def test_no_bad_words_dist_processor(self):
vocab_size = 5
batch_size = 2
eos_token_id = 4
input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]],
device=torch_device,
dtype=torch.long)
bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]]
scores = self._get_uniform_logits(batch_size, vocab_size)
no_bad_words_dist_proc = NoBadWordsLogitsProcessor(
bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id)
filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
# batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden
# batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden
# Note that 5th element cannot be forbidden as it is EOS token
self.assertListEqual(
torch.isinf(filtered_scores).tolist(),
[[True, True, False, True, False],
[True, True, True, False, False]])
# check edge case
no_bad_words_dist_proc = NoBadWordsLogitsProcessor(
bad_words_ids=[[4]], eos_token_id=eos_token_id)
filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3))
def test_processor_list(self):
batch_size = 4
sequence_length = 10
vocab_size = 15
eos_token_id = 0
# dummy input_ids and scores
input_ids = ids_tensor((batch_size, sequence_length), vocab_size)
input_ids_comp = input_ids.clone()
scores = self._get_uniform_logits(batch_size, vocab_size)
scores_comp = scores.clone()
# instantiate all dist processors
min_dist_proc = MinLengthLogitsProcessor(min_length=10,
eos_token_id=eos_token_id)
temp_dist_warp = TemperatureLogitsWarper(temperature=0.5)
rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
top_k_warp = TopKLogitsWarper(3)
top_p_warp = TopPLogitsWarper(0.8)
no_repeat_proc = NoRepeatNGramLogitsProcessor(2)
no_bad_words_dist_proc = NoBadWordsLogitsProcessor(
bad_words_ids=[[1]], eos_token_id=eos_token_id)
# no processor list
scores = min_dist_proc(input_ids, scores)
scores = temp_dist_warp(input_ids, scores)
scores = rep_penalty_proc(input_ids, scores)
scores = top_k_warp(input_ids, scores)
scores = top_p_warp(input_ids, scores)
scores = no_repeat_proc(input_ids, scores)
scores = no_bad_words_dist_proc(input_ids, scores)
# with processor list
processor = LogitsProcessorList([
min_dist_proc,
temp_dist_warp,
rep_penalty_proc,
top_k_warp,
top_p_warp,
no_repeat_proc,
no_bad_words_dist_proc,
])
scores_comp = processor(input_ids, scores_comp)
# scores should be equal
self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())
def _get_logits_processor_and_kwargs(input_length, eos_token_id):
process_kwargs = {
"min_length": input_length + 1,
"bad_words_ids": [[1, 0]],
"no_repeat_ngram_size": 2,
"repetition_penalty": 1.2,
}
logits_processor = LogitsProcessorList(([
MinLengthLogitsProcessor(process_kwargs["min_length"], eos_token_id
),
] if eos_token_id is not None else []) + [
NoBadWordsLogitsProcessor(process_kwargs["bad_words_ids"],
eos_token_id),
NoRepeatNGramLogitsProcessor(
process_kwargs["no_repeat_ngram_size"]),
RepetitionPenaltyLogitsProcessor(
process_kwargs["repetition_penalty"]),
])
return process_kwargs, logits_processor