app.py

import gradio as gr
import torch
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
import matplotlib
from IPython.display import display, HTML
from transformers import AutoTokenizer
from DecompX.src.globenc_utils import GlobencConfig
from DecompX.src.modeling_bert import BertForSequenceClassification
from DecompX.src.modeling_roberta import RobertaForSequenceClassification

plt.style.use("ggplot")
MODELS = ["WillHeld/roberta-base-sst2"]

def plot_clf(tokens, logits, label_names, title="", file_name=None):
    print(tokens)
    plt.figure(figsize=(4.5, 5))
    colors = ["#019875" if l else "#B8293D" for l in (logits >= 0)]
    plt.barh(range(len(tokens)), logits, color=colors)
    plt.axvline(0, color='black', ls='-', lw=2, alpha=0.2)
    plt.gca().invert_yaxis()
    
    max_limit = np.max(np.abs(logits)) + 0.2
    min_limit = -0.01 if np.min(logits) > 0 else -max_limit
    plt.xlim(min_limit, max_limit)
    plt.gca().set_xticks([min_limit, max_limit])
    plt.gca().set_xticklabels(label_names, fontsize=14, fontweight="bold")
    plt.gca().set_yticks(range(len(tokens)))
    plt.gca().set_yticklabels(tokens)

    plt.gca().yaxis.tick_right()
    for xtick, color in zip(plt.gca().get_yticklabels(), colors):
        xtick.set_color(color)
        xtick.set_fontweight("bold")
        xtick.set_verticalalignment("center")

    for xtick, color in zip(plt.gca().get_xticklabels(), ["#B8293D", "#019875"]):
        xtick.set_color(color)
    # plt.title(title, fontsize=14, fontweight="bold")
    plt.title(title)
    plt.tight_layout()

def print_importance(importance, tokenized_text, discrete=False, prefix="", no_cls_sep=False):
    """
    importance: (sent_len)
    """
    if no_cls_sep:
        importance = importance[1:-1]
        tokenized_text = tokenized_text[1:-1]
    importance = importance / np.abs(importance).max() / 1.5  # Normalize
    if discrete:
        importance = np.argsort(np.argsort(importance)) / len(importance) / 1.6
    
    html = "<pre style='color:black; padding: 3px;'>"+prefix
    for i in range(len(tokenized_text)):
        if importance[i] >= 0:
            rgba = matplotlib.colormaps.get_cmap('Greens')(importance[i])   # Wistia
        else:
            rgba = matplotlib.colormaps.get_cmap('Reds')(np.abs(importance[i]))   # Wistia
        text_color = "color: rgba(255, 255, 255, 1.0); " if np.abs(importance[i]) > 0.9 else ""
        color = f"background-color: rgba({rgba[0]*255}, {rgba[1]*255}, {rgba[2]*255}, {rgba[3]}); " + text_color
        html += (f"<span style='"
                 f"{color}"
                 f"color:black; border-radius: 5px; padding: 3px;"
                 f"font-weight: {int(800)};"
                 "'>")
        html += tokenized_text[i].replace('<', "[").replace(">", "]")
        html += "</span> "
    html += "</pre>"
    # display(HTML(html))
    return html

def print_preview(decompx_outputs_df, idx=0, discrete=False):
    html = ""
    NO_CLS_SEP = False
    df = decompx_outputs_df
    for col in ["importance_last_layer_aggregated", "importance_last_layer_classifier"]:
        if col in df and df[col][idx] is not None:
            if "aggregated" in col:
                sentence_importance = df[col].iloc[idx][0, :]
            if "classifier" in col:
                for label in range(df[col].iloc[idx].shape[-1]):
                    sentence_importance = df[col].iloc[idx][:, label]
                    html += print_importance(
                        sentence_importance,
                        df["tokens"].iloc[idx], 
                        prefix=f"{col.split('_')[-1]} Label{label}:".ljust(20),
                        no_cls_sep=NO_CLS_SEP,
                        discrete=False
                    )
                break
                sentence_importance = df[col].iloc[idx][:, df["label"].iloc[idx]]
            html += print_importance(
                sentence_importance,
                df["tokens"].iloc[idx], 
                prefix=f"{col.split('_')[-1]}:".ljust(20),
                no_cls_sep=NO_CLS_SEP,
                discrete=discrete
            )
    return "<div style='overflow:auto; background-color:white; padding: 10px;'>" + html

def run_decompx(text, model):
    """
    Provide DecompX Token Explanation of Model on Text
    """
    SENTENCES = [text, "nothing"]
    CONFIGS = {
        "DecompX":
            GlobencConfig(
                include_biases=True,
                bias_decomp_type="absdot",
                include_LN1=True,
                include_FFN=True,
                FFN_approx_type="GeLU_ZO",
                include_LN2=True,
                aggregation="vector",
                include_classifier_w_pooler=True,
                tanh_approx_type="ZO",
                output_all_layers=True,
                output_attention=None,
                output_res1=None,
                output_LN1=None,
                output_FFN=None,
                output_res2=None,
                output_encoder=None,
                output_aggregated="norm",
                output_pooler="norm",
                output_classifier=True,
            ),
    }
    MODEL = model
    # LOAD MODEL AND TOKENIZER
    tokenizer = AutoTokenizer.from_pretrained(MODEL)
    tokenized_sentence = tokenizer(SENTENCES, return_tensors="pt", padding=True)
    batch_lengths = tokenized_sentence['attention_mask'].sum(dim=-1)
    if "roberta" in MODEL:
        model = RobertaForSequenceClassification.from_pretrained(MODEL)
    elif "bert" in MODEL:
        model = BertForSequenceClassification.from_pretrained(MODEL)
    else:
        raise Exception(f"Not implemented model: {MODEL}")
    # RUN DECOMPX
    with torch.no_grad():
        model.eval()
        logits, hidden_states, globenc_last_layer_outputs, globenc_all_layers_outputs = model(
            **tokenized_sentence, 
            output_attentions=False, 
            return_dict=False, 
            output_hidden_states=True, 
            globenc_config=CONFIGS["DecompX"]
        )
    decompx_outputs = {
        "tokens": [tokenizer.convert_ids_to_tokens(tokenized_sentence["input_ids"][i][:batch_lengths[i]]) for i in range(len(SENTENCES))],
        "logits": logits.cpu().detach().numpy().tolist(),  # (batch, classes)
        "cls": hidden_states[-1][:, 0, :].cpu().detach().numpy().tolist()# Last layer & only CLS -> (batch, emb_dim)
    }

    ### globenc_last_layer_outputs.classifier ~ (8, 55, 2) ###
    importance = np.array([g.squeeze().cpu().detach().numpy() for g in globenc_last_layer_outputs.classifier]).squeeze()  # (batch, seq_len, classes)
    importance = [importance[j][:batch_lengths[j], :] for j in range(len(importance))]
    decompx_outputs["importance_last_layer_classifier"] = importance

    ### globenc_all_layers_outputs.aggregated ~ (12, 8, 55, 55) ###
    importance = np.array([g.squeeze().cpu().detach().numpy() for g in globenc_all_layers_outputs.aggregated])  # (layers, batch, seq_len, seq_len)
    importance = np.einsum('lbij->blij', importance)  # (batch, layers, seq_len, seq_len)
    importance = [importance[j][:, :batch_lengths[j], :batch_lengths[j]] for j in range(len(importance))]
    decompx_outputs["importance_all_layers_aggregated"] = importance

    decompx_outputs_df = pd.DataFrame(decompx_outputs)
    idx = 0
    pred_label = np.argmax(decompx_outputs_df.iloc[idx]["logits"], axis=-1)
    label = decompx_outputs_df.iloc[idx]["importance_last_layer_classifier"][:, pred_label]
    tokens = decompx_outputs_df.iloc[idx]["tokens"][1:-1]
    label = label[1:-1]
    label = label / np.max(np.abs(label))
    plot_clf(tokens, label, ['-','+'], title=f"DecompX for Predicted Label: {pred_label}", file_name="example_sst2_our_method")
    return plt, print_preview(decompx_outputs_df)

demo = gr.Interface(
    fn=run_decompx,
    inputs=[
        gr.components.Textbox(label="Text"),
        gr.components.Dropdown(label="Model", choices=MODELS),
    ],
    outputs=["plot", "html"],
    examples=[["Building a translation demo with Gradio is so easy!", "WillHeld/roberta-base-sst2"]],
    cache_examples=False,
    title="DecompX Demo",
    description="This demo is a simplified version of the original [NLLB-Translator](https://huggingface.co/spaces/Narrativaai/NLLB-Translator) space"
)

demo.launch()