app.py

import numpy as np
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns

#import jax
#import jax.numpy as jnp

import torch
import torch.nn.functional as F

#from custom_modeling_albert_flax import CustomFlaxAlbertForMaskedLM

def wide_setup():
    max_width = 1500
    padding_top = 0
    padding_right = 2
    padding_bottom = 0
    padding_left = 2

    define_margins = f"""
    <style>
        .appview-container .main .block-container{{
            max-width: {max_width}px;
            padding-top: {padding_top}rem;
            padding-right: {padding_right}rem;
            padding-left: {padding_left}rem;
            padding-bottom: {padding_bottom}rem;
        }}
    </style>
    """
    hide_table_row_index = """
                <style>
                tbody th {display:none}
                .blank {display:none}
                </style>
                """
    st.markdown(define_margins, unsafe_allow_html=True)
    st.markdown(hide_table_row_index, unsafe_allow_html=True)

def load_css(file_name):
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

@st.cache(show_spinner=True,allow_output_mutation=True)
def load_model(model_name):
    if model_name.startswith('albert'):
        from transformers import AlbertTokenizer, AlbertForMaskedLM
        from skeleton_modeling_albert import SkeletonAlbertForMaskedLM
        tokenizer = AlbertTokenizer.from_pretrained(model_name)
        model = AlbertForMaskedLM.from_pretrained(model_name)
        skeleton_model = SkeletonAlbertForMaskedLM
    elif model_name.startswith('bert'):
        from transformers import BertTokenizer, BertForMaskedLM
        from skeleton_modeling_bert import SkeletonBertForMaskedLM
        tokenizer = BertTokenizer.from_pretrained(model_name)
        model = BertForMaskedLM.from_pretrained(model_name)
        skeleton_model = SkeletonBertForMaskedLM
    elif model_name.startswith('roberta'):
        from transformers import RobertaTokenizer, RobertaForMaskedLM
        from skeleton_modeling_roberta import SkeletonRobertaForMaskedLM
        tokenizer = RobertaTokenizer.from_pretrained(model_name)
        model = RobertaForMaskedLM.from_pretrained(model_name)
        skeleton_model = SkeletonRobertaForMaskedLM
    return tokenizer,model,skeleton_model

def clear_data():
    for key in st.session_state:
        del st.session_state[key]

def annotate_mask(sent_id,sent):
    show_instruction(f'Sentence {sent_id}',fontsize=16)
    input_sent = tokenizer(sent).input_ids
    decoded_sent = [tokenizer.decode([token]) for token in input_sent[1:-1]]
    st.session_state[f'decoded_sent_{sent_id}'] = decoded_sent
    char_nums = [len(word)+2 for word in decoded_sent]
    cols = st.columns(char_nums)
    if f'mask_locs_{sent_id}' not in st.session_state:
        st.session_state[f'mask_locs_{sent_id}'] = []
    for word_id,(col,word) in enumerate(zip(cols,decoded_sent)):
        with col:
            if st.button(word,key=f'word_mask_{sent_id}_{word_id}'):
                if word_id not in st.session_state[f'mask_locs_{sent_id}']:
                    st.session_state[f'mask_locs_{sent_id}'].append(word_id)
                else:
                    st.session_state[f'mask_locs_{sent_id}'].remove(word_id)
    show_annotated_sentence(decoded_sent,
                    mask_locs=st.session_state[f'mask_locs_{sent_id}'])

def annotate_options(sent_id,sent):
    show_instruction(f'Sentence {sent_id}',fontsize=16)
    input_sent = tokenizer(sent).input_ids
    decoded_sent = [tokenizer.decode([token]) for token in input_sent[1:-1]]
    char_nums = [len(word)+2 for word in decoded_sent]
    cols = st.columns(char_nums)
    if f'option_locs_{sent_id}' not in st.session_state:
        st.session_state[f'option_locs_{sent_id}'] = []
    for word_id,(col,word) in enumerate(zip(cols,decoded_sent)):
        with col:
            if st.button(word,key=f'word_option_{sent_id}_{word_id}'):
                if word_id not in st.session_state[f'option_locs_{sent_id}']:
                    st.session_state[f'option_locs_{sent_id}'].append(word_id)
                else:
                    st.session_state[f'option_locs_{sent_id}'].remove(word_id)
    show_annotated_sentence(decoded_sent,
                            option_locs=st.session_state[f'option_locs_{sent_id}'],
                            mask_locs=st.session_state[f'mask_locs_{sent_id}'])
    st.session_state[f'option_locs_{sent_id}'] = list(np.sort(st.session_state[f'option_locs_{sent_id}']))
    st.session_state[f'mask_locs_{sent_id}'] = list(np.sort(st.session_state[f'mask_locs_{sent_id}']))

def show_annotated_sentence(sent,option_locs=[],mask_locs=[]):
    disp_style = '"font-family:san serif; color:Black; font-size: 20px"'
    prefix = f'<p style={disp_style}><span style="font-weight:bold">'
    style_list = []
    for i, word in enumerate(sent):
        if i in mask_locs:
            style_list.append(f'<span style="color:Red">{word}</span>')
        elif i in option_locs:
            style_list.append(f'<span style="color:Blue">{word}</span>')
        else:
            style_list.append(f'{word}')
    disp = ' '.join(style_list)
    suffix = '</span></p>'
    return st.markdown(prefix + disp + suffix, unsafe_allow_html = True)

def show_instruction(sent,fontsize=20):
    disp_style = f'"font-family:san serif; color:Black; font-size: {fontsize}px"'
    prefix = f'<p style={disp_style}><span style="font-weight:bold">'
    suffix = '</span></p>'
    return st.markdown(prefix + sent + suffix, unsafe_allow_html = True)

def create_interventions(token_id,interv_types,num_heads,multihead=False,heads=[]):
    interventions = {}
    for rep in ['lay','qry','key','val']:
        if rep in interv_types:
            if multihead:
                interventions[rep] = [(head_id,token_id,[0,1]) for head_id in range(num_heads)]
            else:
                interventions[rep] = [(head_id,token_id,[i,i+len(heads)]) for i,head_id in enumerate(heads)]
        else:
            interventions[rep] = []
    return interventions

def separate_options(option_locs):
    assert np.sum(np.diff(option_locs)>1)==1
    sep = list(np.diff(option_locs)>1).index(1)+1
    option_1_locs, option_2_locs = option_locs[:sep], option_locs[sep:]
    if len(option_1_locs)>1:
        assert np.all(np.diff(option_1_locs)==1)
    if len(option_2_locs)>1:
        assert np.all(np.diff(option_2_locs)==1)
    return option_1_locs, option_2_locs

def mask_out(input_ids,pron_locs,option_locs,mask_id):
    if len(pron_locs)>1:
        assert np.all(np.diff(pron_locs)==1)
    # note annotations are shifted by 1 because special tokens were omitted
    return input_ids[:pron_locs[0]+1] + [mask_id for _ in range(len(option_locs))] + input_ids[pron_locs[-1]+2:]


def run_intervention(interventions,batch_size,skeleton_model,model,masked_ids_option_1,masked_ids_option_2,option_1_tokens,option_2_tokens,pron_locs):
    probs = []
    for masked_ids, option_tokens in zip([masked_ids_option_1, masked_ids_option_2],[option_1_tokens,option_2_tokens]):
        input_ids = torch.tensor([
                                *[masked_ids['sent_1'] for _ in range(batch_size)],
                                *[masked_ids['sent_2'] for _ in range(batch_size)]
                                ])
        outputs = skeleton_model(model,input_ids,interventions=interventions)
        logprobs = F.log_softmax(outputs['logits'], dim = -1)
        logprobs_1, logprobs_2 = logprobs[:batch_size], logprobs[batch_size:]
        evals_1 = [logprobs_1[:,pron_locs['sent_1'][0]+1+i,token].numpy() for i,token in enumerate(option_tokens)]
        evals_2 = [logprobs_2[:,pron_locs['sent_2'][0]+1+i,token].numpy() for i,token in enumerate(option_tokens)]
        probs.append([np.exp(np.mean(evals_1,axis=0)),np.exp(np.mean(evals_2,axis=0))])
    probs = np.array(probs)
    assert probs.shape[0]==2 and probs.shape[1]==2 and probs.shape[2]==batch_size
    return probs

def show_results(effect_array,masked_sent,token_id_list,num_layers):
    cols = st.columns(len(masked_sent)-2)
    for col_id,col in enumerate(cols):
        with col:
            st.write(tokenizer.decode([masked_sent[col_id+1]]))
            if col_id in token_id_list:
                interv_id = token_id_list.index(col_id)
                fig,ax = plt.subplots()
                ax.set_box_aspect(num_layers)
                ax.imshow(effect_array[:,interv_id:interv_id+1],cmap=sns.color_palette("light:r", as_cmap=True),
                                vmin=effect_array.min(),vmax=effect_array.max())
                ax.set_xticks([])
                ax.set_xticklabels([])
                ax.set_yticks([])
                ax.set_yticklabels([])
                ax.spines['top'].set_visible(False)
                ax.spines['bottom'].set_visible(False)
                ax.spines['right'].set_visible(False)
                ax.spines['left'].set_visible(False)
                st.pyplot(fig)

if __name__=='__main__':
    wide_setup()
    load_css('style.css')

    if 'page_status' not in st.session_state:
        st.session_state['page_status'] = 'model_selection'

    if st.session_state['page_status']=='model_selection':
        show_instruction('0. Select the model and click "Confirm"',fontsize=16)
        model_name = st.selectbox('Please select the model from below.',
                                    ('bert-base-uncased','bert-large-cased',
                                    'roberta-base','roberta-large',
                                    'albert-base-v2','albert-large-v2','albert-xlarge-v2','albert-xxlarge-v2'),
                                    index=3,label_visibility='collapsed')
        st.session_state['model_name'] = model_name
        if st.button('Confirm',key='confirm_models'):
            st.session_state['page_status'] = 'type_in'
            st.experimental_rerun()

    if st.session_state['page_status']!='model_selection':
        tokenizer,model,skeleton_model = load_model(st.session_state['model_name'])
        num_layers, num_heads = model.config.num_hidden_layers, model.config.num_attention_heads
        mask_id = tokenizer(tokenizer.mask_token).input_ids[1:-1][0]

    if st.session_state['page_status']=='type_in':
        show_instruction('1. Type in the sentences and click "Tokenize"',fontsize=16)
        sent_1 = st.text_input('Sentence 1',value="Paul tried to call George on the phone, but he wasn't successful.")
        sent_2 = st.text_input('Sentence 2',value="Paul tried to call George on the phone, but he wasn't available.")
        if st.button('Tokenize'):
            st.session_state['page_status'] = 'annotate_mask'
            st.session_state['sent_1'] = sent_1
            st.session_state['sent_2'] = sent_2
            st.experimental_rerun()

    if st.session_state['page_status']=='annotate_mask':
        sent_1 = st.session_state['sent_1']
        sent_2 = st.session_state['sent_2']

        show_instruction('2. Select sites to mask out and click "Confirm"',fontsize=16)
        #show_instruction('------------------------------',fontsize=32)
        annotate_mask(1,sent_1)
        show_instruction('------------------------------',fontsize=24)
        annotate_mask(2,sent_2)
        if st.button('Confirm',key='confirm_mask'):
            st.session_state['page_status'] = 'annotate_options'
            st.experimental_rerun()

    if st.session_state['page_status'] == 'annotate_options':
        sent_1 = st.session_state['sent_1']
        sent_2 = st.session_state['sent_2']

        show_instruction('3. Select options and click "Confirm"',fontsize=16)
        #show_instruction('------------------------------',fontsize=32)
        annotate_options(1,sent_1)
        show_instruction('------------------------------',fontsize=24)
        annotate_options(2,sent_2)
        if st.button('Confirm',key='confirm_option'):
            st.session_state['page_status'] = 'analysis'
            st.experimental_rerun()

    if st.session_state['page_status']=='analysis':
        interv_reps = st.multiselect('Select the types of representations to intervene.',['layer','query','key','value'])
        rep_dict = {'layer':'lay','query':'qry','key':'key','value':'val'}
        multihead = not st.checkbox('Perform individual head analysis (takes time)')
        if not multihead:
            heads = st.multiselect('Select heads to intervene.',list(np.arange(1,num_heads+1)))
        else:
            heads = []

        if st.button('Run',key='run'):
            st.session_state['reps'] = [rep_dict[rep] for rep in interv_reps]
            st.session_state['multihead'] = multihead
            st.session_state['heads'] = heads
            st.session_state['page_status'] = 'results'
            st.experimental_rerun()

    if st.session_state['page_status']=='results':
        sent_1 = st.session_state['sent_1']
        sent_2 = st.session_state['sent_2']
        multihead = st.session_state['multihead']
        heads = st.session_state['heads']
        reps = st.session_state['reps']

        option_1_locs, option_2_locs = {}, {}
        pron_locs = {}
        input_ids_dict = {}
        masked_ids_option_1 = {}
        masked_ids_option_2 = {}
        for sent_id in [1,2]:
            option_1_locs[f'sent_{sent_id}'], option_2_locs[f'sent_{sent_id}'] = separate_options(st.session_state[f'option_locs_{sent_id}'])
            pron_locs[f'sent_{sent_id}'] = st.session_state[f'mask_locs_{sent_id}']
            input_ids_dict[f'sent_{sent_id}'] = tokenizer(st.session_state[f'sent_{sent_id}']).input_ids

            masked_ids_option_1[f'sent_{sent_id}'] = mask_out(input_ids_dict[f'sent_{sent_id}'],
                                                                    pron_locs[f'sent_{sent_id}'],
                                                                    option_1_locs[f'sent_{sent_id}'],mask_id)
            masked_ids_option_2[f'sent_{sent_id}'] = mask_out(input_ids_dict[f'sent_{sent_id}'],
                                                                    pron_locs[f'sent_{sent_id}'],
                                                                    option_2_locs[f'sent_{sent_id}'],mask_id)

        option_1_tokens_1 = np.array(input_ids_dict['sent_1'])[np.array(option_1_locs['sent_1'])+1]
        option_1_tokens_2 = np.array(input_ids_dict['sent_2'])[np.array(option_1_locs['sent_2'])+1]
        option_2_tokens_1 = np.array(input_ids_dict['sent_1'])[np.array(option_2_locs['sent_1'])+1]
        option_2_tokens_2 = np.array(input_ids_dict['sent_2'])[np.array(option_2_locs['sent_2'])+1]
        assert np.all(option_1_tokens_1==option_1_tokens_2) and np.all(option_2_tokens_1==option_2_tokens_2)
        option_1_tokens = option_1_tokens_1
        option_2_tokens = option_2_tokens_1

        interventions = [{'lay':[],'qry':[],'key':[],'val':[]} for i in range(num_layers)]
        probs_original = run_intervention(interventions,1,skeleton_model,model,masked_ids_option_1,masked_ids_option_2,option_1_tokens,option_2_tokens,pron_locs)
        df = pd.DataFrame(data=[[probs_original[0,0][0],probs_original[1,0][0]],
                                [probs_original[0,1][0],probs_original[1,1][0]]],
                                columns=[tokenizer.decode(option_1_tokens),tokenizer.decode(option_2_tokens)],
                                index=['Sentence 1','Sentence 2'])
        cols = st.columns(3)
        with cols[1]:
            show_instruction('Probability of predicting each option in each sentence',fontsize=12)
            st.dataframe(df.style.highlight_max(axis=1),use_container_width=True)

        compare_1 = np.array(masked_ids_option_1['sent_1'])!=np.array(masked_ids_option_1['sent_2'])
        compare_2 = np.array(masked_ids_option_2['sent_1'])!=np.array(masked_ids_option_2['sent_2'])
        assert np.all(compare_1.astype(int)==compare_2.astype(int))
        context_locs = list(np.arange(len(masked_ids_option_1['sent_1']))[compare_1]-1)  # match the indexing for annotation

        assert np.all(np.array(pron_locs['sent_1'])==np.array(pron_locs['sent_2']))
        assert np.all(np.array(option_1_locs['sent_1'])==np.array(option_1_locs['sent_2']))
        assert np.all(np.array(option_2_locs['sent_1'])==np.array(option_2_locs['sent_2']))
        token_id_list = pron_locs['sent_1'] + option_1_locs['sent_1'] + option_2_locs['sent_1'] + context_locs

        effect_array = []
        for token_id in token_id_list:
            token_id += 1
            effect_list = []
            for layer_id in range(num_layers):
                interventions = [create_interventions(token_id,reps,num_heads,multihead,[head_id-1 for head_id in heads])
                                    if i==layer_id else {'lay':[],'qry':[],'key':[],'val':[]} for i in range(num_layers)]
                if multihead:
                    probs = run_intervention(interventions,1,skeleton_model,model,masked_ids_option_1,masked_ids_option_2,option_1_tokens,option_2_tokens,pron_locs)
                else:
                    probs = run_intervention(interventions,len(heads),skeleton_model,model,masked_ids_option_1,masked_ids_option_2,option_1_tokens,option_2_tokens,pron_locs)
                effect = ((probs_original-probs)[0,0] + (probs_original-probs)[1,1] + (probs-probs_original)[0,1] + (probs-probs_original)[1,0])/4
                effect_list.append(effect)
            effect_array.append(effect_list)
        effect_array = np.transpose(np.array(effect_array),(1,0,2))

        if multihead:
            show_results(effect_array[:,:,0],masked_ids_option_1['sent_1'],token_id_list,num_layers)
        else:
            tabs = st.tabs([str(head_id) for head_id in heads])
            for i,tab in enumerate(tabs):
                with tab:
                    show_results(effect_array[:,:,i],masked_ids_option_1['sent_1'],token_id_list,num_layers)