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<h1><a href="index.html">Annotated Research Paper Implementations: Transformers, StyleGAN, Stable Diffusion, DDPM/DDIM, LayerNorm, Nucleus Sampling and more</a></h1>
<p>This is a collection of simple PyTorch implementations of neural networks and related algorithms. <a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations">These implementations</a> are documented with explanations, and the <a href="index.html">website</a> renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.</p>
<p><img alt="Screenshot" src="dqn-light.png"></p>
<p>We are actively maintaining this repo and adding new implementations. <a href="https://twitter.com/labmlai"><img alt="Twitter" src="https://img.shields.io/twitter/follow/labmlai?style=social"></a> for updates.</p>
<h2>Translations</h2>
<h3><strong><a href="https://nn.labml.ai">English (original)</a></strong></h3>
<h3><strong><a href="https://nn.labml.ai/zh/">Chinese (translated)</a></strong></h3>
<h3><strong><a href="https://nn.labml.ai/ja/">Japanese (translated)</a></strong></h3>
<h2>Paper Implementations</h2>
<h4>✨ <a href="transformers/index.html">Transformers</a></h4>
<ul><li><a href="transformers/mha.html">Multi-headed attention</a> </li>
<li><a href="transformers/models.html">Transformer building blocks</a> </li>
<li><a href="transformers/xl/index.html">Transformer XL</a> </li>
<li><a href="transformers/xl/relative_mha.html">Relative multi-headed attention</a> </li>
<li><a href="transformers/rope/index.html">Rotary Positional Embeddings (RoPE)</a> </li>
<li><a href="transformers/alibi/index.html">Attention with Linear Biases (ALiBi)</a> </li>
<li><a href="transformers/retro/index.html">RETRO</a> </li>
<li><a href="transformers/compressive/index.html">Compressive Transformer</a> </li>
<li><a href="transformers/gpt/index.html">GPT Architecture</a> </li>
<li><a href="transformers/glu_variants/simple.html">GLU Variants</a> </li>
<li><a href="transformers/knn/index.html">kNN-LM: Generalization through Memorization</a> </li>
<li><a href="transformers/feedback/index.html">Feedback Transformer</a> </li>
<li><a href="transformers/switch/index.html">Switch Transformer</a> </li>
<li><a href="transformers/fast_weights/index.html">Fast Weights Transformer</a> </li>
<li><a href="transformers/fnet/index.html">FNet</a> </li>
<li><a href="transformers/aft/index.html">Attention Free Transformer</a> </li>
<li><a href="transformers/mlm/index.html">Masked Language Model</a> </li>
<li><a href="transformers/mlp_mixer/index.html">MLP-Mixer: An all-MLP Architecture for Vision</a> </li>
<li><a href="transformers/gmlp/index.html">Pay Attention to MLPs (gMLP)</a> </li>
<li><a href="transformers/vit/index.html">Vision Transformer (ViT)</a> </li>
<li><a href="transformers/primer_ez/index.html">Primer EZ</a> </li>
<li><a href="transformers/hour_glass/index.html">Hourglass</a></li></ul>
<h4>✨ <a href="lora/index.html">Low-Rank Adaptation (LoRA)</a></h4>
<h4>✨ <a href="neox/index.html">Eleuther GPT-NeoX</a></h4>
<ul><li><a href="neox/samples/generate.html">Generate on a 48GB GPU</a> </li>
<li><a href="neox/samples/finetune.html">Finetune on two 48GB GPUs</a> </li>
<li><a href="neox/utils/llm_int8.html">LLM.int8()</a></li></ul>
<h4>✨ <a href="diffusion/index.html">Diffusion models</a></h4>
<ul><li><a href="diffusion/ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a> </li>
<li><a href="diffusion/stable_diffusion/sampler/ddim.html">Denoising Diffusion Implicit Models (DDIM)</a> </li>
<li><a href="diffusion/stable_diffusion/latent_diffusion.html">Latent Diffusion Models</a> </li>
<li><a href="diffusion/stable_diffusion/index.html">Stable Diffusion</a></li></ul>
<h4>✨ <a href="gan/index.html">Generative Adversarial Networks</a></h4>
<ul><li><a href="gan/original/index.html">Original GAN</a> </li>
<li><a href="gan/dcgan/index.html">GAN with deep convolutional network</a> </li>
<li><a href="gan/cycle_gan/index.html">Cycle GAN</a> </li>
<li><a href="gan/wasserstein/index.html">Wasserstein GAN</a> </li>
<li><a href="gan/wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a> </li>
<li><a href="gan/stylegan/index.html">StyleGAN 2</a></li></ul>
<h4>✨ <a href="recurrent_highway_networks/index.html">Recurrent Highway Networks</a></h4>
<h4>✨ <a href="lstm/index.html">LSTM</a></h4>
<h4>✨ <a href="hypernetworks/hyper_lstm.html">HyperNetworks - HyperLSTM</a></h4>
<h4>✨ <a href="resnet/index.html">ResNet</a></h4>
<h4>✨ <a href="conv_mixer/index.html">ConvMixer</a></h4>
<h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4>
<h4>✨ <a href="unet/index.html">U-Net</a></h4>
<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
<h4>✨ Graph Neural Networks</h4>
<ul><li><a href="graphs/gat/index.html">Graph Attention Networks (GAT)</a> </li>
<li><a href="graphs/gatv2/index.html">Graph Attention Networks v2 (GATv2)</a></li></ul>
<h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4>
<ul><li><a href="rl/ppo/index.html">Proximal Policy Optimization</a> with <a href="rl/ppo/gae.html">Generalized Advantage Estimation</a> </li>
<li><a href="rl/dqn/index.html">Deep Q Networks</a> with with <a href="rl/dqn/model.html">Dueling Network</a>, <a href="rl/dqn/replay_buffer.html">Prioritized Replay</a> and Double Q Network.</li></ul>
<h4>✨ <a href="cfr/index.html">Counterfactual Regret Minimization (CFR)</a></h4>
<p>Solving games with incomplete information such as poker with CFR.</p>
<ul><li><a href="cfr/kuhn/index.html">Kuhn Poker</a></li></ul>
<h4>✨ <a href="optimizers/index.html">Optimizers</a></h4>
<ul><li><a href="optimizers/adam.html">Adam</a> </li>
<li><a href="optimizers/amsgrad.html">AMSGrad</a> </li>
<li><a href="optimizers/adam_warmup.html">Adam Optimizer with warmup</a> </li>
<li><a href="optimizers/noam.html">Noam Optimizer</a> </li>
<li><a href="optimizers/radam.html">Rectified Adam Optimizer</a> </li>
<li><a href="optimizers/ada_belief.html">AdaBelief Optimizer</a> </li>
<li><a href="optimizers/sophia.html">Sophia-G Optimizer</a></li></ul>
<h4>✨ <a href="normalization/index.html">Normalization Layers</a></h4>
<ul><li><a href="normalization/batch_norm/index.html">Batch Normalization</a> </li>
<li><a href="normalization/layer_norm/index.html">Layer Normalization</a> </li>
<li><a href="normalization/instance_norm/index.html">Instance Normalization</a> </li>
<li><a href="normalization/group_norm/index.html">Group Normalization</a> </li>
<li><a href="normalization/weight_standardization/index.html">Weight Standardization</a> </li>
<li><a href="normalization/batch_channel_norm/index.html">Batch-Channel Normalization</a> </li>
<li><a href="normalization/deep_norm/index.html">DeepNorm</a></li></ul>
<h4>✨ <a href="distillation/index.html">Distillation</a></h4>
<h4>✨ <a href="adaptive_computation/index.html">Adaptive Computation</a></h4>
<ul><li><a href="adaptive_computation/ponder_net/index.html">PonderNet</a></li></ul>
<h4>✨ <a href="uncertainty/index.html">Uncertainty</a></h4>
<ul><li><a href="uncertainty/evidence/index.html">Evidential Deep Learning to Quantify Classification Uncertainty</a></li></ul>
<h4>✨ <a href="activations/index.html">Activations</a></h4>
<ul><li><a href="activations/fta/index.html">Fuzzy Tiling Activations</a></li></ul>
<h4>✨ <a href="sampling/index.html">Language Model Sampling Techniques</a></h4>
<ul><li><a href="sampling/greedy.html">Greedy Sampling</a> </li>
<li><a href="sampling/temperature.html">Temperature Sampling</a> </li>
<li><a href="sampling/top_k.html">Top-k Sampling</a> </li>
<li><a href="sampling/nucleus.html">Nucleus Sampling</a></li></ul>
<h4>✨ <a href="scaling/index.html">Scalable Training/Inference</a></h4>
<ul><li><a href="scaling/zero3/index.html">Zero3 memory optimizations</a></li></ul>
<h3>Installation</h3>
<pre class="highlight lang-bash"><code><span></span>pip<span class="w"> </span>install<span class="w"> </span>labml-nn</code></pre>
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