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import streamlit as st
import pandas as pd
import time
import importlib
import algs
import config
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
EDIT_ALGS = [
"MEND: Model editor networks using gradient decomposition",
"SERAC: Semi-parametric editing with a retrieval-augmented counterfactual model",
"ENN: Editable neural networks",
"KE: KnowledgeEditor",
"FT: Fine-tuning",
"LU: Lookup Cache",
]
tokenizer = None
model = None
editable_model = None
def reset():
st.session_state.edits.drop(st.session_state.edits.index, inplace=True)
st.session_state.model_outputs.drop(st.session_state.edits.index, inplace=True)
selected_alg = st.session_state.alg_selector
selected_alg_idx = EDIT_ALGS.index(selected_alg)
############# TODO: show progress spinner
global tokenizer
global model
global editable_model
if tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained("google/t5-large-ssm-nq")
if model is None:
model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-large-ssm-nq").eval()
del editable_model
alg_name = st.session_state.alg_selector
alg_abbrv = alg_name[:alg_name.index(":")]
alg_module = importlib.import_module(f"algs.{alg_abbrv.lower()}")
alg_class = getattr(alg_module, alg_abbrv.upper())
cfg = getattr(config, f"{alg_abbrv.lower()}_config")
editable_model = alg_class(
model,
cfg,
lambda: copy.deepcopy(model),
).eval()
def apply_edit():
st.session_state.edits.loc[len(st.session_state.edits)] = [str(edit_input), str(edit_label)]
############# Actually do the edit to the model
def sample_model():
input_str = str(test_input)
model_output = "blah blah blah" ############## Actually sample the model
n_edits = len(st.session_state.edits)
alg_name = st.session_state.alg_selector
alg_abbrv = alg_name[:alg_name.index(":")]
st.session_state.model_outputs.loc[len(st.session_state.model_outputs)] = [input_str, model_output, n_edits, alg_abbrv]
################################
#### Backend initialization ####
################################
if "init" not in st.session_state:
st.session_state.edits = pd.DataFrame([], columns=["Edit input", "Edit label"])
st.session_state.model_outputs = pd.DataFrame([], columns=["Input", "Output", "N edits", "Alg"])
st.session_state.init = True
st.session_state.model = None ##############
########################
#### Interface code ####
########################
st.title("Language Model Editing")
st.markdown("**Note: this HF space is currently under development and doesn't actually work yet!**")
st.markdown("The goal of this demo is to give you a sense of the *abilities* and *limitations* of existing methods for **editing** pre-trained language models. **Model editing** algorithms use a single input-output pair to update a pre-trained model's behavior for that input (and ideally, related inputs).")
st.markdown("This demo uses a [T5-large](https://huggingface.co/google/t5-large-ssm-nq) model fine-tuned on [Natural Questions](https://arxiv.org/pdf/2002.08910.pdf) as the base pre-trained model.")
st.write("You can choose from a variety of algorithms for model editing in the dropdown below. At the bottom of the page, you can query the model for whatever input you want before/after editing.")
st.markdown("***")
col1, col2 = st.columns([5,1])
with col1:
alg_selector = st.selectbox("Editing algorithm:", EDIT_ALGS, key="alg_selector", on_change=reset)
with col2:
st.text("ㅤ")
st.button("Clear edits", on_click=reset)
st.write("Edits applied so far:")
st.table(st.session_state.edits)
col1, col2, col3 = st.columns([3, 2, 1])
with col1:
edit_input = st.text_input("Edit input:", placeholder="e.g., 'What is the tallest mountain on Earth?'")
with col2:
edit_label = st.text_input("Edit target:", placeholder="e.g., 'Denali'")
with col3:
st.text("ㅤ")
edit_button = st.button("Apply edit", on_click=apply_edit)
st.markdown("***")
if len(st.session_state.edits) == 0:
title = "Input to sample from *unedited* model:"
else:
title = f"Input to sample from *edited* model:"
col1, col2 = st.columns([5, 1])
with col1:
test_input = st.text_input(title, placeholder="e.g., 'What is the earth's tallest mountain?'")
with col2:
st.text("ㅤ")
generate_button = st.button("Generate", on_click=sample_model)
st.write("Model generation history:")
st.table(st.session_state.model_outputs) |