Move Accelerator preparation into strategy

1 files changed, 14 insertions, 14 deletions

diff --git a/training/functional.py b/training/functional.py
index a450ef6..fb135c4 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -7,6 +7,7 @@ from pathlib import Path
 import itertools
 
 import torch
+import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils.data import DataLoader
 
@@ -45,10 +46,20 @@ class TrainingCallbacks():
     on_checkpoint: Callable[[int, str], None] = const()
 
 
+class TrainingStrategyPrepareCallable(Protocol):
+    def __call__(
+        self,
+        accelerator: Accelerator,
+        text_encoder: CLIPTextModel,
+        unet: UNet2DConditionModel,
+        *args
+    ) -> Tuple: ...
+
+
 @dataclass
 class TrainingStrategy():
     callbacks: Callable[..., TrainingCallbacks]
-    prepare_unet: bool = False
+    prepare: TrainingStrategyPrepareCallable
 
 
 def make_grid(images, rows, cols):
@@ -535,19 +546,8 @@ def train(
     prior_loss_weight: float = 1.0,
     **kwargs,
 ):
-    prep = [text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler]
-
-    if strategy.prepare_unet:
-        prep.append(unet)
-
-    prep = accelerator.prepare(*prep)
-
-    if strategy.prepare_unet:
-        text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler, unet = prep
-    else:
-        text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = prep
-
-        unet.to(accelerator.device, dtype=dtype)
+    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = strategy.prepare(
+        accelerator, text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
 
     vae.to(accelerator.device, dtype=dtype)