In CUDA programming, memory management functions are essential for optimizing data transfer between the host (CPU) and the device (GPU).
In this case you move data manually from host/device side to the other side.
You first malloc memory on host and copy it to device via cudaMemcpy. When the computation on device is finished, you copy the result back via cudaMemcpy again.
The starting point of all optimizations is to better "squeeze" hardware performance through programming.
The GPU architecture is built around a scalable array of Streaming Multiprocessors (SM). GPU hardware parallelism is achieved through the replication of this architectural building block.
Each SM in a GPU is designed to support concurrent execution of hundreds of threads, and there are generally multiple SMs per GPU, so it is possible to have thousands of threads executing concurrently on a single GPU. When a kernel grid is launched, the thread blocks of that kernel grid are distributed among available SMs for execution. Once scheduled on an SM, the threads of a thread block execute concurrently only on that assigned SM. Multiple thread blocks may be assigned to the same SM at once and are scheduled based on the availability of SM resources. Instructions within a single thread are pipelined to leverage instruction-level parallelism, in addition to the thread-level parallelism you are already familiar with in CUDA.
Key components of a Fermi SM are:
Figure: Key components of a Fermi(a GPU architecture) SM
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It enables developers to utilize the immense computational power of NVIDIA GPUs (Graphics Processing Units) for general-purpose processing tasks beyond graphics rendering.
Key features of CUDA programming include:
CUDA programming allows developers to harness the massive parallel processing power of GPUs to accelerate a wide range of computational tasks, including scientific simulations, image and video processing, machine learning, and more.
The main purpose of this article is to use basic self-attention blocks to build a simple large language model for learning purposes. Due to limitations in model scale and embedding methods, the model built in this article will not be very effective, but this does not affect the ability to learn various basic concepts of language models similar to Transformer through the code provided in this article.
What happens on this page:
Code available at github.com/visualDust/naive-llm-from-scratch
Download the code via git clone before continue.
AutoEncoders are a type of artificial neural network used primarily for unsupervised learning tasks, particularly in the field of dimensionality reduction and feature learning. Their general purpose is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction or noise reduction.
The main components of an autoencoder are:
Encoder: This part of the network compresses the input into a latent space representation. It encodes the input data as an encoded representation in a reduced dimension. The encoder layer is typically composed of a series of layers that gradually decrease in size.
Bottleneck: This is the layer that contains the encoded representation of the input data. It is the heart of the network, which holds the compressed knowledge of the input data. The bottleneck is where the dimensionality reduction takes place.
Decoder: The decoder network performs the reverse operation of the encoder. It takes the encoded data from the bottleneck and reconstructs the input data as closely as possible. This part of the network is typically symmetrical to the encoder, with layers increasing in size.
The objective of an autoencoder is to minimize the difference between the original input and its reconstruction, typically measured by a loss function like mean squared error. By learning to reconstruct the input data, the network learns valuable properties about the data and its structure. AutoEncoders are used in various applications like anomaly detection, image denoising, and as a pre-training step for deep learning models.
The objective of an autoencoder is to minimize the difference between the original input and its reconstruction, typically measured by a loss function like mean squared error. By learning to reconstruct the input data, the network learns valuable properties about the data and its structure.
Code available at gist : single-file-linear-ave.py
This is a single file approach consists of model architecture, training code, testing code, and inference code. We will talk about each part of the code separately later. If you're in hurry, run this single file python script and open localhost:20202 to see the result.
Please make a new directory for putting this file, once you run this file, it automatically download MNIST dataset to the relative path ./data. You might not want the code download data into unwanted place.
I'm working on some semantic segmentation related code, where I need to enhance segmentation accuracy on boundaries. Therefore, I tried to use boundary loss to assist model training. This article is my attempt and codes.
Our purpose is clear. In Cityscapes, we have indexed image that represents pixels of each class named gtFine_labelIds. What we want is to generate class level boundary from gtFine_labelIds so that we can use it to optimize boundary regions for specific class.
注意力机制在很多AI领域内得到了成功的应用。这是人工神经网络在模仿人类进行决策过程的重要发展。
In humans, Attention is a core property of all perceptual and cognitive operations. Given our limited ability to process competing sources, attention mechanisms select, modulate, and focus on the information most relevant to behavior.
上面这段文字摘自Alana de Santana Correia, and Esther Luna Colombini的论文 ATTENTION, PLEASE ! A SURVEY OF NEURAL ATTENTION MODELS IN DEEP LEARNING。你应该注意到了，在你的视野中，只有一部分区域是很清晰的。对于视野周围的场景，你往往需要转转眼珠，把视野朝向它，才能完全看清。或者，你还发现，比起历史老师开始强调重点，你似乎对下课铃声的响起更加敏感——这就是注意力。你所处的环境包含着远超你的处理能力的信息，而注意力机制让你的大脑集中精力处理你视野中心的场景，或是你“更应该”关心的事物。
Attention机制听上去是一个很高大上的词汇，实际上，Attention在不经意间就会被使用。例如，循环神经网络中每一步计算都依赖于上一步计算结果的过程就可以被视为一种Attention：在 Attention 机制引入之前，有一个问题大家一直很苦恼：长距离的信息会被弱化，就好像记忆能力弱的人，记不住过去的事情是一样的。
如上图，在处理序列的循环神经网咯中，Attention的功能是关注重点，就算文本比较长，也能从中间抓住重点，不丢失重要的信息。上图中红色的预期就是被挑出来的重点。
对于文字序列的输入，有基于编码器解码器的注意力；对图像输入，有各种各样的空间注意力；在神经网络处理得过程中，还有通道注意力；还有强大的自注意力，并且具有能够将自注意力适用于各种输入的广泛化设计。
Attention具有以下三大优点：参数少、速度快、效果好。Attention机制如果浅层的理解，跟他的名字非常匹配。他的核心逻辑就是从关注全部到关注重点。在认知科学中，由于信息处理的瓶颈，人类会选择性地关注所有信息的一部分，同时忽略其他信息。同理，当神经网络处理大量的信息输入时，快速聚焦其中一些关键信息来进行处理，这便是注意力机制。
Scientific articles are how scholars and researchers communicate with each other. Reading scientific articles helps you to participate in your comprehension by wondering how the researchers explain their ideas. Books, websites, papers, scientific magazines are general places to start with.
This tutorial will discuss:
论文作者：Yongming Rao, Wenliang Zhao, Zheng Zhu Jiwen Lu, Jie Zhou。论文原文点此URL。
本文提出概念简洁、计算性能优异的 Global Filter Network (GFNet)，该模型主要结构框架基于 Vision Transformer ，在频域中学习空间长距离（long-term spatial dependencies）关系，其具有较小的对数线性复杂性。
其主要创新是使用 Global Filter Layer 替换了 Vision Transformer 中的 self-attention 层中的每一个子层。该模型取得了可喜的高精度，同时仅具有 CNN 的复杂度。
