60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
import os
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from typing import Union, Dict, Any
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from modelscope.pipelines.builder import PIPELINES
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from modelscope.models.builder import MODELS
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from modelscope.utils.constant import Tasks
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from modelscope.pipelines.base import Pipeline
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from modelscope.models.base import Model, TorchModel
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from modelscope.utils.logger import get_logger
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from transformers import AutoModelForCausalLM, AutoTokenizer
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os.environ['CUDA_VISIBLE_DEVICES'] = "0"
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@PIPELINES.register_module(Tasks.text_generation, module_name='opt-1b3-text-generation-pipe')
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class Opt1b3TextGenerationPipeline(Pipeline):
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def __init__(
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self,
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model: Union[Model, str],
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*args,
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**kwargs):
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model = Opt1b3TextGeneration(model) if isinstance(model, str) else model
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super().__init__(model=model, **kwargs)
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def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
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return inputs
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# define the forward pass
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def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
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return self.model(inputs)
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# format the outputs from pipeline
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def postprocess(self, input, **kwargs) -> Dict[str, Any]:
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return input
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@MODELS.register_module(Tasks.text_generation, module_name='opt-1b3')
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class Opt1b3TextGeneration(TorchModel):
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def __init__(self, model_dir=None, *args, **kwargs):
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super().__init__(model_dir, *args, **kwargs)
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self.logger = get_logger()
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# loading tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
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self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto")
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self.model = self.model.eval()
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def forward(self,input: Dict, *args, **kwargs) -> Dict[str, Any]:
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output = {}
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res = self.infer(input)
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output['text'] = res
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return output
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def quantize(self, bits: int):
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self.model = self.model.quantize(bits)
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return self
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def infer(self, input):
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device = self.model.device
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input_ids = self.tokenizer(input, return_tensors="pt").input_ids.to(device)
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logits = self.model.generate(input_ids, num_beams=1, max_length=512)
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out = self.tokenizer.decode(logits[0].tolist())
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return out |