Duplicate from bigcode/santacoder-demo

Co-authored-by: Leandro von Werra <[email protected]>

.gitattributes

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README.md

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+---
+title: SantaCoder Demo
+emoji: 🎅
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.13.0
+app_file: app.py
+pinned: false
+duplicated_from: bigcode/santacoder-demo
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+from transformers import pipeline
+import os
+import torch
+
+description = """# <p style="text-align: center; color: white;"> 🎅 <span style='color: #ff75b3;'>SantaCoder:</span> Code Generation </p>
+<span style='color: white;'>This is a demo to generate code with <a href="https://huggingface.co/bigcode/santacoder" style="color: #ff75b3;">SantaCoder</a>,
+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 <span style='color: #ff75b3;'>&lt;FILL-HERE&gt;</span> token.</span>"""
+
+token = os.environ["HUB_TOKEN"]
+device="cuda:0"
+
+
+FIM_PREFIX = "<fim-prefix>"
+FIM_MIDDLE = "<fim-middle>"
+FIM_SUFFIX = "<fim-suffix>"
+FIM_PAD = "<fim-pad>"
+EOD = "<|endoftext|>"
+
+GENERATION_TITLE= "<p style='font-size: 16px; color: white;'>Generated code:</p>"
+
+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("bigcode/christmas-models", 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 = "<span style='color: #ff75b3;'>" + completion + "</span>"
+    prompt = "<span style='color: #727cd6;'>" + prompt + "</span>"
+    code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prompt}{completion}</code></pre><br><hr>"
+    return GENERATION_TITLE + code_html
+
+def post_processing_fim(prefix, middle, suffix):
+    prefix = "<span style='color: #727cd6;'>" + prefix + "</span>"
+    middle = "<span style='color: #ff75b3;'>" + middle + "</span>"
+    suffix = "<span style='color: #727cd6;'>" + suffix + "</span>"
+    code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prefix}{middle}{suffix}</code></pre><br><hr>"
+    return GENERATION_TITLE + code_html
+
+def fim_generation(prompt, max_new_tokens, temperature):
+    prefix = prompt.split("<FILL-HERE>")[0]
+    suffix = prompt.split("<FILL-HERE>")[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 "<FILL-HERE>" 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 all_odd_elements(sequence):\n    \"\"\"Returns every odd element of the sequence.\"\"\"")
+            
+            with gr.Accordion("Advanced settings", open=False):
+                max_new_tokens= gr.Slider(
+                    minimum=8,
+                    maximum=1024,
+                    step=1,
+                    value=48,
+                    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="<img src='https://huggingface.co/datasets/bigcode/admin/resolve/main/bigcode_contact.png' alt='contact' style='display: block; margin: auto; max-width: 800px;'>")
+
+demo.launch()

requirements.txt

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+torch
+transformers