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[Feature] bad words support v1 scheduler and specifiy token ids (#3608)

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* support bad_words_token_ids

* docs

* fix test

* fix

* bad words support kvcache v1 and token ids

* fix
This commit is contained in:
Sunny-bot1
2025-08-26 11:14:51 +08:00
committed by GitHub
parent c43a4bec00
commit c68c3c4b8b
16 changed files with 420 additions and 62 deletions
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52
fastdeploy/input/text_processor.py
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@@ -214,6 +214,12 @@ class DataProcessor(BaseDataProcessor):
request.set("stop_token_ids", stop_seqs)
request.set("stop_seqs_len", stop_seqs_len)
bad_words = request.get("bad_words")
bad_words_token_ids = request.get("bad_words_token_ids")
if bad_words:
bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
request["bad_words_token_ids"] = bad_words_token_ids
if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
if request.prompt is not None:
request.prompt_token_ids = self.text2ids(request.prompt, max_model_len)
@@ -270,6 +276,13 @@ class DataProcessor(BaseDataProcessor):
request["stop_token_ids"] = stop_seqs
request["stop_seqs_len"] = stop_seqs_len
# processing bad_words
bad_words = request.get("bad_words")
bad_words_token_ids = request.get("bad_words_token_ids")
if bad_words:
bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
request["bad_words_token_ids"] = bad_words_token_ids
data_processor_logger.info(f"Processing request {request}")
# processing prompt_token_ids
if not request.get("prompt_token_ids"):
@@ -652,3 +665,42 @@ class DataProcessor(BaseDataProcessor):
stop_seqs, stop_seqs_len = self.pad_batch_data(stop_seqs, pad_id=-1, return_seq_len=True, return_array=False)
data_processor_logger.debug(f"processed stop_seqs: {stop_seqs}, {stop_seqs_len}")
return stop_seqs, stop_seqs_len
def update_bad_words(self, bad_words, bad_words_token_ids):
"""Support bad words"""
token_ids = bad_words_token_ids
if token_ids is None:
token_ids = []
for bad_word in bad_words:
# To prohibit words both at the beginning
# and in the middle of text
# (related to add_prefix_space tokenizer parameter)
for add_prefix_space in [False, True]:
prefix = " " if add_prefix_space else ""
prompt = prefix + bad_word.lstrip()
prompt_token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(prompt))
if len(prompt_token_ids) != 1:
if not add_prefix_space:
data_processor_logger.warning(
f"Skip bad_words: <{prompt}>."
f"Bad words should be a single token."
f"Got tokens: {prompt_token_ids}."
)
continue
if prompt_token_ids[0] > self.tokenizer.vocab_size:
if not add_prefix_space:
data_processor_logger.warning(
f"Skip bad_words: <{prompt}>."
f"All token id values should be satisfying:"
f" 0 <= token_id < {self.tokenizer.vocab_size}."
f"Got token: {prompt_token_ids}."
)
continue
if prompt_token_ids not in token_ids:
token_ids.extend(prompt_token_ids)
return token_ids