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" : "", "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" : "", "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" : "", "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( """

""") gr.Markdown( """

🏠 Homepage｜💻 GitHub｜🛠 Tools VS Code, Jetbrains｜🤗 HF Repo｜📄 Paper

""") 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=True) if __name__ == '__main__': with torch.no_grad(): main()