File size: 3,338 Bytes
0881ae0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
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
|