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70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import torch
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from pilot.singleton import Singleton
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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AutoModel
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)
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from pilot.model.compression import compress_module
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class ModelLoader(metaclass=Singleton):
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"""Model loader is a class for model load
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Args: model_path
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"""
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kwargs = {}
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def __init__(self,
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model_path) -> None:
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model_path = model_path
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self.kwargs = {
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"torch_dtype": torch.float16,
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"device_map": "auto",
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}
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def loader(self, num_gpus, load_8bit=False, debug=False):
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if self.device == "cpu":
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kwargs = {}
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elif self.device == "cuda":
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kwargs = {"torch_dtype": torch.float16}
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if num_gpus == "auto":
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kwargs["device_map"] = "auto"
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else:
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num_gpus = int(num_gpus)
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if num_gpus != 1:
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kwargs.update({
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"device_map": "auto",
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"max_memory": {i: "13GiB" for i in range(num_gpus)},
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})
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else:
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raise ValueError(f"Invalid device: {self.device}")
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if "chatglm" in self.model_path:
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tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
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model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).half().cuda()
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else:
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tokenizer = AutoTokenizer.from_pretrained(self.model_path, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(self.model_path,
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low_cpu_mem_usage=True, **kwargs)
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if load_8bit:
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compress_module(model, self.device)
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if (self.device == "cuda" and num_gpus == 1):
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model.to(self.device)
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if debug:
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print(model)
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return model, tokenizer
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