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| import os | |
| import json | |
| import numpy | |
| import torch | |
| import random | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModel | |
| def get_model(): | |
| tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True) | |
| model = AutoModel.from_pretrained("THUDM/codegeex2-6b", trust_remote_code=True).to('cpu') | |
| # 如需实现多显卡模型加载,请将上面一行注释并启用一下两行,"num_gpus"调整为自己需求的显卡数量 / To enable Multiple GPUs model loading, please uncomment the line above and enable the following two lines. Adjust "num_gpus" to the desired number of graphics cards. | |
| # from gpus import load_model_on_gpus | |
| # model = load_model_on_gpus("THUDM/codegeex2-6b", num_gpus=2) | |
| model = model.eval() | |
| return tokenizer, model | |
| tokenizer, model = get_model() | |
| examples = [] | |
| with open(os.path.join(os.path.split(os.path.realpath(__file__))[0], "example_inputs.jsonl"), "r", encoding="utf-8") as f: | |
| for line in f: | |
| examples.append(list(json.loads(line).values())) | |
| LANGUAGE_TAG = { | |
| "Abap" : "* language: Abap", | |
| "ActionScript" : "// language: ActionScript", | |
| "Ada" : "-- language: Ada", | |
| "Agda" : "-- language: Agda", | |
| "ANTLR" : "// language: ANTLR", | |
| "AppleScript" : "-- language: AppleScript", | |
| "Assembly" : "; language: Assembly", | |
| "Augeas" : "// language: Augeas", | |
| "AWK" : "// language: AWK", | |
| "Basic" : "' language: Basic", | |
| "C" : "// language: C", | |
| "C#" : "// language: C#", | |
| "C++" : "// language: C++", | |
| "CMake" : "# language: CMake", | |
| "Cobol" : "// language: Cobol", | |
| "CSS" : "/* language: CSS */", | |
| "CUDA" : "// language: Cuda", | |
| "Dart" : "// language: Dart", | |
| "Delphi" : "{language: Delphi}", | |
| "Dockerfile" : "# language: Dockerfile", | |
| "Elixir" : "# language: Elixir", | |
| "Erlang" : f"% language: Erlang", | |
| "Excel" : "' language: Excel", | |
| "F#" : "// language: F#", | |
| "Fortran" : "!language: Fortran", | |
| "GDScript" : "# language: GDScript", | |
| "GLSL" : "// language: GLSL", | |
| "Go" : "// language: Go", | |
| "Groovy" : "// language: Groovy", | |
| "Haskell" : "-- language: Haskell", | |
| "HTML" : "<!--language: HTML-->", | |
| "Isabelle" : "(*language: Isabelle*)", | |
| "Java" : "// language: Java", | |
| "JavaScript" : "// language: JavaScript", | |
| "Julia" : "# language: Julia", | |
| "Kotlin" : "// language: Kotlin", | |
| "Lean" : "-- language: Lean", | |
| "Lisp" : "; language: Lisp", | |
| "Lua" : "// language: Lua", | |
| "Markdown" : "<!--language: Markdown-->", | |
| "Matlab" : f"% language: Matlab", | |
| "Objective-C" : "// language: Objective-C", | |
| "Objective-C++": "// language: Objective-C++", | |
| "Pascal" : "// language: Pascal", | |
| "Perl" : "# language: Perl", | |
| "PHP" : "// language: PHP", | |
| "PowerShell" : "# language: PowerShell", | |
| "Prolog" : f"% language: Prolog", | |
| "Python" : "# language: Python", | |
| "R" : "# language: R", | |
| "Racket" : "; language: Racket", | |
| "RMarkdown" : "# language: RMarkdown", | |
| "Ruby" : "# language: Ruby", | |
| "Rust" : "// language: Rust", | |
| "Scala" : "// language: Scala", | |
| "Scheme" : "; language: Scheme", | |
| "Shell" : "# language: Shell", | |
| "Solidity" : "// language: Solidity", | |
| "SPARQL" : "# language: SPARQL", | |
| "SQL" : "-- language: SQL", | |
| "Swift" : "// language: swift", | |
| "TeX" : f"% language: TeX", | |
| "Thrift" : "/* language: Thrift */", | |
| "TypeScript" : "// language: TypeScript", | |
| "Vue" : "<!--language: Vue-->", | |
| "Verilog" : "// language: Verilog", | |
| "Visual Basic" : "' language: Visual Basic", | |
| } | |
| def set_random_seed(seed): | |
| """Set random seed for reproducability.""" | |
| random.seed(seed) | |
| numpy.random.seed(seed) | |
| torch.manual_seed(seed) | |
| def main(): | |
| def predict( | |
| prompt, | |
| lang, | |
| seed, | |
| out_seq_length, | |
| temperature, | |
| top_k, | |
| top_p, | |
| ): | |
| set_random_seed(seed) | |
| if lang != "None": | |
| prompt = LANGUAGE_TAG[lang] + "\n" + prompt | |
| inputs = tokenizer([prompt], return_tensors="pt") | |
| inputs = inputs.to(model.device) | |
| outputs = model.generate(**inputs, | |
| max_length=inputs['input_ids'].shape[-1] + out_seq_length, | |
| do_sample=True, | |
| top_p=top_p, | |
| top_k=top_k, | |
| temperature=temperature, | |
| pad_token_id=2, | |
| eos_token_id=2) | |
| response = tokenizer.decode(outputs[0]) | |
| return response | |
| with gr.Blocks(title="CodeGeeX2 DEMO") as demo: | |
| gr.Markdown( | |
| """ | |
| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/THUDM/CodeGeeX2/main/resources/codegeex_logo.png"> | |
| </p> | |
| """) | |
| gr.Markdown( | |
| """ | |
| <p align="center"> | |
| 🏠 <a href="https://codegeex.cn" target="_blank">Homepage</a>｜💻 <a href="https://github.com/THUDM/CodeGeeX2" target="_blank">GitHub</a>｜🛠 Tools <a href="https://marketplace.visualstudio.com/items?itemName=aminer.codegeex" target="_blank">VS Code</a>, <a href="https://plugins.jetbrains.com/plugin/20587-codegeex" target="_blank">Jetbrains</a>｜🤗 <a href="https://huggingface.co./THUDM/codegeex2-6b" target="_blank">HF Repo</a>｜📄 <a href="https://arxiv.org/abs/2303.17568" target="_blank">Paper</a> | |
| </p> | |
| """) | |
| gr.Markdown( | |
| """ | |
| This is the DEMO for CodeGeeX2. Please note that: | |
| * CodeGeeX2 is a base model, which is not instruction-tuned for chatting. It can do tasks like code completion/translation/explaination. To try the instruction-tuned version in CodeGeeX plugins ([VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)). | |
| * Programming languages can be controled by adding `language tag`, e.g., `# language: Python`. The format should be respected to ensure performance, full list can be found [here](https://github.com/THUDM/CodeGeeX2/blob/main/evaluation/utils.py#L14). | |
| * Write comments under the format of the selected programming language to achieve better results, see examples below. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(lines=13, placeholder='Please enter the description or select an example input below.',label='Input') | |
| with gr.Row(): | |
| gen = gr.Button("Generate") | |
| clr = gr.Button("Clear") | |
| outputs = gr.Textbox(lines=15, label='Output') | |
| gr.Markdown( | |
| """ | |
| Generation Parameter | |
| """) | |
| with gr.Row(): | |
| with gr.Row(): | |
| seed = gr.Slider(maximum=10000, value=8888, step=1, label='Seed') | |
| with gr.Row(): | |
| out_seq_length = gr.Slider(maximum=8192, value=128, minimum=1, step=1, label='Output Sequence Length') | |
| temperature = gr.Slider(maximum=1, value=0.2, minimum=0, label='Temperature') | |
| with gr.Row(): | |
| top_k = gr.Slider(maximum=100, value=0, minimum=0, step=1, label='Top K') | |
| top_p = gr.Slider(maximum=1, value=0.95, minimum=0, label='Top P') | |
| with gr.Row(): | |
| lang = gr.Radio( | |
| choices=["None"] + list(LANGUAGE_TAG.keys()), value='None', label='Programming Language') | |
| inputs = [prompt, lang, seed, out_seq_length, temperature, top_k, top_p] | |
| gen.click(fn=predict, inputs=inputs, outputs=outputs) | |
| clr.click(fn=lambda value: gr.update(value=""), inputs=clr, outputs=prompt) | |
| gr_examples = gr.Examples(examples=examples, inputs=[prompt, lang], | |
| label="Example Inputs (Click to insert an examplet it into the input box)", | |
| examples_per_page=20) | |
| demo.launch(share=False) | |
| if __name__ == '__main__': | |
| with torch.no_grad(): | |
| main() | |