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