68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
|
import os
|
||
|
|
from typing import Union, Dict, Any
|
||
|
|
from modelscope.pipelines.builder import PIPELINES
|
||
|
|
from modelscope.models.builder import MODELS
|
||
|
|
from modelscope.utils.constant import Tasks
|
||
|
|
from modelscope.pipelines.base import Pipeline
|
||
|
|
from modelscope.models.base import Model, TorchModel
|
||
|
|
from modelscope.utils.logger import get_logger
|
||
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
|
|
from transformers import LlamaForCausalLM
|
||
|
|
|
||
|
|
if 'CUDA_VISIBLE_DEVICES' not in os.environ:
|
||
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
@PIPELINES.register_module(Tasks.text_generation, module_name='Ziya-LLaMA-13B-v1-text-generation-pipe')
|
||
|
|
class ZiyaLLaMA13Bv1TextGenerationPipeline(Pipeline):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
model: Union[Model, str],
|
||
|
|
*args,
|
||
|
|
**kwargs):
|
||
|
|
model = ZiyaLLaMA13Bv1TextGeneration(model) if isinstance(model, str) else model
|
||
|
|
super().__init__(model=model, **kwargs)
|
||
|
|
|
||
|
|
def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
|
||
|
|
return inputs
|
||
|
|
|
||
|
|
# define the forward pass
|
||
|
|
def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
|
||
|
|
return self.model(inputs)
|
||
|
|
|
||
|
|
# format the outputs from pipeline
|
||
|
|
def postprocess(self, input, **kwargs) -> Dict[str, Any]:
|
||
|
|
return input
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
@MODELS.register_module(Tasks.text_generation, module_name='Ziya-LLaMA-13B-v1')
|
||
|
|
class ZiyaLLaMA13Bv1TextGeneration(TorchModel):
|
||
|
|
def __init__(self, model_dir=None, *args, **kwargs):
|
||
|
|
super().__init__(model_dir, *args, **kwargs)
|
||
|
|
self.logger = get_logger()
|
||
|
|
# loading tokenizer
|
||
|
|
self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
|
||
|
|
self.model = LlamaForCausalLM.from_pretrained(model_dir, device_map="auto")
|
||
|
|
self.model = self.model.eval()
|
||
|
|
|
||
|
|
def forward(self,input: Dict) -> Dict[str, Any]:
|
||
|
|
output = {}
|
||
|
|
res = self.infer(input)
|
||
|
|
output['text'] = res
|
||
|
|
return output
|
||
|
|
|
||
|
|
def quantize(self, bits: int):
|
||
|
|
self.model = self.model.quantize(bits)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def infer(self, input):
|
||
|
|
device = self.model.device
|
||
|
|
input_ids = self.tokenizer(input, return_tensors="pt").input_ids.to(device)
|
||
|
|
logits = self.model.generate(input_ids, max_new_tokens=1024, do_sample = True,
|
||
|
|
top_p = 0.85, temperature = 1.0, repetition_penalty=1.,
|
||
|
|
eos_token_id=2,bos_token_id=1,pad_token_id=0)
|
||
|
|
out = self.tokenizer.batch_decode(logits)[0]
|
||
|
|
return out
|