pipeline.py

import torch
import transformers
from accelerate import dispatch_model, infer_auto_device_map
from accelerate.utils import get_balanced_memory
from typing import Dict, List, Any

class PreTrainedPipeline():
    def __init__(self, path=""):
        path = "oleksandrfluxon/mpt-7b-instruct-evaluate"
        print("===> path", path)
        
        with torch.autocast('cuda'):
            config = transformers.AutoConfig.from_pretrained(
                path, 
                trust_remote_code=True
            )
            # config.attn_config['attn_impl'] = 'triton'
            config.init_device = 'cuda:0' # For fast initialization directly on GPU!
            config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096
    
            print("===> loading model")
            model = transformers.AutoModelForCausalLM.from_pretrained(
              path,
              config=config,
              # torch_dtype=torch.bfloat16, # Load model weights in bfloat16
              torch_dtype=torch.float16,
              trust_remote_code=True,
              device_map="auto", 
              load_in_8bit=True # Load model in the lowest 4-bit precision quantization
            )
            model.to('cuda')
            print("===> model loaded")
    
            # removed device_map="auto"
            tokenizer = transformers.AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b', padding_side="left")


            max_memory = get_balanced_memory(
                model,
                max_memory=None,
                no_split_module_classes=["MPTBlock"],
                dtype='float16',
                low_zero=False
            )
            
            device_map = infer_auto_device_map(
                model,
                max_memory=max_memory,
                no_split_module_classes=["MPTBlock"],
                dtype='float16'
            )
            model = dispatch_model(model, device_map=device_map)
                

            # device='cuda:0'
            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)

        with torch.autocast('cuda'):
            result = self.pipeline(inputs, **parameters)
            print("===> result", result)

        return result