refactor: inline parallel utils into executor module

- Move create_ref_model from astrai/parallel/utils.py into executor.py
- Remove unused ColumnParallelLinear/RowParallelLinear (module.py)
- Update imports in strategy.py and train_context.py
- Drop unused astrai.parallel.utils and astrai.parallel.module

astrai/parallel/executor.py

@@ -4,7 +4,7 @@ import contextlib
 import logging
 import os
 from contextlib import contextmanager
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Callable, Dict, Optional, Tuple
 
 import torch
 import torch.distributed as dist
@@ -24,6 +24,31 @@ from astrai.parallel.setup import get_rank, get_world_size
 logger = logging.getLogger(__name__)
 
 
+def create_ref_model(
+    model_fn: Callable[[], nn.Module],
+    executor: Optional["BaseExecutor"] = None,
+    model: Optional[nn.Module] = None,
+    state_dict: Optional[Dict[str, torch.Tensor]] = None,
+    device: Optional[str] = None,
+) -> Optional[nn.Module]:
+    """Create a frozen reference model from executor or state dict.
+
+    On non-rank-0, returns None (executor.unwrap_model returns None).
+    """
+    if state_dict is None and executor is not None and model is not None:
+        state_dict = executor.unwrap_model(model)
+    if state_dict is None:
+        return None
+
+    ref_model = model_fn()
+    ref_model.load_state_dict(state_dict)
+    ref_model.requires_grad_(False)
+    ref_model.eval()
+    if device is not None:
+        ref_model = ref_model.to(device=device)
+    return ref_model
+
+
 class GradientState:
     def __init__(self, grad_accum_steps: int = 1):
         self.num_steps = max(grad_accum_steps, 1)