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Merge pull request #3225 from patrickvonplaten/finalize_merge_bart_generate_into_default_generate

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Complete merge Seq-2-Seq generation into default generation
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Thomas Wolf 2020-03-14 15:08:59 +01:00 committed by GitHub
commit 3814e167d9
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3 changed files with 25 additions and 8 deletions
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9
examples/summarization/bart/evaluate_cnn.py
View file

@ -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:

13
src/transformers/modeling_utils.py
View file

@ -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 <EOS> token
return decoded[:, 1:]
return decoded

11
tests/test_modeling_bart.py
View file

@ -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 = "<s>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 = [