dehardcode test string

Signed-off-by: Jimmy Zhang <jiemingz@nvidia.com>

nemo/export/trt_llm/nemo_utils.py

@@ -196,7 +196,11 @@ def nemo_llm_to_model_config(
  return model_configs, tokenizer
 
 
-def to_word_list_format(word_dict: List[List[str]], tokenizer=None):
+def to_word_list_format(
+ word_dict: List[List[str]], 
+ tokenizer=None, 
+ ref_str="<extra_id_1>",
+):
  '''
  format of word_dict
  len(word_dict) should be same to batch_size
@@ -213,7 +217,7 @@ def to_word_list_format(word_dict: List[List[str]], tokenizer=None):
  # We use a similar trick as in NeMo to deal with the fact that the encoding of a single word
  # can't always be trusted. See
  # https://github.com/NVIDIA/NeMo/blob/bb575b72fd0be51ae10cc77d9f89ddb9e9d3b96d/nemo/collections/nlp/modules/common/text_generation_strategy.py#L229
- ids_ref = tokenizer.encode("<extra_id_1>")
+ ids_ref = tokenizer.encode(ref_str)
  for word_dict_item in word_dict:
  item_flat_ids = []
  item_offsets = []
@@ -223,7 +227,7 @@ def to_word_list_format(word_dict: List[List[str]], tokenizer=None):
 
  words = list(csv.reader(word_dict_item))[0]
  for word in words:
- ids = tokenizer.encode(f"<extra_id_1>{word}")
+ ids = tokenizer.encode(f"{ref_str}{word}")
  if ids[0 : len(ids_ref)] == ids_ref:
  # It worked! We can obtain the token(s) associated to `word` by stripping the prefix tokens.
  ids = ids[len(ids_ref) :]