| 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 |