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from torch import cuda
import transformers
from accelerate import dispatch_model, infer_auto_device_map
from accelerate.utils import get_balanced_memory
from transformers import BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
from typing import Dict, List, Any
class PreTrainedPipeline():
def __init__(self, path=""):
path = "oleksandrfluxon/mpt-7b-instruct-evaluate"
print("===> path", path)
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
print("===> device", device)
model = transformers.AutoModelForCausalLM.from_pretrained(
'oleksandrfluxon/mpt-7b-instruct-evaluate',
trust_remote_code=True,
load_in_8bit=True, # this requires the `bitsandbytes` library
max_seq_len=8192,
init_device=device
)
model.eval()
#model.to(device)
print(f"===> Model loaded on {device}")
tokenizer = transformers.AutoTokenizer.from_pretrained("mosaicml/mpt-7b")
self.pipeline = transformers.pipeline('text-generation', model=model, tokenizer=tokenizer)
print("===> init finished")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
parameters (:obj: `str`)
Return:
A :obj:`str`: todo
"""
# get inputs
inputs = data.pop("inputs",data)
parameters = data.pop("parameters", {})
date = data.pop("date", None)
print("===> inputs", inputs)
print("===> parameters", parameters)
result = self.pipeline(inputs, **parameters)
print("===> result", result)
return result |