import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed from transformers import pipeline import os import torch description = """#

🎅 SantaCoder-Ruby: Code Generation

This is a demo to generate code with SantaCoder-Ruby which is a fine-tuned version of SantaCoder, a 1.1B parameter model for code generation in Python, Java & JavaScript. The model can also do infilling, just specify where you would like the model to complete code with the <FILL-HERE> token.""" token = os.environ["HUB_TOKEN"] device="cpu" FIM_PREFIX = "" FIM_MIDDLE = "" FIM_SUFFIX = "" FIM_PAD = "" EOD = "<|endoftext|>" GENERATION_TITLE= "

Generated code:

" tokenizer_fim = AutoTokenizer.from_pretrained("bigcode/santacoder", use_auth_token=token, padding_side="left") tokenizer_fim.add_special_tokens({ "additional_special_tokens": [EOD, FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD], "pad_token": EOD, }) tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token) model = AutoModelForCausalLM.from_pretrained("stillerman/santacoder-ruby", trust_remote_code=True, use_auth_token=token).to(device) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device) def post_processing(prompt, completion): completion = "" + completion + "" prompt = "" + prompt + "" code_html = f"


{prompt}{completion}


" return GENERATION_TITLE + code_html def post_processing_fim(prefix, middle, suffix): prefix = "" + prefix + "" middle = "" + middle + "" suffix = "" + suffix + "" code_html = f"


{prefix}{middle}{suffix}


" return GENERATION_TITLE + code_html def fim_generation(prompt, max_new_tokens, temperature): prefix = prompt.split("")[0] suffix = prompt.split("")[1] [middle] = infill((prefix, suffix), max_new_tokens, temperature) return post_processing_fim(prefix, middle, suffix) def extract_fim_part(s: str): # Find the index of start = s.find(FIM_MIDDLE) + len(FIM_MIDDLE) stop = s.find(EOD, start) or len(s) return s[start:stop] def infill(prefix_suffix_tuples, max_new_tokens, temperature): if type(prefix_suffix_tuples) == tuple: prefix_suffix_tuples = [prefix_suffix_tuples] prompts = [f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}" for prefix, suffix in prefix_suffix_tuples] # `return_token_type_ids=False` is essential, or we get nonsense output. inputs = tokenizer_fim(prompts, return_tensors="pt", padding=True, return_token_type_ids=False).to(device) with torch.no_grad(): outputs = model.generate( **inputs, do_sample=True, temperature=temperature, max_new_tokens=max_new_tokens, pad_token_id=tokenizer.pad_token_id ) # WARNING: cannot use skip_special_tokens, because it blows away the FIM special tokens. return [ extract_fim_part(tokenizer_fim.decode(tensor, skip_special_tokens=False)) for tensor in outputs ] def code_generation(prompt, max_new_tokens, temperature=0.2, seed=42): #set_seed(seed) if "" in prompt: return fim_generation(prompt, max_new_tokens, temperature=0.2) else: completion = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_new_tokens)[0]['generated_text'] completion = completion[len(prompt):] return post_processing(prompt, completion) demo = gr.Blocks( css=".gradio-container {background-color: #20233fff; color:white}" ) with demo: with gr.Row(): _, colum_2, _ = gr.Column(scale=1), gr.Column(scale=6), gr.Column(scale=1) with colum_2: gr.Markdown(value=description) code = gr.Textbox(lines=5, label="Input code", value='''def fib(n) if n <= 1 n else ''') with gr.Accordion("Advanced settings", open=False): max_new_tokens= gr.Slider( minimum=8, maximum=1024, step=1, value=80, label="Number of tokens to generate", ) temperature = gr.Slider( minimum=0.1, maximum=2.5, step=0.1, value=0.2, label="Temperature", ) seed = gr.Slider( minimum=0, maximum=1000, step=1, label="Random seed to use for the generation" ) run = gr.Button() output = gr.HTML(label="Generated code") event = run.click(code_generation, [code, max_new_tokens, temperature, seed], output, api_name="predict") gr.HTML(label="Contact", value="contact") #demo.launch(share=True) demo.launch()