|
import numpy as np |
|
import pandas as pd |
|
import time |
|
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 transformers import AlbertTokenizer, AlbertForMaskedLM |
|
|
|
|
|
from skeleton_modeling_albert import SkeletonAlbertForMaskedLM |
|
|
|
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(): |
|
tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2') |
|
|
|
model = AlbertForMaskedLM.from_pretrained('albert-xxlarge-v2') |
|
return tokenizer,model |
|
|
|
def clear_data(): |
|
for key in st.session_state: |
|
del st.session_state[key] |
|
|
|
def annotate_mask(sent_id,sent): |
|
st.write(f'Sentence {sent_id}') |
|
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): |
|
st.write(f'Sentence {sent_id}') |
|
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}']) |
|
|
|
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): |
|
interventions = {} |
|
for rep in ['lay','qry','key','val']: |
|
if rep in interv_types: |
|
interventions[rep] = [(head_id,token_id,[head_id,head_id+num_heads]) for head_id in range(num_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:] |
|
assert np.all(np.diff(option_1_locs)==1) and 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): |
|
assert np.all(np.diff(pron_locs)==1) |
|
return input_ids[:pron_locs[0]] + [mask_id for _ in range(len(option_locs))] + input_ids[pron_locs[-1]+1:] |
|
|
|
if __name__=='__main__': |
|
wide_setup() |
|
load_css('style.css') |
|
tokenizer,model = load_model() |
|
num_layers, num_heads = 12, 64 |
|
mask_id = tokenizer('[MASK]').input_ids[1:-1][0] |
|
|
|
main_area = st.empty() |
|
|
|
if 'page_status' not in st.session_state: |
|
st.session_state['page_status'] = 'type_in' |
|
|
|
if st.session_state['page_status']=='type_in': |
|
show_instruction('1. Type in the sentences and click "Tokenize"') |
|
sent_1 = st.text_input('Sentence 1',value='It is better to play a prank on Samuel than Craig because he gets angry less often.') |
|
sent_2 = st.text_input('Sentence 2',value='It is better to play a prank on Samuel than Craig because he gets angry more often.') |
|
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"') |
|
annotate_mask(1,sent_1) |
|
annotate_mask(2,sent_2) |
|
if st.button('Confirm',key='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"') |
|
annotate_options(1,sent_1) |
|
annotate_options(2,sent_2) |
|
if st.button('Confirm',key='option'): |
|
st.session_state['page_status'] = 'analysis' |
|
st.experimental_rerun() |
|
|
|
if st.session_state['page_status']=='analysis': |
|
with main_area.container(): |
|
sent_1 = st.session_state['sent_1'] |
|
sent_2 = st.session_state['sent_2'] |
|
show_annotated_sentence(st.session_state['decoded_sent_1'], |
|
option_locs=st.session_state['option_locs_1'], |
|
mask_locs=st.session_state['mask_locs_1']) |
|
show_annotated_sentence(st.session_state['decoded_sent_2'], |
|
option_locs=st.session_state['option_locs_2'], |
|
mask_locs=st.session_state['mask_locs_2']) |
|
|
|
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) |
|
|
|
st.write(option_1_locs) |
|
st.write(option_2_locs) |
|
st.write(pron_locs) |
|
for token_ids in [masked_ids_option_1['sent_1'],masked_ids_option_1['sent_2'],masked_ids_option_2['sent_1'],masked_ids_option_2['sent_2']]: |
|
st.write(' '.join([tokenizer.decode([token]) for token in token_ids])) |
|
|
|
if st.session_state['page_status'] == 'finish_debug': |
|
option_1_tokens_1 = np.array(input_ids_dict['sent_1'])[np.array(option_1_locs['sent_1'])] |
|
option_1_tokens_2 = np.array(input_ids_dict['sent_2'])[np.array(option_1_locs['sent_2'])] |
|
option_2_tokens_1 = np.array(input_ids_dict['sent_1'])[np.array(option_2_locs['sent_1'])] |
|
option_2_tokens_2 = np.array(input_ids_dict['sent_2'])[np.array(option_2_locs['sent_2'])] |
|
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 |
|
|
|
for layer_id in range(num_layers): |
|
interventions = [create_interventions(16,['lay','qry','key','val'],num_heads) if i==layer_id else {'lay':[],'qry':[],'key':[],'val':[]} for i in range(num_layers)] |
|
for masked_ids in [masked_ids_option_1, masked_ids_option_2]: |
|
input_ids = torch.tensor([ |
|
*[masked_ids['sent_1'] for _ in range(num_heads)], |
|
*[masked_ids['sent_2'] for _ in range(num_heads)] |
|
]) |
|
outputs = SkeletonAlbertForMaskedLM(model,input_ids,interventions=interventions) |
|
logprobs = F.log_softmax(outputs['logits'], dim = -1) |
|
|
|
|
|
preds_0 = [torch.multinomial(torch.exp(probs), num_samples=1).squeeze(dim=-1) for probs in logprobs[0][1:-1]] |
|
preds_1 = [torch.multinomial(torch.exp(probs), num_samples=1).squeeze(dim=-1) for probs in logprobs[1][1:-1]] |
|
st.write([tokenizer.decode([token]) for token in preds_0]) |
|
st.write([tokenizer.decode([token]) for token in preds_1]) |
|
|
