import logging from typing import Dict, List, Any from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList # 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 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]]: 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