72 lines
3.1 KiB
Python
72 lines
3.1 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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def main():
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
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examples = [
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tokenizer(
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"auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
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)
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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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desc_act=False, # set to False can significantly speed up inference but the perplexity may slightly bad
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)
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# load un-quantized model, by default, the model will always be loaded into CPU memory
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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 can only be "input_ids" and "attention_mask"
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model.quantize(examples)
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# save quantized model
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model.save_quantized(quantized_model_dir)
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# push quantized model to Hugging Face Hub.
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# to use use_auth_token=True, Login first via huggingface-cli login.
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# or pass explcit token with: use_auth_token="hf_xxxxxxx"
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# (uncomment the following three lines to enable this feature)
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# repo_id = f"YourUserName/{quantized_model_dir}"
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# commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
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# model.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True)
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# alternatively you can save and push at the same time
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# (uncomment the following three lines to enable this feature)
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# repo_id = f"YourUserName/{quantized_model_dir}"
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# commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
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# model.push_to_hub(repo_id, save_dir=quantized_model_dir, use_safetensors=True, commit_message=commit_message, use_auth_token=True)
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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 to the first GPU
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model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0")
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# download quantized model from Hugging Face Hub and load to the first GPU
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# model = AutoGPTQForCausalLM.from_quantized(repo_id, device="cuda:0", use_safetensors=True, 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(model.device))[0]))
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# or you can also use pipeline
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pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer)
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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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