SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

do not allow do_sample and weird force bos token things

Browse Source
This commit is contained in:
patrickvonplaten
2020-03-07 11:45:45 +01:00
committed by Patrick von Platen
parent d880a5fbde
commit 629aac92ec
1 changed files with 6 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
src/transformers/modeling_utils.py
View File

@@ -962,7 +962,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
# Top-p/top-k filtering # Top-p/top-k filtering
next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p) next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
# Sample # Sample
next_token = torch.multinomial(F.softmax(next_token_logits, dim=-1), num_samples=1).squeeze(1) probs = F.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
else: else:
# Greedy decoding # Greedy decoding
next_token = torch.argmax(next_token_logits, dim=-1) next_token = torch.argmax(next_token_logits, dim=-1)
@@ -1079,8 +1080,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size)
if ( if (
self.config.is_encoder_decoder self.config.is_encoder_decoder and do_sample is False
): # TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? ): # TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? The prepare scores fn is ugly here
scores = self.prepare_scores_for_generation(scores, cur_len, max_length) scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
# set eos token prob to zero if min_length is not reached # set eos token prob to zero if min_length is not reached
@@ -1114,9 +1115,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
) # (batch_size, num_beams * vocab_size) ) # (batch_size, num_beams * vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search) # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
next_tokens = torch.multinomial( probs = F.softmax(_scores, dim=-1)
F.softmax(_scores, dim=-1), num_samples=2 * num_beams next_tokens = torch.multinomial(probs, num_samples=2 * num_beams) # (batch_size, num_beams * 2)
) # (batch_size, num_beams * 2)
# Compute next scores # Compute next scores
next_scores = torch.gather(_scores, -1, next_tokens) # (batch_size, num_beams * 2) next_scores = torch.gather(_scores, -1, next_tokens) # (batch_size, num_beams * 2)
# sort the sampled vector to make sure that the first num_beams samples are the best # sort the sampled vector to make sure that the first num_beams samples are the best