diff --git a/examples/summarization/bart/evaluate_cnn.py b/examples/summarization/bart/evaluate_cnn.py index 474fcd46..fded7e51 100644 --- a/examples/summarization/bart/evaluate_cnn.py +++ b/examples/summarization/bart/evaluate_cnn.py @@ -20,6 +20,10 @@ def generate_summaries(lns, out_file, batch_size=8, device=DEFAULT_DEVICE): fout = Path(out_file).open("w") model = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(device) tokenizer = BartTokenizer.from_pretrained("bart-large") + + max_length = 140 + min_length = 55 + for batch in tqdm(list(chunks(lns, batch_size))): dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True) summaries = model.generate( @@ -27,11 +31,12 @@ def generate_summaries(lns, out_file, batch_size=8, device=DEFAULT_DEVICE): attention_mask=dct["attention_mask"].to(device), num_beams=4, length_penalty=2.0, - max_length=142, # +2 from original because we start at step=1 and stop before max_length - min_length=56, # +1 from original because we start at step=1 + max_length=max_length + 2, # +2 from original because we start at step=1 and stop before max_length + min_length=min_length + 1, # +1 from original because we start at step=1 no_repeat_ngram_size=3, early_stopping=True, do_sample=False, + decoder_start_token_id=model.config.eos_token_ids[0], ) dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries] for hypothesis in dec: diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py index 6e26a931..57b4204a 100644 --- a/src/transformers/modeling_utils.py +++ b/src/transformers/modeling_utils.py @@ -628,6 +628,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): no_repeat_ngram_size=None, num_return_sequences=None, attention_mask=None, + decoder_start_token_id=None, ): r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling and beam-search. @@ -739,6 +740,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): num_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) + # TODO: think about how to make this cleaner + decoder_start_token_id = ( + decoder_start_token_id if decoder_start_token_id is not None else self.config.bos_token_id + ) if input_ids is not None: batch_size = input_ids.shape[0] # overriden by the input batch_size @@ -765,6 +770,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): assert (eos_token_ids is None) or ( isinstance(eos_token_ids, (list, tuple)) and ((isinstance(e, int) and e >= 0) for e in eos_token_ids) ), "`eos_token_ids` should be a positive integer or a list/tuple of positive integers." + assert ( + decoder_start_token_id is not None or self.config.is_encoder_decoder is False + ), "`decoder_start_token_id` has to be defined if model is encoder-decoder model" assert length_penalty > 0, "`length_penalty` should be strictly positive." assert ( isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0 @@ -845,7 +853,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): encoder_inputs = input_ids input_ids = torch.full( (effective_batch_size * num_beams, 1), - bos_token_id, + decoder_start_token_id, # TODO: see whether this is the best result dtype=torch.long, device=next(self.parameters()).device, ) @@ -1082,7 +1090,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) if 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? The prepare scores fn is ugly here + # TODO: maybe give better naming scores = self.prepare_scores_for_generation(scores, cur_len, max_length) # set eos token prob to zero if min_length is not reached @@ -1276,7 +1284,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device) if self.config.is_encoder_decoder: - # do not return first token return decoded[:, 1:] return decoded diff --git a/tests/test_modeling_bart.py b/tests/test_modeling_bart.py index 865c989a..1ea46a09 100644 --- a/tests/test_modeling_bart.py +++ b/tests/test_modeling_bart.py @@ -61,7 +61,7 @@ class ModelTester: self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 20 - self.eos_token_id = 2 + self.eos_token_ids = [2] self.pad_token_id = 1 self.bos_token_id = 0 torch.manual_seed(0) @@ -82,7 +82,7 @@ class ModelTester: dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, - eos_token_ids=[self.eos_token_id], + eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, ) @@ -438,7 +438,11 @@ class BartModelIntegrationTest(unittest.TestCase): tokens = tok.encode(text, return_tensors="pt").to(torch_device) extra_len = 20 gen_tokens = hf.generate( - tokens, num_beams=4, max_length=extra_len + 2, do_sample=False + tokens, + num_beams=4, + max_length=extra_len + 2, + do_sample=False, + decoder_start_token_id=hf.config.eos_token_ids[0], ) # repetition_penalty=10., expected_result = "The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday." generated = [tok.decode(g,) for g in gen_tokens] @@ -483,6 +487,7 @@ class BartModelIntegrationTest(unittest.TestCase): no_repeat_ngram_size=3, do_sample=False, early_stopping=True, + decoder_start_token_id=hf.config.eos_token_ids[0], ) decoded = [