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--- |
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license: gpl-3.0 |
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base_model: deepseek-ai/deepseek-coder-7b-instruct-v1.5 |
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library_name: peft |
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--- |
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# OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection |
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### Introduction |
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OriGen is a fine-tuned lora model designed for Verilog code generation. It is trained on top of DeepSeek Coder 7B using datasets generated from code-to-code augmentation and self-reflection. |
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The model has been uploaded to Hugging Face, and the repository contains the inference scripts. The dataset and data generation flow will be released soon. |
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- **Huggingface**: https://huggingface.co/henryen/OriGen |
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- **Repository**: https://github.com/pku-liang/OriGen |
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### Evaluation Results |
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<img src="figures/evaluation.png" alt="evaluation" width="1000"/> |
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### Quick Start |
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Before running the following code, please install the required packages: |
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```bash |
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conda create -n origen python=3.11 |
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conda activate origen |
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pip install -r requirements.txt |
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``` |
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Here is an example of how to use the model. Please note that the base model, DeepSeek Coder 7B, is loaded in float16 precision, even though its default precision is bfloat16. This choice was made because our experiments showed that Lora trained in float16 outperforms those trained in bfloat16. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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import torch |
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from peft import PeftModel |
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model_name = "deepseek-ai/deepseek-coder-7b-instruct-v1.5" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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low_cpu_mem_usage=True, |
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torch_dtype=torch.float16, |
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attn_implementation="flash_attention_2", |
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device_map="auto", |
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).to("cuda") |
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model = PeftModel.from_pretrained(model, model_id="henryen/OriGen") |
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model.eval() |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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prompt = "### Instruction: Please act as a professional Verilog designer and provide Verilog code based on the given description. Create a 8 bit full adder with only one assign statement.\n### Response: " |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=1000, |
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do_sample=False, |
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temperature=0, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.pad_token_id, |
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streamer=streamer |
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) |
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``` |
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The output will be: |
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```verilog |
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module full_adder( |
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input [7:0] a, |
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input [7:0] b, |
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input cin, |
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output [7:0] sum, |
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output cout |
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); |
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assign {cout, sum} = a + b + cin; |
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endmodule |
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``` |
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### Verilog-Eval Benchmark |
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We have released the scripts for the Verilog-Eval benchmark. Please refer to the [README](https://github.com/pku-liang/OriGen/blob/main/evaluation/README.md) for details. |
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### Paper |
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**Arxiv:** https://arxiv.org/abs/2407.16237 |
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Please cite our paper if you use this model. |
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``` |
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@article{2024origen, |
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title={OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection}, |
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author={Cui, Fan and Yin, Chenyang and Zhou, Kexing and Xiao, Youwei and Sun, Guangyu and Xu, Qiang and Guo, Qipeng and Song, Demin and Lin, Dahua and Zhang, Xingcheng and others}, |
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journal={arXiv preprint arXiv:2407.16237}, |
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year={2024} |
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} |
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``` |
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