{"cells": [{"cell_type": "markdown", "id": "f529af98", "metadata": {"papermill": {"duration": 0.018004, "end_time": "2023-10-11T15:59:09.861812", "exception": false, "start_time": "2023-10-11T15:59:09.843808", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 5: Transformers and Multi-Head Attention\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-10-11T15:57:09.389944\n", "\n", "In this tutorial, we will discuss one of the most impactful architectures of the last 2 years: the Transformer model.\n", "Since the paper Attention Is All You Need by Vaswani et al. had been published in 2017,\n", "the Transformer architecture has continued to beat benchmarks in many domains, most importantly in Natural Language Processing.\n", "Transformers with an incredible amount of parameters can generate long, convincing essays, and opened up new application fields of AI.\n", "As the hype of the Transformer architecture seems not to come to an end in the next years,\n", "it is important to understand how it works, and have implemented it yourself, which we will do in this notebook.\n", "This notebook is part of a lecture series on Deep Learning at the University of Amsterdam.\n", "The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.\n", "\n", "\n", "---\n", "Open in [![Open In Colab](data:image/png;base64,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){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/05-transformers-and-MH-attention.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "51b8a03d", "metadata": {"papermill": {"duration": 0.01505, "end_time": "2023-10-11T15:59:09.892188", "exception": false, "start_time": "2023-10-11T15:59:09.877138", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "33c4328b", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-10-11T15:59:09.919787Z", "iopub.status.busy": "2023-10-11T15:59:09.919616Z", "iopub.status.idle": "2023-10-11T16:00:43.272093Z", "shell.execute_reply": "2023-10-11T16:00:43.270950Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 93.367236, "end_time": "2023-10-11T16:00:43.274306", "exception": false, "start_time": "2023-10-11T15:59:09.907070", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"matplotlib>=3.0.0, <3.9.0\" \"torchmetrics>=0.7, <1.3\" \"urllib3\" \"pytorch-lightning>=1.4, <2.1.0\" \"torchvision\" \"matplotlib\" \"lightning>=2.0.0\" \"setuptools>=68.0.0, <68.3.0\" \"ipython[notebook]>=8.0.0, <8.17.0\" \"seaborn\" \"torch>=1.8.1, <2.1.0\""]}, {"cell_type": "markdown", "id": "fd4f8056", "metadata": {"papermill": {"duration": 0.013152, "end_time": "2023-10-11T16:00:43.302735", "exception": false, "start_time": "2023-10-11T16:00:43.289583", "status": "completed"}, "tags": []}, "source": ["
\n", "Despite the huge success of Transformers in NLP, we will _not_ include the NLP domain in our notebook here.\n", "There are many courses at the University of Amsterdam that focus on Natural Language Processing\n", "and take a closer look at the application of the Transformer architecture in NLP\n", "([NLP2](https://studiegids.uva.nl/xmlpages/page/2020-2021/zoek-vak/vak/79628),\n", "[Advanced Topics in Computational Semantics](https://studiegids.uva.nl/xmlpages/page/2020-2021/zoek-vak/vak/80162)).\n", "Furthermore, and most importantly, there is so much more to the Transformer architecture.\n", "NLP is the domain the Transformer architecture has been originally proposed for and had the greatest impact on,\n", "but it also accelerated research in other domains, recently even [Computer Vision](https://arxiv.org/abs/2010.11929).\n", "Thus, we focus here on what makes the Transformer and self-attention so powerful in general.\n", "In a second notebook, we will look at Vision Transformers, i.e. Transformers for image classification\n", "([link to notebook](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial15/Vision_Transformer.html)).\n", "\n", "Below, we import our standard libraries."]}, {"cell_type": "code", "execution_count": 2, "id": "debdc261", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:43.334263Z", "iopub.status.busy": "2023-10-11T16:00:43.333737Z", "iopub.status.idle": "2023-10-11T16:00:47.564569Z", "shell.execute_reply": "2023-10-11T16:00:47.563598Z"}, "papermill": {"duration": 4.247711, "end_time": "2023-10-11T16:00:47.567149", "exception": false, "start_time": "2023-10-11T16:00:43.319438", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Device: cuda:0\n"]}, {"data": {"text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Standard libraries\n", "import math\n", "import os\n", "import urllib.request\n", "from functools import partial\n", "from urllib.error import HTTPError\n", "\n", "# PyTorch Lightning\n", "import lightning as L\n", "\n", "# Plotting\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib_inline.backend_inline\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "# PyTorch\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import torch.utils.data as data\n", "\n", "# Torchvision\n", "import torchvision\n", "from lightning.pytorch.callbacks import ModelCheckpoint\n", "from torchvision import transforms\n", "from torchvision.datasets import CIFAR100\n", "from tqdm.notebook import tqdm\n", "\n", "plt.set_cmap(\"cividis\")\n", "%matplotlib inline\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "matplotlib.rcParams[\"lines.linewidth\"] = 2.0\n", "sns.reset_orig()\n", "\n", "# Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)\n", "DATASET_PATH = os.environ.get(\"PATH_DATASETS\", \"data/\")\n", "# Path to the folder where the pretrained models are saved\n", "CHECKPOINT_PATH = os.environ.get(\"PATH_CHECKPOINT\", \"saved_models/Transformers/\")\n", "\n", "# Setting the seed\n", "L.seed_everything(42)\n", "\n", "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.deterministic = True\n", "torch.backends.cudnn.benchmark = False\n", "\n", "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "print(\"Device:\", device)"]}, {"cell_type": "markdown", "id": "87e9a925", "metadata": {"papermill": {"duration": 0.011524, "end_time": "2023-10-11T16:00:47.656735", "exception": false, "start_time": "2023-10-11T16:00:47.645211", "status": "completed"}, "tags": []}, "source": ["Two pre-trained models are downloaded below.\n", "Make sure to have adjusted your `CHECKPOINT_PATH` before running this code if not already done."]}, {"cell_type": "code", "execution_count": 3, "id": "b72afc91", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:47.681776Z", "iopub.status.busy": "2023-10-11T16:00:47.680706Z", "iopub.status.idle": "2023-10-11T16:00:48.284845Z", "shell.execute_reply": "2023-10-11T16:00:48.283431Z"}, "papermill": {"duration": 0.618852, "end_time": "2023-10-11T16:00:48.286796", "exception": false, "start_time": "2023-10-11T16:00:47.667944", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/ReverseTask.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/SetAnomalyTask.ckpt...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/\"\n", "# Files to download\n", "pretrained_files = [\"ReverseTask.ckpt\", \"SetAnomalyTask.ckpt\"]\n", "\n", "# Create checkpoint path if it doesn't exist yet\n", "os.makedirs(CHECKPOINT_PATH, exist_ok=True)\n", "\n", "# For each file, check whether it already exists. If not, try downloading it.\n", "for file_name in pretrained_files:\n", " file_path = os.path.join(CHECKPOINT_PATH, file_name)\n", " if \"/\" in file_name:\n", " os.makedirs(file_path.rsplit(\"/\", 1)[0], exist_ok=True)\n", " if not os.path.isfile(file_path):\n", " file_url = base_url + file_name\n", " print(\"Downloading %s...\" % file_url)\n", " try:\n", " urllib.request.urlretrieve(file_url, file_path)\n", " except HTTPError as e:\n", " print(\n", " \"Something went wrong. Please try to download the file manually,\"\n", " \" or contact the author with the full output including the following error:\\n\",\n", " e,\n", " )"]}, {"cell_type": "markdown", "id": "d6aa4032", "metadata": {"papermill": {"duration": 0.011368, "end_time": "2023-10-11T16:00:48.312197", "exception": false, "start_time": "2023-10-11T16:00:48.300829", "status": "completed"}, "tags": []}, "source": ["## The Transformer architecture\n", "\n", "In the first part of this notebook, we will implement the Transformer architecture by hand.\n", "As the architecture is so popular, there already exists a Pytorch module `nn.Transformer`\n", "([documentation](https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html))\n", "and a [tutorial](https://pytorch.org/tutorials/beginner/transformer_tutorial.html)\n", "on how to use it for next token prediction.\n", "However, we will implement it here ourselves, to get through to the smallest details.\n", "\n", "There are of course many more tutorials out there about attention and Transformers.\n", "Below, we list a few that are worth exploring if you are interested in the topic\n", "and might want yet another perspective on the topic after this one:\n", "\n", "* [Transformer: A Novel Neural Network Architecture for Language Understanding\n", "(Jakob Uszkoreit, 2017)](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) - The original Google blog post about the Transformer paper, focusing on the application in machine translation.\n", "* [The Illustrated Transformer (Jay Alammar, 2018)](http://jalammar.github.io/illustrated-transformer/) - A very popular and great blog post intuitively explaining the Transformer architecture with many nice visualizations.\n", "The focus is on NLP.\n", "* [Attention?\n", "Attention!\n", "(Lilian Weng, 2018)](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) - A nice blog post summarizing attention mechanisms in many domains including vision.\n", "* [Illustrated: Self-Attention (Raimi Karim, 2019)](https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a) - A nice visualization of the steps of self-attention.\n", "Recommended going through if the explanation below is too abstract for you.\n", "* [The Transformer family (Lilian Weng, 2020)](https://lilianweng.github.io/lil-log/2020/04/07/the-transformer-family.html) - A very detailed blog post reviewing more variants of Transformers besides the original one."]}, {"cell_type": "markdown", "id": "c9f97649", "metadata": {"papermill": {"duration": 0.011308, "end_time": "2023-10-11T16:00:48.334935", "exception": false, "start_time": "2023-10-11T16:00:48.323627", "status": "completed"}, "tags": []}, "source": ["### What is Attention?\n", "\n", "The attention mechanism describes a recent new group of layers in neural networks that has attracted\n", "a lot of interest in the past few years, especially in sequence tasks.\n", "There are a lot of different possible definitions of \"attention\" in the literature,\n", "but the one we will use here is the following: _the attention mechanism describes a weighted average\n", "of (sequence) elements with the weights dynamically computed based on an input query and elements' keys_.\n", "So what does this exactly mean?\n", "The goal is to take an average over the features of multiple elements.\n", "However, instead of weighting each element equally, we want to weight them depending on their actual values.\n", "In other words, we want to dynamically decide on which inputs we want to \"attend\" more than others.\n", "In particular, an attention mechanism has usually four parts we need to specify:\n", "\n", "* **Query**: The query is a feature vector that describes what we are looking for in the sequence, i.e. what would we maybe want to pay attention to.\n", "* **Keys**: For each input element, we have a key which is again a feature vector.\n", "This feature vector roughly describes what the element is \"offering\", or when it might be important.\n", "The keys should be designed such that we can identify the elements we want to pay attention to based on the query.\n", "* **Values**: For each input element, we also have a value vector.\n", "This feature vector is the one we want to average over.\n", "* **Score function**: To rate which elements we want to pay attention to, we need to specify a score function $f_{attn}$.\n", "The score function takes the query and a key as input, and output the score/attention weight of the query-key pair.\n", "It is usually implemented by simple similarity metrics like a dot product, or a small MLP.\n", "\n", "\n", "The weights of the average are calculated by a softmax over all score function outputs.\n", "Hence, we assign those value vectors a higher weight whose corresponding key is most similar to the query.\n", "If we try to describe it with pseudo-math, we can write:\n", "\n", "$$\n", "\\alpha_i = \\frac{\\exp\\left(f_{attn}\\left(\\text{key}_i, \\text{query}\\right)\\right)}{\\sum_j \\exp\\left(f_{attn}\\left(\\text{key}_j, \\text{query}\\right)\\right)}, \\hspace{5mm} \\text{out} = \\sum_i \\alpha_i \\cdot \\text{value}_i\n", "$$\n", "\n", "Visually, we can show the attention over a sequence of words as follows:\n", "\n", "
\n", "\n", "For every word, we have one key and one value vector.\n", "The query is compared to all keys with a score function (in this case the dot product) to determine the weights.\n", "The softmax is not visualized for simplicity.\n", "Finally, the value vectors of all words are averaged using the attention weights.\n", "\n", "Most attention mechanisms differ in terms of what queries they use, how the key and value vectors are defined,\n", "and what score function is used.\n", "The attention applied inside the Transformer architecture is called **self-attention**.\n", "In self-attention, each sequence element provides a key, value, and query.\n", "For each element, we perform an attention layer where based on its query,\n", "we check the similarity of the all sequence elements' keys, and returned a different,\n", "averaged value vector for each element.\n", "We will now go into a bit more detail by first looking at the specific implementation of the attention mechanism\n", "which is in the Transformer case the scaled dot product attention."]}, {"cell_type": "markdown", "id": "bc3e18be", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011336, "end_time": "2023-10-11T16:00:48.357565", "exception": false, "start_time": "2023-10-11T16:00:48.346229", "status": "completed"}, "tags": []}, "source": ["### Scaled Dot Product Attention\n", "\n", "The core concept behind self-attention is the scaled dot product attention.\n", "Our goal is to have an attention mechanism with which any element in a sequence can attend to any other while\n", "still being efficient to compute.\n", "The dot product attention takes as input a set of queries\n", "$Q\\in\\mathbb{R}^{T\\times d_k}$, keys $K\\in\\mathbb{R}^{T\\times d_k}$\n", "and values $V\\in\\mathbb{R}^{T\\times d_v}$ where $T$ is the sequence length,\n", "and $d_k$ and $d_v$ are the hidden dimensionality for queries/keys and values respectively.\n", "For simplicity, we neglect the batch dimension for now.\n", "The attention value from element $i$ to $j$ is based on its similarity of the query $Q_i$ and key $K_j$,\n", "using the dot product as the similarity metric.\n", "In math, we calculate the dot product attention as follows:\n", "\n", "$$\\text{Attention}(Q,K,V)=\\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n", "\n", "The matrix multiplication $QK^T$ performs the dot product for every possible pair of queries and keys,\n", "resulting in a matrix of the shape $T\\times T$.\n", "Each row represents the attention logits for a specific element $i$ to all other elements in the sequence.\n", "On these, we apply a softmax and multiply with the value vector to obtain a weighted mean\n", "(the weights being determined by the attention).\n", "Another perspective on this attention mechanism offers the computation graph which is visualized below\n", "(figure credit - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)).\n", "\n", "
\n", "\n", "One aspect we haven't discussed yet is the scaling factor of $1/\\sqrt{d_k}$.\n", "This scaling factor is crucial to maintain an appropriate variance of attention values after initialization.\n", "Remember that we initialize our layers with the intention of having equal variance throughout the model, and hence,\n", "$Q$ and $K$ might also have a variance close to $1$.\n", "However, performing a dot product over two vectors with a variance $\\sigma$ results\n", "in a scalar having $d_k$-times higher variance:\n", "\n", "$$q_i \\sim \\mathcal{N}(0,\\sigma), k_i \\sim \\mathcal{N}(0,\\sigma) \\to \\text{Var}\\left(\\sum_{i=1}^{d_k} q_i\\cdot k_i\\right) = \\sigma\\cdot d_k$$\n", "\n", "\n", "If we do not scale down the variance back to $\\sigma$, the softmax over the logits will already saturate\n", "to $1$ for one random element and $0$ for all others.\n", "The gradients through the softmax will be close to zero so that we can't learn the parameters appropriately.\n", "\n", "The block `Mask (opt.\n", ")` in the diagram above represents the optional masking of specific entries in the attention matrix.\n", "This is for instance used if we stack multiple sequences with different lengths into a batch.\n", "To still benefit from parallelization in PyTorch, we pad the sentences to the same length and mask out the padding\n", "tokens during the calculation of the attention values.\n", "This is usually done by setting the respective attention logits to a very low value.\n", "\n", "After we have discussed the details of the scaled dot product attention block, we can write a function below\n", "which computes the output features given the triple of queries, keys, and values:"]}, {"cell_type": "code", "execution_count": 4, "id": "40b728f1", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.381832Z", "iopub.status.busy": "2023-10-11T16:00:48.381238Z", "iopub.status.idle": "2023-10-11T16:00:48.388709Z", "shell.execute_reply": "2023-10-11T16:00:48.387502Z"}, "papermill": {"duration": 0.021433, "end_time": "2023-10-11T16:00:48.390296", "exception": false, "start_time": "2023-10-11T16:00:48.368863", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def scaled_dot_product(q, k, v, mask=None):\n", " d_k = q.size()[-1]\n", " attn_logits = torch.matmul(q, k.transpose(-2, -1))\n", " attn_logits = attn_logits / math.sqrt(d_k)\n", " if mask is not None:\n", " attn_logits = attn_logits.masked_fill(mask == 0, -9e15)\n", " attention = F.softmax(attn_logits, dim=-1)\n", " values = torch.matmul(attention, v)\n", " return values, attention"]}, {"cell_type": "markdown", "id": "8844c805", "metadata": {"papermill": {"duration": 0.020493, "end_time": "2023-10-11T16:00:48.423896", "exception": false, "start_time": "2023-10-11T16:00:48.403403", "status": "completed"}, "tags": []}, "source": ["Note that our code above supports any additional dimensionality in front of the sequence length\n", "so that we can also use it for batches.\n", "However, for a better understanding, let's generate a few random queries, keys, and value vectors,\n", "and calculate the attention outputs:"]}, {"cell_type": "code", "execution_count": 5, "id": "d3e0aab2", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.451343Z", "iopub.status.busy": "2023-10-11T16:00:48.450570Z", "iopub.status.idle": "2023-10-11T16:00:48.490229Z", "shell.execute_reply": "2023-10-11T16:00:48.489147Z"}, "papermill": {"duration": 0.055185, "end_time": "2023-10-11T16:00:48.491761", "exception": false, "start_time": "2023-10-11T16:00:48.436576", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Q\n", " tensor([[ 0.3367, 0.1288],\n", " [ 0.2345, 0.2303],\n", " [-1.1229, -0.1863]])\n", "K\n", " tensor([[ 2.2082, -0.6380],\n", " [ 0.4617, 0.2674],\n", " [ 0.5349, 0.8094]])\n", "V\n", " tensor([[ 1.1103, -1.6898],\n", " [-0.9890, 0.9580],\n", " [ 1.3221, 0.8172]])\n", "Values\n", " tensor([[ 0.5698, -0.1520],\n", " [ 0.5379, -0.0265],\n", " [ 0.2246, 0.5556]])\n", "Attention\n", " tensor([[0.4028, 0.2886, 0.3086],\n", " [0.3538, 0.3069, 0.3393],\n", " [0.1303, 0.4630, 0.4067]])\n"]}], "source": ["seq_len, d_k = 3, 2\n", "L.seed_everything(42)\n", "q = torch.randn(seq_len, d_k)\n", "k = torch.randn(seq_len, d_k)\n", "v = torch.randn(seq_len, d_k)\n", "values, attention = scaled_dot_product(q, k, v)\n", "print(\"Q\\n\", q)\n", "print(\"K\\n\", k)\n", "print(\"V\\n\", v)\n", "print(\"Values\\n\", values)\n", "print(\"Attention\\n\", attention)"]}, {"cell_type": "markdown", "id": "72de3e2b", "metadata": {"papermill": {"duration": 0.011482, "end_time": "2023-10-11T16:00:48.515225", "exception": false, "start_time": "2023-10-11T16:00:48.503743", "status": "completed"}, "tags": []}, "source": ["Before continuing, make sure you can follow the calculation of the specific values here, and also check it by hand.\n", "It is important to fully understand how the scaled dot product attention is calculated."]}, {"cell_type": "markdown", "id": "51adfddd", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011439, "end_time": "2023-10-11T16:00:48.538180", "exception": false, "start_time": "2023-10-11T16:00:48.526741", "status": "completed"}, "tags": []}, "source": ["### Multi-Head Attention\n", "\n", "The scaled dot product attention allows a network to attend over a sequence.\n", "However, often there are multiple different aspects a sequence element wants to attend to,\n", "and a single weighted average is not a good option for it.\n", "This is why we extend the attention mechanisms to multiple heads,\n", "i.e. multiple different query-key-value triplets on the same features.\n", "Specifically, given a query, key, and value matrix, we transform those into $h$ sub-queries, sub-keys,\n", "and sub-values, which we pass through the scaled dot product attention independently.\n", "Afterward, we concatenate the heads and combine them with a final weight matrix.\n", "Mathematically, we can express this operation as:\n", "\n", "$$\n", "\\begin{split}\n", " \\text{Multihead}(Q,K,V) & = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^{O}\\\\\n", " \\text{where } \\text{head}_i & = \\text{Attention}(QW_i^Q,KW_i^K, VW_i^V)\n", "\\end{split}\n", "$$\n", "\n", "We refer to this as Multi-Head Attention layer with the learnable parameters\n", "$W_{1...h}^{Q}\\in\\mathbb{R}^{D\\times d_k}$,\n", "$W_{1...h}^{K}\\in\\mathbb{R}^{D\\times d_k}$,\n", "$W_{1...h}^{V}\\in\\mathbb{R}^{D\\times d_v}$,\n", "and $W^{O}\\in\\mathbb{R}^{h\\cdot d_k\\times d_{out}}$ ($D$ being the input dimensionality).\n", "Expressed in a computational graph, we can visualize it as below\n", "(figure credit - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)).\n", "\n", "
\n", "\n", "How are we applying a Multi-Head Attention layer in a neural network,\n", "where we don't have an arbitrary query, key, and value vector as input?\n", "Looking at the computation graph above, a simple but effective implementation is to set the current\n", "feature map in a NN, $X\\in\\mathbb{R}^{B\\times T\\times d_{\\text{model}}}$, as $Q$, $K$ and $V$\n", "($B$ being the batch size, $T$ the sequence length, $d_{\\text{model}}$ the hidden dimensionality of $X$).\n", "The consecutive weight matrices $W^{Q}$, $W^{K}$, and $W^{V}$ can transform $X$ to the corresponding\n", "feature vectors that represent the queries, keys, and values of the input.\n", "Using this approach, we can implement the Multi-Head Attention module below."]}, {"cell_type": "code", "execution_count": 6, "id": "c7314d01", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.562846Z", "iopub.status.busy": "2023-10-11T16:00:48.562393Z", "iopub.status.idle": "2023-10-11T16:00:48.573355Z", "shell.execute_reply": "2023-10-11T16:00:48.572388Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.024936, "end_time": "2023-10-11T16:00:48.574673", "exception": false, "start_time": "2023-10-11T16:00:48.549737", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class MultiheadAttention(nn.Module):\n", " def __init__(self, input_dim, embed_dim, num_heads):\n", " super().__init__()\n", " assert embed_dim % num_heads == 0, \"Embedding dimension must be 0 modulo number of heads.\"\n", "\n", " self.embed_dim = embed_dim\n", " self.num_heads = num_heads\n", " self.head_dim = embed_dim // num_heads\n", "\n", " # Stack all weight matrices 1...h together for efficiency\n", " # Note that in many implementations you see \"bias=False\" which is optional\n", " self.qkv_proj = nn.Linear(input_dim, 3 * embed_dim)\n", " self.o_proj = nn.Linear(embed_dim, embed_dim)\n", "\n", " self._reset_parameters()\n", "\n", " def _reset_parameters(self):\n", " # Original Transformer initialization, see PyTorch documentation\n", " nn.init.xavier_uniform_(self.qkv_proj.weight)\n", " self.qkv_proj.bias.data.fill_(0)\n", " nn.init.xavier_uniform_(self.o_proj.weight)\n", " self.o_proj.bias.data.fill_(0)\n", "\n", " def forward(self, x, mask=None, return_attention=False):\n", " batch_size, seq_length, embed_dim = x.size()\n", " qkv = self.qkv_proj(x)\n", "\n", " # Separate Q, K, V from linear output\n", " qkv = qkv.reshape(batch_size, seq_length, self.num_heads, 3 * self.head_dim)\n", " qkv = qkv.permute(0, 2, 1, 3) # [Batch, Head, SeqLen, Dims]\n", " q, k, v = qkv.chunk(3, dim=-1)\n", "\n", " # Determine value outputs\n", " values, attention = scaled_dot_product(q, k, v, mask=mask)\n", " values = values.permute(0, 2, 1, 3) # [Batch, SeqLen, Head, Dims]\n", " values = values.reshape(batch_size, seq_length, embed_dim)\n", " o = self.o_proj(values)\n", "\n", " if return_attention:\n", " return o, attention\n", " else:\n", " return o"]}, {"cell_type": "markdown", "id": "898e2438", "metadata": {"papermill": {"duration": 0.011495, "end_time": "2023-10-11T16:00:48.597710", "exception": false, "start_time": "2023-10-11T16:00:48.586215", "status": "completed"}, "tags": []}, "source": ["One crucial characteristic of the multi-head attention is that it is permutation-equivariant with respect to its inputs.\n", "This means that if we switch two input elements in the sequence, e.g. $X_1\\leftrightarrow X_2$\n", "(neglecting the batch dimension for now), the output is exactly the same besides the elements 1 and 2 switched.\n", "Hence, the multi-head attention is actually looking at the input not as a sequence, but as a set of elements.\n", "This property makes the multi-head attention block and the Transformer architecture so powerful and widely applicable!\n", "But what if the order of the input is actually important for solving the task, like language modeling?\n", "The answer is to encode the position in the input features, which we will take a closer look at later\n", "(topic _Positional encodings_ below).\n", "\n", "Before moving on to creating the Transformer architecture, we can compare the self-attention operation\n", "with our other common layer competitors for sequence data: convolutions and recurrent neural networks.\n", "Below you can find a table by [Vaswani et al.\n", "(2017)](https://arxiv.org/abs/1706.03762) on the complexity per layer, the number of sequential operations,\n", "and maximum path length.\n", "The complexity is measured by the upper bound of the number of operations to perform, while the maximum path\n", "length represents the maximum number of steps a forward or backward signal has to traverse to reach any other position.\n", "The lower this length, the better gradient signals can backpropagate for long-range dependencies.\n", "Let's take a look at the table below:\n", "\n", "\n", "
\n", "\n", "$n$ is the sequence length, $d$ is the representation dimension and $k$ is the kernel size of convolutions.\n", "In contrast to recurrent networks, the self-attention layer can parallelize all its operations making it much faster\n", "to execute for smaller sequence lengths.\n", "However, when the sequence length exceeds the hidden dimensionality, self-attention becomes more expensive than RNNs.\n", "One way of reducing the computational cost for long sequences is by restricting the self-attention to a neighborhood\n", "of inputs to attend over, denoted by $r$.\n", "Nevertheless, there has been recently a lot of work on more efficient Transformer architectures that still allow long\n", "dependencies, of which you can find an overview in the paper by [Tay et al.\n", "(2020)](https://arxiv.org/abs/2009.06732) if interested."]}, {"cell_type": "markdown", "id": "941a94d7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011503, "end_time": "2023-10-11T16:00:48.620800", "exception": false, "start_time": "2023-10-11T16:00:48.609297", "status": "completed"}, "tags": []}, "source": ["### Transformer Encoder\n", "\n", "
\n", "\n", "Next, we will look at how to apply the multi-head attention blog inside the Transformer architecture.\n", "Originally, the Transformer model was designed for machine translation.\n", "Hence, it got an encoder-decoder structure where the encoder takes as input the sentence in the original language\n", "and generates an attention-based representation.\n", "On the other hand, the decoder attends over the encoded information and generates the translated sentence\n", "in an autoregressive manner, as in a standard RNN.\n", "While this structure is extremely useful for Sequence-to-Sequence tasks with the necessity of autoregressive decoding,\n", "we will focus here on the encoder part.\n", "Many advances in NLP have been made using pure encoder-based Transformer models (if interested, models include the\n", "[BERT](https://arxiv.org/abs/1810.04805)-family,\n", "the [Vision Transformer](https://arxiv.org/abs/2010.11929), and more),\n", "and in our tutorial, we will also mainly focus on the encoder part.\n", "If you have understood the encoder architecture, the decoder is a very small step to implement as well.\n", "The full Transformer architecture looks as follows\n", "(figure credit - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)).\n", ":\n", "\n", "
\n", "\n", "The encoder consists of $N$ identical blocks that are applied in sequence.\n", "Taking as input $x$, it is first passed through a Multi-Head Attention block as we have implemented above.\n", "The output is added to the original input using a residual connection,\n", "and we apply a consecutive Layer Normalization on the sum.\n", "Overall, it calculates $\\text{LayerNorm}(x+\\text{Multihead}(x,x,x))$\n", "($x$ being $Q$, $K$ and $V$ input to the attention layer).\n", "The residual connection is crucial in the Transformer architecture for two reasons:\n", "\n", "1.\n", "Similar to ResNets, Transformers are designed to be very deep.\n", "Some models contain more than 24 blocks in the encoder.\n", "Hence, the residual connections are crucial for enabling a smooth gradient flow through the model.\n", "2.\n", "Without the residual connection, the information about the original sequence is lost.\n", "Remember that the Multi-Head Attention layer ignores the position of elements in a sequence,\n", "and can only learn it based on the input features.\n", "Removing the residual connections would mean that this information is lost after the first attention layer\n", "(after initialization), and with a randomly initialized query and key vector,\n", "the output vectors for position $i$ has no relation to its original input.\n", "All outputs of the attention are likely to represent similar/same information,\n", "and there is no chance for the model to distinguish which information came from which input element.\n", "An alternative option to residual connection would be to fix at least one head to focus on its original input,\n", "but this is very inefficient and does not have the benefit of the improved gradient flow.\n", "\n", "The Layer Normalization also plays an important role in the Transformer architecture as it enables faster\n", "training and provides small regularization.\n", "Additionally, it ensures that the features are in a similar magnitude among the elements in the sequence.\n", "We are not using Batch Normalization because it depends on the batch size which is often small with Transformers\n", "(they require a lot of GPU memory), and BatchNorm has shown to perform particularly bad in language\n", "as the features of words tend to have a much higher variance (there are many, very rare words\n", "which need to be considered for a good distribution estimate).\n", "\n", "Additionally to the Multi-Head Attention, a small fully connected feed-forward network is added to the model,\n", "which is applied to each position separately and identically.\n", "Specifically, the model uses a Linear$\\to$ReLU$\\to$Linear MLP.\n", "The full transformation including the residual connection can be expressed as:\n", "\n", "$$\n", "\\begin{split}\n", " \\text{FFN}(x) & = \\max(0, xW_1+b_1)W_2 + b_2\\\\\n", " x & = \\text{LayerNorm}(x + \\text{FFN}(x))\n", "\\end{split}\n", "$$\n", "\n", "This MLP adds extra complexity to the model and allows transformations on each sequence element separately.\n", "You can imagine as this allows the model to \"post-process\" the new information added\n", "by the previous Multi-Head Attention, and prepare it for the next attention block.\n", "Usually, the inner dimensionality of the MLP is 2-8$\\times$ larger than $d_{\\text{model}}$,\n", "i.e. the dimensionality of the original input $x$.\n", "The general advantage of a wider layer instead of a narrow, multi-layer MLP is the faster, parallelizable execution.\n", "\n", "Finally, after looking at all parts of the encoder architecture, we can start implementing it below.\n", "We first start by implementing a single encoder block.\n", "Additionally to the layers described above, we will add dropout layers in the MLP and on the output\n", "of the MLP and Multi-Head Attention for regularization."]}, {"cell_type": "code", "execution_count": 7, "id": "01ae2ebc", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.645370Z", "iopub.status.busy": "2023-10-11T16:00:48.644918Z", "iopub.status.idle": "2023-10-11T16:00:48.653893Z", "shell.execute_reply": "2023-10-11T16:00:48.652957Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.02288, "end_time": "2023-10-11T16:00:48.655214", "exception": false, "start_time": "2023-10-11T16:00:48.632334", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class EncoderBlock(nn.Module):\n", " def __init__(self, input_dim, num_heads, dim_feedforward, dropout=0.0):\n", " \"\"\"EncoderBlock.\n", "\n", " Args:\n", " input_dim: Dimensionality of the input\n", " num_heads: Number of heads to use in the attention block\n", " dim_feedforward: Dimensionality of the hidden layer in the MLP\n", " dropout: Dropout probability to use in the dropout layers\n", " \"\"\"\n", " super().__init__()\n", "\n", " # Attention layer\n", " self.self_attn = MultiheadAttention(input_dim, input_dim, num_heads)\n", "\n", " # Two-layer MLP\n", " self.linear_net = nn.Sequential(\n", " nn.Linear(input_dim, dim_feedforward),\n", " nn.Dropout(dropout),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(dim_feedforward, input_dim),\n", " )\n", "\n", " # Layers to apply in between the main layers\n", " self.norm1 = nn.LayerNorm(input_dim)\n", " self.norm2 = nn.LayerNorm(input_dim)\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, x, mask=None):\n", " # Attention part\n", " attn_out = self.self_attn(x, mask=mask)\n", " x = x + self.dropout(attn_out)\n", " x = self.norm1(x)\n", "\n", " # MLP part\n", " linear_out = self.linear_net(x)\n", " x = x + self.dropout(linear_out)\n", " x = self.norm2(x)\n", "\n", " return x"]}, {"cell_type": "markdown", "id": "65a3ecb6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.01153, "end_time": "2023-10-11T16:00:48.678285", "exception": false, "start_time": "2023-10-11T16:00:48.666755", "status": "completed"}, "tags": []}, "source": ["Based on this block, we can implement a module for the full Transformer encoder.\n", "Additionally to a forward function that iterates through the sequence of encoder blocks,\n", "we also provide a function called `get_attention_maps`.\n", "The idea of this function is to return the attention probabilities for all Multi-Head Attention blocks in the encoder.\n", "This helps us in understanding, and in a sense, explaining the model.\n", "However, the attention probabilities should be interpreted with a grain of salt as it does not necessarily\n", "reflect the true interpretation of the model (there is a series of papers about this,\n", "including [Attention is not Explanation](https://arxiv.org/abs/1902.10186)\n", "and [Attention is not not Explanation](https://arxiv.org/abs/1908.04626))."]}, {"cell_type": "code", "execution_count": 8, "id": "d5c1bef5", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.703222Z", "iopub.status.busy": "2023-10-11T16:00:48.702833Z", "iopub.status.idle": "2023-10-11T16:00:48.710623Z", "shell.execute_reply": "2023-10-11T16:00:48.709565Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.022585, "end_time": "2023-10-11T16:00:48.712426", "exception": false, "start_time": "2023-10-11T16:00:48.689841", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class TransformerEncoder(nn.Module):\n", " def __init__(self, num_layers, **block_args):\n", " super().__init__()\n", " self.layers = nn.ModuleList([EncoderBlock(**block_args) for _ in range(num_layers)])\n", "\n", " def forward(self, x, mask=None):\n", " for layer in self.layers:\n", " x = layer(x, mask=mask)\n", " return x\n", "\n", " def get_attention_maps(self, x, mask=None):\n", " attention_maps = []\n", " for layer in self.layers:\n", " _, attn_map = layer.self_attn(x, mask=mask, return_attention=True)\n", " attention_maps.append(attn_map)\n", " x = layer(x)\n", " return attention_maps"]}, {"cell_type": "markdown", "id": "56b27f70", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011598, "end_time": "2023-10-11T16:00:48.735728", "exception": false, "start_time": "2023-10-11T16:00:48.724130", "status": "completed"}, "tags": []}, "source": ["### Positional encoding\n", "\n", "We have discussed before that the Multi-Head Attention block is permutation-equivariant,\n", "and cannot distinguish whether an input comes before another one in the sequence or not.\n", "In tasks like language understanding, however, the position is important for interpreting the input words.\n", "The position information can therefore be added via the input features.\n", "We could learn a embedding for every possible position, but this would not generalize to a dynamical\n", "input sequence length.\n", "Hence, the better option is to use feature patterns that the network can identify from the features\n", "and potentially generalize to larger sequences.\n", "The specific pattern chosen by Vaswani et al.\n", "are sine and cosine functions of different frequencies, as follows:\n", "\n", "$$\n", "PE_{(pos,i)} = \\begin{cases}\n", " \\sin\\left(\\frac{pos}{10000^{i/d_{\\text{model}}}}\\right) & \\text{if}\\hspace{3mm} i \\text{ mod } 2=0\\\\\n", " \\cos\\left(\\frac{pos}{10000^{(i-1)/d_{\\text{model}}}}\\right) & \\text{otherwise}\\\\\n", "\\end{cases}\n", "$$\n", "\n", "$PE_{(pos,i)}$ represents the position encoding at position $pos$ in the sequence, and hidden dimensionality $i$.\n", "These values, concatenated for all hidden dimensions, are added to the original input features\n", "(in the Transformer visualization above, see \"Positional encoding\"), and constitute the position information.\n", "We distinguish between even ($i \\text{ mod } 2=0$) and uneven ($i \\text{ mod } 2=1$)\n", "hidden dimensionalities where we apply a sine/cosine respectively.\n", "The intuition behind this encoding is that you can represent $PE_{(pos+k,:)}$ as a linear function\n", "of $PE_{(pos,:)}$, which might allow the model to easily attend to relative positions.\n", "The wavelengths in different dimensions range from $2\\pi$ to $10000\\cdot 2\\pi$.\n", "\n", "The positional encoding is implemented below.\n", "The code is taken from the [PyTorch tutorial](https://pytorch.org/tutorials/beginner/transformer_tutorial.html#define-the-model)\n", "about Transformers on NLP and adjusted for our purposes."]}, {"cell_type": "code", "execution_count": 9, "id": "ff7fb799", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.761094Z", "iopub.status.busy": "2023-10-11T16:00:48.760532Z", "iopub.status.idle": "2023-10-11T16:00:48.769867Z", "shell.execute_reply": "2023-10-11T16:00:48.768617Z"}, "papermill": {"duration": 0.024002, "end_time": "2023-10-11T16:00:48.771336", "exception": false, "start_time": "2023-10-11T16:00:48.747334", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class PositionalEncoding(nn.Module):\n", " def __init__(self, d_model, max_len=5000):\n", " \"\"\"Positional Encoding.\n", "\n", " Args:\n", " d_model: Hidden dimensionality of the input.\n", " max_len: Maximum length of a sequence to expect.\n", " \"\"\"\n", " super().__init__()\n", "\n", " # Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs\n", " pe = torch.zeros(max_len, d_model)\n", " position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n", " div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n", " pe[:, 0::2] = torch.sin(position * div_term)\n", " pe[:, 1::2] = torch.cos(position * div_term)\n", " pe = pe.unsqueeze(0)\n", "\n", " # register_buffer => Tensor which is not a parameter, but should be part of the modules state.\n", " # Used for tensors that need to be on the same device as the module.\n", " # persistent=False tells PyTorch to not add the buffer to the state dict (e.g. when we save the model)\n", " self.register_buffer(\"pe\", pe, persistent=False)\n", "\n", " def forward(self, x):\n", " x = x + self.pe[:, : x.size(1)]\n", " return x"]}, {"cell_type": "markdown", "id": "3d8bd748", "metadata": {"papermill": {"duration": 0.011964, "end_time": "2023-10-11T16:00:48.795009", "exception": false, "start_time": "2023-10-11T16:00:48.783045", "status": "completed"}, "tags": []}, "source": ["To understand the positional encoding, we can visualize it below.\n", "We will generate an image of the positional encoding over hidden dimensionality and position in a sequence.\n", "Each pixel, therefore, represents the change of the input feature we perform to encode the specific position.\n", "Let's do it below."]}, {"cell_type": "code", "execution_count": 10, "id": "6209348a", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:48.820421Z", "iopub.status.busy": "2023-10-11T16:00:48.819994Z", "iopub.status.idle": "2023-10-11T16:00:49.515500Z", "shell.execute_reply": "2023-10-11T16:00:49.514443Z"}, "papermill": {"duration": 0.71019, "end_time": "2023-10-11T16:00:49.516941", "exception": false, "start_time": "2023-10-11T16:00:48.806751", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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sPfiRIXM3/Rt+otm7WXqSydn/mOciU1H4UqguYkJdiBvPoRHwPaFrElmxvfE5LKOZc74HH4W4BDOhAWN9STK5qOaVIRNODHUcDlqkwrhrYsPiWtE8jdxu+0ZmZSaEDY9kQtwYgIgg6wKyGCyUNjYTMlnOA+0NyQ1aYNepG1GLgiuU1gl0olbEqszgs+bWdjdDLfLgqH3x+mhWl2CF0Uv1WHhfhT6YqZl27pJCeuFNOyLMHgqkMjstK7V7xOpugfo/y1Lw/cn3+B2vD838XJwKZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvTGVuZ3RoIDk0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWNwRHAIAgE/1RBCQoK2k8mk4f2/40QMnxg5w7uhAULtnlGHwWVJl4VWAdKY9xQj0C94XItydwFD3Anf9rQVJyW03dpkUlVKdykEnn/DmcmkKh50WOd9wtj+yM8CmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRVJLbkQxCNu/U3CBSOGXkPO0qrqY3n9bm0zVzeAJYGx4y1OmZMqwuSUjJNeUT30iQ6ym/DRyJCKm+EkJBXaVj8drS6yN7JGoFJ/a8eOx9Eam2RVa9e7Rpc2iUc3KyDnIEKGeFbqye9QO2fB6XEi675TNIRzL/1CBLGXdcgolQVvQd+wR3w8droIrgmGway6D7WUy1P/6hxZc7333YscugBas577BDgCopxO0BcgZ2u42KWgAVbqLScKj8npudqJso1Xp+RwAMw4wcsCIJVsdvtHeAJZ9XehFjYr9K0BRWUD8yNV2wd4xyUhwFuYGjr1wPMWZcEs4xgJAir3iGHrwJdjmL1euiJrwCXW6ZC+8wp7a5udCkwh3rQAOXmTDraujqJbt6TyC9mdFckaM1Is4OiGSWtI5guLSoB5a41w3seJtI7G5V9/uH+GcL1z26xdL7ITECmVuZHN0cmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0xlbmd0aCAxNjQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZDHcQUxDEPvqgIlMIAK9azH8w/r/q+G9NNBehhCDGJPwrBcV3FhdMOPty0zDX9HGe7G+jJjvNVYICfoAwyRiavRpPp2xRmq9OTVYq6jolwvOiISzJLjq0AjfDqyx5O2tjP9dF4f7CHvE/8qKuduYQEuqu5A+VIf8dSP2VHqmqGPKitrHmraV4RdEUrbPi6nMk7dvQNa4b2Vqz3a7z8edjryCmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCA3MiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohngJggbRDFIBZEsZmJGUQdnAGRy+BKAwAl2xbJCmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCA0NyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlyWEFYuF0wsB8wC0ZZwCiKewZUGALlnDScKZW5kc3RyZWFtCmVuZG9iagozMiAwIG9iago8PCAvTGVuZ3RoIDI1OCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkUtyBCAIRPeegiOA/OQ8k0plMbn/Ng3OZDZ2l6j9hEojphIs5xR5MH3J8s1ktul3OVY7GwUURSiYyVXosQKrO1PEmWuJautjZeS40zsGxRvOXTmpZHGjjHVUdSpwTM+V9VHd+XZZlH1HDmUK2KxzHGzgym3DGCdGm63uDveJIE8nU0fF7SDZ8AcnjX2VqytwnWz20UswDgT9QhOY5ItA6wyBxs1T9OQS7OPjdueBYG95EUjZEMiRIRgdgnadXP/i1vm9/3GGO8+1Ga4c7+J3mNZ2x19ikhVzAYvcKajnay5a1xk63pMzx+Sm+4bOuWCXu4NM7/k/1s/6/gMeKWb6CmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL1R5cGUgL1hPYmplY3QgL1N1YnR5cGUgL0Zvcm0gL0JCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9MZW5ndGggMzkKL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnic4zI0MFMwNjVVyOUyNzYCs3LALCNzIyALJItgQWQzuNIAFfMKfAplbmRzdHJlYW0KZW5kb2JqCjM0IDAgb2JqCjw8IC9MZW5ndGggMTYzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQOxIDIQxDe06hI/gjAz7PZjIpNvdvY9hsUsDTWCCDuxOC1NqCieiCh7Yl3QXvrQRnY/zpNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwutEx1mDnqUoxmgEDoV3u2i5HKm7s75Q3D1X/W/Yt05m4mBycodCM3qU9z5NjuiurrJ/qTH3KzXfivsVWFpWUvLCbedu2ZACdxTOdqrPT8fCjr2CmVuZHN0cmVhbQplbmRvYmoKMzUgMCBvYmoKPDwgL0xlbmd0aCAzMjIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVG7bcUwDOw1BRcwIH4lzeMgSJG3f5s72qlI07wfVV4ypVwudckqWWHypUN1iqZ8nmam/A71kOOYHtkhulPWlnsYFpaJeUodsZos93ALNr4AmhJzC/H3CPArgFHARKBu8fcPulkSQBoU/BTomquWWGICDYuFrdkV4lbdKVi4q/h2JLkHCXIxWehTDkWKKbfAfBks2ZFanOtyWQr/bn0CGmGFOOyzi0TgecADTCT+ZIBszz5b7OrqRTZ2hjjp0ICLgJvNJAFBUzirPrhh+2q75ueZKCc4OdavojG+DU7mS1LeV7nHz6BB3vgzPGd3jlAOmlAI9N0CIIfdwEaEPrXPwC4Dtkm7d2NK+ZxkKb4ENgr2qFMdyvBi7MxWb9j8x+jKZlFskJX10ekOytygE2Ieb2ShW7K2+zcPs33/AV8Ze2QKZW5kc3RyZWFtCmVuZG9iagozNiAwIG9iago8PCAvTGVuZ3RoIDIxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9ULmNBDEMy12FGljAeu2pZxaLS6b/9Ej59iLRFkVSKjWZkikvdZQlWVPeOnyWxA55huVuZDYlKkUvk7Al99AK8X2J5hT33dWWs0M0l2g5fgszKqobHdNLNppwKhO6oNzDM/oNbXQDVocesVsg0KRg17YgcscPGAzBmROLIgxKTQb/rnKPn16LGz7D8UMUkZIO5jX/WP3ycw2vU48nkW5vvuJenKkOAxEckpq8I11YsS4SEWk1QU3PwFotgLu3Xv4btCO6DED2icRxmlKOob9rcKXPL+UnU9gKZW5kc3RyZWFtCmVuZG9iagozNyAwIG9iago8PCAvTGVuZ3RoIDgzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4m9j5RlMLevw0QJW64J909XB0JmSluM8NDBp4MLIZdcYH0ljALXEdQjp3so2HVvuoEjfWmUvPvD5Se7KzihusBAkIaZgplbmRzdHJlYW0KZW5kb2JqCjM4IDAgb2JqCjw8IC9MZW5ndGggNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhyuDKw0A4bQNmAplbmRzdHJlYW0KZW5kb2JqCjM5IDAgb2JqCjw8IC9MZW5ndGggMjQzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1Ru60DMQzrPYUWOMD62b55Lnh4xWX/NqScBKlEQxRJycNTumTKYX1KRkiOLg9tGktsujw3QlOHioKpa4nqlKuZpsxTLE3Q895ZruYY4HtVN9Tf9IheApFRglVhgQ6QO7hg+NlrJmxRCyIxhlAzgGnCCnO4EjEEGYy1ZxiUKgxO1c8qV/svp2XYKrB4MJ0iP7KaaKdfuhx46ykHQtjclbt6IU0I7o0GY8wsXHepsp0AHEx0mYmMWLwNx9MhDA1emgascNaNmCCxGyOlD14HGdOwd0UedbcY8b5bxpS71c99UX3mXe0fCMEbJ/h7AcobXV4KZW5kc3RyZWFtCmVuZG9iago0MCAwIG9iago8PCAvTGVuZ3RoIDE2MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDkSAzEIBHO9gidIXIL3rMu1wfr/qQfWR6LpAjQcuhZNynoUaD7psUahutBr6CxKkkTBFpIdUKdjiDsoSExIY5JIth6DI5pYs12YmVQqs1LhtGnFwr/ZWtXIRI1wjfyJ6QZU/E/qXJTwTYOvkjH6GFS8O4OMSfheRdxaMe3+RDCxGfYJb0UmBYSJsanZvs9ghsz3Ctc4x/MNTII36wplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9MZW5ndGggMzM0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nC1SS3LFIAzbcwpdoDP4B+Q86XS6eL3/tpKTRUYOYPQx5YaJSnxZILej1sS3jcxAheGvq8yFz0jbyDqIy5CLuJIthXtELOQxxDzEgu+r8R4e+azMybMHxi/Zdw8r9tSEZSHjxRnaYRXHYRXkWLB1Iap7eFOkw6kk2OOL/z7Fcy0ELXxG0IBf5J+vjuD5khZp95ht0656sEw7qqSwHGxPc14mX1pnuToezwfJ9q7YEVK7AhSFuTPOc+Eo01ZGtBZ2NkhqXGxvjv1YStCFblxGiiOQn6kiPKCkycwmCuKPnB5yKgNh6pqudHIbVXGnnsw1m4u3M0lm675IsZnCeV04s/4MU2a1eSfPcqLUqQjvsWdL0NA5rp69lllodJsTvKSEz8ZOT06+VzPrITkVCaliWlfBaRSZYgnbEl9TUVOaehn++/Lu8Tt+/gEsc3xzCmVuZHN0cmVhbQplbmRvYmoKNDIgMCBvYmoKPDwgL0xlbmd0aCA3MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMzZTMFCwMAISpqaGCuZGlgophlxAPoiVywUTywGzzCzMgSwjC5CWHC5DC2MwbWJspGBmYgZkWSAxILoyuNIAmJoTAwplbmRzdHJlYW0KZW5kb2JqCjQzIDAgb2JqCjw8IC9MZW5ndGggMzIwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSS24FMQjbzym4QKXwT87zqqqLvvtvaxO9FUwwYOMpL1nSS77UJdulw+RbH/clsULej+2azFLF9xazFM8tr0fPEbctCgRREz1YmS8VItTP9Og6qHBKn4FXCLcUG7yDSQCDavgHHqUzIFDnQMa7YjJSA4Ik2HNpcQiJciaJf6S8nt8nraSh9D1Zmcvfk0ul0B1NTugBxcrFSaBdSfmgmZhKRJKX632xQvSGwJI8PkcxyYDsNoltogUm5x6lJczEFDqwxwK8ZprVVehgwh6HKYxXC7OoHmzyWxOVpB2t4xnZMN7LMFNioeGwBdTmYmWC7uXjNa/CiO1Rk13DcO6WzXcI0Wj+GxbK4GMVkoBHp7ESDWk4wIjAnl44xV7zEzkOwIhjnZosDGNoJqd6jonA0J6zpWHGxx5a9fMPVOl8hwplbmRzdHJlYW0KZW5kb2JqCjQ0IDAgb2JqCjw8IC9MZW5ndGggMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDCAwxRDrjQAHeYDUgplbmRzdHJlYW0KZW5kb2JqCjQ1IDAgb2JqCjw8IC9MZW5ndGggMTMzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWPSw4EIQhE95yijsDHH+dxMumFc//tgJ1uE2M9hVSBuYKhPS5rA50VHyEZtvG3qZaORVk+VHpSVg/J4Iesxssh3KAs8IJJKoYhUIuYGpEtZW63gNs2DbKylVOljrCLozCP9rRsFR5folsidZI/g8QqL9zjuh3Ipda73qKLvn+kATEJCmVuZHN0cmVhbQplbmRvYmoKNDYgMCBvYmoKPDwgL0xlbmd0aCAzNDAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVI5bgQxDOv9Cn0ggG7b79kgSJH8vw2p2RQDcXRSlDtaVHbLh4VUtex0+bSV2hI35HdlhcQJyasS7VKGSKi8ViHV75kyr7c1ZwTIUqXC5KTkccmCP8OlpwvH+baxr+XIHY8eWBUjoUTAMsXE6BqWzu6wZlt+lmnAj3iEnCvWLcdYBVIb3TjtiveheS2yBoi9mZaKCh1WiRZ+QfGgR4199hhUWCDR7RxJcIyJUJGAdoHaSAw5eyx2UR/0MygxE+jaG0XcQYElkpg5xbp09N/40LGg/tiMN786KulbWllj0j4b7ZTGLDLpelj0dPPWx4MLNO+i/OfVDBI0ZY2Sxget2jmGoplRVni3Q5MNzTHHIfMOnsMZCUr6PBS/jyUTHZTI3w4NoX9fHqOMnDbeAuaiP20VBw7is8NeuYEVShdrkvcBqUzogen/r/G1vtfXHx3tgMYKZW5kc3RyZWFtCmVuZG9iago0NyAwIG9iago8PCAvTGVuZ3RoIDI1MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwtUUlyA0EIu88r9IRmp99jlyuH5P/XCMoHBg2LQHRa4qCMnyAsV7zlkatow98zMYLfBYd+K9dtWORAVCBJY1A1oXbxevQe2HGYCcyT1rAMZqwP/Iwp3OjF4TEZZ7fXZdQQ7F2vPZlByaxcxCUTF0zVYSNnDj+ZMi60cz03IOdGWJdhkG5WGjMSjjSFSCGFqpukzgRBEoyuRo02chT7pS+PdIZVjagx7HMtbV/PTThr0OxYrPLklB5dcS4nFy+sHPT1NgMXUWms8kBIwP1uD/VzspPfeEvnzhbT43vNyfLCVGDFm9duQDbV4t+8iOP7jK/n5/n8A19gW4gKZW5kc3RyZWFtCmVuZG9iago0OCAwIG9iago8PCAvTGVuZ3RoIDE3NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNkEkOQyEMQ/ecwheohDPA5zy/qrpo77+tQwd1gfzkIHA8PNBxJC50ZOiMjiubHOPAsyBj4tE4/8m4PsQxQd2iLViXdsfZzBJzwjIxArZGydk8osAPx1wIEmSXH77AICJdj/lW81mT9M+3O92PurRmXz2iwInsCMWwAVeA/brHgUvC+V7T5JcqJWMTh/KB6iJSNjuhELVU7HKqirPdmytwFfT80UPu7QW1IzzfCmVuZHN0cmVhbQplbmRvYmoKNDkgMCBvYmoKPDwgL0xlbmd0aCA3NSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwztTRSMFAwNgASpmZGCqYm5gophlxAPoiVy2VoZApm5XAZWZopWFgAGSZm5lAhmIYcLmNTc6ABQEXGpmAaqj+HK4MrDQCVkBLvCmVuZHN0cmVhbQplbmRvYmoKNTAgMCBvYmoKPDwgL0xlbmd0aCAyMTUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVE5DgMhDOz3Ff5AJIwveE+iKM3+v82M0VYewVyGtJQhmfJSk6gh5VM+epkunLrc18xqNOeWtC1zgLi2vC+tksCJZoiDwWmYuAGaPAFD19GoUUMXHtDUpVMosNwEPoq3bg/dY7WBl7Yh54kgYigZLEHNqUUTFm3PJ6Q1v16LG96X7d3IU6XGlhiBBgFWOBzX6NfwlT1PJtF0FTLUqzXLGAkTRSI8+Y6m1RPrWjTSMhLUxhGsagO8O/0wTgAAE3HLAmSfSpSz5MRvsfSzBlf6/gGfR1SWCmVuZHN0cmVhbQplbmRvYmoKMTcgMCBvYmoKPDwgL1R5cGUgL0ZvbnQgL0Jhc2VGb250IC9CTVFRRFYrRGVqYVZ1U2FucyAvRmlyc3RDaGFyIDAgL0xhc3RDaGFyIDI1NQovRm9udERlc2NyaXB0b3IgMTYgMCBSIC9TdWJ0eXBlIC9UeXBlMyAvTmFtZSAvQk1RUURWK0RlamFWdVNhbnMKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXQovQ2hhclByb2NzIDE4IDAgUgovRW5jb2RpbmcgPDwgL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDMyIC9zcGFjZSA0NiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgL2ZvdXIgL2ZpdmUgL3NpeCAvc2V2ZW4KL2VpZ2h0IC9uaW5lIDcyIC9IIDgwIC9QIDk3IC9hIDk5IC9jIC9kIC9lIDEwMyAvZyAvaCAvaSAxMDggL2wgL20gL24gL28gMTEzCi9xIC9yIC9zIC90IC91IC92IF0KPj4KL1dpZHRocyAxNSAwIFIgPj4KZW5kb2JqCjE2IDAgb2JqCjw8IC9UeXBlIC9Gb250RGVzY3JpcHRvciAvRm9udE5hbWUgL0JNUVFEVitEZWphVnVTYW5zIC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Bc2NlbnQgOTI5IC9EZXNjZW50IC0yMzYgL0NhcEhlaWdodCAwCi9YSGVpZ2h0IDAgL0l0YWxpY0FuZ2xlIDAgL1N0ZW1WIDAgL01heFdpZHRoIDEzNDIgPj4KZW5kb2JqCjE1IDAgb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNDIgNjM1IDQwMCAxMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUyMSA0MDAgMTAyMyA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUwMCAxMDAwIDQ3MSA2MTIgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxMiA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA1CjYzMCA2MTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5ODIgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5MiA2MzUgNTkyIF0KZW5kb2JqCjE4IDAgb2JqCjw8IC9IIDE5IDAgUiAvUCAyMCAwIFIgL2EgMjEgMCBSIC9jIDIyIDAgUiAvZCAyMyAwIFIgL2UgMjQgMCBSCi9laWdodCAyNSAwIFIgL2ZpdmUgMjYgMCBSIC9mb3VyIDI3IDAgUiAvZyAyOCAwIFIgL2ggMjkgMCBSIC9pIDMwIDAgUgovbCAzMSAwIFIgL20gMzIgMCBSIC9uIDM0IDAgUiAvbmluZSAzNSAwIFIgL28gMzYgMCBSIC9vbmUgMzcgMCBSCi9wZXJpb2QgMzggMCBSIC9xIDM5IDAgUiAvciA0MCAwIFIgL3MgNDEgMCBSIC9zZXZlbiA0MiAwIFIgL3NpeCA0MyAwIFIKL3NwYWNlIDQ0IDAgUiAvdCA0NSAwIFIgL3RocmVlIDQ2IDAgUiAvdHdvIDQ3IDAgUiAvdSA0OCAwIFIgL3YgNDkgMCBSCi96ZXJvIDUwIDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTcgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAwIC9jYSAxID4+Ci9BMiA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAxIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9JMSAxMyAwIFIgL0kyIDE0IDAgUiAvRjEtRGVqYVZ1U2Fucy1taW51cyAzMyAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9UeXBlIC9YT2JqZWN0IC9TdWJ0eXBlIC9JbWFnZSAvV2lkdGggNDYzIC9IZWlnaHQgMjMxCi9Db2xvclNwYWNlIFsgL0luZGV4ZWQgL0RldmljZVJHQiAyNTUKKP7x6v3n2uSAZfv7+/nFq/i9of3fzfX19fOjgPi/o++beu3t7eyTdOqNcOnp6eiLbufn5+aFauWDaODg4OPj4+J9Y+F7YbUfLt91Xf7+/tttV97e3thlUf3k1tXV1dRdS9HR0c/Pz85RRs1PRMvLy8pJQsnJycdDP8XFxcM7O8A1OL+/v/7t47wtNP3dy7knMre3t7YhL7W1tbOzs7IZK7GxsbAXKq+vr62traurq6oVXCmpqamnp6elpaWkE1woo6OjoaGhn5+fnhEn/vz7/dzJmZmZl5eXlg8mlZWVk5OTkZGRkFxyJY+Pj42NjYuLi4oLJImJiYeHh4QJI/Wujn4HIn19fXt7e/rOtnl5eXgFIXd3d/Hx8XIDIO/v752dnfe7nm1tbWwBH2tra2lpaWdnZ/7691tbW1lZWVdXV/r6+ry8vElJSUVFRcI4OkFBQT09Pdvb2/Kgfjk5Of39/TU1Nfn5+TExMfe5m7m5ub29vf749fzYxPrMtP7+/f3i0fzWwe6YePrKsfj4+Pe2mPayk/Wsi/SohvnHrvLy8vGefO7u7u2Wduzs7OuQcurq6ujo6OeIbObm5uTk5Jubm+Li4uB4X95yW91wWf3m2NpqVdloU9jY2NdiT9ZgTdTU1P7u5dLS0tBUR87OzsxMQ/Smg8lGQcjIyMZAPsU+PMLCwsDAwL8yN74wNv718MzMzLsqM7q6urgkMbQcLf738q0WKt3d3dfX18bGxqcUXCnRV0mhElwo/enc/vTumxAnmRAn+9S+9/f3kw4m9rSWjQwl9PT0h1xuJISEhIKCgoEII4CAgP7z7P3j1P3r4XUEIXR0dPnDqXJycnBwcG8CIGkAH2cAH/Pz82RkZGJiYmBgYHsGIl5eXvvSvFRUVFJSUlBQUE5OTkxMTEhISNNaStra2kREREBAQDw8PP3bxzg4ODQ0NPjBpjAwMC4uLiwsLP77+SoqKlwoXChcKCYmJiQkJCIiIiAgIB4eHv7w5xwcHP3gzxoaGvvQufWwkMPDw/3q3/SqiOHh4SkKXQovQml0c1BlckNvbXBvbmVudCA4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9EZWNvZGVQYXJtcyA8PCAvUHJlZGljdG9yIDEwIC9Db2xvcnMgMSAvQ29sdW1ucyA0NjMgL0JpdHNQZXJDb21wb25lbnQgOCA+PgovTGVuZ3RoIDUxIDAgUiA+PgpzdHJlYW0KeJzt3XeYrVdVx/EgIthiBCwxgqKABTtKBCuIHTGgoqioICpRBBt2RUGqFWwUwY4IArEEgzX2BhYuCigIqCCYxGjkGmLD9/eZZ/ae9533nJl579w54Tzr90ee3Jkz5+y9fus885211t7nnG+LHhRdMej7ow+MXhC9efTn0bWDPij6kehF0TtHHxLlm58b/UX0+ZHnOG9QnvUKz/9x0V2iT49uMOgB0TXRT0S3iU5FNxn0ydF10cdHbxXle+8Z/VT0H9GXRt8e3XPQR0c3jF4dfX30PYM+OLKn74z+Msqyr71k0LtE9vSO0YdFXvheg24U2dP7R4+Prhz0wOhLot+Mfi/63+jlg/49umX0XpE9PTK62yBP/QXRd0de+CcH3Tayp5dEbxH9cXTZoIdEr4leFz02eqfo3oM+O/qi6Jzys/wsP8vP8rP8LD+P7qcn/6ToMYO88E9Ht4qE+suiWwz6g+jXovMjAWPXuYN+MVqVBD8a9SS4fZRvfk70L5GAPSdKEpyXgP1TdHAS/GzUkuBTop4E3xHldd8jenYkYH8bzSTBg6OWBLIg8bq3JPiraFUSXBgdmARXHpgEz41+LlqVBG8TtSSQBa+PJEH5WX6Wn+Vn+Vl+lp8L/MS3nxWdHvR10TdGdvArkR/Nqv4sunN0deTlPyHKgt438lDRecvodoO+JhIEFsO0p0QvHCRDQN5XRX8fWXgse2P0dlFifk+58sRBfx2BOU/Lgl+NHj5ImERCTM6JEg7+XxV9ZPQBkRTMij80+u2IK3JUiO4x6JujTt6fGD1ikOSQc1b8N9FbR8lYSfCZ0ZtFclQO5aFfET01Sgbe5FOj+w5iz9iP02B2hR/dkO5H+Vl+lp/lZ/l5Qn5aTH4bv/yCQd8bCZ9f5u8a3ThqKKBE8fPRF0bwKC/wPtH7Reo6LX4fE70hUjKymDtEKWmoLlmy8P1R9K9RSkDZ4n1/JwIF7x4FJ6zYL//vi9DIS3flX77+4shj80N+2vN4Rs+t0JRX87pWAIXgmpJL1mnF1m4X9pON2aG9/nhk/x4beBOb/4nES+TgVGIpquIr0mKe4HNh7MeOIcl6j/+xiB+9wDX2o/wsP8vP8rP8LD/LzwV+4qkPj145CFNZqYBZ0fOjdMl+K7JkXRxlJNu+dBAkQ4ZW9NooYKkD56W+MgJ+QDFNoB+IACRnbcMSUsNRl/qmCKW+Q7SbPS/CjhBWOeqHotCo5LAC/5cv3ePmg3TyPJ7tnkMuBj//NPrySO3p9U1ZjwLXL0dWm2VflvXbiT0pBtnnp0XZuRWIxp9ErPnWKJwselaAxpXMEmOuoGAOCERMeWWgm09KQ94NQgj5f2HQd0XguvwsP8vP8rP8LD/LzwV+jvd+8/HeRa3tXUTF1t7F27bzwrzgCn/a3k+N987ZtvfXjvcuJ+w9WXKbFXt/ftu7nBvv/cIVe29b37N3XcLgoRUoBpqC0rJ6aJQCnJqkFSDjVByf+IRBpn3uHym0Yc1XRaknKmumAfmYX4rgdJur+r9I1VL/S98xK1C1Q8FoXE3y3aJ01axAf0211HCRnmYKn/8W2UX5WX6Wn+Vn+Vl+lp8L/LT3W0cXDbIgEfJdK4VYib4ZZuW4dBof7qXgXmLl9bCjHZiuedSgh0U4GemZrDbC03YgE8TWAPJvRGkwGvkxOpRW6QsbWGtbquv9XfToSILFC91QSWYDZnYycK1tGw6/VDp8dWTyKO1M4fb8zGP720d3HWSQSAlQz1UKJnsYqD6osvgRkTmhjFrp6SoB6m6eaoohElu6aWD6ofy0Fqq31rVNXu5+gwx5K2Ty2fvFQlu62UX5WX6Wn+Vn+Vl+lp8L/Oy+ZUrp7pFJJ4PM3xDhqTsNesHYN1NUcC88fHH03pFuokGpXdt2fDPtpWFoFupZgz4jMgal9oYAm2+OhP1w9F8RIEz1zxD2nG/hbcRouExX8meicLJa5cQ3M1U3HfTPEeQF6BDTHFt88/x8g9MfFaW6GMZ9gnntp0dj34CoETkNTJPbhsLuOAgUT3zLHN4XRwA9A+rnQljzcM8cZJzcc/e+sL9Bcuzu1yPzc+Vn+Vl+lp/l5wn5iYeAQrbyh5GmkV/EDVyuy2tyhVFe2YEfg8z5LS18pqgToZtijSSBEZkeSGhjt6EmViIDfTeVJSWq4I3mk/aRGRw5d92uVESYikYcQ8r49uMiSBUYe9TbRhkTknxi0uHKqmKsKpJyFMxQH1MHSx1LJQqu4RXVnLw4G6wFP/LfWfFwpZF0AcaPwI33zxskUfnfEzhGtbPYlzFKOU49LQTnQLaalJTFZo6Ip6zV11J+lp/lZ/lZfpaf5ecCP/1XuykdK7UgTSPHsPRtfj9KIQONOmqN3JRukG4WY1UI2Aks7Ji5YYSppIGAnxxhuFSLlIaQXs8ca8ki1aqwpuKOGtZ9BjnPjLwVidClM21p7p1qaxErUUs9STHLlTfOkaF3nblMB5ky0v6Txb0+1rJYWUsWO1AXjMeg/xk5YK6eJItzBmwui2VTsljhC/caKTKblBqRWanMXN0NScvWcRb7l697hMcmi5WYPE/5WX6Wn+Vn+Vl+lp8L/MS3BnrSDdMXUpNzgOllkZ9PgHt5rQceZ6ZY5RA5AsTIymLpkgmaI1EIGOmlG/aIDDKH9p6n6wPrcG+buG7cd4o17bYcZ70McysGOrPuNHqKXpIGImNBg+Lh5LSc7qfiCESZNN6Pf/m6R7wiyg8pgaZMd0fP6LnzItLK60pZa3FiLctTvrRiKX5qtB9f4q5d239A39F8sVHgFC+Ru9kgpVRRlX1qhmKe4HOBH+Vn+Vl+lp/lZ/lZfi7ws3HctaE6fT4lPaWrXEV3rx+MUoAzZ6zuBKUyXHNXk0QpqjkKbmJG9Ux/LuzbIa+TnsJWGpIu5FXc0kc1daNmmH4hBjUxA7Od80rZzT1GapJqb1qfHY/bdrAgdg4nqwrqzH5s9IxImzRdVbU9A9T/HUHVf4jSeEzR8k4908b76afm0b7RptQN3fpsntrxN73Sr41SjtRfxvgAFcIH9JU1r1rxzmEZ/PY3CD9TWbwok0eqrnv62eVn+Vl+lp/lZ/lZfh7JT3ybMtp9QlFGpg0nQymvZ/4q4AoPVb6ECQsKZOu+9WhaUSLq4JiVojFpYgehNK08TG1FioFYO0PYupgmqZTFnAuL4UhSuJX64K6gp84otjOBJ19ClzJKMTCFQg1JU84askqaOqdhTYYoWCruufY4o2N6xoqQmp3SBAsn3ZTjIHL/mAmO7IZrLl4e4KH+pOCb/m+e0VMrfEpsrC0hM+6N6mVl+Vl+lp/lZ/lZfpafC/wcU5TWH2KEUgaOoGRafw6ImYxS/sKIPmIh3Tfj6z65IJ/BcAN1vfQoQZ5KFgLWIdQcDQU7Q6/ypY8KfnttLOr1Pmf/g5lI0nxX8POZKn9PioKlqn8uBPqWSFEunPx5kVuKHFLXanQkLLVNGI8YNWTxvZu6272Lp9uk/Chcp0G0eCkSatmmj2pcS+HT1ByYdcguJ+gMyl8e+Tsh59FukXG4341MjWnsatn6uKx87IWrqU35KyNK2T7a1lR+lp/lZ/lZfp6Qn3jIiFDmYcJEN+MRQMnnAF3CvNSIDDi7U8Zy/c4XgOzCr3BBsL32y/90O8s8DgcM8E1I5fF+0nPkyQTH84MCr5nAIwNhYgga0U1KFUl7DlJJMB/kFK6SZM6RaXEZP3K9cYa5TS6rUGngiaFcCScJpBzCT6KZ5MNVCGucgRP8kouij8GSkFzRKnOUXXaH7HTbZL0r/Rwtc5A8JGjIWw3LoW5spoKWvHCGfE99qPwsP8vP8rP8LD/LzyP5uSjArBRg9ibAumqTACcvBNjscQ+w+4PHAdZ3M4hj0DgBxqwKNgJszCYBVgvqAVYRgseJ3KoA+1IPsMf7yQQYZs8E+OkZfPZpDGpS4wCrGwkwQO0BzsRPD7C7BZ4WKbtlnklVTOnNXwyqZWnnmdZSRVNU8heG8loKcTBb1W3uzdGAu/wsP8vP8rP8LD/LzwV+4luX7KUiJvDKYv8YGdzRmkltj1EmlH3oE8S0rNS1dNUsTeEMkmZ9uj7qhgjYIrXbkipabmpmcshyGxDPLLnl3U7iebyNyr5U5+zf8yvACUzSUO9K5VIuOipmZDz5aA7HkX1Z6Wid+/KC98p0+mKs0SZLgZNlctTlxazU4svpeAwqW10kAE6lbK4yVgKVtuqgZo2SNHqAypdGqhC2bmCmrGC2Y3TwWCqrlIaaX9qAu/wsP8vP8rP8LD/LzwV+ro2VKIt3Aj8XK04lVuCMi/wUq1TnnIbqsTKWpB2Y5HC3j1ipMZqHcYArU9QuKhYrOZf7G5+VzxeFk4a5HcMSK/W7TPvAT7FyDZLCWThZrBTUxMqFAWkrPmccK6RrFlysAsD6rgp2yLj1aGcKfHM47fFCLeiJvpIhQ1jjLFqYHVwzEMsjbLcipQipDmiySXUSa7s/IEVLnVOD6OVn+Vl+lp/lZ/lZfi7wU6xELT1Qg9XOdLnYUMVKlDPHhHZFXzUvF9/cDvNmIFqXFPcqZ7mmJ+yLHR3sQsB8dvg9rmNNF/8oI4JQVcVMZMkXk9gdipNRVzYyRrX4tuHx6aZVZbFVxDyXynnqls5XX9UAOjPPyqASG7ibM7f2AL3twGkp7h4CTJ0/ANzvpNQn2bWVM5Ym4c1lq5vKesPTGV6D2eqGZt6xtr9B0rF2/5C/TcrP8rP8LD/LzxPy8wjbu6Ztr5VjrhLcVdsLK+n/OMmEmJxHclg6fmq5OW9tpNkAsvGg8JP6kxM4CjYKU7lzWd/JMLcZJ6ed4VROM2nPQSpDRD60IVxlfNlZIoWcnbvuolRzHJ9+Q6SkYxZcFy8X80Eth6F8ziPgyrEoB5ZAl+lnqKLFl4/DMIek1KPX59w6BksrziluR9MdZdf/CxOBMbcf9uoPdgpEdSwDV53NWoOQys/ys/wsP8vP8rP8XOAnvrXR9MwMVjuL5V4apOs4VAAK7eoXOUh9y8a8iaiuGu5V9HGAK+0mnyCl+YSAWYPhGgW7LlDfjZVMDfyxGAuCYt4HEcEiMpYcKFKuJGm05+SQypW0apws3TosOwrdiFl6dmyWtw2dJ+28xs9dhyPple+c8btnTNiT91Fnbeve5e2dd1T5WX6Wn+Vn+Vl+lp8L/Dzq600qYuOy2MzrXTF+PdFsGKyk53j7AxsLc6TxsGIg3zjY6mbc5TPHed8qaPJChsiVcLIipDySUXJLliXdHKOTgXJRVsrPVNZkrNxVXpPPSWw1NhAt6+W/Fl/eEgayvEu8X9xDgKkzSe6iYjcBOPVm1iif9akHaEJbI9AtPaa20wjzwVf6Yj5mA2u7XCDDSC4+1CUsP8vP8rP8LD/Lz/JzgZ9ez1W7uQPOOnymgEnhTrqpm1m3HWiOqqph3tTY7NOOXXusP5dI6NY9uxGwEp1eXiKnhap+p4+q2ec6v7T+FPh0Ag0d6a2m+qdNqBjoLqQxHmu3QmQ9V8ff4rXmq8pih2Vt2EbMipAdmyVSQ+fenm0ArdcpDSVkI+lXJ1EnOC2VX7erxtV7wHoM152wV2F21xi4y8/ys/wsP8vP8rP8XODnWr5dR7sT5p28yoh9r+lL68ttFDwuXe2wcNv7BIoTpgc3MhbDjscJcEdk0edDDFEVnMAyBxsxc7djM++TBNBZXsgQuZKkmUC03JJlSTdtWxkoF2Wl/Eyiyli5K4vlcxIbXMt1WS//vRMaZnuXeL901s6bybvK+6v8LD/Lz/Kz/DwhPzsPpZpxcYMiAzfISA8thQ90ZOLaWI4Dyhgpg7y6ajhJ4aTBkhsBLxgTk3ZbVqXsYvbHcl01DKAytG0/Johtr/XijA4hKSWdMU5pzwmY6s+Fu2AlokpDHa6UixJ4xaM5zMoYE8vMPN+9AVeKUB26jEY38oJeilYdv5xbbwwmfe7f1GhMkpm/HhPZA8ZYZhjrFU2tQQjQys/ys/wsP8vP8rP8XODnEfj2sMw77q2tIuARBe9h4QkQ74fiq2fIuONxQ+RVnNxheUzME2xu7NzQeUcNoHtEBbiR9MuaBR2nmdSYmoHdz0bXDGd9J2yJMcZs6dNZO7l1UQPu8rP8LD/Lz/Kz/Cw/F/iJb90yvMu4O5DrCH4n3THtaqY9esS8j20aw69T4Jps5zU1DEbBrgt8Y2PhBsR42GH/MRQ/t5Gx8tcEjxsiq5TdeheUn9bUYbm17Z7RNMHmS3bV23kdoPdD9I4ymjTB6TFT37lpAtaREfNO2GPMnmPtXd7eAe7ys/wsP8vP8rP8LD8X+HmGfLsIftdR8FoenoPiOTw+kJNniHmCzRN2HvHzKojeg9JjnB4z9WuaJmDdNCbs3uGdY+3W8KXys/wsP8vP8rP8LD8X+Dnm29YIveGEdBvtTpqjnXnH3NvhtzVNJwTcKHjcRz23A3GD4sc1PX5XnYw7Hu9H5B1Gbpw8geUZYp5gc0PnMT/fqvFzh+hG0rddhdOdqXe5ekczcN0JewazJ6w94u3bl5/lZ/lZfpaf5Wf5ucDPs8m3x0TBy/D4YFA+JDavQOdVEL0ep/cj9Uq4niHsGcyeAHf5WX6Wn+Vn+XlCfnYeGjHRHijqZDSiowkjXT7WflbaQ0wTbBqh0w47Nc1A1BxJNc0g1VyzbhVhde1HrQlwXTDWpTPo1fBrwmAjDtsDYg3GumawbIbNusrP8rP8LD/Lz/Kz/Fzg56bp9oy1HI+XE/OZkfRRwPoImE3lZ/l5/VL5WX6Wn9dflZ8jP8d8O4HcVbh78X49ZEYPG+ny/brxjFbx8Aow3kPGKxB5wskzsDwh5oPZeQVET1F6BqdHSL1HK+B6piM4x9oduMvP8rP8LD/Lz/Kz/Fzg56bx9HqtY2XnEyHs8nOdys/tUvm5XSo/t0tvgn7O8O160p1pjq7j3vUEvJ+C1/LwejKeabeu4+SVxLyWnVcB9EqUXsfU6+l6P2GvZW0qP8vP8rP8LD9PyM9NI0dprY6KU+Xn9Vvl53ap/NwulZ/bpSP7uZZvDybdw3HvUTD4QB4+JB4fEpQPJOZDAvQRcPoQYH04zJ7h7fKz/Cw/y8/ys/wsPxf4uWmAKx2rys/tUvm5XSo/t0vl53bpsHy7GHmXEfBiKF7GyIuweTFJnwFdH8ja5Wf5WX6Wn+Vn+Vl+LvBz00BWOlaVn9ul8nO7VH5ul86Ah46Vk84SRB0rXJ0l8jpWIis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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["encod_block = PositionalEncoding(d_model=48, max_len=96)\n", "pe = encod_block.pe.squeeze().T.cpu().numpy()\n", "\n", "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 3))\n", "pos = ax.imshow(pe, cmap=\"RdGy\", extent=(1, pe.shape[1] + 1, pe.shape[0] + 1, 1))\n", "fig.colorbar(pos, ax=ax)\n", "ax.set_xlabel(\"Position in sequence\")\n", "ax.set_ylabel(\"Hidden dimension\")\n", "ax.set_title(\"Positional encoding over hidden dimensions\")\n", "ax.set_xticks([1] + [i * 10 for i in range(1, 1 + pe.shape[1] // 10)])\n", "ax.set_yticks([1] + [i * 10 for i in range(1, 1 + pe.shape[0] // 10)])\n", "plt.show()"]}, {"cell_type": "markdown", "id": "0bc6f1ee", "metadata": {"papermill": {"duration": 0.017526, "end_time": "2023-10-11T16:00:49.551880", "exception": false, "start_time": "2023-10-11T16:00:49.534354", "status": "completed"}, "tags": []}, "source": ["You can clearly see the sine and cosine waves with different wavelengths that encode the position\n", "in the hidden dimensions.\n", "Specifically, we can look at the sine/cosine wave for each hidden dimension separately,\n", "to get a better intuition of the pattern.\n", "Below we visualize the positional encoding for the hidden dimensions $1$, $2$, $3$ and $4$."]}, {"cell_type": "code", "execution_count": 11, "id": "5bd759b3", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:49.589037Z", "iopub.status.busy": "2023-10-11T16:00:49.588569Z", "iopub.status.idle": "2023-10-11T16:00:51.025017Z", "shell.execute_reply": "2023-10-11T16:00:51.023913Z"}, "papermill": {"duration": 1.457152, "end_time": "2023-10-11T16:00:51.026538", "exception": false, "start_time": "2023-10-11T16:00:49.569386", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.set_theme()\n", "fig, ax = plt.subplots(2, 2, figsize=(12, 4))\n", "ax = [a for a_list in ax for a in a_list]\n", "for i in range(len(ax)):\n", " ax[i].plot(np.arange(1, 17), pe[i, :16], color=\"C%i\" % i, marker=\"o\", markersize=6, markeredgecolor=\"black\")\n", " ax[i].set_title(\"Encoding in hidden dimension %i\" % (i + 1))\n", " ax[i].set_xlabel(\"Position in sequence\", fontsize=10)\n", " ax[i].set_ylabel(\"Positional encoding\", fontsize=10)\n", " ax[i].set_xticks(np.arange(1, 17))\n", " ax[i].tick_params(axis=\"both\", which=\"major\", labelsize=10)\n", " ax[i].tick_params(axis=\"both\", which=\"minor\", labelsize=8)\n", " ax[i].set_ylim(-1.2, 1.2)\n", "fig.subplots_adjust(hspace=0.8)\n", "sns.reset_orig()\n", "plt.show()"]}, {"cell_type": "markdown", "id": "5483c7d6", "metadata": {"papermill": {"duration": 0.016616, "end_time": "2023-10-11T16:00:51.061270", "exception": false, "start_time": "2023-10-11T16:00:51.044654", "status": "completed"}, "tags": []}, "source": ["As we can see, the patterns between the hidden dimension $1$ and $2$ only differ in the starting angle.\n", "The wavelength is $2\\pi$, hence the repetition after position $6$.\n", "The hidden dimensions $2$ and $3$ have about twice the wavelength."]}, {"cell_type": "markdown", "id": "41114dc6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017751, "end_time": "2023-10-11T16:00:51.096060", "exception": false, "start_time": "2023-10-11T16:00:51.078309", "status": "completed"}, "tags": []}, "source": ["### Learning rate warm-up\n", "\n", "One commonly used technique for training a Transformer is learning rate warm-up.\n", "This means that we gradually increase the learning rate from 0 on to our originally specified\n", "learning rate in the first few iterations.\n", "Thus, we slowly start learning instead of taking very large steps from the beginning.\n", "In fact, training a deep Transformer without learning rate warm-up can make the model diverge\n", "and achieve a much worse performance on training and testing.\n", "Take for instance the following plot by [Liu et al.\n", "(2019)](https://arxiv.org/pdf/1908.03265.pdf) comparing Adam-vanilla (i.e. Adam without warm-up)\n", "vs Adam with a warm-up:\n", "\n", "
\n", "\n", "Clearly, the warm-up is a crucial hyperparameter in the Transformer architecture.\n", "Why is it so important?\n", "There are currently two common explanations.\n", "Firstly, Adam uses the bias correction factors which however can lead to a higher variance in the adaptive\n", "learning rate during the first iterations.\n", "Improved optimizers like [RAdam](https://arxiv.org/abs/1908.03265) have been shown to overcome this issue,\n", "not requiring warm-up for training Transformers.\n", "Secondly, the iteratively applied Layer Normalization across layers can lead to very high gradients during\n", "the first iterations, which can be solved by using Pre-Layer Normalization\n", "(similar to Pre-Activation ResNet), or replacing Layer Normalization by other techniques\n", "(Adaptive Normalization,\n", "[Power Normalization](https://arxiv.org/abs/2003.07845)).\n", "\n", "Nevertheless, many applications and papers still use the original Transformer architecture with Adam,\n", "because warm-up is a simple, yet effective way of solving the gradient problem in the first iterations.\n", "There are many different schedulers we could use.\n", "For instance, the original Transformer paper used an exponential decay scheduler with a warm-up.\n", "However, the currently most popular scheduler is the cosine warm-up scheduler,\n", "which combines warm-up with a cosine-shaped learning rate decay.\n", "We can implement it below, and visualize the learning rate factor over epochs."]}, {"cell_type": "code", "execution_count": 12, "id": "785bc806", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.128681Z", "iopub.status.busy": "2023-10-11T16:00:51.128182Z", "iopub.status.idle": "2023-10-11T16:00:51.135400Z", "shell.execute_reply": "2023-10-11T16:00:51.134431Z"}, "papermill": {"duration": 0.025208, "end_time": "2023-10-11T16:00:51.136743", "exception": false, "start_time": "2023-10-11T16:00:51.111535", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class CosineWarmupScheduler(optim.lr_scheduler._LRScheduler):\n", " def __init__(self, optimizer, warmup, max_iters):\n", " self.warmup = warmup\n", " self.max_num_iters = max_iters\n", " super().__init__(optimizer)\n", "\n", " def get_lr(self):\n", " lr_factor = self.get_lr_factor(epoch=self.last_epoch)\n", " return [base_lr * lr_factor for base_lr in self.base_lrs]\n", "\n", " def get_lr_factor(self, epoch):\n", " lr_factor = 0.5 * (1 + np.cos(np.pi * epoch / self.max_num_iters))\n", " if epoch <= self.warmup:\n", " lr_factor *= epoch * 1.0 / self.warmup\n", " return lr_factor"]}, {"cell_type": "code", "execution_count": 13, "id": "fa229b2c", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.168809Z", "iopub.status.busy": "2023-10-11T16:00:51.168403Z", "iopub.status.idle": "2023-10-11T16:00:51.498122Z", "shell.execute_reply": "2023-10-11T16:00:51.497317Z"}, "papermill": {"duration": 0.347145, "end_time": "2023-10-11T16:00:51.499402", "exception": false, "start_time": "2023-10-11T16:00:51.152257", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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yZWFtCnicRZFNcgUhCIT3nqIv8KrkVz3PpFJZTO6/Dc28JCtaheYD0wITR/ASQ+yJlRMfMnwv6DJ8tzI78DrZmXBPuG5cw2XDM2Fb4DsqyzteQ3e2Uj+doarvGjneLlI1dGVkn3qhmgvMkIiuEVl0K5d1QNOU7lLhGmxbghT1SqwnnaA06BHK8HeUa3x1E0+vseRUzSFaza0TGoqwbHhB1MkkEbUNiyeWcyFR+aobqzouYJMl4vSA3KCVZnx6UkkRMIN8rMlozAI20JO7ZxfGmkseRY5XNJiwO0k18ID34ra+9zZxj/MX+IV33/8rDn3XAj5/AEv+XQYKZW5kc3RyZWFtCmVuZG9iagoyNSAwIG9iago8PCAvTGVuZ3RoIDIzMiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UUluxDAMu/sV/MAA1u68J8Wgh/b/11LKFAhAJba4JWJjIwIvMfg5iNz4kjWjJn5nclf8LE+FR8Kt4EkUgZfhXnaCyxvGZT8OMx+8l1bOpMaTDMhFNj08ETLYJRA6MLsGddhm2om+IeGzI1LNRpbT1xL00ioEylO23+mCEm2r+nP7rAtt+9oTTnZ76knlE4jnlqzAZeMVk8VYBj1RuUsxfZDqbKEnobwon4NsPmqIRJcoZ+CJwcEo0A7sue1n4lUhaF3dp21jqEZKx9O/DU1Nkgj5RAlntjTuFv5/z72+1/sPTiFUEQplbmRzdHJlYW0KZW5kb2JqCjI2IDAgb2JqCjw8IC9MZW5ndGggMjMxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVPOZIEIQzLeYU+MFUY20C/p6e2Ntj5f7qSmU6Q8CHJ0xMdmXiZIyOwZsfbWmQgZuBTTMW/9rQPE6r34B4ilIsLYYaRcNas426ejhf/dpXPWAfvNviKWV4Q2MJM1lcWZy7bBWNpnMQ5yW6MXROxjXWtp1NYRzChDIR0tsOUIHNUpPTJjjLm6DiRJ56L7/bbLHY5fg7rCzaNIRXn+Cp6gjaDoux57wIackH/Xd34HkW76CUgGwkW1lFi7pzlhF+9dnQetSgSc0KaQS4TIc3pKqYQmlCss6OgUlFwqT6n6Kyff+VfXC0KZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvTGVuZ3RoIDI0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UDuORCEM6zmFL/Ak8iNwHkarLWbv364DmilQTH62MyTQEYFHDDGUr+MlraCugb+LQvFu4uuDwiCrQ1IgznoPiHTspjaREzodnDM/YTdjjsBFMQac6XSmPQcmOfvCCoRzG2XsVkgniaoijuozjimeKnufeBYs7cg2WyeSPeQg4VJSicmln5TKP23KlAo6ZtEELBK54GQTTTjLu0lSjBmUMuoepnYifaw8yKM66GRNzqwjmdnTT9uZ+Bxwt1/aZE6Vx3QezPictM6DORW69+OJNgdNjdro7PcTaSovUrsdWp1+dRKV3RjnGBKXZ38Z32T/+Qf+h1oiCmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0xlbmd0aCAzOTUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVJLbsVACNvnFFyg0vCbz3lSVd28+29rQ1KpKryJMcYwfcqQueVLXRJxhcm3Xq5bPKZ8LltamXmIu4uNJT623JfuIbZddC6xOB1H8gsynSpEqM2q0aH4QpaFB5BO8KELwn05/uMvgMHXsA244T0yQbAk5ilCxm5RGZoSQRFh55EVqKRQn1nC31Hu6/cyBWpvjKULYxz0CbQFQm1IxALqQABE7JRUrZCOZyQTvxXdZ2IcYOfRsgGuGVRElnvsx4ipzqiMvETEPk9N+iiWTC1Wxm5TGV/8lIzUfHQFKqk08pTy0FWz0AtYiXkS9jn8SPjn1mwhhjpu1vKJ5R8zxTISzmBLOWChl+NH4NtZdRGuHbm4znSBH5XWcEy0637I9U/+dNtazXW8cgiiQOVNQfC7Dq5GscTEMj6djSl6oiywGpq8RjPBYRAR1vfDyAMa/XK8EDSnayK0WCKbtWJEjYpscz29BNZM78U51sMTwmzvndahsjMzKiGC2rqGautAdrO+83C2nz8z6KJtCmVuZHN0cmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0xlbmd0aCAxMzYgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTY9BDgMxCAPveYWfQCBAeM9WVQ/b/19L2HbTCx7JgGxRBoElh3iHG+HR2w/fRTYVZ+OcX1IpYiGYT3CfMFMcjSl38mOPgHGUaiynaHheS85NwxctdxMtpa2XkxlvuO6X90eVbZENRc8tC0LXbJL5MoEHfBiYR3XjaaXH3fZsr/b8AM5sNEkKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8PCAvTGVuZ3RoIDI0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNUUmKAzAMu+cV+kAhXpO8p0OZQ+f/18oOhTkECa+Sk5aYWAsPMYQfLD34kSFzN/0bfqLZu1l6ksnZ/5jnIlNR+FKoLmJCXYgbz6ER8D2haxJZsb3xOSyjmXO+Bx+FuAQzoQFjfUkyuajmlSETTgx1HA5apMK4a2LD4lrRPI3cbvtGZmUmhA2PZELcGICIIOsCshgslDY2EzJZzgPtDckNWmDXqRtRi4IrlNYJdKJWxKrM4LPm1nY3Qy3y4Kh98fpoVpdghdFL9Vh4X4U+mKmZdu6SQnrhTTsizB4KpDI7LSu1e8TqboH6P8tS8P3J9/gdrw/N/FycCmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCA5NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjcERwCAIBP9UQQkKCtpPJpOH9v+NEDJ8YOcO7oQFC7Z5Rh8FlSZeFVgHSmPcUI9AveFyLcncBQ9wJ3/a0FScltN3aZFJVSncpBJ5/w5nJpCoedFjnfcLY/sjPAplbmRzdHJlYW0KZW5kb2JqCjMyIDAgb2JqCjw8IC9MZW5ndGggMzQxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEVSS25EMQjbv1NwgUjhl5DztKq6mN5/W5tM1c3gCWBseMtTpmTKsLklIyTXlE99IkOspvw0ciQipvhJCQV2lY/Ha0usjeyRqBSf2vHjsfRGptkVWvXu0aXNolHNysg5yBChnhW6snvUDtnwelxIuu+UzSEcy/9QgSxl3XIKJUFb0HfsEd8PHa6CK4JhsGsug+1lMtT/+ocWXO9992LHLoAWrOe+wQ4AqKcTtAXIGdruNiloAFW6i0nCo/J6bnaibKNV6fkcADMOMHLAiCVbHb7R3gCWfV3oRY2K/StAUVlA/MjVdsHeMclIcBbmBo69cDzFmXBLOMYCQIq94hh68CXY5i9Xroia8Al1umQvvMKe2ubnQpMId60ADl5kw62ro6iW7ek8gvZnRXJGjNSLODohklrSOYLi0qAeWuNcN7HibSOxuVff7h/hnC9c9usXS+yExAplbmRzdHJlYW0KZW5kb2JqCjMzIDAgb2JqCjw8IC9MZW5ndGggMTY0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQx3EFMQxD76oCJTCACvWsx/MP6/6vhvTTQXoYQgxiT8KwXFdxYXTDj7ctMw1/RxnuxvoyY7zVWCAn6AMMkYmr0aT6dsUZqvTk1WKuo6JcLzoiEsyS46tAI3w6sseTtrYz/XReH+wh7xP/KirnbmEBLqruQPlSH/HUj9lR6pqhjyorax5q2leEXRFK2z4upzJO3b0DWuG9las92u8/HnY68gplbmRzdHJlYW0KZW5kb2JqCjM0IDAgb2JqCjw8IC9MZW5ndGggNTQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzYzVDBQMLFUMDI2UTA2NAJiE4UUQy6gCIiVywUTywGzQKpyuKDKc2CqcrgyuNIABRgOMgplbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8IC9MZW5ndGggNzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZcQL6piblCLhdIDMTKAbMMgLQlnIKIZ4CYIG0QxSAWRLGZiRlEHZwBkcvgSgMAJdsWyQplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggNDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZclhBWLhdMLAfMAtGWcAoinsGVBgC5Zw0nCmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0xlbmd0aCAyNTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZFLcgQgCET3noIjgPzkPJNKZTG5/zYNzmQ2dpeo/YRKI6YSLOcUeTB9yfLNZLbpdzlWOxsFFEUomMlV6LECqztTxJlriWrrY2XkuNM7BsUbzl05qWRxo4x1VHUqcEzPlfVR3fl2WZR9Rw5lCtiscxxs4MptwxgnRput7g73iSBPJ1NHxe0g2fAHJ419lasrcJ1s9tFLMA4E/UITmOSLQOsMgcbNU/TkEuzj43bngWBveRFI2RDIkSEYHYJ2nVz/4tb5vf9xhjvPtRmuHO/id5jWdsdfYpIVcwGL3Cmo52suWtcZOt6TM8fkpvuGzrlgl7uDTO/5P9bP+v4DHilm+gplbmRzdHJlYW0KZW5kb2JqCjM4IDAgb2JqCjw8IC9MZW5ndGggMTYzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQOxIDIQxDe06hI/gjAz7PZjIpNvdvY9hsUsDTWCCDuxOC1NqCieiCh7Yl3QXvrQRnY/zpNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwutEx1mDnqUoxmgEDoV3u2i5HKm7s75Q3D1X/W/Yt05m4mBycodCM3qU9z5NjuiurrJ/qTH3KzXfivsVWFpWUvLCbedu2ZACdxTOdqrPT8fCjr2CmVuZHN0cmVhbQplbmRvYmoKMzkgMCBvYmoKPDwgL0xlbmd0aCAyMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVC5jQQxDMtdhRpYwHrtqWcWi0um//RI+fYi0RZFUio1mZIpL3WUJVlT3jp8lsQOeYblbmQ2JSpFL5OwJffQCvF9ieYU993VlrNDNJdoOX4LMyqqGx3TSzaacCoTuqDcwzP6DW10A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/65yj59eixs+w/FDFJGSDuY1/1j98nMNr1OPJ5Fub77iXpypDgMRHJKavCNdWLEuEhFpNUFNz8BaLYC7t17+G7QjugxA9onEcZpSjqG/a3Clzy/lJ1PYCmVuZHN0cmVhbQplbmRvYmoKNDAgMCBvYmoKPDwgL0xlbmd0aCA4MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjLsNwDAIRHumYAR+JvY+UZTC3r8NECVuuCfdPVwdCZkpbjPDQwaeDCyGXXGB9JYwC1xHUI6d7KNh1b7qBI31plLz7w+Unuys4obrAQJCGmYKZW5kc3RyZWFtCmVuZG9iago0MSAwIG9iago8PCAvTGVuZ3RoIDIzOSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNUMltBDEM+7sKNTDA6By7HgeLPLL9f0PKCZKXaEviofKUW5bKZfcjOW/JuuVDh06VafJu0M2vsf6jDAJ2/1BUEK0lsUrMXNJusTRJL9nDOI2Xa7WO56l7hFmjePDj2NMpgek9MsFms705MKs9zg6QTrjGr+rTO5UkA4m6kPNCpQrrHtQloo8r25hSnU4t5RiXn+h7fI4APcXejdzRx8sXjEa1LajRapU4DzATU9GVcauRgZQTBkNnR1c0C6XIynpCNcKNOaGZvcNwYAPLs4Skpa1SvA9lAegCXdo64zRKgo4Awt8ojPX6Bqr8XjcKZW5kc3RyZWFtCmVuZG9iago0MiAwIG9iago8PCAvTGVuZ3RoIDE1MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9TzkOwzAM2/0KfiCAdVi23pMi6JD+f63ooB0EEaB4yLKjYwUOMYFJxxyJl7Qf/DSNQCyDmiN6QsUwLHA2SYGHQVZJVz5bnEwhtQVeSPjWFDwbTWSCnseIHbiTyegD71JbsXXoAe0QVSRdswxjsa26cD1hBDXFehXm9TBjiZJHn1VL6wEFE/jS+X/ubu92fQFgxTBdCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0xlbmd0aCAxNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNY/LDcMwDEPvmoILBNDPsjxPiqCHdP9rJacFDJgwySfZFoORjENMYOyYY+ElVE+tPiQjt7pJORCpUDcET2hMDDOcpEvglem+ZTy3eDmt1AWdkMjdWW00RBnNPIajp+wVTvovc5OolRllDsisU91OyMqCFZgX1HLfz7itcqETHrYrw6I7xYhymxlp+P3vpDddX9x4MNUKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvTGVuZ3RoIDUxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwUDA0MAeSRoZAlpGJQoohF0gAxMzlggnmgFkGQBqiOAeuJocrgysNAOG0DZgKZW5kc3RyZWFtCmVuZG9iago0NSAwIG9iago8PCAvTGVuZ3RoIDE2MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDkSAzEIBHO9gidIXIL3rMu1wfr/qQfWR6LpAjQcuhZNynoUaD7psUahutBr6CxKkkTBFpIdUKdjiDsoSExIY5JIth6DI5pYs12YmVQqs1LhtGnFwr/ZWtXIRI1wjfyJ6QZU/E/qXJTwTYOvkjH6GFS8O4OMSfheRdxaMe3+RDCxGfYJb0UmBYSJsanZvs9ghsz3Ctc4x/MNTII36wplbmRzdHJlYW0KZW5kb2JqCjQ2IDAgb2JqCjw8IC9MZW5ndGggMzM0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nC1SS3LFIAzbcwpdoDP4B+Q86XS6eL3/tpKTRUYOYPQx5YaJSnxZILej1sS3jcxAheGvq8yFz0jbyDqIy5CLuJIthXtELOQxxDzEgu+r8R4e+azMybMHxi/Zdw8r9tSEZSHjxRnaYRXHYRXkWLB1Iap7eFOkw6kk2OOL/z7Fcy0ELXxG0IBf5J+vjuD5khZp95ht0656sEw7qqSwHGxPc14mX1pnuToezwfJ9q7YEVK7AhSFuTPOc+Eo01ZGtBZ2NkhqXGxvjv1YStCFblxGiiOQn6kiPKCkycwmCuKPnB5yKgNh6pqudHIbVXGnnsw1m4u3M0lm675IsZnCeV04s/4MU2a1eSfPcqLUqQjvsWdL0NA5rp69lllodJsTvKSEz8ZOT06+VzPrITkVCaliWlfBaRSZYgnbEl9TUVOaehn++/Lu8Tt+/gEsc3xzCmVuZHN0cmVhbQplbmRvYmoKNDcgMCBvYmoKPDwgL0xlbmd0aCA3MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMzZTMFCwMAISpqaGCuZGlgophlxAPoiVywUTywGzzCzMgSwjC5CWHC5DC2MwbWJspGBmYgZkWSAxILoyuNIAmJoTAwplbmRzdHJlYW0KZW5kb2JqCjQ4IDAgb2JqCjw8IC9MZW5ndGggMzIwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSS24FMQjbzym4QKXwT87zqqqLvvtvaxO9FUwwYOMpL1nSS77UJdulw+RbH/clsULej+2azFLF9xazFM8tr0fPEbctCgRREz1YmS8VItTP9Og6qHBKn4FXCLcUG7yDSQCDavgHHqUzIFDnQMa7YjJSA4Ik2HNpcQiJciaJf6S8nt8nraSh9D1Zmcvfk0ul0B1NTugBxcrFSaBdSfmgmZhKRJKX632xQvSGwJI8PkcxyYDsNoltogUm5x6lJczEFDqwxwK8ZprVVehgwh6HKYxXC7OoHmzyWxOVpB2t4xnZMN7LMFNioeGwBdTmYmWC7uXjNa/CiO1Rk13DcO6WzXcI0Wj+GxbK4GMVkoBHp7ESDWk4wIjAnl44xV7zEzkOwIhjnZosDGNoJqd6jonA0J6zpWHGxx5a9fMPVOl8hwplbmRzdHJlYW0KZW5kb2JqCjQ5IDAgb2JqCjw8IC9MZW5ndGggMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDCAwxRDrjQAHeYDUgplbmRzdHJlYW0KZW5kb2JqCjUwIDAgb2JqCjw8IC9MZW5ndGggMTMzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWPSw4EIQhE95yijsDHH+dxMumFc//tgJ1uE2M9hVSBuYKhPS5rA50VHyEZtvG3qZaORVk+VHpSVg/J4Iesxssh3KAs8IJJKoYhUIuYGpEtZW63gNs2DbKylVOljrCLozCP9rRsFR5folsidZI/g8QqL9zjuh3Ipda73qKLvn+kATEJCmVuZHN0cmVhbQplbmRvYmoKNTEgMCBvYmoKPDwgL0xlbmd0aCAyNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVFJcgNBCLvPK/SEZqffY5crh+T/1wjKBwYNi0B0WuKgjJ8gLFe85ZGraMPfMzGC3wWHfivXbVjkQFQgSWNQNaF28Xr0HthxmAnMk9awDGasD/yMKdzoxeExGWe312XUEOxdrz2ZQcmsXMQlExdM1WEjZw4/mTIutHM9NyDnRliXYZBuVhozEo40hUghhaqbpM4EQRKMrkaNNnIU+6Uvj3SGVY2oMexzLW1fz004a9DsWKzy5JQeXXEuJxcvrBz09TYDF1FprPJASMD9bg/1c7KT33hL584W0+N7zcnywlRgxZvXbkA21eLfvIjj+4yv5+f5/ANfYFuICmVuZHN0cmVhbQplbmRvYmoKNTIgMCBvYmoKPDwgL0xlbmd0aCAxNzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTZBJDkMhDEP3nMIXqIQzwOc8v6q6aO+/rUMHdYH85CBwPDzQcSQudGTojI4rmxzjwLMgY+LROP/JuD7EMUHdoi1Yl3bH2cwSc8IyMQK2RsnZPKLAD8dcCBJklx++wCAiXY/5VvNZk/TPtzvdj7q0Zl89osCJ7AjFsAFXgP26x4FLwvle0+SXKiVjE4fygeoiUjY7oRC1VOxyqoqz3ZsrcBX0/NFD7u0FtSM83wplbmRzdHJlYW0KZW5kb2JqCjUzIDAgb2JqCjw8IC9MZW5ndGggMjE1IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVROQ4DIQzs9xX+QCSML3hPoijN/r/NjNFWHsFchrSUIZnyUpOoIeVTPnqZLpy63NfMajTnlrQtc4C4trwvrZLAiWaIg8FpmLgBmjwBQ9fRqFFDFx7Q1KVTKLDcBD6Kt24P3WO1gZe2IeeJIGIoGSxBzalFExZtzyekNb9eixvel+3dyFOlxpYYgQYBVjgc1+jX8JU9TybRdBUy1Ks1yxgJE0UiPPmOptUT61o00jIS1MYRrGoDvDv9ME4AABNxywJkn0qUs+TEb7H0swZX+v4Bn0dUlgplbmRzdHJlYW0KZW5kb2JqCjE1IDAgb2JqCjw8IC9UeXBlIC9Gb250IC9CYXNlRm9udCAvQk1RUURWK0RlamFWdVNhbnMgL0ZpcnN0Q2hhciAwIC9MYXN0Q2hhciAyNTUKL0ZvbnREZXNjcmlwdG9yIDE0IDAgUiAvU3VidHlwZSAvVHlwZTMgL05hbWUgL0JNUVFEVitEZWphVnVTYW5zCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0KL0NoYXJQcm9jcyAxNiAwIFIKL0VuY29kaW5nIDw8IC9UeXBlIC9FbmNvZGluZwovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDAgL3BhcmVubGVmdCAvcGFyZW5yaWdodCA0NSAvaHlwaGVuIC9wZXJpb2QgNDggL3plcm8gL29uZQovdHdvIDUyIC9mb3VyIC9maXZlIC9zaXggL3NldmVuIC9laWdodCA2NyAvQyA3MyAvSSA3NiAvTCA4MiAvUiAvUyA4NyAvVyA5NwovYSAvYiAvYyAvZCAvZSAvZiAvZyAvaCAvaSAxMDggL2wgL20gL24gL28gL3AgMTE0IC9yIC9zIC90IC91IF0KPj4KL1dpZHRocyAxMyAwIFIgPj4KZW5kb2JqCjE0IDAgb2JqCjw8IC9UeXBlIC9Gb250RGVzY3JpcHRvciAvRm9udE5hbWUgL0JNUVFEVitEZWphVnVTYW5zIC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Bc2NlbnQgOTI5IC9EZXNjZW50IC0yMzYgL0NhcEhlaWdodCAwCi9YSGVpZ2h0IDAgL0l0YWxpY0FuZ2xlIDAgL1N0ZW1WIDAgL01heFdpZHRoIDEzNDIgPj4KZW5kb2JqCjEzIDAgb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNDIgNjM1IDQwMCAxMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUyMSA0MDAgMTAyMyA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUwMCAxMDAwIDQ3MSA2MTIgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxMiA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA1CjYzMCA2MTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5ODIgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5MiA2MzUgNTkyIF0KZW5kb2JqCjE2IDAgb2JqCjw8IC9DIDE3IDAgUiAvSSAxOCAwIFIgL0wgMTkgMCBSIC9SIDIwIDAgUiAvUyAyMSAwIFIgL1cgMjIgMCBSIC9hIDIzIDAgUgovYiAyNCAwIFIgL2MgMjUgMCBSIC9kIDI2IDAgUiAvZSAyNyAwIFIgL2VpZ2h0IDI4IDAgUiAvZiAyOSAwIFIKL2ZpdmUgMzAgMCBSIC9mb3VyIDMxIDAgUiAvZyAzMiAwIFIgL2ggMzMgMCBSIC9oeXBoZW4gMzQgMCBSIC9pIDM1IDAgUgovbCAzNiAwIFIgL20gMzcgMCBSIC9uIDM4IDAgUiAvbyAzOSAwIFIgL29uZSA0MCAwIFIgL3AgNDEgMCBSCi9wYXJlbmxlZnQgNDIgMCBSIC9wYXJlbnJpZ2h0IDQzIDAgUiAvcGVyaW9kIDQ0IDAgUiAvciA0NSAwIFIgL3MgNDYgMCBSCi9zZXZlbiA0NyAwIFIgL3NpeCA0OCAwIFIgL3NwYWNlIDQ5IDAgUiAvdCA1MCAwIFIgL3R3byA1MSAwIFIgL3UgNTIgMCBSCi96ZXJvIDUzIDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTUgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAwIC9jYSAxID4+Ci9BMiA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAxIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8ID4+CmVuZG9iagoyIDAgb2JqCjw8IC9UeXBlIC9QYWdlcyAvS2lkcyBbIDExIDAgUiBdIC9Db3VudCAxID4+CmVuZG9iago1NCAwIG9iago8PCAvQ3JlYXRvciAoTWF0cGxvdGxpYiB2My44LjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcpCi9Qcm9kdWNlciAoTWF0cGxvdGxpYiBwZGYgYmFja2VuZCB2My44LjApIC9DcmVhdGlvbkRhdGUgKEQ6MjAyMzEwMTExNjAwNTFaKQo+PgplbmRvYmoKeHJlZgowIDU1CjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNiAwMDAwMCBuIAowMDAwMDEzNzkwIDAwMDAwIG4gCjAwMDAwMTM1OTYgMDAwMDAgbiAKMDAwMDAxMzYyOCAwMDAwMCBuIAowMDAwMDEzNzI3IDAwMDAwIG4gCjAwMDAwMTM3NDggMDAwMDAgbiAKMDAwMDAxMzc2OSAwMDAwMCBuIAowMDAwMDAwMDY1IDAwMDAwIG4gCjAwMDAwMDAzNDEgMDAwMDAgbiAKMDAwMDAwMTg1MiAwMDAwMCBuIAowMDAwMDAwMjA4IDAwMDAwIG4gCjAwMDAwMDE4MzEgMDAwMDAgbiAKMDAwMDAxMjA5NyAwMDAwMCBuIAowMDAwMDExODkwIDAwMDAwIG4gCjAwMDAwMTEzNzcgMDAwMDAgbiAKMDAwMDAxMzE1MCAwMDAwMCBuIAowMDAwMDAxODcyIDAwMDAwIG4gCjAwMDAwMDIxODAgMDAwMDAgbiAKMDAwMDAwMjMwMyAwMDAwMCBuIAowMDAwMDAyNDM2IDAwMDAwIG4gCjAwMDAwMDI3NDEgMDAwMDAgbiAKMDAwMDAwMzE1NSAwMDAwMCBuIAowMDAwMDAzMzE5IDAwMDAwIG4gCjAwMDAwMDM2OTkgMDAwMDAgbiAKMDAwMDAwNDAxNiAwMDAwMCBuIAowMDAwMDA0MzIxIDAwMDAwIG4gCjAwMDAwMDQ2MjUgMDAwMDAgbiAKMDAwMDAwNDk0NyAwMDAwMCBuIAowMDAwMDA1NDE1IDAwMDAwIG4gCjAwMDAwMDU2MjQgMDAwMDAgbiAKMDAwMDAwNTk0NiAwMDAwMCBuIAowMDAwMDA2MTEyIDAwMDAwIG4gCjAwMDAwMDY1MjYgMDAwMDAgbiAKMDAwMDAwNjc2MyAwMDAwMCBuIAowMDAwMDA2ODg5IDAwMDAwIG4gCjAwMDAwMDcwMzMgMDAwMDAgbiAKMDAwMDAwNzE1MiAwMDAwMCBuIAowMDAwMDA3NDgzIDAwMDAwIG4gCjAwMDAwMDc3MTkgMDAwMDAgbiAKMDAwMDAwODAxMCAwMDAwMCBuIAowMDAwMDA4MTY1IDAwMDAwIG4gCjAwMDAwMDg0NzcgMDAwMDAgbiAKMDAwMDAwODcwMCAwMDAwMCBuIAowMDAwMDA4OTI0IDAwMDAwIG4gCjAwMDAwMDkwNDcgMDAwMDAgbiAKMDAwMDAwOTI4MCAwMDAwMCBuIAowMDAwMDA5Njg3IDAwMDAwIG4gCjAwMDAwMDk4MjkgMDAwMDAgbiAKMDAwMDAxMDI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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Needed for initializing the lr scheduler\n", "p = nn.Parameter(torch.empty(4, 4))\n", "optimizer = optim.Adam([p], lr=1e-3)\n", "lr_scheduler = CosineWarmupScheduler(optimizer=optimizer, warmup=100, max_iters=2000)\n", "\n", "# Plotting\n", "epochs = list(range(2000))\n", "sns.set()\n", "plt.figure(figsize=(8, 3))\n", "plt.plot(epochs, [lr_scheduler.get_lr_factor(e) for e in epochs])\n", "plt.ylabel(\"Learning rate factor\")\n", "plt.xlabel(\"Iterations (in batches)\")\n", "plt.title(\"Cosine Warm-up Learning Rate Scheduler\")\n", "plt.show()\n", "sns.reset_orig()"]}, {"cell_type": "markdown", "id": "e2cc3314", "metadata": {"papermill": {"duration": 0.016598, "end_time": "2023-10-11T16:00:51.533830", "exception": false, "start_time": "2023-10-11T16:00:51.517232", "status": "completed"}, "tags": []}, "source": ["In the first 100 iterations, we increase the learning rate factor from 0 to 1,\n", "whereas for all later iterations, we decay it using the cosine wave.\n", "Pre-implementations of this scheduler can be found in the popular NLP Transformer library\n", "[huggingface](https://huggingface.co/transformers/main_classes/optimizer_schedules.html?highlight=cosine#transformers.get_cosine_schedule_with_warmup)."]}, {"cell_type": "markdown", "id": "ec3cbcbd", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.016984, "end_time": "2023-10-11T16:00:51.573272", "exception": false, "start_time": "2023-10-11T16:00:51.556288", "status": "completed"}, "tags": []}, "source": ["### PyTorch Lightning Module\n", "\n", "Finally, we can embed the Transformer architecture into a PyTorch lightning module.\n", "From Tutorial 5, you know that PyTorch Lightning simplifies our training and test code,\n", "as well as structures the code nicely in separate functions.\n", "We will implement a template for a classifier based on the Transformer encoder.\n", "Thereby, we have a prediction output per sequence element.\n", "If we would need a classifier over the whole sequence, the common approach is to add an additional\n", "`[CLS]` token to the sequence, representing the classifier token.\n", "However, here we focus on tasks where we have an output per element.\n", "\n", "Additionally to the Transformer architecture, we add a small input network (maps input dimensions to model dimensions),\n", "the positional encoding, and an output network (transforms output encodings to predictions).\n", "We also add the learning rate scheduler, which takes a step each iteration instead of once per epoch.\n", "This is needed for the warmup and the smooth cosine decay.\n", "The training, validation, and test step is left empty for now and will be filled for our task-specific models."]}, {"cell_type": "code", "execution_count": 14, "id": "6c7b65c8", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.608521Z", "iopub.status.busy": "2023-10-11T16:00:51.608056Z", "iopub.status.idle": "2023-10-11T16:00:51.621557Z", "shell.execute_reply": "2023-10-11T16:00:51.620826Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.032923, "end_time": "2023-10-11T16:00:51.622852", "exception": false, "start_time": "2023-10-11T16:00:51.589929", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class TransformerPredictor(L.LightningModule):\n", " def __init__(\n", " self,\n", " input_dim,\n", " model_dim,\n", " num_classes,\n", " num_heads,\n", " num_layers,\n", " lr,\n", " warmup,\n", " max_iters,\n", " dropout=0.0,\n", " input_dropout=0.0,\n", " ):\n", " \"\"\"TransformerPredictor.\n", "\n", " Args:\n", " input_dim: Hidden dimensionality of the input\n", " model_dim: Hidden dimensionality to use inside the Transformer\n", " num_classes: Number of classes to predict per sequence element\n", " num_heads: Number of heads to use in the Multi-Head Attention blocks\n", " num_layers: Number of encoder blocks to use.\n", " lr: Learning rate in the optimizer\n", " warmup: Number of warmup steps. Usually between 50 and 500\n", " max_iters: Number of maximum iterations the model is trained for. This is needed for the CosineWarmup scheduler\n", " dropout: Dropout to apply inside the model\n", " input_dropout: Dropout to apply on the input features\n", " \"\"\"\n", " super().__init__()\n", " self.save_hyperparameters()\n", " self._create_model()\n", "\n", " def _create_model(self):\n", " # Input dim -> Model dim\n", " self.input_net = nn.Sequential(\n", " nn.Dropout(self.hparams.input_dropout), nn.Linear(self.hparams.input_dim, self.hparams.model_dim)\n", " )\n", " # Positional encoding for sequences\n", " self.positional_encoding = PositionalEncoding(d_model=self.hparams.model_dim)\n", " # Transformer\n", " self.transformer = TransformerEncoder(\n", " num_layers=self.hparams.num_layers,\n", " input_dim=self.hparams.model_dim,\n", " dim_feedforward=2 * self.hparams.model_dim,\n", " num_heads=self.hparams.num_heads,\n", " dropout=self.hparams.dropout,\n", " )\n", " # Output classifier per sequence lement\n", " self.output_net = nn.Sequential(\n", " nn.Linear(self.hparams.model_dim, self.hparams.model_dim),\n", " nn.LayerNorm(self.hparams.model_dim),\n", " nn.ReLU(inplace=True),\n", " nn.Dropout(self.hparams.dropout),\n", " nn.Linear(self.hparams.model_dim, self.hparams.num_classes),\n", " )\n", "\n", " def forward(self, x, mask=None, add_positional_encoding=True):\n", " \"\"\"\n", " Args:\n", " x: Input features of shape [Batch, SeqLen, input_dim]\n", " mask: Mask to apply on the attention outputs (optional)\n", " add_positional_encoding: If True, we add the positional encoding to the input.\n", " Might not be desired for some tasks.\n", " \"\"\"\n", " x = self.input_net(x)\n", " if add_positional_encoding:\n", " x = self.positional_encoding(x)\n", " x = self.transformer(x, mask=mask)\n", " x = self.output_net(x)\n", " return x\n", "\n", " @torch.no_grad()\n", " def get_attention_maps(self, x, mask=None, add_positional_encoding=True):\n", " \"\"\"Function for extracting the attention matrices of the whole Transformer for a single batch.\n", "\n", " Input arguments same as the forward pass.\n", " \"\"\"\n", " x = self.input_net(x)\n", " if add_positional_encoding:\n", " x = self.positional_encoding(x)\n", " attention_maps = self.transformer.get_attention_maps(x, mask=mask)\n", " return attention_maps\n", "\n", " def configure_optimizers(self):\n", " optimizer = optim.Adam(self.parameters(), lr=self.hparams.lr)\n", "\n", " # We don't return the lr scheduler because we need to apply it per iteration, not per epoch\n", " self.lr_scheduler = CosineWarmupScheduler(\n", " optimizer, warmup=self.hparams.warmup, max_iters=self.hparams.max_iters\n", " )\n", " return optimizer\n", "\n", " def optimizer_step(self, *args, **kwargs):\n", " super().optimizer_step(*args, **kwargs)\n", " self.lr_scheduler.step() # Step per iteration\n", "\n", " def training_step(self, batch, batch_idx):\n", " raise NotImplementedError\n", "\n", " def validation_step(self, batch, batch_idx):\n", " raise NotImplementedError\n", "\n", " def test_step(self, batch, batch_idx):\n", " raise NotImplementedError"]}, {"cell_type": "markdown", "id": "ac9c031f", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.018782, "end_time": "2023-10-11T16:00:51.661627", "exception": false, "start_time": "2023-10-11T16:00:51.642845", "status": "completed"}, "tags": []}, "source": ["## Experiments\n", "\n", "
\n", "\n", "After having finished the implementation of the Transformer architecture, we can start experimenting\n", "and apply it to various tasks.\n", "In this notebook, we will focus on two tasks: parallel Sequence-to-Sequence, and set anomaly detection.\n", "The two tasks focus on different properties of the Transformer architecture, and we go through them below.\n", "\n", "### Sequence to Sequence\n", "\n", "A Sequence-to-Sequence task represents a task where the input _and_ the output is a sequence,\n", "not necessarily of the same length.\n", "Popular tasks in this domain include machine translation and summarization.\n", "For this, we usually have a Transformer encoder for interpreting the input sequence,\n", "and a decoder for generating the output in an autoregressive manner.\n", "Here, however, we will go back to a much simpler example task and use only the encoder.\n", "Given a sequence of $N$ numbers between $0$ and $M$, the task is to reverse the input sequence.\n", "In Numpy notation, if our input is $x$, the output should be $x$[::-1].\n", "Although this task sounds very simple, RNNs can have issues with such because the task requires long-term dependencies.\n", "Transformers are built to support such, and hence, we expect it to perform very well.\n", "\n", "First, let's create a dataset class below."]}, {"cell_type": "code", "execution_count": 15, "id": "d8f1e8d2", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.699509Z", "iopub.status.busy": "2023-10-11T16:00:51.699048Z", "iopub.status.idle": "2023-10-11T16:00:51.706468Z", "shell.execute_reply": "2023-10-11T16:00:51.705338Z"}, "papermill": {"duration": 0.029009, "end_time": "2023-10-11T16:00:51.708326", "exception": false, "start_time": "2023-10-11T16:00:51.679317", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ReverseDataset(data.Dataset):\n", " def __init__(self, num_categories, seq_len, size):\n", " super().__init__()\n", " self.num_categories = num_categories\n", " self.seq_len = seq_len\n", " self.size = size\n", "\n", " self.data = torch.randint(self.num_categories, size=(self.size, self.seq_len))\n", "\n", " def __len__(self):\n", " return self.size\n", "\n", " def __getitem__(self, idx):\n", " inp_data = self.data[idx]\n", " labels = torch.flip(inp_data, dims=(0,))\n", " return inp_data, labels"]}, {"cell_type": "markdown", "id": "5e94d651", "metadata": {"papermill": {"duration": 0.018097, "end_time": "2023-10-11T16:00:51.745583", "exception": false, "start_time": "2023-10-11T16:00:51.727486", "status": "completed"}, "tags": []}, "source": ["We create an arbitrary number of random sequences of numbers between 0 and `num_categories-1`.\n", "The label is simply the tensor flipped over the sequence dimension.\n", "We can create the corresponding data loaders below."]}, {"cell_type": "code", "execution_count": 16, "id": "eb1b911d", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.783802Z", "iopub.status.busy": "2023-10-11T16:00:51.783311Z", "iopub.status.idle": "2023-10-11T16:00:51.808221Z", "shell.execute_reply": "2023-10-11T16:00:51.807241Z"}, "papermill": {"duration": 0.046636, "end_time": "2023-10-11T16:00:51.809632", "exception": false, "start_time": "2023-10-11T16:00:51.762996", "status": "completed"}, "tags": []}, "outputs": [], "source": ["dataset = partial(ReverseDataset, 10, 16)\n", "train_loader = data.DataLoader(dataset(50000), batch_size=128, shuffle=True, drop_last=True, pin_memory=True)\n", "val_loader = data.DataLoader(dataset(1000), batch_size=128)\n", "test_loader = data.DataLoader(dataset(10000), batch_size=128)"]}, {"cell_type": "markdown", "id": "b6a1fee3", "metadata": {"papermill": {"duration": 0.016957, "end_time": "2023-10-11T16:00:51.843757", "exception": false, "start_time": "2023-10-11T16:00:51.826800", "status": "completed"}, "tags": []}, "source": ["Let's look at an arbitrary sample of the dataset:"]}, {"cell_type": "code", "execution_count": 17, "id": "5cc4c9c0", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.881735Z", "iopub.status.busy": "2023-10-11T16:00:51.880963Z", "iopub.status.idle": "2023-10-11T16:00:51.890662Z", "shell.execute_reply": "2023-10-11T16:00:51.889816Z"}, "papermill": {"duration": 0.030983, "end_time": "2023-10-11T16:00:51.891949", "exception": false, "start_time": "2023-10-11T16:00:51.860966", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Input data: tensor([9, 6, 2, 0, 6, 2, 7, 9, 7, 3, 3, 4, 3, 7, 0, 9])\n", "Labels: tensor([9, 0, 7, 3, 4, 3, 3, 7, 9, 7, 2, 6, 0, 2, 6, 9])\n"]}], "source": ["inp_data, labels = train_loader.dataset[0]\n", "print(\"Input data:\", inp_data)\n", "print(\"Labels: \", labels)"]}, {"cell_type": "markdown", "id": "8277ca0f", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017823, "end_time": "2023-10-11T16:00:51.929755", "exception": false, "start_time": "2023-10-11T16:00:51.911932", "status": "completed"}, "tags": []}, "source": ["During training, we pass the input sequence through the Transformer encoder and predict the output for each input token.\n", "We use the standard Cross-Entropy loss to perform this.\n", "Every number is represented as a one-hot vector.\n", "Remember that representing the categories as single scalars decreases the expressiveness of the model extremely\n", "as $0$ and $1$ are not closer related than $0$ and $9$ in our example.\n", "An alternative to a one-hot vector is using a learned embedding vector as it is provided by the PyTorch module `nn.Embedding`.\n", "However, using a one-hot vector with an additional linear layer as in our case has the same effect\n", "as an embedding layer (`self.input_net` maps one-hot vector to a dense vector,\n", "where each row of the weight matrix represents the embedding for a specific category).\n", "\n", "To implement the training dynamic, we create a new class inheriting from `TransformerPredictor`\n", "and overwriting the training, validation and test step functions."]}, {"cell_type": "code", "execution_count": 18, "id": "98af1a45", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:51.967429Z", "iopub.status.busy": "2023-10-11T16:00:51.966708Z", "iopub.status.idle": "2023-10-11T16:00:51.978842Z", "shell.execute_reply": "2023-10-11T16:00:51.977943Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.032725, "end_time": "2023-10-11T16:00:51.980527", "exception": false, "start_time": "2023-10-11T16:00:51.947802", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ReversePredictor(TransformerPredictor):\n", " def _calculate_loss(self, batch, mode=\"train\"):\n", " # Fetch data and transform categories to one-hot vectors\n", " inp_data, labels = batch\n", " inp_data = F.one_hot(inp_data, num_classes=self.hparams.num_classes).float()\n", "\n", " # Perform prediction and calculate loss and accuracy\n", " preds = self.forward(inp_data, add_positional_encoding=True)\n", " loss = F.cross_entropy(preds.view(-1, preds.size(-1)), labels.view(-1))\n", " acc = (preds.argmax(dim=-1) == labels).float().mean()\n", "\n", " # Logging\n", " self.log(\"%s_loss\" % mode, loss)\n", " self.log(\"%s_acc\" % mode, acc)\n", " return loss, acc\n", "\n", " def training_step(self, batch, batch_idx):\n", " loss, _ = self._calculate_loss(batch, mode=\"train\")\n", " return loss\n", "\n", " def validation_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"val\")\n", "\n", " def test_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"test\")"]}, {"cell_type": "markdown", "id": "f21924ec", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.01959, "end_time": "2023-10-11T16:00:52.019692", "exception": false, "start_time": "2023-10-11T16:00:52.000102", "status": "completed"}, "tags": []}, "source": ["Finally, we can create a training function similar to the one we have seen in Tutorial 5 for PyTorch Lightning.\n", "We create a `L.Trainer` object, running for $N$ epochs, logging in TensorBoard, and saving our best model based on the validation.\n", "Afterward, we test our models on the test set.\n", "An additional parameter we pass to the trainer here is `gradient_clip_val`.\n", "This clips the norm of the gradients for all parameters before taking an optimizer step and prevents the model\n", "from diverging if we obtain very high gradients at, for instance, sharp loss surfaces (see many good blog posts\n", "on gradient clipping, like [DeepAI glossary](https://deepai.org/machine-learning-glossary-and-terms/gradient-clipping)).\n", "For Transformers, gradient clipping can help to further stabilize the training during the first few iterations, and also afterward.\n", "In plain PyTorch, you can apply gradient clipping via `torch.nn.utils.clip_grad_norm_(...)`\n", "(see [documentation](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_)).\n", "The clip value is usually between 0.5 and 10, depending on how harsh you want to clip large gradients.\n", "After having explained this, let's implement the training function:"]}, {"cell_type": "code", "execution_count": 19, "id": "e0133574", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:52.061573Z", "iopub.status.busy": "2023-10-11T16:00:52.060960Z", "iopub.status.idle": "2023-10-11T16:00:52.069948Z", "shell.execute_reply": "2023-10-11T16:00:52.069214Z"}, "papermill": {"duration": 0.031698, "end_time": "2023-10-11T16:00:52.071158", "exception": false, "start_time": "2023-10-11T16:00:52.039460", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_reverse(**kwargs):\n", " # Create a PyTorch Lightning trainer with the generation callback\n", " root_dir = os.path.join(CHECKPOINT_PATH, \"ReverseTask\")\n", " os.makedirs(root_dir, exist_ok=True)\n", " trainer = L.Trainer(\n", " default_root_dir=root_dir,\n", " callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\")],\n", " accelerator=\"auto\",\n", " devices=1,\n", " max_epochs=10,\n", " gradient_clip_val=5,\n", " )\n", " trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n", "\n", " # Check whether pretrained model exists. If yes, load it and skip training\n", " pretrained_filename = os.path.join(CHECKPOINT_PATH, \"ReverseTask.ckpt\")\n", " if os.path.isfile(pretrained_filename):\n", " print(\"Found pretrained model, loading...\")\n", " model = ReversePredictor.load_from_checkpoint(pretrained_filename)\n", " else:\n", " model = ReversePredictor(max_iters=trainer.max_epochs * len(train_loader), **kwargs)\n", " trainer.fit(model, train_loader, val_loader)\n", "\n", " # Test best model on validation and test set\n", " val_result = trainer.test(model, dataloaders=val_loader, verbose=False)\n", " test_result = trainer.test(model, dataloaders=test_loader, verbose=False)\n", " result = {\"test_acc\": test_result[0][\"test_acc\"], \"val_acc\": val_result[0][\"test_acc\"]}\n", "\n", " model = model.to(device)\n", " return model, result"]}, {"cell_type": "markdown", "id": "2f41f356", "metadata": {"papermill": {"duration": 0.017018, "end_time": "2023-10-11T16:00:52.105251", "exception": false, "start_time": "2023-10-11T16:00:52.088233", "status": "completed"}, "tags": []}, "source": ["Finally, we can train the model.\n", "In this setup, we will use a single encoder block and a single head in the Multi-Head Attention.\n", "This is chosen because of the simplicity of the task, and in this case, the attention can actually be interpreted\n", "as an \"explanation\" of the predictions (compared to the other papers above dealing with deep Transformers)."]}, {"cell_type": "code", "execution_count": 20, "id": "331afd24", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:52.142692Z", "iopub.status.busy": "2023-10-11T16:00:52.142061Z", "iopub.status.idle": "2023-10-11T16:00:53.767754Z", "shell.execute_reply": "2023-10-11T16:00:53.767018Z"}, "papermill": {"duration": 1.647606, "end_time": "2023-10-11T16:00:53.769914", "exception": false, "start_time": "2023-10-11T16:00:52.122308", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", " warning_cache.warn(\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Lightning automatically upgraded your loaded checkpoint from v1.0.2 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/Transformers/ReverseTask.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, test_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "5959c58303064c8da3e7dabd6bf8eee0", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "542a82578bf84669abe2821fdb85f52f", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["reverse_model, reverse_result = train_reverse(\n", " input_dim=train_loader.dataset.num_categories,\n", " model_dim=32,\n", " num_heads=1,\n", " num_classes=train_loader.dataset.num_categories,\n", " num_layers=1,\n", " dropout=0.0,\n", " lr=5e-4,\n", " warmup=50,\n", ")"]}, {"cell_type": "markdown", "id": "16a00075", "metadata": {"papermill": {"duration": 0.021558, "end_time": "2023-10-11T16:00:53.821491", "exception": false, "start_time": "2023-10-11T16:00:53.799933", "status": "completed"}, "tags": []}, "source": ["The warning of PyTorch Lightning regarding the number of workers can be ignored for now.\n", "As the data set is so simple and the `__getitem__` finishes a neglectable time, we don't need subprocesses\n", "to provide us the data (in fact, more workers can slow down the training as we have communication overhead among processes/threads).\n", "First, let's print the results:"]}, {"cell_type": "code", "execution_count": 21, "id": "9ce05c50", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:53.858370Z", "iopub.status.busy": "2023-10-11T16:00:53.858192Z", "iopub.status.idle": "2023-10-11T16:00:53.863277Z", "shell.execute_reply": "2023-10-11T16:00:53.862570Z"}, "papermill": {"duration": 0.025435, "end_time": "2023-10-11T16:00:53.864692", "exception": false, "start_time": "2023-10-11T16:00:53.839257", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Val accuracy: 100.00%\n", "Test accuracy: 100.00%\n"]}], "source": ["print(\"Val accuracy: %4.2f%%\" % (100.0 * reverse_result[\"val_acc\"]))\n", "print(\"Test accuracy: %4.2f%%\" % (100.0 * reverse_result[\"test_acc\"]))"]}, {"cell_type": "markdown", "id": "1dd75550", "metadata": {"papermill": {"duration": 0.017767, "end_time": "2023-10-11T16:00:53.900340", "exception": false, "start_time": "2023-10-11T16:00:53.882573", "status": "completed"}, "tags": []}, "source": ["As we would have expected, the Transformer can correctly solve the task.\n", "However, how does the attention in the Multi-Head Attention block looks like for an arbitrary input?\n", "Let's try to visualize it below."]}, {"cell_type": "code", "execution_count": 22, "id": "955abb74", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:53.937354Z", "iopub.status.busy": "2023-10-11T16:00:53.937150Z", "iopub.status.idle": "2023-10-11T16:00:53.944216Z", "shell.execute_reply": "2023-10-11T16:00:53.943550Z"}, "papermill": {"duration": 0.027236, "end_time": "2023-10-11T16:00:53.945461", "exception": false, "start_time": "2023-10-11T16:00:53.918225", "status": "completed"}, "tags": []}, "outputs": [], "source": ["data_input, labels = next(iter(val_loader))\n", "inp_data = F.one_hot(data_input, num_classes=reverse_model.hparams.num_classes).float()\n", "inp_data = inp_data.to(device)\n", "attention_maps = reverse_model.get_attention_maps(inp_data)"]}, {"cell_type": "markdown", "id": "0df53fc3", "metadata": {"papermill": {"duration": 0.017793, "end_time": "2023-10-11T16:00:53.981159", "exception": false, "start_time": "2023-10-11T16:00:53.963366", "status": "completed"}, "tags": []}, "source": ["The object `attention_maps` is a list of length $N$ where $N$ is the number of layers.\n", "Each element is a tensor of shape [Batch, Heads, SeqLen, SeqLen], which we can verify below."]}, {"cell_type": "code", "execution_count": 23, "id": "f96ae814", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:54.019752Z", "iopub.status.busy": "2023-10-11T16:00:54.018541Z", "iopub.status.idle": "2023-10-11T16:00:54.026188Z", "shell.execute_reply": "2023-10-11T16:00:54.025168Z"}, "papermill": {"duration": 0.028683, "end_time": "2023-10-11T16:00:54.027621", "exception": false, "start_time": "2023-10-11T16:00:53.998938", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/plain": ["torch.Size([128, 1, 16, 16])"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["attention_maps[0].shape"]}, {"cell_type": "markdown", "id": "6b89a797", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017919, "end_time": "2023-10-11T16:00:54.063651", "exception": false, "start_time": "2023-10-11T16:00:54.045732", "status": "completed"}, "tags": []}, "source": ["Next, we will write a plotting function that takes as input the sequences, attention maps, and an index\n", "indicating for which batch element we want to visualize the attention map.\n", "We will create a plot where over rows, we have different layers, while over columns, we show the different heads.\n", "Remember that the softmax has been applied for each row separately."]}, {"cell_type": "code", "execution_count": 24, "id": "9c1ab48c", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:54.102281Z", "iopub.status.busy": "2023-10-11T16:00:54.101501Z", "iopub.status.idle": "2023-10-11T16:00:54.115851Z", "shell.execute_reply": "2023-10-11T16:00:54.114787Z"}, "papermill": {"duration": 0.03557, "end_time": "2023-10-11T16:00:54.117157", "exception": false, "start_time": "2023-10-11T16:00:54.081587", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def plot_attention_maps(input_data, attn_maps, idx=0):\n", " if input_data is not None:\n", " input_data = input_data[idx].detach().cpu().numpy()\n", " else:\n", " input_data = np.arange(attn_maps[0][idx].shape[-1])\n", " attn_maps = [m[idx].detach().cpu().numpy() for m in attn_maps]\n", "\n", " num_heads = attn_maps[0].shape[0]\n", " num_layers = len(attn_maps)\n", " seq_len = input_data.shape[0]\n", " fig_size = 4 if num_heads == 1 else 3\n", " fig, ax = plt.subplots(num_layers, num_heads, figsize=(num_heads * fig_size, num_layers * fig_size))\n", " if num_layers == 1:\n", " ax = [ax]\n", " if num_heads == 1:\n", " ax = [[a] for a in ax]\n", " for row in range(num_layers):\n", " for column in range(num_heads):\n", " ax[row][column].imshow(attn_maps[row][column], origin=\"lower\", vmin=0)\n", " ax[row][column].set_xticks(list(range(seq_len)))\n", " ax[row][column].set_xticklabels(input_data.tolist())\n", " ax[row][column].set_yticks(list(range(seq_len)))\n", " ax[row][column].set_yticklabels(input_data.tolist())\n", " ax[row][column].set_title(\"Layer %i, Head %i\" % (row + 1, column + 1))\n", " fig.subplots_adjust(hspace=0.5)\n", " plt.show()"]}, {"cell_type": "markdown", "id": "1f6520ce", "metadata": {"papermill": {"duration": 0.017867, "end_time": "2023-10-11T16:00:54.152957", "exception": false, "start_time": "2023-10-11T16:00:54.135090", "status": "completed"}, "tags": []}, "source": ["Finally, we can plot the attention map of our trained Transformer on the reverse task:"]}, {"cell_type": "code", "execution_count": 25, "id": "bedebfd5", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:54.191275Z", "iopub.status.busy": "2023-10-11T16:00:54.190547Z", "iopub.status.idle": "2023-10-11T16:00:54.512869Z", "shell.execute_reply": "2023-10-11T16:00:54.511926Z"}, "papermill": {"duration": 0.343446, "end_time": "2023-10-11T16:00:54.514400", "exception": false, "start_time": "2023-10-11T16:00:54.170954", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["plot_attention_maps(data_input, attention_maps, idx=0)"]}, {"cell_type": "markdown", "id": "ddbc6bb5", "metadata": {"papermill": {"duration": 0.019004, "end_time": "2023-10-11T16:00:54.552939", "exception": false, "start_time": "2023-10-11T16:00:54.533935", "status": "completed"}, "tags": []}, "source": ["The model has learned to attend to the token that is on the flipped index of itself.\n", "Hence, it actually does what we intended it to do.\n", "We see that it however also pays some attention to values close to the flipped index.\n", "This is because the model doesn't need the perfect, hard attention to solve this problem,\n", "but is fine with this approximate, noisy attention map.\n", "The close-by indices are caused by the similarity of the positional encoding,\n", "which we also intended with the positional encoding."]}, {"cell_type": "markdown", "id": "82d07d8b", "metadata": {"papermill": {"duration": 0.018923, "end_time": "2023-10-11T16:00:54.590858", "exception": false, "start_time": "2023-10-11T16:00:54.571935", "status": "completed"}, "tags": []}, "source": ["### Set Anomaly Detection\n", "\n", "Besides sequences, sets are another data structure that is relevant for many applications.\n", "In contrast to sequences, elements are unordered in a set.\n", "RNNs can only be applied on sets by assuming an order in the data, which however biases the model towards\n", "a non-existing order in the data.\n", "[Vinyals et al.\n", "(2015)](https://arxiv.org/abs/1511.06391) and other papers have shown that the assumed order can have a significant\n", "impact on the model's performance, and hence, we should try to not use RNNs on sets.\n", "Ideally, our model should be permutation-equivariant/invariant such that the output is the same no matter how we sort the elements in a set.\n", "\n", "Transformers offer the perfect architecture for this as the Multi-Head Attention is permutation-equivariant, and thus,\n", "outputs the same values no matter in what order we enter the inputs (inputs and outputs are permuted equally).\n", "The task we are looking at for sets is _Set Anomaly Detection_ which means that we try to find the element(s)\n", "in a set that does not fit the others.\n", "In the research community, the common application of anomaly detection is performed on a set of images,\n", "where $N-1$ images belong to the same category/have the same high-level features while one belongs to another category.\n", "Note that category does not necessarily have to relate to a class in a standard classification problem,\n", "but could be the combination of multiple features.\n", "For instance, on a face dataset, this could be people with glasses, male, beard, etc.\n", "An example of distinguishing different animals can be seen below.\n", "The first four images show foxes, while the last represents a different animal.\n", "We want to recognize that the last image shows a different animal, but it is not relevant which class of animal it is.\n", "\n", "
\n", "\n", "In this tutorial, we will use the CIFAR100 dataset.\n", "CIFAR100 has 600 images for 100 classes each with a resolution of 32x32, similar to CIFAR10.\n", "The larger amount of classes requires the model to attend to specific features in the images instead\n", "of coarse features as in CIFAR10, therefore making the task harder.\n", "We will show the model a set of 9 images of one class, and 1 image from another class.\n", "The task is to find the image that is from a different class than the other images.\n", "Using the raw images directly as input to the Transformer is not a good idea, because it is not translation\n", "invariant as a CNN, and would need to learn to detect image features from high-dimensional input first of all.\n", "Instead, we will use a pre-trained ResNet34 model from the torchvision package to obtain high-level,\n", "low-dimensional features of the images.\n", "The ResNet model has been pre-trained on the [ImageNet](http://image-net.org/) dataset which contains\n", "1 million images of 1k classes and varying resolutions.\n", "However, during training and testing, the images are usually scaled to a resolution of 224x224,\n", "and hence we rescale our CIFAR images to this resolution as well.\n", "Below, we will load the dataset, and prepare the data for being processed by the ResNet model."]}, {"cell_type": "code", "execution_count": 26, "id": "db25fb31", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:00:54.630484Z", "iopub.status.busy": "2023-10-11T16:00:54.630295Z", "iopub.status.idle": "2023-10-11T16:01:01.568468Z", "shell.execute_reply": "2023-10-11T16:01:01.567266Z"}, "papermill": {"duration": 6.959947, "end_time": "2023-10-11T16:01:01.569923", "exception": false, "start_time": "2023-10-11T16:00:54.609976", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to /__w/15/s/.datasets/cifar-100-python.tar.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 0%| | 0/169001437 [00:00150MB free disk space.\n", "So it is recommended to run this only on a local computer if you have enough free disk and a GPU (GoogleColab is fine for this).\n", "If you do not have a GPU, you can download the features from the\n", "[GoogleDrive folder](https://drive.google.com/drive/folders/1DF7POc6j03pRiWQPWSl5QJX5iY-xK0sV?usp=sharing)."]}, {"cell_type": "code", "execution_count": 28, "id": "22f55355", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:04.021213Z", "iopub.status.busy": "2023-10-11T16:01:04.020691Z", "iopub.status.idle": "2023-10-11T16:01:31.682695Z", "shell.execute_reply": "2023-10-11T16:01:31.681859Z"}, "papermill": {"duration": 27.687889, "end_time": "2023-10-11T16:01:31.685055", "exception": false, "start_time": "2023-10-11T16:01:03.997166", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "3f713a70aeb14bbe9f52030f6e1f56d9", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/391 [00:00= anomaly_label:\n", " set_label += 1\n", "\n", " # Sample images from the class determined above\n", " img_indices = np.random.choice(self.img_idx_by_label.shape[1], size=self.set_size, replace=False)\n", " img_indices = self.img_idx_by_label[set_label, img_indices]\n", " return img_indices\n", "\n", " def __len__(self):\n", " return self.img_feats.shape[0]\n", "\n", " def __getitem__(self, idx):\n", " anomaly = self.img_feats[idx]\n", " if self.train: # If train => sample\n", " img_indices = self.sample_img_set(self.labels[idx])\n", " else: # If test => use pre-generated ones\n", " img_indices = self.test_sets[idx]\n", "\n", " # Concatenate images. The anomaly is always the last image for simplicity\n", " img_set = torch.cat([self.img_feats[img_indices], anomaly[None]], dim=0)\n", " indices = torch.cat([img_indices, torch.LongTensor([idx])], dim=0)\n", " label = img_set.shape[0] - 1\n", "\n", " # We return the indices of the images for visualization purpose. \"Label\" is the index of the anomaly\n", " return img_set, indices, label"]}, {"cell_type": "markdown", "id": "9b48255c", "metadata": {"papermill": {"duration": 0.022377, "end_time": "2023-10-11T16:01:32.107228", "exception": false, "start_time": "2023-10-11T16:01:32.084851", "status": "completed"}, "tags": []}, "source": ["Next, we can setup our datasets and data loaders below.\n", "Here, we will use a set size of 10, i.e. 9 images from one category + 1 anomaly.\n", "Feel free to change it if you want to experiment with the sizes."]}, {"cell_type": "code", "execution_count": 32, "id": "b7ef71d6", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:32.154015Z", "iopub.status.busy": "2023-10-11T16:01:32.153668Z", "iopub.status.idle": "2023-10-11T16:01:33.124371Z", "shell.execute_reply": "2023-10-11T16:01:33.123271Z"}, "papermill": {"duration": 0.997062, "end_time": "2023-10-11T16:01:33.126681", "exception": false, "start_time": "2023-10-11T16:01:32.129619", "status": "completed"}, "tags": []}, "outputs": [], "source": ["SET_SIZE = 10\n", "test_labels = torch.LongTensor(test_set.targets)\n", "\n", "train_anom_dataset = SetAnomalyDataset(train_feats, train_labels, set_size=SET_SIZE, train=True)\n", "val_anom_dataset = SetAnomalyDataset(val_feats, val_labels, set_size=SET_SIZE, train=False)\n", "test_anom_dataset = SetAnomalyDataset(test_feats, test_labels, set_size=SET_SIZE, train=False)\n", "\n", "train_anom_loader = data.DataLoader(\n", " train_anom_dataset, batch_size=64, shuffle=True, drop_last=True, num_workers=4, pin_memory=True\n", ")\n", "val_anom_loader = data.DataLoader(val_anom_dataset, batch_size=64, shuffle=False, drop_last=False, num_workers=4)\n", "test_anom_loader = data.DataLoader(test_anom_dataset, batch_size=64, shuffle=False, drop_last=False, num_workers=4)"]}, {"cell_type": "markdown", "id": "cc549472", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.022564, "end_time": "2023-10-11T16:01:33.173506", "exception": false, "start_time": "2023-10-11T16:01:33.150942", "status": "completed"}, "tags": []}, "source": ["To understand the dataset a little better, we can plot below a few sets from the test dataset.\n", "Each row shows a different input set, where the first 9 are from the same class."]}, {"cell_type": "code", "execution_count": 33, "id": "175d8842", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:33.219470Z", "iopub.status.busy": "2023-10-11T16:01:33.219291Z", "iopub.status.idle": "2023-10-11T16:01:34.868153Z", "shell.execute_reply": "2023-10-11T16:01:34.867272Z"}, "papermill": {"duration": 1.675441, "end_time": "2023-10-11T16:01:34.871396", "exception": false, "start_time": "2023-10-11T16:01:33.195955", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["def visualize_exmp(indices, orig_dataset):\n", " images = [orig_dataset[idx][0] for idx in indices.reshape(-1)]\n", " images = torch.stack(images, dim=0)\n", " images = images * TORCH_DATA_STD + TORCH_DATA_MEANS\n", "\n", " img_grid = torchvision.utils.make_grid(images, nrow=SET_SIZE, normalize=True, pad_value=0.5, padding=16)\n", " img_grid = img_grid.permute(1, 2, 0)\n", "\n", " plt.figure(figsize=(12, 8))\n", " plt.title(\"Anomaly examples on CIFAR100\")\n", " plt.imshow(img_grid)\n", " plt.axis(\"off\")\n", " plt.show()\n", " plt.close()\n", "\n", "\n", "_, indices, _ = next(iter(test_anom_loader))\n", "visualize_exmp(indices[:4], test_set)"]}, {"cell_type": "markdown", "id": "b63555a7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.039237, "end_time": "2023-10-11T16:01:34.951052", "exception": false, "start_time": "2023-10-11T16:01:34.911815", "status": "completed"}, "tags": []}, "source": ["We can already see that for some sets the task might be easier than for others.\n", "Difficulties can especially arise if the anomaly is in a different, but yet visually similar class\n", "(e.g. train vs bus, flour vs worm, etc.\n", ").\n", "\n", "After having prepared the data, we can look closer at the model.\n", "Here, we have a classification of the whole set.\n", "For the prediction to be permutation-equivariant, we will output one logit for each image.\n", "Over these logits, we apply a softmax and train the anomaly image to have the highest score/probability.\n", "This is a bit different than a standard classification layer as the softmax is applied over images,\n", "not over output classes in the classical sense.\n", "However, if we swap two images in their position, we effectively swap their position in the output softmax.\n", "Hence, the prediction is equivariant with respect to the input.\n", "We implement this idea below in the subclass of the Transformer Lightning module."]}, {"cell_type": "code", "execution_count": 34, "id": "cef2f171", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:35.030621Z", "iopub.status.busy": "2023-10-11T16:01:35.030328Z", "iopub.status.idle": "2023-10-11T16:01:35.037386Z", "shell.execute_reply": "2023-10-11T16:01:35.036854Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.048858, "end_time": "2023-10-11T16:01:35.038561", "exception": false, "start_time": "2023-10-11T16:01:34.989703", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class AnomalyPredictor(TransformerPredictor):\n", " def _calculate_loss(self, batch, mode=\"train\"):\n", " img_sets, _, labels = batch\n", " # No positional encodings as it is a set, not a sequence!\n", " preds = self.forward(img_sets, add_positional_encoding=False)\n", " preds = preds.squeeze(dim=-1) # Shape: [Batch_size, set_size]\n", " loss = F.cross_entropy(preds, labels) # Softmax/CE over set dimension\n", " acc = (preds.argmax(dim=-1) == labels).float().mean()\n", " self.log(\"%s_loss\" % mode, loss)\n", " self.log(\"%s_acc\" % mode, acc, on_step=False, on_epoch=True)\n", " return loss, acc\n", "\n", " def training_step(self, batch, batch_idx):\n", " loss, _ = self._calculate_loss(batch, mode=\"train\")\n", " return loss\n", "\n", " def validation_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"val\")\n", "\n", " def test_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"test\")"]}, {"cell_type": "markdown", "id": "fed9ec94", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.038384, "end_time": "2023-10-11T16:01:35.116399", "exception": false, "start_time": "2023-10-11T16:01:35.078015", "status": "completed"}, "tags": []}, "source": ["Finally, we write our train function below.\n", "It has the exact same structure as the reverse task one, hence not much of an explanation is needed here."]}, {"cell_type": "code", "execution_count": 35, "id": "c430806b", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:35.195375Z", "iopub.status.busy": "2023-10-11T16:01:35.194940Z", "iopub.status.idle": "2023-10-11T16:01:35.207949Z", "shell.execute_reply": "2023-10-11T16:01:35.207240Z"}, "papermill": {"duration": 0.054687, "end_time": "2023-10-11T16:01:35.209684", "exception": false, "start_time": "2023-10-11T16:01:35.154997", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_anomaly(**kwargs):\n", " # Create a PyTorch Lightning trainer with the generation callback\n", " root_dir = os.path.join(CHECKPOINT_PATH, \"SetAnomalyTask\")\n", " os.makedirs(root_dir, exist_ok=True)\n", " trainer = L.Trainer(\n", " default_root_dir=root_dir,\n", " callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\")],\n", " accelerator=\"auto\",\n", " devices=1,\n", " max_epochs=100,\n", " gradient_clip_val=2,\n", " )\n", " trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n", "\n", " # Check whether pretrained model exists. If yes, load it and skip training\n", " pretrained_filename = os.path.join(CHECKPOINT_PATH, \"SetAnomalyTask.ckpt\")\n", " if os.path.isfile(pretrained_filename):\n", " print(\"Found pretrained model, loading...\")\n", " model = AnomalyPredictor.load_from_checkpoint(pretrained_filename)\n", " else:\n", " model = AnomalyPredictor(max_iters=trainer.max_epochs * len(train_anom_loader), **kwargs)\n", " trainer.fit(model, train_anom_loader, val_anom_loader)\n", " model = AnomalyPredictor.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n", "\n", " # Test best model on validation and test set\n", " train_result = trainer.test(model, dataloaders=train_anom_loader, verbose=False)\n", " val_result = trainer.test(model, dataloaders=val_anom_loader, verbose=False)\n", " test_result = trainer.test(model, dataloaders=test_anom_loader, verbose=False)\n", " result = {\n", " \"test_acc\": test_result[0][\"test_acc\"],\n", " \"val_acc\": val_result[0][\"test_acc\"],\n", " \"train_acc\": train_result[0][\"test_acc\"],\n", " }\n", "\n", " model = model.to(device)\n", " return model, result"]}, {"cell_type": "markdown", "id": "bb880657", "metadata": {"papermill": {"duration": 0.038668, "end_time": "2023-10-11T16:01:35.287777", "exception": false, "start_time": "2023-10-11T16:01:35.249109", "status": "completed"}, "tags": []}, "source": ["Let's finally train our model.\n", "We will use 4 layers with 4 attention heads each.\n", "The hidden dimensionality of the model is 256, and we use a dropout of 0.1 throughout the model for good regularization.\n", "Note that we also apply the dropout on the input features, as this makes the model more robust against\n", "image noise and generalizes better.\n", "Again, we use warmup to slowly start our model training."]}, {"cell_type": "code", "execution_count": 36, "id": "62fecfe3", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:35.367594Z", "iopub.status.busy": "2023-10-11T16:01:35.367292Z", "iopub.status.idle": "2023-10-11T16:01:41.114270Z", "shell.execute_reply": "2023-10-11T16:01:41.113675Z"}, "papermill": {"duration": 5.788933, "end_time": "2023-10-11T16:01:41.116164", "exception": false, "start_time": "2023-10-11T16:01:35.327231", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Lightning automatically upgraded your loaded checkpoint from v1.0.2 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/Transformers/SetAnomalyTask.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:490: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n", " rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e98f387ac6b94e95b6c87c7d89b300a1", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c2dedf410225494e9828f5ed27cce074", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "7c94f4de93a44bb4bd269e0c930191eb", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["anomaly_model, anomaly_result = train_anomaly(\n", " input_dim=train_anom_dataset.img_feats.shape[-1],\n", " model_dim=256,\n", " num_heads=4,\n", " num_classes=1,\n", " num_layers=4,\n", " dropout=0.1,\n", " input_dropout=0.1,\n", " lr=5e-4,\n", " warmup=100,\n", ")"]}, {"cell_type": "markdown", "id": "918d22d7", "metadata": {"papermill": {"duration": 0.045371, "end_time": "2023-10-11T16:01:41.224099", "exception": false, "start_time": "2023-10-11T16:01:41.178728", "status": "completed"}, "tags": []}, "source": ["We can print the achieved accuracy below."]}, {"cell_type": "code", "execution_count": 37, "id": "4bdc1182", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:41.305998Z", "iopub.status.busy": "2023-10-11T16:01:41.305283Z", "iopub.status.idle": "2023-10-11T16:01:41.312756Z", "shell.execute_reply": "2023-10-11T16:01:41.311881Z"}, "papermill": {"duration": 0.050167, "end_time": "2023-10-11T16:01:41.314077", "exception": false, "start_time": "2023-10-11T16:01:41.263910", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Train accuracy: 96.45%\n", "Val accuracy: 95.94%\n", "Test accuracy: 94.41%\n"]}], "source": ["print(\"Train accuracy: %4.2f%%\" % (100.0 * anomaly_result[\"train_acc\"]))\n", "print(\"Val accuracy: %4.2f%%\" % (100.0 * anomaly_result[\"val_acc\"]))\n", "print(\"Test accuracy: %4.2f%%\" % (100.0 * anomaly_result[\"test_acc\"]))"]}, {"cell_type": "markdown", "id": "e8b27ef8", "metadata": {"papermill": {"duration": 0.039768, "end_time": "2023-10-11T16:01:41.394576", "exception": false, "start_time": "2023-10-11T16:01:41.354808", "status": "completed"}, "tags": []}, "source": ["With ~94% validation and test accuracy, the model generalizes quite well.\n", "It should be noted that you might see slightly different scores depending on what computer/device you are running this notebook.\n", "This is because despite setting the seed before generating the test dataset, it is not the same across platforms and numpy versions.\n", "Nevertheless, we can conclude that the model performs quite well and can solve the task for most sets.\n", "Before trying to interpret the model, let's verify that our model is permutation-equivariant,\n", "and assigns the same predictions for different permutations of the input set.\n", "For this, we sample a batch from the test set and run it through the model to obtain the probabilities."]}, {"cell_type": "code", "execution_count": 38, "id": "0a4dcb41", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:41.475791Z", "iopub.status.busy": "2023-10-11T16:01:41.475433Z", "iopub.status.idle": "2023-10-11T16:01:41.900244Z", "shell.execute_reply": "2023-10-11T16:01:41.899141Z"}, "papermill": {"duration": 0.467281, "end_time": "2023-10-11T16:01:41.901646", "exception": false, "start_time": "2023-10-11T16:01:41.434365", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Preds\n", " [2.7609802e-05 1.8916349e-05 1.7331060e-05 2.7779470e-05 1.6092694e-05\n", " 1.6961278e-05 5.7101843e-05 9.9977833e-01 2.1296861e-05 1.8621107e-05]\n", "Permuted preds\n", " [2.7609802e-05 1.8916349e-05 1.7331060e-05 2.7779470e-05 1.6092694e-05\n", " 1.6961278e-05 5.7101788e-05 9.9977833e-01 2.1296861e-05 1.8621089e-05]\n"]}], "source": ["inp_data, indices, labels = next(iter(test_anom_loader))\n", "inp_data = inp_data.to(device)\n", "\n", "anomaly_model.eval()\n", "\n", "with torch.no_grad():\n", " preds = anomaly_model.forward(inp_data, add_positional_encoding=False)\n", " preds = F.softmax(preds.squeeze(dim=-1), dim=-1)\n", "\n", " # Permut input data\n", " permut = np.random.permutation(inp_data.shape[1])\n", " perm_inp_data = inp_data[:, permut]\n", " perm_preds = anomaly_model.forward(perm_inp_data, add_positional_encoding=False)\n", " perm_preds = F.softmax(perm_preds.squeeze(dim=-1), dim=-1)\n", "\n", "assert (preds[:, permut] - perm_preds).abs().max() < 1e-5, \"Predictions are not permutation equivariant\"\n", "\n", "print(\"Preds\\n\", preds[0, permut].cpu().numpy())\n", "print(\"Permuted preds\\n\", perm_preds[0].cpu().numpy())"]}, {"cell_type": "markdown", "id": "b66adff3", "metadata": {"papermill": {"duration": 0.041949, "end_time": "2023-10-11T16:01:41.984202", "exception": false, "start_time": "2023-10-11T16:01:41.942253", "status": "completed"}, "tags": []}, "source": ["You can see that the predictions are almost exactly the same, and only differ because of slight numerical\n", "differences inside the network operation.\n", "\n", "To interpret the model a little more, we can plot the attention maps inside the model.\n", "This will give us an idea of what information the model is sharing/communicating between images,\n", "and what each head might represent.\n", "First, we need to extract the attention maps for the test batch above, and determine the discrete predictions for simplicity."]}, {"cell_type": "code", "execution_count": 39, "id": "c1c1e3ad", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:42.066221Z", "iopub.status.busy": "2023-10-11T16:01:42.065899Z", "iopub.status.idle": "2023-10-11T16:01:42.074397Z", "shell.execute_reply": "2023-10-11T16:01:42.073489Z"}, "papermill": {"duration": 0.051677, "end_time": "2023-10-11T16:01:42.075784", "exception": false, "start_time": "2023-10-11T16:01:42.024107", "status": "completed"}, "tags": []}, "outputs": [], "source": ["attention_maps = anomaly_model.get_attention_maps(inp_data, add_positional_encoding=False)\n", "predictions = preds.argmax(dim=-1)"]}, {"cell_type": "markdown", "id": "48e6e923", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.03973, "end_time": "2023-10-11T16:01:42.156086", "exception": false, "start_time": "2023-10-11T16:01:42.116356", "status": "completed"}, "tags": []}, "source": ["Below we write a plot function which plots the images in the input set, the prediction of the model,\n", "and the attention maps of the different heads on layers of the transformer.\n", "Feel free to explore the attention maps for different input examples as well."]}, {"cell_type": "code", "execution_count": 40, "id": "714c1715", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:42.237449Z", "iopub.status.busy": "2023-10-11T16:01:42.236843Z", "iopub.status.idle": "2023-10-11T16:01:45.603221Z", "shell.execute_reply": "2023-10-11T16:01:45.602497Z"}, "papermill": {"duration": 3.41105, "end_time": "2023-10-11T16:01:45.606883", "exception": false, "start_time": "2023-10-11T16:01:42.195833", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Prediction: 9\n"]}, {"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["def visualize_prediction(idx):\n", " visualize_exmp(indices[idx : idx + 1], test_set)\n", " print(\"Prediction:\", predictions[idx].item())\n", " plot_attention_maps(input_data=None, attn_maps=attention_maps, idx=idx)\n", "\n", "\n", "visualize_prediction(0)"]}, {"cell_type": "markdown", "id": "941f4861", "metadata": {"papermill": {"duration": 0.050088, "end_time": "2023-10-11T16:01:45.741407", "exception": false, "start_time": "2023-10-11T16:01:45.691319", "status": "completed"}, "tags": []}, "source": ["Depending on the random seed, you might see a slightly different input set.\n", "For the version on the website, we compare 9 tree images with a volcano.\n", "We see that multiple heads, for instance, Layer 2 Head 1, Layer 2 Head 3, and Layer 3 Head 1 focus on the last image.\n", "Additionally, the heads in Layer 4 all seem to ignore the last image and assign a very low attention probability to it.\n", "This shows that the model has indeed recognized that the image doesn't fit the setting, and hence predicted it to be the anomaly.\n", "Layer 3 Head 2-4 seems to take a slightly weighted average of all images.\n", "That might indicate that the model extracts the \"average\" information of all images, to compare it to the image features itself.\n", "\n", "Let's try to find where the model actually makes a mistake.\n", "We can do this by identifying the sets where the model predicts something else than 9, as in the dataset,\n", "we ensured that the anomaly is always at the last position in the set."]}, {"cell_type": "code", "execution_count": 41, "id": "a656622e", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:45.844599Z", "iopub.status.busy": "2023-10-11T16:01:45.844065Z", "iopub.status.idle": "2023-10-11T16:01:45.848585Z", "shell.execute_reply": "2023-10-11T16:01:45.848172Z"}, "papermill": {"duration": 0.058655, "end_time": "2023-10-11T16:01:45.849656", "exception": false, "start_time": "2023-10-11T16:01:45.791001", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Indices with mistake: [49]\n"]}], "source": ["mistakes = torch.where(predictions != 9)[0].cpu().numpy()\n", "print(\"Indices with mistake:\", mistakes)"]}, {"cell_type": "markdown", "id": "c4b84b07", "metadata": {"papermill": {"duration": 0.050159, "end_time": "2023-10-11T16:01:45.950043", "exception": false, "start_time": "2023-10-11T16:01:45.899884", "status": "completed"}, "tags": []}, "source": ["As our model achieves ~94% accuracy, we only have very little number of mistakes in a batch of 64 sets.\n", "Still, let's visualize one of them, for example the last one:"]}, {"cell_type": "code", "execution_count": 42, "id": "f91aa40d", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:01:46.051579Z", "iopub.status.busy": "2023-10-11T16:01:46.050933Z", "iopub.status.idle": "2023-10-11T16:01:49.187966Z", "shell.execute_reply": "2023-10-11T16:01:49.187301Z"}, "papermill": {"duration": 3.191974, "end_time": "2023-10-11T16:01:49.191547", "exception": false, "start_time": "2023-10-11T16:01:45.999573", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Prediction: 7\n"]}, {"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Probabilities:\n", "Image 0: 0.07%\n", "Image 1: 0.11%\n", "Image 2: 0.07%\n", "Image 3: 0.11%\n", "Image 4: 0.17%\n", "Image 5: 23.15%\n", "Image 6: 0.16%\n", "Image 7: 48.89%\n", "Image 8: 0.10%\n", "Image 9: 27.18%\n"]}], "source": ["visualize_prediction(mistakes[-1])\n", "print(\"Probabilities:\")\n", "for i, p in enumerate(preds[mistakes[-1]].cpu().numpy()):\n", " print(\"Image %i: %4.2f%%\" % (i, 100.0 * p))"]}, {"cell_type": "markdown", "id": "9e5c5a5f", "metadata": {"papermill": {"duration": 0.060799, "end_time": "2023-10-11T16:01:49.332812", "exception": false, "start_time": "2023-10-11T16:01:49.272013", "status": "completed"}, "tags": []}, "source": ["In this example, the model confuses a palm tree with a building, giving a probability of ~90% to image 2, and 8% to the actual anomaly.\n", "However, the difficulty here is that the picture of the building has been taken at a similar angle as the palms.\n", "Meanwhile, image 2 shows a rather unusual palm with a different color palette, which is why the model fails here.\n", "Nevertheless, in general, the model performs quite well."]}, {"cell_type": "markdown", "id": "633b62b2", "metadata": {"papermill": {"duration": 0.067198, "end_time": "2023-10-11T16:01:49.473323", "exception": false, "start_time": "2023-10-11T16:01:49.406125", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we took a closer look at the Multi-Head Attention layer which uses a scaled dot product between\n", "queries and keys to find correlations and similarities between input elements.\n", "The Transformer architecture is based on the Multi-Head Attention layer and applies multiple of them in a ResNet-like block.\n", "The Transformer is a very important, recent architecture that can be applied to many tasks and datasets.\n", "Although it is best known for its success in NLP, there is so much more to it.\n", "We have seen its application on sequence-to-sequence tasks and set anomaly detection.\n", "Its property of being permutation-equivariant if we do not provide any positional encodings, allows it to generalize to many settings.\n", "Hence, it is important to know the architecture, but also its possible issues such as the gradient problem during\n", "the first iterations solved by learning rate warm-up.\n", "If you are interested in continuing with the study of the Transformer architecture,\n", "please have a look at the blog posts listed at the beginning of the tutorial notebook."]}, {"cell_type": "markdown", "id": "479c0788", "metadata": {"papermill": {"duration": 0.065582, "end_time": "2023-10-11T16:01:49.606550", "exception": false, "start_time": "2023-10-11T16:01:49.540968", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://www.pytorchlightning.ai/community)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[![Pytorch Lightning](data:image/png;base64,NDA0OiBOb3QgRm91bmQ=){height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", " :header: Tutorial 5: Transformers and Multi-Head Attention\n", " :card_description: In this tutorial, we will discuss one of the most impactful architectures of the last 2 years: the Transformer model. Since the paper Attention Is All You Need by Vaswani et...\n", " :tags: Text,GPU/TPU,UvA-DL-Course\n", " :image: _static/images/course_UvA-DL/05-transformers-and-MH-attention.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab,colab_type,id,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12"}, "papermill": {"default_parameters": {}, "duration": 162.325794, "end_time": "2023-10-11T16:01:51.294238", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/05-transformers-and-MH-attention/Transformers_MHAttention.ipynb", "output_path": ".notebooks/course_UvA-DL/05-transformers-and-MH-attention.ipynb", "parameters": {}, "start_time": "2023-10-11T15:59:08.968444", "version": "2.4.0"}, "widgets": 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