feat: Creating a centralized script for latent creation (#101)

* Add support for a uniform data processor

Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>

* Adding processing logic for video models

Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>

* Linting fixes

Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>

* Fix secrets error

Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>

* Formatting fix

Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>

---------

Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>

Author: pthombre

.github/workflows/config/.secrets.baseline

@@ -90,10 +90,6 @@
     {
       "path": "detect_secrets.filters.allowlist.is_line_allowlisted"
     },
-    {
-      "path": "detect_secrets.filters.common.is_baseline_file",
-      "filename": ".github/workflows/config/.secrets.baseline"
-    },
     {
       "path": "detect_secrets.filters.common.is_ignored_due_to_verification_policies",
       "min_level": 2
@@ -139,10 +135,10 @@
         "filename": "examples/automodel/pretrain/cicd/wan21_cicd_nightly_video.yaml",
         "hashed_secret": "c70f071570ba65f9c4079d6051e955ff4f802eea",
         "is_verified": false,
-        "line_number": 67,
+        "line_number": 72,
         "is_secret": false
       }
     ]
   },
-  "generated_at": "2026-01-30T18:50:34Z"
+  "generated_at": "2026-02-12T07:45:24Z"
 }

dfm/src/automodel/recipes/train.py

@@ -25,6 +25,7 @@
 from nemo_automodel.components.checkpoint.checkpointing import Checkpointer, CheckpointingConfig
 from nemo_automodel.components.loggers.log_utils import setup_logging
 from nemo_automodel.components.loggers.wandb_utils import suppress_wandb_log_messages
+from nemo_automodel.components.optim.scheduler import OptimizerParamScheduler
 from nemo_automodel.components.training.rng import StatefulRNG
 from nemo_automodel.components.training.step_scheduler import StepScheduler
 from nemo_automodel.recipes.base_recipe import BaseRecipe
@@ -195,20 +196,93 @@ def build_model_and_optimizer(
 
 
 def build_lr_scheduler(
+    cfg,
     optimizer: torch.optim.Optimizer,
-    *,
-    num_epochs: int,
-    steps_per_epoch: int,
-    eta_min: float = 1e-6,
-) -> torch.optim.lr_scheduler.CosineAnnealingLR:
-    """Build the cosine annealing learning rate scheduler."""
-
-    total_steps = max(1, num_epochs * max(1, steps_per_epoch))
-    logging.info(f"[INFO] Scheduler configured for {total_steps} total steps")
-    return torch.optim.lr_scheduler.CosineAnnealingLR(
-        optimizer,
-        T_max=total_steps,
-        eta_min=eta_min,
+    total_steps: int,
+) -> Optional[OptimizerParamScheduler]:
+    """Build the learning rate scheduler.
+
+    Args:
+        cfg: Configuration for the OptimizerParamScheduler from YAML. If None, no scheduler
+            is created and constant LR is used. Supports:
+            - lr_decay_style: constant, linear, cosine, inverse-square-root, WSD
+            - lr_warmup_steps: Number of warmup steps (or fraction < 1 for percentage)
+            - min_lr: Minimum LR after decay
+            - init_lr: Initial LR for warmup (defaults to 10% of max_lr if warmup enabled)
+            - wd_incr_style: constant, linear, cosine (for weight decay scheduling)
+            - wsd_decay_steps: WSD-specific decay steps
+            - lr_wsd_decay_style: WSD-specific decay style (cosine, linear, exponential, minus_sqrt)
+        optimizer: The optimizer to be scheduled.
+        total_steps: Total number of optimizer steps for the training run.
+
+    Returns:
+        OptimizerParamScheduler instance, or None if cfg is None.
+    """
+    if cfg is None:
+        return None
+
+    user_cfg = cfg.to_dict() if hasattr(cfg, "to_dict") else dict(cfg)
+
+    base_lr = optimizer.param_groups[0]["lr"]
+    base_wd = optimizer.param_groups[0].get("weight_decay", 0.0)
+
+    # Compute defaults from runtime values
+    default_cfg: Dict[str, Any] = {
+        "optimizer": optimizer,
+        "lr_warmup_steps": min(1000, total_steps // 10),
+        "lr_decay_steps": total_steps,
+        "lr_decay_style": "cosine",
+        "init_lr": base_lr * 0.1,
+        "max_lr": base_lr,
+        "min_lr": base_lr * 0.01,
+        "start_wd": base_wd,
+        "end_wd": base_wd,
+        "wd_incr_steps": total_steps,
+        "wd_incr_style": "constant",
+    }
+
+    # Handle warmup as fraction before merging
+    if "lr_warmup_steps" in user_cfg:
+        warmup = user_cfg["lr_warmup_steps"]
+        if isinstance(warmup, float) and 0 < warmup < 1:
+            user_cfg["lr_warmup_steps"] = int(warmup * total_steps)
+
+    # WSD defaults if user specifies WSD style
+    if user_cfg.get("lr_decay_style") == "WSD":
+        default_cfg["wsd_decay_steps"] = max(1, total_steps // 10)
+        default_cfg["lr_wsd_decay_style"] = "cosine"
+
+    # User config overrides defaults
+    default_cfg.update(user_cfg)
+
+    # If user disabled warmup, set init_lr = max_lr
+    if default_cfg["lr_warmup_steps"] == 0:
+        default_cfg["init_lr"] = default_cfg["max_lr"]
+
+    # Ensure warmup < decay steps
+    if default_cfg["lr_warmup_steps"] >= default_cfg["lr_decay_steps"]:
+        default_cfg["lr_warmup_steps"] = max(0, default_cfg["lr_decay_steps"] - 1)
+
+    logging.info(
+        f"[INFO] LR Scheduler: style={default_cfg['lr_decay_style']}, "
+        f"warmup={default_cfg['lr_warmup_steps']}, total={default_cfg['lr_decay_steps']}, "
+        f"max_lr={default_cfg['max_lr']}, min_lr={default_cfg['min_lr']}"
+    )
+
+    return OptimizerParamScheduler(
+        optimizer=default_cfg["optimizer"],
+        init_lr=default_cfg["init_lr"],
+        max_lr=default_cfg["max_lr"],
+        min_lr=default_cfg["min_lr"],
+        lr_warmup_steps=default_cfg["lr_warmup_steps"],
+        lr_decay_steps=default_cfg["lr_decay_steps"],
+        lr_decay_style=default_cfg["lr_decay_style"],
+        start_wd=default_cfg["start_wd"],
+        end_wd=default_cfg["end_wd"],
+        wd_incr_steps=default_cfg["wd_incr_steps"],
+        wd_incr_style=default_cfg["wd_incr_style"],
+        wsd_decay_steps=default_cfg.get("wsd_decay_steps"),
+        lr_wsd_decay_style=default_cfg.get("lr_wsd_decay_style"),
     )
 
 
@@ -390,11 +464,17 @@ def setup(self):
         grad_acc_steps = max(1, self.global_batch_size // max(1, self.local_batch_size * self.dp_size))
         self.steps_per_epoch = ceil(self.raw_steps_per_epoch / grad_acc_steps)
 
-        self.lr_scheduler = build_lr_scheduler(
+        # Calculate total optimizer steps for LR scheduler
+        total_steps = self.num_epochs * self.steps_per_epoch
+
+        # Build LR scheduler (returns None if lr_scheduler not in config)
+        # Wrap in list for compatibility with checkpointing (OptimizerState expects list)
+        lr_scheduler = build_lr_scheduler(
+            self.cfg.get("lr_scheduler", None),
             self.optimizer,
-            num_epochs=self.num_epochs,
-            steps_per_epoch=self.steps_per_epoch,
+            total_steps,
         )
+        self.lr_scheduler = [lr_scheduler] if lr_scheduler is not None else None
 
         self.global_step = 0
         self.start_epoch = 0
@@ -490,7 +570,8 @@ def run_train_validation_loop(self):
                 grad_norm = float(grad_norm) if torch.is_tensor(grad_norm) else grad_norm
 
                 self.optimizer.step()
-                self.lr_scheduler.step()
+                if self.lr_scheduler is not None:
+                    self.lr_scheduler[0].step(1)
 
                 group_loss_mean = float(sum(micro_losses) / len(micro_losses))
                 epoch_loss += group_loss_mean