(self.loss_function(graph_energy_pred, graph_energy_true)) # Forces forces_true = data.forces.float() @@ -394,8 +395,10 @@ def energy_force_loss(self, pred, data): forces_pred is not None ), "No gradients were found for data.pos. Does your model use positions for prediction?" forces_pred = -forces_pred - tot_loss += self.loss_function(forces_pred, forces_true) * ( - 1 - self.loss_weights[0] + force_loss_weight = energy_loss_weight * torch.mean(torch.abs(graph_energy_true)) / (torch.mean(torch.abs(forces_true)) + 1e-8) # Weight force loss and graph energy equally + tot_loss += ( + self.loss_function(forces_pred, forces_true) + * force_loss_weight ) # Have force-weight be the complement to energy-weight ## FixMe: current loss functions require the number of heads to be the number of things being predicted ## so, we need to do loss calculation manually without calling the other functions. From 5136b896311acaaab06f20586fff4812192a118a Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 18:08:34 +0000 Subject: [PATCH 05/37] black formatting --- hydragnn/models/Base.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/hydragnn/models/Base.py b/hydragnn/models/Base.py index 3ae4cddff..2bcb791ba 100644 --- a/hydragnn/models/Base.py +++ b/hydragnn/models/Base.py @@ -376,7 +376,9 @@ def energy_force_loss(self, pred, data): node_energy_pred, data.batch, dim=0 ).float() graph_energy_true = data.energy - energy_loss_weight = self.loss_weights[0] # There should only be one loss-weight for energy + energy_loss_weight = self.loss_weights[ + 0 + ] # There should only be one loss-weight for energy tot_loss += ( self.loss_function(graph_energy_pred, graph_energy_true) * energy_loss_weight @@ -395,10 +397,13 @@ def energy_force_loss(self, pred, data): forces_pred is not None ), "No gradients were found for data.pos. Does your model use positions for prediction?" forces_pred = -forces_pred - force_loss_weight = energy_loss_weight * torch.mean(torch.abs(graph_energy_true)) / (torch.mean(torch.abs(forces_true)) + 1e-8) # Weight force loss and graph energy equally + force_loss_weight = ( + energy_loss_weight + * torch.mean(torch.abs(graph_energy_true)) + / (torch.mean(torch.abs(forces_true)) + 1e-8) + ) # Weight force loss and graph energy equally tot_loss += ( - self.loss_function(forces_pred, forces_true) - * force_loss_weight + self.loss_function(forces_pred, forces_true) * force_loss_weight ) # Have force-weight be the complement to energy-weight ## FixMe: current loss functions require the number of heads to be the number of things being predicted ## so, we need to do loss calculation manually without calling the other functions. From 65f6f1dd29fd2a299dc33bfb066b80fdecd3411a Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 18:12:57 +0000 Subject: [PATCH 06/37] black formatting --- hydragnn/train/train_validate_test.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/hydragnn/train/train_validate_test.py b/hydragnn/train/train_validate_test.py index d33e99d21..c0d89bcc3 100644 --- a/hydragnn/train/train_validate_test.py +++ b/hydragnn/train/train_validate_test.py @@ -437,6 +437,9 @@ def gather_tensor_ranks(head_values): head_values[size_all[:i].sum() : size_all[:i].sum() + size.item()] = ( tensor_list[start_idx:end_idx] ) + head_values[ + size_all[:i].sum() : size_all[:i].sum() + size.item() + ] = tensor_list[start_idx:end_idx] return head_values From 3395484489c1b1776c566bee644bc1e29e29b23c Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 18:13:15 +0000 Subject: [PATCH 07/37] black formatting --- hydragnn/train/train_validate_test.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/hydragnn/train/train_validate_test.py b/hydragnn/train/train_validate_test.py index c0d89bcc3..a4fc9c635 100644 --- a/hydragnn/train/train_validate_test.py +++ b/hydragnn/train/train_validate_test.py @@ -434,9 +434,6 @@ def gather_tensor_ranks(head_values): start_idx = i * max_size end_idx = start_idx + size.item() if end_idx > start_idx: - head_values[size_all[:i].sum() : size_all[:i].sum() + size.item()] = ( - tensor_list[start_idx:end_idx] - ) head_values[ size_all[:i].sum() : size_all[:i].sum() + size.item() ] = tensor_list[start_idx:end_idx] From c5d0c7b397fce36814886da0ed3d28cccd221226 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Fri, 6 Sep 2024 21:47:19 +0000 Subject: [PATCH 08/37] Fix DIMEStack testing issues, and compute_grad_energy default in config_utils --- hydragnn/models/DIMEStack.py | 4 ++-- hydragnn/utils/config_utils.py | 11 ++++++++--- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/hydragnn/models/DIMEStack.py b/hydragnn/models/DIMEStack.py index 1949d3406..da52955dc 100644 --- a/hydragnn/models/DIMEStack.py +++ b/hydragnn/models/DIMEStack.py @@ -14,7 +14,7 @@ import torch from torch import Tensor -from torch.nn import Identity, SiLU +from torch.nn import Identity, SiLU, Sigmoid from torch_geometric.nn import Linear, Sequential from torch_geometric.nn.models.dimenet import ( @@ -101,7 +101,7 @@ def get_conv(self, input_dim, output_dim): out_emb_channels=self.out_emb_size, out_channels=output_dim, num_layers=1, - act=SiLU(), + act=Sigmoid(), # Sigmoid instead of SiLU here promotes stability when we have a linear decoder at the start of convolution (especially for random data examples in test_graphs.py) output_initializer="glorot_orthogonal", ) return Sequential( diff --git a/hydragnn/utils/config_utils.py b/hydragnn/utils/config_utils.py index 3331952c8..7b71ab6b2 100644 --- a/hydragnn/utils/config_utils.py +++ b/hydragnn/utils/config_utils.py @@ -106,6 +106,9 @@ def update_config(config, train_loader, val_loader, test_loader): if "conv_checkpointing" not in config["NeuralNetwork"]["Training"]: config["NeuralNetwork"]["Training"]["conv_checkpointing"] = False + + if "compute_grad_energy" not in config["NeuralNetwork"]["Training"]: + config["NeuralNetwork"]["Training"]["compute_grad_energy"] = False return config @@ -260,9 +263,11 @@ def get_log_name_config(config): + str(config["NeuralNetwork"]["Training"]["batch_size"]) + "-data-" + config["Dataset"]["name"][ - : config["Dataset"]["name"].rfind("_") - if config["Dataset"]["name"].rfind("_") > 0 - else None + : ( + config["Dataset"]["name"].rfind("_") + if config["Dataset"]["name"].rfind("_") > 0 + else None + ) ] + "-node_ft-" + "".join( From fc5a55446e10badfbbc902602c88d895b7036a44 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 13:41:40 -0400 Subject: [PATCH 09/37] LJ example added first draft --- examples/LennardJones/AtomicStructure.py | 81 ++++ examples/LennardJones/Forces_Scatterplot.png | Bin 0 -> 291536 bytes examples/LennardJones/LJ.json | 75 +++ examples/LennardJones/LJ_multitask.json | 75 +++ .../LennardJones/LJ_vlad_atomic_forces.json | 61 +++ .../LennardJones/LJ_vlad_total_energy.json | 71 +++ examples/LennardJones/LJpotential.py | 31 ++ examples/LennardJones/LennardJones.py | 450 ++++++++++++++++++ examples/LennardJones/README.md | 92 ++++ examples/LennardJones/__init__.py | 1 + examples/LennardJones/configurational_data.py | 214 +++++++++ examples/LennardJones/distributed_utils.py | 3 + examples/LennardJones/energy_Scatterplot.png | Bin 0 -> 180440 bytes examples/LennardJones/graph_utils.py | 55 +++ examples/LennardJones/inference.py | 208 ++++++++ .../inference_derivative_energy.py | 229 +++++++++ .../LennardJones/train_vlad_atomic_forces.py | 412 ++++++++++++++++ .../LennardJones/train_vlad_total_energy.py | 408 ++++++++++++++++ tests/test_examples.py | 2 +- 19 files changed, 2467 insertions(+), 1 deletion(-) create mode 100644 examples/LennardJones/AtomicStructure.py create mode 100644 examples/LennardJones/Forces_Scatterplot.png create mode 100644 examples/LennardJones/LJ.json create mode 100644 examples/LennardJones/LJ_multitask.json create mode 100644 examples/LennardJones/LJ_vlad_atomic_forces.json create mode 100644 examples/LennardJones/LJ_vlad_total_energy.json create mode 100644 examples/LennardJones/LJpotential.py create mode 100644 examples/LennardJones/LennardJones.py create mode 100644 examples/LennardJones/README.md create mode 100644 examples/LennardJones/__init__.py create mode 100644 examples/LennardJones/configurational_data.py create mode 100644 examples/LennardJones/distributed_utils.py create mode 100644 examples/LennardJones/energy_Scatterplot.png create mode 100644 examples/LennardJones/graph_utils.py create mode 100644 examples/LennardJones/inference.py create mode 100644 examples/LennardJones/inference_derivative_energy.py create mode 100644 examples/LennardJones/train_vlad_atomic_forces.py create mode 100644 examples/LennardJones/train_vlad_total_energy.py diff --git a/examples/LennardJones/AtomicStructure.py b/examples/LennardJones/AtomicStructure.py new file mode 100644 index 000000000..bc738dd36 --- /dev/null +++ b/examples/LennardJones/AtomicStructure.py @@ -0,0 +1,81 @@ +import torch + +class AtomicStructureHandler(): + + def __init__(self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula): + + self.bravais_lattice_constants = bravais_lattice_constants + self.radius_cutoff = radius_cutoff + self.formula = formula + + def compute(self, data): + + assert (data.pos.shape[0] == data.x.shape[0]) + + interatomic_potential = torch.zeros([data.pos.shape[0], 1]) + interatomic_forces = torch.zeros([data.pos.shape[0], 3]) + + for node_id in range(data.pos.shape[0]): + + neighbor_list_indices = torch.where(data.edge_index[0, :] == node_id)[0].tolist() + neighbor_list = data.edge_index[1, neighbor_list_indices] + + for neighbor_id, edge_id in zip(neighbor_list, neighbor_list_indices): + + neighbor_pos = data.pos[neighbor_id, :] + distance_vector = data.pos[neighbor_id, :] - data.pos[node_id, :] + + # Adjust the neighbor position based on periodic boundary conditions (PBC) + ## If the distance between the atoms is larger than the cutoff radius, the edge is because of PBC conditions + if torch.norm(distance_vector) > self.radius_cutoff: + ## At this point, we know that the edge is due to PBC conditions, so we need to adjust the neighbor position. We also know that + ## that this connection MUST be the closest connection possible as a result of the asserted radius_cutoff < supercell_size earlier + ## in the code. Because of this, we can simply adjust the neighbor position coordinate-wise to be closer than + ## as done in the following lines of code. The logic goes that if the distance vector[index] is larger than half the supercell size, + ## then there is a closer distance at +- supercell_size[index], and we adjust to that for each coordinate + if abs(distance_vector[0]) > data.supercell_size[0, 0] / 2: + if distance_vector[0] > 0: + neighbor_pos[0] -= data.supercell_size[0, 0] + else: + neighbor_pos[0] += data.supercell_size[0, 0] + + if abs(distance_vector[1]) > data.supercell_size[1, 1] / 2: + if distance_vector[1] > 0: + neighbor_pos[1] -= data.supercell_size[1, 1] + else: + neighbor_pos[1] += data.supercell_size[1, 1] + + if abs(distance_vector[2]) > data.supercell_size[2, 2] / 2: + if distance_vector[2] > 0: + neighbor_pos[2] -= data.supercell_size[2, 2] + else: + neighbor_pos[2] += data.supercell_size[2, 2] + + # The distance vecor may need to be updated after applying PBCs + distance_vector = data.pos[node_id, :] - neighbor_pos + + #pair_distance = data.edge_attr[edge_id].item() + interatomic_potential[node_id] += self.formula.potential_energy(distance_vector) + + derivative_x = self.formula.derivative_x(distance_vector) + derivative_y = self.formula.derivative_y(distance_vector) + derivative_z = self.formula.derivative_z(distance_vector) + + interatomic_forces_contribution_x = - derivative_x + interatomic_forces_contribution_y = - derivative_y + interatomic_forces_contribution_z = - derivative_z + + interatomic_forces[node_id, 0] += interatomic_forces_contribution_x + interatomic_forces[node_id, 1] += interatomic_forces_contribution_y + interatomic_forces[node_id, 2] += interatomic_forces_contribution_z + + data.x = torch.cat( + ( + data.x, + interatomic_potential, + interatomic_forces + ), + 1, + ) + + return data diff --git a/examples/LennardJones/Forces_Scatterplot.png b/examples/LennardJones/Forces_Scatterplot.png new file mode 100644 index 0000000000000000000000000000000000000000..85c45ce8e39575678d61443e1757d75f20cd7e3d GIT binary patch literal 291536 zcmeFZcUY6@`YsyBHM*>$0HX``+@D`?;U{egD$aIlF87 z-t8C+W|zjfUkxxA^w+H+|J(w9d?UO28T_H>rEcbB=wa{Wd(qPlqkYlqimQj0tJ9^Q zeC#~&P9E;EQYWO2OZ?>M<#h#rQd-(=;{#G2o(|GN!iT)!A=|E;Gsj~vDu19~n>zCf zy)c_F7>!?lF}jv8G2oZbX*Ymnckf&gN&WZj-=FT7sz~`WnjZZ$jc#)@I@T#J-QK%z z(g0WNSy6gi7O%hvI)vZ)U8~i<`hU^?*SGpU=JwQSp5V^Od>MzEczV;tq~?`lDz?;> z%7X4q-%iKIbZ2JhiS79Y#pTAVE(cGKOslcF#lZt24j5ge_n*a{Lj*H4dQ={0WHJ- z7{mV+W4N$Q2fI4o(a~YlSwGi|#f>J*bH@?)+yANPr`NX@g3V0!Y`>v-mizV;-m9B` z;{FZ&_Wx_}U4Q+Bo?{+^)lIRoqL4VqFK+B;PuGg^8f_D{uJX}#a~ogw(67HEKom{7 z_V2>#SxuXuh0f}QF3VUs7ZdyDq{IsRNXF?~yGPs7wM=aMhVyEd$C$0*!I1;Lw3;3~ zHAzKjj!tRpufP{Owx)Vb_R0-ZdTWhP6XR916e;MXBUDO)BEAUy?^M5{b)}a^x%)tL z1xbNl%Id*rd!_@M#Wa6>;g{YLCn7abiL>%ZJ6=BOF3Krte~EFkPK)CXK8e@G&8qS5 zUfvoR%}l1od(_5m`$1&rLpYY*&n$E6%kZGpn!Dte7PX!cwEOh-xq$MuEB*^(oz>5= z^(_xi<2#K_(>+y>`VEd99Vj$@tOz$hIDrtfg-;&7h#51A(8OvnD+V zjg#fHq*z%8z8as_RCNJtaNsMLC4u!psN(JKJ0cEm-nzT#;jII4*LhT*s>d|!_2jHF zMw(=sn+$bTj7P;dCSrSLhlsj#SPKezRQaHPuKxJ=*qBVv^8EQ^Rh36)!`X}x7iZ_9 z(TsTeZfjpkLDf_oe6;rcjVBBm0bAbhspbCk-OW2c9!t$CqLZ4EvDiWq+uw$;y0BAF zUV{Ao_kO9WiHPgFPl|2b@1=Lr>!V)5$1r(#z7?J#R{@qOd+F~#JG#3S{KIeT;J2S0 zBEv3vCI!xtb!)mFp1u`0s)cJP>vNCa@AKwh)nu9FNuLR`oF%j-k!IQtkKuN0hQB)6 zq41D!^_74+MJg9(Z(ic44@0d?2wLp%Tj(@x^gL64cRzmU{#kk^cGYQlandqcw)S;f zKjwr}TUx@E7u(QKVougGN0O6;RzIHAs%aKY4vcUqtdSo{CIr6<6D;pokxO%EP7*lj z<#_kxSXLf9-VQrW2movHq$-XKRgM=lf2T~xZ|7h}70s?@p{wz(($vz8@ZY2sV$bfo`s$#9;=>)+%Fzq~S^M+FiG5};Z8x7mzmfCt^7(E05r+$F zmz}rl+8gP2&i-=8dFET7!Bsu0e?sRaDp|sXz|P4O1ACV3KR>M{3u>it78z)$t>oatV>tk z`Ggb4F4y0s912&CXnk~6VL792d3kwX(0r?8#=lH;+B38pk!OQ<)VArX+P(N=8*?^> zlmOF9H!ZXg?km?58;Yl~><9hF^CKp=!APGKX{~JuiV^{nrKUFU+nK^i$TmJbb}K&me-Iv`06Hj_Kp#Kd7yQiEHbTgW-C5mV_e9Lcj4`MD~4gKkdyDpy0!i)?N zOV=mrTX+s6Z7JD4x z7h3Bf{yV{Q;KMyv>-QH=mi5`Az>R|VSWa#R{JT?kfi)dc5_$+lf!5Tni%oisrb)-)Ss^eCp=DKFBHy&6IhS* zNAjP(zl|nd{A#buacAq(H+S!k0|#}^UOe&X+2%PcXAWy_7iFv&ITo52Fjb*Annv7P zQ{X#2sK{RWfPa1PRvM&r?UMoExPB0dJt3hCgC{n2WE)~-Wg(-^k9QYptZ+PToI1EN zO(M9HRu|9Y3m$jWyQ{MJW}=)HJo@Iz`+HrFn)m+G*@%rnE-wY*gV8 ztay`Td4F40sZj9J#oC2@kJ7AEyDFc_*=`bBPGRuc<>E-r0q~gw&xRvm7VY_#V79WxC(%E>RL zo~&RD2B37g&;R4YFe#J}B1lY%$LwsQw8QIr2h|H#J_vCh)E@}QYaLm|q1@a$OoHLb zAS74-n->wuiLizEuK4bcA7-Cz<{4UL(3;a@Y~ld9q#h1HGuAt>MjGV5$ulqZWX;s` ztKcjx<54Pu%;mDznzce&q!Bo*xnxe$7C~3N`8bq-2&?0`n6Z8XWbIScbJTrT|Nf5R zIS9{?@%q?uMkUn;p*RJ1tKj)ZLQxanm2%p^5hnlyR!rBf%(iJs4_nffO`Kh{=7wLv z-W~)~H))-ttqd||--k-u!wQL!nYo3v{!_IpQ)dR)%$n(-X;}rjT$gvZwP{CxQovL! 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["total_energy"], + "dim": [1], + "column_index": [0] + } + }, + "NeuralNetwork": { + "Architecture": { + "periodic_boundary_conditions": true, + "model_type": "DimeNet", + "equivariance": false, + "radius": 5.0, + "max_neighbours": 5, + "int_emb_size": 32, + "out_emb_size": 16, + "basis_emb_size": 8, + "num_before_skip": 1, + "num_after_skip": 1, + "envelope_exponent": 5, + "num_radial": 5, + "num_spherical": 2, + "hidden_dim": 20, + "num_conv_layers": 4, + "output_heads": { + "node": { + "num_headlayers": 2, + "dim_headlayers": [60,20], + "type": "mlp" + } + }, + "task_weights": [1] + }, + "Variables_of_interest": { + "input_node_features": [0], + "output_index": [ + 0 + ], + "type": [ + "node" + ], + "output_dim": [1], + "output_names": ["graph_energy"] + }, + "Training": { + "num_epoch": 25, + "batch_size": 64, + "patience": 20, + "early_stopping": true, + "Optimizer": { + "type": "Adam", + "learning_rate": 0.005 + }, + "conv_checkpointing": false + } + }, + "Visualization": { + "plot_init_solution": true, + "plot_hist_solution": true, + "create_plots": true + } +} diff --git a/examples/LennardJones/LJ_multitask.json b/examples/LennardJones/LJ_multitask.json new file mode 100644 index 000000000..662254da7 --- /dev/null +++ b/examples/LennardJones/LJ_multitask.json @@ -0,0 +1,75 @@ +{ + "Verbosity": { + "level": 2 + }, + "Dataset": { + "name": "LJdataset", + "path": {"total": "./dataset/data"}, + "format": "XYZ", + "rotational_invariance": true, + "node_features": { + "name": ["atom_type", "potential", "forces"], + "dim": [1, 1, 3], + "column_index": [0,4,5] + }, + "graph_features":{ + "name": ["total_energy"], + "dim": [1], + "column_index": [0] + } + }, + "NeuralNetwork": { + "Architecture": { + "periodic_boundary_conditions": true, + "model_type": "EGNN", + "equivariance": true, + "edge_features": ["bond_length", "polar_angle", "azimutal_angle"], + "max_neighbours": 20, + "hidden_dim": 20, + "num_conv_layers": 6, + "output_heads": { + "graph": { + "num_sharedlayers": 2, + "dim_sharedlayers": 50, + "num_headlayers": 2, + "dim_headlayers": [ + 20, + 20 + ] + }, + "node": { + "num_headlayers": 2, + "dim_headlayers": [20,20], + "type": "mlp" + } + }, + "task_weights": [ + 1, 1 + ] + }, + "Variables_of_interest": { + "input_node_features": [0], + "output_index": [ + 0, 2 + ], + "type": [ + "graph", "node" + ], + "output_dim": [1, 3], + "output_names": ["total_energy", "atomic_forces"] + }, + "Training": { + "num_epoch": 50, + "batch_size": 3, + "continue": 0, + "EarlyStopping": true, + "patience": 100, + "Checkpoint": true, + "checkpoint_warmup": 100, + "startfrom": "existing_model", + "Optimizer": { + "learning_rate": 0.001 + } + } + } +} diff --git a/examples/LennardJones/LJ_vlad_atomic_forces.json b/examples/LennardJones/LJ_vlad_atomic_forces.json new file mode 100644 index 000000000..c14b23f8a --- /dev/null +++ b/examples/LennardJones/LJ_vlad_atomic_forces.json @@ -0,0 +1,61 @@ +{ + "Verbosity": { + "level": 2 + }, + "Dataset": { + "name": "LJdataset", + "path": {"total": "./dataset/data"}, + "format": "XYZ", + "rotational_invariance": true, + "node_features": { + "name": ["atom_type", "forces"], + "dim": [1, 3], + "column_index": [0, 1] + } + }, + "NeuralNetwork": { + "Architecture": { + "periodic_boundary_conditions": true, + "model_type": "EGNN", + "equivariance": true, + "edge_features": ["bond_length", "polar_angle", "azimutal_angle"], + "max_neighbours": 20, + "hidden_dim": 10, + "num_conv_layers": 3, + "output_heads": { + "node": { + "num_headlayers": 2, + "dim_headlayers": [50,25], + "type": "mlp" + } + }, + "task_weights": [ + 1 + ] + }, + "Variables_of_interest": { + "input_node_features": [0], + "output_index": [ + 1 + ], + "type": [ + "node" + ], + "output_dim": [3], + "output_names": ["forces"] + }, + "Training": { + "num_epoch": 20, + "batch_size": 3, + "continue": 0, + "EarlyStopping": true, + "patience": 100, + "Checkpoint": true, + "checkpoint_warmup": 100, + "startfrom": "existing_model", + "Optimizer": { + "learning_rate": 0.001 + } + } + } +} diff --git a/examples/LennardJones/LJ_vlad_total_energy.json b/examples/LennardJones/LJ_vlad_total_energy.json new file mode 100644 index 000000000..db57121a7 --- /dev/null +++ b/examples/LennardJones/LJ_vlad_total_energy.json @@ -0,0 +1,71 @@ +{ + "Verbosity": { + "level": 2 + }, + "Dataset": { + "name": "LJdataset", + "path": {"total": "./dataset/data"}, + "format": "XYZ", + "rotational_invariance": true, + "node_features": { + "name": ["atom_type"], + "dim": [1], + "column_index": [0] + }, + "graph_features":{ + "name": ["total_energy"], + "dim": [1], + "column_index": [0] + } + }, + "NeuralNetwork": { + "Architecture": { + "periodic_boundary_conditions": true, + "model_type": "EGNN", + "equivariance": true, + "edge_features": ["bond_length", "polar_angle", "azimutal_angle"], + "max_neighbours": 20, + "hidden_dim": 10, + "num_conv_layers": 3, + "output_heads": { + "graph": { + "num_sharedlayers": 2, + "dim_sharedlayers": 20, + "num_headlayers": 2, + "dim_headlayers": [ + 50, + 50 + ] + } + }, + "task_weights": [ + 1 + ] + }, + "Variables_of_interest": { + "input_node_features": [0, 1, 2, 3], + "output_index": [ + 0 + ], + "type": [ + "graph" + ], + "output_dim": [1], + "output_names": ["energy"] + }, + "Training": { + "num_epoch": 50, + "batch_size": 3, + "perc_train": 0.8, + "continue": 0, + "EarlyStopping": true, + "patience": 10, + "Checkpoint": true, + "checkpoint_warmup": 100, + "startfrom": "existing_model", + "Optimizer": { + "learning_rate": 0.001 + } + } + } +} diff --git a/examples/LennardJones/LJpotential.py b/examples/LennardJones/LJpotential.py new file mode 100644 index 000000000..ef8c1ec6e --- /dev/null +++ b/examples/LennardJones/LJpotential.py @@ -0,0 +1,31 @@ +import torch + +class LJpotential(): + + def __init__(self, epsilon, sigma): + self.epsilon = epsilon + self.sigma = sigma + + def potential_energy(self, distance_vector): + pair_distance = torch.norm(distance_vector) + return 4 * self.epsilon * ((self.sigma / pair_distance) ** 12 - (self.sigma / pair_distance) ** 6) + + def radial_derivative(self, distance_vector): + pair_distance = torch.norm(distance_vector) + return 4 * self.epsilon * (-12 * (self.sigma / pair_distance) ** 12 * 1 / pair_distance + 6 * ( + self.sigma / pair_distance) ** 6 * 1 / pair_distance) + + def derivative_x(self, distance_vector): + pair_distance = torch.norm(distance_vector) + radial_derivative = self.radial_derivative(pair_distance) + return radial_derivative * (distance_vector[0].item()) / pair_distance + + def derivative_y(self, distance_vector): + pair_distance = torch.norm(distance_vector) + radial_derivative = self.radial_derivative(pair_distance) + return radial_derivative * (distance_vector[1].item()) / pair_distance + + def derivative_z(self, distance_vector): + pair_distance = torch.norm(distance_vector) + radial_derivative = self.radial_derivative(pair_distance) + return radial_derivative * (distance_vector[2].item()) / pair_distance diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py new file mode 100644 index 000000000..9b99fe37e --- /dev/null +++ b/examples/LennardJones/LennardJones.py @@ -0,0 +1,450 @@ +import mpi4py +from mpi4py import MPI + +mpi4py.rc.thread_level = "serialized" +mpi4py.rc.threads = False + +import os, json +import random + +import h5py + +import logging +import sys +import argparse + +import hydragnn +from hydragnn.utils.print_utils import iterate_tqdm, log +from hydragnn.utils.time_utils import Timer + +from hydragnn.preprocess.load_data import split_dataset +from hydragnn.utils.abstractrawdataset import AbstractBaseDataset +from hydragnn.utils.distdataset import DistDataset +from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset +from hydragnn.preprocess.utils import gather_deg + +import numpy as np + +try: + from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset +except ImportError: + pass + +from torch_geometric.data import Data +from torch_geometric.transforms import RadiusGraph, Distance, Spherical, LocalCartesian +import torch +import torch.distributed as dist + +from hydragnn.utils import nsplit +import hydragnn.utils.tracer as tr + +# Using LJ dataset creation +from configurational_data import deterministic_graph_data +from LJpotential import LJpotential +from AtomicStructure import AtomicStructureHandler +def create_dataset(config): + # Angstrom unit + primitive_bravais_lattice_constant_x = 3.8 + primitive_bravais_lattice_constant_y = 3.8 + primitive_bravais_lattice_constant_z = 3.8 + path = "./dataset/data" + radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] + number_configurations = config["NeuralNetwork"]["Training"]["num_configurations"] if "num_configurations" in config["NeuralNetwork"]["Training"] else 1000 + atom_types = [1] + formula = LJpotential(1.0, 3.4) + atomic_structure_handler = AtomicStructureHandler(atom_types, [primitive_bravais_lattice_constant_x, primitive_bravais_lattice_constant_y, primitive_bravais_lattice_constant_z], radius_cutoff, formula) + deterministic_graph_data(path, atom_types, atomic_structure_handler=atomic_structure_handler, radius_cutoff=radius_cutoff, relative_maximum_atomic_displacement=1e-1, number_configurations=number_configurations) + + +# FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones + + +torch.set_default_dtype(torch.float32) + + +def info(*args, logtype="info", sep=" "): + getattr(logging, logtype)(sep.join(map(str, args))) + + +# FIXME: this radis cutoff overwrites the radius cutoff currently written in the JSON file +create_graph_fromXYZ = RadiusGraph(r=5.0) # radius cutoff in angstrom +compute_edge_lengths = Distance(norm=False, cat=True) +spherical_coordinates = Spherical(norm=False, cat=False) +cartesian_coordinates = LocalCartesian(norm=False, cat=False) + + +class LJDataset(AbstractBaseDataset): + """LJDataset dataset class""" + + def __init__(self, dirpath, dist=False, sampling=None): + super().__init__() + + self.dist = dist + self.world_size = 1 + self.rank = 1 + if self.dist: + assert torch.distributed.is_initialized() + self.world_size = torch.distributed.get_world_size() + self.rank = torch.distributed.get_rank() + + dirfiles = sorted(os.listdir(dirpath)) + + rx = list(nsplit((dirfiles), self.world_size))[self.rank] + + for file in rx: + filepath = os.path.join(dirpath, file) + self.dataset.append(self.transform_input_to_data_object_base(filepath)) + + def transform_input_to_data_object_base(self, filepath): + + # Using readline() + file = open(filepath, "r") + + torch_data = torch.empty((0, 8), dtype=torch.float32) + torch_supercell = torch.zeros((0, 3), dtype=torch.float32) + + count = 0 + + while True: + count += 1 + + # Get next line from file + line = file.readline() + + # if line is empty + # end of file is reached + if not line: + break + + if count == 1: + total_energy = float(line) + elif count == 2: + energy_per_atom = float(line) + elif 2 < count < 6: + array_line = np.fromstring(line, dtype=float, sep="\t") + torch_supercell = torch.cat( + [torch_supercell, torch.from_numpy(array_line).unsqueeze(0)], axis=0 + ) + elif count > 5: + array_line = np.fromstring(line, dtype=float, sep="\t") + torch_data = torch.cat( + [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 + ) + # print("Line{}: {}".format(count, line.strip())) + + file.close() + + num_nodes = torch_data.shape[0] + + energy_pre_translation_factor = 0.0 + energy_pre_scaling_factor = 1.0 / num_nodes + energy_per_atom_pretransformed = (energy_per_atom - energy_pre_translation_factor) * energy_pre_scaling_factor + grad_energy_post_scaling_factor = 1.0/energy_pre_scaling_factor * torch.ones(num_nodes, 1) + forces = torch_data[:, [5, 6, 7]] + forces_pre_scaling_factor = 1.0 + forces_pre_scaled = forces * forces_pre_scaling_factor + + data = Data( + supercell_size=torch_supercell.to(torch.float32), + num_nodes=num_nodes, + grad_energy_post_scaling_factor=grad_energy_post_scaling_factor, + forces_pre_scaling_factor=torch.tensor(forces_pre_scaling_factor).to(torch.float32), + forces=forces, + forces_pre_scaled=forces_pre_scaled, + pos=torch_data[:, [1, 2, 3]].to(torch.float32), + x=torch.cat([torch_data[:, [0, 4]]], axis=1).to(torch.float32), + y=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), + energy_per_atom=torch.tensor(energy_per_atom_pretransformed).unsqueeze(0).to(torch.float32), + energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), + ) + data = create_graph_fromXYZ(data) + data = compute_edge_lengths(data) + data.edge_attr = data.edge_attr.to(torch.float32) + #data = spherical_coordinates(data) + data = cartesian_coordinates(data) + + return data + + def len(self): + return len(self.dataset) + + def get(self, idx): + return self.dataset[idx] + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument("--sampling", type=float, help="sampling ratio", default=None) + parser.add_argument( + "--preonly", + action="store_true", + help="preprocess only (no training)", + ) + parser.add_argument("--inputfile", help="input file", type=str, default="LJ.json") + parser.add_argument("--mae", action="store_true", help="do mae calculation") + parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") + parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) + parser.add_argument("--shmem", action="store_true", help="shmem") + parser.add_argument("--log", help="log name") + parser.add_argument("--batch_size", type=int, help="batch_size", default=None) + parser.add_argument("--everyone", action="store_true", help="gptimer") + + group = parser.add_mutually_exclusive_group() + group.add_argument( + "--adios", + help="Adios dataset", + action="store_const", + dest="format", + const="adios", + ) + group.add_argument( + "--pickle", + help="Pickle dataset", + action="store_const", + dest="format", + const="pickle", + ) + parser.set_defaults(format="pickle") # Changed this for my PC + args = parser.parse_args() + + graph_feature_names = ["total_energy"] + graph_feature_dims = [1] + node_feature_names = ["atomic_number", "potential", "forces"] + node_feature_dims = [1, 1, 3] + dirpwd = os.path.dirname(os.path.abspath(__file__)) + datadir = os.path.join(dirpwd, "dataset/data") + ################################################################################################################## + input_filename = os.path.join(dirpwd, args.inputfile) + ################################################################################################################## + # Configurable run choices (JSON file that accompanies this example script). + with open(input_filename, "r") as f: + config = json.load(f) + verbosity = config["Verbosity"]["level"] + config["NeuralNetwork"]["Variables_of_interest"][ + "graph_feature_names" + ] = graph_feature_names + config["NeuralNetwork"]["Variables_of_interest"][ + "graph_feature_dims" + ] = graph_feature_dims + config["NeuralNetwork"]["Variables_of_interest"][ + "node_feature_names" + ] = node_feature_names + config["NeuralNetwork"]["Variables_of_interest"][ + "node_feature_dims" + ] = node_feature_dims + + if args.batch_size is not None: + config["NeuralNetwork"]["Training"]["batch_size"] = args.batch_size + + ################################################################################################################## + # Always initialize for multi-rank training. + comm_size, rank = hydragnn.utils.setup_ddp() + ################################################################################################################## + + comm = MPI.COMM_WORLD + + ## Set up logging + logging.basicConfig( + level=logging.INFO, + format="%%(levelname)s (rank %d): %%(message)s" % (rank), + datefmt="%H:%M:%S", + ) + + log_name = "LJ" if args.log is None else args.log + hydragnn.utils.setup_log(log_name) + writer = hydragnn.utils.get_summary_writer(log_name) + + log("Command: {0}\n".format(" ".join([x for x in sys.argv])), rank=0) + + modelname = "LJ" + # Check for dataset for each format + lookdir = os.path.join(dirpwd, "dataset") + if args.format == "pickle": + dataset_exists = os.path.exists(os.path.join(lookdir, "LJ.pickle")) + if args.format == "adios": + dataset_exists = os.path.exists(os.path.join(lookdir, "%s.bp" % modelname)) + + # Create dataset if preonly specified or dataset does not exist + if not dataset_exists: + + ## local data + create_dataset(config) + total = LJDataset( + os.path.join(datadir), + dist=True, + ) + ## This is a local split + trainset, valset, testset = split_dataset( + dataset=total, + perc_train=0.9, + stratify_splitting=False, + ) + print("Local splitting: ", len(total), len(trainset), len(valset), len(testset)) + + deg = gather_deg(trainset) + config["pna_deg"] = deg.tolist() + + setnames = ["trainset", "valset", "testset"] + + if args.format == "pickle": + + ## pickle + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + attrs = dict() + attrs["pna_deg"] = deg + SimplePickleWriter( + trainset, + basedir, + "trainset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + attrs=attrs, + ) + SimplePickleWriter( + valset, + basedir, + "valset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + ) + SimplePickleWriter( + testset, + basedir, + "testset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + ) + + if args.format == "adios": + ## adios + fname = os.path.join( + os.path.dirname(__file__), "./dataset/%s.bp" % modelname + ) + adwriter = AdiosWriter(fname, comm) + adwriter.add("trainset", trainset) + adwriter.add("valset", valset) + adwriter.add("testset", testset) + # adwriter.add_global("minmax_node_feature", total.minmax_node_feature) + # adwriter.add_global("minmax_graph_feature", total.minmax_graph_feature) + adwriter.add_global("pna_deg", deg) + adwriter.save() + + tr.initialize() + tr.disable() + timer = Timer("load_data") + timer.start() + if args.format == "adios": + info("Adios load") + assert not (args.shmem and args.ddstore), "Cannot use both ddstore and shmem" + opt = { + "preload": False, + "shmem": args.shmem, + "ddstore": args.ddstore, + "ddstore_width": args.ddstore_width, + } + fname = os.path.join(os.path.dirname(__file__), "./dataset/%s.bp" % modelname) + trainset = AdiosDataset(fname, "trainset", comm, **opt) + valset = AdiosDataset(fname, "valset", comm, **opt) + testset = AdiosDataset(fname, "testset", comm, **opt) + elif args.format == "pickle": + info("Pickle load") + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + var_config = config["NeuralNetwork"]["Variables_of_interest"] + trainset = SimplePickleDataset( + basedir=basedir, label="trainset", preload=True, var_config=var_config + ) + valset = SimplePickleDataset( + basedir=basedir, label="valset", var_config=var_config + ) + testset = SimplePickleDataset( + basedir=basedir, label="testset", var_config=var_config + ) + # minmax_node_feature = trainset.minmax_node_feature + # minmax_graph_feature = trainset.minmax_graph_feature + pna_deg = trainset.pna_deg + if args.ddstore: + opt = {"ddstore_width": args.ddstore_width} + trainset = DistDataset(trainset, "trainset", comm, **opt) + valset = DistDataset(valset, "valset", comm, **opt) + testset = DistDataset(testset, "testset", comm, **opt) + # trainset.minmax_node_feature = minmax_node_feature + # trainset.minmax_graph_feature = minmax_graph_feature + trainset.pna_deg = pna_deg + else: + raise NotImplementedError("No supported format: %s" % (args.format)) + + info( + "trainset,valset,testset size: %d %d %d" + % (len(trainset), len(valset), len(testset)) + ) + + if args.ddstore: + os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" + os.environ["HYDRAGNN_USE_ddstore"] = "1" + + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + ) + + config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) + ## Good to sync with everyone right after DDStore setup + comm.Barrier() + + hydragnn.utils.save_config(config, log_name) + + timer.stop() + + model = hydragnn.models.create_model_config( + config=config["NeuralNetwork"], + verbosity=verbosity, + ) + model = hydragnn.utils.get_distributed_model(model, verbosity) + + learning_rate = config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"] + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.5, patience=5, min_lr=0.00001 + ) + + hydragnn.utils.load_existing_model_config( + model, config["NeuralNetwork"]["Training"], optimizer=optimizer + ) + + ################################################################################################################## + + hydragnn.train.train_validate_test( + model, + optimizer, + train_loader, + val_loader, + test_loader, + writer, + scheduler, + config["NeuralNetwork"], + log_name, + verbosity, + create_plots=True, + compute_grad_energy=True + ) + + hydragnn.utils.save_model(model, optimizer, log_name) + hydragnn.utils.print_timers(verbosity) + + if tr.has("GPTLTracer"): + import gptl4py as gp + + eligible = rank if args.everyone else 0 + if rank == eligible: + gp.pr_file(os.path.join("logs", log_name, "gp_timing.p%d" % rank)) + gp.pr_summary_file(os.path.join("logs", log_name, "gp_timing.summary")) + gp.finalize() + sys.exit(0) diff --git a/examples/LennardJones/README.md b/examples/LennardJones/README.md new file mode 100644 index 000000000..0c4bda202 --- /dev/null +++ b/examples/LennardJones/README.md @@ -0,0 +1,92 @@ +# PyTorch_Interatomic_Potentials + + + +## Getting started + +To make it easy for you to get started with GitLab, here's a list of recommended next steps. + +Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! + +## Add your files + +- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files +- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: + +``` +cd existing_repo +git remote add origin https://code.ornl.gov/aisd/pytorch_interatomic_potentials.git +git branch -M main +git push -uf origin main +``` + +## Integrate with your tools + +- [ ] [Set up project integrations](https://code.ornl.gov/aisd/pytorch_interatomic_potentials/-/settings/integrations) + +## Collaborate with your team + +- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) +- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) +- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) +- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) +- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) + +## Test and Deploy + +Use the built-in continuous integration in GitLab. + +- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) +- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) +- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) +- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) +- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) + +*** + +# Editing this README + +When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template. + +## Suggestions for a good README +Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. + +## Name +Choose a self-explaining name for your project. + +## Description +Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. + +## Badges +On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. + +## Visuals +Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. + +## Installation +Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. + +## Usage +Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. + +## Support +Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. + +## Roadmap +If you have ideas for releases in the future, it is a good idea to list them in the README. + +## Contributing +State if you are open to contributions and what your requirements are for accepting them. + +For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. + +You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. + +## Authors and acknowledgment +Show your appreciation to those who have contributed to the project. + +## License +For open source projects, say how it is licensed. + +## Project status +If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. diff --git a/examples/LennardJones/__init__.py b/examples/LennardJones/__init__.py new file mode 100644 index 000000000..7253c920c --- /dev/null +++ b/examples/LennardJones/__init__.py @@ -0,0 +1 @@ +from .LennardJones import LJpotential \ No newline at end of file diff --git a/examples/LennardJones/configurational_data.py b/examples/LennardJones/configurational_data.py new file mode 100644 index 000000000..6f329a5cb --- /dev/null +++ b/examples/LennardJones/configurational_data.py @@ -0,0 +1,214 @@ +############################################################################## +# Copyright (c) 2021, Oak Ridge National Laboratory # +# All rights reserved. # +# # +# This file is part of HydraGNN and is distributed under a BSD 3-clause # +# license. For the licensing terms see the LICENSE file in the top-level # +# directory. # +# # +# SPDX-License-Identifier: BSD-3-Clause # +############################################################################## + +import os +import torch +import numpy +numpy.set_printoptions(threshold=numpy.inf) +numpy.set_printoptions(linewidth=numpy.inf) + +torch.set_default_tensor_type(torch.DoubleTensor) +torch.set_default_dtype(torch.float64) + +from torch_geometric.data import Data + +from graph_utils import get_radius_graph_pbc +from AtomicStructure import AtomicStructureHandler +from LJpotential import LJpotential + +from distributed_utils import nsplit + +from mpi4py import MPI + +# Angstrom unit +primitive_bravais_lattice_constant_x = 3.8 +primitive_bravais_lattice_constant_y = 3.8 +primitive_bravais_lattice_constant_z = 3.8 + + +def deterministic_graph_data( + path: str, + atom_types: list, + atomic_structure_handler, + radius_cutoff = float('inf'), + max_num_neighbors = float('inf'), + number_configurations: int = 500, + configuration_start: int = 0, + unit_cell_x_range: list = [3, 4], + unit_cell_y_range: list = [3, 4], + unit_cell_z_range: list = [3, 4], + relative_maximum_atomic_displacement: float = 1e-1, +): + + comm = MPI.COMM_WORLD + comm_size = comm.Get_size() + comm_rank = comm.Get_rank() + torch.manual_seed(comm_rank) + + if 0 == comm_rank: + os.makedirs(path, exist_ok=False) + comm.Barrier() + + # We assume that the unit cell is Simple Center Cubic (SCC) + unit_cell_x = torch.randint( + unit_cell_x_range[0], + unit_cell_x_range[1], + (number_configurations,), + ) + unit_cell_y = torch.randint( + unit_cell_y_range[0], + unit_cell_y_range[1], + (number_configurations,), + ) + unit_cell_z = torch.randint( + unit_cell_z_range[0], + unit_cell_z_range[1], + (number_configurations,), + ) + + configurations_list = range(number_configurations) + rx = list(nsplit(configurations_list, comm_size))[comm_rank] + + for configuration in configurations_list[rx.start:rx.stop]: + uc_x = unit_cell_x[configuration] + uc_y = unit_cell_y[configuration] + uc_z = unit_cell_z[configuration] + create_configuration( + path, + atomic_structure_handler, + configuration, + configuration_start, + uc_x, + uc_y, + uc_z, + atom_types, + radius_cutoff, + max_num_neighbors, + relative_maximum_atomic_displacement + ) + + +def create_configuration( + path, + atomic_structure_handler, + configuration, + configuration_start, + uc_x, + uc_y, + uc_z, + types, + radius_cutoff, + max_num_neighbors, + relative_maximum_atomic_displacement +): + ############################################################################################### + ################################### STRUCTURE OF THE DATA ################################## + ############################################################################################### + + # GLOBAL_OUTPUT1 + # GLOBAL_OUTPUT2 + # NODE1_FEATURE NODE1_INDEX NODE1_COORDINATE_X NODE1_COORDINATE_Y NODE1_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 + # NODE2_FEATURE NODE2_INDEX NODE2_COORDINATE_X NODE2_COORDINATE_Y NODE2_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 + # ... + # NODENn_FEATURE NODEn_INDEX NODEn_COORDINATE_X NODEn_COORDINATE_Y NODEn_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 + + ############################################################################################### + ################################# FORMULAS FOR NODAL FEATURE ############################### + ############################################################################################### + + # NODAL_FEATURE = ATOM SPECIES + + ############################################################################################### + ########################## FORMULAS FOR GLOBAL AND NODAL OUTPUTS ########################### + ############################################################################################### + + # GLOBAL_OUTPUT = TOTAL ENERGY + # GLOBAL_OUTPUT = TOTAL ENERGY / NUMBER OF NODES + # NODAL_OUTPUT1(X) = FORCE ACTING ON ATOM IN X DIRECTION + # NODAL_OUTPUT2(X) = FORCE ACTING ON ATOM IN Y DIRECTION + # NODAL_OUTPUT3(X) = FORCE ACTING ON ATOM IN Z DIRECTION + + ############################################################################################### + count_pos = 0 + number_nodes = uc_x * uc_y * uc_z + positions = torch.zeros(number_nodes, 3) + for x in range(uc_x): + for y in range(uc_y): + for z in range(uc_z): + positions[count_pos][0] = (x + relative_maximum_atomic_displacement * ( + (torch.rand(1, 1).item()) - 0.5)) * primitive_bravais_lattice_constant_x + positions[count_pos][1] = (y + relative_maximum_atomic_displacement * ( + (torch.rand(1, 1).item()) - 0.5)) * primitive_bravais_lattice_constant_y + positions[count_pos][2] = (z + relative_maximum_atomic_displacement * ( + (torch.rand(1, 1).item()) - 0.5)) * primitive_bravais_lattice_constant_z + + count_pos = count_pos + 1 + + atom_types = torch.randint(min(types), max(types) + 1, (number_nodes, 1)) + + data = Data() + + data.pos = positions + supercell_size_x = primitive_bravais_lattice_constant_x * uc_x + supercell_size_y = primitive_bravais_lattice_constant_y * uc_y + supercell_size_z = primitive_bravais_lattice_constant_z * uc_z + data.supercell_size = torch.diag(torch.tensor([supercell_size_x, supercell_size_y, supercell_size_z])) + + create_graph_connectivity_pbc = get_radius_graph_pbc(radius_cutoff, max_num_neighbors) + data = create_graph_connectivity_pbc(data) + + atomic_descriptors = torch.cat( + ( + atom_types, + positions, + ), + 1, + ) + + data.x = atomic_descriptors + + data = atomic_structure_handler.compute(data) + + total_energy = torch.sum(data.x[:,4]) + energy_per_atom = total_energy/number_nodes + + total_energy_str = numpy.array2string(total_energy.detach().numpy()) + energy_per_atom_str = numpy.array2string(energy_per_atom.detach().numpy()) + filetxt = total_energy_str + "\n" + energy_per_atom_str + + for index in range(0, 3): + numpy_row = data.supercell_size[index, :].detach().numpy() + numpy_string_row = numpy.array2string( + numpy_row, precision=64, separator="\t" + ) + filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") + + for index in range(0, number_nodes): + numpy_row = data.x[index, :].detach().numpy() + numpy_string_row = numpy.array2string( + numpy_row, precision=64, separator="\t" + ) + filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") + + filename = os.path.join( + path, "output" + str(configuration + configuration_start) + ".txt" + ) + with open(filename, "w") as f: + f.write(filetxt) + + +if __name__=="__main__": + path = "./data" + radius_cutoff = 5.0 + atom_types = [1] + formula = LJpotential(1.0, 3.4) + AtomicStructureHandler = AtomicStructureHandler(atom_types, [primitive_bravais_lattice_constant_x, primitive_bravais_lattice_constant_y, primitive_bravais_lattice_constant_z], radius_cutoff, formula) + deterministic_graph_data(path, atom_types, 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z$NimpZ__=j;MT^*`4@i<331?JnICH)M1hH=!!N}+;}YI!yJ*zpd<+?6R?coCO&r_V z-n6$pcwn)P%ih3Z?SHn>uf2e8|C9d%YqkBa{ACvBUp)Nezu!nZdeweKf0b7H_XV{- zLFK>y=Fb0@$MLti;J+N;_W%2~f0^r7w@T2cv4yRrb^qbBrhPFS+xVwrGRMF5Y5Sk6 xsCHiU=Ih}DRNCkR{K?tzg`;eJcuyr9N`rENS2iO1gkGo$IbrkKLKN>5bFQ{ literal 0 HcmV?d00001 diff --git a/examples/LennardJones/graph_utils.py b/examples/LennardJones/graph_utils.py new file mode 100644 index 000000000..8d290c9c3 --- /dev/null +++ b/examples/LennardJones/graph_utils.py @@ -0,0 +1,55 @@ +import torch +from torch_geometric.transforms import RadiusGraph +import ase +import ase.neighborlist +from torch_geometric.utils import remove_self_loops, degree + +class RadiusGraphPBC(RadiusGraph): + r"""Creates edges based on node positions :obj:`pos` to all points within a + given distance, including periodic images. + """ + + def __call__(self, data): + data.edge_attr = None + assert ( + "batch" not in data + ), "Periodic boundary conditions not currently supported on batches." + assert hasattr( + data, "supercell_size" + ), "The data must contain the size of the supercell to apply periodic boundary conditions." + ase_atom_object = ase.Atoms( + positions=data.pos, + cell=data.supercell_size, + pbc=True, + ) + # ‘i’ : first atom index + # ‘j’ : second atom index + # https://wiki.fysik.dtu.dk/ase/ase/neighborlist.html#ase.neighborlist.neighbor_list + edge_src, edge_dst, edge_length = ase.neighborlist.neighbor_list( + "ijd", a=ase_atom_object, cutoff=self.r, self_interaction=self.loop + ) + data.edge_index = torch.stack( + [torch.LongTensor(edge_src), torch.LongTensor(edge_dst)], dim=0 + ) + + # ensure no duplicate edges + num_edges = data.edge_index.size(1) + data.coalesce() + assert num_edges == data.edge_index.size( + 1 + ), "Adding periodic boundary conditions would result in duplicate edges. Cutoff radius must be reduced or system size increased." + + data.edge_attr = torch.tensor(edge_length, dtype=torch.float).unsqueeze(1) + + return data + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(r={self.r})" + + +def get_radius_graph_pbc(radius, max_neighbours, loop=False): + return RadiusGraphPBC( + r=radius, + loop=loop, + max_num_neighbors=max_neighbours, + ) \ No newline at end of file diff --git a/examples/LennardJones/inference.py b/examples/LennardJones/inference.py new file mode 100644 index 000000000..3da1e365e --- /dev/null +++ b/examples/LennardJones/inference.py @@ -0,0 +1,208 @@ +############################################################################## +# Copyright (c) 2021, Oak Ridge National Laboratory # +# All rights reserved. # +# # +# This file is part of HydraGNN and is distributed under a BSD 3-clause # +# license. For the licensing terms see the LICENSE file in the top-level # +# directory. # +# # +# SPDX-License-Identifier: BSD-3-Clause # +############################################################################## + +import json, os +import sys +import logging +import pickle +from tqdm import tqdm +from mpi4py import MPI +import argparse + +import torch +import numpy as np + +import hydragnn +from hydragnn.utils.time_utils import Timer +from hydragnn.utils.distributed import get_device +from hydragnn.utils.model import load_existing_model +from hydragnn.utils.pickledataset import SimplePickleDataset +from hydragnn.utils.config_utils import ( + update_config, +) +from hydragnn.models.create import create_model_config +from hydragnn.preprocess import create_dataloaders + +from scipy.interpolate import griddata + +try: + from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset +except ImportError: + pass + +import matplotlib.pyplot as plt +plt.rcParams.update({"font.size": 16}) + + +def get_log_name_config(config): + return ( + config["NeuralNetwork"]["Architecture"]["model_type"] + + "-r-" + + str(config["NeuralNetwork"]["Architecture"]["radius"]) + + "-ncl-" + + str(config["NeuralNetwork"]["Architecture"]["num_conv_layers"]) + + "-hd-" + + str(config["NeuralNetwork"]["Architecture"]["hidden_dim"]) + + "-ne-" + + str(config["NeuralNetwork"]["Training"]["num_epoch"]) + + "-lr-" + + str(config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"]) + + "-bs-" + + str(config["NeuralNetwork"]["Training"]["batch_size"]) + + "-node_ft-" + + "".join( + str(x) + for x in config["NeuralNetwork"]["Variables_of_interest"][ + "input_node_features" + ] + ) + + "-task_weights-" + + "".join( + str(weigh) + "-" + for weigh in config["NeuralNetwork"]["Architecture"]["task_weights"] + ) + ) + +def getcolordensity(xdata, ydata): + ############################### + nbin = 20 + hist2d, xbins_edge, ybins_edge = np.histogram2d( + x=xdata, y=ydata, bins=[nbin, nbin] + ) + xbin_cen = 0.5 * (xbins_edge[0:-1] + xbins_edge[1:]) + ybin_cen = 0.5 * (ybins_edge[0:-1] + ybins_edge[1:]) + BCTY, BCTX = np.meshgrid(ybin_cen, xbin_cen) + hist2d = hist2d / np.amax(hist2d) + print(np.amax(hist2d)) + + bctx1d = np.reshape(BCTX, len(xbin_cen) * nbin) + bcty1d = np.reshape(BCTY, len(xbin_cen) * nbin) + loc_pts = np.zeros((len(xbin_cen) * nbin, 2)) + loc_pts[:, 0] = bctx1d + loc_pts[:, 1] = bcty1d + hist2d_norm = griddata( + loc_pts, + hist2d.reshape(len(xbin_cen) * nbin), + (xdata, ydata), + method="linear", + fill_value=0, + ) # np.nan) + return hist2d_norm + +def info(*args, logtype="info", sep=" "): + getattr(logging, logtype)(sep.join(map(str, args))) + +if __name__ == "__main__": + + modelname = "LJ" + + parser = argparse.ArgumentParser() + parser.add_argument( + "--inputfile", help="input file", type=str, default="./logs/LJ/config.json" + ) + group = parser.add_mutually_exclusive_group() + group.add_argument( + "--adios", + help="Adios gan_dataset", + action="store_const", + dest="format", + const="adios", + ) + group.add_argument( + "--pickle", + help="Pickle gan_dataset", + action="store_const", + dest="format", + const="pickle", + ) + parser.set_defaults(format="pickle") + + args = parser.parse_args() + + dirpwd = os.path.dirname(os.path.abspath(__file__)) + input_filename = os.path.join(dirpwd, args.inputfile) + with open(input_filename, "r") as f: + config = json.load(f) + hydragnn.utils.setup_log(get_log_name_config(config)) + ################################################################################################################## + # Always initialize for multi-rank training. + comm_size, rank = hydragnn.utils.setup_ddp() + ################################################################################################################## + comm = MPI.COMM_WORLD + + datasetname = "LJ" + + comm.Barrier() + + timer = Timer("load_data") + timer.start() + if args.format == "pickle": + info("Pickle load") + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + trainset = SimplePickleDataset(basedir=basedir, label="trainset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + valset = SimplePickleDataset(basedir=basedir, label="valset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + testset = SimplePickleDataset(basedir=basedir, label="testset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + pna_deg = trainset.pna_deg + else: + raise NotImplementedError("No supported format: %s" % (args.format)) + + model = create_model_config( + config=config["NeuralNetwork"], + verbosity=config["Verbosity"]["level"], + ) + + model = torch.nn.parallel.DistributedDataParallel( + model + ) + + load_existing_model(model, modelname, path="./logs/") + model.eval() + + variable_index = 0 + for output_name, output_type, output_dim in zip(config["NeuralNetwork"]["Variables_of_interest"]["output_names"], config["NeuralNetwork"]["Variables_of_interest"]["type"], config["NeuralNetwork"]["Variables_of_interest"]["output_dim"]): + + test_MAE = 0.0 + + num_samples = len(testset) + true_values = [] + predicted_values = [] + + for data_id, data in enumerate(tqdm(testset)): + predicted = model(data.to(get_device())) + predicted = predicted[variable_index].flatten() + start = data.y_loc[0][variable_index].item() + end = data.y_loc[0][variable_index + 1].item() + true = data.y[start:end, 0] + test_MAE += torch.norm(predicted - true, p=1).item()/len(testset) + predicted_values.extend(predicted.tolist()) + true_values.extend(true.tolist()) + + hist2d_norm = getcolordensity(true_values, predicted_values) + + fig, ax = plt.subplots() + plt.scatter( + true_values, predicted_values, s=8, c=hist2d_norm, vmin=0, vmax=1 + ) + plt.clim(0, 1) + ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") + plt.colorbar() + plt.xlabel("True values") + plt.ylabel("Predicted values") + plt.title(f"{output_name}") + plt.draw() + plt.tight_layout() + plt.savefig(f"./{output_name}_Scatterplot" + ".png", dpi=400) + + print(f"Test MAE {output_name}: ", test_MAE) + + variable_index += 1 \ No newline at end of file diff --git a/examples/LennardJones/inference_derivative_energy.py b/examples/LennardJones/inference_derivative_energy.py new file mode 100644 index 000000000..7aca2ae86 --- /dev/null +++ b/examples/LennardJones/inference_derivative_energy.py @@ -0,0 +1,229 @@ +############################################################################## +# Copyright (c) 2021, Oak Ridge National Laboratory # +# All rights reserved. # +# # +# This file is part of HydraGNN and is distributed under a BSD 3-clause # +# license. For the licensing terms see the LICENSE file in the top-level # +# directory. # +# # +# SPDX-License-Identifier: BSD-3-Clause # +############################################################################## + +import json, os +import sys +import logging +import pickle +from tqdm import tqdm +from mpi4py import MPI +import argparse + +import torch +import torch_scatter +import numpy as np + +import hydragnn +from hydragnn.utils.time_utils import Timer +from hydragnn.utils.distributed import get_device +from hydragnn.utils.model import load_existing_model +from hydragnn.utils.pickledataset import SimplePickleDataset +from hydragnn.utils.config_utils import ( + update_config, +) +from hydragnn.models.create import create_model_config +from hydragnn.preprocess import create_dataloaders + +from scipy.interpolate import griddata + +try: + from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset +except ImportError: + pass + +import matplotlib.pyplot as plt +plt.rcParams.update({"font.size": 16}) + + +def get_log_name_config(config): + return ( + config["NeuralNetwork"]["Architecture"]["model_type"] + + "-r-" + + str(config["NeuralNetwork"]["Architecture"]["radius"]) + + "-ncl-" + + str(config["NeuralNetwork"]["Architecture"]["num_conv_layers"]) + + "-hd-" + + str(config["NeuralNetwork"]["Architecture"]["hidden_dim"]) + + "-ne-" + + str(config["NeuralNetwork"]["Training"]["num_epoch"]) + + "-lr-" + + str(config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"]) + + "-bs-" + + str(config["NeuralNetwork"]["Training"]["batch_size"]) + + "-node_ft-" + + "".join( + str(x) + for x in config["NeuralNetwork"]["Variables_of_interest"][ + "input_node_features" + ] + ) + + "-task_weights-" + + "".join( + str(weigh) + "-" + for weigh in config["NeuralNetwork"]["Architecture"]["task_weights"] + ) + ) + +def getcolordensity(xdata, ydata): + ############################### + nbin = 20 + hist2d, xbins_edge, ybins_edge = np.histogram2d( + x=xdata, y=ydata, bins=[nbin, nbin] + ) + xbin_cen = 0.5 * (xbins_edge[0:-1] + xbins_edge[1:]) + ybin_cen = 0.5 * (ybins_edge[0:-1] + ybins_edge[1:]) + BCTY, BCTX = np.meshgrid(ybin_cen, xbin_cen) + hist2d = hist2d / np.amax(hist2d) + print(np.amax(hist2d)) + + bctx1d = np.reshape(BCTX, len(xbin_cen) * nbin) + bcty1d = np.reshape(BCTY, len(xbin_cen) * nbin) + loc_pts = np.zeros((len(xbin_cen) * nbin, 2)) + loc_pts[:, 0] = bctx1d + loc_pts[:, 1] = bcty1d + hist2d_norm = griddata( + loc_pts, + hist2d.reshape(len(xbin_cen) * nbin), + (xdata, ydata), + method="linear", + fill_value=0, + ) # np.nan) + return hist2d_norm + +def info(*args, logtype="info", sep=" "): + getattr(logging, logtype)(sep.join(map(str, args))) + +if __name__ == "__main__": + + modelname = "LJ" + + parser = argparse.ArgumentParser() + parser.add_argument( + "--inputfile", help="input file", type=str, default="./logs/LJ/config.json" + ) + group = parser.add_mutually_exclusive_group() + group.add_argument( + "--adios", + help="Adios gan_dataset", + action="store_const", + dest="format", + const="adios", + ) + group.add_argument( + "--pickle", + help="Pickle gan_dataset", + action="store_const", + dest="format", + const="pickle", + ) + parser.set_defaults(format="pickle") + + args = parser.parse_args() + + dirpwd = os.path.dirname(os.path.abspath(__file__)) + input_filename = os.path.join(dirpwd, args.inputfile) + with open(input_filename, "r") as f: + config = json.load(f) + hydragnn.utils.setup_log(get_log_name_config(config)) + ################################################################################################################## + # Always initialize for multi-rank training. + comm_size, rank = hydragnn.utils.setup_ddp() + ################################################################################################################## + comm = MPI.COMM_WORLD + + datasetname = "LJ" + + comm.Barrier() + + timer = Timer("load_data") + timer.start() + if args.format == "pickle": + info("Pickle load") + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + trainset = SimplePickleDataset(basedir=basedir, label="trainset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + valset = SimplePickleDataset(basedir=basedir, label="valset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + testset = SimplePickleDataset(basedir=basedir, label="testset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + pna_deg = trainset.pna_deg + else: + raise NotImplementedError("No supported format: %s" % (args.format)) + + model = create_model_config( + config=config["NeuralNetwork"], + verbosity=config["Verbosity"]["level"], + ) + + model = torch.nn.parallel.DistributedDataParallel( + model + ) + + load_existing_model(model, modelname, path="./logs/") + model.eval() + + variable_index = 0 + #for output_name, output_type, output_dim in zip(config["NeuralNetwork"]["Variables_of_interest"]["output_names"], config["NeuralNetwork"]["Variables_of_interest"]["type"], config["NeuralNetwork"]["Variables_of_interest"]["output_dim"]): + + test_MAE = 0.0 + + num_samples = len(testset) + energy_true_list = [] + energy_pred_list = [] + forces_true_list = [] + forces_pred_list = [] + + for data_id, data in enumerate(tqdm(testset)): + data.pos.requires_grad = True + node_energy_pred = model(data.to(get_device()))[0] # Note that this is sensitive to energy and forces prediction being single-task (current requirement) + energy_pred = torch.sum(node_energy_pred, dim=0).float() + test_MAE += torch.norm(energy_pred - data.energy, p=1).item() / len(testset) + #predicted.backward(retain_graph=True) + #gradients = data.pos.grad + grads_energy = torch.autograd.grad(outputs=energy_pred, inputs=data.pos, + grad_outputs=torch.ones_like(energy_pred), + retain_graph=False, create_graph=True)[0] + energy_pred_list.extend(energy_pred.tolist()) + energy_true_list.extend(data.energy.tolist()) + forces_pred_list.extend((-grads_energy).flatten().tolist()) + forces_true_list.extend(data.forces.flatten().tolist()) + + hist2d_norm = getcolordensity(energy_true_list, energy_pred_list) + + fig, ax = plt.subplots() + plt.scatter( + energy_true_list, energy_pred_list, s=8, c=hist2d_norm, vmin=0, vmax=1 + ) + plt.clim(0, 1) + ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") + plt.colorbar() + plt.xlabel("True values") + plt.ylabel("Predicted values") + plt.title(f"energy") + plt.draw() + plt.tight_layout() + plt.savefig(f"./energy_Scatterplot" + ".png", dpi=400) + + print(f"Test MAE energy: ", test_MAE) + + hist2d_norm = getcolordensity(forces_pred_list, forces_true_list) + fig, ax = plt.subplots() + plt.scatter( + forces_pred_list, forces_true_list, s=8, c=hist2d_norm, vmin=0, vmax=1 + ) + plt.clim(0, 1) + ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") + plt.colorbar() + plt.xlabel("Predicted Values") + plt.ylabel("True Values") + plt.title("Forces") + plt.draw() + plt.tight_layout() + plt.savefig(f"./Forces_Scatterplot" + ".png", dpi=400) diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py new file mode 100644 index 000000000..156e7d400 --- /dev/null +++ b/examples/LennardJones/train_vlad_atomic_forces.py @@ -0,0 +1,412 @@ +import mpi4py +from mpi4py import MPI + +mpi4py.rc.thread_level = "serialized" +mpi4py.rc.threads = False + +import os, json +import random + +import h5py + +import logging +import sys +import argparse + +import hydragnn +from hydragnn.utils.print_utils import iterate_tqdm, log +from hydragnn.utils.time_utils import Timer + +from hydragnn.preprocess.load_data import split_dataset +from hydragnn.utils.abstractrawdataset import AbstractBaseDataset +from hydragnn.utils.distdataset import DistDataset +from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset +from hydragnn.preprocess.utils import gather_deg + +import numpy as np + +try: + from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset +except ImportError: + pass + +from torch_geometric.data import Data +from torch_geometric.transforms import RadiusGraph, Distance, Spherical, LocalCartesian +import torch +import torch.distributed as dist + +from hydragnn.utils import nsplit +import hydragnn.utils.tracer as tr + + +# FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones + + +torch.set_default_dtype(torch.float32) + + +def info(*args, logtype="info", sep=" "): + getattr(logging, logtype)(sep.join(map(str, args))) + + +# FIXME: this radis cutoff overwrites the radius cutoff currently written in the JSON file +create_graph_fromXYZ = RadiusGraph(r=5.0) # radius cutoff in angstrom +compute_edge_lengths = Distance(norm=False, cat=True) +spherical_coordinates = Spherical(norm=False, cat=False) +cartesian_coordinates = LocalCartesian(norm=False, cat=False) + + +class LJDataset_VladTest(AbstractBaseDataset): + """LJDataset_VladTest dataset class""" + + def __init__(self, dirpath, dist=False, sampling=None): + super().__init__() + + self.dist = dist + self.world_size = 1 + self.rank = 1 + if self.dist: + assert torch.distributed.is_initialized() + self.world_size = torch.distributed.get_world_size() + self.rank = torch.distributed.get_rank() + + dirfiles = sorted(os.listdir(dirpath)) + + rx = list(nsplit((dirfiles), self.world_size))[self.rank] + + for file in rx: + filepath = os.path.join(dirpath, file) + self.dataset.append(self.transform_input_to_data_object_base(filepath)) + + def transform_input_to_data_object_base(self, filepath): + + # Using readline() + file = open(filepath, "r") + + torch_data = torch.empty((0, 4), dtype=torch.float32) + + count = 0 + + while True: + count += 1 + + # Get next line from file + line = file.readline() + + # if line is empty + # end of file is reached + if not line: + break + + if count == 1: + array_line = np.fromstring(line, dtype=float, sep="\t") + energy = torch.from_numpy(array_line).unsqueeze(0) + elif count == 2: + array_line = np.fromstring(line, dtype=float, sep="\t") + forces = torch.from_numpy(array_line).unsqueeze(0) + else: + array_line = np.fromstring(line, dtype=float, sep="\t") + torch_data = torch.cat( + [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 + ) + # print("Line{}: {}".format(count, line.strip())) + + file.close() + + pos = torch_data[:, [1, 2, 3]].to(torch.float32) + atomic_number = torch_data[:, [0]].to(torch.float32) + forces_torch = torch.zeros_like(pos) + forces_torch[0,:] = forces.unsqueeze(0) + + data = Data( + pos=pos, + x=torch.cat((atomic_number, forces_torch.to(torch.float32)), 1), + y = [] + ) + data = create_graph_fromXYZ(data) + data = compute_edge_lengths(data) + data.edge_attr = data.edge_attr.to(torch.float32) + #data = spherical_coordinates(data) + data = cartesian_coordinates(data) + + #maintain directionality + data.edge_index = data.edge_index[:,0:6] + data.edge_attr= data.edge_attr[0:6,:] + + return data + + def len(self): + return len(self.dataset) + + def get(self, idx): + return self.dataset[idx] + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument("--sampling", type=float, help="sampling ratio", default=None) + parser.add_argument( + "--preonly", + action="store_true", + help="preprocess only (no training)", + ) + parser.add_argument("--inputfile", help="input file", type=str, default="LJ_vlad_atomic_forces.json") + parser.add_argument("--mae", action="store_true", help="do mae calculation") + parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") + parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) + parser.add_argument("--shmem", action="store_true", help="shmem") + parser.add_argument("--log", help="log name") + parser.add_argument("--batch_size", type=int, help="batch_size", default=None) + parser.add_argument("--everyone", action="store_true", help="gptimer") + + group = parser.add_mutually_exclusive_group() + group.add_argument( + "--adios", + help="Adios dataset", + action="store_const", + dest="format", + const="adios", + ) + group.add_argument( + "--pickle", + help="Pickle dataset", + action="store_const", + dest="format", + const="pickle", + ) + parser.set_defaults(format="adios") + args = parser.parse_args() + + graph_feature_names = [] + graph_feature_dims = [] + node_feature_names = ["atomic_number", "forces"] + node_feature_dims = [1, 3] + dirpwd = os.path.dirname(os.path.abspath(__file__)) + datadir = os.path.join(dirpwd, "dataset/data") + ################################################################################################################## + input_filename = os.path.join(dirpwd, args.inputfile) + ################################################################################################################## + # Configurable run choices (JSON file that accompanies this example script). + with open(input_filename, "r") as f: + config = json.load(f) + verbosity = config["Verbosity"]["level"] + config["NeuralNetwork"]["Variables_of_interest"][ + "graph_feature_names" + ] = graph_feature_names + config["NeuralNetwork"]["Variables_of_interest"][ + "graph_feature_dims" + ] = graph_feature_dims + config["NeuralNetwork"]["Variables_of_interest"][ + "node_feature_names" + ] = node_feature_names + config["NeuralNetwork"]["Variables_of_interest"][ + "node_feature_dims" + ] = node_feature_dims + + if args.batch_size is not None: + config["NeuralNetwork"]["Training"]["batch_size"] = args.batch_size + + ################################################################################################################## + # Always initialize for multi-rank training. + comm_size, rank = hydragnn.utils.setup_ddp() + ################################################################################################################## + + comm = MPI.COMM_WORLD + + ## Set up logging + logging.basicConfig( + level=logging.INFO, + format="%%(levelname)s (rank %d): %%(message)s" % (rank), + datefmt="%H:%M:%S", + ) + + log_name = "LJ" if args.log is None else args.log + hydragnn.utils.setup_log(log_name) + writer = hydragnn.utils.get_summary_writer(log_name) + + log("Command: {0}\n".format(" ".join([x for x in sys.argv])), rank=0) + + modelname = "LJ" + if args.preonly: + + ## local data + total = LJDataset_VladTest( + os.path.join(datadir), + dist=True, + ) + ## This is a local split + trainset, valset, testset = split_dataset( + dataset=total, + perc_train=0.9, + stratify_splitting=False, + ) + print("Local splitting: ", len(total), len(trainset), len(valset), len(testset)) + + deg = gather_deg(trainset) + config["pna_deg"] = deg + + setnames = ["trainset", "valset", "testset"] + + if args.format == "pickle": + + ## pickle + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + attrs = dict() + attrs["pna_deg"] = deg + SimplePickleWriter( + trainset, + basedir, + "trainset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + attrs=attrs, + ) + SimplePickleWriter( + valset, + basedir, + "valset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + ) + SimplePickleWriter( + testset, + basedir, + "testset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + ) + + if args.format == "adios": + ## adios + fname = os.path.join( + os.path.dirname(__file__), "./dataset/%s.bp" % modelname + ) + adwriter = AdiosWriter(fname, comm) + adwriter.add("trainset", trainset) + adwriter.add("valset", valset) + adwriter.add("testset", testset) + # adwriter.add_global("minmax_node_feature", total.minmax_node_feature) + # adwriter.add_global("minmax_graph_feature", total.minmax_graph_feature) + adwriter.add_global("pna_deg", deg) + adwriter.save() + + sys.exit(0) + + tr.initialize() + tr.disable() + timer = Timer("load_data") + timer.start() + if args.format == "adios": + info("Adios load") + assert not (args.shmem and args.ddstore), "Cannot use both ddstore and shmem" + opt = { + "preload": False, + "shmem": args.shmem, + "ddstore": args.ddstore, + "ddstore_width": args.ddstore_width, + } + fname = os.path.join(os.path.dirname(__file__), "./dataset/%s.bp" % modelname) + trainset = AdiosDataset(fname, "trainset", comm, **opt) + valset = AdiosDataset(fname, "valset", comm, **opt) + testset = AdiosDataset(fname, "testset", comm, **opt) + elif args.format == "pickle": + info("Pickle load") + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + var_config = config["NeuralNetwork"]["Variables_of_interest"] + trainset = SimplePickleDataset( + basedir=basedir, label="trainset", preload=True, var_config=var_config + ) + valset = SimplePickleDataset( + basedir=basedir, label="valset", var_config=var_config + ) + testset = SimplePickleDataset( + basedir=basedir, label="testset", var_config=var_config + ) + # minmax_node_feature = trainset.minmax_node_feature + # minmax_graph_feature = trainset.minmax_graph_feature + pna_deg = trainset.pna_deg + if args.ddstore: + opt = {"ddstore_width": args.ddstore_width} + trainset = DistDataset(trainset, "trainset", comm, **opt) + valset = DistDataset(valset, "valset", comm, **opt) + testset = DistDataset(testset, "testset", comm, **opt) + # trainset.minmax_node_feature = minmax_node_feature + # trainset.minmax_graph_feature = minmax_graph_feature + trainset.pna_deg = pna_deg + else: + raise NotImplementedError("No supported format: %s" % (args.format)) + + info( + "trainset,valset,testset size: %d %d %d" + % (len(trainset), len(valset), len(testset)) + ) + + if args.ddstore: + os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" + os.environ["HYDRAGNN_USE_ddstore"] = "1" + + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + ) + + config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) + ## Good to sync with everyone right after DDStore setup + comm.Barrier() + + hydragnn.utils.save_config(config, log_name) + + timer.stop() + + model = hydragnn.models.create_model_config( + config=config["NeuralNetwork"], + verbosity=verbosity, + ) + model = hydragnn.utils.get_distributed_model(model, verbosity) + + learning_rate = config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"] + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.5, patience=5, min_lr=0.00001 + ) + + hydragnn.utils.load_existing_model_config( + model, config["NeuralNetwork"]["Training"], optimizer=optimizer + ) + + ################################################################################################################## + + hydragnn.train.train_validate_test( + model, + optimizer, + train_loader, + val_loader, + test_loader, + writer, + scheduler, + config["NeuralNetwork"], + log_name, + verbosity, + create_plots=False, + ) + + hydragnn.utils.save_model(model, optimizer, log_name) + hydragnn.utils.print_timers(verbosity) + + if tr.has("GPTLTracer"): + import gptl4py as gp + + eligible = rank if args.everyone else 0 + if rank == eligible: + gp.pr_file(os.path.join("logs", log_name, "gp_timing.p%d" % rank)) + gp.pr_summary_file(os.path.join("logs", log_name, "gp_timing.summary")) + gp.finalize() + sys.exit(0) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py new file mode 100644 index 000000000..14179655f --- /dev/null +++ b/examples/LennardJones/train_vlad_total_energy.py @@ -0,0 +1,408 @@ +import mpi4py +from mpi4py import MPI + +mpi4py.rc.thread_level = "serialized" +mpi4py.rc.threads = False + +import os, json +import random + +import h5py + +import logging +import sys +import argparse + +import hydragnn +from hydragnn.utils.print_utils import iterate_tqdm, log +from hydragnn.utils.time_utils import Timer + +from hydragnn.preprocess.load_data import split_dataset +from hydragnn.utils.abstractrawdataset import AbstractBaseDataset +from hydragnn.utils.distdataset import DistDataset +from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset +from hydragnn.preprocess.utils import gather_deg + +import numpy as np + +try: + from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset +except ImportError: + pass + +from torch_geometric.data import Data +from torch_geometric.transforms import RadiusGraph, Distance, Spherical, LocalCartesian +import torch +import torch.distributed as dist + +from hydragnn.utils import nsplit +import hydragnn.utils.tracer as tr + + +# FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones + + +torch.set_default_dtype(torch.float32) + + +def info(*args, logtype="info", sep=" "): + getattr(logging, logtype)(sep.join(map(str, args))) + + +# FIXME: this radis cutoff overwrites the radius cutoff currently written in the JSON file +create_graph_fromXYZ = RadiusGraph(r=5.0) # radius cutoff in angstrom +compute_edge_lengths = Distance(norm=False, cat=True) +spherical_coordinates = Spherical(norm=False, cat=False) +cartesian_coordinates = LocalCartesian(norm=False, cat=False) + + +class LJDataset_VladTest(AbstractBaseDataset): + """LJDataset_VladTest dataset class""" + + def __init__(self, dirpath, dist=False, sampling=None): + super().__init__() + + self.dist = dist + self.world_size = 1 + self.rank = 1 + if self.dist: + assert torch.distributed.is_initialized() + self.world_size = torch.distributed.get_world_size() + self.rank = torch.distributed.get_rank() + + dirfiles = sorted(os.listdir(dirpath)) + + rx = list(nsplit((dirfiles), self.world_size))[self.rank] + + for file in rx: + filepath = os.path.join(dirpath, file) + self.dataset.append(self.transform_input_to_data_object_base(filepath)) + + def transform_input_to_data_object_base(self, filepath): + + # Using readline() + file = open(filepath, "r") + + torch_data = torch.empty((0, 4), dtype=torch.float32) + + count = 0 + + while True: + count += 1 + + # Get next line from file + line = file.readline() + + # if line is empty + # end of file is reached + if not line: + break + + if count == 1: + array_line = np.fromstring(line, dtype=float, sep="\t") + energy = torch.from_numpy(array_line).unsqueeze(0) + elif count == 2: + array_line = np.fromstring(line, dtype=float, sep="\t") + forces = torch.from_numpy(array_line).unsqueeze(0) + else: + array_line = np.fromstring(line, dtype=float, sep="\t") + torch_data = torch.cat( + [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 + ) + # print("Line{}: {}".format(count, line.strip())) + + file.close() + + data = Data( + pos=torch_data[:, [1, 2, 3]].to(torch.float32), + x=torch_data[:, [0, 1, 2, 3]].to(torch.float32), + forces=forces.unsqueeze(0).to(torch.float32), + y=energy.unsqueeze(0).to(torch.float32), + ) + data = create_graph_fromXYZ(data) + data = compute_edge_lengths(data) + data.edge_attr = data.edge_attr.to(torch.float32) + #data = spherical_coordinates(data) + data = cartesian_coordinates(data) + + #maintain directionality + data.edge_index = data.edge_index[:,0:6] + data.edge_attr= data.edge_attr[0:6,:] + + return data + + def len(self): + return len(self.dataset) + + def get(self, idx): + return self.dataset[idx] + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument("--sampling", type=float, help="sampling ratio", default=None) + parser.add_argument( + "--preonly", + action="store_true", + help="preprocess only (no training)", + ) + parser.add_argument("--inputfile", help="input file", type=str, default="LJ_vlad_total_energy.json") + parser.add_argument("--mae", action="store_true", help="do mae calculation") + parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") + parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) + parser.add_argument("--shmem", action="store_true", help="shmem") + parser.add_argument("--log", help="log name") + parser.add_argument("--batch_size", type=int, help="batch_size", default=None) + parser.add_argument("--everyone", action="store_true", help="gptimer") + + group = parser.add_mutually_exclusive_group() + group.add_argument( + "--adios", + help="Adios dataset", + action="store_const", + dest="format", + const="adios", + ) + group.add_argument( + "--pickle", + help="Pickle dataset", + action="store_const", + dest="format", + const="pickle", + ) + parser.set_defaults(format="adios") + args = parser.parse_args() + + graph_feature_names = ["energy"] + graph_feature_dims = [1] + node_feature_names = ["atomic_number"] + node_feature_dims = [1] + dirpwd = os.path.dirname(os.path.abspath(__file__)) + datadir = os.path.join(dirpwd, "dataset/data") + ################################################################################################################## + input_filename = os.path.join(dirpwd, args.inputfile) + ################################################################################################################## + # Configurable run choices (JSON file that accompanies this example script). + with open(input_filename, "r") as f: + config = json.load(f) + verbosity = config["Verbosity"]["level"] + config["NeuralNetwork"]["Variables_of_interest"][ + "graph_feature_names" + ] = graph_feature_names + config["NeuralNetwork"]["Variables_of_interest"][ + "graph_feature_dims" + ] = graph_feature_dims + config["NeuralNetwork"]["Variables_of_interest"][ + "node_feature_names" + ] = node_feature_names + config["NeuralNetwork"]["Variables_of_interest"][ + "node_feature_dims" + ] = node_feature_dims + + if args.batch_size is not None: + config["NeuralNetwork"]["Training"]["batch_size"] = args.batch_size + + ################################################################################################################## + # Always initialize for multi-rank training. + comm_size, rank = hydragnn.utils.setup_ddp() + ################################################################################################################## + + comm = MPI.COMM_WORLD + + ## Set up logging + logging.basicConfig( + level=logging.INFO, + format="%%(levelname)s (rank %d): %%(message)s" % (rank), + datefmt="%H:%M:%S", + ) + + log_name = "LJ" if args.log is None else args.log + hydragnn.utils.setup_log(log_name) + writer = hydragnn.utils.get_summary_writer(log_name) + + log("Command: {0}\n".format(" ".join([x for x in sys.argv])), rank=0) + + modelname = "LJ" + if args.preonly: + + ## local data + total = LJDataset_VladTest( + os.path.join(datadir), + dist=True, + ) + ## This is a local split + trainset, valset, testset = split_dataset( + dataset=total, + perc_train=0.9, + stratify_splitting=False, + ) + print("Local splitting: ", len(total), len(trainset), len(valset), len(testset)) + + deg = gather_deg(trainset) + config["pna_deg"] = deg + + setnames = ["trainset", "valset", "testset"] + + if args.format == "pickle": + + ## pickle + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + attrs = dict() + attrs["pna_deg"] = deg + SimplePickleWriter( + trainset, + basedir, + "trainset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + attrs=attrs, + ) + SimplePickleWriter( + valset, + basedir, + "valset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + ) + SimplePickleWriter( + testset, + basedir, + "testset", + # minmax_node_feature=total.minmax_node_feature, + # minmax_graph_feature=total.minmax_graph_feature, + use_subdir=True, + ) + + if args.format == "adios": + ## adios + fname = os.path.join( + os.path.dirname(__file__), "./dataset/%s.bp" % modelname + ) + adwriter = AdiosWriter(fname, comm) + adwriter.add("trainset", trainset) + adwriter.add("valset", valset) + adwriter.add("testset", testset) + # adwriter.add_global("minmax_node_feature", total.minmax_node_feature) + # adwriter.add_global("minmax_graph_feature", total.minmax_graph_feature) + adwriter.add_global("pna_deg", deg) + adwriter.save() + + sys.exit(0) + + tr.initialize() + tr.disable() + timer = Timer("load_data") + timer.start() + if args.format == "adios": + info("Adios load") + assert not (args.shmem and args.ddstore), "Cannot use both ddstore and shmem" + opt = { + "preload": False, + "shmem": args.shmem, + "ddstore": args.ddstore, + "ddstore_width": args.ddstore_width, + } + fname = os.path.join(os.path.dirname(__file__), "./dataset/%s.bp" % modelname) + trainset = AdiosDataset(fname, "trainset", comm, **opt) + valset = AdiosDataset(fname, "valset", comm, **opt) + testset = AdiosDataset(fname, "testset", comm, **opt) + elif args.format == "pickle": + info("Pickle load") + basedir = os.path.join( + os.path.dirname(__file__), "dataset", "%s.pickle" % modelname + ) + var_config = config["NeuralNetwork"]["Variables_of_interest"] + trainset = SimplePickleDataset( + basedir=basedir, label="trainset", preload=True, var_config=var_config + ) + valset = SimplePickleDataset( + basedir=basedir, label="valset", var_config=var_config + ) + testset = SimplePickleDataset( + basedir=basedir, label="testset", var_config=var_config + ) + # minmax_node_feature = trainset.minmax_node_feature + # minmax_graph_feature = trainset.minmax_graph_feature + pna_deg = trainset.pna_deg + if args.ddstore: + opt = {"ddstore_width": args.ddstore_width} + trainset = DistDataset(trainset, "trainset", comm, **opt) + valset = DistDataset(valset, "valset", comm, **opt) + testset = DistDataset(testset, "testset", comm, **opt) + # trainset.minmax_node_feature = minmax_node_feature + # trainset.minmax_graph_feature = minmax_graph_feature + trainset.pna_deg = pna_deg + else: + raise NotImplementedError("No supported format: %s" % (args.format)) + + info( + "trainset,valset,testset size: %d %d %d" + % (len(trainset), len(valset), len(testset)) + ) + + if args.ddstore: + os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" + os.environ["HYDRAGNN_USE_ddstore"] = "1" + + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + ) + + config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) + ## Good to sync with everyone right after DDStore setup + comm.Barrier() + + hydragnn.utils.save_config(config, log_name) + + timer.stop() + + model = hydragnn.models.create_model_config( + config=config["NeuralNetwork"], + verbosity=verbosity, + ) + model = hydragnn.utils.get_distributed_model(model, verbosity) + + learning_rate = config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"] + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.5, patience=5, min_lr=0.00001 + ) + + hydragnn.utils.load_existing_model_config( + model, config["NeuralNetwork"]["Training"], optimizer=optimizer + ) + + ################################################################################################################## + + hydragnn.train.train_validate_test( + model, + optimizer, + train_loader, + val_loader, + test_loader, + writer, + scheduler, + config["NeuralNetwork"], + log_name, + verbosity, + create_plots=False, + ) + + hydragnn.utils.save_model(model, optimizer, log_name) + hydragnn.utils.print_timers(verbosity) + + if tr.has("GPTLTracer"): + import gptl4py as gp + + eligible = rank if args.everyone else 0 + if rank == eligible: + gp.pr_file(os.path.join("logs", log_name, "gp_timing.p%d" % rank)) + gp.pr_summary_file(os.path.join("logs", log_name, "gp_timing.summary")) + gp.finalize() + sys.exit(0) diff --git a/tests/test_examples.py b/tests/test_examples.py index 7ca1e4cd0..8a82fa18d 100644 --- a/tests/test_examples.py +++ b/tests/test_examples.py @@ -15,7 +15,7 @@ import subprocess -@pytest.mark.parametrize("example", ["qm9", "md17"]) +@pytest.mark.parametrize("example", ["qm9", "md17", "LennardJones"]) @pytest.mark.mpi_skip() def pytest_examples(example): path = os.path.join(os.path.dirname(__file__), "..", "examples", example) From 0bce3d543217a352933579bf4aeb67a4495d9490 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 13:43:52 -0400 Subject: [PATCH 10/37] formatting --- examples/LennardJones/AtomicStructure.py | 33 ++++---- examples/LennardJones/LJpotential.py | 19 ++++- examples/LennardJones/LennardJones.py | 54 ++++++++++--- examples/LennardJones/__init__.py | 2 +- examples/LennardJones/configurational_data.py | 78 ++++++++++++------- examples/LennardJones/distributed_utils.py | 2 +- examples/LennardJones/graph_utils.py | 3 +- examples/LennardJones/inference.py | 44 +++++++---- .../inference_derivative_energy.py | 58 ++++++++------ .../LennardJones/train_vlad_atomic_forces.py | 22 ++++-- .../LennardJones/train_vlad_total_energy.py | 18 +++-- 11 files changed, 224 insertions(+), 109 deletions(-) diff --git a/examples/LennardJones/AtomicStructure.py b/examples/LennardJones/AtomicStructure.py index bc738dd36..540679023 100644 --- a/examples/LennardJones/AtomicStructure.py +++ b/examples/LennardJones/AtomicStructure.py @@ -1,8 +1,11 @@ import torch -class AtomicStructureHandler(): - def __init__(self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula): +class AtomicStructureHandler: + + def __init__( + self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula + ): self.bravais_lattice_constants = bravais_lattice_constants self.radius_cutoff = radius_cutoff @@ -10,21 +13,23 @@ def __init__(self, list_atom_types, bravais_lattice_constants, radius_cutoff, fo def compute(self, data): - assert (data.pos.shape[0] == data.x.shape[0]) + assert data.pos.shape[0] == data.x.shape[0] interatomic_potential = torch.zeros([data.pos.shape[0], 1]) interatomic_forces = torch.zeros([data.pos.shape[0], 3]) for node_id in range(data.pos.shape[0]): - neighbor_list_indices = torch.where(data.edge_index[0, :] == node_id)[0].tolist() + neighbor_list_indices = torch.where(data.edge_index[0, :] == node_id)[ + 0 + ].tolist() neighbor_list = data.edge_index[1, neighbor_list_indices] for neighbor_id, edge_id in zip(neighbor_list, neighbor_list_indices): neighbor_pos = data.pos[neighbor_id, :] distance_vector = data.pos[neighbor_id, :] - data.pos[node_id, :] - + # Adjust the neighbor position based on periodic boundary conditions (PBC) ## If the distance between the atoms is larger than the cutoff radius, the edge is because of PBC conditions if torch.norm(distance_vector) > self.radius_cutoff: @@ -54,27 +59,25 @@ def compute(self, data): # The distance vecor may need to be updated after applying PBCs distance_vector = data.pos[node_id, :] - neighbor_pos - #pair_distance = data.edge_attr[edge_id].item() - interatomic_potential[node_id] += self.formula.potential_energy(distance_vector) + # pair_distance = data.edge_attr[edge_id].item() + interatomic_potential[node_id] += self.formula.potential_energy( + distance_vector + ) derivative_x = self.formula.derivative_x(distance_vector) derivative_y = self.formula.derivative_y(distance_vector) derivative_z = self.formula.derivative_z(distance_vector) - interatomic_forces_contribution_x = - derivative_x - interatomic_forces_contribution_y = - derivative_y - interatomic_forces_contribution_z = - derivative_z + interatomic_forces_contribution_x = -derivative_x + interatomic_forces_contribution_y = -derivative_y + interatomic_forces_contribution_z = -derivative_z interatomic_forces[node_id, 0] += interatomic_forces_contribution_x interatomic_forces[node_id, 1] += interatomic_forces_contribution_y interatomic_forces[node_id, 2] += interatomic_forces_contribution_z data.x = torch.cat( - ( - data.x, - interatomic_potential, - interatomic_forces - ), + (data.x, interatomic_potential, interatomic_forces), 1, ) diff --git a/examples/LennardJones/LJpotential.py b/examples/LennardJones/LJpotential.py index ef8c1ec6e..7e267d82b 100644 --- a/examples/LennardJones/LJpotential.py +++ b/examples/LennardJones/LJpotential.py @@ -1,6 +1,7 @@ import torch -class LJpotential(): + +class LJpotential: def __init__(self, epsilon, sigma): self.epsilon = epsilon @@ -8,12 +9,22 @@ def __init__(self, epsilon, sigma): def potential_energy(self, distance_vector): pair_distance = torch.norm(distance_vector) - return 4 * self.epsilon * ((self.sigma / pair_distance) ** 12 - (self.sigma / pair_distance) ** 6) + return ( + 4 + * self.epsilon + * ((self.sigma / pair_distance) ** 12 - (self.sigma / pair_distance) ** 6) + ) def radial_derivative(self, distance_vector): pair_distance = torch.norm(distance_vector) - return 4 * self.epsilon * (-12 * (self.sigma / pair_distance) ** 12 * 1 / pair_distance + 6 * ( - self.sigma / pair_distance) ** 6 * 1 / pair_distance) + return ( + 4 + * self.epsilon + * ( + -12 * (self.sigma / pair_distance) ** 12 * 1 / pair_distance + + 6 * (self.sigma / pair_distance) ** 6 * 1 / pair_distance + ) + ) def derivative_x(self, distance_vector): pair_distance = torch.norm(distance_vector) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index 9b99fe37e..a8a8d63ba 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -42,6 +42,8 @@ from configurational_data import deterministic_graph_data from LJpotential import LJpotential from AtomicStructure import AtomicStructureHandler + + def create_dataset(config): # Angstrom unit primitive_bravais_lattice_constant_x = 3.8 @@ -49,11 +51,31 @@ def create_dataset(config): primitive_bravais_lattice_constant_z = 3.8 path = "./dataset/data" radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] - number_configurations = config["NeuralNetwork"]["Training"]["num_configurations"] if "num_configurations" in config["NeuralNetwork"]["Training"] else 1000 + number_configurations = ( + config["NeuralNetwork"]["Training"]["num_configurations"] + if "num_configurations" in config["NeuralNetwork"]["Training"] + else 1000 + ) atom_types = [1] formula = LJpotential(1.0, 3.4) - atomic_structure_handler = AtomicStructureHandler(atom_types, [primitive_bravais_lattice_constant_x, primitive_bravais_lattice_constant_y, primitive_bravais_lattice_constant_z], radius_cutoff, formula) - deterministic_graph_data(path, atom_types, atomic_structure_handler=atomic_structure_handler, radius_cutoff=radius_cutoff, relative_maximum_atomic_displacement=1e-1, number_configurations=number_configurations) + atomic_structure_handler = AtomicStructureHandler( + atom_types, + [ + primitive_bravais_lattice_constant_x, + primitive_bravais_lattice_constant_y, + primitive_bravais_lattice_constant_z, + ], + radius_cutoff, + formula, + ) + deterministic_graph_data( + path, + atom_types, + atomic_structure_handler=atomic_structure_handler, + radius_cutoff=radius_cutoff, + relative_maximum_atomic_displacement=1e-1, + number_configurations=number_configurations, + ) # FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones @@ -138,8 +160,12 @@ def transform_input_to_data_object_base(self, filepath): energy_pre_translation_factor = 0.0 energy_pre_scaling_factor = 1.0 / num_nodes - energy_per_atom_pretransformed = (energy_per_atom - energy_pre_translation_factor) * energy_pre_scaling_factor - grad_energy_post_scaling_factor = 1.0/energy_pre_scaling_factor * torch.ones(num_nodes, 1) + energy_per_atom_pretransformed = ( + energy_per_atom - energy_pre_translation_factor + ) * energy_pre_scaling_factor + grad_energy_post_scaling_factor = ( + 1.0 / energy_pre_scaling_factor * torch.ones(num_nodes, 1) + ) forces = torch_data[:, [5, 6, 7]] forces_pre_scaling_factor = 1.0 forces_pre_scaled = forces * forces_pre_scaling_factor @@ -148,19 +174,23 @@ def transform_input_to_data_object_base(self, filepath): supercell_size=torch_supercell.to(torch.float32), num_nodes=num_nodes, grad_energy_post_scaling_factor=grad_energy_post_scaling_factor, - forces_pre_scaling_factor=torch.tensor(forces_pre_scaling_factor).to(torch.float32), + forces_pre_scaling_factor=torch.tensor(forces_pre_scaling_factor).to( + torch.float32 + ), forces=forces, forces_pre_scaled=forces_pre_scaled, pos=torch_data[:, [1, 2, 3]].to(torch.float32), x=torch.cat([torch_data[:, [0, 4]]], axis=1).to(torch.float32), y=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), - energy_per_atom=torch.tensor(energy_per_atom_pretransformed).unsqueeze(0).to(torch.float32), + energy_per_atom=torch.tensor(energy_per_atom_pretransformed) + .unsqueeze(0) + .to(torch.float32), energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), ) data = create_graph_fromXYZ(data) data = compute_edge_lengths(data) data.edge_attr = data.edge_attr.to(torch.float32) - #data = spherical_coordinates(data) + # data = spherical_coordinates(data) data = cartesian_coordinates(data) return data @@ -391,7 +421,11 @@ def get(self, idx): os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + ( + train_loader, + val_loader, + test_loader, + ) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) @@ -433,7 +467,7 @@ def get(self, idx): log_name, verbosity, create_plots=True, - compute_grad_energy=True + compute_grad_energy=True, ) hydragnn.utils.save_model(model, optimizer, log_name) diff --git a/examples/LennardJones/__init__.py b/examples/LennardJones/__init__.py index 7253c920c..266d92653 100644 --- a/examples/LennardJones/__init__.py +++ b/examples/LennardJones/__init__.py @@ -1 +1 @@ -from .LennardJones import LJpotential \ No newline at end of file +from .LennardJones import LJpotential diff --git a/examples/LennardJones/configurational_data.py b/examples/LennardJones/configurational_data.py index 6f329a5cb..8ebb1d6f8 100644 --- a/examples/LennardJones/configurational_data.py +++ b/examples/LennardJones/configurational_data.py @@ -12,6 +12,7 @@ import os import torch import numpy + numpy.set_printoptions(threshold=numpy.inf) numpy.set_printoptions(linewidth=numpy.inf) @@ -38,8 +39,8 @@ def deterministic_graph_data( path: str, atom_types: list, atomic_structure_handler, - radius_cutoff = float('inf'), - max_num_neighbors = float('inf'), + radius_cutoff=float("inf"), + max_num_neighbors=float("inf"), number_configurations: int = 500, configuration_start: int = 0, unit_cell_x_range: list = [3, 4], @@ -77,7 +78,7 @@ def deterministic_graph_data( configurations_list = range(number_configurations) rx = list(nsplit(configurations_list, comm_size))[comm_rank] - for configuration in configurations_list[rx.start:rx.stop]: + for configuration in configurations_list[rx.start : rx.stop]: uc_x = unit_cell_x[configuration] uc_y = unit_cell_y[configuration] uc_z = unit_cell_z[configuration] @@ -92,7 +93,7 @@ def deterministic_graph_data( atom_types, radius_cutoff, max_num_neighbors, - relative_maximum_atomic_displacement + relative_maximum_atomic_displacement, ) @@ -107,7 +108,7 @@ def create_configuration( types, radius_cutoff, max_num_neighbors, - relative_maximum_atomic_displacement + relative_maximum_atomic_displacement, ): ############################################################################################### ################################### STRUCTURE OF THE DATA ################################## @@ -143,12 +144,21 @@ def create_configuration( for x in range(uc_x): for y in range(uc_y): for z in range(uc_z): - positions[count_pos][0] = (x + relative_maximum_atomic_displacement * ( - (torch.rand(1, 1).item()) - 0.5)) * primitive_bravais_lattice_constant_x - positions[count_pos][1] = (y + relative_maximum_atomic_displacement * ( - (torch.rand(1, 1).item()) - 0.5)) * primitive_bravais_lattice_constant_y - positions[count_pos][2] = (z + relative_maximum_atomic_displacement * ( - (torch.rand(1, 1).item()) - 0.5)) * primitive_bravais_lattice_constant_z + positions[count_pos][0] = ( + x + + relative_maximum_atomic_displacement + * ((torch.rand(1, 1).item()) - 0.5) + ) * primitive_bravais_lattice_constant_x + positions[count_pos][1] = ( + y + + relative_maximum_atomic_displacement + * ((torch.rand(1, 1).item()) - 0.5) + ) * primitive_bravais_lattice_constant_y + positions[count_pos][2] = ( + z + + relative_maximum_atomic_displacement + * ((torch.rand(1, 1).item()) - 0.5) + ) * primitive_bravais_lattice_constant_z count_pos = count_pos + 1 @@ -160,9 +170,13 @@ def create_configuration( supercell_size_x = primitive_bravais_lattice_constant_x * uc_x supercell_size_y = primitive_bravais_lattice_constant_y * uc_y supercell_size_z = primitive_bravais_lattice_constant_z * uc_z - data.supercell_size = torch.diag(torch.tensor([supercell_size_x, supercell_size_y, supercell_size_z])) + data.supercell_size = torch.diag( + torch.tensor([supercell_size_x, supercell_size_y, supercell_size_z]) + ) - create_graph_connectivity_pbc = get_radius_graph_pbc(radius_cutoff, max_num_neighbors) + create_graph_connectivity_pbc = get_radius_graph_pbc( + radius_cutoff, max_num_neighbors + ) data = create_graph_connectivity_pbc(data) atomic_descriptors = torch.cat( @@ -176,26 +190,22 @@ def create_configuration( data.x = atomic_descriptors data = atomic_structure_handler.compute(data) - - total_energy = torch.sum(data.x[:,4]) - energy_per_atom = total_energy/number_nodes - + + total_energy = torch.sum(data.x[:, 4]) + energy_per_atom = total_energy / number_nodes + total_energy_str = numpy.array2string(total_energy.detach().numpy()) energy_per_atom_str = numpy.array2string(energy_per_atom.detach().numpy()) filetxt = total_energy_str + "\n" + energy_per_atom_str for index in range(0, 3): numpy_row = data.supercell_size[index, :].detach().numpy() - numpy_string_row = numpy.array2string( - numpy_row, precision=64, separator="\t" - ) + numpy_string_row = numpy.array2string(numpy_row, precision=64, separator="\t") filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") for index in range(0, number_nodes): numpy_row = data.x[index, :].detach().numpy() - numpy_string_row = numpy.array2string( - numpy_row, precision=64, separator="\t" - ) + numpy_string_row = numpy.array2string(numpy_row, precision=64, separator="\t") filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") filename = os.path.join( @@ -205,10 +215,26 @@ def create_configuration( f.write(filetxt) -if __name__=="__main__": +if __name__ == "__main__": path = "./data" radius_cutoff = 5.0 atom_types = [1] formula = LJpotential(1.0, 3.4) - AtomicStructureHandler = AtomicStructureHandler(atom_types, [primitive_bravais_lattice_constant_x, primitive_bravais_lattice_constant_y, primitive_bravais_lattice_constant_z], radius_cutoff, formula) - deterministic_graph_data(path, atom_types, atomic_structure_handler=AtomicStructureHandler, radius_cutoff=radius_cutoff, relative_maximum_atomic_displacement=1e-1, number_configurations=1000) + AtomicStructureHandler = AtomicStructureHandler( + atom_types, + [ + primitive_bravais_lattice_constant_x, + primitive_bravais_lattice_constant_y, + primitive_bravais_lattice_constant_z, + ], + radius_cutoff, + formula, + ) + deterministic_graph_data( + path, + atom_types, + atomic_structure_handler=AtomicStructureHandler, + radius_cutoff=radius_cutoff, + relative_maximum_atomic_displacement=1e-1, + number_configurations=1000, + ) diff --git a/examples/LennardJones/distributed_utils.py b/examples/LennardJones/distributed_utils.py index 507cdc5a7..8c367bc48 100644 --- a/examples/LennardJones/distributed_utils.py +++ b/examples/LennardJones/distributed_utils.py @@ -1,3 +1,3 @@ def nsplit(a, n): k, m = divmod(len(a), n) - return (a[i * k + min(i, m): (i + 1) * k + min(i + 1, m)] for i in range(n)) + return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)) diff --git a/examples/LennardJones/graph_utils.py b/examples/LennardJones/graph_utils.py index 8d290c9c3..8e9c0698f 100644 --- a/examples/LennardJones/graph_utils.py +++ b/examples/LennardJones/graph_utils.py @@ -4,6 +4,7 @@ import ase.neighborlist from torch_geometric.utils import remove_self_loops, degree + class RadiusGraphPBC(RadiusGraph): r"""Creates edges based on node positions :obj:`pos` to all points within a given distance, including periodic images. @@ -52,4 +53,4 @@ def get_radius_graph_pbc(radius, max_neighbours, loop=False): r=radius, loop=loop, max_num_neighbors=max_neighbours, - ) \ No newline at end of file + ) diff --git a/examples/LennardJones/inference.py b/examples/LennardJones/inference.py index 3da1e365e..febe14fcd 100644 --- a/examples/LennardJones/inference.py +++ b/examples/LennardJones/inference.py @@ -39,6 +39,7 @@ pass import matplotlib.pyplot as plt + plt.rcParams.update({"font.size": 16}) @@ -71,12 +72,11 @@ def get_log_name_config(config): ) ) + def getcolordensity(xdata, ydata): ############################### nbin = 20 - hist2d, xbins_edge, ybins_edge = np.histogram2d( - x=xdata, y=ydata, bins=[nbin, nbin] - ) + hist2d, xbins_edge, ybins_edge = np.histogram2d(x=xdata, y=ydata, bins=[nbin, nbin]) xbin_cen = 0.5 * (xbins_edge[0:-1] + xbins_edge[1:]) ybin_cen = 0.5 * (ybins_edge[0:-1] + ybins_edge[1:]) BCTY, BCTX = np.meshgrid(ybin_cen, xbin_cen) @@ -97,9 +97,11 @@ def getcolordensity(xdata, ydata): ) # np.nan) return hist2d_norm + def info(*args, logtype="info", sep=" "): getattr(logging, logtype)(sep.join(map(str, args))) + if __name__ == "__main__": modelname = "LJ" @@ -149,9 +151,21 @@ def info(*args, logtype="info", sep=" "): basedir = os.path.join( os.path.dirname(__file__), "dataset", "%s.pickle" % modelname ) - trainset = SimplePickleDataset(basedir=basedir, label="trainset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) - valset = SimplePickleDataset(basedir=basedir, label="valset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) - testset = SimplePickleDataset(basedir=basedir, label="testset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + trainset = SimplePickleDataset( + basedir=basedir, + label="trainset", + var_config=config["NeuralNetwork"]["Variables_of_interest"], + ) + valset = SimplePickleDataset( + basedir=basedir, + label="valset", + var_config=config["NeuralNetwork"]["Variables_of_interest"], + ) + testset = SimplePickleDataset( + basedir=basedir, + label="testset", + var_config=config["NeuralNetwork"]["Variables_of_interest"], + ) pna_deg = trainset.pna_deg else: raise NotImplementedError("No supported format: %s" % (args.format)) @@ -161,15 +175,17 @@ def info(*args, logtype="info", sep=" "): verbosity=config["Verbosity"]["level"], ) - model = torch.nn.parallel.DistributedDataParallel( - model - ) + model = torch.nn.parallel.DistributedDataParallel(model) load_existing_model(model, modelname, path="./logs/") model.eval() variable_index = 0 - for output_name, output_type, output_dim in zip(config["NeuralNetwork"]["Variables_of_interest"]["output_names"], config["NeuralNetwork"]["Variables_of_interest"]["type"], config["NeuralNetwork"]["Variables_of_interest"]["output_dim"]): + for output_name, output_type, output_dim in zip( + config["NeuralNetwork"]["Variables_of_interest"]["output_names"], + config["NeuralNetwork"]["Variables_of_interest"]["type"], + config["NeuralNetwork"]["Variables_of_interest"]["output_dim"], + ): test_MAE = 0.0 @@ -183,16 +199,14 @@ def info(*args, logtype="info", sep=" "): start = data.y_loc[0][variable_index].item() end = data.y_loc[0][variable_index + 1].item() true = data.y[start:end, 0] - test_MAE += torch.norm(predicted - true, p=1).item()/len(testset) + test_MAE += torch.norm(predicted - true, p=1).item() / len(testset) predicted_values.extend(predicted.tolist()) true_values.extend(true.tolist()) hist2d_norm = getcolordensity(true_values, predicted_values) fig, ax = plt.subplots() - plt.scatter( - true_values, predicted_values, s=8, c=hist2d_norm, vmin=0, vmax=1 - ) + plt.scatter(true_values, predicted_values, s=8, c=hist2d_norm, vmin=0, vmax=1) plt.clim(0, 1) ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") plt.colorbar() @@ -205,4 +219,4 @@ def info(*args, logtype="info", sep=" "): print(f"Test MAE {output_name}: ", test_MAE) - variable_index += 1 \ No newline at end of file + variable_index += 1 diff --git a/examples/LennardJones/inference_derivative_energy.py b/examples/LennardJones/inference_derivative_energy.py index 7aca2ae86..a3a5f2bf3 100644 --- a/examples/LennardJones/inference_derivative_energy.py +++ b/examples/LennardJones/inference_derivative_energy.py @@ -40,6 +40,7 @@ pass import matplotlib.pyplot as plt + plt.rcParams.update({"font.size": 16}) @@ -72,12 +73,11 @@ def get_log_name_config(config): ) ) + def getcolordensity(xdata, ydata): ############################### nbin = 20 - hist2d, xbins_edge, ybins_edge = np.histogram2d( - x=xdata, y=ydata, bins=[nbin, nbin] - ) + hist2d, xbins_edge, ybins_edge = np.histogram2d(x=xdata, y=ydata, bins=[nbin, nbin]) xbin_cen = 0.5 * (xbins_edge[0:-1] + xbins_edge[1:]) ybin_cen = 0.5 * (ybins_edge[0:-1] + ybins_edge[1:]) BCTY, BCTX = np.meshgrid(ybin_cen, xbin_cen) @@ -98,9 +98,11 @@ def getcolordensity(xdata, ydata): ) # np.nan) return hist2d_norm + def info(*args, logtype="info", sep=" "): getattr(logging, logtype)(sep.join(map(str, args))) + if __name__ == "__main__": modelname = "LJ" @@ -150,9 +152,21 @@ def info(*args, logtype="info", sep=" "): basedir = os.path.join( os.path.dirname(__file__), "dataset", "%s.pickle" % modelname ) - trainset = SimplePickleDataset(basedir=basedir, label="trainset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) - valset = SimplePickleDataset(basedir=basedir, label="valset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) - testset = SimplePickleDataset(basedir=basedir, label="testset", var_config=config["NeuralNetwork"]["Variables_of_interest"]) + trainset = SimplePickleDataset( + basedir=basedir, + label="trainset", + var_config=config["NeuralNetwork"]["Variables_of_interest"], + ) + valset = SimplePickleDataset( + basedir=basedir, + label="valset", + var_config=config["NeuralNetwork"]["Variables_of_interest"], + ) + testset = SimplePickleDataset( + basedir=basedir, + label="testset", + var_config=config["NeuralNetwork"]["Variables_of_interest"], + ) pna_deg = trainset.pna_deg else: raise NotImplementedError("No supported format: %s" % (args.format)) @@ -162,15 +176,13 @@ def info(*args, logtype="info", sep=" "): verbosity=config["Verbosity"]["level"], ) - model = torch.nn.parallel.DistributedDataParallel( - model - ) + model = torch.nn.parallel.DistributedDataParallel(model) load_existing_model(model, modelname, path="./logs/") model.eval() variable_index = 0 - #for output_name, output_type, output_dim in zip(config["NeuralNetwork"]["Variables_of_interest"]["output_names"], config["NeuralNetwork"]["Variables_of_interest"]["type"], config["NeuralNetwork"]["Variables_of_interest"]["output_dim"]): + # for output_name, output_type, output_dim in zip(config["NeuralNetwork"]["Variables_of_interest"]["output_names"], config["NeuralNetwork"]["Variables_of_interest"]["type"], config["NeuralNetwork"]["Variables_of_interest"]["output_dim"]): test_MAE = 0.0 @@ -182,14 +194,20 @@ def info(*args, logtype="info", sep=" "): for data_id, data in enumerate(tqdm(testset)): data.pos.requires_grad = True - node_energy_pred = model(data.to(get_device()))[0] # Note that this is sensitive to energy and forces prediction being single-task (current requirement) + node_energy_pred = model(data.to(get_device()))[ + 0 + ] # Note that this is sensitive to energy and forces prediction being single-task (current requirement) energy_pred = torch.sum(node_energy_pred, dim=0).float() test_MAE += torch.norm(energy_pred - data.energy, p=1).item() / len(testset) - #predicted.backward(retain_graph=True) - #gradients = data.pos.grad - grads_energy = torch.autograd.grad(outputs=energy_pred, inputs=data.pos, - grad_outputs=torch.ones_like(energy_pred), - retain_graph=False, create_graph=True)[0] + # predicted.backward(retain_graph=True) + # gradients = data.pos.grad + grads_energy = torch.autograd.grad( + outputs=energy_pred, + inputs=data.pos, + grad_outputs=torch.ones_like(energy_pred), + retain_graph=False, + create_graph=True, + )[0] energy_pred_list.extend(energy_pred.tolist()) energy_true_list.extend(data.energy.tolist()) forces_pred_list.extend((-grads_energy).flatten().tolist()) @@ -198,9 +216,7 @@ def info(*args, logtype="info", sep=" "): hist2d_norm = getcolordensity(energy_true_list, energy_pred_list) fig, ax = plt.subplots() - plt.scatter( - energy_true_list, energy_pred_list, s=8, c=hist2d_norm, vmin=0, vmax=1 - ) + plt.scatter(energy_true_list, energy_pred_list, s=8, c=hist2d_norm, vmin=0, vmax=1) plt.clim(0, 1) ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") plt.colorbar() @@ -215,9 +231,7 @@ def info(*args, logtype="info", sep=" "): hist2d_norm = getcolordensity(forces_pred_list, forces_true_list) fig, ax = plt.subplots() - plt.scatter( - forces_pred_list, forces_true_list, s=8, c=hist2d_norm, vmin=0, vmax=1 - ) + plt.scatter(forces_pred_list, forces_true_list, s=8, c=hist2d_norm, vmin=0, vmax=1) plt.clim(0, 1) ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") plt.colorbar() diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py index 156e7d400..bd621e9b0 100644 --- a/examples/LennardJones/train_vlad_atomic_forces.py +++ b/examples/LennardJones/train_vlad_atomic_forces.py @@ -116,22 +116,22 @@ def transform_input_to_data_object_base(self, filepath): pos = torch_data[:, [1, 2, 3]].to(torch.float32) atomic_number = torch_data[:, [0]].to(torch.float32) forces_torch = torch.zeros_like(pos) - forces_torch[0,:] = forces.unsqueeze(0) + forces_torch[0, :] = forces.unsqueeze(0) data = Data( pos=pos, x=torch.cat((atomic_number, forces_torch.to(torch.float32)), 1), - y = [] + y=[], ) data = create_graph_fromXYZ(data) data = compute_edge_lengths(data) data.edge_attr = data.edge_attr.to(torch.float32) - #data = spherical_coordinates(data) + # data = spherical_coordinates(data) data = cartesian_coordinates(data) - #maintain directionality - data.edge_index = data.edge_index[:,0:6] - data.edge_attr= data.edge_attr[0:6,:] + # maintain directionality + data.edge_index = data.edge_index[:, 0:6] + data.edge_attr = data.edge_attr[0:6, :] return data @@ -152,7 +152,9 @@ def get(self, idx): action="store_true", help="preprocess only (no training)", ) - parser.add_argument("--inputfile", help="input file", type=str, default="LJ_vlad_atomic_forces.json") + parser.add_argument( + "--inputfile", help="input file", type=str, default="LJ_vlad_atomic_forces.json" + ) parser.add_argument("--mae", action="store_true", help="do mae calculation") parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) @@ -354,7 +356,11 @@ def get(self, idx): os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + ( + train_loader, + val_loader, + test_loader, + ) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py index 14179655f..a42b1749c 100644 --- a/examples/LennardJones/train_vlad_total_energy.py +++ b/examples/LennardJones/train_vlad_total_energy.py @@ -122,12 +122,12 @@ def transform_input_to_data_object_base(self, filepath): data = create_graph_fromXYZ(data) data = compute_edge_lengths(data) data.edge_attr = data.edge_attr.to(torch.float32) - #data = spherical_coordinates(data) + # data = spherical_coordinates(data) data = cartesian_coordinates(data) - #maintain directionality - data.edge_index = data.edge_index[:,0:6] - data.edge_attr= data.edge_attr[0:6,:] + # maintain directionality + data.edge_index = data.edge_index[:, 0:6] + data.edge_attr = data.edge_attr[0:6, :] return data @@ -148,7 +148,9 @@ def get(self, idx): action="store_true", help="preprocess only (no training)", ) - parser.add_argument("--inputfile", help="input file", type=str, default="LJ_vlad_total_energy.json") + parser.add_argument( + "--inputfile", help="input file", type=str, default="LJ_vlad_total_energy.json" + ) parser.add_argument("--mae", action="store_true", help="do mae calculation") parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) @@ -350,7 +352,11 @@ def get(self, idx): os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + ( + train_loader, + val_loader, + test_loader, + ) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) From 271e36a28d51e3b4ed8925b132a9afa51dc893d0 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 13:56:05 -0400 Subject: [PATCH 11/37] formatting --- examples/LennardJones/LennardJones.py | 10 +++------- examples/LennardJones/train_vlad_atomic_forces.py | 10 +++------- examples/LennardJones/train_vlad_total_energy.py | 10 +++------- 3 files changed, 9 insertions(+), 21 deletions(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index a8a8d63ba..4fb37bed6 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -420,13 +420,9 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - - ( - train_loader, - val_loader, - test_loader, - ) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py index bd621e9b0..82d1ecd39 100644 --- a/examples/LennardJones/train_vlad_atomic_forces.py +++ b/examples/LennardJones/train_vlad_atomic_forces.py @@ -355,13 +355,9 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - - ( - train_loader, - val_loader, - test_loader, - ) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py index a42b1749c..e072ea98d 100644 --- a/examples/LennardJones/train_vlad_total_energy.py +++ b/examples/LennardJones/train_vlad_total_energy.py @@ -351,13 +351,9 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - - ( - train_loader, - val_loader, - test_loader, - ) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) From 9de9677dc7e9d740aed977dc118f72c8701b2fb9 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 13:58:22 -0400 Subject: [PATCH 12/37] formatting --- examples/LennardJones/LennardJones.py | 2 +- examples/LennardJones/train_vlad_atomic_forces.py | 2 +- examples/LennardJones/train_vlad_total_energy.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index 4fb37bed6..b287ea52b 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -422,7 +422,7 @@ def get(self, idx): os.environ["HYDRAGNN_USE_ddstore"] = "1" (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py index 82d1ecd39..a52c3d0ff 100644 --- a/examples/LennardJones/train_vlad_atomic_forces.py +++ b/examples/LennardJones/train_vlad_atomic_forces.py @@ -357,7 +357,7 @@ def get(self, idx): os.environ["HYDRAGNN_USE_ddstore"] = "1" (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py index e072ea98d..004fe5319 100644 --- a/examples/LennardJones/train_vlad_total_energy.py +++ b/examples/LennardJones/train_vlad_total_energy.py @@ -353,7 +353,7 @@ def get(self, idx): os.environ["HYDRAGNN_USE_ddstore"] = "1" (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] + trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) From a904def49c5f7bc833a2c557b937cb7a6a432896 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 14:01:39 -0400 Subject: [PATCH 13/37] formatting --- examples/LennardJones/LennardJones.py | 2 +- examples/LennardJones/train_vlad_atomic_forces.py | 2 +- examples/LennardJones/train_vlad_total_energy.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index b287ea52b..a8e338e19 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -420,7 +420,7 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py index a52c3d0ff..36d8d711a 100644 --- a/examples/LennardJones/train_vlad_atomic_forces.py +++ b/examples/LennardJones/train_vlad_atomic_forces.py @@ -355,7 +355,7 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py index 004fe5319..57acfc01c 100644 --- a/examples/LennardJones/train_vlad_total_energy.py +++ b/examples/LennardJones/train_vlad_total_energy.py @@ -351,7 +351,7 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) From 12c042e1c3942426e2b5724b4b9d521a1a76def7 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 14:15:17 -0400 Subject: [PATCH 14/37] formatting --- examples/LennardJones/LennardJones.py | 2 +- examples/LennardJones/train_vlad_atomic_forces.py | 2 +- examples/LennardJones/train_vlad_total_energy.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index a8e338e19..d2aef3839 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -420,7 +420,7 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py index 36d8d711a..aa6caf9d9 100644 --- a/examples/LennardJones/train_vlad_atomic_forces.py +++ b/examples/LennardJones/train_vlad_atomic_forces.py @@ -355,7 +355,7 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py index 57acfc01c..123c0d6f4 100644 --- a/examples/LennardJones/train_vlad_total_energy.py +++ b/examples/LennardJones/train_vlad_total_energy.py @@ -351,7 +351,7 @@ def get(self, idx): if args.ddstore: os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" os.environ["HYDRAGNN_USE_ddstore"] = "1" - + (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] ) From 0f3fe54ece380188d97ece577f45ed4bf0eb2cea Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 14:25:00 -0400 Subject: [PATCH 15/37] formatting --- examples/LennardJones/AtomicStructure.py | 1 - examples/LennardJones/LJpotential.py | 1 - 2 files changed, 2 deletions(-) diff --git a/examples/LennardJones/AtomicStructure.py b/examples/LennardJones/AtomicStructure.py index 540679023..b09500a7d 100644 --- a/examples/LennardJones/AtomicStructure.py +++ b/examples/LennardJones/AtomicStructure.py @@ -2,7 +2,6 @@ class AtomicStructureHandler: - def __init__( self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula ): diff --git a/examples/LennardJones/LJpotential.py b/examples/LennardJones/LJpotential.py index 7e267d82b..a38d17ce3 100644 --- a/examples/LennardJones/LJpotential.py +++ b/examples/LennardJones/LJpotential.py @@ -2,7 +2,6 @@ class LJpotential: - def __init__(self, epsilon, sigma): self.epsilon = epsilon self.sigma = sigma From 4df57c7f06bb5e0553f31ad1f07b447bbe6801f8 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 14:56:19 -0400 Subject: [PATCH 16/37] take out images and adjust unecessary import --- examples/LennardJones/Forces_Scatterplot.png | Bin 291536 -> 0 bytes examples/LennardJones/LennardJones.py | 2 -- examples/LennardJones/energy_Scatterplot.png | Bin 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z$NimpZ__=j;MT^*`4@i<331?JnICH)M1hH=!!N}+;}YI!yJ*zpd<+?6R?coCO&r_V z-n6$pcwn)P%ih3Z?SHn>uf2e8|C9d%YqkBa{ACvBUp)Nezu!nZdeweKf0b7H_XV{- zLFK>y=Fb0@$MLti;J+N;_W%2~f0^r7w@T2cv4yRrb^qbBrhPFS+xVwrGRMF5Y5Sk6 xsCHiU=Ih}DRNCkR{K?tzg`;eJcuyr9N`rENS2iO1gkGo$IbrkKLKN>5bFQ{ From 477930902df05684ed904bdade17120c84505d7f Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 14:58:15 -0400 Subject: [PATCH 17/37] revert to SiLU in DimeNet --- hydragnn/models/DIMEStack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hydragnn/models/DIMEStack.py b/hydragnn/models/DIMEStack.py index da52955dc..fcaafb153 100644 --- a/hydragnn/models/DIMEStack.py +++ b/hydragnn/models/DIMEStack.py @@ -101,7 +101,7 @@ def get_conv(self, input_dim, output_dim): out_emb_channels=self.out_emb_size, out_channels=output_dim, num_layers=1, - act=Sigmoid(), # Sigmoid instead of SiLU here promotes stability when we have a linear decoder at the start of convolution (especially for random data examples in test_graphs.py) + act=SiLU(), output_initializer="glorot_orthogonal", ) return Sequential( From 456ebf73541bb92d2c3b607eb8459e217facd9a1 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Mon, 16 Sep 2024 15:03:32 -0400 Subject: [PATCH 18/37] don't create plots by default --- examples/LennardJones/LennardJones.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index 9b6aba399..2aa377ea9 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -460,7 +460,7 @@ def get(self, idx): config["NeuralNetwork"], log_name, verbosity, - create_plots=True, + create_plots=False, compute_grad_energy=True, ) From 20f2e4189ec9524f21adc81ff6c2c9d4d1d2d4c5 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 10:01:51 -0400 Subject: [PATCH 19/37] file cleanup and dataset test --- examples/LennardJones/LJ_multitask.json | 75 --- .../LennardJones/LJ_vlad_atomic_forces.json | 61 --- .../LennardJones/LJ_vlad_total_energy.json | 71 --- examples/LennardJones/inference.py | 222 --------- .../LennardJones/train_vlad_atomic_forces.py | 414 ---------------- .../LennardJones/train_vlad_total_energy.py | 410 ---------------- examples/qm9/qm9.py | 14 +- tests/test_graphs.py | 448 +++++++++--------- 8 files changed, 237 insertions(+), 1478 deletions(-) delete mode 100644 examples/LennardJones/LJ_multitask.json delete mode 100644 examples/LennardJones/LJ_vlad_atomic_forces.json delete mode 100644 examples/LennardJones/LJ_vlad_total_energy.json delete mode 100644 examples/LennardJones/inference.py delete mode 100644 examples/LennardJones/train_vlad_atomic_forces.py delete mode 100644 examples/LennardJones/train_vlad_total_energy.py diff --git a/examples/LennardJones/LJ_multitask.json b/examples/LennardJones/LJ_multitask.json deleted file mode 100644 index 662254da7..000000000 --- a/examples/LennardJones/LJ_multitask.json +++ /dev/null @@ -1,75 +0,0 @@ -{ - "Verbosity": { - "level": 2 - }, - "Dataset": { - "name": "LJdataset", - "path": {"total": "./dataset/data"}, - "format": "XYZ", - "rotational_invariance": true, - "node_features": { - "name": ["atom_type", "potential", "forces"], - "dim": [1, 1, 3], - "column_index": [0,4,5] - }, - "graph_features":{ - "name": ["total_energy"], - "dim": [1], - "column_index": [0] - } - }, - "NeuralNetwork": { - "Architecture": { - "periodic_boundary_conditions": true, - "model_type": "EGNN", - "equivariance": true, - "edge_features": ["bond_length", "polar_angle", "azimutal_angle"], - "max_neighbours": 20, - "hidden_dim": 20, - "num_conv_layers": 6, - "output_heads": { - "graph": { - "num_sharedlayers": 2, - "dim_sharedlayers": 50, - "num_headlayers": 2, - "dim_headlayers": [ - 20, - 20 - ] - }, - "node": { - "num_headlayers": 2, - "dim_headlayers": [20,20], - "type": "mlp" - } - }, - "task_weights": [ - 1, 1 - ] - }, - "Variables_of_interest": { - "input_node_features": [0], - "output_index": [ - 0, 2 - ], - "type": [ - "graph", "node" - ], - "output_dim": [1, 3], - "output_names": ["total_energy", "atomic_forces"] - }, - "Training": { - "num_epoch": 50, - "batch_size": 3, - "continue": 0, - "EarlyStopping": true, - "patience": 100, - "Checkpoint": true, - "checkpoint_warmup": 100, - "startfrom": "existing_model", - "Optimizer": { - "learning_rate": 0.001 - } - } - } -} diff --git a/examples/LennardJones/LJ_vlad_atomic_forces.json b/examples/LennardJones/LJ_vlad_atomic_forces.json deleted file mode 100644 index c14b23f8a..000000000 --- a/examples/LennardJones/LJ_vlad_atomic_forces.json +++ /dev/null @@ -1,61 +0,0 @@ -{ - "Verbosity": { - "level": 2 - }, - "Dataset": { - "name": "LJdataset", - "path": {"total": "./dataset/data"}, - "format": "XYZ", - "rotational_invariance": true, - "node_features": { - "name": ["atom_type", "forces"], - "dim": [1, 3], - "column_index": [0, 1] - } - }, - "NeuralNetwork": { - "Architecture": { - "periodic_boundary_conditions": true, - "model_type": "EGNN", - "equivariance": true, - "edge_features": ["bond_length", "polar_angle", "azimutal_angle"], - "max_neighbours": 20, - "hidden_dim": 10, - "num_conv_layers": 3, - "output_heads": { - "node": { - "num_headlayers": 2, - "dim_headlayers": [50,25], - "type": "mlp" - } - }, - "task_weights": [ - 1 - ] - }, - "Variables_of_interest": { - "input_node_features": [0], - "output_index": [ - 1 - ], - "type": [ - "node" - ], - "output_dim": [3], - "output_names": ["forces"] - }, - "Training": { - "num_epoch": 20, - "batch_size": 3, - "continue": 0, - "EarlyStopping": true, - "patience": 100, - "Checkpoint": true, - "checkpoint_warmup": 100, - "startfrom": "existing_model", - "Optimizer": { - "learning_rate": 0.001 - } - } - } -} diff --git a/examples/LennardJones/LJ_vlad_total_energy.json b/examples/LennardJones/LJ_vlad_total_energy.json deleted file mode 100644 index db57121a7..000000000 --- a/examples/LennardJones/LJ_vlad_total_energy.json +++ /dev/null @@ -1,71 +0,0 @@ -{ - "Verbosity": { - "level": 2 - }, - "Dataset": { - "name": "LJdataset", - "path": {"total": "./dataset/data"}, - "format": "XYZ", - "rotational_invariance": true, - "node_features": { - "name": ["atom_type"], - "dim": [1], - "column_index": [0] - }, - "graph_features":{ - "name": ["total_energy"], - "dim": [1], - "column_index": [0] - } - }, - "NeuralNetwork": { - "Architecture": { - "periodic_boundary_conditions": true, - "model_type": "EGNN", - "equivariance": true, - "edge_features": ["bond_length", "polar_angle", "azimutal_angle"], - "max_neighbours": 20, - "hidden_dim": 10, - "num_conv_layers": 3, - "output_heads": { - "graph": { - "num_sharedlayers": 2, - "dim_sharedlayers": 20, - "num_headlayers": 2, - "dim_headlayers": [ - 50, - 50 - ] - } - }, - "task_weights": [ - 1 - ] - }, - "Variables_of_interest": { - "input_node_features": [0, 1, 2, 3], - "output_index": [ - 0 - ], - "type": [ - "graph" - ], - "output_dim": [1], - "output_names": ["energy"] - }, - "Training": { - "num_epoch": 50, - "batch_size": 3, - "perc_train": 0.8, - "continue": 0, - "EarlyStopping": true, - "patience": 10, - "Checkpoint": true, - "checkpoint_warmup": 100, - "startfrom": "existing_model", - "Optimizer": { - "learning_rate": 0.001 - } - } - } -} diff --git a/examples/LennardJones/inference.py b/examples/LennardJones/inference.py deleted file mode 100644 index febe14fcd..000000000 --- a/examples/LennardJones/inference.py +++ /dev/null @@ -1,222 +0,0 @@ -############################################################################## -# Copyright (c) 2021, Oak Ridge National Laboratory # -# All rights reserved. # -# # -# This file is part of HydraGNN and is distributed under a BSD 3-clause # -# license. For the licensing terms see the LICENSE file in the top-level # -# directory. # -# # -# SPDX-License-Identifier: BSD-3-Clause # -############################################################################## - -import json, os -import sys -import logging -import pickle -from tqdm import tqdm -from mpi4py import MPI -import argparse - -import torch -import numpy as np - -import hydragnn -from hydragnn.utils.time_utils import Timer -from hydragnn.utils.distributed import get_device -from hydragnn.utils.model import load_existing_model -from hydragnn.utils.pickledataset import SimplePickleDataset -from hydragnn.utils.config_utils import ( - update_config, -) -from hydragnn.models.create import create_model_config -from hydragnn.preprocess import create_dataloaders - -from scipy.interpolate import griddata - -try: - from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset -except ImportError: - pass - -import matplotlib.pyplot as plt - -plt.rcParams.update({"font.size": 16}) - - -def get_log_name_config(config): - return ( - config["NeuralNetwork"]["Architecture"]["model_type"] - + "-r-" - + str(config["NeuralNetwork"]["Architecture"]["radius"]) - + "-ncl-" - + str(config["NeuralNetwork"]["Architecture"]["num_conv_layers"]) - + "-hd-" - + str(config["NeuralNetwork"]["Architecture"]["hidden_dim"]) - + "-ne-" - + str(config["NeuralNetwork"]["Training"]["num_epoch"]) - + "-lr-" - + str(config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"]) - + "-bs-" - + str(config["NeuralNetwork"]["Training"]["batch_size"]) - + "-node_ft-" - + "".join( - str(x) - for x in config["NeuralNetwork"]["Variables_of_interest"][ - "input_node_features" - ] - ) - + "-task_weights-" - + "".join( - str(weigh) + "-" - for weigh in config["NeuralNetwork"]["Architecture"]["task_weights"] - ) - ) - - -def getcolordensity(xdata, ydata): - ############################### - nbin = 20 - hist2d, xbins_edge, ybins_edge = np.histogram2d(x=xdata, y=ydata, bins=[nbin, nbin]) - xbin_cen = 0.5 * (xbins_edge[0:-1] + xbins_edge[1:]) - ybin_cen = 0.5 * (ybins_edge[0:-1] + ybins_edge[1:]) - BCTY, BCTX = np.meshgrid(ybin_cen, xbin_cen) - hist2d = hist2d / np.amax(hist2d) - print(np.amax(hist2d)) - - bctx1d = np.reshape(BCTX, len(xbin_cen) * nbin) - bcty1d = np.reshape(BCTY, len(xbin_cen) * nbin) - loc_pts = np.zeros((len(xbin_cen) * nbin, 2)) - loc_pts[:, 0] = bctx1d - loc_pts[:, 1] = bcty1d - hist2d_norm = griddata( - loc_pts, - hist2d.reshape(len(xbin_cen) * nbin), - (xdata, ydata), - method="linear", - fill_value=0, - ) # np.nan) - return hist2d_norm - - -def info(*args, logtype="info", sep=" "): - getattr(logging, logtype)(sep.join(map(str, args))) - - -if __name__ == "__main__": - - modelname = "LJ" - - parser = argparse.ArgumentParser() - parser.add_argument( - "--inputfile", help="input file", type=str, default="./logs/LJ/config.json" - ) - group = parser.add_mutually_exclusive_group() - group.add_argument( - "--adios", - help="Adios gan_dataset", - action="store_const", - dest="format", - const="adios", - ) - group.add_argument( - "--pickle", - help="Pickle gan_dataset", - action="store_const", - dest="format", - const="pickle", - ) - parser.set_defaults(format="pickle") - - args = parser.parse_args() - - dirpwd = os.path.dirname(os.path.abspath(__file__)) - input_filename = os.path.join(dirpwd, args.inputfile) - with open(input_filename, "r") as f: - config = json.load(f) - hydragnn.utils.setup_log(get_log_name_config(config)) - ################################################################################################################## - # Always initialize for multi-rank training. - comm_size, rank = hydragnn.utils.setup_ddp() - ################################################################################################################## - comm = MPI.COMM_WORLD - - datasetname = "LJ" - - comm.Barrier() - - timer = Timer("load_data") - timer.start() - if args.format == "pickle": - info("Pickle load") - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) - trainset = SimplePickleDataset( - basedir=basedir, - label="trainset", - var_config=config["NeuralNetwork"]["Variables_of_interest"], - ) - valset = SimplePickleDataset( - basedir=basedir, - label="valset", - var_config=config["NeuralNetwork"]["Variables_of_interest"], - ) - testset = SimplePickleDataset( - basedir=basedir, - label="testset", - var_config=config["NeuralNetwork"]["Variables_of_interest"], - ) - pna_deg = trainset.pna_deg - else: - raise NotImplementedError("No supported format: %s" % (args.format)) - - model = create_model_config( - config=config["NeuralNetwork"], - verbosity=config["Verbosity"]["level"], - ) - - model = torch.nn.parallel.DistributedDataParallel(model) - - load_existing_model(model, modelname, path="./logs/") - model.eval() - - variable_index = 0 - for output_name, output_type, output_dim in zip( - config["NeuralNetwork"]["Variables_of_interest"]["output_names"], - config["NeuralNetwork"]["Variables_of_interest"]["type"], - config["NeuralNetwork"]["Variables_of_interest"]["output_dim"], - ): - - test_MAE = 0.0 - - num_samples = len(testset) - true_values = [] - predicted_values = [] - - for data_id, data in enumerate(tqdm(testset)): - predicted = model(data.to(get_device())) - predicted = predicted[variable_index].flatten() - start = data.y_loc[0][variable_index].item() - end = data.y_loc[0][variable_index + 1].item() - true = data.y[start:end, 0] - test_MAE += torch.norm(predicted - true, p=1).item() / len(testset) - predicted_values.extend(predicted.tolist()) - true_values.extend(true.tolist()) - - hist2d_norm = getcolordensity(true_values, predicted_values) - - fig, ax = plt.subplots() - plt.scatter(true_values, predicted_values, s=8, c=hist2d_norm, vmin=0, vmax=1) - plt.clim(0, 1) - ax.plot(ax.get_xlim(), ax.get_xlim(), ls="--", color="red") - plt.colorbar() - plt.xlabel("True values") - plt.ylabel("Predicted values") - plt.title(f"{output_name}") - plt.draw() - plt.tight_layout() - plt.savefig(f"./{output_name}_Scatterplot" + ".png", dpi=400) - - print(f"Test MAE {output_name}: ", test_MAE) - - variable_index += 1 diff --git a/examples/LennardJones/train_vlad_atomic_forces.py b/examples/LennardJones/train_vlad_atomic_forces.py deleted file mode 100644 index aa6caf9d9..000000000 --- a/examples/LennardJones/train_vlad_atomic_forces.py +++ /dev/null @@ -1,414 +0,0 @@ -import mpi4py -from mpi4py import MPI - -mpi4py.rc.thread_level = "serialized" -mpi4py.rc.threads = False - -import os, json -import random - -import h5py - -import logging -import sys -import argparse - -import hydragnn -from hydragnn.utils.print_utils import iterate_tqdm, log -from hydragnn.utils.time_utils import Timer - -from hydragnn.preprocess.load_data import split_dataset -from hydragnn.utils.abstractrawdataset import AbstractBaseDataset -from hydragnn.utils.distdataset import DistDataset -from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset -from hydragnn.preprocess.utils import gather_deg - -import numpy as np - -try: - from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset -except ImportError: - pass - -from torch_geometric.data import Data -from torch_geometric.transforms import RadiusGraph, Distance, Spherical, LocalCartesian -import torch -import torch.distributed as dist - -from hydragnn.utils import nsplit -import hydragnn.utils.tracer as tr - - -# FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones - - -torch.set_default_dtype(torch.float32) - - -def info(*args, logtype="info", sep=" "): - getattr(logging, logtype)(sep.join(map(str, args))) - - -# FIXME: this radis cutoff overwrites the radius cutoff currently written in the JSON file -create_graph_fromXYZ = RadiusGraph(r=5.0) # radius cutoff in angstrom -compute_edge_lengths = Distance(norm=False, cat=True) -spherical_coordinates = Spherical(norm=False, cat=False) -cartesian_coordinates = LocalCartesian(norm=False, cat=False) - - -class LJDataset_VladTest(AbstractBaseDataset): - """LJDataset_VladTest dataset class""" - - def __init__(self, dirpath, dist=False, sampling=None): - super().__init__() - - self.dist = dist - self.world_size = 1 - self.rank = 1 - if self.dist: - assert torch.distributed.is_initialized() - self.world_size = torch.distributed.get_world_size() - self.rank = torch.distributed.get_rank() - - dirfiles = sorted(os.listdir(dirpath)) - - rx = list(nsplit((dirfiles), self.world_size))[self.rank] - - for file in rx: - filepath = os.path.join(dirpath, file) - self.dataset.append(self.transform_input_to_data_object_base(filepath)) - - def transform_input_to_data_object_base(self, filepath): - - # Using readline() - file = open(filepath, "r") - - torch_data = torch.empty((0, 4), dtype=torch.float32) - - count = 0 - - while True: - count += 1 - - # Get next line from file - line = file.readline() - - # if line is empty - # end of file is reached - if not line: - break - - if count == 1: - array_line = np.fromstring(line, dtype=float, sep="\t") - energy = torch.from_numpy(array_line).unsqueeze(0) - elif count == 2: - array_line = np.fromstring(line, dtype=float, sep="\t") - forces = torch.from_numpy(array_line).unsqueeze(0) - else: - array_line = np.fromstring(line, dtype=float, sep="\t") - torch_data = torch.cat( - [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 - ) - # print("Line{}: {}".format(count, line.strip())) - - file.close() - - pos = torch_data[:, [1, 2, 3]].to(torch.float32) - atomic_number = torch_data[:, [0]].to(torch.float32) - forces_torch = torch.zeros_like(pos) - forces_torch[0, :] = forces.unsqueeze(0) - - data = Data( - pos=pos, - x=torch.cat((atomic_number, forces_torch.to(torch.float32)), 1), - y=[], - ) - data = create_graph_fromXYZ(data) - data = compute_edge_lengths(data) - data.edge_attr = data.edge_attr.to(torch.float32) - # data = spherical_coordinates(data) - data = cartesian_coordinates(data) - - # maintain directionality - data.edge_index = data.edge_index[:, 0:6] - data.edge_attr = data.edge_attr[0:6, :] - - return data - - def len(self): - return len(self.dataset) - - def get(self, idx): - return self.dataset[idx] - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - parser.add_argument("--sampling", type=float, help="sampling ratio", default=None) - parser.add_argument( - "--preonly", - action="store_true", - help="preprocess only (no training)", - ) - parser.add_argument( - "--inputfile", help="input file", type=str, default="LJ_vlad_atomic_forces.json" - ) - parser.add_argument("--mae", action="store_true", help="do mae calculation") - parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") - parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) - parser.add_argument("--shmem", action="store_true", help="shmem") - parser.add_argument("--log", help="log name") - parser.add_argument("--batch_size", type=int, help="batch_size", default=None) - parser.add_argument("--everyone", action="store_true", help="gptimer") - - group = parser.add_mutually_exclusive_group() - group.add_argument( - "--adios", - help="Adios dataset", - action="store_const", - dest="format", - const="adios", - ) - group.add_argument( - "--pickle", - help="Pickle dataset", - action="store_const", - dest="format", - const="pickle", - ) - parser.set_defaults(format="adios") - args = parser.parse_args() - - graph_feature_names = [] - graph_feature_dims = [] - node_feature_names = ["atomic_number", "forces"] - node_feature_dims = [1, 3] - dirpwd = os.path.dirname(os.path.abspath(__file__)) - datadir = os.path.join(dirpwd, "dataset/data") - ################################################################################################################## - input_filename = os.path.join(dirpwd, args.inputfile) - ################################################################################################################## - # Configurable run choices (JSON file that accompanies this example script). - with open(input_filename, "r") as f: - config = json.load(f) - verbosity = config["Verbosity"]["level"] - config["NeuralNetwork"]["Variables_of_interest"][ - "graph_feature_names" - ] = graph_feature_names - config["NeuralNetwork"]["Variables_of_interest"][ - "graph_feature_dims" - ] = graph_feature_dims - config["NeuralNetwork"]["Variables_of_interest"][ - "node_feature_names" - ] = node_feature_names - config["NeuralNetwork"]["Variables_of_interest"][ - "node_feature_dims" - ] = node_feature_dims - - if args.batch_size is not None: - config["NeuralNetwork"]["Training"]["batch_size"] = args.batch_size - - ################################################################################################################## - # Always initialize for multi-rank training. - comm_size, rank = hydragnn.utils.setup_ddp() - ################################################################################################################## - - comm = MPI.COMM_WORLD - - ## Set up logging - logging.basicConfig( - level=logging.INFO, - format="%%(levelname)s (rank %d): %%(message)s" % (rank), - datefmt="%H:%M:%S", - ) - - log_name = "LJ" if args.log is None else args.log - hydragnn.utils.setup_log(log_name) - writer = hydragnn.utils.get_summary_writer(log_name) - - log("Command: {0}\n".format(" ".join([x for x in sys.argv])), rank=0) - - modelname = "LJ" - if args.preonly: - - ## local data - total = LJDataset_VladTest( - os.path.join(datadir), - dist=True, - ) - ## This is a local split - trainset, valset, testset = split_dataset( - dataset=total, - perc_train=0.9, - stratify_splitting=False, - ) - print("Local splitting: ", len(total), len(trainset), len(valset), len(testset)) - - deg = gather_deg(trainset) - config["pna_deg"] = deg - - setnames = ["trainset", "valset", "testset"] - - if args.format == "pickle": - - ## pickle - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) - attrs = dict() - attrs["pna_deg"] = deg - SimplePickleWriter( - trainset, - basedir, - "trainset", - # minmax_node_feature=total.minmax_node_feature, - # minmax_graph_feature=total.minmax_graph_feature, - use_subdir=True, - attrs=attrs, - ) - SimplePickleWriter( - valset, - basedir, - "valset", - # minmax_node_feature=total.minmax_node_feature, - # minmax_graph_feature=total.minmax_graph_feature, - use_subdir=True, - ) - SimplePickleWriter( - testset, - basedir, - "testset", - # minmax_node_feature=total.minmax_node_feature, - # minmax_graph_feature=total.minmax_graph_feature, - use_subdir=True, - ) - - if args.format == "adios": - ## adios - fname = os.path.join( - os.path.dirname(__file__), "./dataset/%s.bp" % modelname - ) - adwriter = AdiosWriter(fname, comm) - adwriter.add("trainset", trainset) - adwriter.add("valset", valset) - adwriter.add("testset", testset) - # adwriter.add_global("minmax_node_feature", total.minmax_node_feature) - # adwriter.add_global("minmax_graph_feature", total.minmax_graph_feature) - adwriter.add_global("pna_deg", deg) - adwriter.save() - - sys.exit(0) - - tr.initialize() - tr.disable() - timer = Timer("load_data") - timer.start() - if args.format == "adios": - info("Adios load") - assert not (args.shmem and args.ddstore), "Cannot use both ddstore and shmem" - opt = { - "preload": False, - "shmem": args.shmem, - "ddstore": args.ddstore, - "ddstore_width": args.ddstore_width, - } - fname = os.path.join(os.path.dirname(__file__), "./dataset/%s.bp" % modelname) - trainset = AdiosDataset(fname, "trainset", comm, **opt) - valset = AdiosDataset(fname, "valset", comm, **opt) - testset = AdiosDataset(fname, "testset", comm, **opt) - elif args.format == "pickle": - info("Pickle load") - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) - var_config = config["NeuralNetwork"]["Variables_of_interest"] - trainset = SimplePickleDataset( - basedir=basedir, label="trainset", preload=True, var_config=var_config - ) - valset = SimplePickleDataset( - basedir=basedir, label="valset", var_config=var_config - ) - testset = SimplePickleDataset( - basedir=basedir, label="testset", var_config=var_config - ) - # minmax_node_feature = trainset.minmax_node_feature - # minmax_graph_feature = trainset.minmax_graph_feature - pna_deg = trainset.pna_deg - if args.ddstore: - opt = {"ddstore_width": args.ddstore_width} - trainset = DistDataset(trainset, "trainset", comm, **opt) - valset = DistDataset(valset, "valset", comm, **opt) - testset = DistDataset(testset, "testset", comm, **opt) - # trainset.minmax_node_feature = minmax_node_feature - # trainset.minmax_graph_feature = minmax_graph_feature - trainset.pna_deg = pna_deg - else: - raise NotImplementedError("No supported format: %s" % (args.format)) - - info( - "trainset,valset,testset size: %d %d %d" - % (len(trainset), len(valset), len(testset)) - ) - - if args.ddstore: - os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" - os.environ["HYDRAGNN_USE_ddstore"] = "1" - - (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] - ) - - config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) - ## Good to sync with everyone right after DDStore setup - comm.Barrier() - - hydragnn.utils.save_config(config, log_name) - - timer.stop() - - model = hydragnn.models.create_model_config( - config=config["NeuralNetwork"], - verbosity=verbosity, - ) - model = hydragnn.utils.get_distributed_model(model, verbosity) - - learning_rate = config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"] - optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, mode="min", factor=0.5, patience=5, min_lr=0.00001 - ) - - hydragnn.utils.load_existing_model_config( - model, config["NeuralNetwork"]["Training"], optimizer=optimizer - ) - - ################################################################################################################## - - hydragnn.train.train_validate_test( - model, - optimizer, - train_loader, - val_loader, - test_loader, - writer, - scheduler, - config["NeuralNetwork"], - log_name, - verbosity, - create_plots=False, - ) - - hydragnn.utils.save_model(model, optimizer, log_name) - hydragnn.utils.print_timers(verbosity) - - if tr.has("GPTLTracer"): - import gptl4py as gp - - eligible = rank if args.everyone else 0 - if rank == eligible: - gp.pr_file(os.path.join("logs", log_name, "gp_timing.p%d" % rank)) - gp.pr_summary_file(os.path.join("logs", log_name, "gp_timing.summary")) - gp.finalize() - sys.exit(0) diff --git a/examples/LennardJones/train_vlad_total_energy.py b/examples/LennardJones/train_vlad_total_energy.py deleted file mode 100644 index 123c0d6f4..000000000 --- a/examples/LennardJones/train_vlad_total_energy.py +++ /dev/null @@ -1,410 +0,0 @@ -import mpi4py -from mpi4py import MPI - -mpi4py.rc.thread_level = "serialized" -mpi4py.rc.threads = False - -import os, json -import random - -import h5py - -import logging -import sys -import argparse - -import hydragnn -from hydragnn.utils.print_utils import iterate_tqdm, log -from hydragnn.utils.time_utils import Timer - -from hydragnn.preprocess.load_data import split_dataset -from hydragnn.utils.abstractrawdataset import AbstractBaseDataset -from hydragnn.utils.distdataset import DistDataset -from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset -from hydragnn.preprocess.utils import gather_deg - -import numpy as np - -try: - from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset -except ImportError: - pass - -from torch_geometric.data import Data -from torch_geometric.transforms import RadiusGraph, Distance, Spherical, LocalCartesian -import torch -import torch.distributed as dist - -from hydragnn.utils import nsplit -import hydragnn.utils.tracer as tr - - -# FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones - - -torch.set_default_dtype(torch.float32) - - -def info(*args, logtype="info", sep=" "): - getattr(logging, logtype)(sep.join(map(str, args))) - - -# FIXME: this radis cutoff overwrites the radius cutoff currently written in the JSON file -create_graph_fromXYZ = RadiusGraph(r=5.0) # radius cutoff in angstrom -compute_edge_lengths = Distance(norm=False, cat=True) -spherical_coordinates = Spherical(norm=False, cat=False) -cartesian_coordinates = LocalCartesian(norm=False, cat=False) - - -class LJDataset_VladTest(AbstractBaseDataset): - """LJDataset_VladTest dataset class""" - - def __init__(self, dirpath, dist=False, sampling=None): - super().__init__() - - self.dist = dist - self.world_size = 1 - self.rank = 1 - if self.dist: - assert torch.distributed.is_initialized() - self.world_size = torch.distributed.get_world_size() - self.rank = torch.distributed.get_rank() - - dirfiles = sorted(os.listdir(dirpath)) - - rx = list(nsplit((dirfiles), self.world_size))[self.rank] - - for file in rx: - filepath = os.path.join(dirpath, file) - self.dataset.append(self.transform_input_to_data_object_base(filepath)) - - def transform_input_to_data_object_base(self, filepath): - - # Using readline() - file = open(filepath, "r") - - torch_data = torch.empty((0, 4), dtype=torch.float32) - - count = 0 - - while True: - count += 1 - - # Get next line from file - line = file.readline() - - # if line is empty - # end of file is reached - if not line: - break - - if count == 1: - array_line = np.fromstring(line, dtype=float, sep="\t") - energy = torch.from_numpy(array_line).unsqueeze(0) - elif count == 2: - array_line = np.fromstring(line, dtype=float, sep="\t") - forces = torch.from_numpy(array_line).unsqueeze(0) - else: - array_line = np.fromstring(line, dtype=float, sep="\t") - torch_data = torch.cat( - [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 - ) - # print("Line{}: {}".format(count, line.strip())) - - file.close() - - data = Data( - pos=torch_data[:, [1, 2, 3]].to(torch.float32), - x=torch_data[:, [0, 1, 2, 3]].to(torch.float32), - forces=forces.unsqueeze(0).to(torch.float32), - y=energy.unsqueeze(0).to(torch.float32), - ) - data = create_graph_fromXYZ(data) - data = compute_edge_lengths(data) - data.edge_attr = data.edge_attr.to(torch.float32) - # data = spherical_coordinates(data) - data = cartesian_coordinates(data) - - # maintain directionality - data.edge_index = data.edge_index[:, 0:6] - data.edge_attr = data.edge_attr[0:6, :] - - return data - - def len(self): - return len(self.dataset) - - def get(self, idx): - return self.dataset[idx] - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - parser.add_argument("--sampling", type=float, help="sampling ratio", default=None) - parser.add_argument( - "--preonly", - action="store_true", - help="preprocess only (no training)", - ) - parser.add_argument( - "--inputfile", help="input file", type=str, default="LJ_vlad_total_energy.json" - ) - parser.add_argument("--mae", action="store_true", help="do mae calculation") - parser.add_argument("--ddstore", action="store_true", help="ddstore dataset") - parser.add_argument("--ddstore_width", type=int, help="ddstore width", default=None) - parser.add_argument("--shmem", action="store_true", help="shmem") - parser.add_argument("--log", help="log name") - parser.add_argument("--batch_size", type=int, help="batch_size", default=None) - parser.add_argument("--everyone", action="store_true", help="gptimer") - - group = parser.add_mutually_exclusive_group() - group.add_argument( - "--adios", - help="Adios dataset", - action="store_const", - dest="format", - const="adios", - ) - group.add_argument( - "--pickle", - help="Pickle dataset", - action="store_const", - dest="format", - const="pickle", - ) - parser.set_defaults(format="adios") - args = parser.parse_args() - - graph_feature_names = ["energy"] - graph_feature_dims = [1] - node_feature_names = ["atomic_number"] - node_feature_dims = [1] - dirpwd = os.path.dirname(os.path.abspath(__file__)) - datadir = os.path.join(dirpwd, "dataset/data") - ################################################################################################################## - input_filename = os.path.join(dirpwd, args.inputfile) - ################################################################################################################## - # Configurable run choices (JSON file that accompanies this example script). - with open(input_filename, "r") as f: - config = json.load(f) - verbosity = config["Verbosity"]["level"] - config["NeuralNetwork"]["Variables_of_interest"][ - "graph_feature_names" - ] = graph_feature_names - config["NeuralNetwork"]["Variables_of_interest"][ - "graph_feature_dims" - ] = graph_feature_dims - config["NeuralNetwork"]["Variables_of_interest"][ - "node_feature_names" - ] = node_feature_names - config["NeuralNetwork"]["Variables_of_interest"][ - "node_feature_dims" - ] = node_feature_dims - - if args.batch_size is not None: - config["NeuralNetwork"]["Training"]["batch_size"] = args.batch_size - - ################################################################################################################## - # Always initialize for multi-rank training. - comm_size, rank = hydragnn.utils.setup_ddp() - ################################################################################################################## - - comm = MPI.COMM_WORLD - - ## Set up logging - logging.basicConfig( - level=logging.INFO, - format="%%(levelname)s (rank %d): %%(message)s" % (rank), - datefmt="%H:%M:%S", - ) - - log_name = "LJ" if args.log is None else args.log - hydragnn.utils.setup_log(log_name) - writer = hydragnn.utils.get_summary_writer(log_name) - - log("Command: {0}\n".format(" ".join([x for x in sys.argv])), rank=0) - - modelname = "LJ" - if args.preonly: - - ## local data - total = LJDataset_VladTest( - os.path.join(datadir), - dist=True, - ) - ## This is a local split - trainset, valset, testset = split_dataset( - dataset=total, - perc_train=0.9, - stratify_splitting=False, - ) - print("Local splitting: ", len(total), len(trainset), len(valset), len(testset)) - - deg = gather_deg(trainset) - config["pna_deg"] = deg - - setnames = ["trainset", "valset", "testset"] - - if args.format == "pickle": - - ## pickle - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) - attrs = dict() - attrs["pna_deg"] = deg - SimplePickleWriter( - trainset, - basedir, - "trainset", - # minmax_node_feature=total.minmax_node_feature, - # minmax_graph_feature=total.minmax_graph_feature, - use_subdir=True, - attrs=attrs, - ) - SimplePickleWriter( - valset, - basedir, - "valset", - # minmax_node_feature=total.minmax_node_feature, - # minmax_graph_feature=total.minmax_graph_feature, - use_subdir=True, - ) - SimplePickleWriter( - testset, - basedir, - "testset", - # minmax_node_feature=total.minmax_node_feature, - # minmax_graph_feature=total.minmax_graph_feature, - use_subdir=True, - ) - - if args.format == "adios": - ## adios - fname = os.path.join( - os.path.dirname(__file__), "./dataset/%s.bp" % modelname - ) - adwriter = AdiosWriter(fname, comm) - adwriter.add("trainset", trainset) - adwriter.add("valset", valset) - adwriter.add("testset", testset) - # adwriter.add_global("minmax_node_feature", total.minmax_node_feature) - # adwriter.add_global("minmax_graph_feature", total.minmax_graph_feature) - adwriter.add_global("pna_deg", deg) - adwriter.save() - - sys.exit(0) - - tr.initialize() - tr.disable() - timer = Timer("load_data") - timer.start() - if args.format == "adios": - info("Adios load") - assert not (args.shmem and args.ddstore), "Cannot use both ddstore and shmem" - opt = { - "preload": False, - "shmem": args.shmem, - "ddstore": args.ddstore, - "ddstore_width": args.ddstore_width, - } - fname = os.path.join(os.path.dirname(__file__), "./dataset/%s.bp" % modelname) - trainset = AdiosDataset(fname, "trainset", comm, **opt) - valset = AdiosDataset(fname, "valset", comm, **opt) - testset = AdiosDataset(fname, "testset", comm, **opt) - elif args.format == "pickle": - info("Pickle load") - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) - var_config = config["NeuralNetwork"]["Variables_of_interest"] - trainset = SimplePickleDataset( - basedir=basedir, label="trainset", preload=True, var_config=var_config - ) - valset = SimplePickleDataset( - basedir=basedir, label="valset", var_config=var_config - ) - testset = SimplePickleDataset( - basedir=basedir, label="testset", var_config=var_config - ) - # minmax_node_feature = trainset.minmax_node_feature - # minmax_graph_feature = trainset.minmax_graph_feature - pna_deg = trainset.pna_deg - if args.ddstore: - opt = {"ddstore_width": args.ddstore_width} - trainset = DistDataset(trainset, "trainset", comm, **opt) - valset = DistDataset(valset, "valset", comm, **opt) - testset = DistDataset(testset, "testset", comm, **opt) - # trainset.minmax_node_feature = minmax_node_feature - # trainset.minmax_graph_feature = minmax_graph_feature - trainset.pna_deg = pna_deg - else: - raise NotImplementedError("No supported format: %s" % (args.format)) - - info( - "trainset,valset,testset size: %d %d %d" - % (len(trainset), len(valset), len(testset)) - ) - - if args.ddstore: - os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi" - os.environ["HYDRAGNN_USE_ddstore"] = "1" - - (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( - trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"] - ) - - config = hydragnn.utils.update_config(config, train_loader, val_loader, test_loader) - ## Good to sync with everyone right after DDStore setup - comm.Barrier() - - hydragnn.utils.save_config(config, log_name) - - timer.stop() - - model = hydragnn.models.create_model_config( - config=config["NeuralNetwork"], - verbosity=verbosity, - ) - model = hydragnn.utils.get_distributed_model(model, verbosity) - - learning_rate = config["NeuralNetwork"]["Training"]["Optimizer"]["learning_rate"] - optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, mode="min", factor=0.5, patience=5, min_lr=0.00001 - ) - - hydragnn.utils.load_existing_model_config( - model, config["NeuralNetwork"]["Training"], optimizer=optimizer - ) - - ################################################################################################################## - - hydragnn.train.train_validate_test( - model, - optimizer, - train_loader, - val_loader, - test_loader, - writer, - scheduler, - config["NeuralNetwork"], - log_name, - verbosity, - create_plots=False, - ) - - hydragnn.utils.save_model(model, optimizer, log_name) - hydragnn.utils.print_timers(verbosity) - - if tr.has("GPTLTracer"): - import gptl4py as gp - - eligible = rank if args.everyone else 0 - if rank == eligible: - gp.pr_file(os.path.join("logs", log_name, "gp_timing.p%d" % rank)) - gp.pr_summary_file(os.path.join("logs", log_name, "gp_timing.summary")) - gp.finalize() - sys.exit(0) diff --git a/examples/qm9/qm9.py b/examples/qm9/qm9.py index 2e6c3da8e..04492b25f 100644 --- a/examples/qm9/qm9.py +++ b/examples/qm9/qm9.py @@ -16,6 +16,7 @@ import hydragnn + # Update each sample prior to loading. def qm9_pre_transform(data): # Set descriptor as element type. @@ -60,10 +61,21 @@ def qm9_pre_filter(data): dataset = torch_geometric.datasets.QM9( root="dataset/qm9", pre_transform=qm9_pre_transform, pre_filter=qm9_pre_filter ) +# Check for dataset filepath +datadir = os.path.join(os.getcwd(), "dataset/qm9") +if os.exists(datadir): + print("----------------------------DATASET FOUND----------------------------") +else: + print("----------------------------DATASET NOT FOUND----------------------------") + raise FileNotFoundError train, val, test = hydragnn.preprocess.split_dataset( dataset, config["NeuralNetwork"]["Training"]["perc_train"], False ) -(train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( +( + train_loader, + val_loader, + test_loader, +) = hydragnn.preprocess.create_dataloaders( train, val, test, config["NeuralNetwork"]["Training"]["batch_size"] ) diff --git a/tests/test_graphs.py b/tests/test_graphs.py index e2b36be60..dac8ab294 100755 --- a/tests/test_graphs.py +++ b/tests/test_graphs.py @@ -1,224 +1,224 @@ -############################################################################## -# Copyright (c) 2021, Oak Ridge National Laboratory # -# All rights reserved. # -# # -# This file is part of HydraGNN and is distributed under a BSD 3-clause # -# license. For the licensing terms see the LICENSE file in the top-level # -# directory. # -# # -# SPDX-License-Identifier: BSD-3-Clause # -############################################################################## - -import sys, os, json -import pytest - -import torch - -torch.manual_seed(97) -import shutil - -import hydragnn, tests -from hydragnn.utils.config_utils import merge_config - - -# Main unit test function called by pytest wrappers. -def unittest_train_model( - model_type, - ci_input, - use_lengths, - overwrite_data=False, - use_deepspeed=False, - overwrite_config=None, -): - world_size, rank = hydragnn.utils.get_comm_size_and_rank() - - os.environ["SERIALIZED_DATA_PATH"] = os.getcwd() - - # Read in config settings and override model type. - config_file = os.path.join(os.getcwd(), "tests/inputs", ci_input) - with open(config_file, "r") as f: - config = json.load(f) - config["NeuralNetwork"]["Architecture"]["model_type"] = model_type - - # Overwrite config settings if provided - if overwrite_config: - config = merge_config(config, overwrite_config) - - """ - to test this locally, set ci.json as - "Dataset": { - ... - "path": { - "train": "serialized_dataset/unit_test_singlehead_train.pkl", - "test": "serialized_dataset/unit_test_singlehead_test.pkl", - "validate": "serialized_dataset/unit_test_singlehead_validate.pkl"} - ... - """ - # use pkl files if exist by default - for dataset_name in config["Dataset"]["path"].keys(): - if dataset_name == "total": - pkl_file = ( - os.environ["SERIALIZED_DATA_PATH"] - + "/serialized_dataset/" - + config["Dataset"]["name"] - + ".pkl" - ) - else: - pkl_file = ( - os.environ["SERIALIZED_DATA_PATH"] - + "/serialized_dataset/" - + config["Dataset"]["name"] - + "_" - + dataset_name - + ".pkl" - ) - if os.path.exists(pkl_file): - config["Dataset"]["path"][dataset_name] = pkl_file - - # In the unit test runs, it is found MFC favors graph-level features over node-level features, compared with other models; - # hence here we decrease the loss weight coefficient for graph-level head in MFC. - if model_type == "MFC" and ci_input == "ci_multihead.json": - config["NeuralNetwork"]["Architecture"]["task_weights"][0] = 2 - - # Only run with edge lengths for models that support them. - if use_lengths: - config["NeuralNetwork"]["Architecture"]["edge_features"] = ["lengths"] - - if rank == 0: - num_samples_tot = 500 - # check if serialized pickle files or folders for raw files provided - pkl_input = False - if list(config["Dataset"]["path"].values())[0].endswith(".pkl"): - pkl_input = True - # only generate new datasets, if not pkl - if not pkl_input: - for dataset_name, data_path in config["Dataset"]["path"].items(): - if overwrite_data: - shutil.rmtree(data_path) - if not os.path.exists(data_path): - os.makedirs(data_path) - if dataset_name == "total": - num_samples = num_samples_tot - elif dataset_name == "train": - num_samples = int( - num_samples_tot - * config["NeuralNetwork"]["Training"]["perc_train"] - ) - elif dataset_name == "test": - num_samples = int( - num_samples_tot - * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) - * 0.5 - ) - elif dataset_name == "validate": - num_samples = int( - num_samples_tot - * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) - * 0.5 - ) - if not os.listdir(data_path): - tests.deterministic_graph_data( - data_path, number_configurations=num_samples - ) - - # Since the config file uses PNA already, test the file overload here. - # All the other models need to use the locally modified dictionary. - if model_type == "PNA" and not use_lengths: - hydragnn.run_training(config_file, use_deepspeed) - else: - hydragnn.run_training(config, use_deepspeed) - - ( - error, - error_mse_task, - true_values, - predicted_values, - ) = hydragnn.run_prediction(config, use_deepspeed) - - # Set RMSE and sample MAE error thresholds - thresholds = { - "SAGE": [0.20, 0.20], - "PNA": [0.20, 0.20], - "MFC": [0.20, 0.20], - "GIN": [0.25, 0.20], - "GAT": [0.60, 0.70], - "CGCNN": [0.50, 0.40], - "SchNet": [0.20, 0.20], - "DimeNet": [0.50, 0.50], - "EGNN": [0.20, 0.20], - } - if use_lengths and ("vector" not in ci_input): - thresholds["CGCNN"] = [0.175, 0.175] - thresholds["PNA"] = [0.10, 0.10] - if use_lengths and "vector" in ci_input: - thresholds["PNA"] = [0.2, 0.15] - if ci_input == "ci_conv_head.json": - thresholds["GIN"] = [0.25, 0.40] - - verbosity = 2 - - for ihead in range(len(true_values)): - error_head_mse = error_mse_task[ihead] - error_str = ( - str("{:.6f}".format(error_head_mse)) - + " < " - + str(thresholds[model_type][0]) - ) - hydragnn.utils.print_distributed(verbosity, "head: " + error_str) - assert ( - error_head_mse < thresholds[model_type][0] - ), "Head RMSE checking failed for " + str(ihead) - - head_true = true_values[ihead] - head_pred = predicted_values[ihead] - # Check individual samples - mae = torch.nn.L1Loss() - sample_mean_abs_error = mae(head_true, head_pred) - error_str = ( - "{:.6f}".format(sample_mean_abs_error) - + " < " - + str(thresholds[model_type][1]) - ) - assert ( - sample_mean_abs_error < thresholds[model_type][1] - ), "MAE sample checking failed!" - - # Check RMSE error - error_str = str("{:.6f}".format(error)) + " < " + str(thresholds[model_type][0]) - hydragnn.utils.print_distributed(verbosity, "total: " + error_str) - assert error < thresholds[model_type][0], "Total RMSE checking failed!" + str(error) - - -# Test across all models with both single/multihead -@pytest.mark.parametrize( - "model_type", - ["SAGE", "GIN", "GAT", "MFC", "PNA", "CGCNN", "SchNet", "DimeNet", "EGNN"], -) -@pytest.mark.parametrize("ci_input", ["ci.json", "ci_multihead.json"]) -def pytest_train_model(model_type, ci_input, overwrite_data=False): - unittest_train_model(model_type, ci_input, False, overwrite_data) - - -# Test only models -@pytest.mark.parametrize("model_type", ["PNA", "CGCNN", "SchNet", "EGNN"]) -def pytest_train_model_lengths(model_type, overwrite_data=False): - unittest_train_model(model_type, "ci.json", True, overwrite_data) - - -# Test across equivariant models -@pytest.mark.parametrize("model_type", ["EGNN", "SchNet"]) -def pytest_train_equivariant_model(model_type, overwrite_data=False): - unittest_train_model(model_type, "ci_equivariant.json", False, overwrite_data) - - -# Test vector output -@pytest.mark.parametrize("model_type", ["PNA"]) -def pytest_train_model_vectoroutput(model_type, overwrite_data=False): - unittest_train_model(model_type, "ci_vectoroutput.json", True, overwrite_data) - - -@pytest.mark.parametrize( - "model_type", ["SAGE", "GIN", "GAT", "MFC", "PNA", "SchNet", "DimeNet", "EGNN"] -) -def pytest_train_model_conv_head(model_type, overwrite_data=False): - unittest_train_model(model_type, "ci_conv_head.json", False, overwrite_data) +# ############################################################################## +# # Copyright (c) 2021, Oak Ridge National Laboratory # +# # All rights reserved. # +# # # +# # This file is part of HydraGNN and is distributed under a BSD 3-clause # +# # license. For the licensing terms see the LICENSE file in the top-level # +# # directory. # +# # # +# # SPDX-License-Identifier: BSD-3-Clause # +# ############################################################################## + +# import sys, os, json +# import pytest + +# import torch + +# torch.manual_seed(97) +# import shutil + +# import hydragnn, tests +# from hydragnn.utils.config_utils import merge_config + + +# # Main unit test function called by pytest wrappers. +# def unittest_train_model( +# model_type, +# ci_input, +# use_lengths, +# overwrite_data=False, +# use_deepspeed=False, +# overwrite_config=None, +# ): +# world_size, rank = hydragnn.utils.get_comm_size_and_rank() + +# os.environ["SERIALIZED_DATA_PATH"] = os.getcwd() + +# # Read in config settings and override model type. +# config_file = os.path.join(os.getcwd(), "tests/inputs", ci_input) +# with open(config_file, "r") as f: +# config = json.load(f) +# config["NeuralNetwork"]["Architecture"]["model_type"] = model_type + +# # Overwrite config settings if provided +# if overwrite_config: +# config = merge_config(config, overwrite_config) + +# """ +# to test this locally, set ci.json as +# "Dataset": { +# ... +# "path": { +# "train": "serialized_dataset/unit_test_singlehead_train.pkl", +# "test": "serialized_dataset/unit_test_singlehead_test.pkl", +# "validate": "serialized_dataset/unit_test_singlehead_validate.pkl"} +# ... +# """ +# # use pkl files if exist by default +# for dataset_name in config["Dataset"]["path"].keys(): +# if dataset_name == "total": +# pkl_file = ( +# os.environ["SERIALIZED_DATA_PATH"] +# + "/serialized_dataset/" +# + config["Dataset"]["name"] +# + ".pkl" +# ) +# else: +# pkl_file = ( +# os.environ["SERIALIZED_DATA_PATH"] +# + "/serialized_dataset/" +# + config["Dataset"]["name"] +# + "_" +# + dataset_name +# + ".pkl" +# ) +# if os.path.exists(pkl_file): +# config["Dataset"]["path"][dataset_name] = pkl_file + +# # In the unit test runs, it is found MFC favors graph-level features over node-level features, compared with other models; +# # hence here we decrease the loss weight coefficient for graph-level head in MFC. +# if model_type == "MFC" and ci_input == "ci_multihead.json": +# config["NeuralNetwork"]["Architecture"]["task_weights"][0] = 2 + +# # Only run with edge lengths for models that support them. +# if use_lengths: +# config["NeuralNetwork"]["Architecture"]["edge_features"] = ["lengths"] + +# if rank == 0: +# num_samples_tot = 500 +# # check if serialized pickle files or folders for raw files provided +# pkl_input = False +# if list(config["Dataset"]["path"].values())[0].endswith(".pkl"): +# pkl_input = True +# # only generate new datasets, if not pkl +# if not pkl_input: +# for dataset_name, data_path in config["Dataset"]["path"].items(): +# if overwrite_data: +# shutil.rmtree(data_path) +# if not os.path.exists(data_path): +# os.makedirs(data_path) +# if dataset_name == "total": +# num_samples = num_samples_tot +# elif dataset_name == "train": +# num_samples = int( +# num_samples_tot +# * config["NeuralNetwork"]["Training"]["perc_train"] +# ) +# elif dataset_name == "test": +# num_samples = int( +# num_samples_tot +# * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) +# * 0.5 +# ) +# elif dataset_name == "validate": +# num_samples = int( +# num_samples_tot +# * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) +# * 0.5 +# ) +# if not os.listdir(data_path): +# tests.deterministic_graph_data( +# data_path, number_configurations=num_samples +# ) + +# # Since the config file uses PNA already, test the file overload here. +# # All the other models need to use the locally modified dictionary. +# if model_type == "PNA" and not use_lengths: +# hydragnn.run_training(config_file, use_deepspeed) +# else: +# hydragnn.run_training(config, use_deepspeed) + +# ( +# error, +# error_mse_task, +# true_values, +# predicted_values, +# ) = hydragnn.run_prediction(config, use_deepspeed) + +# # Set RMSE and sample MAE error thresholds +# thresholds = { +# "SAGE": [0.20, 0.20], +# "PNA": [0.20, 0.20], +# "MFC": [0.20, 0.20], +# "GIN": [0.25, 0.20], +# "GAT": [0.60, 0.70], +# "CGCNN": [0.50, 0.40], +# "SchNet": [0.20, 0.20], +# "DimeNet": [0.50, 0.50], +# "EGNN": [0.20, 0.20], +# } +# if use_lengths and ("vector" not in ci_input): +# thresholds["CGCNN"] = [0.175, 0.175] +# thresholds["PNA"] = [0.10, 0.10] +# if use_lengths and "vector" in ci_input: +# thresholds["PNA"] = [0.2, 0.15] +# if ci_input == "ci_conv_head.json": +# thresholds["GIN"] = [0.25, 0.40] + +# verbosity = 2 + +# for ihead in range(len(true_values)): +# error_head_mse = error_mse_task[ihead] +# error_str = ( +# str("{:.6f}".format(error_head_mse)) +# + " < " +# + str(thresholds[model_type][0]) +# ) +# hydragnn.utils.print_distributed(verbosity, "head: " + error_str) +# assert ( +# error_head_mse < thresholds[model_type][0] +# ), "Head RMSE checking failed for " + str(ihead) + +# head_true = true_values[ihead] +# head_pred = predicted_values[ihead] +# # Check individual samples +# mae = torch.nn.L1Loss() +# sample_mean_abs_error = mae(head_true, head_pred) +# error_str = ( +# "{:.6f}".format(sample_mean_abs_error) +# + " < " +# + str(thresholds[model_type][1]) +# ) +# assert ( +# sample_mean_abs_error < thresholds[model_type][1] +# ), "MAE sample checking failed!" + +# # Check RMSE error +# error_str = str("{:.6f}".format(error)) + " < " + str(thresholds[model_type][0]) +# hydragnn.utils.print_distributed(verbosity, "total: " + error_str) +# assert error < thresholds[model_type][0], "Total RMSE checking failed!" + str(error) + + +# # Test across all models with both single/multihead +# @pytest.mark.parametrize( +# "model_type", +# ["SAGE", "GIN", "GAT", "MFC", "PNA", "CGCNN", "SchNet", "DimeNet", "EGNN"], +# ) +# @pytest.mark.parametrize("ci_input", ["ci.json", "ci_multihead.json"]) +# def pytest_train_model(model_type, ci_input, overwrite_data=False): +# unittest_train_model(model_type, ci_input, False, overwrite_data) + + +# # Test only models +# @pytest.mark.parametrize("model_type", ["PNA", "CGCNN", "SchNet", "EGNN"]) +# def pytest_train_model_lengths(model_type, overwrite_data=False): +# unittest_train_model(model_type, "ci.json", True, overwrite_data) + + +# # Test across equivariant models +# @pytest.mark.parametrize("model_type", ["EGNN", "SchNet"]) +# def pytest_train_equivariant_model(model_type, overwrite_data=False): +# unittest_train_model(model_type, "ci_equivariant.json", False, overwrite_data) + + +# # Test vector output +# @pytest.mark.parametrize("model_type", ["PNA"]) +# def pytest_train_model_vectoroutput(model_type, overwrite_data=False): +# unittest_train_model(model_type, "ci_vectoroutput.json", True, overwrite_data) + + +# @pytest.mark.parametrize( +# "model_type", ["SAGE", "GIN", "GAT", "MFC", "PNA", "SchNet", "DimeNet", "EGNN"] +# ) +# def pytest_train_model_conv_head(model_type, overwrite_data=False): +# unittest_train_model(model_type, "ci_conv_head.json", False, overwrite_data) From 2da742e3329f1cf180477222cfc392797191bf4c Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 10:03:05 -0400 Subject: [PATCH 20/37] unnecessary import --- hydragnn/models/DIMEStack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hydragnn/models/DIMEStack.py b/hydragnn/models/DIMEStack.py index fcaafb153..1949d3406 100644 --- a/hydragnn/models/DIMEStack.py +++ b/hydragnn/models/DIMEStack.py @@ -14,7 +14,7 @@ import torch from torch import Tensor -from torch.nn import Identity, SiLU, Sigmoid +from torch.nn import Identity, SiLU from torch_geometric.nn import Linear, Sequential from torch_geometric.nn.models.dimenet import ( From 70206dc500692aff83d534ab1265d7a8d8302adf Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 10:04:22 -0400 Subject: [PATCH 21/37] file cleanup --- examples/LennardJones/README.md | 92 --------------------------------- examples/qm9/qm9.py | 1 - 2 files changed, 93 deletions(-) delete mode 100644 examples/LennardJones/README.md diff --git a/examples/LennardJones/README.md b/examples/LennardJones/README.md deleted file mode 100644 index 0c4bda202..000000000 --- a/examples/LennardJones/README.md +++ /dev/null @@ -1,92 +0,0 @@ -# PyTorch_Interatomic_Potentials - - - -## Getting started - -To make it easy for you to get started with GitLab, here's a list of recommended next steps. - -Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! - -## Add your files - -- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files -- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: - -``` -cd existing_repo -git remote add origin https://code.ornl.gov/aisd/pytorch_interatomic_potentials.git -git branch -M main -git push -uf origin main -``` - -## Integrate with your tools - -- [ ] [Set up project integrations](https://code.ornl.gov/aisd/pytorch_interatomic_potentials/-/settings/integrations) - -## Collaborate with your team - -- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) -- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) -- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) -- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) -- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) - -## Test and Deploy - -Use the built-in continuous integration in GitLab. - -- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) -- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) -- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) -- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) -- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) - -*** - -# Editing this README - -When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template. - -## Suggestions for a good README -Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. - -## Name -Choose a self-explaining name for your project. - -## Description -Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. - -## Badges -On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. - -## Visuals -Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. - -## Installation -Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. - -## Usage -Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. - -## Support -Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. - -## Roadmap -If you have ideas for releases in the future, it is a good idea to list them in the README. - -## Contributing -State if you are open to contributions and what your requirements are for accepting them. - -For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. - -You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. - -## Authors and acknowledgment -Show your appreciation to those who have contributed to the project. - -## License -For open source projects, say how it is licensed. - -## Project status -If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. diff --git a/examples/qm9/qm9.py b/examples/qm9/qm9.py index 04492b25f..73a9b7417 100644 --- a/examples/qm9/qm9.py +++ b/examples/qm9/qm9.py @@ -16,7 +16,6 @@ import hydragnn - # Update each sample prior to loading. def qm9_pre_transform(data): # Set descriptor as element type. From 7032acd3f9b052999cbe874e3b71d2cfef5a087c Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 10:05:27 -0400 Subject: [PATCH 22/37] add some tests back in --- tests/test_graphs.py | 386 +++++++++++++++++++++---------------------- 1 file changed, 193 insertions(+), 193 deletions(-) diff --git a/tests/test_graphs.py b/tests/test_graphs.py index dac8ab294..f2b8484c3 100755 --- a/tests/test_graphs.py +++ b/tests/test_graphs.py @@ -1,192 +1,192 @@ -# ############################################################################## -# # Copyright (c) 2021, Oak Ridge National Laboratory # -# # All rights reserved. # -# # # -# # This file is part of HydraGNN and is distributed under a BSD 3-clause # -# # license. For the licensing terms see the LICENSE file in the top-level # -# # directory. # -# # # -# # SPDX-License-Identifier: BSD-3-Clause # -# ############################################################################## - -# import sys, os, json -# import pytest - -# import torch - -# torch.manual_seed(97) -# import shutil - -# import hydragnn, tests -# from hydragnn.utils.config_utils import merge_config - - -# # Main unit test function called by pytest wrappers. -# def unittest_train_model( -# model_type, -# ci_input, -# use_lengths, -# overwrite_data=False, -# use_deepspeed=False, -# overwrite_config=None, -# ): -# world_size, rank = hydragnn.utils.get_comm_size_and_rank() - -# os.environ["SERIALIZED_DATA_PATH"] = os.getcwd() - -# # Read in config settings and override model type. -# config_file = os.path.join(os.getcwd(), "tests/inputs", ci_input) -# with open(config_file, "r") as f: -# config = json.load(f) -# config["NeuralNetwork"]["Architecture"]["model_type"] = model_type - -# # Overwrite config settings if provided -# if overwrite_config: -# config = merge_config(config, overwrite_config) - -# """ -# to test this locally, set ci.json as -# "Dataset": { -# ... -# "path": { -# "train": "serialized_dataset/unit_test_singlehead_train.pkl", -# "test": "serialized_dataset/unit_test_singlehead_test.pkl", -# "validate": "serialized_dataset/unit_test_singlehead_validate.pkl"} -# ... -# """ -# # use pkl files if exist by default -# for dataset_name in config["Dataset"]["path"].keys(): -# if dataset_name == "total": -# pkl_file = ( -# os.environ["SERIALIZED_DATA_PATH"] -# + "/serialized_dataset/" -# + config["Dataset"]["name"] -# + ".pkl" -# ) -# else: -# pkl_file = ( -# os.environ["SERIALIZED_DATA_PATH"] -# + "/serialized_dataset/" -# + config["Dataset"]["name"] -# + "_" -# + dataset_name -# + ".pkl" -# ) -# if os.path.exists(pkl_file): -# config["Dataset"]["path"][dataset_name] = pkl_file - -# # In the unit test runs, it is found MFC favors graph-level features over node-level features, compared with other models; -# # hence here we decrease the loss weight coefficient for graph-level head in MFC. -# if model_type == "MFC" and ci_input == "ci_multihead.json": -# config["NeuralNetwork"]["Architecture"]["task_weights"][0] = 2 - -# # Only run with edge lengths for models that support them. -# if use_lengths: -# config["NeuralNetwork"]["Architecture"]["edge_features"] = ["lengths"] - -# if rank == 0: -# num_samples_tot = 500 -# # check if serialized pickle files or folders for raw files provided -# pkl_input = False -# if list(config["Dataset"]["path"].values())[0].endswith(".pkl"): -# pkl_input = True -# # only generate new datasets, if not pkl -# if not pkl_input: -# for dataset_name, data_path in config["Dataset"]["path"].items(): -# if overwrite_data: -# shutil.rmtree(data_path) -# if not os.path.exists(data_path): -# os.makedirs(data_path) -# if dataset_name == "total": -# num_samples = num_samples_tot -# elif dataset_name == "train": -# num_samples = int( -# num_samples_tot -# * config["NeuralNetwork"]["Training"]["perc_train"] -# ) -# elif dataset_name == "test": -# num_samples = int( -# num_samples_tot -# * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) -# * 0.5 -# ) -# elif dataset_name == "validate": -# num_samples = int( -# num_samples_tot -# * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) -# * 0.5 -# ) -# if not os.listdir(data_path): -# tests.deterministic_graph_data( -# data_path, number_configurations=num_samples -# ) - -# # Since the config file uses PNA already, test the file overload here. -# # All the other models need to use the locally modified dictionary. -# if model_type == "PNA" and not use_lengths: -# hydragnn.run_training(config_file, use_deepspeed) -# else: -# hydragnn.run_training(config, use_deepspeed) - -# ( -# error, -# error_mse_task, -# true_values, -# predicted_values, -# ) = hydragnn.run_prediction(config, use_deepspeed) - -# # Set RMSE and sample MAE error thresholds -# thresholds = { -# "SAGE": [0.20, 0.20], -# "PNA": [0.20, 0.20], -# "MFC": [0.20, 0.20], -# "GIN": [0.25, 0.20], -# "GAT": [0.60, 0.70], -# "CGCNN": [0.50, 0.40], -# "SchNet": [0.20, 0.20], -# "DimeNet": [0.50, 0.50], -# "EGNN": [0.20, 0.20], -# } -# if use_lengths and ("vector" not in ci_input): -# thresholds["CGCNN"] = [0.175, 0.175] -# thresholds["PNA"] = [0.10, 0.10] -# if use_lengths and "vector" in ci_input: -# thresholds["PNA"] = [0.2, 0.15] -# if ci_input == "ci_conv_head.json": -# thresholds["GIN"] = [0.25, 0.40] - -# verbosity = 2 - -# for ihead in range(len(true_values)): -# error_head_mse = error_mse_task[ihead] -# error_str = ( -# str("{:.6f}".format(error_head_mse)) -# + " < " -# + str(thresholds[model_type][0]) -# ) -# hydragnn.utils.print_distributed(verbosity, "head: " + error_str) -# assert ( -# error_head_mse < thresholds[model_type][0] -# ), "Head RMSE checking failed for " + str(ihead) - -# head_true = true_values[ihead] -# head_pred = predicted_values[ihead] -# # Check individual samples -# mae = torch.nn.L1Loss() -# sample_mean_abs_error = mae(head_true, head_pred) -# error_str = ( -# "{:.6f}".format(sample_mean_abs_error) -# + " < " -# + str(thresholds[model_type][1]) -# ) -# assert ( -# sample_mean_abs_error < thresholds[model_type][1] -# ), "MAE sample checking failed!" - -# # Check RMSE error -# error_str = str("{:.6f}".format(error)) + " < " + str(thresholds[model_type][0]) -# hydragnn.utils.print_distributed(verbosity, "total: " + error_str) -# assert error < thresholds[model_type][0], "Total RMSE checking failed!" + str(error) +############################################################################## +# Copyright (c) 2021, Oak Ridge National Laboratory # +# All rights reserved. # +# # +# This file is part of HydraGNN and is distributed under a BSD 3-clause # +# license. For the licensing terms see the LICENSE file in the top-level # +# directory. # +# # +# SPDX-License-Identifier: BSD-3-Clause # +############################################################################## + +import sys, os, json +import pytest + +import torch + +torch.manual_seed(97) +import shutil + +import hydragnn, tests +from hydragnn.utils.config_utils import merge_config + + +# Main unit test function called by pytest wrappers. +def unittest_train_model( + model_type, + ci_input, + use_lengths, + overwrite_data=False, + use_deepspeed=False, + overwrite_config=None, +): + world_size, rank = hydragnn.utils.get_comm_size_and_rank() + + os.environ["SERIALIZED_DATA_PATH"] = os.getcwd() + + # Read in config settings and override model type. + config_file = os.path.join(os.getcwd(), "tests/inputs", ci_input) + with open(config_file, "r") as f: + config = json.load(f) + config["NeuralNetwork"]["Architecture"]["model_type"] = model_type + + # Overwrite config settings if provided + if overwrite_config: + config = merge_config(config, overwrite_config) + + """ + to test this locally, set ci.json as + "Dataset": { + ... + "path": { + "train": "serialized_dataset/unit_test_singlehead_train.pkl", + "test": "serialized_dataset/unit_test_singlehead_test.pkl", + "validate": "serialized_dataset/unit_test_singlehead_validate.pkl"} + ... + """ + # use pkl files if exist by default + for dataset_name in config["Dataset"]["path"].keys(): + if dataset_name == "total": + pkl_file = ( + os.environ["SERIALIZED_DATA_PATH"] + + "/serialized_dataset/" + + config["Dataset"]["name"] + + ".pkl" + ) + else: + pkl_file = ( + os.environ["SERIALIZED_DATA_PATH"] + + "/serialized_dataset/" + + config["Dataset"]["name"] + + "_" + + dataset_name + + ".pkl" + ) + if os.path.exists(pkl_file): + config["Dataset"]["path"][dataset_name] = pkl_file + + # In the unit test runs, it is found MFC favors graph-level features over node-level features, compared with other models; + # hence here we decrease the loss weight coefficient for graph-level head in MFC. + if model_type == "MFC" and ci_input == "ci_multihead.json": + config["NeuralNetwork"]["Architecture"]["task_weights"][0] = 2 + + # Only run with edge lengths for models that support them. + if use_lengths: + config["NeuralNetwork"]["Architecture"]["edge_features"] = ["lengths"] + + if rank == 0: + num_samples_tot = 500 + # check if serialized pickle files or folders for raw files provided + pkl_input = False + if list(config["Dataset"]["path"].values())[0].endswith(".pkl"): + pkl_input = True + # only generate new datasets, if not pkl + if not pkl_input: + for dataset_name, data_path in config["Dataset"]["path"].items(): + if overwrite_data: + shutil.rmtree(data_path) + if not os.path.exists(data_path): + os.makedirs(data_path) + if dataset_name == "total": + num_samples = num_samples_tot + elif dataset_name == "train": + num_samples = int( + num_samples_tot + * config["NeuralNetwork"]["Training"]["perc_train"] + ) + elif dataset_name == "test": + num_samples = int( + num_samples_tot + * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) + * 0.5 + ) + elif dataset_name == "validate": + num_samples = int( + num_samples_tot + * (1 - config["NeuralNetwork"]["Training"]["perc_train"]) + * 0.5 + ) + if not os.listdir(data_path): + tests.deterministic_graph_data( + data_path, number_configurations=num_samples + ) + + # Since the config file uses PNA already, test the file overload here. + # All the other models need to use the locally modified dictionary. + if model_type == "PNA" and not use_lengths: + hydragnn.run_training(config_file, use_deepspeed) + else: + hydragnn.run_training(config, use_deepspeed) + + ( + error, + error_mse_task, + true_values, + predicted_values, + ) = hydragnn.run_prediction(config, use_deepspeed) + + # Set RMSE and sample MAE error thresholds + thresholds = { + "SAGE": [0.20, 0.20], + "PNA": [0.20, 0.20], + "MFC": [0.20, 0.20], + "GIN": [0.25, 0.20], + "GAT": [0.60, 0.70], + "CGCNN": [0.50, 0.40], + "SchNet": [0.20, 0.20], + "DimeNet": [0.50, 0.50], + "EGNN": [0.20, 0.20], + } + if use_lengths and ("vector" not in ci_input): + thresholds["CGCNN"] = [0.175, 0.175] + thresholds["PNA"] = [0.10, 0.10] + if use_lengths and "vector" in ci_input: + thresholds["PNA"] = [0.2, 0.15] + if ci_input == "ci_conv_head.json": + thresholds["GIN"] = [0.25, 0.40] + + verbosity = 2 + + for ihead in range(len(true_values)): + error_head_mse = error_mse_task[ihead] + error_str = ( + str("{:.6f}".format(error_head_mse)) + + " < " + + str(thresholds[model_type][0]) + ) + hydragnn.utils.print_distributed(verbosity, "head: " + error_str) + assert ( + error_head_mse < thresholds[model_type][0] + ), "Head RMSE checking failed for " + str(ihead) + + head_true = true_values[ihead] + head_pred = predicted_values[ihead] + # Check individual samples + mae = torch.nn.L1Loss() + sample_mean_abs_error = mae(head_true, head_pred) + error_str = ( + "{:.6f}".format(sample_mean_abs_error) + + " < " + + str(thresholds[model_type][1]) + ) + assert ( + sample_mean_abs_error < thresholds[model_type][1] + ), "MAE sample checking failed!" + + # Check RMSE error + error_str = str("{:.6f}".format(error)) + " < " + str(thresholds[model_type][0]) + hydragnn.utils.print_distributed(verbosity, "total: " + error_str) + assert error < thresholds[model_type][0], "Total RMSE checking failed!" + str(error) # # Test across all models with both single/multihead @@ -211,10 +211,10 @@ # unittest_train_model(model_type, "ci_equivariant.json", False, overwrite_data) -# # Test vector output -# @pytest.mark.parametrize("model_type", ["PNA"]) -# def pytest_train_model_vectoroutput(model_type, overwrite_data=False): -# unittest_train_model(model_type, "ci_vectoroutput.json", True, overwrite_data) +# Test vector output +@pytest.mark.parametrize("model_type", ["PNA"]) +def pytest_train_model_vectoroutput(model_type, overwrite_data=False): + unittest_train_model(model_type, "ci_vectoroutput.json", True, overwrite_data) # @pytest.mark.parametrize( From f98fb3ebf27dca96a04e9117d61815340e61382c Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 10:18:12 -0400 Subject: [PATCH 23/37] Restore all tests --- tests/test_graphs.py | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/tests/test_graphs.py b/tests/test_graphs.py index f2b8484c3..e2b36be60 100755 --- a/tests/test_graphs.py +++ b/tests/test_graphs.py @@ -189,26 +189,26 @@ def unittest_train_model( assert error < thresholds[model_type][0], "Total RMSE checking failed!" + str(error) -# # Test across all models with both single/multihead -# @pytest.mark.parametrize( -# "model_type", -# ["SAGE", "GIN", "GAT", "MFC", "PNA", "CGCNN", "SchNet", "DimeNet", "EGNN"], -# ) -# @pytest.mark.parametrize("ci_input", ["ci.json", "ci_multihead.json"]) -# def pytest_train_model(model_type, ci_input, overwrite_data=False): -# unittest_train_model(model_type, ci_input, False, overwrite_data) +# Test across all models with both single/multihead +@pytest.mark.parametrize( + "model_type", + ["SAGE", "GIN", "GAT", "MFC", "PNA", "CGCNN", "SchNet", "DimeNet", "EGNN"], +) +@pytest.mark.parametrize("ci_input", ["ci.json", "ci_multihead.json"]) +def pytest_train_model(model_type, ci_input, overwrite_data=False): + unittest_train_model(model_type, ci_input, False, overwrite_data) -# # Test only models -# @pytest.mark.parametrize("model_type", ["PNA", "CGCNN", "SchNet", "EGNN"]) -# def pytest_train_model_lengths(model_type, overwrite_data=False): -# unittest_train_model(model_type, "ci.json", True, overwrite_data) +# Test only models +@pytest.mark.parametrize("model_type", ["PNA", "CGCNN", "SchNet", "EGNN"]) +def pytest_train_model_lengths(model_type, overwrite_data=False): + unittest_train_model(model_type, "ci.json", True, overwrite_data) -# # Test across equivariant models -# @pytest.mark.parametrize("model_type", ["EGNN", "SchNet"]) -# def pytest_train_equivariant_model(model_type, overwrite_data=False): -# unittest_train_model(model_type, "ci_equivariant.json", False, overwrite_data) +# Test across equivariant models +@pytest.mark.parametrize("model_type", ["EGNN", "SchNet"]) +def pytest_train_equivariant_model(model_type, overwrite_data=False): + unittest_train_model(model_type, "ci_equivariant.json", False, overwrite_data) # Test vector output @@ -217,8 +217,8 @@ def pytest_train_model_vectoroutput(model_type, overwrite_data=False): unittest_train_model(model_type, "ci_vectoroutput.json", True, overwrite_data) -# @pytest.mark.parametrize( -# "model_type", ["SAGE", "GIN", "GAT", "MFC", "PNA", "SchNet", "DimeNet", "EGNN"] -# ) -# def pytest_train_model_conv_head(model_type, overwrite_data=False): -# unittest_train_model(model_type, "ci_conv_head.json", False, overwrite_data) +@pytest.mark.parametrize( + "model_type", ["SAGE", "GIN", "GAT", "MFC", "PNA", "SchNet", "DimeNet", "EGNN"] +) +def pytest_train_model_conv_head(model_type, overwrite_data=False): + unittest_train_model(model_type, "ci_conv_head.json", False, overwrite_data) From d66de163186dcf114b64ed9a3812d7923bc6d1f7 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 10:31:30 -0400 Subject: [PATCH 24/37] formatting --- examples/qm9/qm9.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/examples/qm9/qm9.py b/examples/qm9/qm9.py index 73a9b7417..1594b1d0f 100644 --- a/examples/qm9/qm9.py +++ b/examples/qm9/qm9.py @@ -70,11 +70,7 @@ def qm9_pre_filter(data): train, val, test = hydragnn.preprocess.split_dataset( dataset, config["NeuralNetwork"]["Training"]["perc_train"], False ) -( - train_loader, - val_loader, - test_loader, -) = hydragnn.preprocess.create_dataloaders( +(train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders( train, val, test, config["NeuralNetwork"]["Training"]["batch_size"] ) From 206b52fde03e2adcfc443b6ed02b83c44559c342 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 11:25:10 -0400 Subject: [PATCH 25/37] check dataset things --- examples/LennardJones/LennardJones.py | 11 +++-------- examples/qm9/qm9.py | 2 +- 2 files changed, 4 insertions(+), 9 deletions(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index 2aa377ea9..c397d33e9 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -42,18 +42,13 @@ from AtomicStructure import AtomicStructureHandler -def create_dataset(config): +def create_dataset(path, config): # Angstrom unit primitive_bravais_lattice_constant_x = 3.8 primitive_bravais_lattice_constant_y = 3.8 primitive_bravais_lattice_constant_z = 3.8 - path = "./dataset/data" radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] - number_configurations = ( - config["NeuralNetwork"]["Training"]["num_configurations"] - if "num_configurations" in config["NeuralNetwork"]["Training"] - else 1000 - ) + number_configurations = 1000 atom_types = [1] formula = LJpotential(1.0, 3.4) atomic_structure_handler = AtomicStructureHandler( @@ -298,7 +293,7 @@ def get(self, idx): if not dataset_exists: ## local data - create_dataset(config) + create_dataset(os.path.join(lookdir, 'data'), config) total = LJDataset( os.path.join(datadir), dist=True, diff --git a/examples/qm9/qm9.py b/examples/qm9/qm9.py index 1594b1d0f..76cec549c 100644 --- a/examples/qm9/qm9.py +++ b/examples/qm9/qm9.py @@ -62,7 +62,7 @@ def qm9_pre_filter(data): ) # Check for dataset filepath datadir = os.path.join(os.getcwd(), "dataset/qm9") -if os.exists(datadir): +if os.path.exists(datadir): print("----------------------------DATASET FOUND----------------------------") else: print("----------------------------DATASET NOT FOUND----------------------------") From 998083483e7496549a966be82788ecf7cfee3daf Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 11:28:16 -0400 Subject: [PATCH 26/37] formatting --- examples/LennardJones/LennardJones.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index c397d33e9..bb48fd842 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -293,7 +293,7 @@ def get(self, idx): if not dataset_exists: ## local data - create_dataset(os.path.join(lookdir, 'data'), config) + create_dataset(os.path.join(lookdir, "data"), config) total = LJDataset( os.path.join(datadir), dist=True, From fa10c20faafbc76440f0cf6b30c8926aa918819e Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 11:53:58 -0400 Subject: [PATCH 27/37] Revise paths to be more succinct and Use radius from config --- examples/LennardJones/LennardJones.py | 48 ++++++++----------- examples/LennardJones/configurational_data.py | 27 +---------- 2 files changed, 20 insertions(+), 55 deletions(-) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index bb48fd842..7e605529e 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -29,7 +29,7 @@ pass from torch_geometric.data import Data -from torch_geometric.transforms import RadiusGraph, Distance, Spherical, LocalCartesian +from torch_geometric.transforms import RadiusGraph, Distance, LocalCartesian import torch import torch.distributed as dist @@ -48,7 +48,7 @@ def create_dataset(path, config): primitive_bravais_lattice_constant_y = 3.8 primitive_bravais_lattice_constant_z = 3.8 radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] - number_configurations = 1000 + number_configurations = config["Dataset"]["number_configurations"] if "number_configurations" in config["Dataset"] else 1000 atom_types = [1] formula = LJpotential(1.0, 3.4) atomic_structure_handler = AtomicStructureHandler( @@ -81,17 +81,11 @@ def info(*args, logtype="info", sep=" "): getattr(logging, logtype)(sep.join(map(str, args))) -# FIXME: this radis cutoff overwrites the radius cutoff currently written in the JSON file -create_graph_fromXYZ = RadiusGraph(r=5.0) # radius cutoff in angstrom -compute_edge_lengths = Distance(norm=False, cat=True) -spherical_coordinates = Spherical(norm=False, cat=False) -cartesian_coordinates = LocalCartesian(norm=False, cat=False) - class LJDataset(AbstractBaseDataset): """LJDataset dataset class""" - def __init__(self, dirpath, dist=False, sampling=None): + def __init__(self, dirpath, config, dist=False, sampling=None): super().__init__() self.dist = dist @@ -101,6 +95,11 @@ def __init__(self, dirpath, dist=False, sampling=None): assert torch.distributed.is_initialized() self.world_size = torch.distributed.get_world_size() self.rank = torch.distributed.get_rank() + + self.create_graph_fromXYZ = RadiusGraph(r=config["NeuralNetwork"]["Architecture"]["radius"]) # radius cutoff in angstrom + self.compute_edge_lengths = Distance(norm=False, cat=True) + # self.spherical_coordinates = Spherical(norm=False, cat=False) + self.cartesian_coordinates = LocalCartesian(norm=False, cat=False) dirfiles = sorted(os.listdir(dirpath)) @@ -180,11 +179,11 @@ def transform_input_to_data_object_base(self, filepath): .to(torch.float32), energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), ) - data = create_graph_fromXYZ(data) - data = compute_edge_lengths(data) + data = self.create_graph_fromXYZ(data) + data = self.compute_edge_lengths(data) data.edge_attr = data.edge_attr.to(torch.float32) - # data = spherical_coordinates(data) - data = cartesian_coordinates(data) + # data = self.spherical_coordinates(data) + data = self.cartesian_coordinates(data) return data @@ -237,7 +236,6 @@ def get(self, idx): node_feature_names = ["atomic_number", "potential", "forces"] node_feature_dims = [1, 1, 3] dirpwd = os.path.dirname(os.path.abspath(__file__)) - datadir = os.path.join(dirpwd, "dataset/data") ################################################################################################################## input_filename = os.path.join(dirpwd, args.inputfile) ################################################################################################################## @@ -283,19 +281,21 @@ def get(self, idx): modelname = "LJ" # Check for dataset for each format - lookdir = os.path.join(dirpwd, "dataset") if args.format == "pickle": - dataset_exists = os.path.exists(os.path.join(lookdir, "LJ.pickle")) + basedir = os.path.join(dirpwd, "dataset", "%s.pickle" % modelname) + dataset_exists = os.path.exists(os.path.join(dirpwd, "dataset/LJ.pickle")) if args.format == "adios": - dataset_exists = os.path.exists(os.path.join(lookdir, "%s.bp" % modelname)) + fname = os.path.join(dirpwd, "./dataset/%s.bp" % modelname) + dataset_exists = os.path.exists(os.path.join(dirpwd, "dataset", "%s.bp" % modelname)) # Create dataset if preonly specified or dataset does not exist if not dataset_exists: ## local data - create_dataset(os.path.join(lookdir, "data"), config) + create_dataset(os.path.join(dirpwd, "dataset/data"), config) total = LJDataset( - os.path.join(datadir), + os.path.join(dirpwd, "dataset"), + config, dist=True, ) ## This is a local split @@ -314,9 +314,6 @@ def get(self, idx): if args.format == "pickle": ## pickle - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) attrs = dict() attrs["pna_deg"] = deg SimplePickleWriter( @@ -347,9 +344,6 @@ def get(self, idx): if args.format == "adios": ## adios - fname = os.path.join( - os.path.dirname(__file__), "./dataset/%s.bp" % modelname - ) adwriter = AdiosWriter(fname, comm) adwriter.add("trainset", trainset) adwriter.add("valset", valset) @@ -372,15 +366,11 @@ def get(self, idx): "ddstore": args.ddstore, "ddstore_width": args.ddstore_width, } - fname = os.path.join(os.path.dirname(__file__), "./dataset/%s.bp" % modelname) trainset = AdiosDataset(fname, "trainset", comm, **opt) valset = AdiosDataset(fname, "valset", comm, **opt) testset = AdiosDataset(fname, "testset", comm, **opt) elif args.format == "pickle": info("Pickle load") - basedir = os.path.join( - os.path.dirname(__file__), "dataset", "%s.pickle" % modelname - ) var_config = config["NeuralNetwork"]["Variables_of_interest"] trainset = SimplePickleDataset( basedir=basedir, label="trainset", preload=True, var_config=var_config diff --git a/examples/LennardJones/configurational_data.py b/examples/LennardJones/configurational_data.py index 8ebb1d6f8..ec41f1a8a 100644 --- a/examples/LennardJones/configurational_data.py +++ b/examples/LennardJones/configurational_data.py @@ -212,29 +212,4 @@ def create_configuration( path, "output" + str(configuration + configuration_start) + ".txt" ) with open(filename, "w") as f: - f.write(filetxt) - - -if __name__ == "__main__": - path = "./data" - radius_cutoff = 5.0 - atom_types = [1] - formula = LJpotential(1.0, 3.4) - AtomicStructureHandler = AtomicStructureHandler( - atom_types, - [ - primitive_bravais_lattice_constant_x, - primitive_bravais_lattice_constant_y, - primitive_bravais_lattice_constant_z, - ], - radius_cutoff, - formula, - ) - deterministic_graph_data( - path, - atom_types, - atomic_structure_handler=AtomicStructureHandler, - radius_cutoff=radius_cutoff, - relative_maximum_atomic_displacement=1e-1, - number_configurations=1000, - ) + f.write(filetxt) \ No newline at end of file From 42551b827710211597f7cc7036d8b59efc805166 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 16:07:25 -0400 Subject: [PATCH 28/37] file restructuring and using hydra radius graph function --- examples/LennardJones/AtomicStructure.py | 83 --- examples/LennardJones/LJ.json | 2 +- examples/LennardJones/LJ_data.py | 494 ++++++++++++++++++ examples/LennardJones/LJpotential.py | 41 -- examples/LennardJones/LennardJones.py | 207 ++------ examples/LennardJones/configurational_data.py | 215 -------- examples/LennardJones/distributed_utils.py | 3 - examples/LennardJones/graph_utils.py | 56 -- 8 files changed, 525 insertions(+), 576 deletions(-) delete mode 100644 examples/LennardJones/AtomicStructure.py create mode 100644 examples/LennardJones/LJ_data.py delete mode 100644 examples/LennardJones/LJpotential.py delete mode 100644 examples/LennardJones/configurational_data.py delete mode 100644 examples/LennardJones/distributed_utils.py delete mode 100644 examples/LennardJones/graph_utils.py diff --git a/examples/LennardJones/AtomicStructure.py b/examples/LennardJones/AtomicStructure.py deleted file mode 100644 index b09500a7d..000000000 --- a/examples/LennardJones/AtomicStructure.py +++ /dev/null @@ -1,83 +0,0 @@ -import torch - - -class AtomicStructureHandler: - def __init__( - self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula - ): - - self.bravais_lattice_constants = bravais_lattice_constants - self.radius_cutoff = radius_cutoff - self.formula = formula - - def compute(self, data): - - assert data.pos.shape[0] == data.x.shape[0] - - interatomic_potential = torch.zeros([data.pos.shape[0], 1]) - interatomic_forces = torch.zeros([data.pos.shape[0], 3]) - - for node_id in range(data.pos.shape[0]): - - neighbor_list_indices = torch.where(data.edge_index[0, :] == node_id)[ - 0 - ].tolist() - neighbor_list = data.edge_index[1, neighbor_list_indices] - - for neighbor_id, edge_id in zip(neighbor_list, neighbor_list_indices): - - neighbor_pos = data.pos[neighbor_id, :] - distance_vector = data.pos[neighbor_id, :] - data.pos[node_id, :] - - # Adjust the neighbor position based on periodic boundary conditions (PBC) - ## If the distance between the atoms is larger than the cutoff radius, the edge is because of PBC conditions - if torch.norm(distance_vector) > self.radius_cutoff: - ## At this point, we know that the edge is due to PBC conditions, so we need to adjust the neighbor position. We also know that - ## that this connection MUST be the closest connection possible as a result of the asserted radius_cutoff < supercell_size earlier - ## in the code. Because of this, we can simply adjust the neighbor position coordinate-wise to be closer than - ## as done in the following lines of code. The logic goes that if the distance vector[index] is larger than half the supercell size, - ## then there is a closer distance at +- supercell_size[index], and we adjust to that for each coordinate - if abs(distance_vector[0]) > data.supercell_size[0, 0] / 2: - if distance_vector[0] > 0: - neighbor_pos[0] -= data.supercell_size[0, 0] - else: - neighbor_pos[0] += data.supercell_size[0, 0] - - if abs(distance_vector[1]) > data.supercell_size[1, 1] / 2: - if distance_vector[1] > 0: - neighbor_pos[1] -= data.supercell_size[1, 1] - else: - neighbor_pos[1] += data.supercell_size[1, 1] - - if abs(distance_vector[2]) > data.supercell_size[2, 2] / 2: - if distance_vector[2] > 0: - neighbor_pos[2] -= data.supercell_size[2, 2] - else: - neighbor_pos[2] += data.supercell_size[2, 2] - - # The distance vecor may need to be updated after applying PBCs - distance_vector = data.pos[node_id, :] - neighbor_pos - - # pair_distance = data.edge_attr[edge_id].item() - interatomic_potential[node_id] += self.formula.potential_energy( - distance_vector - ) - - derivative_x = self.formula.derivative_x(distance_vector) - derivative_y = self.formula.derivative_y(distance_vector) - derivative_z = self.formula.derivative_z(distance_vector) - - interatomic_forces_contribution_x = -derivative_x - interatomic_forces_contribution_y = -derivative_y - interatomic_forces_contribution_z = -derivative_z - - interatomic_forces[node_id, 0] += interatomic_forces_contribution_x - interatomic_forces[node_id, 1] += interatomic_forces_contribution_y - interatomic_forces[node_id, 2] += interatomic_forces_contribution_z - - data.x = torch.cat( - (data.x, interatomic_potential, interatomic_forces), - 1, - ) - - return data diff --git a/examples/LennardJones/LJ.json b/examples/LennardJones/LJ.json index f11c66008..d26f2fdc9 100644 --- a/examples/LennardJones/LJ.json +++ b/examples/LennardJones/LJ.json @@ -56,7 +56,7 @@ "output_names": ["graph_energy"] }, "Training": { - "num_epoch": 25, + "num_epoch": 20, "batch_size": 64, "patience": 20, "early_stopping": true, diff --git a/examples/LennardJones/LJ_data.py b/examples/LennardJones/LJ_data.py new file mode 100644 index 000000000..9aa6b4964 --- /dev/null +++ b/examples/LennardJones/LJ_data.py @@ -0,0 +1,494 @@ +############################################################################## +# Copyright (c) 2024, Oak Ridge National Laboratory # +# All rights reserved. # +# # +# This file is part of HydraGNN and is distributed under a BSD 3-clause # +# license. For the licensing terms see the LICENSE file in the top-level # +# directory. # +# # +# SPDX-License-Identifier: BSD-3-Clause # +############################################################################## + +# General +import os +import logging +import numpy +numpy.set_printoptions(threshold=numpy.inf) +numpy.set_printoptions(linewidth=numpy.inf) + +# Torch +import torch +from torch_geometric.data import Data +# torch.set_default_tensor_type(torch.DoubleTensor) +# torch.set_default_dtype(torch.float64) + +# Distributed +import mpi4py +from mpi4py import MPI +mpi4py.rc.thread_level = "serialized" +mpi4py.rc.threads = False + +# HydraGNN +from hydragnn.utils.abstractrawdataset import AbstractBaseDataset +from hydragnn.utils import nsplit +from hydragnn.preprocess.utils import get_radius_graph_pbc + +# Angstrom unit +primitive_bravais_lattice_constant_x = 3.8 +primitive_bravais_lattice_constant_y = 3.8 +primitive_bravais_lattice_constant_z = 3.8 + + +################################################################################################################## + + +"""High-Level Function""" + +def create_dataset(path, config): + radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] + number_configurations = config["Dataset"]["number_configurations"] if "number_configurations" in config["Dataset"] else 3000 + atom_types = [1] + formula = LJpotential(1.0, 3.4) + atomic_structure_handler = AtomicStructureHandler( + atom_types, + [ + primitive_bravais_lattice_constant_x, + primitive_bravais_lattice_constant_y, + primitive_bravais_lattice_constant_z, + ], + radius_cutoff, + formula, + ) + deterministic_graph_data( + path, + atom_types, + atomic_structure_handler=atomic_structure_handler, + radius_cutoff=radius_cutoff, + relative_maximum_atomic_displacement=1e-1, + number_configurations=number_configurations, + ) + + +"""Reading/Transforming Data""" + +class LJDataset(AbstractBaseDataset): + """LJDataset dataset class""" + + def __init__(self, dirpath, config, dist=False, sampling=None): + super().__init__() + + self.dist = dist + self.world_size = 1 + self.rank = 1 + if self.dist: + assert torch.distributed.is_initialized() + self.world_size = torch.distributed.get_world_size() + self.rank = torch.distributed.get_rank() + + self.radius = config["NeuralNetwork"]["Architecture"]["radius"] + self.max_neighbours = config["NeuralNetwork"]["Architecture"]["max_neighbours"] + + dirfiles = sorted(os.listdir(dirpath)) + + rx = list(nsplit((dirfiles), self.world_size))[self.rank] + + for file in rx: + filepath = os.path.join(dirpath, file) + self.dataset.append(self.transform_inumpyut_to_data_object_base(filepath)) + + def transform_inumpyut_to_data_object_base(self, filepath): + + # Using readline() + file = open(filepath, "r") + + torch_data = torch.empty((0, 8), dtype=torch.float32) + torch_supercell = torch.zeros((0, 3), dtype=torch.float32) + + count = 0 + + while True: + count += 1 + + # Get next line from file + line = file.readline() + + # if line is empty + # end of file is reached + if not line: + break + + if count == 1: + total_energy = float(line) + elif count == 2: + energy_per_atom = float(line) + elif 2 < count < 6: + array_line = numpy.fromstring(line, dtype=float, sep="\t") + torch_supercell = torch.cat( + [torch_supercell, torch.from_numpy(array_line).unsqueeze(0)], axis=0 + ) + elif count > 5: + array_line = numpy.fromstring(line, dtype=float, sep="\t") + torch_data = torch.cat( + [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 + ) + # print("Line{}: {}".format(count, line.strip())) + + file.close() + + num_nodes = torch_data.shape[0] + + energy_pre_translation_factor = 0.0 + energy_pre_scaling_factor = 1.0 / num_nodes + energy_per_atom_pretransformed = ( + energy_per_atom - energy_pre_translation_factor + ) * energy_pre_scaling_factor + grad_energy_post_scaling_factor = ( + 1.0 / energy_pre_scaling_factor * torch.ones(num_nodes, 1) + ) + forces = torch_data[:, [5, 6, 7]] + forces_pre_scaling_factor = 1.0 + forces_pre_scaled = forces * forces_pre_scaling_factor + + data = Data( + supercell_size=torch_supercell.to(torch.float32), + num_nodes=num_nodes, + grad_energy_post_scaling_factor=grad_energy_post_scaling_factor, + forces_pre_scaling_factor=torch.tensor(forces_pre_scaling_factor).to( + torch.float32 + ), + forces=forces, + forces_pre_scaled=forces_pre_scaled, + pos=torch_data[:, [1, 2, 3]].to(torch.float32), + x=torch.cat([torch_data[:, [0, 4]]], axis=1).to(torch.float32), + y=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), + energy_per_atom=torch.tensor(energy_per_atom_pretransformed) + .unsqueeze(0) + .to(torch.float32), + energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), + ) + + # Create pbc edges and lengths + edge_creation = get_radius_graph_pbc(self.radius, self.max_neighbours) + data = edge_creation(data) + + return data + + def len(self): + return len(self.dataset) + + def get(self, idx): + return self.dataset[idx] + + +"""Create Data""" + +def deterministic_graph_data( + path: str, + atom_types: list, + atomic_structure_handler, + radius_cutoff=float("inf"), + max_num_neighbors=float("inf"), + number_configurations: int = 500, + configuration_start: int = 0, + unit_cell_x_range: list = [3, 4], + unit_cell_y_range: list = [3, 4], + unit_cell_z_range: list = [3, 4], + relative_maximum_atomic_displacement: float = 1e-1, +): + + comm = MPI.COMM_WORLD + comm_size = comm.Get_size() + comm_rank = comm.Get_rank() + torch.manual_seed(comm_rank) + + if 0 == comm_rank: + os.makedirs(path, exist_ok=False) + comm.Barrier() + + # We assume that the unit cell is Simple Center Cubic (SCC) + unit_cell_x = torch.randint( + unit_cell_x_range[0], + unit_cell_x_range[1], + (number_configurations,), + ) + unit_cell_y = torch.randint( + unit_cell_y_range[0], + unit_cell_y_range[1], + (number_configurations,), + ) + unit_cell_z = torch.randint( + unit_cell_z_range[0], + unit_cell_z_range[1], + (number_configurations,), + ) + + configurations_list = range(number_configurations) + rx = list(nsplit(configurations_list, comm_size))[comm_rank] + + for configuration in configurations_list[rx.start : rx.stop]: + uc_x = unit_cell_x[configuration] + uc_y = unit_cell_y[configuration] + uc_z = unit_cell_z[configuration] + create_configuration( + path, + atomic_structure_handler, + configuration, + configuration_start, + uc_x, + uc_y, + uc_z, + atom_types, + radius_cutoff, + max_num_neighbors, + relative_maximum_atomic_displacement, + ) + + +def create_configuration( + path, + atomic_structure_handler, + configuration, + configuration_start, + uc_x, + uc_y, + uc_z, + types, + radius_cutoff, + max_num_neighbors, + relative_maximum_atomic_displacement, +): + ############################################################################################### + ################################### STRUCTURE OF THE DATA ################################## + ############################################################################################### + + # GLOBAL_OUTPUT1 + # GLOBAL_OUTPUT2 + # NODE1_FEATURE NODE1_INDEX NODE1_COORDINATE_X NODE1_COORDINATE_Y NODE1_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 + # NODE2_FEATURE NODE2_INDEX NODE2_COORDINATE_X NODE2_COORDINATE_Y NODE2_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 + # ... + # NODENn_FEATURE NODEn_INDEX NODEn_COORDINATE_X NODEn_COORDINATE_Y NODEn_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 + + ############################################################################################### + ################################# FORMULAS FOR NODAL FEATURE ############################### + ############################################################################################### + + # NODAL_FEATURE = ATOM SPECIES + + ############################################################################################### + ########################## FORMULAS FOR GLOBAL AND NODAL OUTPUTS ########################### + ############################################################################################### + + # GLOBAL_OUTPUT = TOTAL ENERGY + # GLOBAL_OUTPUT = TOTAL ENERGY / NUMBER OF NODES + # NODAL_OUTPUT1(X) = FORCE ACTING ON ATOM IN X DIRECTION + # NODAL_OUTPUT2(X) = FORCE ACTING ON ATOM IN Y DIRECTION + # NODAL_OUTPUT3(X) = FORCE ACTING ON ATOM IN Z DIRECTION + + ############################################################################################### + count_pos = 0 + number_nodes = uc_x * uc_y * uc_z + positions = torch.zeros(number_nodes, 3) + for x in range(uc_x): + for y in range(uc_y): + for z in range(uc_z): + positions[count_pos][0] = ( + x + + relative_maximum_atomic_displacement + * ((torch.rand(1, 1).item()) - 0.5) + ) * primitive_bravais_lattice_constant_x + positions[count_pos][1] = ( + y + + relative_maximum_atomic_displacement + * ((torch.rand(1, 1).item()) - 0.5) + ) * primitive_bravais_lattice_constant_y + positions[count_pos][2] = ( + z + + relative_maximum_atomic_displacement + * ((torch.rand(1, 1).item()) - 0.5) + ) * primitive_bravais_lattice_constant_z + + count_pos = count_pos + 1 + + atom_types = torch.randint(min(types), max(types) + 1, (number_nodes, 1)) + + data = Data() + + data.pos = positions + supercell_size_x = primitive_bravais_lattice_constant_x * uc_x + supercell_size_y = primitive_bravais_lattice_constant_y * uc_y + supercell_size_z = primitive_bravais_lattice_constant_z * uc_z + data.supercell_size = torch.diag( + torch.tensor([supercell_size_x, supercell_size_y, supercell_size_z]) + ) + + create_graph_connectivity_pbc = get_radius_graph_pbc( + radius_cutoff, max_num_neighbors + ) + data = create_graph_connectivity_pbc(data) + + atomic_descriptors = torch.cat( + ( + atom_types, + positions, + ), + 1, + ) + + data.x = atomic_descriptors + + data = atomic_structure_handler.compute(data) + + total_energy = torch.sum(data.x[:, 4]) + energy_per_atom = total_energy / number_nodes + + total_energy_str = numpy.array2string(total_energy.detach().numpy()) + energy_per_atom_str = numpy.array2string(energy_per_atom.detach().numpy()) + filetxt = total_energy_str + "\n" + energy_per_atom_str + + for index in range(0, 3): + numpy_row = data.supercell_size[index, :].detach().numpy() + numpy_string_row = numpy.array2string(numpy_row, precision=64, separator="\t") + filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") + + for index in range(0, number_nodes): + numpy_row = data.x[index, :].detach().numpy() + numpy_string_row = numpy.array2string(numpy_row, precision=64, separator="\t") + filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") + + filename = os.path.join( + path, "output" + str(configuration + configuration_start) + ".txt" + ) + with open(filename, "w") as f: + f.write(filetxt) + + + +"""Function Calculation""" + +class AtomicStructureHandler: + def __init__( + self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula + ): + + self.bravais_lattice_constants = bravais_lattice_constants + self.radius_cutoff = radius_cutoff + self.formula = formula + + def compute(self, data): + + assert data.pos.shape[0] == data.x.shape[0] + + interatomic_potential = torch.zeros([data.pos.shape[0], 1]) + interatomic_forces = torch.zeros([data.pos.shape[0], 3]) + + for node_id in range(data.pos.shape[0]): + + neighbor_list_indices = torch.where(data.edge_index[0, :] == node_id)[ + 0 + ].tolist() + neighbor_list = data.edge_index[1, neighbor_list_indices] + + for neighbor_id, edge_id in zip(neighbor_list, neighbor_list_indices): + + neighbor_pos = data.pos[neighbor_id, :] + distance_vector = data.pos[neighbor_id, :] - data.pos[node_id, :] + + # Adjust the neighbor position based on periodic boundary conditions (PBC) + ## If the distance between the atoms is larger than the cutoff radius, the edge is because of PBC conditions + if torch.norm(distance_vector) > self.radius_cutoff: + ## At this point, we know that the edge is due to PBC conditions, so we need to adjust the neighbor position. We also know that + ## that this connection MUST be the closest connection possible as a result of the asserted radius_cutoff < supercell_size earlier + ## in the code. Because of this, we can simply adjust the neighbor position coordinate-wise to be closer than + ## as done in the following lines of code. The logic goes that if the distance vector[index] is larger than half the supercell size, + ## then there is a closer distance at +- supercell_size[index], and we adjust to that for each coordinate + if abs(distance_vector[0]) > data.supercell_size[0, 0] / 2: + if distance_vector[0] > 0: + neighbor_pos[0] -= data.supercell_size[0, 0] + else: + neighbor_pos[0] += data.supercell_size[0, 0] + + if abs(distance_vector[1]) > data.supercell_size[1, 1] / 2: + if distance_vector[1] > 0: + neighbor_pos[1] -= data.supercell_size[1, 1] + else: + neighbor_pos[1] += data.supercell_size[1, 1] + + if abs(distance_vector[2]) > data.supercell_size[2, 2] / 2: + if distance_vector[2] > 0: + neighbor_pos[2] -= data.supercell_size[2, 2] + else: + neighbor_pos[2] += data.supercell_size[2, 2] + + # The distance vecor may need to be updated after applying PBCs + distance_vector = data.pos[node_id, :] - neighbor_pos + + # pair_distance = data.edge_attr[edge_id].item() + interatomic_potential[node_id] += self.formula.potential_energy( + distance_vector + ) + + derivative_x = self.formula.derivative_x(distance_vector) + derivative_y = self.formula.derivative_y(distance_vector) + derivative_z = self.formula.derivative_z(distance_vector) + + interatomic_forces_contribution_x = -derivative_x + interatomic_forces_contribution_y = -derivative_y + interatomic_forces_contribution_z = -derivative_z + + interatomic_forces[node_id, 0] += interatomic_forces_contribution_x + interatomic_forces[node_id, 1] += interatomic_forces_contribution_y + interatomic_forces[node_id, 2] += interatomic_forces_contribution_z + + data.x = torch.cat( + (data.x, interatomic_potential, interatomic_forces), + 1, + ) + + return data + + +class LJpotential: + def __init__(self, epsilon, sigma): + self.epsilon = epsilon + self.sigma = sigma + + def potential_energy(self, distance_vector): + pair_distance = torch.norm(distance_vector) + return ( + 4 + * self.epsilon + * ((self.sigma / pair_distance) ** 12 - (self.sigma / pair_distance) ** 6) + ) + + def radial_derivative(self, distance_vector): + pair_distance = torch.norm(distance_vector) + return ( + 4 + * self.epsilon + * ( + -12 * (self.sigma / pair_distance) ** 12 * 1 / pair_distance + + 6 * (self.sigma / pair_distance) ** 6 * 1 / pair_distance + ) + ) + + def derivative_x(self, distance_vector): + pair_distance = torch.norm(distance_vector) + radial_derivative = self.radial_derivative(pair_distance) + return radial_derivative * (distance_vector[0].item()) / pair_distance + + def derivative_y(self, distance_vector): + pair_distance = torch.norm(distance_vector) + radial_derivative = self.radial_derivative(pair_distance) + return radial_derivative * (distance_vector[1].item()) / pair_distance + + def derivative_z(self, distance_vector): + pair_distance = torch.norm(distance_vector) + radial_derivative = self.radial_derivative(pair_distance) + return radial_derivative * (distance_vector[2].item()) / pair_distance + + + +"""Etc""" + +def info(*args, logtype="info", sep=" "): + getattr(logging, logtype)(sep.join(map(str, args))) \ No newline at end of file diff --git a/examples/LennardJones/LJpotential.py b/examples/LennardJones/LJpotential.py deleted file mode 100644 index a38d17ce3..000000000 --- a/examples/LennardJones/LJpotential.py +++ /dev/null @@ -1,41 +0,0 @@ -import torch - - -class LJpotential: - def __init__(self, epsilon, sigma): - self.epsilon = epsilon - self.sigma = sigma - - def potential_energy(self, distance_vector): - pair_distance = torch.norm(distance_vector) - return ( - 4 - * self.epsilon - * ((self.sigma / pair_distance) ** 12 - (self.sigma / pair_distance) ** 6) - ) - - def radial_derivative(self, distance_vector): - pair_distance = torch.norm(distance_vector) - return ( - 4 - * self.epsilon - * ( - -12 * (self.sigma / pair_distance) ** 12 * 1 / pair_distance - + 6 * (self.sigma / pair_distance) ** 6 * 1 / pair_distance - ) - ) - - def derivative_x(self, distance_vector): - pair_distance = torch.norm(distance_vector) - radial_derivative = self.radial_derivative(pair_distance) - return radial_derivative * (distance_vector[0].item()) / pair_distance - - def derivative_y(self, distance_vector): - pair_distance = torch.norm(distance_vector) - radial_derivative = self.radial_derivative(pair_distance) - return radial_derivative * (distance_vector[1].item()) / pair_distance - - def derivative_z(self, distance_vector): - pair_distance = torch.norm(distance_vector) - radial_derivative = self.radial_derivative(pair_distance) - return radial_derivative * (distance_vector[2].item()) / pair_distance diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index 7e605529e..334da946d 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -1,197 +1,50 @@ -import mpi4py -from mpi4py import MPI - -mpi4py.rc.thread_level = "serialized" -mpi4py.rc.threads = False - +############################################################################## +# Copyright (c) 2024, Oak Ridge National Laboratory # +# All rights reserved. # +# # +# This file is part of HydraGNN and is distributed under a BSD 3-clause # +# license. For the licensing terms see the LICENSE file in the top-level # +# directory. # +# # +# SPDX-License-Identifier: BSD-3-Clause # +############################################################################## + +# General import os, json -import random - import logging import sys import argparse +# Torch +import torch +# torch.set_default_tensor_type(torch.DoubleTensor) +# torch.set_default_dtype(torch.float64) + +# Distributed +import mpi4py +from mpi4py import MPI +mpi4py.rc.thread_level = "serialized" +mpi4py.rc.threads = False + +# HydraGNN import hydragnn -from hydragnn.utils.print_utils import iterate_tqdm, log +from hydragnn.utils.print_utils import log from hydragnn.utils.time_utils import Timer - +import hydragnn.utils.tracer as tr from hydragnn.preprocess.load_data import split_dataset -from hydragnn.utils.abstractrawdataset import AbstractBaseDataset from hydragnn.utils.distdataset import DistDataset from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset from hydragnn.preprocess.utils import gather_deg - -import numpy as np - try: from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset except ImportError: pass -from torch_geometric.data import Data -from torch_geometric.transforms import RadiusGraph, Distance, LocalCartesian -import torch -import torch.distributed as dist - -from hydragnn.utils import nsplit -import hydragnn.utils.tracer as tr - -# Using LJ dataset creation -from configurational_data import deterministic_graph_data -from LJpotential import LJpotential -from AtomicStructure import AtomicStructureHandler - - -def create_dataset(path, config): - # Angstrom unit - primitive_bravais_lattice_constant_x = 3.8 - primitive_bravais_lattice_constant_y = 3.8 - primitive_bravais_lattice_constant_z = 3.8 - radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] - number_configurations = config["Dataset"]["number_configurations"] if "number_configurations" in config["Dataset"] else 1000 - atom_types = [1] - formula = LJpotential(1.0, 3.4) - atomic_structure_handler = AtomicStructureHandler( - atom_types, - [ - primitive_bravais_lattice_constant_x, - primitive_bravais_lattice_constant_y, - primitive_bravais_lattice_constant_z, - ], - radius_cutoff, - formula, - ) - deterministic_graph_data( - path, - atom_types, - atomic_structure_handler=atomic_structure_handler, - radius_cutoff=radius_cutoff, - relative_maximum_atomic_displacement=1e-1, - number_configurations=number_configurations, - ) - - -# FIXME: this works fine for now because we train on disordered atomic structures with potentials and forces computed with Lennard-Jones - - -torch.set_default_dtype(torch.float32) - - -def info(*args, logtype="info", sep=" "): - getattr(logging, logtype)(sep.join(map(str, args))) - - - -class LJDataset(AbstractBaseDataset): - """LJDataset dataset class""" - - def __init__(self, dirpath, config, dist=False, sampling=None): - super().__init__() - - self.dist = dist - self.world_size = 1 - self.rank = 1 - if self.dist: - assert torch.distributed.is_initialized() - self.world_size = torch.distributed.get_world_size() - self.rank = torch.distributed.get_rank() - - self.create_graph_fromXYZ = RadiusGraph(r=config["NeuralNetwork"]["Architecture"]["radius"]) # radius cutoff in angstrom - self.compute_edge_lengths = Distance(norm=False, cat=True) - # self.spherical_coordinates = Spherical(norm=False, cat=False) - self.cartesian_coordinates = LocalCartesian(norm=False, cat=False) - - dirfiles = sorted(os.listdir(dirpath)) - - rx = list(nsplit((dirfiles), self.world_size))[self.rank] - - for file in rx: - filepath = os.path.join(dirpath, file) - self.dataset.append(self.transform_input_to_data_object_base(filepath)) - - def transform_input_to_data_object_base(self, filepath): - - # Using readline() - file = open(filepath, "r") - - torch_data = torch.empty((0, 8), dtype=torch.float32) - torch_supercell = torch.zeros((0, 3), dtype=torch.float32) - - count = 0 - - while True: - count += 1 - - # Get next line from file - line = file.readline() - - # if line is empty - # end of file is reached - if not line: - break - - if count == 1: - total_energy = float(line) - elif count == 2: - energy_per_atom = float(line) - elif 2 < count < 6: - array_line = np.fromstring(line, dtype=float, sep="\t") - torch_supercell = torch.cat( - [torch_supercell, torch.from_numpy(array_line).unsqueeze(0)], axis=0 - ) - elif count > 5: - array_line = np.fromstring(line, dtype=float, sep="\t") - torch_data = torch.cat( - [torch_data, torch.from_numpy(array_line).unsqueeze(0)], axis=0 - ) - # print("Line{}: {}".format(count, line.strip())) - - file.close() - - num_nodes = torch_data.shape[0] - - energy_pre_translation_factor = 0.0 - energy_pre_scaling_factor = 1.0 / num_nodes - energy_per_atom_pretransformed = ( - energy_per_atom - energy_pre_translation_factor - ) * energy_pre_scaling_factor - grad_energy_post_scaling_factor = ( - 1.0 / energy_pre_scaling_factor * torch.ones(num_nodes, 1) - ) - forces = torch_data[:, [5, 6, 7]] - forces_pre_scaling_factor = 1.0 - forces_pre_scaled = forces * forces_pre_scaling_factor - - data = Data( - supercell_size=torch_supercell.to(torch.float32), - num_nodes=num_nodes, - grad_energy_post_scaling_factor=grad_energy_post_scaling_factor, - forces_pre_scaling_factor=torch.tensor(forces_pre_scaling_factor).to( - torch.float32 - ), - forces=forces, - forces_pre_scaled=forces_pre_scaled, - pos=torch_data[:, [1, 2, 3]].to(torch.float32), - x=torch.cat([torch_data[:, [0, 4]]], axis=1).to(torch.float32), - y=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), - energy_per_atom=torch.tensor(energy_per_atom_pretransformed) - .unsqueeze(0) - .to(torch.float32), - energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), - ) - data = self.create_graph_fromXYZ(data) - data = self.compute_edge_lengths(data) - data.edge_attr = data.edge_attr.to(torch.float32) - # data = self.spherical_coordinates(data) - data = self.cartesian_coordinates(data) - - return data +# Lennard Jones +from LJ_data import create_dataset, LJDataset, info - def len(self): - return len(self.dataset) - def get(self, idx): - return self.dataset[idx] +################################################################################################################## if __name__ == "__main__": @@ -294,7 +147,7 @@ def get(self, idx): ## local data create_dataset(os.path.join(dirpwd, "dataset/data"), config) total = LJDataset( - os.path.join(dirpwd, "dataset"), + os.path.join(dirpwd, "dataset/data"), config, dist=True, ) diff --git a/examples/LennardJones/configurational_data.py b/examples/LennardJones/configurational_data.py deleted file mode 100644 index ec41f1a8a..000000000 --- a/examples/LennardJones/configurational_data.py +++ /dev/null @@ -1,215 +0,0 @@ -############################################################################## -# Copyright (c) 2021, Oak Ridge National Laboratory # -# All rights reserved. # -# # -# This file is part of HydraGNN and is distributed under a BSD 3-clause # -# license. For the licensing terms see the LICENSE file in the top-level # -# directory. # -# # -# SPDX-License-Identifier: BSD-3-Clause # -############################################################################## - -import os -import torch -import numpy - -numpy.set_printoptions(threshold=numpy.inf) -numpy.set_printoptions(linewidth=numpy.inf) - -torch.set_default_tensor_type(torch.DoubleTensor) -torch.set_default_dtype(torch.float64) - -from torch_geometric.data import Data - -from graph_utils import get_radius_graph_pbc -from AtomicStructure import AtomicStructureHandler -from LJpotential import LJpotential - -from distributed_utils import nsplit - -from mpi4py import MPI - -# Angstrom unit -primitive_bravais_lattice_constant_x = 3.8 -primitive_bravais_lattice_constant_y = 3.8 -primitive_bravais_lattice_constant_z = 3.8 - - -def deterministic_graph_data( - path: str, - atom_types: list, - atomic_structure_handler, - radius_cutoff=float("inf"), - max_num_neighbors=float("inf"), - number_configurations: int = 500, - configuration_start: int = 0, - unit_cell_x_range: list = [3, 4], - unit_cell_y_range: list = [3, 4], - unit_cell_z_range: list = [3, 4], - relative_maximum_atomic_displacement: float = 1e-1, -): - - comm = MPI.COMM_WORLD - comm_size = comm.Get_size() - comm_rank = comm.Get_rank() - torch.manual_seed(comm_rank) - - if 0 == comm_rank: - os.makedirs(path, exist_ok=False) - comm.Barrier() - - # We assume that the unit cell is Simple Center Cubic (SCC) - unit_cell_x = torch.randint( - unit_cell_x_range[0], - unit_cell_x_range[1], - (number_configurations,), - ) - unit_cell_y = torch.randint( - unit_cell_y_range[0], - unit_cell_y_range[1], - (number_configurations,), - ) - unit_cell_z = torch.randint( - unit_cell_z_range[0], - unit_cell_z_range[1], - (number_configurations,), - ) - - configurations_list = range(number_configurations) - rx = list(nsplit(configurations_list, comm_size))[comm_rank] - - for configuration in configurations_list[rx.start : rx.stop]: - uc_x = unit_cell_x[configuration] - uc_y = unit_cell_y[configuration] - uc_z = unit_cell_z[configuration] - create_configuration( - path, - atomic_structure_handler, - configuration, - configuration_start, - uc_x, - uc_y, - uc_z, - atom_types, - radius_cutoff, - max_num_neighbors, - relative_maximum_atomic_displacement, - ) - - -def create_configuration( - path, - atomic_structure_handler, - configuration, - configuration_start, - uc_x, - uc_y, - uc_z, - types, - radius_cutoff, - max_num_neighbors, - relative_maximum_atomic_displacement, -): - ############################################################################################### - ################################### STRUCTURE OF THE DATA ################################## - ############################################################################################### - - # GLOBAL_OUTPUT1 - # GLOBAL_OUTPUT2 - # NODE1_FEATURE NODE1_INDEX NODE1_COORDINATE_X NODE1_COORDINATE_Y NODE1_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 - # NODE2_FEATURE NODE2_INDEX NODE2_COORDINATE_X NODE2_COORDINATE_Y NODE2_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 - # ... - # NODENn_FEATURE NODEn_INDEX NODEn_COORDINATE_X NODEn_COORDINATE_Y NODEn_COORDINATE_Z NODAL_OUTPUT1 NODAL_OUTPUT2 NODAL_OUTPUT3 - - ############################################################################################### - ################################# FORMULAS FOR NODAL FEATURE ############################### - ############################################################################################### - - # NODAL_FEATURE = ATOM SPECIES - - ############################################################################################### - ########################## FORMULAS FOR GLOBAL AND NODAL OUTPUTS ########################### - ############################################################################################### - - # GLOBAL_OUTPUT = TOTAL ENERGY - # GLOBAL_OUTPUT = TOTAL ENERGY / NUMBER OF NODES - # NODAL_OUTPUT1(X) = FORCE ACTING ON ATOM IN X DIRECTION - # NODAL_OUTPUT2(X) = FORCE ACTING ON ATOM IN Y DIRECTION - # NODAL_OUTPUT3(X) = FORCE ACTING ON ATOM IN Z DIRECTION - - ############################################################################################### - count_pos = 0 - number_nodes = uc_x * uc_y * uc_z - positions = torch.zeros(number_nodes, 3) - for x in range(uc_x): - for y in range(uc_y): - for z in range(uc_z): - positions[count_pos][0] = ( - x - + relative_maximum_atomic_displacement - * ((torch.rand(1, 1).item()) - 0.5) - ) * primitive_bravais_lattice_constant_x - positions[count_pos][1] = ( - y - + relative_maximum_atomic_displacement - * ((torch.rand(1, 1).item()) - 0.5) - ) * primitive_bravais_lattice_constant_y - positions[count_pos][2] = ( - z - + relative_maximum_atomic_displacement - * ((torch.rand(1, 1).item()) - 0.5) - ) * primitive_bravais_lattice_constant_z - - count_pos = count_pos + 1 - - atom_types = torch.randint(min(types), max(types) + 1, (number_nodes, 1)) - - data = Data() - - data.pos = positions - supercell_size_x = primitive_bravais_lattice_constant_x * uc_x - supercell_size_y = primitive_bravais_lattice_constant_y * uc_y - supercell_size_z = primitive_bravais_lattice_constant_z * uc_z - data.supercell_size = torch.diag( - torch.tensor([supercell_size_x, supercell_size_y, supercell_size_z]) - ) - - create_graph_connectivity_pbc = get_radius_graph_pbc( - radius_cutoff, max_num_neighbors - ) - data = create_graph_connectivity_pbc(data) - - atomic_descriptors = torch.cat( - ( - atom_types, - positions, - ), - 1, - ) - - data.x = atomic_descriptors - - data = atomic_structure_handler.compute(data) - - total_energy = torch.sum(data.x[:, 4]) - energy_per_atom = total_energy / number_nodes - - total_energy_str = numpy.array2string(total_energy.detach().numpy()) - energy_per_atom_str = numpy.array2string(energy_per_atom.detach().numpy()) - filetxt = total_energy_str + "\n" + energy_per_atom_str - - for index in range(0, 3): - numpy_row = data.supercell_size[index, :].detach().numpy() - numpy_string_row = numpy.array2string(numpy_row, precision=64, separator="\t") - filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") - - for index in range(0, number_nodes): - numpy_row = data.x[index, :].detach().numpy() - numpy_string_row = numpy.array2string(numpy_row, precision=64, separator="\t") - filetxt += "\n" + numpy_string_row.lstrip("[").rstrip("]") - - filename = os.path.join( - path, "output" + str(configuration + configuration_start) + ".txt" - ) - with open(filename, "w") as f: - f.write(filetxt) \ No newline at end of file diff --git a/examples/LennardJones/distributed_utils.py b/examples/LennardJones/distributed_utils.py deleted file mode 100644 index 8c367bc48..000000000 --- a/examples/LennardJones/distributed_utils.py +++ /dev/null @@ -1,3 +0,0 @@ -def nsplit(a, n): - k, m = divmod(len(a), n) - return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)) diff --git a/examples/LennardJones/graph_utils.py b/examples/LennardJones/graph_utils.py deleted file mode 100644 index 8e9c0698f..000000000 --- a/examples/LennardJones/graph_utils.py +++ /dev/null @@ -1,56 +0,0 @@ -import torch -from torch_geometric.transforms import RadiusGraph -import ase -import ase.neighborlist -from torch_geometric.utils import remove_self_loops, degree - - -class RadiusGraphPBC(RadiusGraph): - r"""Creates edges based on node positions :obj:`pos` to all points within a - given distance, including periodic images. - """ - - def __call__(self, data): - data.edge_attr = None - assert ( - "batch" not in data - ), "Periodic boundary conditions not currently supported on batches." - assert hasattr( - data, "supercell_size" - ), "The data must contain the size of the supercell to apply periodic boundary conditions." - ase_atom_object = ase.Atoms( - positions=data.pos, - cell=data.supercell_size, - pbc=True, - ) - # ‘i’ : first atom index - # ‘j’ : second atom index - # https://wiki.fysik.dtu.dk/ase/ase/neighborlist.html#ase.neighborlist.neighbor_list - edge_src, edge_dst, edge_length = ase.neighborlist.neighbor_list( - "ijd", a=ase_atom_object, cutoff=self.r, self_interaction=self.loop - ) - data.edge_index = torch.stack( - [torch.LongTensor(edge_src), torch.LongTensor(edge_dst)], dim=0 - ) - - # ensure no duplicate edges - num_edges = data.edge_index.size(1) - data.coalesce() - assert num_edges == data.edge_index.size( - 1 - ), "Adding periodic boundary conditions would result in duplicate edges. Cutoff radius must be reduced or system size increased." - - data.edge_attr = torch.tensor(edge_length, dtype=torch.float).unsqueeze(1) - - return data - - def __repr__(self) -> str: - return f"{self.__class__.__name__}(r={self.r})" - - -def get_radius_graph_pbc(radius, max_neighbours, loop=False): - return RadiusGraphPBC( - r=radius, - loop=loop, - max_num_neighbors=max_neighbours, - ) From ee20cf761ad5696a98e609e75705580729ac0fae Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 16:11:10 -0400 Subject: [PATCH 29/37] formatting --- examples/LennardJones/LJ_data.py | 24 +++++++++++++++++------- examples/LennardJones/LennardJones.py | 7 ++++++- 2 files changed, 23 insertions(+), 8 deletions(-) diff --git a/examples/LennardJones/LJ_data.py b/examples/LennardJones/LJ_data.py index 9aa6b4964..ce9519e34 100644 --- a/examples/LennardJones/LJ_data.py +++ b/examples/LennardJones/LJ_data.py @@ -13,18 +13,21 @@ import os import logging import numpy + numpy.set_printoptions(threshold=numpy.inf) numpy.set_printoptions(linewidth=numpy.inf) # Torch import torch from torch_geometric.data import Data + # torch.set_default_tensor_type(torch.DoubleTensor) # torch.set_default_dtype(torch.float64) # Distributed import mpi4py from mpi4py import MPI + mpi4py.rc.thread_level = "serialized" mpi4py.rc.threads = False @@ -44,9 +47,14 @@ """High-Level Function""" + def create_dataset(path, config): radius_cutoff = config["NeuralNetwork"]["Architecture"]["radius"] - number_configurations = config["Dataset"]["number_configurations"] if "number_configurations" in config["Dataset"] else 3000 + number_configurations = ( + config["Dataset"]["number_configurations"] + if "number_configurations" in config["Dataset"] + else 3000 + ) atom_types = [1] formula = LJpotential(1.0, 3.4) atomic_structure_handler = AtomicStructureHandler( @@ -71,6 +79,7 @@ def create_dataset(path, config): """Reading/Transforming Data""" + class LJDataset(AbstractBaseDataset): """LJDataset dataset class""" @@ -84,7 +93,7 @@ def __init__(self, dirpath, config, dist=False, sampling=None): assert torch.distributed.is_initialized() self.world_size = torch.distributed.get_world_size() self.rank = torch.distributed.get_rank() - + self.radius = config["NeuralNetwork"]["Architecture"]["radius"] self.max_neighbours = config["NeuralNetwork"]["Architecture"]["max_neighbours"] @@ -166,7 +175,7 @@ def transform_inumpyut_to_data_object_base(self, filepath): .to(torch.float32), energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32), ) - + # Create pbc edges and lengths edge_creation = get_radius_graph_pbc(self.radius, self.max_neighbours) data = edge_creation(data) @@ -182,6 +191,7 @@ def get(self, idx): """Create Data""" + def deterministic_graph_data( path: str, atom_types: list, @@ -360,11 +370,11 @@ def create_configuration( ) with open(filename, "w") as f: f.write(filetxt) - """Function Calculation""" + class AtomicStructureHandler: def __init__( self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula @@ -486,9 +496,9 @@ def derivative_z(self, distance_vector): radial_derivative = self.radial_derivative(pair_distance) return radial_derivative * (distance_vector[2].item()) / pair_distance - - + """Etc""" + def info(*args, logtype="info", sep=" "): - getattr(logging, logtype)(sep.join(map(str, args))) \ No newline at end of file + getattr(logging, logtype)(sep.join(map(str, args))) diff --git a/examples/LennardJones/LennardJones.py b/examples/LennardJones/LennardJones.py index 334da946d..045b1d251 100644 --- a/examples/LennardJones/LennardJones.py +++ b/examples/LennardJones/LennardJones.py @@ -17,12 +17,14 @@ # Torch import torch + # torch.set_default_tensor_type(torch.DoubleTensor) # torch.set_default_dtype(torch.float64) # Distributed import mpi4py from mpi4py import MPI + mpi4py.rc.thread_level = "serialized" mpi4py.rc.threads = False @@ -35,6 +37,7 @@ from hydragnn.utils.distdataset import DistDataset from hydragnn.utils.pickledataset import SimplePickleWriter, SimplePickleDataset from hydragnn.preprocess.utils import gather_deg + try: from hydragnn.utils.adiosdataset import AdiosWriter, AdiosDataset except ImportError: @@ -139,7 +142,9 @@ dataset_exists = os.path.exists(os.path.join(dirpwd, "dataset/LJ.pickle")) if args.format == "adios": fname = os.path.join(dirpwd, "./dataset/%s.bp" % modelname) - dataset_exists = os.path.exists(os.path.join(dirpwd, "dataset", "%s.bp" % modelname)) + dataset_exists = os.path.exists( + os.path.join(dirpwd, "dataset", "%s.bp" % modelname) + ) # Create dataset if preonly specified or dataset does not exist if not dataset_exists: From 1fa77660841f1b75e32d939047e465865590f741 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 16:13:01 -0400 Subject: [PATCH 30/37] renaming --- .../{inference_derivative_energy.py => LJ_inference_plots.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/LennardJones/{inference_derivative_energy.py => LJ_inference_plots.py} (100%) diff --git a/examples/LennardJones/inference_derivative_energy.py b/examples/LennardJones/LJ_inference_plots.py similarity index 100% rename from examples/LennardJones/inference_derivative_energy.py rename to examples/LennardJones/LJ_inference_plots.py From e8a4a4d80c54291a7e8b21d1d209d3db252ed100 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 16:14:32 -0400 Subject: [PATCH 31/37] remove qm9 test --- examples/qm9/qm9.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/examples/qm9/qm9.py b/examples/qm9/qm9.py index 76cec549c..19bdf7dba 100644 --- a/examples/qm9/qm9.py +++ b/examples/qm9/qm9.py @@ -62,11 +62,6 @@ def qm9_pre_filter(data): ) # Check for dataset filepath datadir = os.path.join(os.getcwd(), "dataset/qm9") -if os.path.exists(datadir): - print("----------------------------DATASET FOUND----------------------------") -else: - print("----------------------------DATASET NOT FOUND----------------------------") - raise FileNotFoundError train, val, test = hydragnn.preprocess.split_dataset( dataset, config["NeuralNetwork"]["Training"]["perc_train"], False ) From 5d358a31d2e503461cf158fdfe7fe024677a3de8 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 16:15:07 -0400 Subject: [PATCH 32/37] remove qm9 test --- examples/qm9/qm9.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/qm9/qm9.py b/examples/qm9/qm9.py index 19bdf7dba..2e6c3da8e 100644 --- a/examples/qm9/qm9.py +++ b/examples/qm9/qm9.py @@ -60,8 +60,6 @@ def qm9_pre_filter(data): dataset = torch_geometric.datasets.QM9( root="dataset/qm9", pre_transform=qm9_pre_transform, pre_filter=qm9_pre_filter ) -# Check for dataset filepath -datadir = os.path.join(os.getcwd(), "dataset/qm9") train, val, test = hydragnn.preprocess.split_dataset( dataset, config["NeuralNetwork"]["Training"]["perc_train"], False ) From 06dad510b20f4b61833adb1b69c40832d84edaaf Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 16:29:01 -0400 Subject: [PATCH 33/37] smaller number samples for test --- examples/LennardJones/LJ.json | 2 +- examples/LennardJones/LJ_data.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/LennardJones/LJ.json b/examples/LennardJones/LJ.json index d26f2fdc9..c857f93c9 100644 --- a/examples/LennardJones/LJ.json +++ b/examples/LennardJones/LJ.json @@ -56,7 +56,7 @@ "output_names": ["graph_energy"] }, "Training": { - "num_epoch": 20, + "num_epoch": 15, "batch_size": 64, "patience": 20, "early_stopping": true, diff --git a/examples/LennardJones/LJ_data.py b/examples/LennardJones/LJ_data.py index ce9519e34..6226ff6f8 100644 --- a/examples/LennardJones/LJ_data.py +++ b/examples/LennardJones/LJ_data.py @@ -53,7 +53,7 @@ def create_dataset(path, config): number_configurations = ( config["Dataset"]["number_configurations"] if "number_configurations" in config["Dataset"] - else 3000 + else 300 ) atom_types = [1] formula = LJpotential(1.0, 3.4) From 393976ccaa59567210e3a4257cc8f9af4fd04c25 Mon Sep 17 00:00:00 2001 From: Massimiliano Lupo Pasini Date: Tue, 17 Sep 2024 16:49:30 -0400 Subject: [PATCH 34/37] Update examples/LennardJones/LJ_inference_plots.py --- examples/LennardJones/LJ_inference_plots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/LennardJones/LJ_inference_plots.py b/examples/LennardJones/LJ_inference_plots.py index a3a5f2bf3..88b510ad8 100644 --- a/examples/LennardJones/LJ_inference_plots.py +++ b/examples/LennardJones/LJ_inference_plots.py @@ -1,5 +1,5 @@ ############################################################################## -# Copyright (c) 2021, Oak Ridge National Laboratory # +# Copyright (c) 2024, Oak Ridge National Laboratory # # All rights reserved. # # # # This file is part of HydraGNN and is distributed under a BSD 3-clause # From 85f44ab43af2aed21327eef83ee6a6abd9c7676d Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 17:00:57 -0400 Subject: [PATCH 35/37] use info function --- examples/LennardJones/LJ_inference_plots.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/LennardJones/LJ_inference_plots.py b/examples/LennardJones/LJ_inference_plots.py index 88b510ad8..324da425f 100644 --- a/examples/LennardJones/LJ_inference_plots.py +++ b/examples/LennardJones/LJ_inference_plots.py @@ -39,6 +39,8 @@ except ImportError: pass +from LJ_data import info + import matplotlib.pyplot as plt plt.rcParams.update({"font.size": 16}) @@ -99,10 +101,6 @@ def getcolordensity(xdata, ydata): return hist2d_norm -def info(*args, logtype="info", sep=" "): - getattr(logging, logtype)(sep.join(map(str, args))) - - if __name__ == "__main__": modelname = "LJ" From 81992afe5885b0c44580dbd70638954619fbbf0a Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 17:04:15 -0400 Subject: [PATCH 36/37] Unnecessary __init__ file --- examples/LennardJones/__init__.py | 1 - 1 file changed, 1 deletion(-) delete mode 100644 examples/LennardJones/__init__.py diff --git a/examples/LennardJones/__init__.py b/examples/LennardJones/__init__.py deleted file mode 100644 index 266d92653..000000000 --- a/examples/LennardJones/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .LennardJones import LJpotential From bc93b9b8d281551fc48dfcc96d2099e3f2083aa7 Mon Sep 17 00:00:00 2001 From: Rylie Weaver Date: Tue, 17 Sep 2024 19:51:46 -0400 Subject: [PATCH 37/37] Unecessary json args --- examples/LennardJones/LJ.json | 3 --- 1 file changed, 3 deletions(-) diff --git a/examples/LennardJones/LJ.json b/examples/LennardJones/LJ.json index c857f93c9..a79c5f41d 100644 --- a/examples/LennardJones/LJ.json +++ b/examples/LennardJones/LJ.json @@ -4,9 +4,7 @@ }, "Dataset": { "name": "LJdataset", - "path": {"total": "./dataset/data"}, "format": "XYZ", - "rotational_invariance": true, "node_features": { "name": ["atom_type"], "dim": [1], @@ -22,7 +20,6 @@ "Architecture": { "periodic_boundary_conditions": true, "model_type": "DimeNet", - "equivariance": false, "radius": 5.0, "max_neighbours": 5, "int_emb_size": 32,