| import logging | |
| from typing import Dict, List, Any | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList, BitsAndBytesConfig | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| logging.info("Initializing EndpointHandler with model path: %s", path) | |
| tokenizer = AutoTokenizer.from_pretrained(path) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16 | |
| ) | |
| self.model = AutoModelForCausalLM.from_pretrained(path, quantization_config=bnb_config) | |
| self.tokenizer = tokenizer | |
| self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)]) | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| logging.info("Starting inference") | |
| inputs = data.pop("inputs", data) | |
| additional_bad_words_ids = data.pop("additional_bad_words_ids", []) | |
| # Log the input size | |
| logging.info("Encoding inputs") | |
| input_ids = self.tokenizer.encode(inputs, return_tensors="pt") | |
| logging.info("Input IDs shape: %s", input_ids.shape) | |
| 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 | |
| # 3070, 10456, [313, 334], [29898, 1068] 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], [3253, 29885, 4430, 29889] is "Admitted." | |
| # [3253, 29885, 4430, 29889] | |
| bad_words_ids = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889], [29898, 1068], [3253, 29885, 4430, 29889]] | |
| bad_words_ids.extend(additional_bad_words_ids) | |
| # Truncation and generation logging | |
| if input_ids.shape[1] > max_input_length: | |
| logging.info("Truncating input IDs to fit within max input length") | |
| input_ids = input_ids[:, -max_input_length:] | |
| max_length = input_ids.shape[1] + max_generation_length | |
| logging.info("Generating output") | |
| generated_ids = self.model.generate( | |
| input_ids, | |
| max_length=max_length, | |
| bad_words_ids=bad_words_ids, | |
| temperature=0.5, | |
| top_k=40, | |
| do_sample=True, | |
| stopping_criteria=self.stopping_criteria, | |
| ) | |
| logging.info("Finished generating output") | |
| 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()}] | |
| logging.info("Inference complete") | |
| return prediction | |
| class StopAtPeriodCriteria(StoppingCriteria): | |
| def __init__(self, tokenizer): | |
| self.tokenizer = tokenizer | |
| def __call__(self, input_ids, scores, **kwargs): | |
| last_token_text = self.tokenizer.decode(input_ids[:, -1], skip_special_tokens=True) | |
| logging.info("StopAtPeriodCriteria called. Last token text: '%s'", last_token_text) | |
| return '.' in last_token_text | |
| # 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) | |
| # 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], [29898, 1068] 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], [29898, 1068]] | |
| # bad_words_ids.extend(additional_bad_words_ids) | |
| # input_ids = self.tokenizer.encode(inputs, return_tensors="pt") | |
| # 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=0.5, | |
| # 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) | |
| # logging.info("StopAtPeriodCriteria called. Last token text: '%s'", last_token_text) | |
| # # Check if the decoded text ends with a period | |
| # return '.' in last_token_text |