handler.py

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