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from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList
class EndpointHandler():
def __init__(self, path=""):
tokenizer = AutoTokenizer.from_pretrained(path)
tokenizer.pad_token = tokenizer.eos_token
self.model = AutoModelForCausalLM.from_pretrained(path).to('cuda')
self.tokenizer = tokenizer
self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)])
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs", data)
additional_bad_words_ids = data.pop("additional_bad_words_ids", [])
# 3070, 10456, [313, 334] corresponds to "(*", and we do not want to output a comment
# 13 is a newline character
# [1976, 441, 29889], [4920, 441, 29889] is "Abort." [4920, 18054, 29889] is "Aborted."
# [2087, 29885, 4430, 29889] is "Admitted."
bad_words_ids = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889]]
bad_words_ids.extend(additional_bad_words_ids)
input_ids = self.tokenizer.encode(inputs, return_tensors="pt").to('cuda')
max_generation_length = 75 # Desired number of tokens to generate
# max_input_length = 4092 - max_generation_length # Maximum input length to allow space for generation
# # Truncate input_ids to the most recent tokens that fit within the max_input_length
# if input_ids.shape[1] > max_input_length:
# input_ids = input_ids[:, -max_input_length:]
max_length = input_ids.shape[1] + max_generation_length
generated_ids = self.model.generate(
input_ids,
max_length=max_length, # 50 new tokens
bad_words_ids=bad_words_ids,
temperature=1,
top_k=40,
do_sample=True,
stopping_criteria=self.stopping_criteria,
)
generated_text = self.tokenizer.decode(generated_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
prediction = [{"generated_text": generated_text, "generated_ids": generated_ids[0][input_ids.shape[1]:].tolist()}]
return prediction
class StopAtPeriodCriteria(StoppingCriteria):
def __init__(self, tokenizer):
self.tokenizer = tokenizer
def __call__(self, input_ids, scores, **kwargs):
# Decode the last generated token to text
last_token_text = self.tokenizer.decode(input_ids[:, -1], skip_special_tokens=True)
# Check if the decoded text ends with a period
return '.' in last_token_text |