accuracy k=1 and k=80 because im probably dumb for k=10 as the default since it does not represent any usecase

docs/train.md

@@ -72,6 +72,7 @@ The optimizer used *mostly* doesn't matter, as AdamW seems to get moving faster,
 * `APOLLO` needs more testing, but seemed adequate in cursory tests
 * `Muon` requires much more testing, but absolutely cannot be used for predicting tokens in place (NAR demasking), and requires `cfg.model.experimental.predict_causally=True`
   * I honestly don't think it gives good enough results from curosry tests for this application
+* `Adagrad` surprisingly seems to "fix" (for now) my problems with the loss / accuracy bouncing.
 
 ## Try Me
 

vall_e/models/arch/attention/__init__.py

@@ -54,6 +54,8 @@ try:
 			heads = config.num_attention_heads
 			dim_head = getattr(config, "head_dim", dim // heads)
 			kv_heads = config.num_key_value_heads
+			causal = False # config.causal # to-do: handle split-causal attention like I do for normal attention
+			# for now though leave it as false since the mask transformer variant of VALL-E is much more preferable to the causal variant
 
 			# to-do: figure out these settings best for VALL-E
 			compress_block_size = 16
@@ -83,6 +85,8 @@ try:
 				num_selected_blocks = num_selected_blocks,
 				num_compressed_mem_kv = num_compressed_mem_kv,
 
+				causal = causal,
+
 				norm = False, # pre/post norm is done here already
 				use_diff_topk = True,
 				use_triton_kernel = False,

vall_e/models/arch/llama.py

@@ -24,12 +24,14 @@ class Config(BaseConfig):
 		self,
 		attn_mode = "sdpa",
 		output_norm = True,
+		causal = True,
 		*args, **kwargs
 	):
 		super().__init__(*args, **kwargs)
 
 		self.attn_mode = attn_mode
 		self.output_norm = output_norm
+		self.causal = causal
 
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
 	batch, num_key_value_heads, slen, head_dim = hidden_states.shape

vall_e/models/base_v2.py

@@ -458,8 +458,11 @@ class Base_V2(nn.Module):
 				is_encoder_decoder=False,
 				is_decoder=True,
 				#gradient_checkpointing=self.gradient_checkpointing,
+
+				# extra parameters
 				output_norm = not per_level_normalization, # moves the LN out to the decoder
 				attn_mode = attention_backend,
+				causal = self.causal,
 			)
 			self.model = LlamaModel(self.model_config)
 
@@ -903,7 +906,7 @@ class Base_V2(nn.Module):
 				sequence = sequence.reshape(-1)
 
 			nll = None
-			metrics = None
+			acc_k1 = None
 
 			if compute_hard_loss:
 				reduction = 'mean' if not batched else 'none'
@@ -917,14 +920,23 @@ class Base_V2(nn.Module):
 			if compute_acc:
 				accuracy_metric = MulticlassAccuracy(
 					logit.shape[-1],
-					top_k = min(logit.shape[0], 10),
+					top_k = 1,
 					average="micro",
 					multidim_average="global",
 					ignore_index = -100
 				).to(logit.device)
-				metrics = accuracy_metric( logit, sequence )
+				acc_k1 = accuracy_metric( logit, sequence )
 
-			return nll, metrics
+				accuracy_metric = MulticlassAccuracy(
+					logit.shape[-1],
+					top_k = min(logit.shape[0], 80),
+					average="micro",
+					multidim_average="global",
+					ignore_index = -100
+				).to(logit.device)
+				acc_k80 = accuracy_metric( logit, sequence )
+			
+			return nll, acc_k1, acc_k80
 		
 		for batch_index, batch in enumerate(inputs):
 			quant_level = quant_levels[batch_index]
@@ -1010,7 +1022,7 @@ class Base_V2(nn.Module):
 						continue
 
 					if logits[batch_index].dim() < 3:
-						nll, metrics = _calc_loss( logits[batch_index][start:end], token.long(), causal )
+						nll, acc_k1, acc_k80 = _calc_loss( logits[batch_index][start:end], token.long(), causal )
 					elif not self.resp_parallel_training:
 						# cringe way to deduce "requested" level
 						level = quant_level
@@ -1023,25 +1035,31 @@ class Base_V2(nn.Module):
 							name = f'{name}[{level}]'
 
 						sequence = token if token.dim() <= 1 else token[:, level]
-						nll, metrics = _calc_loss( logits[batch_index][level][start:end], sequence.long(), causal, level )
+						nll, acc_k1, acc_k80 = _calc_loss( logits[batch_index][level][start:end], sequence.long(), causal, level )
 					else:
 						sequence = token.t()
-						nll, metrics = _calc_loss( logits[batch_index][:, start:end], sequence.long(), causal )
+						nll, acc_k1, acc_k80 = _calc_loss( logits[batch_index][:, start:end], sequence.long(), causal )
 
 						if nll is not None:
 							nll = nll.mean()
 
 					loss_key = f'{name}.nll'
-					acc_key = f'{name}.acc'
+					acc_k1_key = f'{name}.acc[k=1]'
+					acc_k80_key = f'{name}.acc[k=80]'
 					if nll is not None:
 						if loss_key not in loss:
 							loss[loss_key] = []
 						loss[loss_key].append( nll * loss_factor )
 					
-					if metrics is not None:
-						if acc_key not in stats:
-							stats[acc_key] = []
-						stats[acc_key].append( metrics )
+					if acc_k1 is not None:
+						if acc_k1_key not in stats:
+							stats[acc_k1_key] = []
+						stats[acc_k1_key].append( acc_k1 )
+
+					if acc_k80 is not None:
+						if acc_k80_key not in stats:
+							stats[acc_k80_key] = []
+						stats[acc_k80_key].append( acc_k80 )
 				# add to list
 				else:
 					target.append( token )
@@ -1051,7 +1069,7 @@ class Base_V2(nn.Module):
 			if not self.config.loss_factors:
 				if logits[batch_index].dim() < 3:
 					sequence = _join( target, torch.tensor(self.ignore_index, device=target[-1].device) )
-					nll, metrics = _calc_loss( logits[batch_index], sequence, causal )
+					nll, acc_k1, acc_k80 = _calc_loss( logits[batch_index], sequence, causal )
 				elif not self.resp_parallel_training:
 					# cringe way to deduce "requested" level
 					level = 0
@@ -1062,35 +1080,45 @@ class Base_V2(nn.Module):
 
 					sequence = [ x if x.dim() <= 1 else x[:, level] for x in target ] 
 					sequence = _join( sequence, torch.tensor(self.ignore_index, device=sequence[-1].device) )
-					nll, metrics = _calc_loss( logits[batch_index][level], sequence.long(), causal, level )
+					nll, acc_k1, acc_k80 = _calc_loss( logits[batch_index][level], sequence.long(), causal, level )
 				else:
 					nlls = []
-					accs = []
+					acc_k1s = []
+					acc_k80s = []
 					
 					for level, logit in enumerate( logits[batch_index] ):
 						sequence = [ x if x.dim() <= 1 else x[:, level] for x in target ] 
 						sequence = _join( sequence, torch.tensor(self.ignore_index, device=sequence[-1].device) )
-						nll, metrics = _calc_loss( logit, sequence, causal, level )
+						nll, acc_k1, acc_k80 = _calc_loss( logit, sequence, causal, level )
 
 						if nll:
 							nlls.append( nll )
-						if metrics:
-							accs.append( metrics )
+						if acc_k1:
+							acc_k1s.append( acc_k1 )
+						if acc_k80:
+							acc_k80s.append( acc_k80 )
 
 					if nlls:
 						nll = sum(nlls) / len(nlls)
-					if accs:
-						metrics = sum(accs) / len(accs)
+					if acc_k1s:
+						acc_k1 = sum(acc_k1s) / len(acc_k1s)
+					if acc_k80s:
+						acc_k80 = sum(acc_k80s) / len(acc_k80s)
 
 				if nll is not None:
 					if 'nll' not in loss:
 						loss['nll'] = []
 					loss["nll"].append( nll )
 				
-				if metrics is not None:
-					if 'acc' not in stats:
-						stats['acc'] = []
-					stats["acc"].append( metrics )
+				if acc_k1 is not None:
+					if 'acc[k=1]' not in stats:
+						stats['acc[k=1]'] = []
+					stats["acc[k=1]"].append( acc_k1 )
+
+				if acc_k80 is not None:
+					if 'acc[k=80]' not in stats:
+						stats['acc[k=80]'] = []
+					stats["acc[k=80]"].append( acc_k80 )
 
 		# average
 		loss = { name: sum( loss[name] ) / len( loss[name] ) for name in loss.keys() }