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disable accuracy calc because it breaks with actual batched training even though it shouldn't

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mrq 2024-06-04 22:13:44 -05:00
parent d005e24953
commit 687c71e028
1 changed files with 11 additions and 9 deletions
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20
vall_e/models/experimental.py
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@ -162,7 +162,7 @@ class Model(LlmArchClass):
self.backbone.gradient_checkpointing = gradient_checkpointing
self.accuracy_metric = MulticlassAccuracy(
self.accuracy_metric = None if True else MulticlassAccuracy(
vocab_size,
top_k=10,
average="micro",
@ -211,9 +211,10 @@ class Model(LlmArchClass):
nll = loss,
)
self.stats = dict(
acc = (sum([ self.accuracy_metric( logit, target ) for logit, target in zip( logits, labels ) ] ) / len( logits )).item()
)
if self.accuracy_metric is not None:
self.stats = dict(
acc = (sum([ self.accuracy_metric( logit, target ) for logit, target in zip( logits, labels ) ] ) / len( logits )).item()
)
else:
sep = 3
@ -237,12 +238,13 @@ class Model(LlmArchClass):
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(),
if self.accuracy_metric is not None:
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