feat: use num_epoch to set num_steps

deepmd/dpmodel/utils/__init__.py

@@ -36,6 +36,11 @@
     save_dp_model,
     traverse_model_dict,
 )
+from .training_utils import (
+    compute_total_numb_batch,
+    resolve_model_prob,
+    resolve_model_prob_from_epochs,
+)
 
 __all__ = [
     "AtomExcludeMask",
@@ -49,6 +54,7 @@
     "aggregate",
     "build_multiple_neighbor_list",
     "build_neighbor_list",
+    "compute_total_numb_batch",
     "extend_coord_with_ghosts",
     "get_graph_index",
     "get_multiple_nlist_key",
@@ -60,6 +66,8 @@
     "nlist_distinguish_types",
     "normalize_coord",
     "phys2inter",
+    "resolve_model_prob",
+    "resolve_model_prob_from_epochs",
     "save_dp_model",
     "to_face_distance",
     "traverse_model_dict",

deepmd/dpmodel/utils/training_utils.py

@@ -0,0 +1,188 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import logging
+from collections.abc import (
+    Iterable,
+)
+
+import numpy as np
+
+log = logging.getLogger(__name__)
+
+
+def compute_total_numb_batch(
+    numb_batches: Iterable[int],
+    sampler_weights: np.ndarray,
+) -> int:
+    """Compute total number of batches considering sampler weights.
+
+    Parameters
+    ----------
+    numb_batches : Iterable[int]
+        Number of batches for each data system.
+    sampler_weights : np.ndarray
+        Sampling weights for each data system.
+
+    Returns
+    -------
+    int
+        Total number of batches.
+
+    Raises
+    ------
+    ValueError
+        If input validation fails.
+    """
+    weights = np.asarray(sampler_weights, dtype=np.float64)
+    if weights.ndim != 1:
+        raise ValueError("Sampler weights must be 1D.")
+    if weights.size == 0:
+        raise ValueError("Sampler weights are empty.")
+    if not np.all(np.isfinite(weights)):
+        raise ValueError("Sampler weights must be finite.")
+    if np.any(weights < 0.0):
+        raise ValueError("Sampler weights must be non-negative.")
+    weight_sum = float(np.sum(weights))
+    if weight_sum <= 0.0:
+        raise ValueError("Sampler weights must sum to a positive value.")
+    probs = weights / weight_sum
+    nbatches = np.asarray(numb_batches, dtype=np.float64)
+    if nbatches.ndim != 1:
+        raise ValueError("Number of batches must be 1D.")
+    if nbatches.size == 0:
+        raise ValueError("Number of batches is empty.")
+    if not np.all(np.isfinite(nbatches)):
+        raise ValueError("Number of batches must be finite.")
+    if np.any(nbatches < 0.0):
+        raise ValueError("Number of batches must be non-negative.")
+    if nbatches.shape[0] != probs.shape[0]:
+        raise ValueError("Number of batches and sampler weights must match.")
+    valid = probs > 0.0
+    if not np.any(valid):
+        raise ValueError(
+            "Sampler probabilities must contain at least one positive entry."
+        )
+    return int(np.ceil(np.max(nbatches[valid] / probs[valid])))
+
+
+def resolve_model_prob(
+    model_keys: list[str],
+    model_prob_config: dict[str, float] | None,
+    model_training_data: dict[str, object],
+    rank: int = 0,
+) -> np.ndarray:
+    """Resolve model training probability for multi-task training.
+
+    Parameters
+    ----------
+    model_keys : list[str]
+        List of model keys.
+    model_prob_config : dict[str, float] | None
+        User-specified model probabilities. If None, use data size.
+    model_training_data : dict[str, object]
+        Training data for each model.
+    rank : int, optional
+        Process rank for distributed training, by default 0.
+
+    Returns
+    -------
+    np.ndarray
+        Normalized model probabilities.
+
+    Raises
+    ------
+    ValueError
+        If input validation fails.
+    """
+    model_prob = np.zeros(len(model_keys), dtype=np.float64)
+    if model_prob_config:
+        missing = [k for k in model_keys if k not in model_prob_config]
+        if missing:
+            raise ValueError(
+                f"training.model_prob must specify all tasks; missing: {missing}"
+            )
+        for ii, model_key in enumerate(model_keys):
+            if model_key in model_prob_config:
+                model_prob[ii] = float(model_prob_config[model_key])
+    else:
+        if rank == 0:
+            log.info(
+                "training.model_prob is not set or empty; defaulting to the "
+                "number of systems per task."
+            )
+        for ii, model_key in enumerate(model_keys):
+            model_prob[ii] = float(len(model_training_data[model_key]))
+    if not np.all(np.isfinite(model_prob)):
+        raise ValueError("Model prob must be finite.")
+    if np.any(model_prob < 0.0):
+        raise ValueError("Model prob must be non-negative.")
+    sum_prob = float(np.sum(model_prob))
+    if sum_prob <= 0.0:
+        raise ValueError("Sum of model prob must be larger than 0!")
+    return model_prob / sum_prob
+
+
+def resolve_model_prob_from_epochs(
+    model_keys: list[str],
+    num_epoch_dict_config: dict[str, float],
+    per_task_total: np.ndarray,
+) -> tuple[np.ndarray, int, dict[str, float]]:
+    """Resolve model probability and training steps from epoch configuration.
+
+    Parameters
+    ----------
+    model_keys : list[str]
+        List of model keys.
+    num_epoch_dict_config : dict[str, float]
+        Target epochs for each task.
+    per_task_total : np.ndarray
+        Total batches per task.
+
+    Returns
+    -------
+    tuple[np.ndarray, int, dict[str, float]]
+        Model probabilities, total training steps, and per-task steps.
+
+    Raises
+    ------
+    ValueError
+        If input validation fails.
+    """
+    if not num_epoch_dict_config:
+        raise ValueError("training.num_epoch_dict must be set for multi-task epochs.")
+    missing = [k for k in model_keys if k not in num_epoch_dict_config]
+    if missing:
+        raise ValueError(
+            f"training.num_epoch_dict must specify all tasks; missing: {missing}"
+        )
+    epoch_targets = np.zeros(len(model_keys), dtype=np.float64)
+    for ii, model_key in enumerate(model_keys):
+        epoch_value = num_epoch_dict_config[model_key]
+        if epoch_value is None:
+            raise ValueError(
+                f"training.num_epoch_dict['{model_key}'] must be positive."
+            )
+        epoch_value = float(epoch_value)
+        if not np.isfinite(epoch_value) or epoch_value <= 0.0:
+            raise ValueError(
+                f"training.num_epoch_dict['{model_key}'] must be positive, got {epoch_value}."
+            )
+        epoch_targets[ii] = epoch_value
+    per_task_total = np.asarray(per_task_total, dtype=np.float64)
+    if per_task_total.ndim != 1:
+        raise ValueError("Per-task total batches must be 1D.")
+    if per_task_total.shape[0] != epoch_targets.shape[0]:
+        raise ValueError("Per-task totals and epoch targets must match.")
+    if not np.all(np.isfinite(per_task_total)):
+        raise ValueError("Per-task total batches must be finite.")
+    if np.any(per_task_total <= 0.0):
+        raise ValueError("Per-task total batches must be positive.")
+    per_task_steps = per_task_total * epoch_targets
+    total_target_steps = float(np.sum(per_task_steps))
+    if total_target_steps <= 0.0:
+        raise ValueError("Sum of target steps must be positive.")
+    model_prob = per_task_steps / total_target_steps
+    num_steps = int(np.ceil(total_target_steps))
+    per_task_steps_map = {
+        model_key: float(per_task_steps[ii]) for ii, model_key in enumerate(model_keys)
+    }
+    return model_prob, num_steps, per_task_steps_map

deepmd/pd/train/training.py

@@ -30,6 +30,11 @@
 from deepmd.common import (
     symlink_prefix_files,
 )
+from deepmd.dpmodel.utils import (
+    compute_total_numb_batch,
+    resolve_model_prob,
+    resolve_model_prob_from_epochs,
+)
 from deepmd.dpmodel.utils.learning_rate import (
     BaseLR,
 )
@@ -130,9 +135,12 @@ def __init__(
             else 1
         )
         self.num_model = len(self.model_keys)
+        self.model_prob = None
 
         # Iteration config
-        self.num_steps = training_params["numb_steps"]
+        self.num_steps = training_params.get("numb_steps")
+        self.num_epoch = training_params.get("num_epoch")
+        self.num_epoch_dict = training_params.get("num_epoch_dict")
         self.acc_freq: int = training_params.get(
             "acc_freq", 1
         )  # gradient accumulation steps
@@ -390,6 +398,75 @@ def get_lr(lr_params: dict[str, Any]) -> BaseLR:
                         ),
                     )
 
+        per_task_total = []
+        if not self.multi_task:
+            sampler_weights = to_numpy_array(
+                self.training_dataloader.batch_sampler.sampler.weights
+            )
+            total_numb_batch = compute_total_numb_batch(
+                training_data.index,
+                sampler_weights,
+            )
+            if self.num_steps is None:
+                if self.num_epoch is None:
+                    raise ValueError(
+                        "Either training.numb_steps or training.num_epoch must be set."
+                    )
+                if self.num_epoch <= 0:
+                    raise ValueError("training.num_epoch must be positive.")
+                if total_numb_batch <= 0:
+                    raise ValueError(
+                        "Total number of training batches must be positive."
+                    )
+                self.num_steps = int(np.ceil(self.num_epoch * total_numb_batch))
+                log.info(
+                    "Computed num_steps=%d from num_epoch=%s and total_numb_batch=%d.",
+                    self.num_steps,
+                    self.num_epoch,
+                    total_numb_batch,
+                )
+        else:
+            for model_key in self.model_keys:
+                sampler_weights = to_numpy_array(
+                    self.training_dataloader[model_key].batch_sampler.sampler.weights
+                )
+                per_task_total.append(
+                    compute_total_numb_batch(
+                        training_data[model_key].index,
+                        sampler_weights,
+                    )
+                )
+            if self.num_epoch_dict:
+                (
+                    self.model_prob,
+                    self.num_steps,
+                    per_task_steps,
+                ) = resolve_model_prob_from_epochs(
+                    self.model_keys,
+                    self.num_epoch_dict,
+                    np.asarray(per_task_total, dtype=np.float64),
+                )
+                log.info(
+                    "Computed model_prob=%s and num_steps=%d from num_epoch_dict=%s "
+                    "with per-task target steps: %s.",
+                    self.model_prob,
+                    self.num_steps,
+                    self.num_epoch_dict,
+                    {k: int(np.ceil(v)) for k, v in per_task_steps.items()},
+                )
+            else:
+                if self.num_steps is None:
+                    raise ValueError(
+                        "Either training.numb_steps (multi-task only) or "
+                        "training.num_epoch_dict must be set."
+                    )
+                self.model_prob = resolve_model_prob(
+                    self.model_keys,
+                    training_params.get("model_prob"),
+                    training_data,
+                    rank=self.rank,
+                )
+
         # Learning rate
         self.warmup_steps = training_params.get("warmup_steps", 0)
         self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0)
@@ -575,6 +652,15 @@ def single_model_finetune(
             frz_model = paddle.jit.load(init_frz_model)
             self.model.set_state_dict(frz_model.state_dict())
 
+        # Get model prob for multi-task
+        if self.multi_task and self.model_prob is None:
+            self.model_prob = resolve_model_prob(
+                self.model_keys,
+                training_params.get("model_prob"),
+                training_data,
+                rank=self.rank,
+            )
+
         # Multi-task share params
         if shared_links is not None:
             self.wrapper.share_params(
@@ -682,21 +768,6 @@ def warm_up_linear(step: int, warmup_steps: int) -> float:
             )
             self.optimizer = fleet.distributed_optimizer(self.optimizer)
 
-        # Get model prob for multi-task
-        if self.multi_task:
-            self.model_prob = np.array([0.0 for key in self.model_keys])
-            if training_params.get("model_prob", None) is not None:
-                model_prob = training_params["model_prob"]
-                for ii, model_key in enumerate(self.model_keys):
-                    if model_key in model_prob:
-                        self.model_prob[ii] += float(model_prob[model_key])
-            else:
-                for ii, model_key in enumerate(self.model_keys):
-                    self.model_prob[ii] += float(len(self.training_data[model_key]))
-            sum_prob = np.sum(self.model_prob)
-            assert sum_prob > 0.0, "Sum of model prob must be larger than 0!"
-            self.model_prob = self.model_prob / sum_prob
-
         # Tensorboard
         self.enable_tensorboard = training_params.get("tensorboard", False)
         self.tensorboard_log_dir = training_params.get("tensorboard_log_dir", "log")