From 8e9d62225db11913bf7ef67221fc3508d7fe1149 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Tue, 17 Jan 2023 16:39:33 +0100
Subject: Update

---
 training/functional.py          | 12 ++++++++----
 training/lr.py                  |  2 +-
 training/strategy/dreambooth.py |  5 ++---
 training/strategy/ti.py         | 14 ++++++++------
 4 files changed, 19 insertions(+), 14 deletions(-)

(limited to 'training')

diff --git a/training/functional.py b/training/functional.py
index 7a3e821..a450ef6 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 import math
 from contextlib import _GeneratorContextManager, nullcontext
-from typing import Callable, Any, Tuple, Union, Optional, Type
+from typing import Callable, Any, Tuple, Union, Optional, Protocol
 from functools import partial
 from pathlib import Path
 import itertools
@@ -37,7 +37,7 @@ class TrainingCallbacks():
     on_model: Callable[[], torch.nn.Module] = const(None)
     on_log: Callable[[], dict[str, Any]] = const({})
     on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext())
-    on_before_optimize: Callable[[int], None] = const()
+    on_before_optimize: Callable[[float, int], None] = const()
     on_after_optimize: Callable[[float], None] = const()
     on_after_epoch: Callable[[float], None] = const()
     on_eval: Callable[[], _GeneratorContextManager] = const(nullcontext())
@@ -331,13 +331,17 @@ def loss_step(
     return loss, acc, bsz
 
 
+class LossCallable(Protocol):
+    def __call__(self, step: int, batch: dict[str, Any], eval: bool = False) -> Tuple[Any, Any, int]: ...
+
+
 def train_loop(
     accelerator: Accelerator,
     optimizer: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
-    loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]],
+    loss_step: LossCallable,
     sample_frequency: int = 10,
     checkpoint_frequency: int = 50,
     global_step_offset: int = 0,
@@ -406,7 +410,7 @@ def train_loop(
 
                         accelerator.backward(loss)
 
-                        on_before_optimize(epoch)
+                        on_before_optimize(lr_scheduler.get_last_lr()[0], epoch)
 
                         optimizer.step()
                         lr_scheduler.step()
diff --git a/training/lr.py b/training/lr.py
index 902c4eb..9690738 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -101,7 +101,7 @@ class LRFinder():
 
                         self.accelerator.backward(loss)
 
-                        on_before_optimize(epoch)
+                        on_before_optimize(lr_scheduler.get_last_lr()[0], epoch)
 
                         self.optimizer.step()
                         lr_scheduler.step()
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index d813b49..f57e736 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -99,8 +99,7 @@ def dreambooth_strategy_callbacks(
     def on_prepare():
         unet.requires_grad_(True)
         text_encoder.requires_grad_(True)
-        text_encoder.text_model.embeddings.persist()
-        text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(False)
+        text_encoder.text_model.embeddings.requires_grad_(False)
 
         if ema_unet is not None:
             ema_unet.to(accelerator.device)
@@ -125,7 +124,7 @@ def dreambooth_strategy_callbacks(
         with ema_context():
             yield
 
-    def on_before_optimize(epoch: int):
+    def on_before_optimize(lr: float, epoch: int):
         if accelerator.sync_gradients:
             params_to_clip = [unet.parameters()]
             if epoch < train_text_encoder_epochs:
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index ba78b98..e922954 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -117,14 +117,15 @@ def textual_inversion_strategy_callbacks(
         with ema_context():
             yield
 
-    def on_after_optimize(lr: float):
+    @torch.no_grad()
+    def on_before_optimize(lr: float, epoch: int):
         if use_emb_decay:
-            with torch.no_grad():
-                text_encoder.text_model.embeddings.normalize(
-                    emb_decay_target,
-                    min(1.0, emb_decay * lr)
-                )
+            text_encoder.text_model.embeddings.normalize(
+                emb_decay_target,
+                min(1.0, emb_decay * lr)
+            )
 
+    def on_after_optimize(lr: float):
         if ema_embeddings is not None:
             ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
 
@@ -154,6 +155,7 @@ def textual_inversion_strategy_callbacks(
         on_model=on_model,
         on_train=on_train,
         on_eval=on_eval,
+        on_before_optimize=on_before_optimize,
         on_after_optimize=on_after_optimize,
         on_log=on_log,
         on_checkpoint=on_checkpoint,
-- 
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