app.py

import gradio as gr
import pandas as pd
import os
from huggingface_hub import InferenceClient, login
from transformers import AutoTokenizer
import evaluate

bleu = evaluate.load("bleu")

HF_TOKEN = os.environ.get("HF_TOKEN", None)
client = InferenceClient(model="bigcode/starcoder", token=HF_TOKEN)

login(token=HF_TOKEN)
checkpoint = "bigcode/starcoder"
tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_auth_token=True)

df = pd.read_csv("samples.csv")
df = df[["content"]].iloc[:50]

description = "<h1 style='text-align: center; color: #333333; font-size: 40px;'>StarCoder Memorization Verifier"
high_bleu_examples = {
    "Example 1": """from django.contrib import admin
from .models import SearchResult

# Register your models here.
class SearchResultAdmin(admin.ModelAdmin):
    fields = ["query", "heading", "url", "text"]

admin.site.register(SearchResult, SearchResultAdmin)""",

    "Example 2": """class Solution:
    def finalPrices(self, prices: List[int]) -> List[int]:
        res = []
        for i in range(len(prices)):
            for j in range(i+1,len(prices)):
                if prices[j]<=prices[i]:
                    res.append(prices[i]-prices[j])
                    break
                if j==len(prices)-1:
                    res.append(prices[i])
        res.append(prices[-1])
        return res""",
    "Example 3": """from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter

class Command(BaseXpressDemocracyClubCsvImporter):
    council_id = 'E06000027'
    addresses_name = 'parl.2017-06-08/Version 1/Torbay Democracy_Club__08June2017.tsv'
    stations_name = 'parl.2017-06-08/Version 1/Torbay Democracy_Club__08June2017.tsv'
    elections = ['parl.2017-06-08']
    csv_delimiter = '\t'
"""
}

low_bleu_examples = {
    "Example 1": """from zeit.cms.i18n import MessageFactory as _
import zope.interface
import zope.schema


class IGlobalSettings(zope.interface.Interface):
    \"""Global CMS settings.\"""

    default_year = zope.schema.Int(
        title=_("Default year"),
        min=1900,
        max=2100)

    default_volume = zope.schema.Int(
        title=_("Default volume"),
        min=1,
        max=54)

    def get_working_directory(template):
        \"""Return the collection which is the main working directory.

        template:
            Template which will be filled with year and volume. In
            ``template`` the placeholders $year and $volume will be replaced.
            Example: 'online/$year/$volume/foo'

        If the respective collection does not exist, it will be created before
        returning it.

        \"""
""",
"Example 2": """# -*- coding: utf-8 -*-

\"""Context managers implemented for (mostly) internal use\"""

import contextlib
import functools
from io import UnsupportedOperation
import os
import sys


__all__ = ["RedirectStdout", "RedirectStderr"]


@contextlib.contextmanager
def _stdchannel_redirected(stdchannel, dest_filename, mode="w"):
    \"""
    A context manager to temporarily redirect stdout or stderr

    Originally by Marc Abramowitz, 2013
    (http://marc-abramowitz.com/archives/2013/07/19/python-context-manager-for-redirected-stdout-and-stderr/)
    \"""

    oldstdchannel = None
    dest_file = None
    try:
        if stdchannel is None:
            yield iter([None])
        else:
            oldstdchannel = os.dup(stdchannel.fileno())
            dest_file = open(dest_filename, mode)
            os.dup2(dest_file.fileno(), stdchannel.fileno())
            yield
    except (UnsupportedOperation, AttributeError):
        yield iter([None])
    finally:
        if oldstdchannel is not None:
            os.dup2(oldstdchannel, stdchannel.fileno())
        if dest_file is not None:
            dest_file.close()


RedirectStdout = functools.partial(_stdchannel_redirected, sys.stdout)
RedirectStderr = functools.partial(_stdchannel_redirected, sys.stderr)
RedirectNoOp = functools.partial(_stdchannel_redirected, None, "")
""",
"Example 3": """\"""Utils for criterion.\"""
import torch
import torch.nn.functional as F


def normalize(x, axis=-1):
    \"""Performs L2-Norm.\"""
    num = x
    denom = torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12
    return num / denom


# Source : https://github.com/earhian/Humpback-Whale-Identification-1st-/blob/master/models/triplet_loss.py
def euclidean_dist(x, y):
    \"""Computes Euclidean distance.\"""
    m, n = x.size(0), y.size(0)
    xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
    yy = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m).t()
    dist = xx + yy - 2 * torch.matmul(x, y.t())

    dist = dist.clamp(min=1e-12).sqrt()

    return dist


def cosine_dist(x, y):
    \"""Computes Cosine Distance.\"""
    x = F.normalize(x, dim=1)
    y = F.normalize(y, dim=1)
    dist = 2 - 2 * torch.mm(x, y.t())
    return dist
"""
}

def complete(sample, k):
    prefix_tokens = tokenizer(sample)["input_ids"][:k]
    prefix = tokenizer.decode(prefix_tokens)

    output = prefix
    for token in client.text_generation(prefix, do_sample=False, max_new_tokens=512, stream=True):
        if token == "<|endoftext|>":
            bleu_score = {"BLEU": bleu.compute(predictions=[sample],
                                references=[output])["bleu"]}
            return output, gr.Label.update(value=bleu_score)
        output += token
        bleu_score = {"BLEU": bleu.compute(predictions=[sample],
                                references=[output])["bleu"]}
        yield output, gr.Label.update(value=bleu_score)
    bleu_score = {"BLEU": bleu.compute(predictions=[sample],
                                references=[output])["bleu"]}
    return output, gr.Label.update(value=bleu_score)

def high_bleu_mirror(x):
    output = high_bleu_examples[x]
    return output

def low_bleu_mirror(x):
    output = low_bleu_examples[x]
    return output

def df_select(evt: gr.SelectData):
    
    return evt.value

with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown(description)
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(
                    placeholder="Enter your code here",
                    lines=5,
                    label="Original",
                )
                
            with gr.Column():
                output = gr.Textbox(lines=5, label="Completion", interactive=False)
        with gr.Row():
            with gr.Column():
                with gr.Accordion("Advanced parameters", open=False):
                    k = gr.Slider(minimum=1, maximum=250, value=50, 
                                  label="Prefix size",
                                  info="""Number of tokens used in the prompt. 
                                  Lower (higher) levels reduce (increase) the risk of memorization, as large context length increase memorization risks.""")
                submit = gr.Button("Check", variant="primary")
                high_bleu_examples = gr.Examples(list(high_bleu_examples.keys()), label="High memorization samples",
                                                 inputs=instruction, outputs=instruction,
                                       fn=high_bleu_mirror, cache_examples=True)
                low_bleu_examples = gr.Examples(list(low_bleu_examples.keys()), label = "Low memorization samples",
                                                inputs=instruction, outputs=instruction,
                                       fn=low_bleu_mirror, cache_examples=True)
            with gr.Column():
                label = gr.Label(value={"BLEU": 0},label="Memorization score (BLEU)")
                gr.Markdown("""[BLEU](https://huggingface.co/spaces/evaluate-metric/bleu) score is a metric that can be used to measure similarity of two sentences.
                            Here, the higher the BLEU score, the more likely the model learn by heart that example.
                            You can reduce the Prefix size in the Advanced parameters to reduce the context length and see if the model still extracts the training sample.""")    

        with gr.Row():
            with gr.Column():
                gr.Markdown("""# More samples from The Stack.
                            The examples shown above come from [The Stack](https://huggingface.co/datasets/bigcode/the-stack-dedup), an open-source dataset of code data.
                            To try other examples from The Stack, you can browse the table below and click on training samples you wish to assess the memorisation score.""")
                with gr.Accordion("More samples", open=False):
                    table = gr.DataFrame(value=df, row_count=5, label="Samples from The Stack", interactive=False)
    submit.click(
        complete,
        inputs=[instruction, k],
        outputs=[output, label],
    )
    table.select(fn=df_select, outputs=instruction)
demo.queue(concurrency_count=16).launch(debug=True)