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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from transformers import pipeline |
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import numpy as np |
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import pandas as pd |
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import matplotlib.cm as cm |
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import html |
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from torch.nn.functional import softmax |
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import torch |
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from matplotlib.colors import LinearSegmentedColormap |
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cdict = {'red': [[0.0, 0.8, 0.8], |
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[1.0, 1.0, 1.0]], |
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'green': [[0.0, 0.0, 0.0], |
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[1.0, 1.0, 1.0]], |
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'blue': [[0.0, 0.0, 0.0], |
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[1.0, 1.0, 1.0]], |
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'alpha':[[0.0, 1.0, 1.0], |
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[1.0, 0.0, 0.0]]} |
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cmap = LinearSegmentedColormap('codemap', segmentdata=cdict, N=256) |
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def value2rgba(x, cmap=cmap, alpha_mult=1.0): |
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c = cmap(x) |
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rgb = (np.array(c[:-1]) * 255).astype(int) |
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a = c[-1] * alpha_mult |
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return tuple(rgb.tolist() + [a]) |
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def highlight_token_scores(tokens, scores, sep=' ', **kwargs): |
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html_code,spans = [''], [] |
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for t, s in zip(tokens, scores): |
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t = html.escape(t) |
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t = t.replace("\n", " \n") |
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c = str(value2rgba(s, alpha_mult=0.8, **kwargs)) |
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spans.append(f'<span title="{s:.3f}" style="background-color: rgba{c};">{t}</span>') |
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html_code.append(sep.join(spans)) |
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return '<p><code><FONT COLOR=black>' + ''.join(html_code) + '</pre></p>' |
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def color_dataframe(row): |
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styles = [] |
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c = str(value2rgba(row["scores"], alpha_mult=0.8)) |
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for key in row.index: |
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if key in {"tokens", "scores"}: |
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styles.append(f"background-color: rgba{c}") |
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else: |
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styles.append(f"background-color: None") |
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return styles |
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@st.cache_resource |
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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_resource |
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def load_model(model_ckpt): |
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model = AutoModelForCausalLM.from_pretrained(model_ckpt) |
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return model |
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def calculate_scores(probs, token_ids): |
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probs = probs[:-1] |
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token_ids = token_ids[1:] |
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sorted_ids = np.argsort(probs, axis=-1)[:, ::-1] |
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sorted_probs = np.sort(probs, axis=-1)[:, ::-1] |
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selected_token_mask = sorted_ids == token_ids[:, None] |
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masked_probs = np.ma.array(sorted_probs, mask=~selected_token_mask) |
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token_probs = masked_probs.sum(axis=1).data |
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masked_indices = np.cumsum(selected_token_mask[:, ::-1], axis=-1)[:, ::-1].astype(bool) |
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masked_probs = np.ma.array(sorted_probs, mask=~masked_indices) |
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token_rank = masked_indices.sum(axis=-1) |
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cumulative_probs = masked_probs.sum(axis=1).data/token_rank |
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scores = token_probs/cumulative_probs |
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return [1.] + list(scores), sorted_ids |
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def calculate_loss(logits, labels): |
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shift_logits = logits[..., :-1, :].contiguous() |
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shift_labels = labels[..., 1:].contiguous() |
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loss_fct = torch.nn.CrossEntropyLoss(reduction="none") |
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) |
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norm_loss = 1 - (loss/torch.max(loss)) |
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return [1.] + list(norm_loss.numpy()) |
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default_code = """\ |
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from torch import nn |
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from transformers import Model |
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class Transformer: |
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def __init__(config): |
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self.model = Model(config) |
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def forward(inputs): |
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return self.model(inputs)""" |
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solution_code = """\ |
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from torch import nn |
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from transformers import Model |
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class Transformer(nn.Module): |
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def __init__(self, config): |
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super(Transformer, self).__init__() |
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self.config = config |
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self.model = Model(config) |
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def forward(self, inputs): |
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return self.model(inputs) |
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""" |
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st.set_page_config(page_icon=':parrot:', layout="wide") |
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np.random.seed(42) |
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model_ckpt = "codeparrot/codeparrot-small" |
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tokenizer = load_tokenizer(model_ckpt) |
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model = load_model(model_ckpt) |
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st.markdown("<h1 style='text-align: center;'>CodeParrot 🦜</h1>", unsafe_allow_html=True) |
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st.markdown('##') |
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col1, col2 = st.columns(2) |
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col1.subheader("Edit code") |
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code = col1.text_area(label="", value=default_code, height=220,).strip() |
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inputs = tokenizer(code, return_tensors='pt') |
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token_list = [tokenizer.decode(t) for t in inputs["input_ids"][0]] |
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with torch.no_grad(): |
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logits = model(input_ids=inputs["input_ids"]).logits[0] |
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probs = softmax(logits, dim=-1) |
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loss = calculate_loss(logits, inputs["input_ids"][0]) |
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norm_probs, sorted_token_ids = calculate_scores(probs.numpy(), inputs["input_ids"][0].numpy()) |
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if len(inputs['input_ids'])>1024: |
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st.warning("Your input is longer than the maximum 1024 tokens and will be truncated.") |
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st.sidebar.title("Info:") |
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st.sidebar.markdown("This demo uses CodeParrot to highlight the parts of code with low probability. Since CodeParrot is an autoregressive model the tokens at the beginning tend to have a lower probability. E.g. the model can't know what you want to import because it has no access to information later in the code. However, as you can see in the example on the right it still can highlight bugs or unconventional naming.\n\nAt the bottom of the page is an example of how a better solution might look like. Try to copy paste it and press **CMD + Enter** to update the highlighting.") |
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st.sidebar.title("Settings:") |
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if st.sidebar.radio("Highlight mode:", ["Probability heuristics", "Scaled loss per token"]) == "Probability heuristics": |
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scores = norm_probs |
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else: |
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scores = loss |
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suggestion_threshold = st.sidebar.slider("Suggestion threshold", 0.0, 1.0, 0.2) |
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col2.subheader("Highlighted code") |
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col2.markdown('##') |
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html_string = highlight_token_scores(token_list, scores, sep="") |
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col2.markdown(html_string, unsafe_allow_html=True) |
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col2.markdown('##') |
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st.subheader("Model suggestions") |
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top_k = {} |
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for i in range(5): |
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top_k[f"top-{i+1}"] = ["No prediction for first token"] + [repr(tokenizer.decode(idx)) for idx in sorted_token_ids[:, i]] |
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df = pd.DataFrame({"tokens": [repr(t) for t in token_list], "scores": scores, **top_k}) |
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df.index.name = "position" |
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df_filter = df.loc[df["scores"]<=suggestion_threshold] |
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df_filter.reset_index(inplace=True) |
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df_filter = df_filter[["tokens", "scores", "position", "top-1", "top-2", "top-3", "top-4", "top-5",]] |
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df_filter = df_filter.style.apply(color_dataframe, axis=1) |
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st.dataframe(df_filter) |
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st.markdown('##') |
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st.subheader("Possible solution") |
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st.code(solution_code) |
