Training Transformer
TA’s Slide
Slide
Description
In this assignment, we are tasked with utilizing a transformer decoder-only architecture for pretraining, with a focus on next-token prediction, applied to Pokémon images.
Please feel free to mail us if you have any questions.
ntu-ml-2025-spring-ta@googlegroups.com
Utilities
Download packages
!pip install datasets==3.3.2Collecting datasets==3.3.2Using cached datasets-3.3.2-py3-none-any.whl.metadata (19 kB)
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Using cached datasets-3.3.2-py3-none-any.whl (485 kB)
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Installing collected packages: fsspec, dill, datasetsAttempting uninstall: fsspecFound existing installation: fsspec 2025.9.0Uninstalling fsspec-2025.9.0:Successfully uninstalled fsspec-2025.9.0---------------------------------------- 0/3 [fsspec]---------------------------------------- 0/3 [fsspec]---------------------------------------- 0/3 [fsspec]---------------------------------------- 0/3 [fsspec]Attempting uninstall: dill---------------------------------------- 0/3 [fsspec]Found existing installation: dill 0.4.0---------------------------------------- 0/3 [fsspec]Uninstalling dill-0.4.0:---------------------------------------- 0/3 [fsspec]Successfully uninstalled dill-0.4.0---------------------------------------- 0/3 [fsspec]------------- -------------------------- 1/3 [dill]------------- -------------------------- 1/3 [dill]------------- -------------------------- 1/3 [dill]Attempting uninstall: datasets------------- -------------------------- 1/3 [dill]Found existing installation: datasets 4.1.1------------- -------------------------- 1/3 [dill]Uninstalling datasets-4.1.1:------------- -------------------------- 1/3 [dill]Successfully uninstalled datasets-4.1.1------------- -------------------------- 1/3 [dill]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]-------------------------- ------------- 2/3 [datasets]---------------------------------------- 3/3 [datasets]
Successfully installed datasets-3.3.2 dill-0.3.8 fsspec-2024.12.0Import Packages
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.optim as optim
from PIL import Image
from torch import nn
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import AutoModelForCausalLM, GPT2Config, set_seed
from datasets import load_dataset
from typing import Dict, Any, OptionalCheck Devices
!nvidia-smiWed Oct 8 18:50:06 2025
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| NVIDIA-SMI 580.97 Driver Version: 580.97 CUDA Version: 13.0 |
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| | | MIG M. |
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| 0 NVIDIA GeForce RTX 3090 Ti WDDM | 00000000:07:00.0 On | Off |
| 47% 42C P8 25W / 450W | 12684MiB / 24564MiB | 2% Default |
| | | N/A |
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+-----------------------------------------------------------------------------------------+Set Random Seed
set_seed(0)Prepare Data
Define Dataset
from typing import List, Tuple, Union
import torch
from torch.utils.data import Dataset
class PixelSequenceDataset(Dataset):
def __init__(self, data: List[List[int]], mode: str = "train"):
"""
A dataset class for handling pixel sequences.
Args:
data (List[List[int]]): A list of sequences, where each sequence is a list of integers.
mode (str): The mode of operation, either "train", "dev", or "test".
- "train": Returns (input_ids, labels) where input_ids are sequence[:-1] and labels are sequence[1:].
- "dev": Returns (input_ids, labels) where input_ids are sequence[:-160] and labels are sequence[-160:].
- "test": Returns only input_ids, as labels are not available.
"""
self.data = data
self.mode = mode
def __len__(self) -> int:
"""Returns the total number of sequences in the dataset."""
return len(self.data)
def __getitem__(self, idx: int) -> Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
"""
Fetches a sequence from the dataset and processes it based on the mode.
Args:
idx (int): The index of the sequence.
Returns:
- If mode == "train": Tuple[torch.Tensor, torch.Tensor] -> (input_ids, labels)
- If mode == "dev": Tuple[torch.Tensor, torch.Tensor] -> (input_ids, labels)
- If mode == "test": torch.Tensor -> input_ids
"""
sequence = self.data[idx]
if self.mode == "train":
input_ids = torch.tensor(sequence[:-1], dtype=torch.long)
labels = torch.tensor(sequence[1:], dtype=torch.long)
return input_ids, labels
elif self.mode == "dev":
input_ids = torch.tensor(sequence[:-160], dtype=torch.long)
labels = torch.tensor(sequence[-160:], dtype=torch.long)
return input_ids, labels
elif self.mode == "test":
input_ids = torch.tensor(sequence, dtype=torch.long)
return input_ids
raise ValueError(f"Invalid mode: {
self.mode}. Choose from 'train', 'dev', or 'test'.")Download Dataset & Prepare Dataloader
# Load the pokemon dataset from Hugging Face Hub
pokemon_dataset = load_dataset("lca0503/ml2025-hw4-pokemon")
# Load the colormap from Hugging Face Hub
colormap = list(load_dataset("lca0503/ml2025-hw4-colormap")["train"]["color"])
# Define number of classes
num_classes = len(colormap)
# Define batch size
batch_size = 16
# === Prepare Dataset and DataLoader for Training ===
train_dataset: PixelSequenceDataset = PixelSequenceDataset(
pokemon_dataset["train"]["pixel_color"], mode="train"
)
train_dataloader: DataLoader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True
)
# === Prepare Dataset and DataLoader for Validation ===
dev_dataset: PixelSequenceDataset = PixelSequenceDataset(
pokemon_dataset["dev"]["pixel_color"], mode="dev"
)
dev_dataloader: DataLoader = DataLoader(
dev_dataset, batch_size=batch_size, shuffle=False
)
# === Prepare Dataset and DataLoader for Testing ===
test_dataset: PixelSequenceDataset = PixelSequenceDataset(
pokemon_dataset["test"][
![[题解]【MX-S10】梦熊 NOIP 2025 模拟赛 2 FeOI Round 4 T1~T2](http://pic.xiahunao.cn/[题解]【MX-S10】梦熊 NOIP 2025 模拟赛 2 FeOI Round 4 T1~T2)
