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#!/usr/bin/env python3
"""
Optimized Data Loader for Training
This module provides an optimized data loader with prefetching, caching,
and efficient batch processing to improve training performance.
Author: Louis Chua Bean Chong
License: GPLv3
"""
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset, Sampler
from typing import Optional, List, Tuple, Dict, Any
import numpy as np
import threading
import queue
import time
from collections import deque
import psutil
import os
class OptimizedDataset(Dataset):
"""
Optimized dataset with caching and memory management.
This dataset provides efficient data loading with optional caching
and memory management to improve training performance.
"""
def __init__(self,
data: torch.Tensor,
targets: torch.Tensor,
cache_size: Optional[int] = None,
pin_memory: bool = True):
"""
Initialize optimized dataset.
Args:
data: Input data tensor
targets: Target tensor
cache_size: Number of samples to cache in memory
pin_memory: Whether to pin memory for faster GPU transfer
"""
self.data = data
self.targets = targets
self.cache_size = cache_size
self.pin_memory = pin_memory
# Initialize cache
self.cache = {}
self.cache_hits = 0
self.cache_misses = 0
if cache_size and cache_size > 0:
print(f"Initializing cache with {cache_size} samples")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
# Check cache first
if self.cache_size and idx in self.cache:
self.cache_hits += 1
return self.cache[idx]
self.cache_misses += 1
# Get data
sample_data = self.data[idx]
sample_target = self.targets[idx]
# Pin memory if requested
if self.pin_memory and torch.cuda.is_available():
sample_data = sample_data.pin_memory()
sample_target = sample_target.pin_memory()
# Cache if enabled
if self.cache_size and len(self.cache) < self.cache_size:
self.cache[idx] = (sample_data, sample_target)
return sample_data, sample_target
def get_cache_stats(self) -> Dict[str, Any]:
"""Get cache statistics."""
total_requests = self.cache_hits + self.cache_misses
hit_rate = self.cache_hits / total_requests if total_requests > 0 else 0
return {
"cache_hits": self.cache_hits,
"cache_misses": self.cache_misses,
"hit_rate": hit_rate,
"cache_size": len(self.cache),
"max_cache_size": self.cache_size
}
class PrefetchDataLoader:
"""
Data loader with prefetching for improved performance.
This data loader uses background threads to prefetch data,
reducing the time spent waiting for data during training.
"""
def __init__(self,
dataset: Dataset,
batch_size: int = 32,
num_workers: int = 4,
prefetch_factor: int = 2,
pin_memory: bool = True,
shuffle: bool = True,
drop_last: bool = False):
"""
Initialize prefetch data loader.
Args:
dataset: Dataset to load
batch_size: Batch size
num_workers: Number of worker processes
prefetch_factor: Number of batches to prefetch
pin_memory: Whether to pin memory
shuffle: Whether to shuffle data
drop_last: Whether to drop incomplete batches
"""
self.dataset = dataset
self.batch_size = batch_size
self.num_workers = num_workers
self.prefetch_factor = prefetch_factor
self.pin_memory = pin_memory
self.shuffle = shuffle
self.drop_last = drop_last
# Initialize data loader
self.data_loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory,
drop_last=drop_last,
persistent_workers=True if num_workers > 0 else False
)
# Prefetch queue
self.prefetch_queue = queue.Queue(maxsize=prefetch_factor)
self.prefetch_thread = None
self.stop_prefetch = False
# Start prefetching
self._start_prefetch()
print(f"PrefetchDataLoader initialized with {num_workers} workers")
def _start_prefetch(self):
"""Start prefetching thread."""
if self.prefetch_factor > 0:
self.prefetch_thread = threading.Thread(target=self._prefetch_worker)
self.prefetch_thread.daemon = True
self.prefetch_thread.start()
def _prefetch_worker(self):
"""Worker thread for prefetching data."""
try:
for batch in self.data_loader:
if self.stop_prefetch:
break
# Put batch in queue (block if full)
self.prefetch_queue.put(batch, block=True)
except Exception as e:
print(f"Prefetch worker error: {e}")
def __iter__(self):
"""Iterate over prefetched batches."""
return self
def __next__(self):
"""Get next batch from prefetch queue."""
if self.stop_prefetch:
raise StopIteration
try:
# Get batch from prefetch queue
batch = self.prefetch_queue.get(timeout=1.0)
return batch
except queue.Empty:
# If queue is empty, get directly from data loader
return next(self.data_loader.__iter__())
def __len__(self):
return len(self.data_loader)
def stop(self):
"""Stop prefetching."""
self.stop_prefetch = True
if self.prefetch_thread:
self.prefetch_thread.join()
class DynamicBatchSampler(Sampler):
"""
Dynamic batch sampler that adjusts batch size based on memory availability.
This sampler monitors system memory and adjusts batch sizes dynamically
to optimize memory usage and training performance.
"""
def __init__(self,
dataset_size: int,
base_batch_size: int = 32,
max_batch_size: int = 128,
memory_threshold: float = 0.8,
adjustment_factor: float = 1.2):
"""
Initialize dynamic batch sampler.
Args:
dataset_size: Size of the dataset
base_batch_size: Base batch size
max_batch_size: Maximum batch size
memory_threshold: Memory usage threshold for adjustment
adjustment_factor: Factor for batch size adjustment
"""
self.dataset_size = dataset_size
self.base_batch_size = base_batch_size
self.max_batch_size = max_batch_size
self.memory_threshold = memory_threshold
self.adjustment_factor = adjustment_factor
self.current_batch_size = base_batch_size
self.batch_history = deque(maxlen=10)
print(f"DynamicBatchSampler initialized with base batch size: {base_batch_size}")
def _get_memory_usage(self) -> float:
"""Get current memory usage as a fraction."""
memory = psutil.virtual_memory()
return memory.percent / 100.0
def _adjust_batch_size(self):
"""Adjust batch size based on memory usage."""
memory_usage = self._get_memory_usage()
if memory_usage > self.memory_threshold:
# Reduce batch size if memory usage is high
self.current_batch_size = max(
self.base_batch_size,
int(self.current_batch_size / self.adjustment_factor)
)
else:
# Increase batch size if memory usage is low
self.current_batch_size = min(
self.max_batch_size,
int(self.current_batch_size * self.adjustment_factor)
)
self.batch_history.append(self.current_batch_size)
def __iter__(self):
"""Generate batch indices."""
indices = list(range(self.dataset_size))
# Shuffle indices
np.random.shuffle(indices)
# Generate batches
for i in range(0, len(indices), self.current_batch_size):
batch_indices = indices[i:i + self.current_batch_size]
# Adjust batch size for next iteration
self._adjust_batch_size()
yield batch_indices
def __len__(self):
return (self.dataset_size + self.current_batch_size - 1) // self.current_batch_size
def get_stats(self) -> Dict[str, Any]:
"""Get sampler statistics."""
return {
"current_batch_size": self.current_batch_size,
"base_batch_size": self.base_batch_size,
"max_batch_size": self.max_batch_size,
"memory_usage": self._get_memory_usage(),
"batch_history": list(self.batch_history)
}
class OptimizedDataLoader:
"""
High-performance data loader with multiple optimizations.
This data loader combines multiple optimization techniques:
- Prefetching with background threads
- Dynamic batch sizing
- Memory pinning
- Caching
- Efficient memory management
"""
def __init__(self,
dataset: Dataset,
batch_size: int = 32,
num_workers: int = 4,
prefetch_factor: int = 2,
pin_memory: bool = True,
shuffle: bool = True,
drop_last: bool = False,
use_dynamic_batching: bool = True,
cache_size: Optional[int] = None):
"""
Initialize optimized data loader.
Args:
dataset: Dataset to load
batch_size: Base batch size
num_workers: Number of worker processes
prefetch_factor: Number of batches to prefetch
pin_memory: Whether to pin memory
shuffle: Whether to shuffle data
drop_last: Whether to drop incomplete batches
use_dynamic_batching: Whether to use dynamic batch sizing
cache_size: Number of samples to cache
"""
self.dataset = dataset
self.batch_size = batch_size
self.num_workers = num_workers
self.prefetch_factor = prefetch_factor
self.pin_memory = pin_memory
self.shuffle = shuffle
self.drop_last = drop_last
self.use_dynamic_batching = use_dynamic_batching
self.cache_size = cache_size
# Create optimized dataset if caching is enabled
if cache_size and cache_size > 0:
self.dataset = OptimizedDataset(
dataset.data if hasattr(dataset, 'data') else dataset,
dataset.targets if hasattr(dataset, 'targets') else None,
cache_size=cache_size,
pin_memory=pin_memory
)
# Create sampler
if use_dynamic_batching:
self.sampler = DynamicBatchSampler(
dataset_size=len(self.dataset),
base_batch_size=batch_size,
max_batch_size=batch_size * 4
)
else:
self.sampler = None
# Create data loader
self.data_loader = DataLoader(
dataset=self.dataset,
batch_size=batch_size,
sampler=self.sampler,
shuffle=shuffle if not use_dynamic_batching else False,
num_workers=num_workers,
pin_memory=pin_memory,
drop_last=drop_last,
persistent_workers=True if num_workers > 0 else False
)
# Create prefetch loader
self.prefetch_loader = PrefetchDataLoader(
dataset=self.dataset,
batch_size=batch_size,
num_workers=num_workers,
prefetch_factor=prefetch_factor,
pin_memory=pin_memory,
shuffle=shuffle,
drop_last=drop_last
)
print(f"OptimizedDataLoader initialized with {num_workers} workers")
def __iter__(self):
"""Iterate over batches."""
return iter(self.prefetch_loader)
def __len__(self):
return len(self.data_loader)
def get_stats(self) -> Dict[str, Any]:
"""Get loader statistics."""
stats = {
"batch_size": self.batch_size,
"num_workers": self.num_workers,
"prefetch_factor": self.prefetch_factor,
"cache_enabled": self.cache_size is not None,
"dynamic_batching": self.use_dynamic_batching
}
if hasattr(self.dataset, 'get_cache_stats'):
stats.update(self.dataset.get_cache_stats())
if self.sampler:
stats.update(self.sampler.get_stats())
return stats
def stop(self):
"""Stop the data loader."""
self.prefetch_loader.stop()
def create_optimized_loader(dataset: Dataset,
batch_size: int = 32,
num_workers: Optional[int] = None,
**kwargs) -> OptimizedDataLoader:
"""
Create an optimized data loader with automatic configuration.
Args:
dataset: Dataset to load
batch_size: Batch size
num_workers: Number of workers (auto-detect if None)
**kwargs: Additional arguments
Returns:
OptimizedDataLoader: Configured data loader
"""
if num_workers is None:
# Auto-detect optimal number of workers
num_workers = min(4, os.cpu_count() or 1)
return OptimizedDataLoader(
dataset=dataset,
batch_size=batch_size,
num_workers=num_workers,
**kwargs
)
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