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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
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
class EndpointHandler():
def __init__(self, path=""):
self.model_path = path
tokenizer = AutoTokenizer.from_pretrained(path)
tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer = tokenizer
# Initialize the pipeline for text generation
self.text_generation_pipeline = pipeline("text-generation", model=path, tokenizer=self.tokenizer, device=0) # device=0 for CUDA
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", [])
# Define bad words to avoid in the output
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)
# Generate text using the pipeline
generation_kwargs = {
"max_length": 75, # Adjust as needed
"temperature": 1,
"top_k": 40,
"bad_words_ids": bad_words_ids,
"pad_token_id": self.tokenizer.eos_token_id # Ensure padding with EOS token
}
generated_outputs = self.text_generation_pipeline(inputs, **generation_kwargs)
# Format the output
predictions = [{"generated_text": output["generated_text"]} for output in generated_outputs]
return predictions
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 |