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perf: optimize training loop #4426

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Merged
njzjz merged 6 commits into from
Nov 27, 2024
Merged

perf: optimize training loop #4426

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4823dd3
perf: optimize training loop
caic99 Nov 26, 2024
7b9fb5a
use faster norm check
caic99 Nov 26, 2024
f48c18f
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 26, 2024
53bbbdb
remove unused return value
caic99 Nov 26, 2024
078e848
only set training mode on first entrance and exiting valid
caic99 Nov 27, 2024
97c3fcf
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 27, 2024
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14 changes: 7 additions & 7 deletions deepmd/pt/train/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -656,7 +656,6 @@ def step(_step_id, task_key="Default") -> None:
# PyTorch Profiler
if self.enable_profiler or self.profiling:
prof.step()
self.wrapper.train()
if isinstance(self.lr_exp, dict):
_lr = self.lr_exp[task_key]
else:
Expand All @@ -682,12 +681,11 @@ def step(_step_id, task_key="Default") -> None:
)
loss.backward()
if self.gradient_max_norm > 0.0:
grad_norm = torch.nn.utils.clip_grad_norm_(
self.wrapper.parameters(), self.gradient_max_norm
torch.nn.utils.clip_grad_norm_(
self.wrapper.parameters(),
self.gradient_max_norm,
error_if_nonfinite=True,
)
if not torch.isfinite(grad_norm).all():
# check local gradnorm single GPU case, trigger NanDetector
raise FloatingPointError("gradients are Nan/Inf")
with torch.device("cpu"):
self.optimizer.step()
self.scheduler.step()
Expand Down Expand Up @@ -766,7 +764,7 @@ def fake_model():
if self.display_in_training and (
display_step_id % self.disp_freq == 0 or display_step_id == 1
):
self.wrapper.eval()
self.wrapper.eval() # Will set to train mode before fininshing validation

def log_loss_train(_loss, _more_loss, _task_key="Default"):
results = {}
Expand Down Expand Up @@ -872,6 +870,7 @@ def log_loss_valid(_task_key="Default"):
learning_rate=None,
)
)
self.wrapper.train()

current_time = time.time()
train_time = current_time - self.t0
Expand Down Expand Up @@ -927,6 +926,7 @@ def log_loss_valid(_task_key="Default"):
f"{task_key}/{item}", more_loss[item], display_step_id
)

self.wrapper.train()
self.t0 = time.time()
self.total_train_time = 0.0
for step_id in range(self.num_steps):
Expand Down