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refactor(training): remove unused learning_rate_dict multitask handling #5278

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Merged
wanghan-iapcm merged 1 commit into from
Mar 3, 2026
Merged

refactor(training): remove unused learning_rate_dict multitask handling #5278

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20 changes: 3 additions & 17 deletions deepmd/pd/train/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -302,10 +302,7 @@ def get_lr(lr_params: dict[str, Any]) -> BaseLR:
self.loss = {}
for model_key in self.model_keys:
loss_param = config["loss_dict"][model_key]
if config.get("learning_rate_dict", None) is not None:
lr_param = config["learning_rate_dict"][model_key]["start_lr"]
else:
lr_param = config["learning_rate"]["start_lr"]
lr_param = config["learning_rate"]["start_lr"]
ntypes = len(model_params["model_dict"][model_key]["type_map"])
self.loss[model_key] = get_loss(
loss_param, lr_param, ntypes, self.model[model_key]
Expand Down Expand Up @@ -476,14 +473,7 @@ def get_lr(lr_params: dict[str, Any]) -> BaseLR:

# Learning rate
self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0)
if self.multi_task and config.get("learning_rate_dict", None) is not None:
self.lr_schedule = {}
for model_key in self.model_keys:
self.lr_schedule[model_key] = get_lr(
config["learning_rate_dict"][model_key]
)
else:
self.lr_schedule = get_lr(config["learning_rate"])
self.lr_schedule = get_lr(config["learning_rate"])

# JIT
if JIT:
Expand Down Expand Up @@ -806,11 +796,7 @@ def step(_step_id: int, task_key: str = "Default") -> None:
# Paddle Profiler
if enable_profiling:
core.nvprof_nvtx_push(f"Training step {_step_id}")
if isinstance(self.lr_schedule, dict):
_lr = self.lr_schedule[task_key]
else:
_lr = self.lr_schedule
cur_lr = _lr.value(_step_id)
cur_lr = self.lr_schedule.value(_step_id)
pref_lr = cur_lr

with nvprof_context(enable_profiling, "Fetching data"):
Expand Down
20 changes: 3 additions & 17 deletions deepmd/pt/train/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -365,10 +365,7 @@ def get_lr(lr_params: dict[str, Any]) -> BaseLR:
self.loss = {}
for model_key in self.model_keys:
loss_param = config["loss_dict"][model_key]
if config.get("learning_rate_dict", None) is not None:
lr_param = config["learning_rate_dict"][model_key]["start_lr"]
else:
lr_param = config["learning_rate"]["start_lr"]
lr_param = config["learning_rate"]["start_lr"]
ntypes = len(model_params["model_dict"][model_key]["type_map"])
self.loss[model_key] = get_loss(
loss_param, lr_param, ntypes, self.model[model_key]
Expand Down Expand Up @@ -548,14 +545,7 @@ def get_lr(lr_params: dict[str, Any]) -> BaseLR:

# Learning rate
self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0)
if self.multi_task and config.get("learning_rate_dict", None) is not None:
self.lr_schedule = {}
for model_key in self.model_keys:
self.lr_schedule[model_key] = get_lr(
config["learning_rate_dict"][model_key]
)
else:
self.lr_schedule = get_lr(config["learning_rate"])
self.lr_schedule = get_lr(config["learning_rate"])

# JIT
if JIT:
Expand Down Expand Up @@ -1027,11 +1017,7 @@ def step(_step_id: int, task_key: str = "Default") -> None:
# PyTorch Profiler
if self.enable_profiler or self.profiling:
prof.step()
if isinstance(self.lr_schedule, dict):
_lr = self.lr_schedule[task_key]
else:
_lr = self.lr_schedule
cur_lr = _lr.value(_step_id)
cur_lr = self.lr_schedule.value(_step_id)
pref_lr = cur_lr
self.optimizer.zero_grad(set_to_none=True)
input_dict, label_dict, log_dict = self.get_data(
Expand Down
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