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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed |
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from transformers import pipeline |
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import torch |
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import json |
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@st.cache(allow_output_mutation=True) |
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def load_tokenizer(model_ckpt): |
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return AutoTokenizer.from_pretrained(model_ckpt) |
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@st.cache(allow_output_mutation=True) |
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def load_model(model_ckpt): |
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model = AutoModelForCausalLM.from_pretrained(model_ckpt, low_cpu_mem_usage=True) |
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return model |
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@st.cache() |
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def load_examples(): |
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with open("examples.json", "r") as f: |
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examples = json.load(f) |
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return examples |
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st.set_page_config(page_icon=':laptop:', layout="wide") |
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st.sidebar.header("Models") |
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models = ["CodeParrot", "OPT", "InCoder"] |
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selected_models = st.sidebar.multiselect('Select code generation models to compare:', |
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models, |
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default=["CodeParrot"]) |
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st.sidebar.header("Tasks") |
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tasks = [" ","Model architecture", "Model evaluation", "Pretraining dataset", "Code generation"] |
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selected_task = st.sidebar.selectbox("Select a task:", tasks) |
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tokenizer1 = load_tokenizer("lvwerra/codeparrot") |
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model1 = load_model("lvwerra/codeparrot") |
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tokenizer2 = load_tokenizer("facebook/incoder-1B") |
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model2 = load_model("facebook/incoder-1B") |
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tokenizer3 = load_tokenizer("facebook/opt-1.3b") |
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model3 = load_model("facebook/opt-1.3b") |
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pipelines = {} |
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for model in models: |
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if model == "CodeParrot": |
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pipe = pipeline("text-generation", model=model1, tokenizer=tokenizer1) |
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pipelines[model] = pipe |
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elif model == "InCoder": |
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tokenizer = load_tokenizer("facebook/incoder-1B") |
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model = load_model("facebook/incoder-1B") |
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pipe = pipeline("text-generation", model=model2, tokenizer=tokenizer2) |
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pipelines[model] = pipe |
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else: |
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tokenizer = load_tokenizer("facebook/opt-1.3b") |
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model = load_model("facebook/opt-1.3b") |
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pipe = pipeline("text-generation", model=model3, tokenizer=tokenizer3) |
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pipelines[model] = pipe |
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example_names = [example["name"] for example in examples] |
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name2id = dict([(name, i) for i, name in enumerate(example_names)]) |
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set_seed(42) |
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gen_kwargs = {} |
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if selected_task == " ": |
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st.title("Code Generation Models comparison 💻") |
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with open("intro.txt", "r") as f: |
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intro = f.read() |
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st.markdown(intro) |
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elif selected_task == "Pretraining dataset": |
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st.title("Pretraining datasets 📚") |
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for model in selected_models: |
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with open(f"datasets/{model.lower()}.txt", "r") as f: |
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text = f.read() |
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st.markdown(f"## {model}:") |
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st.markdown(text) |
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elif selected_task == "Model architecture": |
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st.title("Model architecture 🔨") |
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for model in selected_models: |
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with open(f"architectures/{model.lower()}.txt", "r") as f: |
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text = f.read() |
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st.markdown(f"## {model}:") |
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st.markdown(text) |
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elif selected_task == "Code generation": |
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st.title("Code generation 💻") |
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st.sidebar.header("Examples") |
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selected_example = st.sidebar.selectbox("Select one of the following examples:", example_names) |
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example_text = examples[name2id[selected_example]]["value"] |
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default_length = examples[name2id[selected_example]]["length"] |
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st.sidebar.header("Generation settings") |
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gen_kwargs["do_sample"] = st.sidebar.radio("Decoding strategy:", ["Greedy", "Sample"]) == "Sample" |
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gen_kwargs["max_new_tokens"] = st.sidebar.slider("Number of tokens to generate:", value=default_length, min_value=8, step=8, max_value=256) |
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if gen_kwargs["do_sample"]: |
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gen_kwargs["temperature"] = 0.2 |
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gen_kwargs["top_k"] = 0 |
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gen_kwargs["top_p"] = 0.95 |
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gen_prompt = st.text_area("Generate code with prompt:", value=example_text, height=220,).strip() |
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if st.button("Generate code!"): |
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with st.spinner("Generating code..."): |
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for model in selected_models: |
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pipe = pipelines[model] |
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generated_text = pipe(gen_prompt, **gen_kwargs)[0]['generated_text'] |
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st.markdown(f"### {model}:") |
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st.code(generated_text) |
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