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mrq 2025-02-26 21:31:10 -06:00
parent 2ea387c08a
commit cbd4d7d7f4
2 changed files with 11 additions and 86 deletions
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88
vall_e/models/base.py
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@ -1473,54 +1473,10 @@ class Base(nn.Module):
if loss_factor == 0.0:
continue
if logits[batch_index].dim() < 3:
nll, metrics = _calc_loss( logits[batch_index][start:end], token.long(), causal )
if name == "resp":
name = f'{name}[{quant_level}]'
elif not self.resp_parallel_training:
# cringe way to deduce "requested" level
level = quant_level
for i in range( self.n_resp_levels ):
if classifier_level.endswith(f':{i}:{i}'):
level = i
break
if name == "resp":
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 )
else:
nlls = []
accs = []
for level, logit in enumerate( logits[batch_index] ):
sequence = token if token.dim() <= 1 else token[:, level]
nll, metrics = _calc_loss( logit[start:end], sequence.long(), causal )
if name == "resp":
if nll is not None:
if f'{name}[{level}].nll' not in loss:
loss[f'{name}[{level}].nll'] = []
loss[f"{name}[{level}].nll"].append( nll * loss_factor )
if metrics is not None:
if f'{name}[{level}].acc' not in stats:
stats[f'{name}[{level}].acc'] = []
stats[f"{name}[{level}].acc"].append( metrics )
nll = None
metrics = None
else:
if nll:
nlls.append( nll )
if metrics:
accs.append( metrics )
else:
if nlls:
nll = sum(nlls) / len(nlls)
if accs:
accs = sum(accs) / len(accs)
"""
if name == "resp":
name = f'{name}[{quant_level}]'
"""
if nll is not None:
if f'{name}.nll' not in loss:
loss[f'{name}.nll'] = []
@ -1536,39 +1492,9 @@ class Base(nn.Module):
# perofrm loss calculation on the entire sequence
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 )
elif not self.resp_parallel_training:
# cringe way to deduce "requested" level
level = 0
for i in range( self.n_resp_levels ):
if classifier_level.endswith(f':{i}:{i}'):
level = i
break
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 )
else:
nlls = []
accs = []
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 )
if nll:
nlls.append( nll )
if metrics:
accs.append( metrics )
if nlls:
nll = sum(nlls) / len(nlls)
if accs:
accs = sum(accs) / len(accs)
sequence = _join( target, torch.tensor(self.ignore_index, device=target[-1].device) )
nll, metrics = _calc_loss( logits[batch_index], sequence, causal )
if nll is not None:
if 'nll' not in loss:
loss['nll'] = []

9
vall_e/models/base_v2.py
View File

@ -898,11 +898,10 @@ class Base_V2(nn.Module):
nlls.append( nll )
if metrics:
accs.append( metrics )
else:
if nlls:
nll = sum(nlls) / len(nlls)
if accs:
accs = sum(accs) / len(accs)
if nlls:
nll = sum(nlls) / len(nlls)
if accs:
accs = sum(accs) / len(accs)
if nll is not None:
if f'{name}.nll' not in loss: