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Energy forces #278

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Energy and Force Prediction changes (loss function in base, and optio…
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comments and renamings
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Black formatting and mak computational graph fixes
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fix loss weighting
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Fix DIMEStack testing issues, and compute_grad_energy default in conf…
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LJ example added first draft
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don't create plots by default
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55 changes: 55 additions & 0 deletions hydragnn/models/Base.py
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -355,6 +356,60 @@ def loss(self, pred, value, head_index):
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_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)
* energy_loss_weight
)
tasks_loss.append(self.loss_function(graph_energy_pred, graph_energy_true))
# Forces
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()
Comment on lines +389 to +395
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Why is grad_outputs used here? This would lead to sum of the gradients, right?

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@RylieWeaver RylieWeaver Sep 19, 2024

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I don't think so.

Here's my understanding of it:
The grad_outputs argument essentially serves as a weight to multiply the gradients. This is necessary since graph_energy_pred is a higher dimension than just a scalar. The torch.ones_like() here will assign a weight of 1 to each gradient, which I believe is what we want. Otherwise, there would be a force prediction which is c*grad_E where c is a constant not equal to 1, which is nonphysical.

Specifically to your question:
No, I don't believe that it results in a sum. The output shape of forces_pred will be in the same shape as data.pos. The grad_outputs specifically is for weighting the gradients before a sum, but that extra step of summing is outside of autograd calculation I believe.

Does this answer your question?

@pzhanggit

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I don't think so.

Here's my understanding of it: The grad_outputs argument essentially serves as a weight to multiply the gradients. This is necessary since graph_energy_pred is a higher dimension than just a scalar. The torch.ones_like() here will assign a weight of 1 to each gradient, which I believe is what we want. Otherwise, there would be a force prediction which is c*grad_E where c is a constant not equal to 1, which is nonphysical.

Specifically to your question: No, I don't believe that it results in a sum. The output shape of forces_pred will be in the same shape as data.pos. The grad_outputs specifically is for weighting the gradients before a sum, but that extra step of summing is outside of autograd calculation I believe.

Does this answer your question?

@pzhanggit

Thanks. So just to verify if my understanding is correct: grad_outputs is needed because we're calculating gradients of a batch of samples here. And since grad_outputs should be a sequence of length matching output containing the “vector” in vector-Jacobian product, it provides a way to aggregate/sum the gradients across all the samples in the batch. This is equivalent to calculate the gradients iteratively for each sample, since the cross-gradients between samples would be zero.

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@pzhanggit

grad_outputs is needed because we're calculating gradients of a batch of samples here

Yep, that's right.

And since grad_outputs should be a sequence of length matching output containing the “vector” in vector-Jacobian product

I think so. Yes, grad_outputs should be a sequence, and it should be the same shape as whatever you're predicting. It then multiplies the matrix of gradients (row-wise, not normal matrix multiplication), which I think is the vector-Jacobian product you're referring to.

it provides a way to aggregate/sum the gradients across all the samples in the batch.

I think yes. It's scaling those gradients, which would be relevant in an aggregation/sum. Although, it does not do that aggregation/sum.

This is equivalent to calculate the gradients iteratively for each sample, since the cross-gradients between samples would be zero.

Yep.

Are there any parts still unclear?

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@pzhanggit

grad_outputs is needed because we're calculating gradients of a batch of samples here

Yep, that's right.

And since grad_outputs should be a sequence of length matching output containing the “vector” in vector-Jacobian product

I think so. Yes, grad_outputs should be a sequence, and it should be the same shape as whatever you're predicting. It then multiplies the matrix of gradients (row-wise, not normal matrix multiplication), which I think is the vector-Jacobian product you're referring to.

it provides a way to aggregate/sum the gradients across all the samples in the batch.

I think yes. It's scaling those gradients, which would be relevant in an aggregation/sum. Although, it does not do that aggregation/sum.

This is equivalent to calculate the gradients iteratively for each sample, since the cross-gradients between samples would be zero.

Yep.

Are there any parts still unclear?

Scaling those gradients?

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@pzhanggit
The scaling is done with torch.ones_like(graph_energy_pred)
In our case, we use vectors of ones because we do NOT want to scale. However, you could apply any multiplying factor (or even provide a customized vector with different values for each entry).

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@pzhanggit @allaffa ^ on what Max said. Also, feel free to @ me so In reply faster :)

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@pzhanggit @RylieWeaver

I personally found the explanation given by ChatGPT very useful. I attach it here hoping that you will find it useful too.

Screenshot 2024-09-19 at 5 46 13 PM Screenshot 2024-09-19 at 5 46 24 PM Screenshot 2024-09-19 at 5 46 35 PM Screenshot 2024-09-19 at 5 46 44 PM Screenshot 2024-09-19 at 5 46 58 PM Screenshot 2024-09-19 at 5 47 21 PM

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Yep. Wow, the o1-preview is great

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thanks.

assert (
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
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.

return tot_loss, tasks_loss

def loss_nll(self, pred, value, head_index, var=None):
# negative log likelihood loss
# uncertainty to weigh losses in https://openaccess.thecvf.com/content_cvpr_2018/papers/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.pdf
Expand Down
1 change: 1 addition & 0 deletions hydragnn/run_training.py
Original file line number Diff line number Diff line change
Expand Up @@ -175,6 +175,7 @@ def _(config: dict, use_deepspeed=False):
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)
Expand Down
59 changes: 49 additions & 10 deletions hydragnn/train/train_validate_test.py
Original file line number Diff line number Diff line change
Expand Up @@ -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 = (
Expand Down Expand Up @@ -162,6 +163,7 @@ def train_validate_test(
verbosity,
profiler=prof,
use_deepspeed=use_deepspeed,
compute_grad_energy=compute_grad_energy,
)
tr.stop("train")
tr.disable()
Expand All @@ -172,14 +174,19 @@ 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,
model,
verbosity,
reduce_ranks=True,
return_samples=plot_hist_solution,
compute_grad_energy=compute_grad_energy,
)
scheduler.step(val_loss)
if writer is not None:
Expand Down Expand Up @@ -434,7 +441,15 @@ def gather_tensor_ranks(head_values):
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()

Expand Down Expand Up @@ -492,8 +507,13 @@ 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: # for force and energy prediction
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()
Expand Down Expand Up @@ -541,7 +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())
Expand All @@ -565,8 +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)):
Expand All @@ -585,7 +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())
Expand All @@ -612,8 +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:
Expand Down
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