gradient accumulation

e2_tts_pytorch/trainer.py

@@ -112,6 +112,7 @@ def __init__(
         model: E2TTS,
         optimizer,
         num_warmup_steps=20000,
+        grad_accumulation_steps=1,
         duration_predictor: DurationPredictor | None = None,
         checkpoint_path = None,
         log_file = "logs.txt",
@@ -125,6 +126,7 @@ def __init__(
 
         self.accelerator = Accelerator(
             log_with="all",
+            grad_accumulation_steps=grad_accumulation_steps,
             **accelerate_kwargs
         )
 
@@ -146,6 +148,7 @@ def __init__(
         self.num_warmup_steps = num_warmup_steps
         self.checkpoint_path = default(checkpoint_path, 'model.pth')
         self.mel_spectrogram = MelSpec(sampling_rate=self.target_sample_rate)
+
         self.model, self.optimizer = self.accelerator.prepare(
             self.model, self.optimizer
         )
@@ -183,8 +186,7 @@ def load_checkpoint(self):
             self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
         return checkpoint['step']
 
-    def train(self, train_dataset, epochs, batch_size, grad_accumulation_steps=1, num_workers=12, save_step=1000):
-        # (todo) gradient accumulation needs to be accounted for
+    def train(self, train_dataset, epochs, batch_size, num_workers=12, save_step=1000):
 
         train_dataloader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True, num_workers=num_workers, pin_memory=True)
         total_steps = len(train_dataloader) * epochs
@@ -204,23 +206,24 @@ def train(self, train_dataset, epochs, batch_size, grad_accumulation_steps=1, nu
             epoch_loss = 0.0
 
             for batch in progress_bar:
-                text_inputs = batch['text']
-                mel_spec = rearrange(batch['mel'], 'b d n -> b n d')
-                mel_lengths = batch["mel_lengths"]
-
-                if self.duration_predictor is not None:
-                    dur_loss = self.duration_predictor(mel_spec, lens=batch.get('durations'))
-                    self.writer.add_scalar('duration loss', dur_loss.item(), global_step)
-
-                loss = self.model(mel_spec, text=text_inputs, lens=mel_lengths)
-                self.accelerator.backward(loss)
-
-                if self.max_grad_norm > 0:
-                    self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
-
-                self.optimizer.step()
-                self.scheduler.step()
-                self.optimizer.zero_grad()
+                with self.accelerator.accumulate(self.model):
+                    text_inputs = batch['text']
+                    mel_spec = rearrange(batch['mel'], 'b d n -> b n d')
+                    mel_lengths = batch["mel_lengths"]
+
+                    if self.duration_predictor is not None:
+                        dur_loss = self.duration_predictor(mel_spec, lens=batch.get('durations'))
+                        self.writer.add_scalar('duration loss', dur_loss.item(), global_step)
+
+                    loss = self.model(mel_spec, text=text_inputs, lens=mel_lengths)
+                    self.accelerator.backward(loss)
+
+                    if self.max_grad_norm > 0:
+                        self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                    self.optimizer.step()
+                    self.scheduler.step()
+                    self.optimizer.zero_grad()
 
                 if self.is_main:
                     self.ema_model.update()

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "e2-tts-pytorch"
-version = "0.1.2"
+version = "0.1.5"
 description = "E2-TTS in Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }
@@ -24,7 +24,7 @@ classifiers=[
   ]
 
 dependencies = [
-    'accelerate>=0.32.1',
+    'accelerate>=0.33.0',
     'einops>=0.8.0',
     'einx>=0.3.0',
     'ema-pytorch>=0.5.2',