56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import os
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from transformers import AutoTokenizer, TextGenerationPipeline
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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pretrained_model_dir = "facebook/opt-125m"
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quantized_model_dir = "opt-125m_4bit_128g"
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# os.makedirs(quantized_model_dir, exist_ok=True)
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def main():
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
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example = tokenizer(
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"auto_gptq is a useful tool that can automatically compress model into 4-bit or even higher rate by using GPTQ algorithm.",
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return_tensors="pt"
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)
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quantize_config = BaseQuantizeConfig(
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bits=4, # quantize model to 4-bit
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group_size=128, # it is recommended to set the value to 128
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)
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# load un-quantized model, the model will always be force loaded into cpu
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model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
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# quantize model, the examples should be list of dict whose keys contains "input_ids" and "attention_mask"
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# with value under torch.LongTensor type.
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model.quantize([example], use_triton=False)
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# save quantized model
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model.save_quantized(quantized_model_dir)
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# save quantized model using safetensors
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model.save_quantized(quantized_model_dir, use_safetensors=True)
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# load quantized model, currently only support cpu or single gpu
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model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0", use_triton=False)
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# inference with model.generate
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print(tokenizer.decode(model.generate(**tokenizer("auto_gptq is", return_tensors="pt").to("cuda:0"))[0]))
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# or you can also use pipeline
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pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer, device="cuda:0")
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print(pipeline("auto_gptq is")[0]["generated_text"])
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if __name__ == "__main__":
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import logging
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logging.basicConfig(
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format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
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)
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main()
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