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import os
import plotly.graph_objects as go
import streamlit as st
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import wandb
from datasets import load_dataset
from pydantic import BaseModel
from rich.progress import track
from safetensors.torch import save_model
from sklearn.metrics import roc_auc_score, roc_curve
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
class DatasetArgs(BaseModel):
dataset_address: str
train_dataset_range: int
test_dataset_range: int
class LlamaGuardFineTuner:
def __init__(
self, wandb_project: str, wandb_entity: str, streamlit_mode: bool = False
):
self.wandb_project = wandb_project
self.wandb_entity = wandb_entity
self.streamlit_mode = streamlit_mode
def load_dataset(self, dataset_args: DatasetArgs):
dataset = load_dataset(dataset_args.dataset_address)
self.train_dataset = (
dataset["train"]
if dataset_args.train_dataset_range <= 0
or dataset_args.train_dataset_range > len(dataset["train"])
else dataset["train"].select(range(dataset_args.train_dataset_range))
)
self.test_dataset = (
dataset["test"]
if dataset_args.test_dataset_range <= 0
or dataset_args.test_dataset_range > len(dataset["test"])
else dataset["test"].select(range(dataset_args.test_dataset_range))
)
def load_model(self, model_name: str = "meta-llama/Prompt-Guard-86M"):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model_name = model_name
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(
self.device
)
def show_dataset_sample(self):
if self.streamlit_mode:
st.markdown("### Train Dataset Sample")
st.dataframe(self.train_dataset.to_pandas().head())
st.markdown("### Test Dataset Sample")
st.dataframe(self.test_dataset.to_pandas().head())
def evaluate_batch(
self,
texts,
batch_size: int = 32,
positive_label: int = 2,
temperature: float = 1.0,
truncation: bool = True,
max_length: int = 512,
) -> list[float]:
self.model.eval()
encoded_texts = self.tokenizer(
texts,
padding=True,
truncation=truncation,
max_length=max_length,
return_tensors="pt",
)
dataset = torch.utils.data.TensorDataset(
encoded_texts["input_ids"], encoded_texts["attention_mask"]
)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)
scores = []
progress_bar = (
st.progress(0, text="Evaluating") if self.streamlit_mode else None
)
for i, batch in track(
enumerate(data_loader), description="Evaluating", total=len(data_loader)
):
input_ids, attention_mask = [b.to(self.device) for b in batch]
with torch.no_grad():
logits = self.model(
input_ids=input_ids, attention_mask=attention_mask
).logits
scaled_logits = logits / temperature
probabilities = F.softmax(scaled_logits, dim=-1)
positive_class_probabilities = (
probabilities[:, positive_label].cpu().numpy()
)
scores.extend(positive_class_probabilities)
if progress_bar:
progress_percentage = (i + 1) * 100 // len(data_loader)
progress_bar.progress(
progress_percentage,
text=f"Evaluating batch {i + 1}/{len(data_loader)}",
)
return scores
def visualize_roc_curve(self, test_scores: list[float]):
test_labels = [int(elt) for elt in self.test_dataset["label"]]
fpr, tpr, _ = roc_curve(test_labels, test_scores)
roc_auc = roc_auc_score(test_labels, test_scores)
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=fpr,
y=tpr,
mode="lines",
name=f"ROC curve (area = {roc_auc:.3f})",
line=dict(color="darkorange", width=2),
)
)
fig.add_trace(
go.Scatter(
x=[0, 1],
y=[0, 1],
mode="lines",
name="Random Guess",
line=dict(color="navy", width=2, dash="dash"),
)
)
fig.update_layout(
title="Receiver Operating Characteristic",
xaxis_title="False Positive Rate",
yaxis_title="True Positive Rate",
xaxis=dict(range=[0.0, 1.0]),
yaxis=dict(range=[0.0, 1.05]),
legend=dict(x=0.8, y=0.2),
)
if self.streamlit_mode:
st.plotly_chart(fig)
else:
fig.show()
def visualize_score_distribution(self, scores: list[float]):
test_labels = [int(elt) for elt in self.test_dataset["label"]]
positive_scores = [scores[i] for i in range(500) if test_labels[i] == 1]
negative_scores = [scores[i] for i in range(500) if test_labels[i] == 0]
fig = go.Figure()
fig.add_trace(
go.Histogram(
x=positive_scores,
histnorm="probability density",
name="Positive",
marker_color="darkblue",
opacity=0.75,
)
)
fig.add_trace(
go.Histogram(
x=negative_scores,
histnorm="probability density",
name="Negative",
marker_color="darkred",
opacity=0.75,
)
)
fig.update_layout(
title="Score Distribution for Positive and Negative Examples",
xaxis_title="Score",
yaxis_title="Density",
barmode="overlay",
legend_title="Scores",
)
if self.streamlit_mode:
st.plotly_chart(fig)
else:
fig.show()
def evaluate_model(
self,
batch_size: int = 32,
positive_label: int = 2,
temperature: float = 3.0,
truncation: bool = True,
max_length: int = 512,
):
test_scores = self.evaluate_batch(
self.test_dataset["text"],
batch_size=batch_size,
positive_label=positive_label,
temperature=temperature,
truncation=truncation,
max_length=max_length,
)
self.visualize_roc_curve(test_scores)
self.visualize_score_distribution(test_scores)
return test_scores
def collate_fn(self, batch):
texts = [item["text"] for item in batch]
labels = torch.tensor([int(item["label"]) for item in batch])
encodings = self.tokenizer(
texts, padding=True, truncation=True, max_length=512, return_tensors="pt"
)
return encodings.input_ids, encodings.attention_mask, labels
def train(self, batch_size: int = 32, lr: float = 5e-6, num_classes: int = 2):
wandb.init(
project=self.wandb_project,
entity=self.wandb_entity,
name=f"{self.model_name}-{self.dataset_name}",
)
self.model.classifier = nn.Linear(
self.model.classifier.in_features, num_classes
)
self.model.num_labels = num_classes
self.model.train()
optimizer = optim.AdamW(self.model.parameters(), lr=lr)
data_loader = DataLoader(
self.train_dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=self.collate_fn,
)
progress_bar = st.progress(0, text="Training") if self.streamlit_mode else None
for i, batch in track(
enumerate(data_loader), description="Training", total=len(data_loader)
):
input_ids, attention_mask, labels = [x.to(self.device) for x in batch]
outputs = self.model(
input_ids=input_ids, attention_mask=attention_mask, labels=labels
)
loss = outputs.loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
wandb.log({"loss": loss.item()})
if progress_bar:
progress_percentage = (i + 1) * 100 // len(data_loader)
progress_bar.progress(
progress_percentage,
text=f"Training batch {i + 1}/{len(data_loader)}, Loss: {loss.item()}",
)
save_model(self.model, f"{self.model_name}-{self.dataset_name}.safetensors")
wandb.log_model(f"{self.model_name}-{self.dataset_name}.safetensors")
wandb.finish()
os.remove(f"{self.model_name}-{self.dataset_name}.safetensors")
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