Improved learning rate finder

3 files changed, 30 insertions, 23 deletions

diff --git a/train_dreambooth.py b/train_dreambooth.py
index a62cec9..325fe90 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -970,9 +970,8 @@ def main():
                     avg_loss_val.update(loss.detach_(), bsz)
                     avg_acc_val.update(acc.detach_(), bsz)
 
-                    if accelerator.sync_gradients:
-                        local_progress_bar.update(1)
-                        global_progress_bar.update(1)
+                    local_progress_bar.update(1)
+                    global_progress_bar.update(1)
 
                     logs = {
                         "val/loss": avg_loss_val.avg.item(),
diff --git a/train_ti.py b/train_ti.py
index 32f44f4..870b2ba 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -548,9 +548,6 @@ def main():
             args.train_batch_size * accelerator.num_processes
         )
 
-    if args.find_lr:
-        args.learning_rate = 1e2
-
     # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
     if args.use_8bit_adam:
         try:
@@ -783,7 +780,7 @@ def main():
 
     if args.find_lr:
         lr_finder = LRFinder(accelerator, text_encoder, optimizer, train_dataloader, val_dataloader, loop)
-        lr_finder.run(num_train_steps=2)
+        lr_finder.run(min_lr=1e-6, num_train_batches=4)
 
         plt.savefig(basepath.joinpath("lr.png"))
         plt.close()
@@ -908,9 +905,8 @@ def main():
                     avg_loss_val.update(loss.detach_(), bsz)
                     avg_acc_val.update(acc.detach_(), bsz)
 
-                    if accelerator.sync_gradients:
-                        local_progress_bar.update(1)
-                        global_progress_bar.update(1)
+                    local_progress_bar.update(1)
+                    global_progress_bar.update(1)
 
                     logs = {
                         "val/loss": avg_loss_val.avg.item(),
diff --git a/training/lr.py b/training/lr.py
index 5343f24..8e558e1 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -1,3 +1,6 @@
+import math
+import copy
+
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -16,15 +19,22 @@ class LRFinder():
         self.val_dataloader = val_dataloader
         self.loss_fn = loss_fn
 
-    def run(self, num_epochs=100, num_train_steps=1, num_val_steps=1, smooth_f=0.05, diverge_th=5):
+        self.model_state = copy.deepcopy(model.state_dict())
+        self.optimizer_state = copy.deepcopy(optimizer.state_dict())
+
+    def run(self, min_lr, num_epochs=100, num_train_batches=1, num_val_batches=math.inf, smooth_f=0.05, diverge_th=5):
         best_loss = None
         lrs = []
         losses = []
 
-        lr_scheduler = get_exponential_schedule(self.optimizer, num_epochs)
+        lr_scheduler = get_exponential_schedule(self.optimizer, min_lr, num_epochs)
+
+        steps = min(num_train_batches, len(self.train_dataloader))
+        steps += min(num_val_batches, len(self.val_dataloader))
+        steps *= num_epochs
 
         progress_bar = tqdm(
-            range(num_epochs * (num_train_steps + num_val_steps)),
+            range(steps),
             disable=not self.accelerator.is_local_main_process,
             dynamic_ncols=True
         )
@@ -38,6 +48,9 @@ class LRFinder():
             self.model.train()
 
             for step, batch in enumerate(self.train_dataloader):
+                if step >= num_train_batches:
+                    break
+
                 with self.accelerator.accumulate(self.model):
                     loss, acc, bsz = self.loss_fn(batch)
 
@@ -49,21 +62,17 @@ class LRFinder():
                 if self.accelerator.sync_gradients:
                     progress_bar.update(1)
 
-                if step >= num_train_steps:
-                    break
-
             self.model.eval()
 
             with torch.inference_mode():
                 for step, batch in enumerate(self.val_dataloader):
+                    if step >= num_val_batches:
+                        break
+
                     loss, acc, bsz = self.loss_fn(batch)
                     avg_loss.update(loss.detach_(), bsz)
 
-                    if self.accelerator.sync_gradients:
-                        progress_bar.update(1)
-
-                    if step >= num_val_steps:
-                        break
+                    progress_bar.update(1)
 
             lr_scheduler.step()
 
@@ -87,6 +96,9 @@ class LRFinder():
                 "lr": lr,
             })
 
+            self.model.load_state_dict(self.model_state)
+            self.optimizer.load_state_dict(self.optimizer_state)
+
             if loss > diverge_th * best_loss:
                 print("Stopping early, the loss has diverged")
                 break
@@ -120,8 +132,8 @@ class LRFinder():
         ax.set_ylabel("Loss")
 
 
-def get_exponential_schedule(optimizer, num_epochs, last_epoch=-1):
+def get_exponential_schedule(optimizer, min_lr, num_epochs, last_epoch=-1):
     def lr_lambda(current_epoch: int):
-        return (current_epoch / num_epochs) ** 5
+        return min_lr + ((current_epoch / num_epochs) ** 10) * (1 - min_lr)
 
     return LambdaLR(optimizer, lr_lambda, last_epoch)