added loss calc split and acc for experimental model

vall_e/models/experimental.py

@@ -11,10 +11,12 @@ from ..config import cfg
 from ..data import fold_inputs, unfold_outputs
 
 import torch
+import torch.nn.functional as F
 from torch.nn.utils.rnn import pad_sequence
 from torch import Tensor
 from torch.nn import CrossEntropyLoss
 from torch.utils.checkpoint import checkpoint
+from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
 
 import random
 import math
@@ -98,7 +100,7 @@ class Model(LlmArchClass):
 		n_heads=16,
 		p_dropout=0.1,
 
-		config = None,
+		config = cfg.model,
 	):
 		self.hyper_config = config
 		
@@ -160,6 +162,14 @@ class Model(LlmArchClass):
 
 			self.backbone.gradient_checkpointing = gradient_checkpointing
 
+		self.accuracy_metric = MulticlassAccuracy(
+			vocab_size,
+			top_k=10,
+			average="micro",
+			multidim_average="global",
+			ignore_index=-100,
+		)
+
 	def generate(
 		self,
 		*args,
@@ -188,33 +198,52 @@ class Model(LlmArchClass):
 			if "attention_mask" in kwargs:
 				kwargs.pop("attention_mask")
 
-		output = super().forward(*args, **kwargs)
+		labels = kwargs.pop("labels") if "labels" in kwargs else None
 
-		if SELECTED_ARCH in ["llama", "retnet"]:
-			if output.loss is not None:
-				self.loss = dict(
-					nll = output.loss,
-				)
-		elif SELECTED_ARCH in ["mamba","mamba2"]:
-			if "labels" in kwargs:
-				labels = kwargs.pop("labels")
+		output = super().forward(*args, **kwargs)
 		logits = output.logits
 
-				# Shift so that tokens < n predict n
-				shift_logits = logits[..., :-1, :].contiguous()
-				shift_labels = labels[..., 1:].contiguous()
-				# Flatten the tokens
-				loss_fct = CrossEntropyLoss()
-				shift_logits = shift_logits.view(-1, shift_logits.size(-1))
-				shift_labels = shift_labels.view(-1)
-				# Enable model parallelism
-				shift_labels = shift_labels.to(shift_logits.device)
-				loss = loss_fct(shift_logits, shift_labels)
-
+		# i HATE the correct way
+		if labels is not None:
+			if self.hyper_config is None or not self.hyper_config.loss_factors:
+				loss = sum([ F.cross_entropy( logit[:-1, :], label[1:], ignore_index=-100 ) for logit, label in zip( logits, labels ) ])
 				self.loss = dict(
 					nll = loss,
 				)
 
+				self.stats = dict(
+					acc = (sum([ self.accuracy_metric( logit, target ) for logit, target in zip( logits, labels ) ] ) / len( logits )).item()
+				)
+
+			else:
+				sep = 3
+				# determine specific sections to focus on
+				indices = [ [ idx for idx, token in enumerate( batch ) if token == sep ] for i, batch in enumerate( labels ) ]
+
+				text_index = 0
+				resp_index = 1 # 1 indluces everything non text, -3 includes pre_resp + resp (ignores prom, probably better to include prom here)
+
+				labels_text = [ batch[:indices[i][text_index] + 1 ] for i, batch in enumerate( labels ) ]
+				labels_resp = [ batch[indices[i][resp_index] + 1:] for i, batch in enumerate( labels ) ]
+
+				logits_text = [ batch[:indices[i][text_index] + 1 ] for i, batch in enumerate( logits ) ]
+				logits_resp = [ batch[indices[i][resp_index] + 1:] for i, batch in enumerate( logits ) ]
+
+				loss_text = sum([ F.cross_entropy( logit[:-1, :], label[1:], ignore_index=-100 ) for logit, label in zip( logits_text, labels_text ) ]) * self.hyper_config.loss_factor("text")
+				loss_resp = sum([ F.cross_entropy( logit[:-1, :], label[1:], ignore_index=-100 ) for logit, label in zip( logits_resp, labels_resp ) ]) * self.hyper_config.loss_factor("resp")
+
+				self.loss = dict(
+					text = loss_text,
+					resp = loss_resp,
+				)
+
+				self.stats = dict(
+					acc = dict(
+						text = (sum([ self.accuracy_metric( logit, target ) for logit, target in zip( logits_text, labels_text ) ] ) / len( logits_text )).item(),
+						resp = (sum([ self.accuracy_metric( logit, target ) for logit, target in zip( logits_resp, labels_resp ) ] ) / len( logits_resp )).item(),
+					)
+				)
+
 		return output
 
 def example_usage():
@@ -412,6 +441,7 @@ def example_usage():
 			target_ids, target_attention_mask = fold_inputs(text_list=text_list, prom_list=prom_list, resp_list=resp_list, targ_list=resp_list, ignore_index=-100, quant_levels=quant_levels)
 			
 			stats |= engine.traverse(input_ids=input_ids, labels=target_ids, attention_mask=attention_mask)
+			stats |= engine.gather_attribute("stats")
 			stats |= {"grad_norm": engine.get_global_grad_norm()}
 
 			tqdm.write(f"{stats}")

vall_e/train.py

@@ -30,10 +30,10 @@ def train_feeder(engine, batch):
 			batch_size = len(batch["text"])
 			if cfg.model.interleave:
 				quant_levels = None	
-				resps_list = [ resp for resp in resp_list ]
+				resps_list = [ resp for resp in batch["resps"] ]
 			else:
 				quant_levels = torch.randint(0, cfg.model.max_levels, (batch_size,))
-				resps_list = [ [] if l == 0 else resp for l, resp in zip(quant_levels, resp_list) ]
+				resps_list = [ [] if l == 0 else resp for l, resp in zip(quant_levels, batch["resps"]) ]
 
 			input_ids, attention_mask = fold_inputs(
 				text_list=batch["text"],