ugh

vall_e.cpp/vall_e.cpp

@@ -14,7 +14,7 @@
 #include <unordered_map>
 #include <iostream>
 
-#define LLAMA_CPP_EXTENDED 1 // whether the underlying llama.cpp has some extra functions
+#define LLAMA_CPP_EXTENDED 0 // whether the underlying llama.cpp has some extra functions
 #define LLAMA_CPP_USE_VALL_E_ARCH 0 // whether the underlying llama.cpp is to use the VALL_E arch
 
 #if !LLAMA_CPP_EXTENDED

vall_e/export.py

@@ -105,7 +105,8 @@ def convert_to_hf_llama( state_dict, config = None, save_path = None ):
 	classifier = torch.nn.Linear( model_dim, n_tokens, bias=classifier_bias )
 
 	if not split_classifiers:
-		classifier.weight[:] = state_dict['module']['classifier.weight'][:]
+		src = state_dict['module']['classifier.weight'][:]
+		classifier.weight[:src.shape[0], ] = src
 
 	# update ranges
 	start = 0

vall_e/models/base.py

@@ -523,7 +523,7 @@ class Base(nn.Module):
 			classifier_l_tokens += [ 11 ]
 			classifier_l_names += ["len"]
 
-		n_vocab = 17701 if not split_classifiers else n_resp_tokens + 1
+		n_vocab = 17702 if not split_classifiers else n_resp_tokens + 1
 
 		self.n_vocab = n_vocab
 		self.unified_position_ids = unified_position_ids
@@ -1436,6 +1436,9 @@ class Base(nn.Module):
 					continue
 
 				# offset to flattened vocab ranges
+				if self.classifier is not None:
+					compute_acc = False
+				"""
 				if self.classifier is not None:
 					offsets = _get_offsets()
 
@@ -1454,6 +1457,7 @@ class Base(nn.Module):
 							if t == self.ignore_index:
 								continue
 							token[i] += start
+				"""
 
 				if token.is_floating_point():
 					ignored = True
@@ -1511,7 +1515,7 @@ class Base(nn.Module):
 			
 			# perofrm loss calculation on the entire sequence
 			if not self.config.loss_factors:
-				target = _join( target, torch.tensor(self.ignore_index if self.classifier is None else 17685, device=target[-1].device) )
+				target = _join( target, torch.tensor(self.ignore_index, device=target[-1].device) )
 				logit = logits[batch_index]
 
 				# shift if causal
@@ -1679,31 +1683,30 @@ class Base(nn.Module):
 			for i, state in enumerate( hidden_states ):
 				hidden_states[i] = [ hi[:li] for hi, li in zip(hidden_states[i], map(len, x_list)) ]
 		
+		# de-offset if needed
+		if self.classifier is not None:
+			offsets = _get_offsets()
+			for batch_index, classifier_level in enumerate( classifier_levels ):
+				if classifier_level == "stt":
+					k = "text"
+				elif classifier_level == "len":
+					k = "len"
+				else:
+					k = f'resps|{classifier_level}'
+
+				if k not in offsets:
+					continue
+
+				start, end = offsets[k]
+
+				logits[batch_index] = logits[batch_index][:, start:end]
+
 		if not training:
 			loss = None
 			stats = None
 
 			self.loss = None
 			self.stats = None
-
-			# de-offset if needed
-			if self.classifier is not None:
-				offsets = _get_offsets()
-				for batch_index, classifier_level in enumerate( classifier_levels ):
-					if classifier_level == "stt":
-						k = "text"
-					elif classifier_level == "len":
-						k = "len"
-					else:
-						k = f'resps|{classifier_level}'
-
-					if k not in offsets:
-						continue
-
-					start, end = offsets[k]
-
-					logits[batch_index] = logits[batch_index][:, start:start+end]
-
 		# compute loss if the target is given
 		else:
 			loss, stats = self.calc_loss( inputs=inputs, logits=logits, quant_levels=quant_levels )