Added functional trainer

1 files changed, 74 insertions, 1 deletions

diff --git a/training/functional.py b/training/functional.py
index c5b514a..1f2ca6d 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -1,6 +1,7 @@
 import math
 from contextlib import _GeneratorContextManager, nullcontext
-from typing import Callable, Any, Tuple, Union
+from typing import Callable, Any, Tuple, Union, Optional
+from functools import partial
 
 import torch
 import torch.nn.functional as F
@@ -376,3 +377,75 @@ def train_loop(
             print("Interrupted")
             on_checkpoint(global_step + global_step_offset, "end")
             accelerator.end_training()
+
+
+def train(
+    accelerator: Accelerator,
+    unet: UNet2DConditionModel,
+    text_encoder: CLIPTextModel,
+    vae: AutoencoderKL,
+    noise_scheduler: DDPMScheduler,
+    train_dataloader: DataLoader,
+    val_dataloader: DataLoader,
+    dtype: torch.dtype,
+    seed: int,
+    optimizer: torch.optim.Optimizer,
+    lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+    num_train_epochs: int = 100,
+    sample_frequency: int = 20,
+    checkpoint_frequency: int = 50,
+    global_step_offset: int = 0,
+    prior_loss_weight: float = 0,
+    on_prepare: Callable[[], dict[str, Any]] = const({}),
+    on_log: Callable[[], dict[str, Any]] = const({}),
+    on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext()),
+    on_before_optimize: Callable[[int], None] = const(),
+    on_after_optimize: Callable[[float], None] = const(),
+    on_eval: Callable[[], _GeneratorContextManager] = const(nullcontext()),
+    on_sample: Callable[[int], None] = const(),
+    on_checkpoint: Callable[[int, str], None] = const(),
+):
+    unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
+        unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler
+    )
+
+    vae.to(accelerator.device, dtype=dtype)
+
+    for model in (unet, text_encoder, vae):
+        model.requires_grad_(False)
+        model.eval()
+
+    on_prepare()
+
+    loss_step_ = partial(
+        loss_step,
+        vae,
+        noise_scheduler,
+        unet,
+        text_encoder,
+        prior_loss_weight,
+        seed,
+    )
+
+    train_loop(
+        accelerator=accelerator,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler,
+        model=text_encoder,
+        train_dataloader=train_dataloader,
+        val_dataloader=val_dataloader,
+        loss_step=loss_step_,
+        sample_frequency=sample_frequency,
+        checkpoint_frequency=checkpoint_frequency,
+        global_step_offset=global_step_offset,
+        num_epochs=num_train_epochs,
+        on_log=on_log,
+        on_train=on_train,
+        on_before_optimize=on_before_optimize,
+        on_after_optimize=on_after_optimize,
+        on_eval=on_eval,
+        on_sample=on_sample,
+        on_checkpoint=on_checkpoint,
+    )
+
+    accelerator.free_memory()