class EnergyTrainer(BaseTrainer):
def __init__(
self,
task,
model,
dataset,
optimizer,
identifier,
run_dir=None,
is_debug=False,
is_vis=False,
print_every=100,
seed=None,
logger="tensorboard",
local_rank=0,
amp=False,
):
if run_dir is None:
run_dir = os.getcwd()
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
if identifier:
timestamp += "-{}".format(identifier)
self.config = {
"task": task,
"model": model.pop("name"),
"model_attributes": model,
"optim": optimizer,
"logger": logger,
"cmd": {
"identifier": identifier,
"print_every": print_every,
"seed": seed,
"timestamp": timestamp,
"checkpoint_dir": os.path.join(run_dir, "checkpoints",
timestamp),
"results_dir": os.path.join(run_dir, "results", timestamp),
"logs_dir": os.path.join(run_dir, "logs", logger, timestamp),
},
"amp": amp,
}
# AMP Scaler
self.scaler = torch.cuda.amp.GradScaler() if amp else None
if isinstance(dataset, list):
self.config["dataset"] = dataset[0]
if len(dataset) > 1:
self.config["val_dataset"] = dataset[1]
else:
self.config["dataset"] = dataset
if not is_debug:
os.makedirs(self.config["cmd"]["checkpoint_dir"])
os.makedirs(self.config["cmd"]["results_dir"])
os.makedirs(self.config["cmd"]["logs_dir"])
self.is_debug = is_debug
self.is_vis = is_vis
if torch.cuda.is_available():
device_ids = list(range(torch.cuda.device_count()))
self.output_device = self.config["optim"].get(
"output_device", device_ids[0])
self.device = f"cuda:{self.output_device}"
else:
self.device = "cpu"
print(yaml.dump(self.config, default_flow_style=False))
self.load()
self.evaluator = Evaluator(task="is2re")
def load_task(self):
print("### Loading dataset: {}".format(self.config["task"]["dataset"]))
self.parallel_collater = ParallelCollater(self.config["optim"].get(
"num_gpus", 1))
if self.config["task"]["dataset"] == "single_point_lmdb":
self.train_dataset = registry.get_dataset_class(
self.config["task"]["dataset"])(self.config["dataset"])
self.train_loader = DataLoader(
self.train_dataset,
batch_size=self.config["optim"]["batch_size"],
shuffle=True,
collate_fn=self.parallel_collater,
num_workers=self.config["optim"]["num_workers"],
pin_memory=True,
)
self.val_loader = self.test_loader = None
if "val_dataset" in self.config:
self.val_dataset = registry.get_dataset_class(
self.config["task"]["dataset"])(self.config["val_dataset"])
self.val_loader = DataLoader(
self.val_dataset,
self.config["optim"].get("eval_batch_size", 64),
shuffle=False,
collate_fn=self.parallel_collater,
num_workers=self.config["optim"]["num_workers"],
pin_memory=True,
)
else:
raise NotImplementedError
self.num_targets = 1
# Normalizer for the dataset.
# Compute mean, std of training set labels.
self.normalizers = {}
if self.config["dataset"].get("normalize_labels", True):
if "target_mean" in self.config["dataset"]:
self.normalizers["target"] = Normalizer(
mean=self.config["dataset"]["target_mean"],
std=self.config["dataset"]["target_std"],
device=self.device,
)
else:
raise NotImplementedError
def load_model(self):
super(EnergyTrainer, self).load_model()
self.model = OCPDataParallel(
self.model,
output_device=self.output_device,
num_gpus=self.config["optim"].get("num_gpus", 1),
)
self.model.to(self.device)
def train(self):
self.best_val_mae = 1e9
for epoch in range(self.config["optim"]["max_epochs"]):
self.model.train()
for i, batch in enumerate(self.train_loader):
# Forward, loss, backward.
with torch.cuda.amp.autocast(enabled=self.scaler is not None):
out = self._forward(batch)
loss = self._compute_loss(out, batch)
loss = self.scaler.scale(loss) if self.scaler else loss
self._backward(loss)
scale = self.scaler.get_scale() if self.scaler else 1.0
# Compute metrics.
self.metrics = self._compute_metrics(
out,
batch,
self.evaluator,
metrics={},
)
self.metrics = self.evaluator.update("loss",
loss.item() / scale,
self.metrics)
# Print metrics, make plots.
log_dict = {k: self.metrics[k]["metric"] for k in self.metrics}
log_dict.update(
{"epoch": epoch + (i + 1) / len(self.train_loader)})
if i % self.config["cmd"]["print_every"] == 0:
log_str = [
"{}: {:.4f}".format(k, v) for k, v in log_dict.items()
]
print(", ".join(log_str))
if self.logger is not None:
self.logger.log(
log_dict,
step=epoch * len(self.train_loader) + i + 1,
split="train",
)
self.scheduler.step()
torch.cuda.empty_cache()
if self.val_loader is not None:
val_metrics = self.validate(split="val", epoch=epoch)
if (val_metrics[self.evaluator.task_primary_metric["is2re"]]
["metric"] < self.best_val_mae):
self.best_val_mae = val_metrics[
self.evaluator.task_primary_metric["is2re"]]["metric"]
if not self.is_debug:
save_checkpoint(
{
"epoch":
epoch + 1,
"state_dict":
self.model.state_dict(),
"optimizer":
self.optimizer.state_dict(),
"normalizers": {
key: value.state_dict()
for key, value in self.normalizers.items()
},
"config":
self.config,
"val_metrics":
val_metrics,
"amp":
self.scaler.state_dict()
if self.scaler else None,
},
self.config["cmd"]["checkpoint_dir"],
)
if self.test_loader is not None:
self.validate(split="test", epoch=epoch)
def validate(self, split="val", epoch=None):
print("### Evaluating on {}.".format(split))
self.model.eval()
evaluator, metrics = Evaluator(task="is2re"), {}
loader = self.val_loader if split == "val" else self.test_loader
for i, batch in tqdm(enumerate(loader), total=len(loader)):
# Forward.
with torch.cuda.amp.autocast(enabled=self.scaler is not None):
out = self._forward(batch)
loss = self._compute_loss(out, batch)
# Compute metrics.
metrics = self._compute_metrics(out, batch, evaluator, metrics)
metrics = evaluator.update("loss", loss.item(), metrics)
log_dict = {k: metrics[k]["metric"] for k in metrics}
log_dict.update({"epoch": epoch + 1})
log_str = ["{}: {:.4f}".format(k, v) for k, v in log_dict.items()]
print(", ".join(log_str))
# Make plots.
if self.logger is not None and epoch is not None:
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
return metrics
def _forward(self, batch_list):
output = self.model(batch_list)
if output.shape[-1] == 1:
output = output.view(-1)
return {
"energy": output,
}
def _compute_loss(self, out, batch_list):
energy_target = torch.cat(
[batch.y_relaxed.to(self.device) for batch in batch_list], dim=0)
if self.config["dataset"].get("normalize_labels", True):
target_normed = self.normalizers["target"].norm(energy_target)
else:
target_normed = energy_target
loss = self.criterion(out["energy"], target_normed)
return loss
def _compute_metrics(self, out, batch_list, evaluator, metrics={}):
energy_target = torch.cat(
[batch.y_relaxed.to(self.device) for batch in batch_list], dim=0)
if self.config["dataset"].get("normalize_labels", True):
out["energy"] = self.normalizers["target"].denorm(out["energy"])
metrics = evaluator.eval(
out,
{"energy": energy_target},
prev_metrics=metrics,
)
return metrics
class ForcesTrainer(BaseTrainer):
def __init__(
self,
task,
model,
dataset,
optimizer,
identifier,
run_dir=None,
is_debug=False,
is_vis=False,
print_every=100,
seed=None,
logger="tensorboard",
):
if run_dir is None:
run_dir = os.getcwd()
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
if identifier:
timestamp += "-{}".format(identifier)
self.config = {
"task": task,
"model": model.pop("name"),
"model_attributes": model,
"optim": optimizer,
"logger": logger,
"cmd": {
"identifier": identifier,
"print_every": print_every,
"seed": seed,
"timestamp": timestamp,
"checkpoint_dir": os.path.join(run_dir, "checkpoints",
timestamp),
"results_dir": os.path.join(run_dir, "results", timestamp),
"logs_dir": os.path.join(run_dir, "logs", logger, timestamp),
},
}
if isinstance(dataset, list):
self.config["dataset"] = dataset[0]
if len(dataset) > 1:
self.config["val_dataset"] = dataset[1]
else:
self.config["dataset"] = dataset
if not is_debug:
os.makedirs(self.config["cmd"]["checkpoint_dir"])
os.makedirs(self.config["cmd"]["results_dir"])
os.makedirs(self.config["cmd"]["logs_dir"])
self.is_debug = is_debug
self.is_vis = is_vis
if torch.cuda.is_available():
device_ids = list(range(torch.cuda.device_count()))
self.output_device = self.config["optim"].get(
"output_device", device_ids[0])
self.device = f"cuda:{self.output_device}"
else:
self.device = "cpu"
print(yaml.dump(self.config, default_flow_style=False))
self.load()
def load_task(self):
print("### Loading dataset: {}".format(self.config["task"]["dataset"]))
self.parallel_collater = ParallelCollater(self.config["optim"].get(
"num_gpus", 1))
if self.config["task"]["dataset"] == "trajectory_lmdb":
self.train_dataset = registry.get_dataset_class(
self.config["task"]["dataset"])(self.config["dataset"])
self.train_loader = DataLoader(
self.train_dataset,
batch_size=self.config["optim"]["batch_size"],
shuffle=True,
collate_fn=self.parallel_collater,
num_workers=self.config["optim"]["num_workers"],
)
self.val_loader = self.test_loader = None
if "val_dataset" in self.config:
self.val_dataset = registry.get_dataset_class(
self.config["task"]["dataset"])(self.config["val_dataset"])
self.val_loader = DataLoader(
self.val_dataset,
self.config["optim"].get("eval_batch_size", 64),
shuffle=False,
collate_fn=self.parallel_collater,
num_workers=self.config["optim"]["num_workers"],
)
else:
self.dataset = registry.get_dataset_class(
self.config["task"]["dataset"])(self.config["dataset"])
(
self.train_loader,
self.val_loader,
self.test_loader,
) = self.dataset.get_dataloaders(
batch_size=self.config["optim"]["batch_size"],
collate_fn=self.parallel_collater,
)
self.num_targets = 1
# Normalizer for the dataset.
# Compute mean, std of training set labels.
self.normalizers = {}
if self.config["dataset"].get("normalize_labels", True):
if "target_mean" in self.config["dataset"]:
self.normalizers["target"] = Normalizer(
mean=self.config["dataset"]["target_mean"],
std=self.config["dataset"]["target_std"],
device=self.device,
)
else:
self.normalizers["target"] = Normalizer(
tensor=self.train_loader.dataset.data.y[
self.train_loader.dataset.__indices__],
device=self.device,
)
# If we're computing gradients wrt input, set mean of normalizer to 0 --
# since it is lost when compute dy / dx -- and std to forward target std
if "grad_input" in self.config["task"]:
if self.config["dataset"].get("normalize_labels", True):
if "target_mean" in self.config["dataset"]:
self.normalizers["grad_target"] = Normalizer(
mean=self.config["dataset"]["grad_target_mean"],
std=self.config["dataset"]["grad_target_std"],
device=self.device,
)
else:
self.normalizers["grad_target"] = Normalizer(
tensor=self.train_loader.dataset.data.y[
self.train_loader.dataset.__indices__],
device=self.device,
)
self.normalizers["grad_target"].mean.fill_(0)
if self.is_vis and self.config["task"]["dataset"] != "qm9":
# Plot label distribution.
plots = [
plot_histogram(
self.train_loader.dataset.data.y.tolist(),
xlabel="{}/raw".format(self.config["task"]["labels"][0]),
ylabel="# Examples",
title="Split: train",
),
plot_histogram(
self.val_loader.dataset.data.y.tolist(),
xlabel="{}/raw".format(self.config["task"]["labels"][0]),
ylabel="# Examples",
title="Split: val",
),
plot_histogram(
self.test_loader.dataset.data.y.tolist(),
xlabel="{}/raw".format(self.config["task"]["labels"][0]),
ylabel="# Examples",
title="Split: test",
),
]
self.logger.log_plots(plots)
def load_model(self):
super(ForcesTrainer, self).load_model()
self.model = OCPDataParallel(
self.model,
output_device=self.output_device,
num_gpus=self.config["optim"].get("num_gpus", 1),
)
self.model.to(self.device)
# Takes in a new data source and generates predictions on it.
def predict(self, dataset, batch_size=32):
if isinstance(dataset, dict):
if self.config["task"]["dataset"] == "trajectory_lmdb":
print("### Generating predictions on {}.".format(
dataset["src"]))
else:
print("### Generating predictions on {}.".format(
dataset["src"] + dataset["traj"]))
dataset = registry.get_dataset_class(
self.config["task"]["dataset"])(dataset)
data_loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=self.parallel_collater,
)
elif isinstance(dataset, torch_geometric.data.Batch):
data_loader = [[dataset]]
else:
raise NotImplementedError
self.model.eval()
predictions = {"energy": [], "forces": []}
for i, batch_list in enumerate(data_loader):
out, _ = self._forward(batch_list, compute_metrics=False)
if self.normalizers is not None and "target" in self.normalizers:
out["output"] = self.normalizers["target"].denorm(
out["output"])
out["force_output"] = self.normalizers["grad_target"].denorm(
out["force_output"])
atoms_sum = 0
predictions["energy"].extend(out["output"].tolist())
batch_natoms = torch.cat([batch.natoms for batch in batch_list])
for natoms in batch_natoms:
predictions["forces"].append(
out["force_output"][atoms_sum:natoms +
atoms_sum].cpu().detach().numpy())
atoms_sum += natoms
return predictions
def train(self):
for epoch in range(self.config["optim"]["max_epochs"]):
self.model.train()
for i, batch in enumerate(self.train_loader):
# Forward, loss, backward.
out, metrics = self._forward(batch)
loss = self._compute_loss(out, batch)
self._backward(loss)
# Update meter.
meter_update_dict = {
"epoch": epoch + (i + 1) / len(self.train_loader),
"loss": loss.item(),
}
meter_update_dict.update(metrics)
self.meter.update(meter_update_dict)
# Make plots.
if self.logger is not None:
self.logger.log(
meter_update_dict,
step=epoch * len(self.train_loader) + i + 1,
split="train",
)
# Print metrics.
if i % self.config["cmd"]["print_every"] == 0:
print(self.meter)
self.scheduler.step()
torch.cuda.empty_cache()
if self.val_loader is not None:
self.validate(split="val", epoch=epoch)
if self.test_loader is not None:
self.validate(split="test", epoch=epoch)
if ("relaxation_dir" in self.config["task"]
and self.config["task"].get("ml_relax", "end") == "train"):
self.validate_relaxation(
split="val",
epoch=epoch,
)
if not self.is_debug:
save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"normalizers": {
key: value.state_dict()
for key, value in self.normalizers.items()
},
"config": self.config,
},
self.config["cmd"]["checkpoint_dir"],
)
if ("relaxation_dir" in self.config["task"]
and self.config["task"].get("ml_relax", "end") == "end"):
self.validate_relaxation(
split="val",
epoch=epoch,
)
def validate(self, split="val", epoch=None):
print("### Evaluating on {}.".format(split))
self.model.eval()
meter = Meter(split=split)
loader = self.val_loader if split == "val" else self.test_loader
for i, batch in enumerate(loader):
# Forward.
out, metrics = self._forward(batch)
loss = self._compute_loss(out, batch)
# Update meter.
meter_update_dict = {"loss": loss.item()}
meter_update_dict.update(metrics)
meter.update(meter_update_dict)
# Make plots.
if self.logger is not None and epoch is not None:
log_dict = meter.get_scalar_dict()
log_dict.update({"epoch": epoch + 1})
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
print(meter)
def validate_relaxation(self, split="val", epoch=None):
print("### Evaluating ML-relaxation")
self.model.eval()
metrics = {}
meter = Meter(split=split)
mae_energy, mae_structure = relax_eval(
trainer=self,
traj_dir=self.config["task"]["relaxation_dir"],
metric=self.config["task"]["metric"],
steps=self.config["task"].get("relaxation_steps", 300),
fmax=self.config["task"].get("relaxation_fmax", 0.01),
results_dir=self.config["cmd"]["results_dir"],
)
metrics["relaxed_energy/{}".format(
self.config["task"]["metric"])] = mae_energy
metrics["relaxed_structure/{}".format(
self.config["task"]["metric"])] = mae_structure
meter.update(metrics)
# Make plots.
if self.logger is not None and epoch is not None:
log_dict = meter.get_scalar_dict()
log_dict.update({"epoch": epoch + 1})
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
print(meter)
return mae_energy, mae_structure
def _forward(self, batch_list, compute_metrics=True):
out = {}
# forward pass.
if self.config["model_attributes"].get("regress_forces", True):
output, output_forces = self.model(batch_list)
else:
output = self.model(batch_list)
if output.shape[-1] == 1:
output = output.view(-1)
out["output"] = output
force_output = None
if self.config["model_attributes"].get("regress_forces", True):
out["force_output"] = output_forces
force_output = output_forces
if not compute_metrics:
return out, None
metrics = {}
energy_target = torch.cat(
[batch.y.to(self.device) for batch in batch_list], dim=0)
if self.config["dataset"].get("normalize_labels", True):
errors = eval(self.config["task"]["metric"])(
self.normalizers["target"].denorm(output).cpu(),
energy_target.cpu(),
).view(-1)
else:
errors = eval(self.config["task"]["metric"])(
output.cpu(), energy_target.cpu()).view(-1)
if ("label_index" in self.config["task"]
and self.config["task"]["label_index"] is not False):
# TODO(abhshkdz): Get rid of this edge case for QM9.
# This is only because QM9 has multiple targets and we can either
# jointly predict all of them or one particular target.
metrics["{}/{}".format(
self.config["task"]["labels"][self.config["task"]
["label_index"]],
self.config["task"]["metric"],
)] = errors[0]
else:
for i, label in enumerate(self.config["task"]["labels"]):
metrics["{}/{}".format(
label, self.config["task"]["metric"])] = errors[i]
if "grad_input" in self.config["task"]:
force_pred = force_output
force_target = torch.cat(
[batch.force.to(self.device) for batch in batch_list], dim=0)
if self.config["task"].get("eval_on_free_atoms", True):
fixed = torch.cat([batch.fixed for batch in batch_list])
mask = fixed == 0
force_pred = force_pred[mask]
force_target = force_target[mask]
if self.config["dataset"].get("normalize_labels", True):
grad_input_errors = eval(self.config["task"]["metric"])(
self.normalizers["grad_target"].denorm(force_pred).cpu(),
force_target.cpu(),
)
else:
grad_input_errors = eval(self.config["task"]["metric"])(
force_pred.cpu(), force_target.cpu())
metrics["force_x/{}".format(
self.config["task"]["metric"])] = grad_input_errors[0]
metrics["force_y/{}".format(
self.config["task"]["metric"])] = grad_input_errors[1]
metrics["force_z/{}".format(
self.config["task"]["metric"])] = grad_input_errors[2]
return out, metrics
def _compute_loss(self, out, batch_list):
loss = []
energy_target = torch.cat(
[batch.y.to(self.device) for batch in batch_list], dim=0)
force_target = torch.cat(
[batch.force.to(self.device) for batch in batch_list], dim=0)
if self.config["dataset"].get("normalize_labels", True):
target_normed = self.normalizers["target"].norm(energy_target)
else:
target_normed = energy_target
loss.append(self.criterion(out["output"], target_normed))
# TODO(abhshkdz): Test support for gradients wrt input.
# TODO(abhshkdz): Make this general; remove dependence on `.forces`.
if "grad_input" in self.config["task"]:
if self.config["dataset"].get("normalize_labels", True):
grad_target_normed = self.normalizers["grad_target"].norm(
force_target)
else:
grad_target_normed = force_target
# Force coefficient = 30 has been working well for us.
force_mult = self.config["optim"].get("force_coefficient", 30)
if self.config["task"].get("train_on_free_atoms", False):
fixed = torch.cat([batch.fixed for batch in batch_list])
mask = fixed == 0
loss.append(force_mult * self.criterion(
out["force_output"][mask], grad_target_normed[mask]))
else:
loss.append(
force_mult *
self.criterion(out["force_output"], grad_target_normed))
# Sanity check to make sure the compute graph is correct.
for lc in loss:
assert hasattr(lc, "grad_fn")
loss = sum(loss)
return loss
class ForcesTrainer(BaseTrainer):
def __init__(
self,
task,
model,
dataset,
optimizer,
identifier,
run_dir=None,
is_debug=False,
is_vis=False,
print_every=100,
seed=None,
logger="tensorboard",
local_rank=0,
amp=False,
):
if run_dir is None:
run_dir = os.getcwd()
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
if identifier:
timestamp += "-{}".format(identifier)
self.config = {
"task": task,
"model": model.pop("name"),
"model_attributes": model,
"optim": optimizer,
"logger": logger,
"cmd": {
"identifier": identifier,
"print_every": print_every,
"seed": seed,
"timestamp": timestamp,
"checkpoint_dir": os.path.join(
run_dir, "checkpoints", timestamp
),
"results_dir": os.path.join(run_dir, "results", timestamp),
"logs_dir": os.path.join(run_dir, "logs", logger, timestamp),
},
}
if isinstance(dataset, list):
self.config["dataset"] = dataset[0]
if len(dataset) > 1:
self.config["val_dataset"] = dataset[1]
else:
self.config["dataset"] = dataset
if not is_debug:
os.makedirs(self.config["cmd"]["checkpoint_dir"])
os.makedirs(self.config["cmd"]["results_dir"])
os.makedirs(self.config["cmd"]["logs_dir"])
self.is_debug = is_debug
self.is_vis = is_vis
if torch.cuda.is_available():
device_ids = list(range(torch.cuda.device_count()))
self.output_device = self.config["optim"].get(
"output_device", device_ids[0]
)
self.device = f"cuda:{self.output_device}"
else:
self.device = "cpu"
print(yaml.dump(self.config, default_flow_style=False))
self.load()
self.scaler = None
self.evaluator = Evaluator(task="s2ef")
def load_task(self):
print("### Loading dataset: {}".format(self.config["task"]["dataset"]))
self.parallel_collater = ParallelCollater(
self.config["optim"].get("num_gpus", 1)
)
if self.config["task"]["dataset"] == "trajectory_lmdb":
self.train_dataset = registry.get_dataset_class(
self.config["task"]["dataset"]
)(self.config["dataset"])
self.train_loader = DataLoader(
self.train_dataset,
batch_size=self.config["optim"]["batch_size"],
shuffle=True,
collate_fn=self.parallel_collater,
num_workers=self.config["optim"]["num_workers"],
pin_memory=True,
)
self.val_loader = self.test_loader = None
if "val_dataset" in self.config:
self.val_dataset = registry.get_dataset_class(
self.config["task"]["dataset"]
)(self.config["val_dataset"])
self.val_loader = DataLoader(
self.val_dataset,
self.config["optim"].get("eval_batch_size", 64),
shuffle=False,
collate_fn=self.parallel_collater,
num_workers=self.config["optim"]["num_workers"],
pin_memory=True,
)
else:
self.dataset = registry.get_dataset_class(
self.config["task"]["dataset"]
)(self.config["dataset"])
(
self.train_loader,
self.val_loader,
self.test_loader,
) = self.dataset.get_dataloaders(
batch_size=self.config["optim"]["batch_size"],
collate_fn=self.parallel_collater,
)
self.num_targets = 1
# Normalizer for the dataset.
# Compute mean, std of training set labels.
self.normalizers = {}
if self.config["dataset"].get("normalize_labels", True):
if "target_mean" in self.config["dataset"]:
self.normalizers["target"] = Normalizer(
mean=self.config["dataset"]["target_mean"],
std=self.config["dataset"]["target_std"],
device=self.device,
)
else:
self.normalizers["target"] = Normalizer(
tensor=self.train_loader.dataset.data.y[
self.train_loader.dataset.__indices__
],
device=self.device,
)
# If we're computing gradients wrt input, set mean of normalizer to 0 --
# since it is lost when compute dy / dx -- and std to forward target std
if self.config["model_attributes"].get("regress_forces", True):
if self.config["dataset"].get("normalize_labels", True):
if "grad_target_mean" in self.config["dataset"]:
self.normalizers["grad_target"] = Normalizer(
mean=self.config["dataset"]["grad_target_mean"],
std=self.config["dataset"]["grad_target_std"],
device=self.device,
)
else:
self.normalizers["grad_target"] = Normalizer(
tensor=self.train_loader.dataset.data.y[
self.train_loader.dataset.__indices__
],
device=self.device,
)
self.normalizers["grad_target"].mean.fill_(0)
if self.is_vis and self.config["task"]["dataset"] != "qm9":
# Plot label distribution.
plots = [
plot_histogram(
self.train_loader.dataset.data.y.tolist(),
xlabel="{}/raw".format(self.config["task"]["labels"][0]),
ylabel="# Examples",
title="Split: train",
),
plot_histogram(
self.val_loader.dataset.data.y.tolist(),
xlabel="{}/raw".format(self.config["task"]["labels"][0]),
ylabel="# Examples",
title="Split: val",
),
plot_histogram(
self.test_loader.dataset.data.y.tolist(),
xlabel="{}/raw".format(self.config["task"]["labels"][0]),
ylabel="# Examples",
title="Split: test",
),
]
self.logger.log_plots(plots)
def load_model(self):
super(ForcesTrainer, self).load_model()
self.model = OCPDataParallel(
self.model,
output_device=self.output_device,
num_gpus=self.config["optim"].get("num_gpus", 1),
)
self.model.to(self.device)
# Takes in a new data source and generates predictions on it.
def predict(self, dataset, batch_size=32):
if isinstance(dataset, dict):
if self.config["task"]["dataset"] == "trajectory_lmdb":
print(
"### Generating predictions on {}.".format(dataset["src"])
)
else:
print(
"### Generating predictions on {}.".format(
dataset["src"] + dataset["traj"]
)
)
dataset = registry.get_dataset_class(
self.config["task"]["dataset"]
)(dataset)
data_loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=self.parallel_collater,
)
elif isinstance(dataset, torch_geometric.data.Batch):
data_loader = [[dataset]]
else:
raise NotImplementedError
self.model.eval()
predictions = {"energy": [], "forces": []}
for i, batch_list in enumerate(data_loader):
out = self._forward(batch_list)
if self.normalizers is not None and "target" in self.normalizers:
out["energy"] = self.normalizers["target"].denorm(
out["energy"]
)
out["forces"] = self.normalizers["grad_target"].denorm(
out["forces"]
)
atoms_sum = 0
predictions["energy"].extend(out["energy"].tolist())
batch_natoms = torch.cat([batch.natoms for batch in batch_list])
for natoms in batch_natoms:
predictions["forces"].append(
out["forces"][atoms_sum : natoms + atoms_sum]
.cpu()
.detach()
.numpy()
)
atoms_sum += natoms
return predictions
def train(self):
self.best_val_mae = 1e9
eval_every = self.config["optim"].get("eval_every", -1)
iters = 0
self.metrics = {}
for epoch in range(self.config["optim"]["max_epochs"]):
self.model.train()
for i, batch in enumerate(self.train_loader):
# Forward, loss, backward.
out = self._forward(batch)
loss = self._compute_loss(out, batch)
self._backward(loss)
# Compute metrics.
self.metrics = self._compute_metrics(
out, batch, self.evaluator, self.metrics,
)
self.metrics = self.evaluator.update(
"loss", loss.item(), self.metrics
)
# Print metrics, make plots.
log_dict = {k: self.metrics[k]["metric"] for k in self.metrics}
log_dict.update(
{"epoch": epoch + (i + 1) / len(self.train_loader)}
)
if i % self.config["cmd"]["print_every"] == 0:
log_str = [
"{}: {:.4f}".format(k, v) for k, v in log_dict.items()
]
print(", ".join(log_str))
self.metrics = {}
if self.logger is not None:
self.logger.log(
log_dict,
step=epoch * len(self.train_loader) + i + 1,
split="train",
)
iters += 1
# Evaluate on val set every `eval_every` iterations.
if eval_every != -1 and iters % eval_every == 0:
if self.val_loader is not None:
val_metrics = self.validate(split="val", epoch=epoch)
if (
val_metrics[
self.evaluator.task_primary_metric["s2ef"]
]["metric"]
< self.best_val_mae
):
self.best_val_mae = val_metrics[
self.evaluator.task_primary_metric["s2ef"]
]["metric"]
if not self.is_debug:
save_checkpoint(
{
"epoch": epoch
+ (i + 1) / len(self.train_loader),
"state_dict": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"normalizers": {
key: value.state_dict()
for key, value in self.normalizers.items()
},
"config": self.config,
"val_metrics": val_metrics,
},
self.config["cmd"]["checkpoint_dir"],
)
self.scheduler.step()
torch.cuda.empty_cache()
if eval_every == -1:
if self.val_loader is not None:
val_metrics = self.validate(split="val", epoch=epoch)
if (
val_metrics[
self.evaluator.task_primary_metric["s2ef"]
]["metric"]
< self.best_val_mae
):
self.best_val_mae = val_metrics[
self.evaluator.task_primary_metric["s2ef"]
]["metric"]
if not self.is_debug:
save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"normalizers": {
key: value.state_dict()
for key, value in self.normalizers.items()
},
"config": self.config,
"val_metrics": val_metrics,
},
self.config["cmd"]["checkpoint_dir"],
)
if self.test_loader is not None:
self.validate(split="test", epoch=epoch)
if (
"relaxation_dir" in self.config["task"]
and self.config["task"].get("ml_relax", "end") == "train"
):
self.validate_relaxation(
split="val", epoch=epoch,
)
if (
"relaxation_dir" in self.config["task"]
and self.config["task"].get("ml_relax", "end") == "end"
):
self.validate_relaxation(
split="val", epoch=epoch,
)
def validate(self, split="val", epoch=None):
print("### Evaluating on {}.".format(split))
self.model.eval()
evaluator, metrics = Evaluator(task="s2ef"), {}
loader = self.val_loader if split == "val" else self.test_loader
for i, batch in tqdm(enumerate(loader), total=len(loader)):
# Forward.
out = self._forward(batch)
loss = self._compute_loss(out, batch)
# Compute metrics.
metrics = self._compute_metrics(out, batch, evaluator, metrics)
metrics = evaluator.update("loss", loss.item(), metrics)
log_dict = {k: metrics[k]["metric"] for k in metrics}
log_dict.update({"epoch": epoch + 1})
log_str = ["{}: {:.4f}".format(k, v) for k, v in log_dict.items()]
print(", ".join(log_str))
# Make plots.
if self.logger is not None and epoch is not None:
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
return metrics
def validate_relaxation(self, split="val", epoch=None):
print("### Evaluating ML-relaxation")
self.model.eval()
mae_energy, mae_structure = relax_eval(
trainer=self,
traj_dir=self.config["task"]["relaxation_dir"],
metric=self.config["task"]["metric"],
steps=self.config["task"].get("relaxation_steps", 300),
fmax=self.config["task"].get("relaxation_fmax", 0.01),
results_dir=self.config["cmd"]["results_dir"],
)
log_dict = {
"relaxed_energy_mae": mae_energy,
"relaxed_structure_mae": mae_structure,
"epoch": epoch + 1,
}
# Make plots.
if self.logger is not None and epoch is not None:
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
print(log_dict)
return mae_energy, mae_structure
def _forward(self, batch_list):
if self.config["model_attributes"].get("regress_forces", True):
out_energy, out_forces = self.model(batch_list)
else:
out_energy = self.model(batch_list)
if out_energy.shape[-1] == 1:
out_energy = out_energy.view(-1)
out = {
"energy": out_energy,
}
if self.config["model_attributes"].get("regress_forces", True):
out["forces"] = out_forces
return out
def _compute_loss(self, out, batch_list):
loss = []
# Energy loss.
energy_target = torch.cat(
[batch.y.to(self.device) for batch in batch_list], dim=0
)
if self.config["dataset"].get("normalize_labels", True):
energy_target = self.normalizers["target"].norm(energy_target)
energy_mult = self.config["optim"].get("energy_coefficient", 1)
loss.append(energy_mult * self.criterion(out["energy"], energy_target))
# Forces loss.
if self.config["model_attributes"].get("regress_forces", True):
force_target = torch.cat(
[batch.force.to(self.device) for batch in batch_list], dim=0
)
if self.config["dataset"].get("normalize_labels", True):
force_target = self.normalizers["grad_target"].norm(
force_target
)
# Force coefficient = 30 has been working well for us.
force_mult = self.config["optim"].get("force_coefficient", 30)
if self.config["task"].get("train_on_free_atoms", False):
fixed = torch.cat(
[batch.fixed.to(self.device) for batch in batch_list]
)
mask = fixed == 0
loss.append(
force_mult
* self.criterion(out["forces"][mask], force_target[mask])
)
else:
loss.append(
force_mult * self.criterion(out["forces"], force_target)
)
# Sanity check to make sure the compute graph is correct.
for lc in loss:
assert hasattr(lc, "grad_fn")
loss = sum(loss)
return loss
def _compute_metrics(self, out, batch_list, evaluator, metrics={}):
target = {
"energy": torch.cat(
[batch.y.to(self.device) for batch in batch_list], dim=0
),
"forces": torch.cat(
[batch.force.to(self.device) for batch in batch_list], dim=0
),
}
if self.config["task"].get("eval_on_free_atoms", True):
fixed = torch.cat(
[batch.fixed.to(self.device) for batch in batch_list]
)
mask = fixed == 0
out["forces"] = out["forces"][mask]
target["forces"] = target["forces"][mask]
if self.config["dataset"].get("normalize_labels", True):
out["energy"] = self.normalizers["target"].denorm(out["energy"])
out["forces"] = self.normalizers["grad_target"].denorm(
out["forces"]
)
metrics = evaluator.eval(out, target, prev_metrics=metrics)
return metrics