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update GPT2 loss computation for more flexbility

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thomwolf
2019-05-07 23:27:18 +02:00
parent ce86336545
commit ea9dbea9d5
1 changed files with 6 additions and 7 deletions
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13
pytorch_pretrained_bert/modeling_gpt2.py
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@@ -336,6 +336,7 @@ class GPT2MultipleChoiceHead(nn.Module):
# (bsz, num_choices, 1, hidden_size) # (bsz, num_choices, 1, hidden_size)
multiple_choice_h = hidden_states.gather(2, mc_token_ids).squeeze(2) multiple_choice_h = hidden_states.gather(2, mc_token_ids).squeeze(2)
# (bsz, num_choices, hidden_size) # (bsz, num_choices, hidden_size)
multiple_choice_h = self.dropout(multiple_choice_h.transpose(1, 2)).transpose(1, 2)
multiple_choice_logits = self.linear(multiple_choice_h).squeeze(-1) multiple_choice_logits = self.linear(multiple_choice_h).squeeze(-1)
# (bsz, num_choices) # (bsz, num_choices)
return multiple_choice_logits return multiple_choice_logits
@@ -665,9 +666,8 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
lm_logits = self.lm_head(hidden_states) lm_logits = self.lm_head(hidden_states)
if lm_labels is not None: if lm_labels is not None:
# Shift so that tokens < n predict n # Shift so that tokens < n predict n
shift_logits = lm_logits[:, :-1].contiguous() shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[:, 1:].contiguous() shift_labels = lm_labels[..., 1:].contiguous()
# Flatten the tokens # Flatten the tokens
loss_fct = CrossEntropyLoss(ignore_index=-1) loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
@@ -746,11 +746,10 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids) mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
losses = [] losses = []
if lm_labels is not None: if lm_labels is not None:
shift_logits = lm_logits[:, :-1].contiguous() shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[:, 1:].contiguous() shift_labels = lm_labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss(ignore_index=-1) loss_fct = CrossEntropyLoss(ignore_index=-1)
losses.append(loss_fct(shift_logits.view(-1, losses.append(loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)))
shift_logits.size(-1)), shift_labels.view(-1)))
if mc_labels is not None: if mc_labels is not None:
loss_fct = CrossEntropyLoss() loss_fct = CrossEntropyLoss()
losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))) losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)))