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[SwitchTransformers] Fix return values (#24300)

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* clean history

* remove other changes

* fix

* fix coipes
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Arthur 2023-06-16 15:40:33 +02:00 committed by GitHub
parent 0b7b4429c7
commit ba3fb4b8d7
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2 changed files with 23 additions and 28 deletions
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2
src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py
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@ -1348,7 +1348,7 @@ class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel):
total_router_logits = []
total_expert_indexes = []
for router_output in router_outputs:
if router_output[0] is not None:
if len(router_output[0].shape) > 1:
router_logits, expert_indexes = router_output
total_router_logits.append(router_logits)
total_expert_indexes.append(expert_indexes)

49
src/transformers/models/switch_transformers/modeling_switch_transformers.py
View File

@ -798,7 +798,7 @@ class SwitchTransformersBlock(nn.Module):
if isinstance(hidden_states, tuple):
hidden_states, router_tuple = hidden_states
else:
router_tuple = (None,)
router_tuple = (torch.tensor([0]),)
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
@ -1683,50 +1683,45 @@ class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedMod
decoder_z_loss = None
decoder_aux_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-100)
# todo check in the config if router loss enables
if output_router_logits:
# Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(
encoder_outputs.router_probs
)
if output_router_logits:
# Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
if self.encoder.config.encoder_sparse_step > 1:
encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(encoder_outputs[-1])
encoder_z_loss = router_z_loss_func(encoder_router_logits)
encoder_router_probs = nn.Softmax(dim=-1)(encoder_router_logits)
encoder_aux_loss = load_balancing_loss_func(encoder_router_probs, encoder_expert_indexes)
else:
encoder_z_loss = 0
encoder_aux_loss = 0
decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(
decoder_outputs.router_probs
)
if self.decoder.config.decoder_sparse_step > 1:
decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(decoder_outputs[-1])
decoder_z_loss = router_z_loss_func(decoder_router_logits)
decoder_router_probs = nn.Softmax(dim=-1)(decoder_router_logits)
decoder_aux_loss = load_balancing_loss_func(decoder_router_probs, decoder_expert_indexes)
else:
decoder_z_loss = 0
decoder_aux_loss = 0
if labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-100)
# move labels to correct device to enable PP
labels = labels.to(lm_logits.device)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
if output_router_logits and labels is not None:
if output_router_logits:
z_loss = self.router_z_loss_coef * (encoder_z_loss + decoder_z_loss)
aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss)
loss = loss + z_loss + aux_loss
if not return_dict:
output = (lm_logits,)
if output_router_logits: # only return the loss if they are not None
output += (
encoder_z_loss,
encoder_aux_loss,
decoder_z_loss,
decoder_aux_loss,
*decoder_outputs[1:],
*encoder_outputs,
)
else:
output += (*decoder_outputs[1:], *encoder_outputs)
if output_router_logits:
output += (encoder_z_loss, encoder_aux_loss, decoder_z_loss, decoder_aux_loss)
output += (*decoder_outputs[1:], *encoder_outputs)
return ((loss,) + output) if loss is not None else output
return Seq2SeqMoEOutput(
loss=loss,
logits=lm_logits,
@ -1738,18 +1733,18 @@ class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedMod
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
decoder_router_logits=decoder_outputs.router_probs,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
encoder_router_logits=encoder_outputs.router_probs,
decoder_router_logits=decoder_outputs.router_probs,
)
def _unpack_router_logits(self, router_outputs):
total_router_logits = []
total_expert_indexes = []
for router_output in router_outputs:
if router_output[0] is not None:
if len(router_output[0].shape) > 1:
router_logits, expert_indexes = router_output
total_router_logits.append(router_logits)
total_expert_indexes.append(expert_indexes)