fixed training tqdm being stubborn

vall_e/data.py

@@ -1397,6 +1397,11 @@ class Dataset(_Dataset):
 	def index(self):
 		return (self.sampler.index() if self.sampler is not None else -1) // self.batch_size
 	
+	def batches(self):
+		if isinstance(self.sampler, BatchedOrderedSampler):
+			return len(self.sampler)
+		return len(self.sampler if self.sampler is not None else self) // self.batch_size
+
 	def __len__(self):
 		if self.sampler_type == "group":
 			return min(len(self.spkr_groups), self._head or len(self.spkr_groups))

vall_e/models/arch/llama.py

@@ -22,6 +22,13 @@ AVAILABLE_ATTENTIONS = []
 
 LN_2 = 0.69314718056
 
+try:
+	from torch.nn.attention.flex_attention import flex_attention, create_block_mask
+
+	AVAILABLE_ATTENTIONS.append("flex")
+except Exception as e:
+	_logger.warning(f"Error while querying for `flexattention` support: {str(e)}")
+
 try:
 	from transformers.utils import is_flash_attn_2_available
 

vall_e/utils/trainer.py

@@ -102,12 +102,18 @@ def _non_blocking_input():
 
 
 def _make_infinite_epochs(dl):
+	start = dl.dataset.index()
+	total = dl.dataset.batches()
+
 	while True:
 		if dl.dataset.index() == 0:
 			_logger.info("New epoch starts.")
-		#yield from tqdm(dl, "Epoch progress", dynamic_ncols=True, disable=not is_global_leader())
-		# this number may jump from the dataloader sampling before the actual training step happens
-		yield from tqdm(dl, "Epoch progress", dynamic_ncols=True, disable=not is_global_leader(), initial=dl.dataset.index())
+
+		with tqdm(dl, "Epoch progress", dynamic_ncols=True, disable=not is_global_leader()) as pbar:
+			if start:
+				pbar.n = start
+				start = 0
+			yield from pbar
 
 
 @local_leader_only(default=None)